biki96/hf-stack-v1 · Datasets at Fast360

hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/big_modeling.md

# Handling big models for inference One of the biggest advancements 🤗 Accelerate provides is the concept of [large model inference](../concept_guides/big_model_inference) wherein you can perform *inference* on models that cannot fully fit on your graphics card. This tutorial will be broken down into two parts showcasing how to use both 🤗 Accelerate and 🤗 Transformers (a higher API-level) to make use of this idea. ## Using 🤗 Accelerate For these tutorials, we'll assume a typical workflow for loading your model in such that: ```py import torch my_model = ModelClass(...) state_dict = torch.load(checkpoint_file) my_model.load_state_dict(state_dict) ``` Note that here we assume that `ModelClass` is a model that takes up more video-card memory than what can fit on your device (be it `mps` or `cuda`). The first step is to init an empty skeleton of the model which won't take up any RAM using the [`init_empty_weights`] context manager: ```py from accelerate import init_empty_weights with init_empty_weights(): my_model = ModelClass(...) ``` With this `my_model` currently is "parameterless", hence leaving the smaller footprint than what one would normally get loading this onto the CPU directly. Next we need to load in the weights to our model so we can perform inference. For this we will use [`load_checkpoint_and_dispatch`], which as the name implies will load a checkpoint inside your empty model and dispatch the weights for each layer across all the devices you have available (GPU/MPS and CPU RAM). To determine how this `dispatch` can be performed, generally specifying `device_map="auto"` will be good enough as 🤗 Accelerate will attempt to fill all the space in your GPU(s), then loading them to the CPU, and finally if there is not enough RAM it will be loaded to the disk (the absolute slowest option). <Tip> For more details on designing your own device map, see this section of the [concept guide](../concept_guide/big_model_inference#designing-a-device-map) </Tip> See an example below: ```py from accelerate import load_checkpoint_and_dispatch model = load_checkpoint_and_dispatch( model, checkpoint=checkpoint_file, device_map="auto" ) ``` <Tip> If there are certain "chunks" of layers that shouldn't be split, you can pass them in as `no_split_module_classes`. Read more about it [here](../concept_guides/big_model_inference#loading-weights) </Tip> <Tip> Also to save on memory (such as if the `state_dict` will not fit in RAM), a model's weights can be divided and split into multiple checkpoint files. Read more about it [here](../concept_guides/big_model_inference#sharded-checkpoints) </Tip> Now that the model is dispatched fully, you can perform inference as normal with the model: ```py input = torch.randn(2,3) input = input.to("cuda") output = model(input) ``` What will happen now is each time the input gets passed through a layer, it will be sent from the CPU to the GPU (or disk to CPU to GPU), the output is calculated, and then the layer is pulled back off the GPU going back down the line. While this adds some overhead to the inference being performed, through this method it is possible to run **any size model** on your system, as long as the largest layer is capable of fitting on your GPU. <Tip> Multiple GPUs can be utilized, however this is considered "model parallelism" and as a result only one GPU will be active at a given moment, waiting for the prior one to send it the output. You should launch your script normally with `python` and not need `torchrun`, `accelerate launch`, etc. </Tip> For a visual representation of this, check out the animation below: <Youtube id="MWCSGj9jEAo" /> ### Complete Example Below is the full example showcasing what we performed above: ```py import torch from accelerate import init_empty_weights, load_checkpoint_and_dispatch with init_empty_weights(): model = MyModel(...) model = load_checkpoint_and_dispatch( model, checkpoint=checkpoint_file, device_map="auto" ) input = torch.randn(2,3) input = input.to("cuda") output = model(input) ``` ## Using 🤗 Transformers, 🤗 Diffusers, and other 🤗 Open Source Libraries Libraries that support 🤗 Accelerate big model inference include all of the earlier logic in their `from_pretrained` constructors. These operate by specifying a string representing the model to download from the [🤗 Hub](https://hf.co/models) and then denoting `device_map="auto"` along with a few extra parameters. As a brief example, we will look at using `transformers` and loading in Big Science's T0pp model. ```py from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto") ``` After loading the model in, the initial steps from before to prepare a model have all been done and the model is fully ready to make use of all the resources in your machine. Through these constructors, you can also save *more* memory by specifying the precision the model is loaded into as well, through the `torch_dtype` parameter, such as: ```py from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto", torch_dtype=torch.float16) ``` To learn more about this, check out the 🤗 Transformers documentation available [here](https://huggingface.co/docs/transformers/main/en/main_classes/model#large-model-loading). ## Where to go from here For a much more detailed look at big model inference, be sure to check out the [Conceptual Guide on it](../concept_guides/big_model_inference)

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/explore.md

# Learning how to incorporate 🤗 Accelerate features quickly! Please use the interactive tool below to help you get started with learning about a particular feature of 🤗 Accelerate and how to utilize it! It will provide you with a code diff, an explanation towards what is going on, as well as provide you with some useful links to explore more within the documentation! Most code examples start from the following python code before integrating 🤗 Accelerate in some way: ```python for batch in dataloader: optimizer.zero_grad() inputs, targets = batch inputs = inputs.to(device) targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss.backward() optimizer.step() scheduler.step() ``` <div class="block dark:hidden"> <iframe src="https://hf-accelerate-accelerate-examples.hf.space?__theme=light" width="850" height="1600" ></iframe> </div> <div class="hidden dark:block"> <iframe src="https://hf-accelerate-accelerate-examples.hf.space?__theme=dark" width="850" height="1600" ></iframe> </div>

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/distributed_inference.md

# Distributed Inference with 🤗 Accelerate Distributed inference is a common use case, especially with natural language processing (NLP) models. Users often want to send a number of different prompts, each to a different GPU, and then get the results back. This also has other cases outside of just NLP, however for this tutorial we will focus on just this idea of each GPU receiving a different prompt, and then returning the results. ## The Problem Normally when doing this, users send the model to a specific device to load it from the CPU, and then move each prompt to a different device. A basic pipeline using the `diffusers` library might look something like so: ```python import torch import torch.distributed as dist from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) ``` Followed then by performing inference based on the specific prompt: ```python def run_inference(rank, world_size): dist.init_process_group("nccl", rank=rank, world_size=world_size) pipe.to(rank) if torch.distributed.get_rank() == 0: prompt = "a dog" elif torch.distributed.get_rank() == 1: prompt = "a cat" result = pipe(prompt).images[0] result.save(f"result_{rank}.png") ``` One will notice how we have to check the rank to know what prompt to send, which can be a bit tedious. A user might then also think that with 🤗 Accelerate, using the `Accelerator` to prepare a dataloader for such a task might also be a simple way to manage this. (To learn more, check out the relevant section in the [Quick Tour](../quicktour#distributed-evaluation)) Can it manage it? Yes. Does it add unneeded extra code however: also yes. ## The Solution With 🤗 Accelerate, we can simplify this process by using the [`Accelerator.split_between_processes`] context manager (which also exists in `PartialState` and `AcceleratorState`). This function will automatically split whatever data you pass to it (be it a prompt, a set of tensors, a dictionary of the prior data, etc.) across all the processes (with a potential to be padded) for you to use right away. Let's rewrite the above example using this context manager: ```python from accelerate import PartialState # Can also be Accelerator or AcceleratorState from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) distributed_state = PartialState() pipe.to(distributed_state.device) # Assume two processes with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt: result = pipe(prompt).images[0] result.save(f"result_{distributed_state.process_index}.png") ``` And then to launch the code, we can use the 🤗 Accelerate: If you have generated a config file to be used using `accelerate config`: ```bash accelerate launch distributed_inference.py ``` If you have a specific config file you want to use: ```bash accelerate launch --config_file my_config.json distributed_inference.py ``` Or if don't want to make any config files and launch on two GPUs: > Note: You will get some warnings about values being guessed based on your system. To remove these you can do `accelerate config default` or go through `accelerate config` to create a config file. ```bash accelerate launch --num_processes 2 distributed_inference.py ``` We've now reduced the boilerplate code needed to split this data to a few lines of code quite easily. But what if we have an odd distribution of prompts to GPUs? For example, what if we have 3 prompts, but only 2 GPUs? Under the context manager, the first GPU would receive the first two prompts and the second GPU the third, ensuring that all prompts are split and no overhead is needed. *However*, what if we then wanted to do something with the results of *all the GPUs*? (Say gather them all and perform some kind of post processing) You can pass in `apply_padding=True` to ensure that the lists of prompts are padded to the same length, with extra data being taken from the last sample. This way all GPUs will have the same number of prompts, and you can then gather the results. <Tip> This is only needed when trying to perform an action such as gathering the results, where the data on each device needs to be the same length. Basic inference does not require this. </Tip> For instance: ```python from accelerate import PartialState # Can also be Accelerator or AcceleratorState from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) distributed_state = PartialState() pipe.to(distributed_state.device) # Assume two processes with distributed_state.split_between_processes(["a dog", "a cat", "a chicken"], apply_padding=True) as prompt: result = pipe(prompt).images ``` On the first GPU, the prompts will be `["a dog", "a cat"]`, and on the second GPU it will be `["a chicken", "a chicken"]`. Make sure to drop the final sample, as it will be a duplicate of the previous one.

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/megatron_lm.md

# Megatron-LM [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) enables training large transformer language models at scale. It provides efficient tensor, pipeline and sequence based model parallelism for pre-training transformer based Language Models such as [GPT](https://arxiv.org/abs/2005.14165) (Decoder Only), [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Encoder Only) and [T5](https://arxiv.org/abs/1910.10683) (Encoder-Decoder). For detailed information and how things work behind the scene please refer the github [repo](https://github.com/NVIDIA/Megatron-LM). ## What is integrated? Accelerate integrates following feature of Megatron-LM to enable large scale pre-training/finetuning of BERT (Encoder), GPT (Decoder) or T5 models (Encoder and Decoder): a. **Tensor Parallelism (TP)**: Reduces memory footprint without much additional communication on intra-node ranks. Each tensor is split into multiple chunks with each shard residing on separate GPU. At each step, the same mini-batch of data is processed independently and in parallel by each shard followed by syncing across all GPUs (`all-reduce` operation). In a simple transformer layer, this leads to 2 `all-reduces` in the forward path and 2 in the backward path. For more details, please refer research paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) and this section of 🤗 blogpost [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#tensor-parallelism). b. **Pipeline Parallelism (PP)**: Reduces memory footprint and enables large scale training via inter-node parallelization. Reduces the bubble of naive PP via PipeDream-Flush schedule/1F1B schedule and Interleaved 1F1B schedule. Layers are distributed uniformly across PP stages. For example, if a model has `24` layers and we have `4` GPUs for pipeline parallelism, each GPU will have `6` layers (24/4). For more details on schedules to reduce the idle time of PP, please refer to the research paper [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://arxiv.org/pdf/2104.04473.pdf) and this section of 🤗 blogpost [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#pipeline-parallelism). c. **Sequence Parallelism (SP)**: Reduces memory footprint without any additional communication. Only applicable when using TP. It reduces activation memory required as it prevents the same copies to be on the tensor parallel ranks post `all-reduce` by replacing then with `reduce-scatter` and `no-op` operation would be replaced by `all-gather`. As `all-reduce = reduce-scatter + all-gather`, this saves a ton of activation memory at no added communication cost. To put it simply, it shards the outputs of each transformer layer along sequence dimension, e.g., if the sequence length is `1024` and the TP size is `4`, each GPU will have `256` tokens (1024/4) for each sample. This increases the batch size that can be supported for training. For more details, please refer to the research paper [Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/pdf/2205.05198.pdf). d. **Data Parallelism (DP)** via Distributed Optimizer: Reduces the memory footprint by sharding optimizer states and gradients across DP ranks (versus the traditional method of replicating the optimizer state across data parallel ranks). For example, when using Adam optimizer with mixed-precision training, each parameter accounts for 12 bytes of memory. This gets distributed equally across the GPUs, i.e., each parameter would account for 3 bytes (12/4) if we have 4 GPUs. For more details, please refer the research paper [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/pdf/1910.02054.pdf) and following section of 🤗 blog [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#zero-data-parallelism). e. **Selective Activation Recomputation**: Reduces the memory footprint of activations significantly via smart activation checkpointing. It doesn't store activations occupying large memory while being fast to recompute thereby achieving great tradeoff between memory and recomputation. For example, for GPT-3, this leads to 70% reduction in required memory for activations at the expense of only 2.7% FLOPs overhead for recomputation of activations. For more details, please refer to the research paper [Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/pdf/2205.05198.pdf). f. **Fused Kernels**: Fused Softmax, Mixed Precision Fused Layer Norm and Fused gradient accumulation to weight gradient computation of linear layer. PyTorch JIT compiled Fused GeLU and Fused Bias+Dropout+Residual addition. g. **Support for Indexed datasets**: Efficient binary format of datasets for large scale training. Support for the `mmap`, `cached` index file and the `lazy` loader format. h. **Checkpoint reshaping and interoperability**: Utility for reshaping Megatron-LM checkpoints of variable tensor and pipeline parallel sizes to the beloved 🤗 Transformers sharded checkpoints as it has great support with plethora of tools such as 🤗 Accelerate Big Model Inference, Megatron-DeepSpeed Inference etc. Support is also available for converting 🤗 Transformers sharded checkpoints to Megatron-LM checkpoint of variable tensor and pipeline parallel sizes for large scale training. ## Pre-Requisites You will need to install the latest pytorch, cuda, nccl, and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start) releases and the nltk library. See [documentation](https://github.com/NVIDIA/Megatron-LM#setup) for more details. Another way to setup the environment is to pull an NVIDIA PyTorch Container that comes with all the required installations from NGC. Below is a step-by-step method to set up the conda environment: 1. Create a virtual environment ``` conda create --name ml ``` 2. Assuming that the machine has CUDA 11.3 installed, installing the corresponding PyTorch GPU Version ``` conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch ``` 3. Install Nvidia APEX ``` git clone https://github.com/NVIDIA/apex cd apex pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ cd .. ``` 4. Installing Megatron-LM ``` pip install git+https://github.com/huggingface/Megatron-LM.git ``` ## Accelerate Megatron-LM Plugin Important features are directly supported via the `accelerate config` command. An example of the corresponding questions for using Megatron-LM features is shown below: ```bash :~$ accelerate config --config_file "megatron_gpt_config.yaml" In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 0 Which type of machine are you using? ([0] No distributed training, [1] multi-CPU, [2] multi-GPU, [3] TPU): 2 How many different machines will you use (use more than 1 for multi-node training)? [1]: Do you want to use DeepSpeed? [yes/NO]: Do you want to use FullyShardedDataParallel? [yes/NO]: Do you want to use Megatron-LM ? [yes/NO]: yes What is the Tensor Parallelism degree/size? [1]:2 Do you want to enable Sequence Parallelism? [YES/no]: What is the Pipeline Parallelism degree/size? [1]:2 What is the number of micro-batches? [1]:2 Do you want to enable selective activation recomputation? [YES/no]: Do you want to use distributed optimizer which shards optimizer state and gradients across data parallel ranks? [YES/no]: What is the gradient clipping value based on global L2 Norm (0 to disable)? [1.0]: How many GPU(s) should be used for distributed training? [1]:4 Do you wish to use FP16 or BF16 (mixed precision)? [NO/fp16/bf16]: bf16 ``` The resulting config is shown below: ``` ~$ cat megatron_gpt_config.yaml compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: MEGATRON_LM downcast_bf16: 'no' fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main megatron_lm_config: megatron_lm_gradient_clipping: 1.0 megatron_lm_num_micro_batches: 2 megatron_lm_pp_degree: 2 megatron_lm_recompute_activations: true megatron_lm_sequence_parallelism: true megatron_lm_tp_degree: 2 megatron_lm_use_distributed_optimizer: true mixed_precision: bf16 num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true use_cpu: false ``` We will take the example of GPT pre-training. The minimal changes required to the official `run_clm_no_trainer.py` to use Megatron-LM are as follows: 1. As Megatron-LM uses its own implementation of Optimizer, the corresponding scheduler compatible with it needs to be used. As such, support for only the Megatron-LM's scheduler is present. User will need to create `accelerate.utils.MegatronLMDummyScheduler`. Example is given below: ```python from accelerate.utils import MegatronLMDummyScheduler if accelerator.distributed_type == DistributedType.MEGATRON_LM: lr_scheduler = MegatronLMDummyScheduler( optimizer=optimizer, total_num_steps=args.max_train_steps, warmup_num_steps=args.num_warmup_steps, ) else: lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) ``` 2. Getting the details of the total batch size now needs to be cognization of tensor and pipeline parallel sizes. Example of getting the effective total batch size is shown below: ```python if accelerator.distributed_type == DistributedType.MEGATRON_LM: total_batch_size = accelerator.state.megatron_lm_plugin.global_batch_size else: total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps ``` 3. When using Megatron-LM, the losses are already averaged across the data parallel group ```python if accelerator.distributed_type == DistributedType.MEGATRON_LM: losses.append(loss) else: losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) if accelerator.distributed_type == DistributedType.MEGATRON_LM: losses = torch.tensor(losses) else: losses = torch.cat(losses) ``` 4. For Megatron-LM, we need to save the model using `accelerator.save_state` ```python if accelerator.distributed_type == DistributedType.MEGATRON_LM: accelerator.save_state(args.output_dir) else: unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) ``` That's it! We are good to go 🚀. Please find the example script in the examples folder at the path `accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py`. Let's run it for `gpt-large` model architecture using 4 A100-80GB GPUs. ```bash accelerate launch --config_file megatron_gpt_config.yaml \ examples/by_feature/megatron_lm_gpt_pretraining.py \ --config_name "gpt2-large" \ --tokenizer_name "gpt2-large" \ --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 \ --block_size 1024 \ --learning_rate 5e-5 \ --per_device_train_batch_size 24 \ --per_device_eval_batch_size 24 \ --num_train_epochs 5 \ --with_tracking \ --report_to "wandb" \ --output_dir "awesome_model" ``` Below are some important excerpts from the output logs: ```bash Loading extension module fused_dense_cuda... >>> done with compiling and loading fused kernels. Compilation time: 3.569 seconds > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432) Building gpt model in the pre-training mode. The Megatron LM model weights are initialized at random in `accelerator.prepare`. Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup. Preparing dataloader Preparing dataloader Preparing model > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 210753280 > number of parameters on (tensor, pipeline) model parallel rank (1, 1): 209445120 > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 210753280 > number of parameters on (tensor, pipeline) model parallel rank (0, 1): 209445120 Preparing optimizer Preparing scheduler > learning rate decay style: linear 10/10/2022 22:57:22 - INFO - __main__ - ***** Running training ***** 10/10/2022 22:57:22 - INFO - __main__ - Num examples = 2318 10/10/2022 22:57:22 - INFO - __main__ - Num Epochs = 5 10/10/2022 22:57:22 - INFO - __main__ - Instantaneous batch size per device = 24 10/10/2022 22:57:22 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 48 10/10/2022 22:57:22 - INFO - __main__ - Gradient Accumulation steps = 1 10/10/2022 22:57:22 - INFO - __main__ - Total optimization steps = 245 20%|████████████▍ | 49/245 [01:04<04:09, 1.27s/it] 10/10/2022 22:58:29 - INFO - __main__ - epoch 0: perplexity: 1222.1594275215962 eval_loss: 7.10837459564209 40%|████████████████████████▊ | 98/245 [02:10<03:07, 1.28s/it] 10/10/2022 22:59:35 - INFO - __main__ - epoch 1: perplexity: 894.5236583794557 eval_loss: 6.796291351318359 60%|████████████████████████████████████▌ | 147/245 [03:16<02:05, 1.28s/it] 10/10/2022 23:00:40 - INFO - __main__ - epoch 2: perplexity: 702.8458788508042 eval_loss: 6.555137634277344 80%|████████████████████████████████████████████████▊ | 196/245 [04:22<01:02, 1.28s/it] 10/10/2022 23:01:46 - INFO - __main__ - epoch 3: perplexity: 600.3220028695281 eval_loss: 6.39746618270874 100%|█████████████████████████████████████████████████████████████| 245/245 [05:27<00:00, 1.28s/it] ``` There are a large number of other options/features that one can set using `accelerate.utils.MegatronLMPlugin`. ## Advanced features to leverage writing custom train step and Megatron-LM Indexed Datasets For leveraging more features, please go through below details. 1. Below is an example of changes required to customize the Train Step while using Megatron-LM. You will implement the `accelerate.utils.AbstractTrainStep` or inherit from their corresponding children `accelerate.utils.GPTTrainStep`, `accelerate.utils.BertTrainStep` or `accelerate.utils.T5TrainStep`. ```python from accelerate.utils import MegatronLMDummyScheduler, GPTTrainStep, avg_losses_across_data_parallel_group # Custom loss function for the Megatron model class GPTTrainStepWithCustomLoss(GPTTrainStep): def __init__(self, megatron_args, **kwargs): super().__init__(megatron_args) self.kwargs = kwargs def get_loss_func(self): def loss_func(inputs, loss_mask, output_tensor): batch_size, seq_length = output_tensor.shape losses = output_tensor.float() loss_mask = loss_mask.view(-1).float() loss = losses.view(-1) * loss_mask # Resize and average loss per sample loss_per_sample = loss.view(batch_size, seq_length).sum(axis=1) loss_mask_per_sample = loss_mask.view(batch_size, seq_length).sum(axis=1) loss_per_sample = loss_per_sample / loss_mask_per_sample # Calculate and scale weighting weights = torch.stack([(inputs == kt).float() for kt in self.kwargs["keytoken_ids"]]).sum(axis=[0, 2]) weights = 1.0 + self.kwargs["alpha"] * weights # Calculate weighted average weighted_loss = (loss_per_sample * weights).mean() # Reduce loss across data parallel groups averaged_loss = avg_losses_across_data_parallel_group([weighted_loss]) return weighted_loss, {"lm loss": averaged_loss[0]} return loss_func def get_forward_step_func(self): def forward_step(data_iterator, model): """Forward step.""" # Get the batch. tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator) output_tensor = model(tokens, position_ids, attention_mask, labels=labels) return output_tensor, partial(self.loss_func, tokens, loss_mask) return forward_step def main(): # Custom loss function for the Megatron model keytoken_ids = [] keywords = ["plt", "pd", "sk", "fit", "predict", " plt", " pd", " sk", " fit", " predict"] for keyword in keywords: ids = tokenizer([keyword]).input_ids[0] if len(ids) == 1: keytoken_ids.append(ids[0]) accelerator.print(f"Keytoken ids: {keytoken_ids}") accelerator.state.megatron_lm_plugin.custom_train_step_class = GPTTrainStepWithCustomLoss accelerator.state.megatron_lm_plugin.custom_train_step_kwargs = { "keytoken_ids": keytoken_ids, "alpha": 0.25, } ``` 2. For using the Megatron-LM datasets, a few more changes are required. Dataloaders for these datasets are available only on rank 0 of each tensor parallel group. As such, there are rank where dataloader won't be available and this requires tweaks to the training loop. Being able to do all this shows how flexible and extensible 🤗 Accelerate is. The changes required are as follows. a. For Megatron-LM indexed datasets, we need to use `MegatronLMDummyDataLoader` and pass the required dataset args to it such as `data_path`, `seq_length` etc. See [here](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/arguments.py#L804) for the list of available args. ```python from accelerate.utils import MegatronLMDummyDataLoader megatron_dataloader_config = { "data_path": args.data_path, "splits_string": args.splits_string, "seq_length": args.block_size, "micro_batch_size": args.per_device_train_batch_size, } megatron_dataloader = MegatronLMDummyDataLoader(**megatron_dataloader_config) accelerator.state.megatron_lm_plugin.megatron_dataset_flag = True ``` b. `megatron_dataloader` is repeated 3 times to get training, validation and test dataloaders as per the `args.splits_string` proportions ```python model, optimizer, lr_scheduler, train_dataloader, eval_dataloader, _ = accelerator.prepare( model, optimizer, lr_scheduler, megatron_dataloader, megatron_dataloader, megatron_dataloader ) ``` c. Changes to training and evaluation loops as dataloader is only available on tensor parallel ranks 0 So, we need to iterate only if the dataloader isn't `None` else provide empty dict As such, we loop using `while` loop and break when `completed_steps` is equal to `args.max_train_steps` This is similar to the Megatron-LM setup wherein user has to provide `max_train_steps` when using Megaton-LM indexed datasets. This displays how flexible and extensible 🤗 Accelerate is. ```python while completed_steps < args.max_train_steps: model.train() batch = next(train_dataloader) if train_dataloader is not None else {} outputs = model(**batch) loss = outputs.loss ... if completed_steps % eval_interval == 0: eval_completed_steps = 0 losses = [] while eval_completed_steps < eval_iters: model.eval() with torch.no_grad(): batch = next(eval_dataloader) if eval_dataloader is not None else {} outputs = model(**batch) ``` ## Utility for Checkpoint reshaping and interoperability 1. The scripts for these are present in 🤗 Transformers library under respective models. Currently, it is available for GPT model [checkpoint_reshaping_and_interoperability.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py) 2. Below is an example of conversion of checkpoint from Megatron-LM to universal 🤗 Transformers sharded checkpoint. ```bash python checkpoint_reshaping_and_interoperability.py \ --convert_checkpoint_from_megatron_to_transformers \ --load_path "gpt/iter_0005000" \ --save_path "gpt/trfs_checkpoint" \ --max_shard_size "200MB" \ --tokenizer_name "gpt2" \ --print-checkpoint-structure ``` 3. Conversion of checkpoint from transformers to megatron with `tp_size=2`, `pp_size=2` and `dp_size=2`. ```bash python checkpoint_utils/megatgron_gpt2/checkpoint_reshaping_and_interoperability.py \ --load_path "gpt/trfs_checkpoint" \ --save_path "gpt/megatron_lm_checkpoint" \ --target_tensor_model_parallel_size 2 \ --target_pipeline_model_parallel_size 2 \ --target_data_parallel_size 2 \ --target_params_dtype "bf16" \ --make_vocab_size_divisible_by 128 \ --use_distributed_optimizer \ --print-checkpoint-structure ``` ## Megatron-LM GPT models support returning logits and `megatron_generate` function for text generation 1. Returning logits require setting `require_logits=True` in MegatronLMPlugin as shown below. These would be available on the in the last stage of pipeline. ```python megatron_lm_plugin = MegatronLMPlugin(return_logits=True) ``` 2. `megatron_generate` method for Megatron-LM GPT model: This will use Tensor and Pipeline Parallelism to complete generations for a batch of inputs when using greedy with/without top_k/top_p sampling and for individual prompt inputs when using beam search decoding. Only a subset of features of transformers generate is supported. This will help in using large models via tensor and pipeline parallelism for generation (already does key-value caching and uses fused kernels by default). This requires data parallel size to be 1, sequence parallelism and activation checkpointing to be disabled. It also requires specifying path to tokenizer's vocab file and merges file. Below example shows how to configure and use `megatron_generate` method for Megatron-LM GPT model. ```python # specifying tokenizer's vocab and merges file vocab_file = os.path.join(args.resume_from_checkpoint, "vocab.json") merge_file = os.path.join(args.resume_from_checkpoint, "merges.txt") other_megatron_args = {"vocab_file": vocab_file, "merge_file": merge_file} megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args) # inference using `megatron_generate` functionality tokenizer.pad_token = tokenizer.eos_token max_new_tokens = 64 batch_texts = [ "Are you human?", "The purpose of life is", "The arsenal was constructed at the request of", "How are you doing these days?", ] batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True) # top-p sampling generated_tokens = model.megatron_generate( batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, top_p=0.8, top_p_decay=0.5, temperature=0.9, ) decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy()) accelerator.print(decoded_preds) # top-k sampling generated_tokens = model.megatron_generate( batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, top_k=50, temperature=0.9, ) decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy()) accelerator.print(decoded_preds) # adding `bos` token at the start generated_tokens = model.megatron_generate( batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, add_BOS=True ) decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy()) accelerator.print(decoded_preds) # beam search => only takes single prompt batch_texts = ["The purpose of life is"] batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True) generated_tokens = model.megatron_generate( batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, num_beams=20, length_penalty=1.5, ) decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy()) accelerator.print(decoded_preds) ``` 3. An end-to-end example of using `megatron_generate` method for Megatron-LM GPT model is available at [megatron_gpt2_generation.py](https://github.com/pacman100/accelerate-megatron-test/blob/main/src/inference/megatron_gpt2_generation.py) with config file [megatron_lm_gpt_generate_config.yaml](https://github.com/pacman100/accelerate-megatron-test/blob/main/src/Configs/megatron_lm_gpt_generate_config.yaml). The bash script with accelerate launch command is available at [megatron_lm_gpt_generate.sh](https://github.com/pacman100/accelerate-megatron-test/blob/main/megatron_lm_gpt_generate.sh). The output logs of the script are available at [megatron_lm_gpt_generate.log](https://github.com/pacman100/accelerate-megatron-test/blob/main/output_logs/megatron_lm_gpt_generate.log). ## Support for ROPE and ALiBi Positional embeddings and Multi-Query Attention 1. For ROPE/ALiBi attention, pass `position_embedding_type` with `("absolute" | "rotary" | "alibi")` to `MegatronLMPlugin` as shown below. ```python other_megatron_args = {"position_embedding_type": "alibi"} megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args) ``` 2. For Multi-Query Attention, pass `attention_head_type` with `("multihead" | "multiquery")` to `MegatronLMPlugin` as shown below. ```python other_megatron_args = {"attention_head_type": "multiquery"} megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args) ``` ## Caveats 1. Supports Transformers GPT2, Megatron-BERT and T5 models. This covers Decoder only, Encode only and Encoder-Decoder model classes. 2. Only loss is returned from model forward pass as there is quite complex interplay of pipeline, tensor and data parallelsim behind the scenes. The `model(**batch_data)` call return loss(es) averaged across the data parallel ranks. This is fine for most cases wherein pre-training jobs are run using Megatron-LM features and you can easily compute the `perplexity` using the loss. For GPT model, returning logits in addition to loss(es) is supported. These logits aren't gathered across data parallel ranks. Use `accelerator.utils.gather_across_data_parallel_groups` to gather logits across data parallel ranks. These logits along with labels can be used for computing various performance metrics. 3. The main process is the last rank as the losses/logits are available in the last stage of pipeline. `accelerator.is_main_process` and `accelerator.is_local_main_process` return `True` for last rank when using Megatron-LM integration. 4. In `accelerator.prepare` call, a Megatron-LM model corresponding to a given Transformers model is created with random weights. Please use `accelerator.load_state` to load the Megatron-LM checkpoint with matching TP, PP and DP partitions. 5. Currently, checkpoint reshaping and interoperability support is only available for GPT. Soon it will be extended to BERT and T5. 6. `gradient_accumulation_steps` needs to be 1. When using Megatron-LM, micro batches in pipeline parallelism setting is synonymous with gradient accumulation. 7. When using Megatron-LM, use `accelerator.save_state` and `accelerator.load_state` for saving and loading checkpoints. 8. Below are the mapping from Megatron-LM model architectures to the the equivalent 🤗 transformers model architectures. Only these 🤗 transformers model architectures are supported. a. Megatron-LM [BertModel](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/bert_model.py) : 🤗 transformers models with `megatron-bert` in config's model type, e.g., [MegatronBERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert) b. Megatron-LM [GPTModel](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py) : 🤗 transformers models with `gpt2` in config's model type, e.g., [OpenAI GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2) c. Megatron-LM [T5Model](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/t5_model.py) : 🤗 transformers models with `t5` in config's model type, e.g., [T5](https://huggingface.co/docs/transformers/model_doc/t5) and [MT5](https://huggingface.co/docs/transformers/model_doc/mt5)

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hf_public_repos/accelerate/docs/source/usage_guides/mps.md

# Accelerated PyTorch Training on Mac With PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac. Apple's Metal Performance Shaders (MPS) as a backend for PyTorch enables this and can be used via the new `"mps"` device. This will map computational graphs and primitives on the MPS Graph framework and tuned kernels provided by MPS. For more information please refer official documents [Introducing Accelerated PyTorch Training on Mac](https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/) and [MPS BACKEND](https://pytorch.org/docs/stable/notes/mps.html). ### Benefits of Training and Inference using Apple Silicon Chips 1. Enables users to train larger networks or batch sizes locally 2. Reduces data retrieval latency and provides the GPU with direct access to the full memory store due to unified memory architecture. Therefore, improving end-to-end performance. 3. Reduces costs associated with cloud-based development or the need for additional local GPUs. **Pre-requisites**: To install torch with mps support, please follow this nice medium article [GPU-Acceleration Comes to PyTorch on M1 Macs](https://medium.com/towards-data-science/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1). ## How it works out of the box It is enabled by default on MacOs machines with MPS enabled Apple Silicon GPUs. To disable it, pass `--cpu` flag to `accelerate launch` command or answer the corresponding question when answering the `accelerate config` questionnaire. You can directly run the following script to test it out on MPS enabled Apple Silicon machines: ```bash accelerate launch /examples/cv_example.py --data_dir images ``` ## A few caveats to be aware of 1. We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing) on your MacOS machine. It has major fixes related to model correctness and performance improvements for transformer based models. Please refer to https://github.com/pytorch/pytorch/issues/82707 for more details. 2. Distributed setups `gloo` and `nccl` are not working with `mps` device. This means that currently only single GPU of `mps` device type can be used. Finally, please, remember that, 🤗 `Accelerate` only integrates MPS backend, therefore if you have any problems or questions with regards to MPS backend usage, please, file an issue with [PyTorch GitHub](https://github.com/pytorch/pytorch/issues).

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hf_public_repos/accelerate/docs/source/usage_guides/quantization.md

# Quantization ## `bitsandbytes` Integration 🤗 Accelerate brings `bitsandbytes` quantization to your model. You can now load any pytorch model in 8-bit or 4-bit with a few lines of code. If you want to use 🤗 Transformers models with `bitsandbytes`, you should follow this [documentation](https://huggingface.co/docs/transformers/main_classes/quantization). To learn more about how the `bitsandbytes` quantization works, check out the blog posts on [8-bit quantization](https://huggingface.co/blog/hf-bitsandbytes-integration) and [4-bit quantization](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ### Pre-Requisites You will need to install the following requirements: - Install `bitsandbytes` library ```bash pip install bitsandbytes ``` - Install latest `accelerate` from source ```bash pip install git+https://github.com/huggingface/accelerate.git ``` - Install `minGPT` and `huggingface_hub` to run examples ```bash git clone https://github.com/karpathy/minGPT.git pip install minGPT/ pip install huggingface_hub ``` ### How it works First, we need to initialize our model. To save memory, we can initialize an empty model using the context manager [`init_empty_weights`]. Let's take the GPT2 model from minGPT library. ```py from accelerate import init_empty_weights from mingpt.model import GPT model_config = GPT.get_default_config() model_config.model_type = 'gpt2-xl' model_config.vocab_size = 50257 model_config.block_size = 1024 with init_empty_weights(): empty_model = GPT(model_config) ``` Then, we need to get the path to the weights of your model. The path can be the state_dict file (e.g. "pytorch_model.bin") or a folder containing the sharded checkpoints. ```py from huggingface_hub import snapshot_download weights_location = snapshot_download(repo_id="marcsun13/gpt2-xl-linear-sharded") ``` Finally, you need to set your quantization configuration with [`~utils.BnbQuantizationConfig`]. Here's an example for 8-bit quantization: ```py from accelerate.utils import BnbQuantizationConfig bnb_quantization_config = BnbQuantizationConfig(load_in_8bit=True, llm_int8_threshold = 6) ``` Here's an example for 4-bit quantization: ```py from accelerate.utils import BnbQuantizationConfig bnb_quantization_config = BnbQuantizationConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4") ``` To quantize your empty model with the selected configuration, you need to use [`~utils.load_and_quantize_model`]. ```py from accelerate.utils import load_and_quantize_model quantized_model = load_and_quantize_model(empty_model, weights_location=weights_location, bnb_quantization_config=bnb_quantization_config, device_map = "auto") ``` ### Saving and loading 8-bit model You can save your 8-bit model with accelerate using [`~Accelerator.save_model`]. ```py from accelerate import Accelerator accelerate = Accelerator() new_weights_location = "path/to/save_directory" accelerate.save_model(quantized_model, new_weights_location) quantized_model_from_saved = load_and_quantize_model(empty_model, weights_location=new_weights_location, bnb_quantization_config=bnb_quantization_config, device_map = "auto") ``` Note that 4-bit model serialization is currently not supported. ### Offload modules to cpu and disk You can offload some modules to cpu/disk if you don't have enough space on the GPU to store the entire model on your GPUs. This uses big model inference under the hood. Check this [documentation](https://huggingface.co/docs/accelerate/usage_guides/big_modeling) for more details. For 8-bit quantization, the selected modules will be converted to 8-bit precision. For 4-bit quantization, the selected modules will be kept in `torch_dtype` that the user passed in `BnbQuantizationConfig`. We will add support to convert these offloaded modules in 4-bit when 4-bit serialization will be possible. You just need to pass a custom `device_map` in order to offload modules on cpu/disk. The offload modules will be dispatched on the GPU when needed. Here's an example : ```py device_map = { "transformer.wte": 0, "transformer.wpe": 0, "transformer.drop": 0, "transformer.h": "cpu", "transformer.ln_f": "disk", "lm_head": "disk", } ``` ### Fine-tune a quantized model It is not possible to perform pure 8bit or 4bit training on these models. However, you can train these models by leveraging parameter efficient fine tuning methods (PEFT) and train for example adapters on top of them. Please have a look at [peft](https://github.com/huggingface/peft) library for more details. Currently, you can't add adapters on top of any quantized model. However, with the official support of adapters with 🤗 Transformers models, you can fine-tune quantized models. If you want to finetune a 🤗 Transformers model , follow this [documentation](https://huggingface.co/docs/transformers/main_classes/quantization) instead. Check out this [demo](https://colab.research.google.com/drive/1VoYNfYDKcKRQRor98Zbf2-9VQTtGJ24k?usp=sharing) on how to fine-tune a 4-bit 🤗 Transformers model. Note that you don’t need to pass `device_map` when loading the model for training. It will automatically load your model on your GPU. Please note that `device_map=auto` should be used for inference only. ### Example demo - running GPT2 1.5b on a Google Colab Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GTP2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.

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hf_public_repos/accelerate/docs/source/usage_guides/model_size_estimator.md

# Understanding how big of a model can fit on your machine One very difficult aspect when exploring potential models to use on your machine is knowing just how big of a model will *fit* into memory with your current graphics card (such as loading the model onto CUDA). To help alleviate this, 🤗 Accelerate has a CLI interface through `accelerate estimate-memory`. This tutorial will help walk you through using it, what to expect, and at the end link to the interactive demo hosted on the 🤗 Hub which will even let you post those results directly on the model repo! Currently we support searching for models that can be used in `timm` and `transformers`. <Tip> This API will load the model into memory on the `meta` device, so we are not actually downloading and loading the full weights of the model into memory, nor do we need to. As a result it's perfectly fine to measure 8 billion parameter models (or more), without having to worry about if your CPU can handle it! </Tip> ## Gradio Demos Below are a few gradio demos related to what was described above. The first is the official Hugging Face memory estimation space, utilizing Accelerate directly: <div class="block dark:hidden"> <iframe src="https://hf-accelerate-model-memory-usage.hf.space?__theme=light" width="850" height="1600" ></iframe> </div> <div class="hidden dark:block"> <iframe src="https://hf-accelerate-model-memory-usage.hf.space?__theme=dark" width="850" height="1600" ></iframe> </div> A community member has taken the idea and expended it further, allowing you to filter models directly and see if you can run a particular LLM given GPU constraints and LoRA configurations. To play with it, see [here](https://huggingface.co/spaces/Vokturz/can-it-run-llm) for more details. ## The Command When using `accelerate estimate-memory`, you need to pass in the name of the model you want to use, potentially the framework that model utilizing (if it can't be found automatically), and the data types you want the model to be loaded in with. For example, here is how we can calculate the memory footprint for `bert-base-cased`: ```bash accelerate estimate-memory bert-base-cased ``` This will download the `config.json` for `bert-based-cased`, load the model on the `meta` device, and report back how much space it will use: Memory Usage for loading `bert-base-cased`: | dtype | Largest Layer | Total Size | Training using Adam | |---------|---------------|------------|---------------------| | float32 | 84.95 MB | 418.18 MB | 1.61 GB | | float16 | 42.47 MB | 206.59 MB | 826.36 MB | | int8 | 21.24 MB | 103.29 MB | 413.18 MB | | int4 | 10.62 MB | 51.65 MB | 206.59 MB | By default it will return all the supported dtypes (`int4` through `float32`), but if you are interested in specific ones these can be filtered. ### Specific libraries If the source library cannot be determined automatically (like it could in the case of `bert-base-cased`), a library name can be passed in. ```bash accelerate estimate-memory HuggingFaceM4/idefics-80b-instruct --library_name transformers ``` Memory Usage for loading `HuggingFaceM4/idefics-80b-instruct`: | dtype | Largest Layer | Total Size | Training using Adam | |---------|---------------|------------|---------------------| | float32 | 3.02 GB | 297.12 GB | 1.16 TB | | float16 | 1.51 GB | 148.56 GB | 594.24 GB | | int8 | 772.52 MB | 74.28 GB | 297.12 GB | | int4 | 386.26 MB | 37.14 GB | 148.56 GB | ```bash accelerate estimate-memory timm/resnet50.a1_in1k --library_name timm ``` Memory Usage for loading `timm/resnet50.a1_in1k`: | dtype | Largest Layer | Total Size | Training using Adam | |---------|---------------|------------|---------------------| | float32 | 9.0 MB | 97.7 MB | 390.78 MB | | float16 | 4.5 MB | 48.85 MB | 195.39 MB | | int8 | 2.25 MB | 24.42 MB | 97.7 MB | | int4 | 1.12 MB | 12.21 MB | 48.85 MB | ### Specific dtypes As mentioned earlier, while we return `int4` through `float32` by default, any dtype can be used from `float32`, `float16`, `int8`, and `int4`. To do so, pass them in after specifying `--dtypes`: ```bash accelerate estimate-memory bert-base-cased --dtypes float32 float16 ``` Memory Usage for loading `bert-base-cased`: | dtype | Largest Layer | Total Size | Training using Adam | |---------|---------------|------------|---------------------| | float32 | 84.95 MB | 413.18 MB | 1.61 GB | | float16 | 42.47 MB | 206.59 MB | 826.36 MB | ## Caveats with this calculator This calculator will tell you how much memory is needed to purely load the model in, *not* to perform inference. This calculation is accurate within a few % of the actual value, so it is a very good view of just how much memory it will take. For instance loading `bert-base-cased` actually takes `413.68 MB` when loaded on CUDA in full precision, and the calculator estimates `413.18 MB`. When performing inference you can expect to add up to an additional 20% as found by [EleutherAI](https://blog.eleuther.ai/transformer-math/). We'll be conducting research into finding a more accurate estimate to these values, and will update this calculator once done.

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/training_zoo.md

# Example Zoo Below contains a non-exhaustive list of tutorials and scripts showcasing 🤗 Accelerate ## Official Accelerate Examples: ### Basic Examples These examples showcase the base features of Accelerate and are a great starting point - [Barebones NLP example](https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py) - [Barebones distributed NLP example in a Jupyter Notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb) - [Barebones computer vision example](https://github.com/huggingface/accelerate/blob/main/examples/cv_example.py) - [Barebones distributed computer vision example in a Jupyter Notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb) - [Using Accelerate in Kaggle](https://www.kaggle.com/code/muellerzr/multi-gpu-and-accelerate) ### Feature Specific Examples These examples showcase specific features that the Accelerate framework offers - [Automatic memory-aware gradient accumulation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/automatic_gradient_accumulation.py) - [Checkpointing states](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py) - [Cross validation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/cross_validation.py) - [DeepSpeed](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/deepspeed_with_config_support.py) - [Fully Sharded Data Parallelism](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/fsdp_with_peak_mem_tracking.py) - [Gradient accumulation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/gradient_accumulation.py) - [Memory-aware batch size finder](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/memory.py) - [Metric Computation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/multi_process_metrics.py) - [Using Trackers](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/tracking.py) - [Using Megatron-LM](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/megatron_lm_gpt_pretraining.py) ### Full Examples These examples showcase every feature in Accelerate at once that was shown in "Feature Specific Examples" - [Complete NLP example](https://github.com/huggingface/accelerate/blob/main/examples/complete_nlp_example.py) - [Complete computer vision example](https://github.com/huggingface/accelerate/blob/main/examples/complete_cv_example.py) - [Very complete and extensible vision example showcasing SLURM, hydra, and a very extensible usage of the framework](https://github.com/yuvalkirstain/PickScore) - [Causal language model fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm_no_trainer.py) - [Masked language model fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_no_trainer.py) - [Speech pretraining example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py) - [Translation fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py) - [Text classification fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py) - [Semantic segmentation fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py) - [Question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_no_trainer.py) - [Beam search question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py) - [Multiple choice question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/multiple-choice/run_swag_no_trainer.py) - [Named entity recognition fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/token-classification/run_ner_no_trainer.py) - [Image classification fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py) - [Summarization fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization_no_trainer.py) - [End-to-end examples on how to use AWS SageMaker integration of Accelerate](https://github.com/huggingface/notebooks/blob/main/sagemaker/22_accelerate_sagemaker_examples/README.md) - [Megatron-LM examples for various NLp tasks](https://github.com/pacman100/accelerate-megatron-test) ## Integration Examples These are tutorials from libraries that integrate with 🤗 Accelerate: > Don't find your integration here? Make a PR to include it! ### Amphion - [Training Text-to-Speech Models with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/tts/README.md) - [Training Singing Voice Conversion Models with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/svc/README.md) - [Training Vocoders with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/README.md) ### Catalyst - [Distributed training tutorial with Catalyst](https://catalyst-team.github.io/catalyst/tutorials/ddp.html) ### DALLE2-pytorch - [Fine-tuning DALLE2](https://github.com/lucidrains/DALLE2-pytorch#usage) ### 🤗 diffusers - [Performing textual inversion with diffusers](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) - [Training DreamBooth with diffusers](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) ### fastai - [Distributed training from Jupyter Notebooks with fastai](https://docs.fast.ai/tutorial.distributed.html) - [Basic distributed training examples with fastai](https://docs.fast.ai/examples/distributed_app_examples.html) ### GradsFlow - [Auto Image Classification with GradsFlow](https://docs.gradsflow.com/en/latest/examples/nbs/01-ImageClassification/) ### imagen-pytorch - [Fine-tuning Imagen](https://github.com/lucidrains/imagen-pytorch#usage) ### Kornia - [Fine-tuning vision models with Kornia's Trainer](https://kornia.readthedocs.io/en/latest/get-started/training.html) ### PyTorch Accelerated - [Quickstart distributed training tutorial with PyTorch Accelerated](https://pytorch-accelerated.readthedocs.io/en/latest/quickstart.html) ### PyTorch3D - [Perform Deep Learning with 3D data](https://pytorch3d.org/tutorials/) ### Stable-Dreamfusion - [Training with Stable-Dreamfusion to convert text to a 3D model](https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing) ### Tez - [Leaf disease detection with Tez and Accelerate](https://www.kaggle.com/code/abhishek/tez-faster-and-easier-training-for-leaf-detection/notebook) ### trlx - [How to implement a sentiment learning task with trlx](https://github.com/CarperAI/trlx#example-how-to-add-a-task) ### Comfy-UI - [Enabling using large Stable Diffusion Models in low-vram settings using Accelerate](https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/model_management.py#L291-L296) ## In Science Below contains a non-exhaustive list of papers utilizing 🤗 Accelerate. > Don't find your paper here? Make a PR to include it! * Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy: “Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation”, 2023; [arXiv:2305.01569](http://arxiv.org/abs/2305.01569). * Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim: “Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models”, 2023; [arXiv:2305.04091](http://arxiv.org/abs/2305.04091). * Arthur Câmara, Claudia Hauff: “Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models”, 2022; [arXiv:2205.08343](http://arxiv.org/abs/2205.08343). * Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang: “High-throughput Generative Inference of Large Language Models with a Single GPU”, 2023; [arXiv:2303.06865](http://arxiv.org/abs/2303.06865). * Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding: “Autoencoding Galaxy Spectra I: Architecture”, 2022; [arXiv:2211.07890](http://arxiv.org/abs/2211.07890). * Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang: “A Cheaper and Better Diffusion Language Model with Soft-Masked Noise”, 2023; [arXiv:2304.04746](http://arxiv.org/abs/2304.04746). * Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa: “Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions”, 2023; [arXiv:2303.12789](http://arxiv.org/abs/2303.12789). * Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi: “RealFusion: 360° Reconstruction of Any Object from a Single Image”, 2023; [arXiv:2302.10663](http://arxiv.org/abs/2302.10663). * Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li: “Better Aligning Text-to-Image Models with Human Preference”, 2023; [arXiv:2303.14420](http://arxiv.org/abs/2303.14420). * Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang: “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”, 2023; [arXiv:2303.17580](http://arxiv.org/abs/2303.17580). * Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen: “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, 2022; [arXiv:2210.12261](http://arxiv.org/abs/2210.12261). * Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho: “How to Backdoor Diffusion Models?”, 2022; [arXiv:2212.05400](http://arxiv.org/abs/2212.05400). * Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim: “Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation”, 2023; [arXiv:2303.07937](http://arxiv.org/abs/2303.07937). * Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch-Elor, Daniel Cohen-Or: “Localizing Object-level Shape Variations with Text-to-Image Diffusion Models”, 2023; [arXiv:2303.11306](http://arxiv.org/abs/2303.11306). * Dídac Surís, Sachit Menon, Carl Vondrick: “ViperGPT: Visual Inference via Python Execution for Reasoning”, 2023; [arXiv:2303.08128](http://arxiv.org/abs/2303.08128). * Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, Qifeng Chen: “FateZero: Fusing Attentions for Zero-shot Text-based Video Editing”, 2023; [arXiv:2303.09535](http://arxiv.org/abs/2303.09535). * Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi: “NaturalProver: Grounded Mathematical Proof Generation with Language Models”, 2022; [arXiv:2205.12910](http://arxiv.org/abs/2205.12910). * Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; [arXiv:2302.01721](http://arxiv.org/abs/2302.01721). * Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang: “Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement”, 2023; [arXiv:2303.04603](http://arxiv.org/abs/2303.04603). * Shun Shao, Yftah Ziser, Shay Cohen: “Erasure of Unaligned Attributes from Neural Representations”, 2023; [arXiv:2302.02997](http://arxiv.org/abs/2302.02997). * Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, Minjoon Seo: “In-Context Instruction Learning”, 2023; [arXiv:2302.14691](http://arxiv.org/abs/2302.14691). * Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar: “Prismer: A Vision-Language Model with An Ensemble of Experts”, 2023; [arXiv:2303.02506](http://arxiv.org/abs/2303.02506). * Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma: “Learning a Deep Color Difference Metric for Photographic Images”, 2023; [arXiv:2303.14964](http://arxiv.org/abs/2303.14964). * Van-Hoang Le, Hongyu Zhang: “Log Parsing with Prompt-based Few-shot Learning”, 2023; [arXiv:2302.07435](http://arxiv.org/abs/2302.07435). * Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui: “Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?”, 2023; [arXiv:2302.07866](http://arxiv.org/abs/2302.07866). * Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu: “Behavior Cloned Transformers are Neurosymbolic Reasoners”, 2022; [arXiv:2210.07382](http://arxiv.org/abs/2210.07382). * Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde: “Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection”, 2023; [arXiv:2304.13148](http://arxiv.org/abs/2304.13148). DOI: [https://dx.doi.org/10.1145/3539618.3591882 10.1145/3539618.3591882]. * Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or: “Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models”, 2023; [arXiv:2301.13826](http://arxiv.org/abs/2301.13826). * Marcio Fonseca, Yftah Ziser, Shay B. Cohen: “Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents”, 2022; [arXiv:2205.12486](http://arxiv.org/abs/2205.12486). * Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; [arXiv:2302.01721](http://arxiv.org/abs/2302.01721). * Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, Yulia Tsvetkov: “On the Blind Spots of Model-Based Evaluation Metrics for Text Generation”, 2022; [arXiv:2212.10020](http://arxiv.org/abs/2212.10020). * Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham: “In-Context Retrieval-Augmented Language Models”, 2023; [arXiv:2302.00083](http://arxiv.org/abs/2302.00083). * Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang: “MPCFormer: fast, performant and private Transformer inference with MPC”, 2022; [arXiv:2211.01452](http://arxiv.org/abs/2211.01452). * Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao: “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, 2022; [arXiv:2206.11309](http://arxiv.org/abs/2206.11309). * Egil Rønningstad, Erik Velldal, Lilja Øvrelid: “Entity-Level Sentiment Analysis (ELSA): An exploratory task survey”, 2023, Proceedings of the 29th International Conference on Computational Linguistics, 2022, pages 6773-6783; [arXiv:2304.14241](http://arxiv.org/abs/2304.14241). * Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine: “Offline RL for Natural Language Generation with Implicit Language Q Learning”, 2022; [arXiv:2206.11871](http://arxiv.org/abs/2206.11871). * Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig: “Execution-Based Evaluation for Open-Domain Code Generation”, 2022; [arXiv:2212.10481](http://arxiv.org/abs/2212.10481). * Minh-Long Luu, Zeyi Huang, Eric P. Xing, Yong Jae Lee, Haohan Wang: “Expeditious Saliency-guided Mix-up through Random Gradient Thresholding”, 2022; [arXiv:2212.04875](http://arxiv.org/abs/2212.04875). * Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng: “MagicMix: Semantic Mixing with Diffusion Models”, 2022; [arXiv:2210.16056](http://arxiv.org/abs/2210.16056). * Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao: “LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners”, 2021; [arXiv:2110.06274](http://arxiv.org/abs/2110.06274).

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/sagemaker.md

# Amazon SageMaker Hugging Face and Amazon introduced new [Hugging Face Deep Learning Containers (DLCs)](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) to make it easier than ever to train Hugging Face Transformer models in [Amazon SageMaker](https://aws.amazon.com/sagemaker/). ## Getting Started ### Setup & Installation Before you can run your 🤗 Accelerate scripts on Amazon SageMaker you need to sign up for an AWS account. If you do not have an AWS account yet learn more [here](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-set-up.html). After you have your AWS Account you need to install the `sagemaker` sdk for 🤗 Accelerate with: ```bash pip install "accelerate[sagemaker]" --upgrade ``` 🤗 Accelerate currently uses the 🤗 DLCs, with `transformers`, `datasets` and `tokenizers` pre-installed. 🤗 Accelerate is not in the DLC yet (will soon be added!) so to use it within Amazon SageMaker you need to create a `requirements.txt` in the same directory where your training script is located and add it as dependency: ``` accelerate ``` You should also add any other dependencies you have to this `requirements.txt`. ### Configure 🤗 Accelerate You can configure the launch configuration for Amazon SageMaker the same as you do for non SageMaker training jobs with the 🤗 Accelerate CLI: ```bash accelerate config # In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 1 ``` 🤗 Accelerate will go through a questionnaire about your Amazon SageMaker setup and create a config file you can edit. <Tip> 🤗 Accelerate is not saving any of your credentials. </Tip> ### Prepare a 🤗 Accelerate fine-tuning script The training script is very similar to a training script you might run outside of SageMaker, but to save your model after training you need to specify either `/opt/ml/model` or use `os.environ["SM_MODEL_DIR"]` as your save directory. After training, artifacts in this directory are uploaded to S3: ```diff - torch.save('/opt/ml/model`) + accelerator.save('/opt/ml/model') ``` <Tip warning={true}> SageMaker doesn’t support argparse actions. If you want to use, for example, boolean hyperparameters, you need to specify type as bool in your script and provide an explicit True or False value for this hyperparameter. [[REF]](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#prepare-a-pytorch-training-script). </Tip> ### Launch Training You can launch your training with 🤗 Accelerate CLI with: ``` accelerate launch path_to_script.py --args_to_the_script ``` This will launch your training script using your configuration. The only thing you have to do is provide all the arguments needed by your training script as named arguments. **Examples** <Tip> If you run one of the example scripts, don't forget to add `accelerator.save('/opt/ml/model')` to it. </Tip> ```bash accelerate launch ./examples/sagemaker_example.py ``` Outputs: ``` Configuring Amazon SageMaker environment Converting Arguments to Hyperparameters Creating Estimator 2021-04-08 11:56:50 Starting - Starting the training job... 2021-04-08 11:57:13 Starting - Launching requested ML instancesProfilerReport-1617883008: InProgress ......... 2021-04-08 11:58:54 Starting - Preparing the instances for training......... 2021-04-08 12:00:24 Downloading - Downloading input data 2021-04-08 12:00:24 Training - Downloading the training image.................. 2021-04-08 12:03:39 Training - Training image download completed. Training in progress.. ........ epoch 0: {'accuracy': 0.7598039215686274, 'f1': 0.8178438661710037} epoch 1: {'accuracy': 0.8357843137254902, 'f1': 0.882249560632689} epoch 2: {'accuracy': 0.8406862745098039, 'f1': 0.8869565217391304} ........ 2021-04-08 12:05:40 Uploading - Uploading generated training model 2021-04-08 12:05:40 Completed - Training job completed Training seconds: 331 Billable seconds: 331 You can find your model data at: s3://your-bucket/accelerate-sagemaker-1-2021-04-08-11-56-47-108/output/model.tar.gz ``` ## Advanced Features ### Distributed Training: Data Parallelism Set up the accelerate config by running `accelerate config` and answer the SageMaker questions and set it up. To use SageMaker DDP, select it when asked `What is the distributed mode? ([0] No distributed training, [1] data parallelism):`. Example config below: ```yaml base_job_name: accelerate-sagemaker-1 compute_environment: AMAZON_SAGEMAKER distributed_type: DATA_PARALLEL ec2_instance_type: ml.p3.16xlarge iam_role_name: xxxxx image_uri: null mixed_precision: fp16 num_machines: 1 profile: xxxxx py_version: py38 pytorch_version: 1.10.2 region: us-east-1 transformers_version: 4.17.0 use_cpu: false ``` ### Distributed Training: Model Parallelism *currently in development, will be supported soon.* ### Python packages and dependencies 🤗 Accelerate currently uses the 🤗 DLCs, with `transformers`, `datasets` and `tokenizers` pre-installed. If you want to use different/other Python packages you can do this by adding them to the `requirements.txt`. These packages will be installed before your training script is started. ### Local Training: SageMaker Local mode The local mode in the SageMaker SDK allows you to run your training script locally inside the HuggingFace DLC (Deep Learning container) or using your custom container image. This is useful for debugging and testing your training script inside the final container environment. Local mode uses Docker compose (*Note: Docker Compose V2 is not supported yet*). The SDK will handle the authentication against ECR to pull the DLC to your local environment. You can emulate CPU (single and multi-instance) and GPU (single instance) SageMaker training jobs. To use local mode, you need to set your `ec2_instance_type` to `local`. ```yaml ec2_instance_type: local ``` ### Advanced configuration The configuration allows you to override parameters for the [Estimator](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html). These settings have to be applied in the config file and are not part of `accelerate config`. You can control many additional aspects of the training job, e.g. use Spot instances, enable network isolation and many more. ```yaml additional_args: # enable network isolation to restrict internet access for containers enable_network_isolation: True ``` You can find all available configuration [here](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html). ### Use Spot Instances You can use Spot Instances e.g. using (see [Advanced configuration](#advanced-configuration)): ```yaml additional_args: use_spot_instances: True max_wait: 86400 ``` *Note: Spot Instances are subject to be terminated and training to be continued from a checkpoint. This is not handled in 🤗 Accelerate out of the box. Contact us if you would like this feature.* ### Remote scripts: Use scripts located on Github *undecided if feature is needed. Contact us if you would like this feature.*

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hf_public_repos/accelerate/docs/source/usage_guides/fsdp.md

# Fully Sharded Data Parallel To accelerate training huge models on larger batch sizes, we can use a fully sharded data parallel model. This type of data parallel paradigm enables fitting more data and larger models by sharding the optimizer states, gradients and parameters. To read more about it and the benefits, check out the [Fully Sharded Data Parallel blog](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/). We have integrated the latest PyTorch's Fully Sharded Data Parallel (FSDP) training feature. All you need to do is enable it through the config. ## How it works out of the box On your machine(s) just run: ```bash accelerate config ``` and answer the questions asked. This will generate a config file that will be used automatically to properly set the default options when doing ```bash accelerate launch my_script.py --args_to_my_script ``` For instance, here is how you would run `examples/nlp_example.py` (from the root of the repo) with FSDP enabled: ```bash compute_environment: LOCAL_MACHINE debug: false distributed_type: FSDP downcast_bf16: 'no' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_forward_prefetch: false fsdp_cpu_ram_efficient_loading: true fsdp_offload_params: false fsdp_sharding_strategy: FULL_SHARD fsdp_state_dict_type: SHARDED_STATE_DICT fsdp_sync_module_states: true fsdp_transformer_layer_cls_to_wrap: BertLayer fsdp_use_orig_params: true machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ``` ```bash accelerate launch examples/nlp_example.py ``` Currently, `Accelerate` supports the following config through the CLI: `fsdp_sharding_strategy`: [1] FULL_SHARD (shards optimizer states, gradients and parameters), [2] SHARD_GRAD_OP (shards optimizer states and gradients), [3] NO_SHARD (DDP), [4] HYBRID_SHARD (shards optimizer states, gradients and parameters within each node while each node has full copy), [5] HYBRID_SHARD_ZERO2 (shards optimizer states and gradients within each node while each node has full copy) `fsdp_offload_params` : Decides Whether to offload parameters and gradients to CPU `fsdp_auto_wrap_policy`: [1] TRANSFORMER_BASED_WRAP, [2] SIZE_BASED_WRAP, [3] NO_WRAP `fsdp_transformer_layer_cls_to_wrap`: Only applicable for 🤗 Transformers. When using `fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP`, a user may provide a comma-separated string of transformer layer class names (case-sensitive) to wrap, e.g., `BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput`. This is important because submodules that share weights (e.g., embedding layers) should not end up in different FSDP wrapped units. Using this policy, wrapping happens for each block containing Multi-Head Attention followed by a couple of MLP layers. Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit. Therefore, use this for transformer-based models. You can use the `model._no_split_modules` for 🤗 Transformer models by answering `yes` to `Do you want to use the model's `_no_split_modules` to wrap. It will try to use `model._no_split_modules` when possible. `fsdp_min_num_params`: minimum number of parameters when using `fsdp_auto_wrap_policy=SIZE_BASED_WRAP`. `fsdp_backward_prefetch_policy`: [1] BACKWARD_PRE, [2] BACKWARD_POST, [3] NO_PREFETCH `fsdp_forward_prefetch`: if True, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. Should only be used for static-graph models since the prefetching follows the first iteration’s execution order. i.e., if the sub-modules' order changes dynamically during the model's execution do not enable this feature. `fsdp_state_dict_type`: [1] FULL_STATE_DICT, [2] LOCAL_STATE_DICT, [3] SHARDED_STATE_DICT `fsdp_use_orig_params`: If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable parameters. This setting is useful in cases such as parameter-efficient fine-tuning as discussed in [this post](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019). This option also allows one to have multiple optimizer param groups. This should be `True` when creating an optimizer before preparing/wrapping the model with FSDP. `fsdp_cpu_ram_efficient_loading`: Only applicable for 🤗 Transformers models. If True, only the first process loads the pretrained model checkpoint while all other processes have empty weights. This should be set to False if you experience errors when loading the pretrained 🤗 Transformers model via `from_pretrained` method. When this setting is True `fsdp_sync_module_states` also must to be True, otherwise all the processes except the main process would have random weights leading to unexpected behaviour during training. `fsdp_sync_module_states`: If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0. For additional and more nuanced control, you can specify other FSDP parameters via `FullyShardedDataParallelPlugin`. When creating `FullyShardedDataParallelPlugin` object, pass it the parameters that weren't part of the accelerate config or if you want to override them. The FSDP parameters will be picked based on the accelerate config file or launch command arguments and other parameters that you will pass directly through the `FullyShardedDataParallelPlugin` object will set/override that. Below is an example: ```py from accelerate import FullyShardedDataParallelPlugin from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig fsdp_plugin = FullyShardedDataParallelPlugin( state_dict_config=FullStateDictConfig(offload_to_cpu=False, rank0_only=False), optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=False, rank0_only=False), ) accelerator = Accelerator(fsdp_plugin=fsdp_plugin) ``` ## Saving and loading The new recommended way of checkpointing when using FSDP models is to use `SHARDED_STATE_DICT` as `StateDictType` when setting up the accelerate config. Below is the code snippet to save using `save_state` utility of accelerate. ```py accelerator.save_state("ckpt") ``` Inspect the checkpoint folder to see model and optimizer as shards per process: ``` ls ckpt # optimizer_0 pytorch_model_0 random_states_0.pkl random_states_1.pkl scheduler.bin cd ckpt ls optimizer_0 # __0_0.distcp __1_0.distcp ls pytorch_model_0 # __0_0.distcp __1_0.distcp ``` To load them back for resuming the training, use the `load_state` utility of accelerate ```py accelerator.load_state("ckpt") ``` When using transformers `save_pretrained`, pass `state_dict=accelerator.get_state_dict(model)` to save the model state dict. Below is an example: ```diff unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, + state_dict=accelerator.get_state_dict(model), ) ``` ### State Dict `accelerator.get_state_dict` will call the underlying `model.state_dict` implementation using `FullStateDictConfig(offload_to_cpu=True, rank0_only=True)` context manager to get the state dict only for rank 0 and it will be offloaded to CPU. You can then pass `state` into the `save_pretrained` method. There are several modes for `StateDictType` and `FullStateDictConfig` that you can use to control the behavior of `state_dict`. For more information, see the [PyTorch documentation](https://pytorch.org/docs/stable/fsdp.html). ## A few caveats to be aware of - In case of multiple models, pass the optimizers to the prepare call in the same order as corresponding models else `accelerator.save_state()` and `accelerator.load_state()` will result in wrong/unexpected behaviour. - This feature is incompatible with `--predict_with_generate` in the `run_translation.py` script of 🤗 `Transformers` library. For more control, users can leverage the `FullyShardedDataParallelPlugin`. After creating an instance of this class, users can pass it to the Accelerator class instantiation. For more information on these options, please refer to the PyTorch [FullyShardedDataParallel](https://github.com/pytorch/pytorch/blob/0df2e863fbd5993a7b9e652910792bd21a516ff3/torch/distributed/fsdp/fully_sharded_data_parallel.py#L236) code.

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/usage_guides/deepspeed.md

# DeepSpeed [DeepSpeed](https://github.com/microsoft/DeepSpeed) implements everything described in the [ZeRO paper](https://arxiv.org/abs/1910.02054). Some of the salient optimizations are: 1. Optimizer state partitioning (ZeRO stage 1) 2. Gradient partitioning (ZeRO stage 2) 3. Parameter partitioning (ZeRO stage 3) 4. Custom mixed precision training handling 5. A range of fast CUDA-extension-based optimizers 6. ZeRO-Offload to CPU and Disk/NVMe 7. Hierarchical partitioning of model parameters (ZeRO++) ZeRO-Offload has its own dedicated paper: [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840). And NVMe-support is described in the paper [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857). DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. DeepSpeed ZeRO-3 can be used for inference as well since it allows huge models to be loaded on multiple GPUs, which won't be possible on a single GPU. 🤗 Accelerate integrates [DeepSpeed](https://github.com/microsoft/DeepSpeed) via 2 options: 1. Integration of the DeepSpeed features via `deepspeed config file` specification in `accelerate config` . You just supply your custom config file or use our template. Most of this document is focused on this feature. This supports all the core features of DeepSpeed and gives user a lot of flexibility. User may have to change a few lines of code depending on the config. 2. Integration via `deepspeed_plugin`.This supports subset of the DeepSpeed features and uses default options for the rest of the configurations. User need not change any code and is good for those who are fine with most of the default settings of DeepSpeed. ## What is integrated? Training: 1. 🤗 Accelerate integrates all features of DeepSpeed ZeRO. This includes all the ZeRO stages 1, 2 and 3 as well as ZeRO-Offload, ZeRO-Infinity (which can offload to disk/NVMe) and ZeRO++. Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Optimizer along with diagram from this [blog post](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/) ![ZeRO Data Parallelism](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-zero.png) (Source: [link](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)) a. **Stage 1** : Shards optimizer states across data parallel workers/GPUs b. **Stage 2** : Shards optimizer states + gradients across data parallel workers/GPUs c. **Stage 3**: Shards optimizer states + gradients + model parameters across data parallel workers/GPUs d. **Optimizer Offload**: Offloads the gradients + optimizer states to CPU/Disk building on top of ZERO Stage 2 e. **Param Offload**: Offloads the model parameters to CPU/Disk building on top of ZERO Stage 3 f. **Hierarchical Partitioning**: Enables efficient multi-node training with data-parallel training across nodes and ZeRO-3 sharding within a node, built on top of ZeRO Stage 3. <u>Note</u>: With respect to Disk Offload, the disk should be an NVME for decent speed but it technically works on any Disk Inference: 1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see: [deepspeed-zero-inference](#deepspeed-zero-inference). ## How it works? **Pre-Requisites**: Install DeepSpeed version >=0.6.5. Please refer to the [DeepSpeed Installation details](https://github.com/microsoft/DeepSpeed#installation) for more information. We will first look at easy to use integration via `accelerate config`. Followed by more flexible and feature rich `deepspeed config file` integration. ### Accelerate DeepSpeed Plugin On your machine(s) just run: ```bash accelerate config ``` and answer the questions asked. It will ask whether you want to use a config file for DeepSpeed to which you should answer no. Then answer the following questions to generate a basic DeepSpeed config. This will generate a config file that will be used automatically to properly set the default options when doing ```bash accelerate launch my_script.py --args_to_my_script ``` For instance, here is how you would run the NLP example `examples/nlp_example.py` (from the root of the repo) with DeepSpeed Plugin: **ZeRO Stage-2 DeepSpeed Plugin Example** ```bash compute_environment: LOCAL_MACHINE deepspeed_config: gradient_accumulation_steps: 1 gradient_clipping: 1.0 offload_optimizer_device: none offload_param_device: none zero3_init_flag: true zero_stage: 2 distributed_type: DEEPSPEED fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 2 use_cpu: false ``` ```bash accelerate launch examples/nlp_example.py --mixed_precision fp16 ``` **ZeRO Stage-3 with CPU Offload DeepSpeed Plugin Example** ```bash compute_environment: LOCAL_MACHINE deepspeed_config: gradient_accumulation_steps: 1 gradient_clipping: 1.0 offload_optimizer_device: cpu offload_param_device: cpu zero3_init_flag: true zero3_save_16bit_model: true zero_stage: 3 distributed_type: DEEPSPEED fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 2 use_cpu: false ``` ```bash accelerate launch examples/nlp_example.py --mixed_precision fp16 ``` Currently, `Accelerate` supports following config through the CLI: ```bash `zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning `gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them. `gradient_clipping`: Enable gradient clipping with value. `offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2. `offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3. `zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3. `zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3. `mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. ``` To be able to tweak more options, you will need to use a DeepSpeed config file. ### DeepSpeed Config File On your machine(s) just run: ```bash accelerate config ``` and answer the questions asked. It will ask whether you want to use a config file for deepspeed to which you answer yes and provide the path to the deepspeed config file. This will generate a config file that will be used automatically to properly set the default options when doing ```bash accelerate launch my_script.py --args_to_my_script ``` For instance, here is how you would run the NLP example `examples/by_feature/deepspeed_with_config_support.py` (from the root of the repo) with DeepSpeed Config File: **ZeRO Stage-2 DeepSpeed Config File Example** ```bash compute_environment: LOCAL_MACHINE deepspeed_config: deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage2_config.json zero3_init_flag: true distributed_type: DEEPSPEED fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 2 use_cpu: false ``` with the contents of `zero_stage2_config.json` being: ```json { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "weight_decay": "auto", "torch_adam": true, "adam_w_mode": true } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": "auto", "contiguous_gradients": true }, "gradient_accumulation_steps": 1, "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } ``` ```bash accelerate launch examples/by_feature/deepspeed_with_config_support.py \ --config_name "gpt2-large" \ --tokenizer_name "gpt2-large" \ --dataset_name "wikitext" \ --dataset_config_name "wikitext-2-raw-v1" \ --block_size 128 \ --output_dir "./clm/clm_deepspeed_stage2_accelerate" \ --learning_rate 5e-4 \ --per_device_train_batch_size 24 \ --per_device_eval_batch_size 24 \ --num_train_epochs 3 \ --with_tracking \ --report_to "wandb"\ ``` **ZeRO Stage-3 with CPU offload DeepSpeed Config File Example** ```bash compute_environment: LOCAL_MACHINE deepspeed_config: deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage3_offload_config.json zero3_init_flag: true distributed_type: DEEPSPEED fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 2 use_cpu: false ``` with the contents of `zero_stage3_offload_config.json` being: ```json { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto" } }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "sub_group_size": 1e9, "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": "auto" }, "gradient_accumulation_steps": 1, "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } ``` ```bash accelerate launch examples/by_feature/deepspeed_with_config_support.py \ --config_name "gpt2-large" \ --tokenizer_name "gpt2-large" \ --dataset_name "wikitext" \ --dataset_config_name "wikitext-2-raw-v1" \ --block_size 128 \ --output_dir "./clm/clm_deepspeed_stage3_offload_accelerate" \ --learning_rate 5e-4 \ --per_device_train_batch_size 32 \ --per_device_eval_batch_size 32 \ --num_train_epochs 3 \ --with_tracking \ --report_to "wandb"\ ``` **ZeRO++ Config Example** You can use the the features of ZeRO++ by using the appropriate config parameters. Note that ZeRO++ is an extension for ZeRO Stage 3. Here is how the config file can be modified, from [DeepSpeed's ZeRO++ tutorial](https://www.deepspeed.ai/tutorials/zeropp/): ```json { "zero_optimization": { "stage": 3, "reduce_bucket_size": "auto", "zero_quantized_weights": true, "zero_hpz_partition_size": 8, "zero_quantized_gradients": true, "contiguous_gradients": true, "overlap_comm": true } } ``` For hierarchical partitioning, the partition size `zero_hpz_partition_size` should ideally be set to the number of GPUs per node. (For example, the above config file assumes 8 GPUs per node) **Important code changes when using DeepSpeed Config File** 1. DeepSpeed Optimizers and Schedulers. For more information on these, see the [DeepSpeed Optimizers](https://deepspeed.readthedocs.io/en/latest/optimizers.html) and [DeepSpeed Schedulers](https://deepspeed.readthedocs.io/en/latest/schedulers.html) documentation. We will look at the changes needed in the code when using these. a. DS Optim + DS Scheduler: The case when both `optimizer` and `scheduler` keys are present in the DeepSpeed config file. In this situation, those will be used and the user has to use `accelerate.utils.DummyOptim` and `accelerate.utils.DummyScheduler` to replace the PyTorch/Custom optimizers and schedulers in their code. Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this: ```python # Creates Dummy Optimizer if `optimizer` was specified in the config file else creates Adam Optimizer optimizer_cls = ( torch.optim.AdamW if accelerator.state.deepspeed_plugin is None or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) optimizer = optimizer_cls(optimizer_grouped_parameters, lr=args.learning_rate) # Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) else: lr_scheduler = DummyScheduler( optimizer, total_num_steps=args.max_train_steps, warmup_num_steps=args.num_warmup_steps ) ``` b. Custom Optim + Custom Scheduler: The case when both `optimizer` and `scheduler` keys are absent in the DeepSpeed config file. In this situation, no code changes are needed from the user and this is the case when using integration via DeepSpeed Plugin. In the above example we can see that the code remains unchanged if the `optimizer` and `scheduler` keys are absent in the DeepSpeed config file. c. Custom Optim + DS Scheduler: The case when only `scheduler` key is present in the DeepSpeed config file. In this situation, the user has to use `accelerate.utils.DummyScheduler` to replace the PyTorch/Custom scheduler in their code. d. DS Optim + Custom Scheduler: The case when only `optimizer` key is present in the DeepSpeed config file. This will result in an error because you can only use DS Scheduler when using DS Optim. 2. Notice the `auto` values in the above example DeepSpeed config files. These are automatically handled by `prepare` method based on model, dataloaders, dummy optimizer and dummy schedulers provided to `prepare` method. Only the `auto` fields specified in above examples are handled by `prepare` method and the rest have to be explicitly specified by the user. The `auto` values are calculated as: - `reduce_bucket_size`: `hidden_size * hidden_size` - `stage3_prefetch_bucket_size`: `0.9 * hidden_size * hidden_size` - `stage3_param_persistence_threshold`: `10 * hidden_size` For the `auto` feature to work for these 3 config entries - Accelerate will use `model.config.hidden_size` or `max(model.config.hidden_sizes)` as `hidden_size`. If neither of these is available, the launching will fail and you will have to set these 3 config entries manually. Remember the first 2 config entries are the communication buffers - the larger they are the more efficient the comms will be, and the larger they are the more GPU memory they will consume, so it's a tunable performance trade-off. **Things to note when using DeepSpeed Config File** Below is a sample script using `deepspeed_config_file` in different scenarios. Code `test.py`: ```python from accelerate import Accelerator from accelerate.state import AcceleratorState def main(): accelerator = Accelerator() accelerator.print(f"{AcceleratorState()}") if __name__ == "__main__": main() ``` **Scenario 1**: Manually tampered accelerate config file having `deepspeed_config_file` along with other entries. 1. Content of the `accelerate` config: ```yaml command_file: null commands: null compute_environment: LOCAL_MACHINE deepspeed_config: gradient_accumulation_steps: 1 gradient_clipping: 1.0 offload_optimizer_device: 'cpu' offload_param_device: 'cpu' zero3_init_flag: true zero3_save_16bit_model: true zero_stage: 3 deepspeed_config_file: 'ds_config.json' distributed_type: DEEPSPEED downcast_bf16: 'no' dynamo_backend: 'NO' fsdp_config: {} gpu_ids: null machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main megatron_lm_config: {} num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_name: null tpu_zone: null use_cpu: false ``` 2. `ds_config.json`: ```json { "bf16": { "enabled": true }, "zero_optimization": { "stage": 3, "stage3_gather_16bit_weights_on_model_save": false, "offload_optimizer": { "device": "none" }, "offload_param": { "device": "none" } }, "gradient_clipping": 1.0, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": 10, "steps_per_print": 2000000 } ``` 3. Output of `accelerate launch test.py`: ```bash ValueError: When using `deepspeed_config_file`, the following accelerate config variables will be ignored: ['gradient_accumulation_steps', 'gradient_clipping', 'zero_stage', 'offload_optimizer_device', 'offload_param_device', 'zero3_save_16bit_model', 'mixed_precision']. Please specify them appropriately in the DeepSpeed config file. If you are using an accelerate config file, remove others config variables mentioned in the above specified list. The easiest method is to create a new config following the questionnaire via `accelerate config`. It will only ask for the necessary config variables when using `deepspeed_config_file`. ``` **Scenario 2**: Use the solution of the error to create new accelerate config and check that no ambiguity error is now thrown. 1. Run `accelerate config`: ```bash $ accelerate config ------------------------------------------------------------------------------------------------------------------------------- In which compute environment are you running? This machine ------------------------------------------------------------------------------------------------------------------------------- Which type of machine are you using? multi-GPU How many different machines will you use (use more than 1 for multi-node training)? [1]: Do you wish to optimize your script with torch dynamo?[yes/NO]: Do you want to use DeepSpeed? [yes/NO]: yes Do you want to specify a json file to a DeepSpeed config? [yes/NO]: yes Please enter the path to the json DeepSpeed config file: ds_config.json Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]: yes How many GPU(s) should be used for distributed training? [1]:4 accelerate configuration saved at ds_config_sample.yaml ``` 2. Content of the `accelerate` config: ```yaml compute_environment: LOCAL_MACHINE deepspeed_config: deepspeed_config_file: ds_config.json zero3_init_flag: true distributed_type: DEEPSPEED downcast_bf16: 'no' dynamo_backend: 'NO' fsdp_config: {} machine_rank: 0 main_training_function: main megatron_lm_config: {} num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true use_cpu: false ``` 3. Output of `accelerate launch test.py`: ```bash Distributed environment: DEEPSPEED Backend: nccl Num processes: 4 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: bf16 ds_config: {'bf16': {'enabled': True}, 'zero_optimization': {'stage': 3, 'stage3_gather_16bit_weights_on_model_save': False, 'offload_optimizer': {'device': 'none'}, 'offload_param': {'device': 'none'}}, 'gradient_clipping': 1.0, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'gradient_accumulation_steps': 10, 'steps_per_print': inf, 'fp16': {'enabled': False}} ``` **Scenario 3**: Setting the `accelerate launch` command arguments related to DeepSpeed as `"auto"` in the DeepSpeed` configuration file and check that things work as expected. 1. New `ds_config.json` with `"auto"` for the `accelerate launch` DeepSpeed command arguments: ```json { "bf16": { "enabled": "auto" }, "zero_optimization": { "stage": "auto", "stage3_gather_16bit_weights_on_model_save": "auto", "offload_optimizer": { "device": "auto" }, "offload_param": { "device": "auto" } }, "gradient_clipping": "auto", "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "steps_per_print": 2000000 } ``` 2. Output of `accelerate launch --mixed_precision="fp16" --zero_stage=3 --gradient_accumulation_steps=5 --gradient_clipping=1.0 --offload_param_device="cpu" --offload_optimizer_device="nvme" --zero3_save_16bit_model="true" test.py`: ```bash Distributed environment: DEEPSPEED Backend: nccl Num processes: 4 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 ds_config: {'bf16': {'enabled': False}, 'zero_optimization': {'stage': 3, 'stage3_gather_16bit_weights_on_model_save': True, 'offload_optimizer': {'device': 'nvme'}, 'offload_param': {'device': 'cpu'}}, 'gradient_clipping': 1.0, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'gradient_accumulation_steps': 5, 'steps_per_print': inf, 'fp16': {'enabled': True, 'auto_cast': True}} ``` **Note**: 1. Remaining `"auto"` values are handled in `accelerator.prepare()` call as explained in point 2 of `Important code changes when using DeepSpeed Config File`. 2. Only when `gradient_accumulation_steps` is `auto`, the value passed while creating `Accelerator` object via `Accelerator(gradient_accumulation_steps=k)` will be used. When using DeepSpeed Plugin, the value from it will be used and it will overwrite the value passed while creating Accelerator object. ## Saving and loading 1. Saving and loading of models is unchanged for ZeRO Stage-1 and Stage-2. 2. under ZeRO Stage-3, `state_dict` contains just the placeholders since the model weights are partitioned across multiple GPUs. ZeRO Stage-3 has 2 options: a. Saving the entire 16bit model weights to directly load later on using `model.load_state_dict(torch.load(pytorch_model.bin))`. For this, either set `zero_optimization.stage3_gather_16bit_weights_on_model_save` to True in DeepSpeed Config file or set `zero3_save_16bit_model` to True in DeepSpeed Plugin. **Note that this option requires consolidation of the weights on one GPU it can be slow and memory demanding, so only use this feature when needed.** Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this: ```python unwrapped_model = accelerator.unwrap_model(model) # New Code # # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or # `zero3_save_16bit_model` is True in DeepSpeed Plugin. # For Zero Stages 1 and 2, models are saved as usual in the output directory. # The model name saved is `pytorch_model.bin` unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model), ) ``` b. To get 32bit weights, first save the model using `model.save_checkpoint()`. Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this: ```python success = model.save_checkpoint(PATH, ckpt_id, checkpoint_state_dict) status_msg = "checkpointing: PATH={}, ckpt_id={}".format(PATH, ckpt_id) if success: logging.info(f"Success {status_msg}") else: logging.warning(f"Failure {status_msg}") ``` This will create ZeRO model and optimizer partitions along with `zero_to_fp32.py` script in checkpoint directory. You can use this script to do offline consolidation. It requires no configuration files or GPUs. Here is an example of its usage: ```bash $ cd /path/to/checkpoint_dir $ ./zero_to_fp32.py . pytorch_model.bin Processing zero checkpoint at global_step1 Detected checkpoint of type zero stage 3, world_size: 2 Saving fp32 state dict to pytorch_model.bin (total_numel=60506624) ``` To get 32bit model for saving/inference, you can perform: ```python from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint unwrapped_model = accelerator.unwrap_model(model) fp32_model = load_state_dict_from_zero_checkpoint(unwrapped_model, checkpoint_dir) ``` If you are only interested in the `state_dict`, you can do the following: ```python from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) ``` Note that all these functions require ~2x memory (general RAM) of the size of the final checkpoint. ## ZeRO Inference DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. With accelerate integration, you just need to prepare the model and dataloader as shown below: ```python model, eval_dataloader = accelerator.prepare(model, eval_dataloader) ``` ## Few caveats to be aware of 1. Current integration doesn’t support Pipeline Parallelism of DeepSpeed. 2. Current integration doesn’t support `mpu`, limiting the tensor parallelism which is supported in Megatron-LM. 3. Current integration doesn’t support multiple models. ## DeepSpeed Resources The documentation for the internals related to deepspeed can be found [here](../package_reference/deepspeed). - [Project's github](https://github.com/microsoft/deepspeed) - [Usage docs](https://www.deepspeed.ai/getting-started/) - [API docs](https://deepspeed.readthedocs.io/en/latest/index.html) - [Blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed) Papers: - [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054) - [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840) - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857) - [ZeRO++: Extremely Efficient Collective Communication for Giant Model Training](https://arxiv.org/abs/2306.10209) Finally, please, remember that 🤗 `Accelerate` only integrates DeepSpeed, therefore if you have any problems or questions with regards to DeepSpeed usage, please, file an issue with [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/issues).

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hf_public_repos/accelerate/docs/source/usage_guides/ipex.md

# Intel® Extension for PyTorch [IPEX](https://github.com/intel/intel-extension-for-pytorch) is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs with only AVX2 (e.g., AMD CPUs or older Intel CPUs) might result in a better performance under IPEX, but not guaranteed. IPEX provides performance optimizations for CPU training with both Float32 and BFloat16. The usage of BFloat16 is the main focus of the following sections. Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision. ## IPEX installation: IPEX release is following PyTorch, to install via pip: | PyTorch Version | IPEX version | | :---------------: | :----------: | | 2.0 | 2.0.0 | | 1.13 | 1.13.0 | | 1.12 | 1.12.300 | | 1.11 | 1.11.200 | | 1.10 | 1.10.100 | ``` pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu ``` Check more approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html). ## How It Works For Training optimization in CPU 🤗 Accelerate has integrated [IPEX](https://github.com/intel/intel-extension-for-pytorch), all you need to do is enabling it through the config. **Scenario 1**: Acceleration of No distributed CPU training Run <u>accelerate config</u> on your machine: ```bash $ accelerate config ----------------------------------------------------------------------------------------------------------------------------------------------------------- In which compute environment are you running? This machine ----------------------------------------------------------------------------------------------------------------------------------------------------------- Which type of machine are you using? No distributed training Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:yes Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes Do you wish to optimize your script with torch dynamo?[yes/NO]:NO Do you want to use DeepSpeed? [yes/NO]: NO ----------------------------------------------------------------------------------------------------------------------------------------------------------- Do you wish to use FP16 or BF16 (mixed precision)? bf16 ``` This will generate a config file that will be used automatically to properly set the default options when doing ```bash accelerate launch my_script.py --args_to_my_script ``` For instance, here is how you would run the NLP example `examples/nlp_example.py` (from the root of the repo) with IPEX enabled. default_config.yaml that is generated after `accelerate config` ```bash compute_environment: LOCAL_MACHINE distributed_type: 'NO' downcast_bf16: 'no' ipex_config: ipex: true machine_rank: 0 main_training_function: main mixed_precision: bf16 num_machines: 1 num_processes: 1 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: true ``` ```bash accelerate launch examples/nlp_example.py ``` **Scenario 2**: Acceleration of distributed CPU training we use Intel oneCCL for communication, combined with Intel® MPI library to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. you could refer the [here](https://huggingface.co/docs/transformers/perf_train_cpu_many) for the installation guide Run <u>accelerate config</u> on your machine(node0): ```bash $ accelerate config ----------------------------------------------------------------------------------------------------------------------------------------------------------- In which compute environment are you running? This machine ----------------------------------------------------------------------------------------------------------------------------------------------------------- Which type of machine are you using? multi-CPU How many different machines will you use (use more than 1 for multi-node training)? [1]: 4 ----------------------------------------------------------------------------------------------------------------------------------------------------------- What is the rank of this machine? 0 What is the IP address of the machine that will host the main process? 36.112.23.24 What is the port you will use to communicate with the main process? 29500 Are all the machines on the same local network? Answer `no` if nodes are on the cloud and/or on different network hosts [YES/no]: yes Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes Do you wish to optimize your script with torch dynamo?[yes/NO]:NO How many CPU(s) should be used for distributed training? [1]:16 ----------------------------------------------------------------------------------------------------------------------------------------------------------- Do you wish to use FP16 or BF16 (mixed precision)? bf16 ``` For instance, here is how you would run the NLP example `examples/nlp_example.py` (from the root of the repo) with IPEX enabled for distributed CPU training. default_config.yaml that is generated after `accelerate config` ```bash compute_environment: LOCAL_MACHINE distributed_type: MULTI_CPU downcast_bf16: 'no' ipex_config: ipex: true machine_rank: 0 main_process_ip: 36.112.23.24 main_process_port: 29500 main_training_function: main mixed_precision: bf16 num_machines: 4 num_processes: 16 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: true ``` Set following env and using intel MPI to launch the training In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument. ```bash $ cat hostfile xxx.xxx.xxx.xxx #node0 ip xxx.xxx.xxx.xxx #node1 ip xxx.xxx.xxx.xxx #node2 ip xxx.xxx.xxx.xxx #node3 ip ``` Now, run the following command in node0 and **16DDP** will be enabled in node0,node1,node2,node3 with BF16 mixed precision: ```bash oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh export CCL_WORKER_COUNT=1 export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip export CCL_ATL_TRANSPORT=ofi mpirun -f hostfile -n 16 -ppn 4 accelerate launch examples/nlp_example.py ``` ## Related Resources - [Project's github](https://github.com/intel/intel-extension-for-pytorch) - [API docs](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/api_doc.html) - [Tuning guide](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html) - [Blogs & Publications](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/blogs_publications.html)

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hf_public_repos/accelerate/docs/source/usage_guides/local_sgd.md

# Using Local SGD with 🤗 Accelerate Local SGD is a technique for distributed training where gradients are not synchronized every step. Thus, each process updates its own version of the model weights and after a given number of steps these weights are synchronized by averaging across all processes. This improves communication efficiency and can lead to substantial training speed up especially when a computer lacks a faster interconnect such as NVLink. Unlike gradient accumulation (where improving communication efficiency requires increasing the effective batch size), Local SGD does not require changing a batch size or a learning rate / schedule. However, if necessary, Local SGD can be combined with gradient accumulation as well. In this tutorial you will see how to quickly setup Local SGD 🤗 Accelerate. Compared to a standard Accelerate setup, this requires only two extra lines of code. This example will use a very simplistic PyTorch training loop that performs gradient accumulation every two batches: ```python device = "cuda" model.to(device) gradient_accumulation_steps = 2 for index, batch in enumerate(training_dataloader): inputs, targets = batch inputs = inputs.to(device) targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss = loss / gradient_accumulation_steps loss.backward() if (index + 1) % gradient_accumulation_steps == 0: optimizer.step() scheduler.step() optimizer.zero_grad() ``` ## Converting it to 🤗 Accelerate First the code shown earlier will be converted to use 🤗 Accelerate with neither a LocalSGD or a gradient accumulation helper: ```diff + from accelerate import Accelerator + accelerator = Accelerator() + model, optimizer, training_dataloader, scheduler = accelerator.prepare( + model, optimizer, training_dataloader, scheduler + ) for index, batch in enumerate(training_dataloader): inputs, targets = batch - inputs = inputs.to(device) - targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss = loss / gradient_accumulation_steps + accelerator.backward(loss) if (index+1) % gradient_accumulation_steps == 0: optimizer.step() scheduler.step() ``` ## Letting 🤗 Accelerate handle model synchronization All that is left now is to let 🤗 Accelerate handle model parameter synchronization **and** the gradient accumulation for us. For simplicity let us assume we need to synchronize every 8 steps. This is achieved by adding one `with LocalSGD` statement and one call `local_sgd.step()` after every optimizer step: ```diff +local_sgd_steps=8 +with LocalSGD(accelerator=accelerator, model=model, local_sgd_steps=8, enabled=True) as local_sgd: for batch in training_dataloader: with accelerator.accumulate(model): inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() optimizer.zero_grad() + local_sgd.step() ``` Under the hood, the Local SGD code **disables** automatic gradient synchornization (but accumulation still works as expected!). Instead it averages model parameters every `local_sgd_steps` steps (as well as in the end of the training loop). ## Limitations The current implementation works only with basic multi-GPU (or multi-CPU) training without, e.g., [DeepSpeed.](https://github.com/microsoft/DeepSpeed). ## References Although we are not aware of the true origins of this simple approach, the idea of local SGD is quite old and goes back to at least: Zhang, J., De Sa, C., Mitliagkas, I., & Ré, C. (2016). [Parallel SGD: When does averaging help?. arXiv preprint arXiv:1606.07365.](https://arxiv.org/abs/1606.07365) We credit the term Local SGD to the following paper (but there might be earlier references we are not aware of). Stich, Sebastian Urban. ["Local SGD Converges Fast and Communicates Little." ICLR 2019-International Conference on Learning Representations. No. CONF. 2019.](https://arxiv.org/abs/1805.09767)

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hf_public_repos/accelerate/docs/source

hf_public_repos/accelerate/docs/source/basic_tutorials/notebook.md

# Launching Multi-GPU Training from a Jupyter Environment This tutorial teaches you how to fine tune a computer vision model with 🤗 Accelerate from a Jupyter Notebook on a distributed system. You will also learn how to setup a few requirements needed for ensuring your environment is configured properly, your data has been prepared properly, and finally how to launch training. <Tip> This tutorial is also available as a Jupyter Notebook [here](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb) </Tip> ## Configuring the Environment Before any training can be performed, a 🤗 Accelerate config file must exist in the system. Usually this can be done by running the following in a terminal and answering the prompts: ```bash accelerate config ``` However, if general defaults are fine and you are *not* running on a TPU, 🤗Accelerate has a utility to quickly write your GPU configuration into a config file via [`utils.write_basic_config`]. The following code will restart Jupyter after writing the configuration, as CUDA code was called to perform this. <Tip warning={true}> CUDA can't be initialized more than once on a multi-GPU system. It's fine to debug in the notebook and have calls to CUDA, but in order to finally train a full cleanup and restart will need to be performed. </Tip> ```python import os from accelerate.utils import write_basic_config write_basic_config() # Write a config file os._exit(00) # Restart the notebook ``` ## Preparing the Dataset and Model Next you should prepare your dataset. As mentioned at earlier, great care should be taken when preparing the `DataLoaders` and model to make sure that **nothing** is put on *any* GPU. If you do, it is recommended to put that specific code into a function and call that from within the notebook launcher interface, which will be shown later. Make sure the dataset is downloaded based on the directions [here](https://github.com/huggingface/accelerate/tree/main/examples#simple-vision-example) ```python import os, re, torch, PIL import numpy as np from torch.optim.lr_scheduler import OneCycleLR from torch.utils.data import DataLoader, Dataset from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor from accelerate import Accelerator from accelerate.utils import set_seed from timm import create_model ``` First you need to create a function to extract the class name based on a filename: ```python import os data_dir = "../../images" fnames = os.listdir(data_dir) fname = fnames[0] print(fname) ``` ```python out beagle_32.jpg ``` In the case here, the label is `beagle`. Using regex you can extract the label from the filename: ```python import re def extract_label(fname): stem = fname.split(os.path.sep)[-1] return re.search(r"^(.*)_\d+\.jpg$", stem).groups()[0] ``` ```python extract_label(fname) ``` And you can see it properly returned the right name for our file: ```python out "beagle" ``` Next a `Dataset` class should be made to handle grabbing the image and the label: ```python class PetsDataset(Dataset): def __init__(self, file_names, image_transform=None, label_to_id=None): self.file_names = file_names self.image_transform = image_transform self.label_to_id = label_to_id def __len__(self): return len(self.file_names) def __getitem__(self, idx): fname = self.file_names[idx] raw_image = PIL.Image.open(fname) image = raw_image.convert("RGB") if self.image_transform is not None: image = self.image_transform(image) label = extract_label(fname) if self.label_to_id is not None: label = self.label_to_id[label] return {"image": image, "label": label} ``` Now to build the dataset. Outside the training function you can find and declare all the filenames and labels and use them as references inside the launched function: ```python fnames = [os.path.join("../../images", fname) for fname in fnames if fname.endswith(".jpg")] ``` Next gather all the labels: ```python all_labels = [extract_label(fname) for fname in fnames] id_to_label = list(set(all_labels)) id_to_label.sort() label_to_id = {lbl: i for i, lbl in enumerate(id_to_label)} ``` Next, you should make a `get_dataloaders` function that will return your built dataloaders for you. As mentioned earlier, if data is automatically sent to the GPU or a TPU device when building your `DataLoaders`, they must be built using this method. ```python def get_dataloaders(batch_size: int = 64): "Builds a set of dataloaders with a batch_size" random_perm = np.random.permutation(len(fnames)) cut = int(0.8 * len(fnames)) train_split = random_perm[:cut] eval_split = random_perm[cut:] # For training a simple RandomResizedCrop will be used train_tfm = Compose([RandomResizedCrop((224, 224), scale=(0.5, 1.0)), ToTensor()]) train_dataset = PetsDataset([fnames[i] for i in train_split], image_transform=train_tfm, label_to_id=label_to_id) # For evaluation a deterministic Resize will be used eval_tfm = Compose([Resize((224, 224)), ToTensor()]) eval_dataset = PetsDataset([fnames[i] for i in eval_split], image_transform=eval_tfm, label_to_id=label_to_id) # Instantiate dataloaders train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=4) eval_dataloader = DataLoader(eval_dataset, shuffle=False, batch_size=batch_size * 2, num_workers=4) return train_dataloader, eval_dataloader ``` Finally, you should import the scheduler to be used later: ```python from torch.optim.lr_scheduler import CosineAnnealingLR ``` ## Writing the Training Function Now you can build the training loop. [`notebook_launcher`] works by passing in a function to call that will be ran across the distributed system. Here is a basic training loop for the animal classification problem: <Tip> The code has been split up to allow for explanations on each section. A full version that can be copy and pasted will be available at the end </Tip> ```python def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64): set_seed(seed) accelerator = Accelerator(mixed_precision=mixed_precision) ``` First you should set the seed and create an [`Accelerator`] object as early in the training loop as possible. <Tip warning={true}> If training on the TPU, your training loop should take in the model as a parameter and it should be instantiated outside of the training loop function. See the [TPU best practices](../concept_guides/training_tpu) to learn why </Tip> Next you should build your dataloaders and create your model: ```python train_dataloader, eval_dataloader = get_dataloaders(batch_size) model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id)) ``` <Tip> You build the model here so that the seed also controls the new weight initialization </Tip> As you are performing transfer learning in this example, the encoder of the model starts out frozen so the head of the model can be trained only initially: ```python for param in model.parameters(): param.requires_grad = False for param in model.get_classifier().parameters(): param.requires_grad = True ``` Normalizing the batches of images will make training a little faster: ```python mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None] std = torch.tensor(model.default_cfg["std"])[None, :, None, None] ``` To make these constants available on the active device, you should set it to the Accelerator's device: ```python mean = mean.to(accelerator.device) std = std.to(accelerator.device) ``` Next instantiate the rest of the PyTorch classes used for training: ```python optimizer = torch.optim.Adam(params=model.parameters(), lr=3e-2 / 25) lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=3e-2, epochs=5, steps_per_epoch=len(train_dataloader)) ``` Before passing everything to [`~Accelerator.prepare`]. <Tip> There is no specific order to remember, you just need to unpack the objects in the same order you gave them to the prepare method. </Tip> ```python model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) ``` Now train the model: ```python for epoch in range(5): model.train() for batch in train_dataloader: inputs = (batch["image"] - mean) / std outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, batch["label"]) accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() ``` The evaluation loop will look slightly different compared to the training loop. The number of elements passed as well as the overall total accuracy of each batch will be added to two constants: ```python model.eval() accurate = 0 num_elems = 0 ``` Next you have the rest of your standard PyTorch loop: ```python for batch in eval_dataloader: inputs = (batch["image"] - mean) / std with torch.no_grad(): outputs = model(inputs) predictions = outputs.argmax(dim=-1) ``` Before finally the last major difference. When performing distributed evaluation, the predictions and labels need to be passed through [`~Accelerator.gather`] so that all of the data is available on the current device and a properly calculated metric can be achieved: ```python accurate_preds = accelerator.gather(predictions) == accelerator.gather(batch["label"]) num_elems += accurate_preds.shape[0] accurate += accurate_preds.long().sum() ``` Now you just need to calculate the actual metric for this problem, and you can print it on the main process using [`~Accelerator.print`]: ```python eval_metric = accurate.item() / num_elems accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}") ``` A full version of this training loop is available below: ```python def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64): set_seed(seed) # Initialize accelerator accelerator = Accelerator(mixed_precision=mixed_precision) # Build dataloaders train_dataloader, eval_dataloader = get_dataloaders(batch_size) # Instantiate the model (you build the model here so that the seed also controls new weight initaliziations) model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id)) # Freeze the base model for param in model.parameters(): param.requires_grad = False for param in model.get_classifier().parameters(): param.requires_grad = True # You can normalize the batches of images to be a bit faster mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None] std = torch.tensor(model.default_cfg["std"])[None, :, None, None] # To make these constants available on the active device, set it to the accelerator device mean = mean.to(accelerator.device) std = std.to(accelerator.device) # Instantiate the optimizer optimizer = torch.optim.Adam(params=model.parameters(), lr=3e-2 / 25) # Instantiate the learning rate scheduler lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=3e-2, epochs=5, steps_per_epoch=len(train_dataloader)) # Prepare everything # There is no specific order to remember, you just need to unpack the objects in the same order you gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # Now you train the model for epoch in range(5): model.train() for batch in train_dataloader: inputs = (batch["image"] - mean) / std outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, batch["label"]) accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() accurate = 0 num_elems = 0 for batch in eval_dataloader: inputs = (batch["image"] - mean) / std with torch.no_grad(): outputs = model(inputs) predictions = outputs.argmax(dim=-1) accurate_preds = accelerator.gather(predictions) == accelerator.gather(batch["label"]) num_elems += accurate_preds.shape[0] accurate += accurate_preds.long().sum() eval_metric = accurate.item() / num_elems # Use accelerator.print to print only on the main process. accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}") ``` ## Using the notebook_launcher All that's left is to use the [`notebook_launcher`]. You pass in the function, the arguments (as a tuple), and the number of processes to train on. (See the [documentation](../package_reference/launchers) for more information) ```python from accelerate import notebook_launcher ``` ```python args = ("fp16", 42, 64) notebook_launcher(training_loop, args, num_processes=2) ``` In the case of running on multiple nodes, you need to set up a Jupyter session at each node and run the launching cell at the same time. For an environment containing 2 nodes (computers) with 8 GPUs each and the main computer with an IP address of "172.31.43.8", it would look like so: ```python notebook_launcher(training_loop, args, master_addr="172.31.43.8", node_rank=0, num_nodes=2, num_processes=8) ``` And in the second Jupyter session on the other machine: <Tip> Notice how the `node_rank` has changed </Tip> ```python notebook_launcher(training_loop, args, master_addr="172.31.43.8", node_rank=1, num_nodes=2, num_processes=8) ``` In the case of running on the TPU, it would look like so: ```python model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id)) args = (model, "fp16", 42, 64) notebook_launcher(training_loop, args, num_processes=8) ``` As it's running it will print the progress as well as state how many devices you ran on. This tutorial was ran with two GPUs: ```python out Launching training on 2 GPUs. epoch 0: 88.12 epoch 1: 91.73 epoch 2: 92.58 epoch 3: 93.90 epoch 4: 94.71 ``` And that's it! ## Debugging A common issue when running the `notebook_launcher` is receiving a CUDA has already been initialized issue. This usually stems from an import or prior code in the notebook that makes a call to the PyTorch `torch.cuda` sublibrary. To help narrow down what went wrong, you can launch the `notebook_launcher` with `ACCELERATE_DEBUG_MODE=yes` in your environment and an additional check will be made when spawning that a regular process can be created and utilize CUDA without issue. (Your CUDA code can still be ran afterwards). ## Conclusion This notebook showed how to perform distributed training from inside of a Jupyter Notebook. Some key notes to remember: - Make sure to save any code that use CUDA (or CUDA imports) for the function passed to [`notebook_launcher`] - Set the `num_processes` to be the number of devices used for training (such as number of GPUs, CPUs, TPUs, etc) - If using the TPU, declare your model outside the training loop function

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hf_public_repos/accelerate/docs/source/basic_tutorials/migration.md

# Migrating your code to 🤗 Accelerate This tutorial will detail how to easily convert existing PyTorch code to use 🤗 Accelerate! You'll see that by just changing a few lines of code, 🤗 Accelerate can perform its magic and get you on your way toward running your code on distributed systems with ease! ## The base training loop To begin, write out a very basic PyTorch training loop. <Tip> We are under the presumption that `training_dataloader`, `model`, `optimizer`, `scheduler`, and `loss_function` have been defined beforehand. </Tip> ```python device = "cuda" model.to(device) for batch in training_dataloader: optimizer.zero_grad() inputs, targets = batch inputs = inputs.to(device) targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss.backward() optimizer.step() scheduler.step() ``` ## Add in 🤗 Accelerate To start using 🤗 Accelerate, first import and create an [`Accelerator`] instance: ```python from accelerate import Accelerator accelerator = Accelerator() ``` [`Accelerator`] is the main force behind utilizing all the possible options for distributed training! ### Setting the right device The [`Accelerator`] class knows the right device to move any PyTorch object to at any time, so you should change the definition of `device` to come from [`Accelerator`]: ```diff - device = 'cuda' + device = accelerator.device model.to(device) ``` ### Preparing your objects Next, you need to pass all of the important objects related to training into [`~Accelerator.prepare`]. 🤗 Accelerate will make sure everything is setup in the current environment for you to start training: ``` model, optimizer, training_dataloader, scheduler = accelerator.prepare( model, optimizer, training_dataloader, scheduler ) ``` These objects are returned in the same order they were sent in. By default when using `device_placement=True`, all of the objects that can be sent to the right device will be. If you need to work with data that isn't passed to [~Accelerator.prepare] but should be on the active device, you should pass in the `device` you made earlier. <Tip warning={true}> Accelerate will only prepare objects that inherit from their respective PyTorch classes (such as `torch.optim.Optimizer`). </Tip> ### Modifying the training loop Finally, three lines of code need to be changed in the training loop. 🤗 Accelerate's DataLoader classes will automatically handle the device placement by default, and [`~Accelerator.backward`] should be used for performing the backward pass: ```diff - inputs = inputs.to(device) - targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) - loss.backward() + accelerator.backward(loss) ``` With that, your training loop is now ready to use 🤗 Accelerate! ## The finished code Below is the final version of the converted code: ```python from accelerate import Accelerator accelerator = Accelerator() model, optimizer, training_dataloader, scheduler = accelerator.prepare( model, optimizer, training_dataloader, scheduler ) for batch in training_dataloader: optimizer.zero_grad() inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() ``` ## More Resources To check out more ways on how to migrate to 🤗 Accelerate, check out our [interactive migration tutorial](https://huggingface.co/docs/accelerate/usage_guides/explore) which showcases other items that need to be watched for when using Accelerate and how to do so quickly.

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hf_public_repos/accelerate/docs/source/basic_tutorials/install.md

# Installation and Configuration Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 Accelerate. 🤗 Accelerate is tested on **Python 3.8+**. ## Installing 🤗 Accelerate 🤗 Accelerate is available on pypi and conda, as well as on GitHub. Details to install from each are below: ### pip To install 🤗 Accelerate from pypi, perform: ```bash pip install accelerate ``` ### conda 🤗 Accelerate can also be installed with conda with: ```bash conda install -c conda-forge accelerate ``` ### Source New features are added every day that haven't been released yet. To try them out yourself, install from the GitHub repository: ```bash pip install git+https://github.com/huggingface/accelerate ``` If you're working on contributing to the library or wish to play with the source code and see live results as you run the code, an editable version can be installed from a locally-cloned version of the repository: ```bash git clone https://github.com/huggingface/accelerate cd accelerate pip install -e . ``` ## Configuring 🤗 Accelerate After installing, you need to configure 🤗 Accelerate for how the current system is setup for training. To do so run the following and answer the questions prompted to you: ```bash accelerate config ``` To write a barebones configuration that doesn't include options such as DeepSpeed configuration or running on TPUs, you can quickly run: ```bash python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')" ``` 🤗 Accelerate will automatically utilize the maximum number of GPUs available and set the mixed precision mode. To check that your configuration looks fine, run: ```bash accelerate env ``` An example output is shown below, which describes two GPUs on a single machine with no mixed precision being used: ```bash - `Accelerate` version: 0.11.0.dev0 - Platform: Linux-5.10.0-15-cloud-amd64-x86_64-with-debian-11.3 - Python version: 3.7.12 - Numpy version: 1.19.5 - PyTorch version (GPU?): 1.12.0+cu102 (True) - `Accelerate` default config: - compute_environment: LOCAL_MACHINE - distributed_type: MULTI_GPU - mixed_precision: no - use_cpu: False - num_processes: 2 - machine_rank: 0 - num_machines: 1 - main_process_ip: None - main_process_port: None - main_training_function: main - deepspeed_config: {} - fsdp_config: {} ```

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hf_public_repos/accelerate/docs/source/basic_tutorials/overview.md

# Overview Welcome to the 🤗 Accelerate tutorials! These introductory guides will help catch you up to speed on working with 🤗 Accelerate. You'll learn how to modify your code to have it work with the API seamlessly, how to launch your script properly, and more! These tutorials assume some basic knowledge of Python and familiarity with the PyTorch framework. If you have any questions about 🤗 Accelerate, feel free to join and ask the community on our [forum](https://discuss.huggingface.co/c/accelerate/18).

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hf_public_repos/accelerate/docs/source/basic_tutorials/troubleshooting.md

# Troubleshooting guide This guide aims to provide you the tools and knowledge required to navigate some common issues. However, as 🤗 Accelerate continuously evolves and the use cases and setups are diverse, you might encounter an issue not covered in this guide. If the suggestions listed in this guide do not cover your such situation, please refer to the final section of the guide, [Asking for Help](#ask-for-help), to learn where to find help with your specific issue. ## Logging When facing an error, logging can help narrow down where it is coming from. In a distributed setup with multiple processes, logging can be a challenge, but 🤗 Accelerate provides a utility that streamlines the logging process and ensures that logs are synchronized and managed effectively across the distributed setup. To troubleshoot an issue, use `accelerate.logging` instead of the standard Python `logging` module: ```diff - import logging + from accelerate.logging import get_logger - logger = logging.getLogger(__name__) + logger = get_logger(__name__) ``` To set the log level (`INFO`, `DEBUG`, `WARNING`, `ERROR`, `CRITICAL`), export it as the `ACCELERATE_LOG_LEVEL` environment, or pass as `log_level` to `get_logger`: ```python from accelerate.logging import get_logger logger = get_logger(__name__, log_level="INFO") ``` By default, the log is called on main processes only. To call it on all processes, pass `main_process_only=False`. If a log should be called on all processes and in order, also pass `in_order=True`. ## Hanging code and timeout errors ### Mismatched tensor shapes If your code seems to be hanging for a significant amount time on a distributed setup, a common cause is mismatched shapes of tensors on different devices. When running scripts in a distributed fashion, functions such as [`Accelerator.gather`] and [`Accelerator.reduce`] are necessary to grab tensors across devices to perform operations on them collectively. These (and other) functions rely on `torch.distributed` performing a `gather` operation, which requires that tensors have the **exact same shape** across all processes. When the tensor shapes don't match, you will experience handing code, and eventually hit a timeout exception. If you suspect this to be the case, use Accelerate's operational debug mode to immediately catch the issue. The recommended way to enable Accelerate's operational debug mode is during `accelerate config` setup. Alternative ways to enable debug mode are: * From the CLI: ```bash accelerate launch --debug {my_script.py} --arg1 --arg2 ``` * As an environmental variable (which avoids the need for `accelerate launch`): ```bash ACCELERATE_DEBUG_MODE="1" torchrun {my_script.py} --arg1 --arg2 ``` * Manually changing the `config.yaml` file: ```diff compute_environment: LOCAL_MACHINE +debug: true ``` Once you enable the debug mode, you should get a similar traceback that points to the tensor shape mismatch issue: ```py Traceback (most recent call last): File "/home/zach_mueller_huggingface_co/test.py", line 18, in <module> main() File "/home/zach_mueller_huggingface_co/test.py", line 15, in main broadcast_tensor = broadcast(tensor) File "/home/zach_mueller_huggingface_co/accelerate/src/accelerate/utils/operations.py", line 303, in wrapper accelerate.utils.operations.DistributedOperationException: Cannot apply desired operation due to shape mismatches. All shapes across devices must be valid. Operation: `accelerate.utils.operations.broadcast` Input shapes: - Process 0: [1, 5] - Process 1: [1, 2, 5] ``` ### Early stopping leads to hanging When doing early stopping in distributed training, if each process has a specific stopping condition (e.g. validation loss), it may not be synchronized across all of them. As a result, a break can happen on process 0 but not on process 1. This will cause the code to hang indefinitely until a timeout occurs. If you have early stopping conditionals, use `set_breakpoint` and `check_breakpoint` methods to make sure all the processes are ended correctly: ```py # Assume `should_do_breakpoint` is a custom defined function that returns a conditional, # and that conditional might be true only on process 1 if should_do_breakpoint(loss): accelerator.set_breakpoint() # Later in the training script when we need to check for the breakpoint if accelerator.check_breakpoint(): break ``` ### Hanging on low kernel versions on Linux This is a known issue. On Linux with kernel version < 5.5, hanging processes have been reported. To avoid encountering this problem, we recommend upgrading your system to a later kernel version. ## CUDA out of memory One of the most frustrating errors when it comes to running training scripts is hitting "CUDA Out-of-Memory", as the entire script needs to be restarted, progress is lost, and typically a developer would want to simply start their script and let it run. To address this problem, `Accelerate` offers a utility `find_executable_batch_size` that is heavily based on [toma](https://github.com/BlackHC/toma). The utility retries code that fails due to OOM (out-of-memory) conditions and lowers batch sizes automatically. ### find_executable_batch_size This algorithm operates with exponential decay, decreasing the batch size in half after each failed run on some training script. To use it, restructure your training function to include an inner function that includes this wrapper, and build your dataloaders inside it. At a minimum, this could look like 4 new lines of code. <Tip warning={true}> The inner function *must* take in the batch size as the first parameter, but we do not pass one to it when called. The wrapper handles this for us. </Tip> It should also be noted that anything which will consume CUDA memory and passed to the `accelerator` **must** be declared inside the inner function, such as models and optimizers. ```diff def training_function(args): accelerator = Accelerator() + @find_executable_batch_size(starting_batch_size=args.batch_size) + def inner_training_loop(batch_size): + nonlocal accelerator # Ensure they can be used in our context + accelerator.free_memory() # Free all lingering references model = get_model() model.to(accelerator.device) optimizer = get_optimizer() train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size) lr_scheduler = get_scheduler( optimizer, num_training_steps=len(train_dataloader)*num_epochs ) model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) train(model, optimizer, train_dataloader, lr_scheduler) validate(model, eval_dataloader) + inner_training_loop() ``` To find out more, check the documentation [here](../package_reference/utilities#accelerate.find_executable_batch_size). ## Non-reproducible results between device setups If you have changed the device setup and are observing different model performance, this is likely due to the fact that you have not updated your script when moving from one setup to another. The same script with the same batch size across TPU, multi-GPU, and single-GPU with Accelerate will have different results. For example, if you were previously training on a single GPU with a batch size of 16, when moving to two GPU setup, you need to change the batch size to 8 to have the same effective batch size. This is because when training with Accelerate, the batch size passed to the dataloader is the **batch size per GPU**. To make sure you can reproduce the results between the setups, make sure to use the same seed, adjust the batch size accordingly, consider scaling the learning rate. For more details and a quick reference for batch sizes, check out the [Comparing performance between different device setups](../concept_guides/performance) guide. ## Performance issues on different GPUs If your multi-GPU setup consists of different GPUs, you may hit some limitations: - There may be an imbalance in GPU memory between the GPUs. In this case, the GPU with smaller memory will limit the batch size or the size of the model that can be loaded onto the GPUs. - If you are using GPUs with different performance profiles, the performance will be driven by the slowest GPU that you are using as the other GPUs will have to wait for it to complete its workload. Vastly different GPUs within the same setup can lead to performance bottlenecks. ## Ask for help If the above troubleshooting tools and advice did not help you resolve your issue, reach out for help to the community and the team. ### Forums Ask for help on the Hugging Face forums - post your question in the [🤗Accelerate category](https://discuss.huggingface.co/c/accelerate/18) Make sure to write a descriptive post with relevant context about your setup and reproducible code to maximize the likelihood that your problem is solved! ### Discord Post a question on [Discord](http://hf.co/join/discord), and let the team and the community help you. ### GitHub Issues Create an Issue on the 🤗 Accelerate [GitHub repository](https://github.com/huggingface/accelerate/issues) if you suspect to have found a bug related to the library. Include context regarding the bug and details about your distributed setup to help us better figure out what's wrong and how we can fix it.

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hf_public_repos/accelerate/docs/source/basic_tutorials/launch.md

# Launching your 🤗 Accelerate scripts In the previous tutorial, you were introduced to how to modify your current training script to use 🤗 Accelerate. The final version of that code is shown below: ```python from accelerate import Accelerator accelerator = Accelerator() model, optimizer, training_dataloader, scheduler = accelerator.prepare( model, optimizer, training_dataloader, scheduler ) for batch in training_dataloader: optimizer.zero_grad() inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() ``` But how do you run this code and have it utilize the special hardware available to it? First, you should rewrite the above code into a function, and make it callable as a script. For example: ```diff from accelerate import Accelerator + def main(): accelerator = Accelerator() model, optimizer, training_dataloader, scheduler = accelerator.prepare( model, optimizer, training_dataloader, scheduler ) for batch in training_dataloader: optimizer.zero_grad() inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() + if __name__ == "__main__": + main() ``` Next, you need to launch it with `accelerate launch`. <Tip warning={true}> It's recommended you run `accelerate config` before using `accelerate launch` to configure your environment to your liking. Otherwise 🤗 Accelerate will use very basic defaults depending on your system setup. </Tip> ## Using accelerate launch 🤗 Accelerate has a special CLI command to help you launch your code in your system through `accelerate launch`. This command wraps around all of the different commands needed to launch your script on various platforms, without you having to remember what each of them is. <Tip> If you are familiar with launching scripts in PyTorch yourself such as with `torchrun`, you can still do this. It is not required to use `accelerate launch`. </Tip> You can launch your script quickly by using: ```bash accelerate launch {script_name.py} --arg1 --arg2 ... ``` Just put `accelerate launch` at the start of your command, and pass in additional arguments and parameters to your script afterward like normal! Since this runs the various torch spawn methods, all of the expected environment variables can be modified here as well. For example, here is how to use `accelerate launch` with a single GPU: ```bash CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ... ``` You can also use `accelerate launch` without performing `accelerate config` first, but you may need to manually pass in the right configuration parameters. In this case, 🤗 Accelerate will make some hyperparameter decisions for you, e.g., if GPUs are available, it will use all of them by default without the mixed precision. Here is how you would use all GPUs and train with mixed precision disabled: ```bash accelerate launch --multi_gpu {script_name.py} {--arg1} {--arg2} ... ``` Or by specifying a number of GPUs to use: ```bash accelerate launch --num_processes=2 {script_name.py} {--arg1} {--arg2} ... ``` To get more specific you should pass in the needed parameters yourself. For instance, here is how you would also launch that same script on two GPUs using mixed precision while avoiding all of the warnings: ```bash accelerate launch --multi_gpu --mixed_precision=fp16 --num_processes=2 {script_name.py} {--arg1} {--arg2} ... ``` For a complete list of parameters you can pass in, run: ```bash accelerate launch -h ``` <Tip> Even if you are not using 🤗 Accelerate in your code, you can still use the launcher for starting your scripts! </Tip> For a visualization of this difference, that earlier `accelerate launch` on multi-gpu would look something like so with `torchrun`: ```bash MIXED_PRECISION="fp16" torchrun --nproc_per_node=2 --num_machines=1 {script_name.py} {--arg1} {--arg2} ... ``` You can also launch your script utilizing the launch CLI as a python module itself, enabling the ability to pass in other python-specific launching behaviors. To do so, use `accelerate.commands.launch` instead of `accelerate launch`: ```bash python -m accelerate.commands.launch --num_processes=2 {script_name.py} {--arg1} {--arg2} ``` If you want to execute the script with any other python flags, you can pass them in as well similar to `-m`, such as the below example enabling unbuffered stdout and stderr: ```bash python -u -m accelerate.commands.launch --num_processes=2 {script_name.py} {--arg1} {--arg2} ``` <Tip> You can run your code on CPU as well! This is helpful for debugging and testing purposes on toy models and datasets. ```bash accelerate launch --cpu {script_name.py} {--arg1} {--arg2} ``` </Tip> ## Why you should always use `accelerate config` Why is it useful to the point you should **always** run `accelerate config`? Remember that earlier call to `accelerate launch` as well as `torchrun`? Post configuration, to run that script with the needed parts you just need to use `accelerate launch` outright, without passing anything else in: ```bash accelerate launch {script_name.py} {--arg1} {--arg2} ... ``` ## Custom Configurations As briefly mentioned earlier, `accelerate launch` should be mostly used through combining set configurations made with the `accelerate config` command. These configs are saved to a `default_config.yaml` file in your cache folder for 🤗 Accelerate. This cache folder is located at (with decreasing order of priority): - The content of your environment variable `HF_HOME` suffixed with `accelerate`. - If it does not exist, the content of your environment variable `XDG_CACHE_HOME` suffixed with `huggingface/accelerate`. - If this does not exist either, the folder `~/.cache/huggingface/accelerate`. To have multiple configurations, the flag `--config_file` can be passed to the `accelerate launch` command paired with the location of the custom yaml. An example yaml may look something like the following for two GPUs on a single machine using `fp16` for mixed precision: ```yaml compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: MULTI_GPU fsdp_config: {} machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 2 use_cpu: false ``` Launching a script from the location of that custom yaml file looks like the following: ```bash accelerate launch --config_file {path/to/config/my_config_file.yaml} {script_name.py} {--arg1} {--arg2} ... ``` ## Multi-node training Multi-node training with 🤗Accelerate is similar to [multi-node training with torchrun](https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html). The simplest way to launch a multi-node training run is to do the following: - Copy your codebase and data to all nodes. (or place them on a shared filesystem) - Setup your python packages on all nodes. - Run `accelerate config` on the main single node first. After specifying the number of nodes, you will be asked to specify the rank of each node (this will be 0 for the main/master node), along with the IP address and port for the main process. This is required for the worker nodes to communicate with the main process. Afterwards, you can copy or send this config file across all of your nodes, changing the `machine_rank` to 1, 2,3, etc. to avoid having to run the command (or just follow their directions directly for launching with `torchrun` as well) Once you have done this, you can start your multi-node training run by running `accelerate launch` (or `torchrun`) on all nodes. <Tip> It is required that the command be ran on all nodes for everything to start, not just running it from the main node. You can use something like SLURM or a different process executor to wrap around this requirement and call everything from a single command. </Tip> <Tip> It is recommended to use the intranet IP of your main node over the public IP for better latency. This is the `192.168.x.x` or the `172.x.x.x` address you see when you run `hostname -I` on the main node. </Tip> To get a better idea about multi-node training, check out our example for [multi-node training with FSDP](https://huggingface.co/blog/ram-efficient-pytorch-fsdp).

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hf_public_repos/accelerate/docs/source/package_reference/accelerator.md

# Accelerator The [`Accelerator`] is the main class provided by 🤗 Accelerate. It serves at the main entry point for the API. ## Quick adaptation of your code To quickly adapt your script to work on any kind of setup with 🤗 Accelerate just: 1. Initialize an [`Accelerator`] object (that we will call `accelerator` throughout this page) as early as possible in your script. 2. Pass your dataloader(s), model(s), optimizer(s), and scheduler(s) to the [`~Accelerator.prepare`] method. 3. Remove all the `.cuda()` or `.to(device)` from your code and let the `accelerator` handle the device placement for you. <Tip> Step three is optional, but considered a best practice. </Tip> 4. Replace `loss.backward()` in your code with `accelerator.backward(loss)` 5. Gather your predictions and labels before storing them or using them for metric computation using [`~Accelerator.gather`] <Tip warning={true}> Step five is mandatory when using distributed evaluation </Tip> In most cases this is all that is needed. The next section lists a few more advanced use cases and nice features you should search for and replace by the corresponding methods of your `accelerator`: ## Advanced recommendations ### Printing `print` statements should be replaced by [`~Accelerator.print`] to be printed once per process: ```diff - print("My thing I want to print!") + accelerator.print("My thing I want to print!") ``` ### Executing processes #### Once on a single server For statements that should be executed once per server, use [`~Accelerator.is_local_main_process`]: ```python if accelerator.is_local_main_process: do_thing_once_per_server() ``` A function can be wrapped using the [`~Accelerator.on_local_main_process`] function to achieve the same behavior on a function's execution: ```python @accelerator.on_local_main_process def do_my_thing(): "Something done once per server" do_thing_once_per_server() ``` #### Only ever once across all servers For statements that should only ever be executed once, use [`~Accelerator.is_main_process`]: ```python if accelerator.is_main_process: do_thing_once() ``` A function can be wrapped using the [`~Accelerator.on_main_process`] function to achieve the same behavior on a function's execution: ```python @accelerator.on_main_process def do_my_thing(): "Something done once per server" do_thing_once() ``` #### On specific processes If a function should be ran on a specific overall or local process index, there are similar decorators to achieve this: ```python @accelerator.on_local_process(local_process_idx=0) def do_my_thing(): "Something done on process index 0 on each server" do_thing_on_index_zero_on_each_server() ``` ```python @accelerator.on_process(process_index=0) def do_my_thing(): "Something done on process index 0" do_thing_on_index_zero() ``` ### Synchronicity control Use [`~Accelerator.wait_for_everyone`] to make sure all processes join that point before continuing. (Useful before a model save for instance). ### Saving and loading ```python model = MyModel() model = accelerator.prepare(model) ``` Use [`~Accelerator.save_model`] instead of `torch.save` to save a model. It will remove all model wrappers added during the distributed process, get the state_dict of the model and save it. The state_dict will be in the same precision as the model being trained. ```diff - torch.save(state_dict, "my_state.pkl") + accelerator.save_model(model, save_directory) ``` [`~Accelerator.save_model`] can also save a model into sharded checkpoints or with safetensors format. Here is an example: ```python accelerator.save_model(model, save_directory, max_shard_size="1GB", safe_serialization=True) ``` #### 🤗 Transformers models If you are using models from the [🤗 Transformers](https://huggingface.co/docs/transformers/) library, you can use the `.save_pretrained()` method. ```python from transformers import AutoModel model = AutoModel.from_pretrained("bert-base-cased") model = accelerator.prepare(model) # ...fine-tune with PyTorch... unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( "path/to/my_model_directory", is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) ``` This will ensure your model stays compatible with other 🤗 Transformers functionality like the `.from_pretrained()` method. ```python from transformers import AutoModel model = AutoModel.from_pretrained("path/to/my_model_directory") ``` ### Operations Use [`~Accelerator.clip_grad_norm_`] instead of ``torch.nn.utils.clip_grad_norm_`` and [`~Accelerator.clip_grad_value_`] instead of ``torch.nn.utils.clip_grad_value`` ### Gradient Accumulation To perform gradient accumulation use [`~Accelerator.accumulate`] and specify a gradient_accumulation_steps. This will also automatically ensure the gradients are synced or unsynced when on multi-device training, check if the step should actually be performed, and auto-scale the loss: ```diff - accelerator = Accelerator() + accelerator = Accelerator(gradient_accumulation_steps=2) for (input, label) in training_dataloader: + with accelerator.accumulate(model): predictions = model(input) loss = loss_function(predictions, labels) accelerator.backward(loss) optimizer.step() scheduler.step() optimizer.zero_grad() ``` #### GradientAccumulationPlugin [[autodoc]] utils.GradientAccumulationPlugin Instead of passing `gradient_accumulation_steps` you can instantiate a GradientAccumulationPlugin and pass it to the [`Accelerator`]'s `__init__` as `gradient_accumulation_plugin`. You can only pass either one of `gradient_accumulation_plugin` or `gradient_accumulation_steps` passing both will raise an error. ```diff from accelerate.utils import GradientAccumulationPlugin gradient_accumulation_plugin = GradientAccumulationPlugin(num_steps=2) - accelerator = Accelerator() + accelerator = Accelerator(gradient_accumulation_plugin=gradient_accumulation_plugin) ``` In addition to the number of steps, this also lets you configure whether or not you adjust your learning rate scheduler to account for the change in steps due to accumulation. ## Overall API documentation: [[autodoc]] Accelerator

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hf_public_repos/accelerate/docs/source/package_reference/tracking.md

# Experiment Tracking ## The Base Tracker Class [[autodoc]] tracking.GeneralTracker ## Integrated Trackers [[autodoc]] tracking.TensorBoardTracker - __init__ [[autodoc]] tracking.WandBTracker - __init__ [[autodoc]] tracking.CometMLTracker - __init__ [[autodoc]] tracking.AimTracker - __init__ [[autodoc]] tracking.MLflowTracker - __init__ [[autodoc]] tracking.ClearMLTracker - __init__

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hf_public_repos/accelerate/docs/source/package_reference/big_modeling.md

# Working with large models ## Dispatching and Offloading Models [[autodoc]] big_modeling.init_empty_weights [[autodoc]] big_modeling.cpu_offload [[autodoc]] big_modeling.cpu_offload_with_hook [[autodoc]] big_modeling.disk_offload [[autodoc]] big_modeling.dispatch_model [[autodoc]] big_modeling.load_checkpoint_and_dispatch [[autodoc]] big_modeling.load_checkpoint_in_model [[autodoc]] utils.infer_auto_device_map ## Model Hooks ### Hook Classes [[autodoc]] hooks.ModelHook [[autodoc]] hooks.AlignDevicesHook [[autodoc]] hooks.SequentialHook ### Adding Hooks [[autodoc]] hooks.add_hook_to_module [[autodoc]] hooks.attach_execution_device_hook [[autodoc]] hooks.attach_align_device_hook [[autodoc]] hooks.attach_align_device_hook_on_blocks ### Removing Hooks [[autodoc]] hooks.remove_hook_from_module [[autodoc]] hooks.remove_hook_from_submodules

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hf_public_repos/accelerate/docs/source/package_reference/logging.md

# Logging with Accelerate Refer to the [Troubleshooting guide](../usage_guides/troubleshooting#logging) or to the example below to learn how to use 🤗 Accelerate's logger. [[autodoc]] logging.get_logger

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hf_public_repos/accelerate/docs/source/package_reference/megatron_lm.md

# Utilities for Megatron-LM [[autodoc]] utils.MegatronLMPlugin [[autodoc]] utils.MegatronLMDummyScheduler [[autodoc]] utils.MegatronLMDummyDataLoader [[autodoc]] utils.AbstractTrainStep [[autodoc]] utils.GPTTrainStep [[autodoc]] utils.BertTrainStep [[autodoc]] utils.T5TrainStep [[autodoc]] utils.avg_losses_across_data_parallel_group

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# Kwargs Handlers The following objects can be passed to the main [`Accelerator`] to customize how some PyTorch objects related to distributed training or mixed precision are created. ## AutocastKwargs [[autodoc]] AutocastKwargs ## DistributedDataParallelKwargs [[autodoc]] DistributedDataParallelKwargs ## FP8RecipeKwargs [[autodoc]] utils.FP8RecipeKwargs ## GradScalerKwargs [[autodoc]] GradScalerKwargs ## InitProcessGroupKwargs [[autodoc]] InitProcessGroupKwargs

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hf_public_repos/accelerate/docs/source/package_reference/state.md

# Stateful Classes Below are variations of a [singleton class](https://en.wikipedia.org/wiki/Singleton_pattern) in the sense that all instances share the same state, which is initialized on the first instantiation. These classes are immutable and store information about certain configurations or states. [[autodoc]] state.PartialState [[autodoc]] state.AcceleratorState [[autodoc]] state.GradientState

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# Utilities for Fully Sharded Data Parallelism [[autodoc]] utils.FullyShardedDataParallelPlugin

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hf_public_repos/accelerate/docs/source/package_reference/launchers.md

# Launchers Functions for launching training on distributed processes. [[autodoc]] accelerate.notebook_launcher [[autodoc]] accelerate.debug_launcher

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hf_public_repos/accelerate/docs/source/package_reference/deepspeed.md

# Utilities for DeepSpeed [[autodoc]] utils.DeepSpeedPlugin [[autodoc]] utils.DummyOptim [[autodoc]] utils.DummyScheduler [[autodoc]] utils.DeepSpeedEngineWrapper [[autodoc]] utils.DeepSpeedOptimizerWrapper [[autodoc]] utils.DeepSpeedSchedulerWrapper

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hf_public_repos/accelerate/docs/source/package_reference/torch_wrappers.md

# Wrapper classes for torch Dataloaders, Optimizers, and Schedulers The internal classes Accelerate uses to prepare objects for distributed training when calling [`~Accelerator.prepare`]. ## Datasets and DataLoaders [[autodoc]] data_loader.prepare_data_loader [[autodoc]] data_loader.skip_first_batches [[autodoc]] data_loader.BatchSamplerShard [[autodoc]] data_loader.IterableDatasetShard [[autodoc]] data_loader.DataLoaderShard [[autodoc]] data_loader.DataLoaderDispatcher ## Optimizers [[autodoc]] optimizer.AcceleratedOptimizer ## Schedulers [[autodoc]] scheduler.AcceleratedScheduler

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hf_public_repos/accelerate/docs/source/package_reference/utilities.md

# Helpful Utilities Below are a variety of utility functions that 🤗 Accelerate provides, broken down by use-case. ## Constants Constants used throughout 🤗 Accelerate for reference The following are constants used when utilizing [`Accelerator.save_state`] `utils.MODEL_NAME`: `"pytorch_model"` `utils.OPTIMIZER_NAME`: `"optimizer"` `utils.RNG_STATE_NAME`: `"random_states"` `utils.SCALER_NAME`: `"scaler.pt` `utils.SCHEDULER_NAME`: `"scheduler` The following are constants used when utilizing [`Accelerator.save_model`] `utils.WEIGHTS_NAME`: `"pytorch_model.bin"` `utils.SAFE_WEIGHTS_NAME`: `"model.safetensors"` `utils.WEIGHTS_INDEX_NAME`: `"pytorch_model.bin.index.json"` `utils.SAFE_WEIGHTS_INDEX_NAME`: `"model.safetensors.index.json"` ## Data Classes These are basic dataclasses used throughout 🤗 Accelerate and they can be passed in as parameters. ### Standalone These are standalone dataclasses used for checks, such as the type of distributed system being used [[autodoc]] utils.ComputeEnvironment [[autodoc]] utils.DistributedType [[autodoc]] utils.DynamoBackend [[autodoc]] utils.LoggerType [[autodoc]] utils.PrecisionType [[autodoc]] utils.RNGType [[autodoc]] utils.SageMakerDistributedType ### Kwargs These are configurable arguments for specific interactions throughout the PyTorch ecosystem that Accelerate handles under the hood. [[autodoc]] utils.AutocastKwargs [[autodoc]] utils.DistributedDataParallelKwargs [[autodoc]] utils.FP8RecipeKwargs [[autodoc]] utils.GradScalerKwargs [[autodoc]] utils.InitProcessGroupKwargs ## Plugins These are plugins that can be passed to the [`Accelerator`] object. While they are defined elsewhere in the documentation, for convenience all of them are available to see here: [[autodoc]] utils.DeepSpeedPlugin [[autodoc]] utils.FullyShardedDataParallelPlugin [[autodoc]] utils.GradientAccumulationPlugin [[autodoc]] utils.MegatronLMPlugin [[autodoc]] utils.TorchDynamoPlugin ## Configurations These are classes which can be configured and passed through to the appropriate integration [[autodoc]] utils.BnbQuantizationConfig [[autodoc]] utils.ProjectConfiguration ## Environmental Variables These are environmental variables that can be enabled for different use cases * `ACCELERATE_DEBUG_MODE` (`str`): Whether to run accelerate in debug mode. More info available [here](../usage_guides/debug.md). ## Data Manipulation and Operations These include data operations that mimic the same `torch` ops but can be used on distributed processes. [[autodoc]] utils.broadcast [[autodoc]] utils.broadcast_object_list [[autodoc]] utils.concatenate [[autodoc]] utils.convert_outputs_to_fp32 [[autodoc]] utils.convert_to_fp32 [[autodoc]] utils.gather [[autodoc]] utils.gather_object [[autodoc]] utils.listify [[autodoc]] utils.pad_across_processes [[autodoc]] utils.recursively_apply [[autodoc]] utils.reduce [[autodoc]] utils.send_to_device [[autodoc]] utils.slice_tensors ## Environment Checks These functionalities check the state of the current working environment including information about the operating system itself, what it can support, and if particular dependencies are installed. [[autodoc]] utils.is_bf16_available [[autodoc]] utils.is_ipex_available [[autodoc]] utils.is_mps_available [[autodoc]] utils.is_npu_available [[autodoc]] utils.is_torch_version [[autodoc]] utils.is_tpu_available [[autodoc]] utils.is_xpu_available ## Environment Manipulation [[autodoc]] utils.patch_environment [[autodoc]] utils.clear_environment [[autodoc]] utils.write_basic_config When setting up 🤗 Accelerate for the first time, rather than running `accelerate config` [~utils.write_basic_config] can be used as an alternative for quick configuration. ## Memory [[autodoc]] utils.find_executable_batch_size ## Modeling These utilities relate to interacting with PyTorch models [[autodoc]] utils.calculate_maximum_sizes [[autodoc]] utils.compute_module_sizes [[autodoc]] utils.extract_model_from_parallel [[autodoc]] utils.get_balanced_memory [[autodoc]] utils.get_max_layer_size [[autodoc]] utils.infer_auto_device_map [[autodoc]] utils.load_checkpoint_in_model [[autodoc]] utils.load_offloaded_weights [[autodoc]] utils.load_state_dict [[autodoc]] utils.offload_state_dict [[autodoc]] utils.retie_parameters [[autodoc]] utils.set_module_tensor_to_device [[autodoc]] utils.shard_checkpoint ## Parallel These include general utilities that should be used when working in parallel. [[autodoc]] utils.extract_model_from_parallel [[autodoc]] utils.save [[autodoc]] utils.wait_for_everyone ## Random These utilities relate to setting and synchronizing of all the random states. [[autodoc]] utils.set_seed [[autodoc]] utils.synchronize_rng_state [[autodoc]] utils.synchronize_rng_states ## PyTorch XLA These include utilities that are useful while using PyTorch with XLA. [[autodoc]] utils.install_xla ## Loading model weights These include utilities that are useful to load checkpoints. [[autodoc]] utils.load_checkpoint_in_model ## Quantization These include utilities that are useful to quantize model. [[autodoc]] utils.load_and_quantize_model

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hf_public_repos/accelerate/docs/source/package_reference/cli.md

# The Command Line Below is a list of all the available commands 🤗 Accelerate with their parameters ## accelerate config **Command**: `accelerate config` or `accelerate-config` Launches a series of prompts to create and save a `default_config.yml` configuration file for your training system. Should always be ran first on your machine. **Usage**: ```bash accelerate config [arguments] ``` **Optional Arguments**: * `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`. * `-h`, `--help` (`bool`) -- Show a help message and exit ## accelerate config default **Command**: `accelerate config default` or `accelerate-config default` Create a default config file for Accelerate with only a few flags set. **Usage**: ```bash accelerate config default [arguments] ``` **Optional Arguments**: * `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`. * `-h`, `--help` (`bool`) -- Show a help message and exit * `--mixed_precision {no,fp16,bf16}` (`str`) -- Whether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later. ## accelerate config update **Command**: `accelerate config update` or `accelerate-config update` Update an existing config file with the latest defaults while maintaining the old configuration. **Usage**: ```bash accelerate config update [arguments] ``` **Optional Arguments**: * `--config_file CONFIG_FILE` (`str`) -- The path to the config file to update. Will default to a file named default_config.yaml in the cache location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`. * `-h`, `--help` (`bool`) -- Show a help message and exit ## accelerate env **Command**: `accelerate env` or `accelerate-env` or `python -m accelerate.commands.env` Lists the contents of the passed 🤗 Accelerate configuration file. Should always be used when opening an issue on the [GitHub repository](https://github.com/huggingface/accelerate). **Usage**: ```bash accelerate env [arguments] ``` **Optional Arguments**: * `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`. * `-h`, `--help` (`bool`) -- Show a help message and exit ## accelerate launch **Command**: `accelerate launch` or `accelerate-launch` or `python -m accelerate.commands.launch` Launches a specified script on a distributed system with the right parameters. **Usage**: ```bash accelerate launch [arguments] {training_script} --{training_script-argument-1} --{training_script-argument-2} ... ``` **Positional Arguments**: - `{training_script}` -- The full path to the script to be launched in parallel - `--{training_script-argument-1}` -- Arguments of the training script **Optional Arguments**: * `-h`, `--help` (`bool`) -- Show a help message and exit * `--config_file CONFIG_FILE` (`str`)-- The config file to use for the default values in the launching script. * `-m`, `--module` (`bool`) -- Change each process to interpret the launch script as a Python module, executing with the same behavior as 'python -m'. * `--no_python` (`bool`) -- Skip prepending the training script with 'python' - just execute it directly. Useful when the script is not a Python script. * `--debug` (`bool`) -- Whether to print out the torch.distributed stack trace when something fails. * `-q`, `--quiet` (`bool`) -- Silence subprocess errors from the launch stack trace to only show the relevant tracebacks. (Only applicable to DeepSpeed and single-process configurations). The rest of these arguments are configured through `accelerate config` and are read in from the specified `--config_file` (or default configuration) for their values. They can also be passed in manually. **Hardware Selection Arguments**: * `--cpu` (`bool`) -- Whether or not to force the training on the CPU. * `--multi_gpu` (`bool`) -- Whether or not this should launch a distributed GPU training. * `--tpu` (`bool`) -- Whether or not this should launch a TPU training. * `--ipex` (`bool`) -- Whether or not this should launch an Intel Pytorch Extension (IPEX) training. **Resource Selection Arguments**: The following arguments are useful for fine-tuning how available hardware should be used * `--mixed_precision {no,fp16,bf16}` (`str`) -- Whether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later. * `--num_processes NUM_PROCESSES` (`int`) -- The total number of processes to be launched in parallel. * `--num_machines NUM_MACHINES` (`int`) -- The total number of machines used in this training. * `--num_cpu_threads_per_process NUM_CPU_THREADS_PER_PROCESS` (`int`) -- The number of CPU threads per process. Can be tuned for optimal performance. **Training Paradigm Arguments**: The following arguments are useful for selecting which training paradigm to use. * `--use_deepspeed` (`bool`) -- Whether or not to use DeepSpeed for training. * `--use_fsdp` (`bool`) -- Whether or not to use FullyShardedDataParallel for training. * `--use_megatron_lm` (`bool`) -- Whether or not to use Megatron-LM for training. * `--use_xpu` (`bool`) -- Whether to use IPEX plugin to speed up training on XPU specifically. **Distributed GPU Arguments**: The following arguments are only useful when `multi_gpu` is passed or multi-gpu training is configured through `accelerate config`: * `--gpu_ids` (`str`) -- What GPUs (by id) should be used for training on this machine as a comma-seperated list * `--same_network` (`bool`) -- Whether all machines used for multinode training exist on the same local network. * `--machine_rank MACHINE_RANK` (`int`) -- The rank of the machine on which this script is launched. * `--main_process_ip MAIN_PROCESS_IP` (`str`) -- The IP address of the machine of rank 0. * `--main_process_port MAIN_PROCESS_PORT` (`int`) -- The port to use to communicate with the machine of rank 0. * `--rdzv_backend` (`str`) -- The rendezvous method to use, such as "static" or "c10d" * `--rdzv_conf` (`str`) -- Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...). * `--max_restarts` (`int`) -- Maximum number of worker group restarts before failing. * `--monitor_interval` (`float`) -- Interval, in seconds, to monitor the state of workers. **TPU Arguments**: The following arguments are only useful when `tpu` is passed or TPU training is configured through `accelerate config`: * `--main_training_function MAIN_TRAINING_FUNCTION` (`str`) -- The name of the main function to be executed in your script. * `--downcast_bf16` (`bool`) -- Whether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32. **DeepSpeed Arguments**: The following arguments are only useful when `use_deepspeed` is passed or `deepspeed` is configured through `accelerate config`: * `--deepspeed_config_file` (`str`) -- DeepSpeed config file. * `--zero_stage` (`int`) -- DeepSpeed's ZeRO optimization stage. * `--offload_optimizer_device` (`str`) -- Decides where (none|cpu|nvme) to offload optimizer states. * `--offload_param_device` (`str`) -- Decides where (none|cpu|nvme) to offload parameters. * `--gradient_accumulation_steps` (`int`) -- No of gradient_accumulation_steps used in your training script. * `--gradient_clipping` (`float`) -- Gradient clipping value used in your training script. * `--zero3_init_flag` (`str`) -- Decides Whether (true|false) to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with DeepSpeed ZeRO Stage-3. * `--zero3_save_16bit_model` (`str`) -- Decides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. Only applicable with DeepSpeed ZeRO Stage-3. * `--deepspeed_hostfile` (`str`) -- DeepSpeed hostfile for configuring multi-node compute resources. * `--deepspeed_exclusion_filter` (`str`) -- DeepSpeed exclusion filter string when using mutli-node setup. * `--deepspeed_inclusion_filter` (`str`) -- DeepSpeed inclusion filter string when using mutli-node setup. * `--deepspeed_multinode_launcher` (`str`) -- DeepSpeed multi-node launcher to use. **Fully Sharded Data Parallelism Arguments**: The following arguments are only useful when `use_fsdp` is passed or Fully Sharded Data Parallelism is configured through `accelerate config`: * `--fsdp_offload_params` (`str`) -- Decides Whether (true|false) to offload parameters and gradients to CPU. * `--fsdp_min_num_params` (`int`) -- FSDP's minimum number of parameters for Default Auto Wrapping. * `--fsdp_sharding_strategy` (`int`) -- FSDP's Sharding Strategy. * `--fsdp_auto_wrap_policy` (`str`) -- FSDP's auto wrap policy. * `--fsdp_transformer_layer_cls_to_wrap` (`str`) -- Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` ... * `--fsdp_backward_prefetch_policy` (`str`) -- FSDP's backward prefetch policy. * `--fsdp_state_dict_type` (`str`) -- FSDP's state dict type. **Megatron-LM Arguments**: The following arguments are only useful when `use_megatron_lm` is passed or Megatron-LM is configured through `accelerate config`: * `--megatron_lm_tp_degree` (``) -- Megatron-LM's Tensor Parallelism (TP) degree. * `--megatron_lm_pp_degree` (``) -- Megatron-LM's Pipeline Parallelism (PP) degree. * `--megatron_lm_num_micro_batches` (``) -- Megatron-LM's number of micro batches when PP degree > 1. * `--megatron_lm_sequence_parallelism` (``) -- Decides Whether (true|false) to enable Sequence Parallelism when TP degree > 1. * `--megatron_lm_recompute_activations` (``) -- Decides Whether (true|false) to enable Selective Activation Recomputation. * `--megatron_lm_use_distributed_optimizer` (``) -- Decides Whether (true|false) to use distributed optimizer which shards optimizer state and gradients across Data Parallel (DP) ranks. * `--megatron_lm_gradient_clipping` (``) -- Megatron-LM's gradient clipping value based on global L2 Norm (0 to disable). **AWS SageMaker Arguments**: The following arguments are only useful when training in SageMaker * `--aws_access_key_id AWS_ACCESS_KEY_ID` (`str`) -- The AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training job * `--aws_secret_access_key AWS_SECRET_ACCESS_KEY` (`str`) -- The AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training job ## accelerate estimate-memory **Command**: `accelerate estimate-memory` or `accelerate-estimate-memory` or `python -m accelerate.commands.estimate` Estimates the total vRAM a particular model hosted on the Hub needs to be loaded in with an estimate for training. Requires that `huggingface_hub` be installed. <Tip> When performing inference, typically add ≤20% to the result as overall allocation [as referenced here](https://blog.eleuther.ai/transformer-math/). We will have more extensive estimations in the future that will automatically be included in the calculation. </Tip> **Usage**: ```bash accelerate estimate-memory {MODEL_NAME} --library_name {LIBRARY_NAME} --dtypes {dtype_1} {dtype_2} ... ``` **Required Arguments**: * `MODEL_NAME` (`str`)-- The model name on the Hugging Face Hub **Optional Arguments**: * `--library_name {timm,transformers}` (`str`) -- The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub * `--dtypes {float32,float16,int8,int4}` (`[{float32,float16,int8,int4} ...]`) -- The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4` * `--trust_remote_code` (`bool`) -- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be passed for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. ## accelerate tpu-config `accelerate tpu-config` **Usage**: ```bash accelerate tpu-config [arguments] ``` **Optional Arguments**: * `-h`, `--help` (`bool`) -- Show a help message and exit **Config Arguments**: Arguments that can be configured through `accelerate config`. * `--config_file` (`str`) -- Path to the config file to use for accelerate. * `--tpu_name` (`str`) -- The name of the TPU to use. If not specified, will use the TPU specified in the config file. * `--tpu_zone` (`str`) -- The zone of the TPU to use. If not specified, will use the zone specified in the config file. **TPU Arguments**: Arguments for options ran inside the TPU. * `--command_file` (`str`) -- The path to the file containing the commands to run on the pod on startup. * `--command` (`str`) -- A command to run on the pod. Can be passed multiple times. * `--install_accelerate` (`bool`) -- Whether to install accelerate on the pod. Defaults to False. * `--accelerate_version` (`str`) -- The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub. * `--debug` (`bool`) -- If set, will print the command that would be run instead of running it. ## accelerate test `accelerate test` or `accelerate-test` Runs `accelerate/test_utils/test_script.py` to verify that 🤗 Accelerate has been properly configured on your system and runs. **Usage**: ```bash accelerate test [arguments] ``` **Optional Arguments**: * `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`. * `-h`, `--help` (`bool`) -- Show a help message and exit

0

hf_public_repos/accelerate/docker

hf_public_repos/accelerate/docker/accelerate-cpu/Dockerfile

# Builds CPU-only Docker image of PyTorch # Uses multi-staged approach to reduce size # Stage 1 FROM python:3.8-slim as compile-image ARG DEBIAN_FRONTEND=noninteractive RUN apt update RUN apt-get install -y --no-install-recommends \ build-essential \ git \ gcc # Setup virtual environment for Docker ENV VIRTUAL_ENV=/opt/venv RUN python3 -m venv ${VIRTUAL_ENV} # Make sure we use the virtualenv ENV PATH="${VIRTUAL_ENV}/bin:$PATH" WORKDIR /workspace # Install specific CPU torch wheel to save on space RUN python3 -m pip install --upgrade --no-cache-dir pip RUN python3 -m pip install --no-cache-dir \ jupyter \ git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers] \ --extra-index-url https://download.pytorch.org/whl/cpu # Stage 2 FROM python:3.8-slim AS build-image COPY --from=compile-image /opt/venv /opt/venv RUN useradd -ms /bin/bash user USER user # Make sure we use the virtualenv ENV PATH="/opt/venv/bin:$PATH" CMD ["/bin/bash"]

0

hf_public_repos/accelerate/docker

hf_public_repos/accelerate/docker/accelerate-gpu/Dockerfile

# Builds GPU docker image of PyTorch # Uses multi-staged approach to reduce size # Stage 1 # Use base conda image to reduce time FROM continuumio/miniconda3:latest AS compile-image # Specify py version ENV PYTHON_VERSION=3.8 # Install apt libs RUN apt-get update && \ apt-get install -y curl git wget && \ apt-get clean && \ rm -rf /var/lib/apt/lists* # Create our conda env RUN conda create --name accelerate python=${PYTHON_VERSION} ipython jupyter pip # We don't install pytorch here yet since CUDA isn't available # instead we use the direct torch wheel ENV PATH /opt/conda/envs/accelerate/bin:$PATH # Activate our bash shell RUN chsh -s /bin/bash SHELL ["/bin/bash", "-c"] # Activate the conda env and install torch + accelerate RUN source activate accelerate && \ python3 -m pip install --no-cache-dir \ git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers] \ --extra-index-url https://download.pytorch.org/whl/cu117 RUN python3 -m pip install --no-cache-dir bitsandbytes # Stage 2 FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 AS build-image COPY --from=compile-image /opt/conda /opt/conda ENV PATH /opt/conda/bin:$PATH # Install apt libs RUN apt-get update && \ apt-get install -y curl git wget && \ apt-get clean && \ rm -rf /var/lib/apt/lists* RUN echo "source activate accelerate" >> ~/.profile # Activate the virtualenv CMD ["/bin/bash"]

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/checkpointing.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random from pathlib import Path from typing import List import numpy as np import torch from safetensors.torch import load_file from torch.cuda.amp import GradScaler from .utils import ( MODEL_NAME, OPTIMIZER_NAME, RNG_STATE_NAME, SAFE_MODEL_NAME, SAFE_WEIGHTS_NAME, SAMPLER_NAME, SCALER_NAME, SCHEDULER_NAME, WEIGHTS_NAME, get_pretty_name, is_tpu_available, is_xpu_available, save, ) if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm from .logging import get_logger from .state import PartialState logger = get_logger(__name__) def save_accelerator_state( output_dir: str, model_states: List[dict], optimizers: list, schedulers: list, dataloaders: list, process_index: int, scaler: GradScaler = None, save_on_each_node: bool = False, safe_serialization: bool = True, ): """ Saves the current states of the models, optimizers, scaler, and RNG generators to a given directory. <Tip> If `safe_serialization` is `True`, models will be saved with `safetensors` while the rest are saved using native `pickle`. </Tip> Args: output_dir (`str` or `os.PathLike`): The name of the folder to save all relevant weights and states. model_states (`List[torch.nn.Module]`): A list of model states optimizers (`List[torch.optim.Optimizer]`): A list of optimizer instances schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`): A list of learning rate schedulers dataloaders (`List[torch.utils.data.DataLoader]`): A list of dataloader instances to save their sampler states process_index (`int`): The current process index in the Accelerator state scaler (`torch.cuda.amp.GradScaler`, *optional*): An optional gradient scaler instance to save save_on_each_node (`bool`, *optional*): Whether to save on every node, or only the main node. safe_serialization (`bool`, *optional*, defaults to `True`): Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). """ output_dir = Path(output_dir) # Model states for i, state in enumerate(model_states): weights_name = WEIGHTS_NAME if not safe_serialization else SAFE_WEIGHTS_NAME if i > 0: weights_name = weights_name.replace(".", f"_{i}.") output_model_file = output_dir.joinpath(weights_name) save(state, output_model_file, save_on_each_node=save_on_each_node, safe_serialization=safe_serialization) logger.info(f"Model weights saved in {output_model_file}") # Optimizer states for i, opt in enumerate(optimizers): state = opt.state_dict() optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin" output_optimizer_file = output_dir.joinpath(optimizer_name) save(state, output_optimizer_file, save_on_each_node=save_on_each_node, safe_serialization=False) logger.info(f"Optimizer state saved in {output_optimizer_file}") # Scheduler states for i, scheduler in enumerate(schedulers): state = scheduler.state_dict() scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin" output_scheduler_file = output_dir.joinpath(scheduler_name) save(state, output_scheduler_file, save_on_each_node=save_on_each_node, safe_serialization=False) logger.info(f"Scheduler state saved in {output_scheduler_file}") # DataLoader states for i, dataloader in enumerate(dataloaders): sampler_name = f"{SAMPLER_NAME}.bin" if i == 0 else f"{SAMPLER_NAME}_{i}.bin" output_sampler_file = output_dir.joinpath(sampler_name) # Only save if we have our custom sampler from .data_loader import IterableDatasetShard, SeedableRandomSampler if isinstance(dataloader.dataset, IterableDatasetShard): sampler = dataloader.sampler.sampler if isinstance(sampler, SeedableRandomSampler): save(sampler, output_sampler_file, save_on_each_node=save_on_each_node, safe_serialization=False) logger.info(f"Sampler state for dataloader {i} saved in {output_sampler_file}") # GradScaler state if scaler is not None: state = scaler.state_dict() output_scaler_file = output_dir.joinpath(SCALER_NAME) torch.save(state, output_scaler_file) logger.info(f"Gradient scaler state saved in {output_scaler_file}") # Random number generator states states = {} states_name = f"{RNG_STATE_NAME}_{process_index}.pkl" states["random_state"] = random.getstate() states["numpy_random_seed"] = np.random.get_state() states["torch_manual_seed"] = torch.get_rng_state() if is_xpu_available(): states["torch_xpu_manual_seed"] = torch.xpu.get_rng_state_all() else: states["torch_cuda_manual_seed"] = torch.cuda.get_rng_state_all() if is_tpu_available(): states["xm_seed"] = xm.get_rng_state() output_states_file = output_dir.joinpath(states_name) torch.save(states, output_states_file) logger.info(f"Random states saved in {output_states_file}") return output_dir def load_accelerator_state( input_dir, models, optimizers, schedulers, dataloaders, process_index, scaler=None, map_location=None, **load_model_func_kwargs, ): """ Loads states of the models, optimizers, scaler, and RNG generators from a given directory. Args: input_dir (`str` or `os.PathLike`): The name of the folder to load all relevant weights and states. models (`List[torch.nn.Module]`): A list of model instances optimizers (`List[torch.optim.Optimizer]`): A list of optimizer instances schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`): A list of learning rate schedulers process_index (`int`): The current process index in the Accelerator state scaler (`torch.cuda.amp.GradScaler`, *optional*): An optional *GradScaler* instance to load map_location (`str`, *optional*): What device to load the optimizer state onto. Should be one of either "cpu" or "on_device". load_model_func_kwargs (`dict`, *optional*): Additional arguments that can be passed to the model's `load_state_dict` method. """ if map_location not in [None, "cpu", "on_device"]: raise TypeError( "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`" ) if map_location is None: map_location = "cpu" elif map_location == "on_device": map_location = PartialState().device input_dir = Path(input_dir) # Model states for i, model in enumerate(models): ending = f"_{i}" if i > 0 else "" input_model_file = input_dir.joinpath(f"{SAFE_MODEL_NAME}{ending}.safetensors") if input_model_file.exists(): state_dict = load_file(input_model_file, device=str(map_location)) else: # Load with torch input_model_file = input_dir.joinpath(f"{MODEL_NAME}{ending}.bin") state_dict = torch.load(input_model_file, map_location=map_location) models[i].load_state_dict(state_dict, **load_model_func_kwargs) logger.info("All model weights loaded successfully") # Optimizer states for i, opt in enumerate(optimizers): optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin" input_optimizer_file = input_dir.joinpath(optimizer_name) optimizer_state = torch.load(input_optimizer_file, map_location=map_location) optimizers[i].load_state_dict(optimizer_state) logger.info("All optimizer states loaded successfully") # Scheduler states for i, scheduler in enumerate(schedulers): scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin" input_scheduler_file = input_dir.joinpath(scheduler_name) scheduler.load_state_dict(torch.load(input_scheduler_file)) logger.info("All scheduler states loaded successfully") for i, dataloader in enumerate(dataloaders): sampler_name = f"{SAMPLER_NAME}.bin" if i == 0 else f"{SAMPLER_NAME}_{i}.bin" input_sampler_file = input_dir.joinpath(sampler_name) # Only load if we have our custom sampler from .data_loader import IterableDatasetShard, SeedableRandomSampler if isinstance(dataloader.dataset, IterableDatasetShard): sampler = dataloader.sampler.sampler if isinstance(sampler, SeedableRandomSampler): dataloader.sampler.sampler = torch.load(input_sampler_file) logger.info("All dataloader sampler states loaded successfully") # GradScaler state if scaler is not None: input_scaler_file = input_dir.joinpath(SCALER_NAME) scaler.load_state_dict(torch.load(input_scaler_file)) logger.info("GradScaler state loaded successfully") # Random states try: states = torch.load(input_dir.joinpath(f"{RNG_STATE_NAME}_{process_index}.pkl")) random.setstate(states["random_state"]) np.random.set_state(states["numpy_random_seed"]) torch.set_rng_state(states["torch_manual_seed"]) if is_xpu_available(): torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]) else: torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]) if is_tpu_available(): xm.set_rng_state(states["xm_seed"]) logger.info("All random states loaded successfully") except Exception: logger.info("Could not load random states") def save_custom_state(obj, path, index: int = 0, save_on_each_node: bool = False): """ Saves the state of `obj` to `{path}/custom_checkpoint_{index}.pkl` """ # Should this be the right way to get a qual_name type value from `obj`? save_location = Path(path) / f"custom_checkpoint_{index}.pkl" logger.info(f"Saving the state of {get_pretty_name(obj)} to {save_location}") save(obj.state_dict(), save_location, save_on_each_node=save_on_each_node) def load_custom_state(obj, path, index: int = 0): """ Loads the state of `obj` at `{path}/custom_checkpoint_{index}.pkl` """ load_location = f"{path}/custom_checkpoint_{index}.pkl" logger.info(f"Loading the state of {get_pretty_name(obj)} from {load_location}") obj.load_state_dict(torch.load(load_location, map_location="cpu"))

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/big_modeling.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from contextlib import contextmanager from functools import wraps from typing import Dict, List, Optional, Union import torch import torch.nn as nn from .hooks import ( AlignDevicesHook, CpuOffload, UserCpuOffloadHook, add_hook_to_module, attach_align_device_hook, attach_align_device_hook_on_blocks, ) from .utils import ( OffloadedWeightsLoader, check_device_map, extract_submodules_state_dict, find_tied_parameters, get_balanced_memory, infer_auto_device_map, is_npu_available, is_torch_version, load_checkpoint_in_model, offload_state_dict, parse_flag_from_env, retie_parameters, ) from .utils.other import recursive_getattr logger = logging.getLogger(__name__) @contextmanager def init_empty_weights(include_buffers: bool = None): """ A context manager under which models are initialized with all parameters on the meta device, therefore creating an empty model. Useful when just initializing the model would blow the available RAM. Args: include_buffers (`bool`, *optional*): Whether or not to also put all buffers on the meta device while initializing. Example: ```python import torch.nn as nn from accelerate import init_empty_weights # Initialize a model with 100 billions parameters in no time and without using any RAM. with init_empty_weights(): tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)]) ``` <Tip warning={true}> Any model created under this context manager has no weights. As such you can't do something like `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`]. Make sure to overwrite the default device_map param for [`load_checkpoint_and_dispatch`], otherwise dispatch is not called. </Tip> """ if include_buffers is None: include_buffers = parse_flag_from_env("ACCELERATE_INIT_INCLUDE_BUFFERS", False) with init_on_device(torch.device("meta"), include_buffers=include_buffers) as f: yield f @contextmanager def init_on_device(device: torch.device, include_buffers: bool = None): """ A context manager under which models are initialized with all parameters on the specified device. Args: device (`torch.device`): Device to initialize all parameters on. include_buffers (`bool`, *optional*): Whether or not to also put all buffers on the meta device while initializing. Example: ```python import torch.nn as nn from accelerate import init_on_device with init_on_device(device=torch.device("cuda")): tst = nn.Liner(100, 100) # on `cuda` device ``` """ if include_buffers is None: include_buffers = parse_flag_from_env("ACCELERATE_INIT_INCLUDE_BUFFERS", False) # TODO(shingjan): remove the torch version check once older versions are deprecated if is_torch_version(">=", "2.0") and include_buffers: with device: yield return old_register_parameter = nn.Module.register_parameter if include_buffers: old_register_buffer = nn.Module.register_buffer def register_empty_parameter(module, name, param): old_register_parameter(module, name, param) if param is not None: param_cls = type(module._parameters[name]) kwargs = module._parameters[name].__dict__ module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) def register_empty_buffer(module, name, buffer, persistent=True): old_register_buffer(module, name, buffer, persistent=persistent) if buffer is not None: module._buffers[name] = module._buffers[name].to(device) # Patch tensor creation if include_buffers: tensor_constructors_to_patch = { torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ["empty", "zeros", "ones", "full"] } else: tensor_constructors_to_patch = {} def patch_tensor_constructor(fn): def wrapper(*args, **kwargs): kwargs["device"] = device return fn(*args, **kwargs) return wrapper try: nn.Module.register_parameter = register_empty_parameter if include_buffers: nn.Module.register_buffer = register_empty_buffer for torch_function_name in tensor_constructors_to_patch.keys(): setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) yield finally: nn.Module.register_parameter = old_register_parameter if include_buffers: nn.Module.register_buffer = old_register_buffer for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): setattr(torch, torch_function_name, old_torch_function) def cpu_offload( model: nn.Module, execution_device: Optional[torch.device] = None, offload_buffers: bool = False, state_dict: Optional[Dict[str, torch.Tensor]] = None, preload_module_classes: Optional[List[str]] = None, ): """ Activates full CPU offload for a model. As a result, all parameters of the model will be offloaded and only one copy of the state dict of the model will be kept. During the forward pass, parameters will be extracted from that state dict and put on the execution device passed as they are needed, then offloaded again. Args: model (`torch.nn.Module`): The model to offload. execution_device (`torch.device`, *optional*): The device on which the forward pass of the model will be executed (should be a GPU). Will default to the model first parameter device. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to offload the buffers with the model parameters. state_dict (`Dict[str, torch.Tensor]`, *optional*): The state dict of the model that will be kept on CPU. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. """ if execution_device is None: execution_device = next(iter(model.parameters())).device if state_dict is None: state_dict = {n: p.to("cpu") for n, p in model.state_dict().items()} add_hook_to_module(model, AlignDevicesHook(io_same_device=True), append=True) attach_align_device_hook( model, execution_device=execution_device, offload=True, offload_buffers=offload_buffers, weights_map=state_dict, preload_module_classes=preload_module_classes, ) return model def cpu_offload_with_hook( model: torch.nn.Module, execution_device: Optional[Union[int, str, torch.device]] = None, prev_module_hook: Optional[UserCpuOffloadHook] = None, ): """ Offloads a model on the CPU and puts it back to an execution device when executed. The difference with [`cpu_offload`] is that the model stays on the execution device after the forward and is only offloaded again when the `offload` method of the returned `hook` is called. Useful for pipelines running a model in a loop. Args: model (`torch.nn.Module`): The model to offload. execution_device(`str`, `int` or `torch.device`, *optional*): The device on which the model should be executed. Will default to the MPS device if it's available, then GPU 0 if there is a GPU, and finally to the CPU. prev_module_hook (`UserCpuOffloadHook`, *optional*): The hook sent back by this function for a previous model in the pipeline you are running. If passed, its offload method will be called just before the forward of the model to which this hook is attached. Example: ```py model_1, hook_1 = cpu_offload_with_hook(model_1, cuda_device) model_2, hook_2 = cpu_offload_with_hook(model_2, cuda_device, prev_module_hook=hook_1) model_3, hook_3 = cpu_offload_with_hook(model_3, cuda_device, prev_module_hook=hook_2) hid_1 = model_1(input) for i in range(50): # model1 is offloaded on the CPU at the first iteration, model 2 stays on the GPU for this whole loop. hid_2 = model_2(hid_1) # model2 is offloaded to the CPU just before this forward. hid_3 = model_3(hid_3) # For model3, you need to manually call the hook offload method. hook_3.offload() ``` """ hook = CpuOffload(execution_device=execution_device, prev_module_hook=prev_module_hook) add_hook_to_module(model, hook, append=True) user_hook = UserCpuOffloadHook(model, hook) return model, user_hook def disk_offload( model: nn.Module, offload_dir: Union[str, os.PathLike], execution_device: Optional[torch.device] = None, offload_buffers: bool = False, preload_module_classes: Optional[List[str]] = None, ): """ Activates full disk offload for a model. As a result, all parameters of the model will be offloaded as memory-mapped array in a given folder. During the forward pass, parameters will be accessed from that folder and put on the execution device passed as they are needed, then offloaded again. Args: model (`torch.nn.Module`): The model to offload. offload_dir (`str` or `os.PathLike`): The folder in which to offload the model weights (or where the model weights are already offloaded). execution_device (`torch.device`, *optional*): The device on which the forward pass of the model will be executed (should be a GPU). Will default to the model's first parameter device. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to offload the buffers with the model parameters. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. """ if not os.path.isdir(offload_dir) or not os.path.isfile(os.path.join(offload_dir, "index.json")): offload_state_dict(offload_dir, model.state_dict()) if execution_device is None: execution_device = next(iter(model.parameters())).device weights_map = OffloadedWeightsLoader(save_folder=offload_dir) add_hook_to_module(model, AlignDevicesHook(io_same_device=True), append=True) attach_align_device_hook( model, execution_device=execution_device, offload=True, offload_buffers=offload_buffers, weights_map=weights_map, preload_module_classes=preload_module_classes, ) return model def dispatch_model( model: nn.Module, device_map: Dict[str, Union[str, int, torch.device]], main_device: Optional[torch.device] = None, state_dict: Optional[Dict[str, torch.Tensor]] = None, offload_dir: Optional[Union[str, os.PathLike]] = None, offload_index: Optional[Dict[str, str]] = None, offload_buffers: bool = False, skip_keys: Optional[Union[str, List[str]]] = None, preload_module_classes: Optional[List[str]] = None, force_hooks: bool = False, ): """ Dispatches a model according to a given device map. Layers of the model might be spread across GPUs, offloaded on the CPU or even the disk. Args: model (`torch.nn.Module`): The model to dispatch. device_map (`Dict[str, Union[str, int, torch.device]]`): A dictionary mapping module names in the models `state_dict` to the device they should go to. Note that `"disk"` is accepted even if it's not a proper value for `torch.device`. main_device (`str`, `int` or `torch.device`, *optional*): The main execution device. Will default to the first device in the `device_map` different from `"cpu"` or `"disk"`. state_dict (`Dict[str, torch.Tensor]`, *optional*): The state dict of the part of the model that will be kept on CPU. offload_dir (`str` or `os.PathLike`): The folder in which to offload the model weights (or where the model weights are already offloaded). offload_index (`Dict`, *optional*): A dictionary from weight name to their information (`dtype`/ `shape` or safetensors filename). Will default to the index saved in `save_folder`. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to offload the buffers with the model parameters. skip_keys (`str` or `List[str]`, *optional*): A list of keys to ignore when moving inputs or outputs between devices. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. force_hooks (`bool`, *optional*, defaults to `False`): Whether or not to force device hooks to be attached to the model even if all layers are dispatched to a single device. """ # Error early if the device map is incomplete. check_device_map(model, device_map) # for backward compatibility is_bnb_quantized = ( getattr(model, "is_quantized", False) or getattr(model, "is_loaded_in_8bit", False) ) and getattr(model, "quantization_method", "bitsandbytes") == "bitsandbytes" # We attach hooks if the device_map has at least 2 different devices or if # force_hooks is set to `True`. Otherwise, the model in already loaded # in the unique device and the user can decide where to dispatch the model. # If the model is quantized, we always force-dispatch the model if (len(set(device_map.values())) > 1) or is_bnb_quantized or force_hooks: if main_device is None: if set(device_map.values()) == {"cpu"} or set(device_map.values()) == {"cpu", "disk"}: main_device = "cpu" else: main_device = [d for d in device_map.values() if d not in ["cpu", "disk"]][0] if main_device != "cpu": cpu_modules = [name for name, device in device_map.items() if device == "cpu"] if state_dict is None and len(cpu_modules) > 0: state_dict = extract_submodules_state_dict(model.state_dict(), cpu_modules) disk_modules = [name for name, device in device_map.items() if device == "disk"] if offload_dir is None and offload_index is None and len(disk_modules) > 0: raise ValueError( "We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules " f"need to be offloaded: {', '.join(disk_modules)}." ) if ( len(disk_modules) > 0 and offload_index is None and (not os.path.isdir(offload_dir) or not os.path.isfile(os.path.join(offload_dir, "index.json"))) ): disk_state_dict = extract_submodules_state_dict(model.state_dict(), disk_modules) offload_state_dict(offload_dir, disk_state_dict) execution_device = { name: main_device if device in ["cpu", "disk"] else device for name, device in device_map.items() } execution_device[""] = main_device offloaded_devices = ["disk"] if main_device == "cpu" or main_device == "mps" else ["cpu", "disk"] offload = {name: device in offloaded_devices for name, device in device_map.items()} save_folder = offload_dir if len(disk_modules) > 0 else None if state_dict is not None or save_folder is not None or offload_index is not None: device = main_device if offload_index is not None else None weights_map = OffloadedWeightsLoader( state_dict=state_dict, save_folder=save_folder, index=offload_index, device=device ) else: weights_map = None # When dispatching the model's parameters to the devices specified in device_map, we want to avoid allocating memory several times for the # tied parameters. The dictionary tied_params_map keeps track of the already allocated data for a given tied parameter (represented by its # original pointer) on each devices. tied_params = find_tied_parameters(model) tied_params_map = {} for group in tied_params: for param_name in group: # data_ptr() is enough here, as `find_tied_parameters` finds tied params simply by comparing `param1 is param2`, so we don't need # to care about views of tensors through storage_offset. data_ptr = recursive_getattr(model, param_name).data_ptr() tied_params_map[data_ptr] = {} # Note: To handle the disk offloading case, we can not simply use weights_map[param_name].data_ptr() as the reference pointer, # as we have no guarantee that safetensors' `file.get_tensor()` will always give the same pointer. attach_align_device_hook_on_blocks( model, execution_device=execution_device, offload=offload, offload_buffers=offload_buffers, weights_map=weights_map, skip_keys=skip_keys, preload_module_classes=preload_module_classes, tied_params_map=tied_params_map, ) # warn if there is any params on the meta device offloaded_devices_str = " and ".join( [device for device in set(device_map.values()) if device in ("cpu", "disk")] ) if len(offloaded_devices_str) > 0: logging.warning( f"Some parameters are on the meta device device because they were offloaded to the {offloaded_devices_str}." ) # Attaching the hook may break tied weights, so we retie them retie_parameters(model, tied_params) # add warning to cuda and to method def add_warning(fn, model): @wraps(fn) def wrapper(*args, **kwargs): logger.warning("You shouldn't move a model when it is dispatched on multiple devices.") for param in model.parameters(): if param.device == torch.device("meta"): raise RuntimeError("You can't move a model that has some modules offloaded to cpu or disk.") return fn(*args, **kwargs) return wrapper model.to = add_warning(model.to, model) if is_npu_available(): model.npu = add_warning(model.npu, model) else: model.cuda = add_warning(model.cuda, model) else: device = list(device_map.values())[0] # `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)). if is_npu_available() and isinstance(device, int): device = f"npu:{device}" if device != "disk": model.to(device) else: raise ValueError( "You are trying to offload the whole model to the disk. Please use the `disk_offload` function instead." ) # Convert OrderedDict back to dict for easier usage model.hf_device_map = dict(device_map) return model def load_checkpoint_and_dispatch( model: nn.Module, checkpoint: Union[str, os.PathLike], device_map: Optional[Union[str, Dict[str, Union[int, str, torch.device]]]] = None, max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None, no_split_module_classes: Optional[List[str]] = None, offload_folder: Optional[Union[str, os.PathLike]] = None, offload_buffers: bool = False, dtype: Optional[Union[str, torch.dtype]] = None, offload_state_dict: Optional[bool] = None, skip_keys: Optional[Union[str, List[str]]] = None, preload_module_classes: Optional[List[str]] = None, force_hooks: bool = False, ): """ Loads a (potentially sharded) checkpoint inside a model, potentially sending weights to a given device as they are loaded and adds the various hooks that will make this model run properly (even if split across devices). Args: model (`torch.nn.Module`): The model in which we want to load a checkpoint. checkpoint (`str` or `os.PathLike`): The folder checkpoint to load. It can be: - a path to a file containing a whole model state dict - a path to a `.json` file containing the index to a sharded checkpoint - a path to a folder containing a unique `.index.json` file and the shards of a checkpoint. device_map (`Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see [here](../concept_guides/big_model_inference#designing-a-device-map). Defaults to None, which means [`dispatch_model`] will not be called. max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. no_split_module_classes (`List[str]`, *optional*): A list of layer class names that should never be split across device (for instance any layer that has a residual connection). offload_folder (`str` or `os.PathLike`, *optional*): If the `device_map` contains any value `"disk"`, the folder where we will offload weights. offload_buffers (`bool`, *optional*, defaults to `False`): In the layers that are offloaded on the CPU or the hard drive, whether or not to offload the buffers as well as the parameters. dtype (`str` or `torch.dtype`, *optional*): If provided, the weights will be converted to that type when loaded. offload_state_dict (`bool`, *optional*): If `True`, will temporarily offload the CPU state dict on the hard drive to avoid getting out of CPU RAM if the weight of the CPU state dict + the biggest shard does not fit. Will default to `True` if the device map picked contains `"disk"` values. skip_keys (`str` or `List[str]`, *optional*): A list of keys to ignore when moving inputs or outputs between devices. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. force_hooks (`bool`, *optional*, defaults to `False`): Whether or not to force device hooks to be attached to the model even if all layers are dispatched to a single device. Example: ```python >>> from accelerate import init_empty_weights, load_checkpoint_and_dispatch >>> from huggingface_hub import hf_hub_download >>> from transformers import AutoConfig, AutoModelForCausalLM >>> # Download the Weights >>> checkpoint = "EleutherAI/gpt-j-6B" >>> weights_location = hf_hub_download(checkpoint, "pytorch_model.bin") >>> # Create a model and initialize it with empty weights >>> config = AutoConfig.from_pretrained(checkpoint) >>> with init_empty_weights(): ... model = AutoModelForCausalLM.from_config(config) >>> # Load the checkpoint and dispatch it to the right devices >>> model = load_checkpoint_and_dispatch( ... model, weights_location, device_map="auto", no_split_module_classes=["GPTJBlock"] ... ) ``` """ if isinstance(device_map, str) and device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'." ) if isinstance(device_map, str): if device_map != "sequential": max_memory = get_balanced_memory( model, max_memory=max_memory, no_split_module_classes=no_split_module_classes, dtype=dtype, low_zero=(device_map == "balanced_low_0"), ) device_map = infer_auto_device_map( model, max_memory=max_memory, no_split_module_classes=no_split_module_classes, dtype=dtype ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): offload_state_dict = True load_checkpoint_in_model( model, checkpoint, device_map=device_map, offload_folder=offload_folder, dtype=dtype, offload_state_dict=offload_state_dict, offload_buffers=offload_buffers, ) if device_map is None: return model return dispatch_model( model, device_map=device_map, offload_dir=offload_folder, offload_buffers=offload_buffers, skip_keys=skip_keys, preload_module_classes=preload_module_classes, force_hooks=force_hooks, )

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/optimizer.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import warnings import torch from .state import AcceleratorState, GradientState from .utils import DistributedType, honor_type, is_tpu_available if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm def move_to_device(state, device): if isinstance(state, (list, tuple)): return honor_type(state, (move_to_device(t, device) for t in state)) elif isinstance(state, dict): return type(state)({k: move_to_device(v, device) for k, v in state.items()}) elif isinstance(state, torch.Tensor): return state.to(device) return state class AcceleratedOptimizer(torch.optim.Optimizer): """ Internal wrapper around a torch optimizer. Conditionally will perform `step` and `zero_grad` if gradients should be synchronized when performing gradient accumulation. Args: optimizer (`torch.optim.optimizer.Optimizer`): The optimizer to wrap. device_placement (`bool`, *optional*, defaults to `True`): Whether or not the optimizer should handle device placement. If so, it will place the state dictionary of `optimizer` on the right device. scaler (`torch.cuda.amp.grad_scaler.GradScaler`, *optional*): The scaler to use in the step function if training with mixed precision. """ def __init__(self, optimizer, device_placement=True, scaler=None): self.optimizer = optimizer self.scaler = scaler self.accelerator_state = AcceleratorState() self.gradient_state = GradientState() self.device_placement = device_placement self._is_overflow = False if self.scaler is not None: self._accelerate_step_called = False self._optimizer_original_step_method = self.optimizer.step self._optimizer_patched_step_method = patch_optimizer_step(self, self.optimizer.step) # Handle device placement if device_placement: state_dict = self.optimizer.state_dict() if self.accelerator_state.distributed_type == DistributedType.TPU: xm.send_cpu_data_to_device(state_dict, self.accelerator_state.device) else: state_dict = move_to_device(state_dict, self.accelerator_state.device) self.optimizer.load_state_dict(state_dict) @property def state(self): return self.optimizer.state @state.setter def state(self, state): self.optimizer.state = state @property def param_groups(self): return self.optimizer.param_groups @param_groups.setter def param_groups(self, param_groups): self.optimizer.param_groups = param_groups @property def defaults(self): return self.optimizer.defaults @defaults.setter def defaults(self, defaults): self.optimizer.defaults = defaults def add_param_group(self, param_group): self.optimizer.add_param_group(param_group) def load_state_dict(self, state_dict): if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement: xm.send_cpu_data_to_device(state_dict, self.accelerator_state.device) self.optimizer.load_state_dict(state_dict) def state_dict(self): return self.optimizer.state_dict() def zero_grad(self, set_to_none=None): if self.gradient_state.sync_gradients: accept_arg = "set_to_none" in inspect.signature(self.optimizer.zero_grad).parameters if accept_arg: if set_to_none is None: set_to_none = False self.optimizer.zero_grad(set_to_none=set_to_none) else: if set_to_none is not None: raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.") self.optimizer.zero_grad() def step(self, closure=None): if self.gradient_state.sync_gradients: if self.accelerator_state.distributed_type == DistributedType.TPU: optimizer_args = {"closure": closure} if closure is not None else {} xm.optimizer_step(self.optimizer, optimizer_args=optimizer_args) elif self.scaler is not None: self.optimizer.step = self._optimizer_patched_step_method self.scaler.step(self.optimizer, closure) self.scaler.update() if not self._accelerate_step_called: # If the optimizer step was skipped, gradient overflow was detected. self._is_overflow = True else: self._is_overflow = False # Reset the step method to the original one self.optimizer.step = self._optimizer_original_step_method # Reset the indicator self._accelerate_step_called = False else: self.optimizer.step(closure) def _switch_parameters(self, parameters_map): for param_group in self.optimizer.param_groups: param_group["params"] = [parameters_map.get(p, p) for p in param_group["params"]] @property def is_overflow(self): """Whether or not the optimizer step was done, or skipped because of gradient overflow.""" warnings.warn( "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use " "`optimizer.step_was_skipped` instead.", FutureWarning, ) return self._is_overflow @property def step_was_skipped(self): """Whether or not the optimizer step was skipped.""" return self._is_overflow def __getstate__(self): _ignored_keys = [ "_accelerate_step_called", "_optimizer_original_step_method", "_optimizer_patched_step_method", ] return {k: v for k, v in self.__dict__.items() if k not in _ignored_keys} def __setstate__(self, state): self.__dict__.update(state) if self.scaler is not None: self._accelerate_step_called = False self._optimizer_original_step_method = self.optimizer.step self._optimizer_patched_step_method = patch_optimizer_step(self, self.optimizer.step) def patch_optimizer_step(accelerated_optimizer: AcceleratedOptimizer, method): def patched_step(*args, **kwargs): accelerated_optimizer._accelerate_step_called = True return method(*args, **kwargs) return patched_step

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/tracking.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Expectation: # Provide a project dir name, then each type of logger gets stored in project/{`logging_dir`} import json import os import time from functools import wraps from typing import Any, Dict, List, Optional, Union import yaml from .logging import get_logger from .state import PartialState from .utils import ( LoggerType, is_aim_available, is_clearml_available, is_comet_ml_available, is_dvclive_available, is_mlflow_available, is_tensorboard_available, is_wandb_available, listify, ) _available_trackers = [] if is_tensorboard_available(): _available_trackers.append(LoggerType.TENSORBOARD) if is_wandb_available(): _available_trackers.append(LoggerType.WANDB) if is_comet_ml_available(): _available_trackers.append(LoggerType.COMETML) if is_aim_available(): _available_trackers.append(LoggerType.AIM) if is_mlflow_available(): _available_trackers.append(LoggerType.MLFLOW) if is_clearml_available(): _available_trackers.append(LoggerType.CLEARML) if is_dvclive_available(): _available_trackers.append(LoggerType.DVCLIVE) logger = get_logger(__name__) def on_main_process(function): """ Decorator to selectively run the decorated function on the main process only based on the `main_process_only` attribute in a class. Checks at function execution rather than initialization time, not triggering the initialization of the `PartialState`. """ @wraps(function) def execute_on_main_process(self, *args, **kwargs): if getattr(self, "main_process_only", False): return PartialState().on_main_process(function)(self, *args, **kwargs) else: return function(self, *args, **kwargs) return execute_on_main_process def get_available_trackers(): "Returns a list of all supported available trackers in the system" return _available_trackers class GeneralTracker: """ A base Tracker class to be used for all logging integration implementations. Each function should take in `**kwargs` that will automatically be passed in from a base dictionary provided to [`Accelerator`]. Should implement `name`, `requires_logging_directory`, and `tracker` properties such that: `name` (`str`): String representation of the tracker class name, such as "TensorBoard" `requires_logging_directory` (`bool`): Whether the logger requires a directory to store their logs. `tracker` (`object`): Should return internal tracking mechanism used by a tracker class (such as the `run` for wandb) Implementations can also include a `main_process_only` (`bool`) attribute to toggle if relevent logging, init, and other functions should occur on the main process or across all processes (by default will use `True`) """ main_process_only = True def __init__(self, _blank=False): if not _blank: err = "" if not hasattr(self, "name"): err += "`name`" if not hasattr(self, "requires_logging_directory"): if len(err) > 0: err += ", " err += "`requires_logging_directory`" # as tracker is a @property that relies on post-init if "tracker" not in dir(self): if len(err) > 0: err += ", " err += "`tracker`" if len(err) > 0: raise NotImplementedError( f"The implementation for this tracker class is missing the following " f"required attributes. Please define them in the class definition: " f"{err}" ) def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Implementations should use the experiment configuration functionality of a tracking API. Args: values (Dictionary `str` to `bool`, `str`, `float` or `int`): Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`, `str`, `float`, `int`, or `None`. """ pass def log(self, values: dict, step: Optional[int], **kwargs): """ Logs `values` to the current run. Base `log` implementations of a tracking API should go in here, along with special behavior for the `step parameter. Args: values (Dictionary `str` to `str`, `float`, or `int`): Values to be logged as key-value pairs. The values need to have type `str`, `float`, or `int`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. """ pass def finish(self): """ Should run any finalizing functions within the tracking API. If the API should not have one, just don't overwrite that method. """ pass class TensorBoardTracker(GeneralTracker): """ A `Tracker` class that supports `tensorboard`. Should be initialized at the start of your script. Args: run_name (`str`): The name of the experiment run logging_dir (`str`, `os.PathLike`): Location for TensorBoard logs to be stored. kwargs: Additional key word arguments passed along to the `tensorboard.SummaryWriter.__init__` method. """ name = "tensorboard" requires_logging_directory = True @on_main_process def __init__(self, run_name: str, logging_dir: Union[str, os.PathLike], **kwargs): try: from torch.utils import tensorboard except ModuleNotFoundError: import tensorboardX as tensorboard super().__init__() self.run_name = run_name self.logging_dir = os.path.join(logging_dir, run_name) self.writer = tensorboard.SummaryWriter(self.logging_dir, **kwargs) logger.debug(f"Initialized TensorBoard project {self.run_name} logging to {self.logging_dir}") logger.debug( "Make sure to log any initial configurations with `self.store_init_configuration` before training!" ) @property def tracker(self): return self.writer @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Stores the hyperparameters in a yaml file for future use. Args: values (Dictionary `str` to `bool`, `str`, `float` or `int`): Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`, `str`, `float`, `int`, or `None`. """ self.writer.add_hparams(values, metric_dict={}) self.writer.flush() project_run_name = time.time() dir_name = os.path.join(self.logging_dir, str(project_run_name)) os.makedirs(dir_name, exist_ok=True) with open(os.path.join(dir_name, "hparams.yml"), "w") as outfile: try: yaml.dump(values, outfile) except yaml.representer.RepresenterError: logger.error("Serialization to store hyperparameters failed") raise logger.debug("Stored initial configuration hyperparameters to TensorBoard and hparams yaml file") @on_main_process def log(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `values` to the current run. Args: values (Dictionary `str` to `str`, `float`, `int` or `dict` of `str` to `float`/`int`): Values to be logged as key-value pairs. The values need to have type `str`, `float`, `int` or `dict` of `str` to `float`/`int`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to either `SummaryWriter.add_scaler`, `SummaryWriter.add_text`, or `SummaryWriter.add_scalers` method based on the contents of `values`. """ values = listify(values) for k, v in values.items(): if isinstance(v, (int, float)): self.writer.add_scalar(k, v, global_step=step, **kwargs) elif isinstance(v, str): self.writer.add_text(k, v, global_step=step, **kwargs) elif isinstance(v, dict): self.writer.add_scalars(k, v, global_step=step, **kwargs) self.writer.flush() logger.debug("Successfully logged to TensorBoard") @on_main_process def log_images(self, values: dict, step: Optional[int], **kwargs): """ Logs `images` to the current run. Args: values (Dictionary `str` to `List` of `np.ndarray` or `PIL.Image`): Values to be logged as key-value pairs. The values need to have type `List` of `np.ndarray` or step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `SummaryWriter.add_image` method. """ for k, v in values.items(): self.writer.add_images(k, v, global_step=step, **kwargs) logger.debug("Successfully logged images to TensorBoard") @on_main_process def finish(self): """ Closes `TensorBoard` writer """ self.writer.close() logger.debug("TensorBoard writer closed") class WandBTracker(GeneralTracker): """ A `Tracker` class that supports `wandb`. Should be initialized at the start of your script. Args: run_name (`str`): The name of the experiment run. kwargs: Additional key word arguments passed along to the `wandb.init` method. """ name = "wandb" requires_logging_directory = False main_process_only = False @on_main_process def __init__(self, run_name: str, **kwargs): super().__init__() self.run_name = run_name import wandb self.run = wandb.init(project=self.run_name, **kwargs) logger.debug(f"Initialized WandB project {self.run_name}") logger.debug( "Make sure to log any initial configurations with `self.store_init_configuration` before training!" ) @property def tracker(self): return self.run @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Args: values (Dictionary `str` to `bool`, `str`, `float` or `int`): Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`, `str`, `float`, `int`, or `None`. """ import wandb wandb.config.update(values, allow_val_change=True) logger.debug("Stored initial configuration hyperparameters to WandB") @on_main_process def log(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `values` to the current run. Args: values (Dictionary `str` to `str`, `float`, `int` or `dict` of `str` to `float`/`int`): Values to be logged as key-value pairs. The values need to have type `str`, `float`, `int` or `dict` of `str` to `float`/`int`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `wandb.log` method. """ self.run.log(values, step=step, **kwargs) logger.debug("Successfully logged to WandB") @on_main_process def log_images(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `images` to the current run. Args: values (Dictionary `str` to `List` of `np.ndarray` or `PIL.Image`): Values to be logged as key-value pairs. The values need to have type `List` of `np.ndarray` or step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `wandb.log` method. """ import wandb for k, v in values.items(): self.log({k: [wandb.Image(image) for image in v]}, step=step, **kwargs) logger.debug("Successfully logged images to WandB") @on_main_process def log_table( self, table_name: str, columns: List[str] = None, data: List[List[Any]] = None, dataframe: Any = None, step: Optional[int] = None, **kwargs, ): """ Log a Table containing any object type (text, image, audio, video, molecule, html, etc). Can be defined either with `columns` and `data` or with `dataframe`. Args: table_name (`str`): The name to give to the logged table on the wandb workspace columns (list of `str`, *optional*): The name of the columns on the table data (List of List of Any data type, *optional*): The data to be logged in the table dataframe (Any data type, *optional*): The data to be logged in the table step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. """ import wandb values = {table_name: wandb.Table(columns=columns, data=data, dataframe=dataframe)} self.log(values, step=step, **kwargs) @on_main_process def finish(self): """ Closes `wandb` writer """ self.run.finish() logger.debug("WandB run closed") class CometMLTracker(GeneralTracker): """ A `Tracker` class that supports `comet_ml`. Should be initialized at the start of your script. API keys must be stored in a Comet config file. Args: run_name (`str`): The name of the experiment run. kwargs: Additional key word arguments passed along to the `Experiment.__init__` method. """ name = "comet_ml" requires_logging_directory = False @on_main_process def __init__(self, run_name: str, **kwargs): super().__init__() self.run_name = run_name from comet_ml import Experiment self.writer = Experiment(project_name=run_name, **kwargs) logger.debug(f"Initialized CometML project {self.run_name}") logger.debug( "Make sure to log any initial configurations with `self.store_init_configuration` before training!" ) @property def tracker(self): return self.writer @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Args: values (Dictionary `str` to `bool`, `str`, `float` or `int`): Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`, `str`, `float`, `int`, or `None`. """ self.writer.log_parameters(values) logger.debug("Stored initial configuration hyperparameters to CometML") @on_main_process def log(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `values` to the current run. Args: values (Dictionary `str` to `str`, `float`, `int` or `dict` of `str` to `float`/`int`): Values to be logged as key-value pairs. The values need to have type `str`, `float`, `int` or `dict` of `str` to `float`/`int`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to either `Experiment.log_metric`, `Experiment.log_other`, or `Experiment.log_metrics` method based on the contents of `values`. """ if step is not None: self.writer.set_step(step) for k, v in values.items(): if isinstance(v, (int, float)): self.writer.log_metric(k, v, step=step, **kwargs) elif isinstance(v, str): self.writer.log_other(k, v, **kwargs) elif isinstance(v, dict): self.writer.log_metrics(v, step=step, **kwargs) logger.debug("Successfully logged to CometML") @on_main_process def finish(self): """ Closes `comet-ml` writer """ self.writer.end() logger.debug("CometML run closed") class AimTracker(GeneralTracker): """ A `Tracker` class that supports `aim`. Should be initialized at the start of your script. Args: run_name (`str`): The name of the experiment run. kwargs: Additional key word arguments passed along to the `Run.__init__` method. """ name = "aim" requires_logging_directory = True @on_main_process def __init__(self, run_name: str, logging_dir: Optional[Union[str, os.PathLike]] = ".", **kwargs): self.run_name = run_name from aim import Run self.writer = Run(repo=logging_dir, **kwargs) self.writer.name = self.run_name logger.debug(f"Initialized Aim project {self.run_name}") logger.debug( "Make sure to log any initial configurations with `self.store_init_configuration` before training!" ) @property def tracker(self): return self.writer @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Args: values (`dict`): Values to be stored as initial hyperparameters as key-value pairs. """ self.writer["hparams"] = values @on_main_process def log(self, values: dict, step: Optional[int], **kwargs): """ Logs `values` to the current run. Args: values (`dict`): Values to be logged as key-value pairs. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `Run.track` method. """ # Note: replace this with the dictionary support when merged for key, value in values.items(): self.writer.track(value, name=key, step=step, **kwargs) @on_main_process def log_images(self, values: dict, step: Optional[int] = None, kwargs: Optional[Dict[str, dict]] = None): """ Logs `images` to the current run. Args: values (`Dict[str, Union[np.ndarray, PIL.Image, Tuple[np.ndarray, str], Tuple[PIL.Image, str]]]`): Values to be logged as key-value pairs. The values need to have type `np.ndarray` or PIL.Image. If a tuple is provided, the first element should be the image and the second element should be the caption. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs (`Dict[str, dict]`): Additional key word arguments passed along to the `Run.Image` and `Run.track` method specified by the keys `aim_image` and `track`, respectively. """ import aim aim_image_kw = {} track_kw = {} if kwargs is not None: aim_image_kw = kwargs.get("aim_image", {}) track_kw = kwargs.get("track", {}) for key, value in values.items(): if isinstance(value, tuple): img, caption = value else: img, caption = value, "" aim_image = aim.Image(img, caption=caption, **aim_image_kw) self.writer.track(aim_image, name=key, step=step, **track_kw) @on_main_process def finish(self): """ Closes `aim` writer """ self.writer.close() class MLflowTracker(GeneralTracker): """ A `Tracker` class that supports `mlflow`. Should be initialized at the start of your script. Args: experiment_name (`str`, *optional*): Name of the experiment. Environment variable MLFLOW_EXPERIMENT_NAME has priority over this argument. logging_dir (`str` or `os.PathLike`, defaults to `"."`): Location for mlflow logs to be stored. run_id (`str`, *optional*): If specified, get the run with the specified UUID and log parameters and metrics under that run. The run’s end time is unset and its status is set to running, but the run’s other attributes (source_version, source_type, etc.) are not changed. Environment variable MLFLOW_RUN_ID has priority over this argument. tags (`Dict[str, str]`, *optional*): An optional `dict` of `str` keys and values, or a `str` dump from a `dict`, to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run. Environment variable MLFLOW_TAGS has priority over this argument. nested_run (`bool`, *optional*, defaults to `False`): Controls whether run is nested in parent run. True creates a nested run. Environment variable MLFLOW_NESTED_RUN has priority over this argument. run_name (`str`, *optional*): Name of new run (stored as a mlflow.runName tag). Used only when `run_id` is unspecified. description (`str`, *optional*): An optional string that populates the description box of the run. If a run is being resumed, the description is set on the resumed run. If a new run is being created, the description is set on the new run. """ name = "mlflow" requires_logging_directory = False @on_main_process def __init__( self, experiment_name: str = None, logging_dir: Optional[Union[str, os.PathLike]] = None, run_id: Optional[str] = None, tags: Optional[Union[Dict[str, Any], str]] = None, nested_run: Optional[bool] = False, run_name: Optional[str] = None, description: Optional[str] = None, ): experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", experiment_name) run_id = os.getenv("MLFLOW_RUN_ID", run_id) tags = os.getenv("MLFLOW_TAGS", tags) if isinstance(tags, str): tags = json.loads(tags) nested_run = os.getenv("MLFLOW_NESTED_RUN", nested_run) import mlflow exps = mlflow.search_experiments(filter_string=f"name = '{experiment_name}'") if len(exps) > 0: if len(exps) > 1: logger.warning("Multiple experiments with the same name found. Using first one.") experiment_id = exps[0].experiment_id else: experiment_id = mlflow.create_experiment( name=experiment_name, artifact_location=logging_dir, tags=tags, ) self.active_run = mlflow.start_run( run_id=run_id, experiment_id=experiment_id, run_name=run_name, nested=nested_run, tags=tags, description=description, ) logger.debug(f"Initialized mlflow experiment {experiment_name}") logger.debug( "Make sure to log any initial configurations with `self.store_init_configuration` before training!" ) @property def tracker(self): return self.active_run @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Args: values (`dict`): Values to be stored as initial hyperparameters as key-value pairs. """ import mlflow for name, value in list(values.items()): # internally, all values are converted to str in MLflow if len(str(value)) > mlflow.utils.validation.MAX_PARAM_VAL_LENGTH: logger.warning_once( f'Accelerate is attempting to log a value of "{value}" for key "{name}" as a parameter. MLflow\'s' f" log_param() only accepts values no longer than {mlflow.utils.validation.MAX_PARAM_VAL_LENGTH} characters so we dropped this attribute." ) del values[name] values_list = list(values.items()) # MLflow cannot log more than 100 values in one go, so we have to split it for i in range(0, len(values_list), mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH): mlflow.log_params(dict(values_list[i : i + mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH])) logger.debug("Stored initial configuration hyperparameters to MLflow") @on_main_process def log(self, values: dict, step: Optional[int]): """ Logs `values` to the current run. Args: values (`dict`): Values to be logged as key-value pairs. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. """ metrics = {} for k, v in values.items(): if isinstance(v, (int, float)): metrics[k] = v else: logger.warning_once( f'MLflowTracker is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. ' "MLflow's log_metric() only accepts float and int types so we dropped this attribute." ) import mlflow mlflow.log_metrics(metrics, step=step) logger.debug("Successfully logged to mlflow") @on_main_process def finish(self): """ End the active MLflow run. """ import mlflow mlflow.end_run() class ClearMLTracker(GeneralTracker): """ A `Tracker` class that supports `clearml`. Should be initialized at the start of your script. Args: run_name (`str`, *optional*): Name of the experiment. Environment variables `CLEARML_PROJECT` and `CLEARML_TASK` have priority over this argument. kwargs: Kwargs passed along to the `Task.__init__` method. """ name = "clearml" requires_logging_directory = False @on_main_process def __init__(self, run_name: str = None, **kwargs): from clearml import Task current_task = Task.current_task() self._initialized_externally = False if current_task: self._initialized_externally = True self.task = current_task return kwargs.setdefault("project_name", os.environ.get("CLEARML_PROJECT", run_name)) kwargs.setdefault("task_name", os.environ.get("CLEARML_TASK", run_name)) self.task = Task.init(**kwargs) @property def tracker(self): return self.task @on_main_process def store_init_configuration(self, values: dict): """ Connect configuration dictionary to the Task object. Should be run at the beginning of your experiment. Args: values (`dict`): Values to be stored as initial hyperparameters as key-value pairs. """ return self.task.connect_configuration(values) @on_main_process def log(self, values: Dict[str, Union[int, float]], step: Optional[int] = None, **kwargs): """ Logs `values` dictionary to the current run. The dictionary keys must be strings. The dictionary values must be ints or floats Args: values (`Dict[str, Union[int, float]]`): Values to be logged as key-value pairs. If the key starts with 'eval_'/'test_'/'train_', the value will be reported under the 'eval'/'test'/'train' series and the respective prefix will be removed. Otherwise, the value will be reported under the 'train' series, and no prefix will be removed. step (`int`, *optional*): If specified, the values will be reported as scalars, with the iteration number equal to `step`. Otherwise they will be reported as single values. kwargs: Additional key word arguments passed along to the `clearml.Logger.report_single_value` or `clearml.Logger.report_scalar` methods. """ clearml_logger = self.task.get_logger() for k, v in values.items(): if not isinstance(v, (int, float)): logger.warning_once( "Accelerator is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of ClearML logger's report_scalar() " "is incorrect so we dropped this attribute." ) continue if step is None: clearml_logger.report_single_value(name=k, value=v, **kwargs) continue title, series = ClearMLTracker._get_title_series(k) clearml_logger.report_scalar(title=title, series=series, value=v, iteration=step, **kwargs) @on_main_process def log_images(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `images` to the current run. Args: values (`Dict[str, List[Union[np.ndarray, PIL.Image]]`): Values to be logged as key-value pairs. The values need to have type `List` of `np.ndarray` or step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `clearml.Logger.report_image` method. """ clearml_logger = self.task.get_logger() for k, v in values.items(): title, series = ClearMLTracker._get_title_series(k) clearml_logger.report_image(title=title, series=series, iteration=step, image=v, **kwargs) @on_main_process def log_table( self, table_name: str, columns: List[str] = None, data: List[List[Any]] = None, dataframe: Any = None, step: Optional[int] = None, **kwargs, ): """ Log a Table to the task. Can be defined eitherwith `columns` and `data` or with `dataframe`. Args: table_name (`str`): The name of the table columns (list of `str`, *optional*): The name of the columns on the table data (List of List of Any data type, *optional*): The data to be logged in the table. If `columns` is not specified, then the first entry in data will be the name of the columns of the table dataframe (Any data type, *optional*): The data to be logged in the table step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to the `clearml.Logger.report_table` method. """ to_report = dataframe if dataframe is None: if data is None: raise ValueError( "`ClearMLTracker.log_table` requires that `data` to be supplied if `dataframe` is `None`" ) to_report = [columns] + data if columns else data title, series = ClearMLTracker._get_title_series(table_name) self.task.get_logger().report_table(title=title, series=series, table_plot=to_report, iteration=step, **kwargs) @on_main_process def finish(self): """ Close the ClearML task. If the task was initialized externally (e.g. by manually calling `Task.init`), this function is a noop """ if self.task and not self._initialized_externally: self.task.close() @staticmethod def _get_title_series(name): for prefix in ["eval", "test", "train"]: if name.startswith(prefix + "_"): return name[len(prefix) + 1 :], prefix return name, "train" class DVCLiveTracker(GeneralTracker): """ A `Tracker` class that supports `dvclive`. Should be initialized at the start of your script. Args: run_name (`str`, *optional*): Ignored for dvclive. See `kwargs` instead. kwargs: Additional key word arguments passed along to [`dvclive.Live()`](https://dvc.org/doc/dvclive/live). Example: ```py from accelerate import Accelerator accelerator = Accelerator(log_with="dvclive") accelerator.init_trackers(project_name="my_project", init_kwargs={"dvclive": {"dir": "my_directory"}}) ``` """ name = "dvclive" requires_logging_directory = False @on_main_process def __init__(self, run_name: Optional[str] = None, live: Optional[Any] = None, **kwargs): from dvclive import Live super().__init__() self.live = live if live is not None else Live(**kwargs) @property def tracker(self): return self.live @on_main_process def store_init_configuration(self, values: dict): """ Logs `values` as hyperparameters for the run. Should be run at the beginning of your experiment. Stores the hyperparameters in a yaml file for future use. Args: values (Dictionary `str` to `bool`, `str`, `float`, `int`, or a List or Dict of those types): Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`, `str`, `float`, or `int`. """ self.live.log_params(values) @on_main_process def log(self, values: dict, step: Optional[int] = None, **kwargs): """ Logs `values` to the current run. Args: values (Dictionary `str` to `str`, `float`, or `int`): Values to be logged as key-value pairs. The values need to have type `str`, `float`, or `int`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. kwargs: Additional key word arguments passed along to `dvclive.Live.log_metric()`. """ from dvclive.plots import Metric if step is not None: self.live.step = step for k, v in values.items(): if Metric.could_log(v): self.live.log_metric(k, v, **kwargs) else: logger.warning_once( "Accelerator attempted to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of DVCLive's Live.log_metric() " "is incorrect so we dropped this attribute." ) self.live.next_step() @on_main_process def finish(self): """ Closes `dvclive.Live()`. """ self.live.end() LOGGER_TYPE_TO_CLASS = { "aim": AimTracker, "comet_ml": CometMLTracker, "mlflow": MLflowTracker, "tensorboard": TensorBoardTracker, "wandb": WandBTracker, "clearml": ClearMLTracker, "dvclive": DVCLiveTracker, } def filter_trackers( log_with: List[Union[str, LoggerType, GeneralTracker]], logging_dir: Union[str, os.PathLike] = None, ): """ Takes in a list of potential tracker types and checks that: - The tracker wanted is available in that environment - Filters out repeats of tracker types - If `all` is in `log_with`, will return all trackers in the environment - If a tracker requires a `logging_dir`, ensures that `logging_dir` is not `None` Args: log_with (list of `str`, [`~utils.LoggerType`] or [`~tracking.GeneralTracker`], *optional*): A list of loggers to be setup for experiment tracking. Should be one or several of: - `"all"` - `"tensorboard"` - `"wandb"` - `"comet_ml"` - `"mlflow"` - `"dvclive"` If `"all"` is selected, will pick up all available trackers in the environment and initialize them. Can also accept implementations of `GeneralTracker` for custom trackers, and can be combined with `"all"`. logging_dir (`str`, `os.PathLike`, *optional*): A path to a directory for storing logs of locally-compatible loggers. """ loggers = [] if log_with is not None: if not isinstance(log_with, (list, tuple)): log_with = [log_with] if "all" in log_with or LoggerType.ALL in log_with: loggers = [o for o in log_with if issubclass(type(o), GeneralTracker)] + get_available_trackers() else: for log_type in log_with: if log_type not in LoggerType and not issubclass(type(log_type), GeneralTracker): raise ValueError(f"Unsupported logging capability: {log_type}. Choose between {LoggerType.list()}") if issubclass(type(log_type), GeneralTracker): loggers.append(log_type) else: log_type = LoggerType(log_type) if log_type not in loggers: if log_type in get_available_trackers(): tracker_init = LOGGER_TYPE_TO_CLASS[str(log_type)] if getattr(tracker_init, "requires_logging_directory"): if logging_dir is None: raise ValueError( f"Logging with `{log_type}` requires a `logging_dir` to be passed in." ) loggers.append(log_type) else: logger.debug(f"Tried adding logger {log_type}, but package is unavailable in the system.") return loggers

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/local_sgd.py

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from accelerate import Accelerator, DistributedType class LocalSGD: """ A helper class to support local SGD on top of Accelerator. It simply runs a given number of updates independently on each device, and averages model weights every K synchronization step. It should be used only in the multi-GPU (or multi-CPU) setup without extensions such as DeepSpeed. In particular, this is a simple implementation that cannot support scenarios such as model parallelism. Although we are not aware of the true origins of this simple approach, the idea of local SGD is quite old and goes back to at least: Zhang, J., De Sa, C., Mitliagkas, I., & Ré, C. (2016). [Parallel SGD: When does averaging help?. arXiv preprint arXiv:1606.07365.](https://arxiv.org/abs/1606.07365) We credit the term Local SGD to the following paper (but there might be earlier references we are not aware of). Stich, Sebastian Urban. ["Local SGD Converges Fast and Communicates Little." ICLR 2019-International Conference on Learning Representations. No. CONF. 2019.](https://arxiv.org/abs/1805.09767) """ def __enter__(self): if self.enabled: self.model_sync_obj = self.model.no_sync() self.model_sync_obj.__enter__() return self def __exit__(self, type, value, tb): if self.enabled: # Average all models on exit self._sync_and_avg_model_params() self.model_sync_obj.__exit__(type, value, tb) def __init__(self, accelerator: Accelerator, model: torch.nn.Module, local_sgd_steps: int, enabled: bool = True): """ Constructor. Args: model (`torch.nn.Module): The model whose parameters we need to average. accelerator (`Accelerator`): Accelerator object. local_sgd_steps (`int`): A number of local SGD steps (before model parameters are synchronized). enabled (`bool): Local SGD is disabled if this parameter set to `False`. """ if accelerator.distributed_type not in [ DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU, ]: raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)") self.enabled = enabled and accelerator.distributed_type != DistributedType.NO self.num_steps = 0 if self.enabled: self.accelerator = accelerator self.model = model self.local_sgd_steps = local_sgd_steps def step(self): """ This function makes a "step" and synchronizes model parameters if necessary. """ self.num_steps += 1 if not self.enabled: return if self.num_steps % self.local_sgd_steps == 0: self._sync_and_avg_model_params() def _sync_and_avg_model_params(self): """ Synchronize + Average model parameters across all GPUs """ self.accelerator.wait_for_everyone() with self.accelerator.autocast(): for param in self.model.parameters(): param.data = self.accelerator.reduce(param.data, reduction="mean")

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/launchers.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import tempfile import torch from .state import AcceleratorState, PartialState from .utils import ( PrecisionType, PrepareForLaunch, are_libraries_initialized, check_cuda_p2p_ib_support, is_mps_available, patch_environment, ) def test_launch(): "Verify a `PartialState` can be initialized." _ = PartialState() def notebook_launcher( function, args=(), num_processes=None, mixed_precision="no", use_port="29500", master_addr="127.0.0.1", node_rank=0, num_nodes=1, ): """ Launches a training function, using several processes or multiple nodes if it's possible in the current environment (TPU with multiple cores for instance). <Tip warning={true}> To use this function absolutely zero calls to a CUDA device must be made in the notebook session before calling. If any have been made, you will need to restart the notebook and make sure no cells use any CUDA capability. Setting `ACCELERATE_DEBUG_MODE="1"` in your environment will run a test before truly launching to ensure that none of those calls have been made. </Tip> Args: function (`Callable`): The training function to execute. If it accepts arguments, the first argument should be the index of the process run. args (`Tuple`): Tuple of arguments to pass to the function (it will receive `*args`). num_processes (`int`, *optional*): The number of processes to use for training. Will default to 8 in Colab/Kaggle if a TPU is available, to the number of GPUs available otherwise. mixed_precision (`str`, *optional*, defaults to `"no"`): If `fp16` or `bf16`, will use mixed precision training on multi-GPU. use_port (`str`, *optional*, defaults to `"29500"`): The port to use to communicate between processes when launching a multi-GPU training. master_addr (`str`, *optional*, defaults to `"127.0.0.1"`): The address to use for communication between processes. node_rank (`int`, *optional*, defaults to 0): The rank of the current node. num_nodes (`int`, *optional*, defaults to 1): The number of nodes to use for training. Example: ```python # Assume this is defined in a Jupyter Notebook on an instance with two GPUs from accelerate import notebook_launcher def train(*args): # Your training function here ... notebook_launcher(train, args=(arg1, arg2), num_processes=2, mixed_precision="fp16") ``` """ # Are we in a google colab or a Kaggle Kernel? in_colab = False in_kaggle = False if any(key.startswith("KAGGLE") for key in os.environ.keys()): in_kaggle = True elif "IPython" in sys.modules: in_colab = "google.colab" in str(sys.modules["IPython"].get_ipython()) try: mixed_precision = PrecisionType(mixed_precision.lower()) except ValueError: raise ValueError( f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." ) if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME", None) is not None): # TPU launch import torch_xla.distributed.xla_multiprocessing as xmp if len(AcceleratorState._shared_state) > 0: raise ValueError( "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside " "your training function. Restart your notebook and make sure no cells initializes an " "`Accelerator`." ) if num_processes is None: num_processes = 8 launcher = PrepareForLaunch(function, distributed_type="TPU") print(f"Launching a training on {num_processes} TPU cores.") xmp.spawn(launcher, args=args, nprocs=num_processes, start_method="fork") elif in_colab: # No need for a distributed launch otherwise as it's either CPU or one GPU. if torch.cuda.is_available(): print("Launching training on one GPU.") else: print("Launching training on one CPU.") function(*args) else: if num_processes is None: raise ValueError( "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." ) if node_rank >= num_nodes: raise ValueError("The node_rank must be less than the number of nodes.") if num_processes > 1: # Multi-GPU launch from torch.multiprocessing import start_processes from torch.multiprocessing.spawn import ProcessRaisedException if len(AcceleratorState._shared_state) > 0: raise ValueError( "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized " "inside your training function. Restart your notebook and make sure no cells initializes an " "`Accelerator`." ) # Check for specific libraries known to initialize CUDA that users constantly use problematic_imports = are_libraries_initialized("bitsandbytes") if len(problematic_imports) > 0: err = ( "Could not start distributed process. Libraries known to initialize CUDA upon import have been " "imported already. Please keep these imports inside your training function to try and help with this:" ) for lib_name in problematic_imports: err += f"\n\t* `{lib_name}`" raise RuntimeError(err) patched_env = dict( nproc=num_processes, node_rank=node_rank, world_size=num_nodes * num_processes, master_addr=master_addr, master_port=use_port, mixed_precision=mixed_precision, ) # Check for CUDA P2P and IB issues if not check_cuda_p2p_ib_support(): patched_env["nccl_p2p_disable"] = "1" patched_env["nccl_ib_disable"] = "1" # torch.distributed will expect a few environment variable to be here. We set the ones common to each # process here (the other ones will be set be the launcher). with patch_environment(**patched_env): # First dummy launch if os.environ.get("ACCELERATE_DEBUG_MODE", "false").lower() == "true": launcher = PrepareForLaunch(test_launch, distributed_type="MULTI_GPU") try: start_processes(launcher, args=(), nprocs=num_processes, start_method="fork") except ProcessRaisedException as e: err = "An issue was found when verifying a stable environment for the notebook launcher." if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]: raise RuntimeError( f"{err}" "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. " "Please review your imports and test them when running the `notebook_launcher()` to identify " "which one is problematic and causing CUDA to be initialized." ) from e else: raise RuntimeError(f"{err} The following error was raised: {e}") from e # Now the actual launch launcher = PrepareForLaunch(function, distributed_type="MULTI_GPU") print(f"Launching training on {num_processes} GPUs.") try: start_processes(launcher, args=args, nprocs=num_processes, start_method="fork") except ProcessRaisedException as e: if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]: raise RuntimeError( "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. " "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. " "Please review your imports and test them when running the `notebook_launcher()` to identify " "which one is problematic and causing CUDA to be initialized." ) from e else: raise RuntimeError(f"An issue was found when launching the training: {e}") from e else: # No need for a distributed launch otherwise as it's either CPU, GPU or MPS. if is_mps_available(): os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" print("Launching training on MPS.") elif torch.cuda.is_available(): print("Launching training on one GPU.") else: print("Launching training on CPU.") function(*args) def debug_launcher(function, args=(), num_processes=2): """ Launches a training function using several processes on CPU for debugging purposes. <Tip warning={true}> This function is provided for internal testing and debugging, but it's not intended for real trainings. It will only use the CPU. </Tip> Args: function (`Callable`): The training function to execute. args (`Tuple`): Tuple of arguments to pass to the function (it will receive `*args`). num_processes (`int`, *optional*, defaults to 2): The number of processes to use for training. """ from torch.multiprocessing import start_processes with tempfile.NamedTemporaryFile() as tmp_file: # torch.distributed will expect a few environment variable to be here. We set the ones common to each # process here (the other ones will be set be the launcher). with patch_environment( world_size=num_processes, master_addr="127.0.0.1", master_port="29500", accelerate_mixed_precision="no", accelerate_debug_rdv_file=tmp_file.name, accelerate_use_cpu="yes", ): launcher = PrepareForLaunch(function, debug=True) start_processes(launcher, args=args, nprocs=num_processes, start_method="fork")

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/data_loader.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from contextlib import suppress from typing import Callable, List, Optional, Union import torch from torch.utils.data import BatchSampler, DataLoader, IterableDataset, RandomSampler from .logging import get_logger from .state import AcceleratorState, DistributedType, GradientState, is_tpu_available from .utils import ( RNGType, broadcast, broadcast_object_list, concatenate, find_batch_size, get_data_structure, initialize_tensors, is_torch_version, send_to_device, slice_tensors, synchronize_rng_states, ) logger = get_logger(__name__) # kwargs of the DataLoader in min version 1.4.0. _PYTORCH_DATALOADER_KWARGS = { "batch_size": 1, "shuffle": False, "sampler": None, "batch_sampler": None, "num_workers": 0, "collate_fn": None, "pin_memory": False, "drop_last": False, "timeout": 0, "worker_init_fn": None, "multiprocessing_context": None, "generator": None, "prefetch_factor": 2, "persistent_workers": False, } # kwargs added after by version _PYTORCH_DATALOADER_ADDITIONAL_KWARGS = {} for v, additional_kwargs in _PYTORCH_DATALOADER_ADDITIONAL_KWARGS.items(): if is_torch_version(">=", v): _PYTORCH_DATALOADER_KWARGS.update(additional_kwargs) class SeedableRandomSampler(RandomSampler): """ Same as a random sampler, except that in `__iter__` a seed can be used. Needed specifically in distributed cases, when the random generator for each GPU needs to start from the same seed and be fully reproducable on multiple iterations. If a custom `generator` is passed, it will rely on its initial seed as well as the current iteration it is on (stored in `self.epoch`). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.epoch = 0 self.seed = torch.random.initial_seed() def __iter__(self): if self.generator is None: self.generator = torch.Generator() else: self.seed = self.generator.initial_seed() # Allow `self.epoch` to modify the seed of the generator seed = self.epoch + self.seed self.generator.manual_seed(seed) yield from super().__iter__() self.set_epoch(self.epoch + 1) def set_epoch(self, epoch: int): "Sets the current iteration of the sampler." self.epoch = epoch class BatchSamplerShard(BatchSampler): """ Wraps a PyTorch `BatchSampler` to generate batches for one of the processes only. Instances of this class will always yield a number of batches that is a round multiple of `num_processes` and that all have the same size. Depending on the value of the `drop_last` attribute of the batch sampler passed, it will either stop the iteration at the first batch that would be too small / not present on all processes or loop with indices from the beginning. Args: batch_sampler (`torch.utils.data.sampler.BatchSampler`): The batch sampler to split in several shards. num_processes (`int`, *optional*, defaults to 1): The number of processes running concurrently. process_index (`int`, *optional*, defaults to 0): The index of the current process. split_batches (`bool`, *optional*, defaults to `False`): Whether the shards should be created by splitting a batch to give a piece of it on each process, or by yielding different full batches on each process. On two processes with a sampler of `[[0, 1, 2, 3], [4, 5, 6, 7]]`, this will result in: - the sampler on process 0 to yield `[0, 1, 2, 3]` and the sampler on process 1 to yield `[4, 5, 6, 7]` if this argument is set to `False`. - the sampler on process 0 to yield `[0, 1]` then `[4, 5]` and the sampler on process 1 to yield `[2, 3]` then `[6, 7]` if this argument is set to `True`. even_batches (`bool`, *optional*, defaults to `True`): Whether or not to loop back at the beginning of the sampler when the number of samples is not a round multiple of (original batch size / number of processes). <Tip warning={true}> `BatchSampler`s with varying batch sizes are not enabled by default. To enable this behaviour, set `even_batches` equal to `False` </Tip>""" def __init__( self, batch_sampler: BatchSampler, num_processes: int = 1, process_index: int = 0, split_batches: bool = False, even_batches: bool = True, ): if split_batches and batch_sampler.batch_size % num_processes != 0: raise ValueError( f"To use `BatchSamplerShard` in `split_batches` mode, the batch size ({batch_sampler.batch_size}) " f"needs to be a round multiple of the number of processes ({num_processes})." ) self.batch_sampler = batch_sampler self.num_processes = num_processes self.process_index = process_index self.split_batches = split_batches self.even_batches = even_batches self.batch_size = getattr(batch_sampler, "batch_size", None) self.drop_last = getattr(batch_sampler, "drop_last", False) if self.batch_size is None and self.even_batches: raise ValueError( "You need to use `even_batches=False` when the batch sampler has no batch size. If you " "are not calling this method directly, set `accelerator.even_batches=False` instead." ) @property def total_length(self): return len(self.batch_sampler) def __len__(self): if self.split_batches: # Split batches does not change the length of the batch sampler return len(self.batch_sampler) if len(self.batch_sampler) % self.num_processes == 0: # If the length is a round multiple of the number of processes, it's easy. return len(self.batch_sampler) // self.num_processes length = len(self.batch_sampler) // self.num_processes if self.drop_last: # Same if we drop the remainder. return length elif self.even_batches: # When we even batches we always get +1 return length + 1 else: # Otherwise it depends on the process index. return length + 1 if self.process_index < len(self.batch_sampler) % self.num_processes else length def __iter__(self): return self._iter_with_split() if self.split_batches else self._iter_with_no_split() def _iter_with_split(self): initial_data = [] batch_length = self.batch_sampler.batch_size // self.num_processes for idx, batch in enumerate(self.batch_sampler): if idx == 0: initial_data = batch if len(batch) == self.batch_size: # If the batch is full, we yield the part of it this process is responsible of. yield batch[batch_length * self.process_index : batch_length * (self.process_index + 1)] # If drop_last is True of the last batch was full, iteration is over, otherwise... if not self.drop_last and len(initial_data) > 0 and len(batch) < self.batch_size: if not self.even_batches: if len(batch) > batch_length * self.process_index: yield batch[batch_length * self.process_index : batch_length * (self.process_index + 1)] else: # For degenerate cases where the dataset has less than num_process * batch_size samples while len(initial_data) < self.batch_size: initial_data += initial_data batch = batch + initial_data yield batch[batch_length * self.process_index : batch_length * (self.process_index + 1)] def _iter_with_no_split(self): initial_data = [] batch_to_yield = [] for idx, batch in enumerate(self.batch_sampler): # We gather the initial indices in case we need to circle back at the end. if not self.drop_last and idx < self.num_processes: initial_data += batch # We identify the batch to yield but wait until we ar sure every process gets a full batch before actually # yielding it. if idx % self.num_processes == self.process_index: batch_to_yield = batch if idx % self.num_processes == self.num_processes - 1 and ( self.batch_size is None or len(batch) == self.batch_size ): yield batch_to_yield batch_to_yield = [] # If drop_last is True, iteration is over, otherwise... if not self.drop_last and len(initial_data) > 0: if not self.even_batches: if len(batch_to_yield) > 0: yield batch_to_yield else: # ... we yield the complete batch we had saved before if it has the proper length if len(batch_to_yield) == self.batch_size: yield batch_to_yield # For degenerate cases where the dataset has less than num_process * batch_size samples while len(initial_data) < self.num_processes * self.batch_size: initial_data += initial_data # If the last batch seen was of the proper size, it has been yielded by its process so we move to the next if len(batch) == self.batch_size: batch = [] idx += 1 # Make sure we yield a multiple of self.num_processes batches cycle_index = 0 while idx % self.num_processes != 0 or len(batch) > 0: end_index = cycle_index + self.batch_size - len(batch) batch += initial_data[cycle_index:end_index] if idx % self.num_processes == self.process_index: yield batch cycle_index = end_index batch = [] idx += 1 class IterableDatasetShard(IterableDataset): """ Wraps a PyTorch `IterableDataset` to generate samples for one of the processes only. Instances of this class will always yield a number of samples that is a round multiple of the actual batch size (depending of the value of `split_batches`, this is either `batch_size` or `batch_size x num_processes`). Depending on the value of the `drop_last` attribute of the batch sampler passed, it will either stop the iteration at the first batch that would be too small or loop with indices from the beginning. Args: dataset (`torch.utils.data.dataset.IterableDataset`): The batch sampler to split in several shards. batch_size (`int`, *optional*, defaults to 1): The size of the batches per shard (if `split_batches=False`) or the size of the batches (if `split_batches=True`). drop_last (`bool`, *optional*, defaults to `False`): Whether or not to drop the last incomplete batch or complete the last batches by using the samples from the beginning. num_processes (`int`, *optional*, defaults to 1): The number of processes running concurrently. process_index (`int`, *optional*, defaults to 0): The index of the current process. split_batches (`bool`, *optional*, defaults to `False`): Whether the shards should be created by splitting a batch to give a piece of it on each process, or by yielding different full batches on each process. On two processes with an iterable dataset yielding of `[0, 1, 2, 3, 4, 5, 6, 7]`, this will result in: - the shard on process 0 to yield `[0, 1, 2, 3]` and the shard on process 1 to yield `[4, 5, 6, 7]` if this argument is set to `False`. - the shard on process 0 to yield `[0, 1, 4, 5]` and the sampler on process 1 to yield `[2, 3, 6, 7]` if this argument is set to `True`. """ def __init__( self, dataset: IterableDataset, batch_size: int = 1, drop_last: bool = False, num_processes: int = 1, process_index: int = 0, split_batches: bool = False, ): if split_batches and batch_size > 1 and batch_size % num_processes != 0: raise ValueError( f"To use `IterableDatasetShard` in `split_batches` mode, the batch size ({batch_size}) " f"needs to be a round multiple of the number of processes ({num_processes})." ) self.dataset = dataset self.batch_size = batch_size self.drop_last = drop_last self.num_processes = num_processes self.process_index = process_index self.split_batches = split_batches def set_epoch(self, epoch): self.epoch = epoch if hasattr(self.dataset, "set_epoch"): self.dataset.set_epoch(epoch) def __len__(self): # We will just raise the downstream error if the underlying dataset is not sized if self.drop_last: return (len(self.dataset) // (self.batch_size * self.num_processes)) * self.batch_size else: return math.ceil(len(self.dataset) / (self.batch_size * self.num_processes)) * self.batch_size def __iter__(self): if ( not hasattr(self.dataset, "set_epoch") and hasattr(self.dataset, "generator") and isinstance(self.dataset.generator, torch.Generator) ): self.dataset.generator.manual_seed(self.epoch) real_batch_size = self.batch_size if self.split_batches else (self.batch_size * self.num_processes) process_batch_size = (self.batch_size // self.num_processes) if self.split_batches else self.batch_size process_slice = range(self.process_index * process_batch_size, (self.process_index + 1) * process_batch_size) first_batch = None current_batch = [] for element in self.dataset: current_batch.append(element) # Wait to have a full batch before yielding elements. if len(current_batch) == real_batch_size: for i in process_slice: yield current_batch[i] if first_batch is None: first_batch = current_batch.copy() current_batch = [] # Finished if drop_last is True, otherwise complete the last batch with elements from the beginning. if not self.drop_last and len(current_batch) > 0: if first_batch is None: first_batch = current_batch.copy() while len(current_batch) < real_batch_size: current_batch += first_batch for i in process_slice: yield current_batch[i] class DataLoaderStateMixin: """ Mixin class that adds a state to a `DataLoader` to keep track of the status inside the dataloader such as at the end of the iteration, the number of items in the dataset in the last batch relative to the batch size, and other useful information that might be needed. **Available attributes:** - **end_of_dataloader** (`bool`) -- Whether at the last iteration or batch - **remainder** (`int`) -- The number of items that are remaining in the last batch, relative to the total batch size """ def __init_subclass__(cls, **kwargs): cls.end_of_dataloader = False cls.remainder = -1 def reset(self): self.end_of_dataloader = False self.remainder = -1 def begin(self): "Prepares the gradient state for the current dataloader" self.reset() with suppress(Exception): if not self._drop_last: length = getattr(self.dataset, "total_dataset_length", len(self.dataset)) self.remainder = length % self.total_batch_size self.gradient_state._add_dataloader(self) def end(self): "Cleans up the gradient state after exiting the dataloader" self.gradient_state._remove_dataloader(self) class DataLoaderShard(DataLoader, DataLoaderStateMixin): """ Subclass of a PyTorch `DataLoader` that will deal with device placement and current distributed setup. Args: dataset (`torch.utils.data.dataset.Dataset`): The dataset to use to build this datalaoder. device (`torch.device`, *optional*): If passed, the device to put all batches on. rng_types (list of `str` or [`~utils.RNGType`]): The list of random number generators to synchronize at the beginning of each iteration. Should be one or several of: - `"torch"`: the base torch random number generator - `"cuda"`: the CUDA random number generator (GPU only) - `"xla"`: the XLA random number generator (TPU only) - `"generator"`: an optional `torch.Generator` synchronized_generator (`torch.Generator`, *optional*): A random number generator to keep synchronized across processes. skip_batches (`int`, *optional*, defaults to 0): The number of batches to skip at the beginning. kwargs: All other keyword arguments to pass to the regular `DataLoader` initialization. **Available attributes:** - **total_batch_size** (`int`) -- Total batch size of the dataloader across all processes. Equal to the original batch size when `split_batches=True`; otherwise the original batch size * the total number of processes - **total_dataset_length** (`int`) -- Total length of the inner dataset across all processes. """ def __init__( self, dataset, device=None, rng_types=None, synchronized_generator=None, skip_batches=0, _drop_last: bool = False, **kwargs, ): super().__init__(dataset, **kwargs) self.device = device self.rng_types = rng_types self.synchronized_generator = synchronized_generator self.skip_batches = skip_batches self.gradient_state = GradientState() self._drop_last = _drop_last self.iteration = 0 def __iter__(self): if self.rng_types is not None: synchronize_rng_states(self.rng_types, self.synchronized_generator) self.begin() self.set_epoch(self.iteration) dataloader_iter = super().__iter__() # We iterate one batch ahead to check when we are at the end try: current_batch = next(dataloader_iter) except StopIteration: yield batch_index = 0 while True: try: # But we still move it to the device so it is done before `StopIteration` is reached if self.device is not None: current_batch = send_to_device(current_batch, self.device) next_batch = next(dataloader_iter) if batch_index >= self.skip_batches: yield current_batch batch_index += 1 current_batch = next_batch except StopIteration: self.end_of_dataloader = True if batch_index >= self.skip_batches: yield current_batch break self.iteration += 1 self.end() def set_epoch(self, epoch: int): # In case it is manually passed in, the user can set it to what they like if self.iteration != epoch: self.iteration = epoch if hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, "set_epoch"): self.batch_sampler.sampler.set_epoch(epoch) # We support if a custom `Dataset` implementation has `set_epoch` # or in general HF datasets `Datasets` elif hasattr(self.dataset, "set_epoch"): self.dataset.set_epoch(epoch) @property def total_batch_size(self): batch_sampler = self.sampler if isinstance(self.sampler, BatchSampler) else self.batch_sampler return ( batch_sampler.batch_size if getattr(batch_sampler, "split_batches", False) else (batch_sampler.batch_size * getattr(batch_sampler, "num_processes", 1)) ) @property def total_dataset_length(self): if hasattr(self.dataset, "total_length"): return self.dataset.total_length else: return len(self.dataset) if is_tpu_available(check_device=False): import torch_xla.distributed.parallel_loader as xpl class MpDeviceLoaderWrapper(xpl.MpDeviceLoader): """ Wrapper for the xpl.MpDeviceLoader class that knows the total batch size. XLA preloading threads will all call DataLoaderShard's __iter__(). Remove rng_types from DataLoaderShard to prevent it from using the XLA device in the preloading threads, and synchronize the RNG once from the main thread only. **Available attributes:** - **total_batch_size** (`int`) -- Total batch size of the dataloader across all processes. Equal to the original batch size when `split_batches=True`; otherwise the original batch size * the total number of processes - **total_dataset_length** (`int`) -- Total length of the inner dataset across all processes. """ def __init__(self, dataloader: DataLoaderShard, device: torch.device): super().__init__(dataloader, device) self._rng_types = self._loader.rng_types self._loader.rng_types = None def __iter__(self): if self._rng_types is not None: synchronize_rng_states(self._rng_types, self._loader.synchronized_generator) return super().__iter__() @property def total_batch_size(self): return self._loader.total_batch_size @property def total_dataset_length(self): return self._loader.total_dataset_length @property def batch_sampler(self): return self._loader.batch_sampler class DataLoaderDispatcher(DataLoader, DataLoaderStateMixin): """ Subclass of a PyTorch `DataLoader` that will iterate and preprocess on process 0 only, then dispatch on each process their part of the batch. Args: split_batches (`bool`, *optional*, defaults to `False`): Whether the resulting `DataLoader` should split the batches of the original data loader across devices or yield full batches (in which case it will yield batches starting at the `process_index`-th and advancing of `num_processes` batches at each iteration). Another way to see this is that the observed batch size will be the same as the initial `dataloader` if this option is set to `True`, the batch size of the initial `dataloader` multiplied by `num_processes` otherwise. Setting this option to `True` requires that the batch size of the `dataloader` is a round multiple of `batch_size`. skip_batches (`int`, *optional*, defaults to 0): The number of batches to skip at the beginning of an iteration. **Available attributes:** - **total_batch_size** (`int`) -- Total batch size of the dataloader across all processes. Equal to the original batch size when `split_batches=True`; otherwise the original batch size * the total number of processes - **total_dataset_length** (`int`) -- Total length of the inner dataset across all processes. """ def __init__( self, dataset, split_batches: bool = False, skip_batches=0, _drop_last: bool = False, slice_fn=None, **kwargs ): shuffle = False if is_torch_version(">=", "1.11.0"): from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe # We need to save the shuffling state of the DataPipe if isinstance(dataset, ShufflerIterDataPipe): shuffle = dataset._shuffle_enabled super().__init__(dataset, **kwargs) self.split_batches = split_batches if shuffle: torch.utils.data.graph_settings.apply_shuffle_settings(dataset, shuffle=shuffle) self.gradient_state = GradientState() self.state = AcceleratorState() self._drop_last = _drop_last self.skip_batches = skip_batches self.slice_fn = slice_tensors if slice_fn is None else slice_fn self.iteration = 0 def _fetch_batches(self, iterator): batches, batch = None, None # On process 0, we gather the batch to dispatch. if self.state.process_index == 0: try: if self.split_batches: # One batch of the main iterator is dispatched and split. batch = next(iterator) else: # num_processes batches of the main iterator are concatenated then dispatched and split. # We add the batches one by one so we have the remainder available when drop_last=False. batches = [] for _ in range(self.state.num_processes): batches.append(next(iterator)) try: batch = concatenate(batches, dim=0) except RuntimeError as e: raise RuntimeError( "You can't use batches of different size with `dispatch_batches=True` or when using an `IterableDataset`." "either pass `dispatch_batches=False` and have each process fetch its own batch " " or pass `split_batches=True`. By doing so, the main process will fetch a full batch and " "slice it into `num_processes` batches for each process." ) from e # In both cases, we need to get the structure of the batch that we will broadcast on other # processes to initialize the tensors with the right shape. # data_structure, stop_iteration batch_info = [get_data_structure(batch), False] except StopIteration: batch_info = [None, True] else: batch_info = [None, self._stop_iteration] # This is inplace, so after this instruction, every process has the same `batch_info` as process 0. broadcast_object_list(batch_info) self._stop_iteration = batch_info[1] if self._stop_iteration: # If drop_last is False and split_batches is False, we may have a remainder to take care of. if not self.split_batches and not self._drop_last: if self.state.process_index == 0 and len(batches) > 0: batch = concatenate(batches, dim=0) batch_info = [get_data_structure(batch), False] else: batch_info = [None, True] broadcast_object_list(batch_info) return batch, batch_info def __iter__(self): self.begin() self.set_epoch(self.iteration) main_iterator = None if is_torch_version(">=", "2.0.1"): # NOTE PyTorch DataLoader adds forward compatibilities for DataPipes, which broadcasts # shared seed to all dist processes. Thus, we need to create iterator for all dist processes. # But, we only iterate through the DataLoader on process 0. main_iterator = super().__iter__() elif self.state.process_index == 0: main_iterator = super().__iter__() stop_iteration = False self._stop_iteration = False first_batch = None next_batch, next_batch_info = self._fetch_batches(main_iterator) batch_index = 0 while not stop_iteration: batch, batch_info = next_batch, next_batch_info if self.state.process_index != 0: # Initialize tensors on other processes than process 0. batch = initialize_tensors(batch_info[0]) batch = send_to_device(batch, self.state.device) # Broadcast the batch before splitting it. batch = broadcast(batch, from_process=0) if not self._drop_last and first_batch is None: # We keep at least num processes elements of the first batch to be able to complete the last batch first_batch = self.slice_fn( batch, slice(0, self.state.num_processes), process_index=self.state.process_index, num_processes=self.state.num_processes, ) if batch is None: raise ValueError( f"Batch does not contain any data (`{batch}`). At the end of all iterable data available before expected stop iteration." ) observed_batch_size = find_batch_size(batch) batch_size = observed_batch_size // self.state.num_processes stop_iteration = self._stop_iteration if not stop_iteration: # We may still be at the end of the dataloader without knowing it yet: if there is nothing left in # the dataloader since the number of batches is a round multiple of the number of processes. next_batch, next_batch_info = self._fetch_batches(main_iterator) # next_batch_info[0] is None when there are no more batches, otherwise we still need to process them. if self._stop_iteration and next_batch_info[0] is None: stop_iteration = True if not self._drop_last and stop_iteration and observed_batch_size % self.state.num_processes != 0: # If the last batch is not complete, let's add the first batch to it. batch = concatenate([batch, first_batch], dim=0) # Batch size computation above is wrong, it's off by 1 so we fix it. batch_size += 1 data_slice = slice(self.state.process_index * batch_size, (self.state.process_index + 1) * batch_size) batch = self.slice_fn( batch, data_slice, process_index=self.state.process_index, num_processes=self.state.num_processes, ) if stop_iteration: self.end_of_dataloader = True self.remainder = observed_batch_size if batch_index >= self.skip_batches: yield batch batch_index += 1 self.iteration += 1 self.end() def set_epoch(self, epoch: int): # In case it is manually passed in, the user can set it to what they like if self.iteration != epoch: self.iteration = epoch if hasattr(self.batch_sampler.sampler, "set_epoch"): self.batch_sampler.sampler.set_epoch(epoch) elif hasattr(self.dataset, "set_epoch"): self.dataset.set_epoch(epoch) def __len__(self): whole_length = super().__len__() if self.split_batches: return whole_length elif self._drop_last: return whole_length // self.state.num_processes else: return math.ceil(whole_length / self.state.num_processes) @property def total_batch_size(self): return ( self.dataset.batch_size if self.split_batches else (self.dataset.batch_size * self.dataset.num_processes) ) @property def total_dataset_length(self): return len(self.dataset) def prepare_data_loader( dataloader: DataLoader, device: Optional[torch.device] = None, num_processes: Optional[int] = None, process_index: Optional[int] = None, split_batches: bool = False, put_on_device: bool = False, rng_types: Optional[List[Union[str, RNGType]]] = None, dispatch_batches: Optional[bool] = None, even_batches: bool = True, slice_fn_for_dispatch: Optional[Callable] = None, use_seedable_sampler: bool = False, ) -> DataLoader: """ Wraps a PyTorch `DataLoader` to generate batches for one of the processes only. Depending on the value of the `drop_last` attribute of the `dataloader` passed, it will either stop the iteration at the first batch that would be too small / not present on all processes or loop with indices from the beginning. Args: dataloader (`torch.utils.data.dataloader.DataLoader`): The data loader to split across several devices. device (`torch.device`): The target device for the returned `DataLoader`. num_processes (`int`, *optional*): The number of processes running concurrently. Will default to the value given by [`~state.AcceleratorState`]. process_index (`int`, *optional*): The index of the current process. Will default to the value given by [`~state.AcceleratorState`]. split_batches (`bool`, *optional*, defaults to `False`): Whether the resulting `DataLoader` should split the batches of the original data loader across devices or yield full batches (in which case it will yield batches starting at the `process_index`-th and advancing of `num_processes` batches at each iteration). Another way to see this is that the observed batch size will be the same as the initial `dataloader` if this option is set to `True`, the batch size of the initial `dataloader` multiplied by `num_processes` otherwise. Setting this option to `True` requires that the batch size of the `dataloader` is a round multiple of `batch_size`. put_on_device (`bool`, *optional*, defaults to `False`): Whether or not to put the batches on `device` (only works if the batches are nested list, tuples or dictionaries of tensors). rng_types (list of `str` or [`~utils.RNGType`]): The list of random number generators to synchronize at the beginning of each iteration. Should be one or several of: - `"torch"`: the base torch random number generator - `"cuda"`: the CUDA random number generator (GPU only) - `"xla"`: the XLA random number generator (TPU only) - `"generator"`: the `torch.Generator` of the sampler (or batch sampler if there is no sampler in your dataloader) or of the iterable dataset (if it exists) if the underlying dataset is of that type. dispatch_batches (`bool`, *optional*): If set to `True`, the datalaoder prepared is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to `True` when the underlying dataset is an `IterableDataset`, `False` otherwise. even_batches (`bool`, *optional*, defaults to `True`): If set to `True`, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers. slice_fn_for_dispatch (`Callable`, *optional*`): If passed, this function will be used to slice tensors across `num_processes`. Will default to [`~utils.slice_tensors`]. This argument is used only when `dispatch_batches` is set to `True` and will be ignored otherwise. use_seedable_sampler (`bool`, *optional*, defaults to `False`): Whether to use the [`~data_loader.SeedableRandomSampler`] instead of a `RandomSampler` for better reproducability. Comes at a cost of potentially different performances due to different shuffling algorithms but ensures results will be the *exact* same. Returns: `torch.utils.data.dataloader.DataLoader`: A new data loader that will yield the portion of the batches <Tip warning={true}> `BatchSampler`s with varying batch sizes are not enabled by default. To enable this behaviour, set `even_batches` equal to `False` </Tip> """ if dispatch_batches is None: if not put_on_device: dispatch_batches = False else: dispatch_batches = isinstance(dataloader.dataset, IterableDataset) if dispatch_batches and not put_on_device: raise ValueError("Using `dispatch_batches=True` requires `put_on_device=True`.") # Grab defaults from AcceleratorState state = AcceleratorState() if num_processes is None: num_processes = state.num_processes if process_index is None: process_index = state.process_index # Sanity check batch_size = dataloader.batch_size if dataloader.batch_size is not None else dataloader.batch_sampler.batch_size if split_batches and batch_size > 1 and batch_size % num_processes != 0: raise ValueError( f"To use a `DataLoader` in `split_batches` mode, the batch size ({dataloader.batch_size}) " f"needs to be a round multiple of the number of processes ({num_processes})." ) new_dataset = dataloader.dataset # Iterable dataset doesn't like batch_sampler, but data_loader creates a default one for it new_batch_sampler = dataloader.batch_sampler if not isinstance(new_dataset, IterableDataset) else None sampler_is_batch_sampler = False synchronized_generator = None sampler_is_batch_sampler = isinstance(dataloader.sampler, BatchSampler) if sampler_is_batch_sampler: sampler = getattr(dataloader.sampler, "sampler", None) else: sampler = getattr(dataloader.batch_sampler, "sampler", None) if isinstance(sampler, RandomSampler) and use_seedable_sampler: # When iterating through the dataloader during distributed processes # we want to ensure that on each process we are iterating through the same # samples in the same order if a seed is set. This requires a tweak # to the `torch.utils.data.RandomSampler` class (if used). sampler = SeedableRandomSampler( data_source=sampler.data_source, replacement=sampler.replacement, num_samples=sampler._num_samples, generator=getattr(sampler, "generator", torch.Generator()), ) # No change if no multiprocess if (num_processes != 1 or state.distributed_type == DistributedType.MEGATRON_LM) and not dispatch_batches: if isinstance(new_dataset, IterableDataset): if getattr(dataloader.dataset, "generator", None) is not None: synchronized_generator = dataloader.dataset.generator new_dataset = IterableDatasetShard( new_dataset, batch_size=dataloader.batch_size, drop_last=dataloader.drop_last, num_processes=num_processes, process_index=process_index, split_batches=split_batches, ) else: batch_sampler = dataloader.sampler if sampler_is_batch_sampler else dataloader.batch_sampler new_batch_sampler = BatchSamplerShard( batch_sampler, num_processes=num_processes, process_index=process_index, split_batches=split_batches, even_batches=even_batches, ) # We ignore all of those since they are all dealt with by our new_batch_sampler ignore_kwargs = [ "batch_size", "shuffle", "sampler", "batch_sampler", "drop_last", ] if rng_types is not None and synchronized_generator is None and "generator" in rng_types: rng_types.remove("generator") kwargs = { k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k]) for k in _PYTORCH_DATALOADER_KWARGS if k not in ignore_kwargs } # Need to provide batch_size as batch_sampler is None for Iterable dataset if new_batch_sampler is None: kwargs["drop_last"] = dataloader.drop_last kwargs["batch_size"] = ( dataloader.batch_size // num_processes if split_batches and not dispatch_batches else dataloader.batch_size ) if isinstance(sampler, SeedableRandomSampler) and use_seedable_sampler: if sampler_is_batch_sampler: dataloader.sampler.sampler = sampler else: dataloader.batch_sampler.sampler = sampler if dispatch_batches: kwargs.pop("generator") dataloader = DataLoaderDispatcher( new_dataset, split_batches=split_batches, batch_sampler=new_batch_sampler, _drop_last=dataloader.drop_last, slice_fn=slice_fn_for_dispatch, **kwargs, ) elif sampler_is_batch_sampler: dataloader = DataLoaderShard( new_dataset, device=device if put_on_device and state.distributed_type != DistributedType.TPU else None, sampler=new_batch_sampler, batch_size=dataloader.batch_size, rng_types=rng_types, _drop_last=dataloader.drop_last, synchronized_generator=synchronized_generator, **kwargs, ) else: dataloader = DataLoaderShard( new_dataset, device=device if put_on_device and state.distributed_type != DistributedType.TPU else None, batch_sampler=new_batch_sampler, rng_types=rng_types, synchronized_generator=synchronized_generator, _drop_last=dataloader.drop_last, **kwargs, ) if state.distributed_type == DistributedType.TPU: return MpDeviceLoaderWrapper(dataloader, device) return dataloader class SkipBatchSampler(BatchSampler): """ A `torch.utils.data.BatchSampler` that skips the first `n` batches of another `torch.utils.data.BatchSampler`. """ def __init__(self, batch_sampler, skip_batches=0): self.batch_sampler = batch_sampler self.skip_batches = skip_batches def __iter__(self): for index, samples in enumerate(self.batch_sampler): if index >= self.skip_batches: yield samples @property def total_length(self): return len(self.batch_sampler) def __len__(self): return len(self.batch_sampler) - self.skip_batches class SkipDataLoader(DataLoader): """ Subclass of a PyTorch `DataLoader` that will skip the first batches. Args: dataset (`torch.utils.data.dataset.Dataset`): The dataset to use to build this datalaoder. skip_batches (`int`, *optional*, defaults to 0): The number of batches to skip at the beginning. kwargs: All other keyword arguments to pass to the regular `DataLoader` initialization. """ def __init__(self, dataset, skip_batches=0, **kwargs): super().__init__(dataset, **kwargs) self.skip_batches = skip_batches def __iter__(self): for index, batch in enumerate(super().__iter__()): if index >= self.skip_batches: yield batch def skip_first_batches(dataloader, num_batches=0): """ Creates a `torch.utils.data.DataLoader` that will efficiently skip the first `num_batches`. """ dataset = dataloader.dataset sampler_is_batch_sampler = False if isinstance(dataset, IterableDataset): new_batch_sampler = None else: sampler_is_batch_sampler = isinstance(dataloader.sampler, BatchSampler) batch_sampler = dataloader.sampler if sampler_is_batch_sampler else dataloader.batch_sampler new_batch_sampler = SkipBatchSampler(batch_sampler, skip_batches=num_batches) # We ignore all of those since they are all dealt with by our new_batch_sampler ignore_kwargs = [ "batch_size", "shuffle", "sampler", "batch_sampler", "drop_last", ] kwargs = { k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k]) for k in _PYTORCH_DATALOADER_KWARGS if k not in ignore_kwargs } # Need to provide batch_size as batch_sampler is None for Iterable dataset if new_batch_sampler is None: kwargs["drop_last"] = dataloader.drop_last kwargs["batch_size"] = dataloader.batch_size if isinstance(dataloader, DataLoaderDispatcher): if new_batch_sampler is None: # Need to manually skip batches in the dataloader kwargs["skip_batches"] = num_batches dataloader = DataLoaderDispatcher( dataset, split_batches=dataloader.split_batches, batch_sampler=new_batch_sampler, _drop_last=dataloader._drop_last, **kwargs, ) elif isinstance(dataloader, DataLoaderShard): if new_batch_sampler is None: # Need to manually skip batches in the dataloader kwargs["skip_batches"] = num_batches elif sampler_is_batch_sampler: kwargs["sampler"] = new_batch_sampler kwargs["batch_size"] = dataloader.batch_size else: kwargs["batch_sampler"] = new_batch_sampler dataloader = DataLoaderShard( dataset, device=dataloader.device, rng_types=dataloader.rng_types, synchronized_generator=dataloader.synchronized_generator, **kwargs, ) else: if new_batch_sampler is None: # Need to manually skip batches in the dataloader dataloader = SkipDataLoader(dataset, skip_batches=num_batches, **kwargs) else: dataloader = DataLoader(dataset, batch_sampler=new_batch_sampler, **kwargs) return dataloader

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/memory_utils.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings warnings.warn( "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: " "`from accelerate import find_executable_batch_size` to avoid this warning.", FutureWarning, )

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/hooks.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools from typing import Dict, List, Mapping, Optional, Union import torch import torch.nn as nn from .state import PartialState from .utils import ( PrefixedDataset, find_device, named_module_tensors, send_to_device, set_module_tensor_to_device, ) from .utils.modeling import get_non_persistent_buffers from .utils.other import recursive_getattr class ModelHook: """ A hook that contains callbacks to be executed just before and after the forward method of a model. The difference with PyTorch existing hooks is that they get passed along the kwargs. Class attribute: - **no_grad** (`bool`, *optional*, defaults to `False`) -- Whether or not to execute the actual forward pass under the `torch.no_grad()` context manager. """ no_grad = False def init_hook(self, module): """ To be executed when the hook is attached to the module. Args: module (`torch.nn.Module`): The module attached to this hook. """ return module def pre_forward(self, module, *args, **kwargs): """ To be executed just before the forward method of the model. Args: module (`torch.nn.Module`): The module whose forward pass will be executed just after this event. args (`Tuple[Any]`): The positional arguments passed to the module. kwargs (`Dict[Str, Any]`): The keyword arguments passed to the module. Returns: `Tuple[Tuple[Any], Dict[Str, Any]]`: A tuple with the treated `args` and `kwargs`. """ return args, kwargs def post_forward(self, module, output): """ To be executed just after the forward method of the model. Args: module (`torch.nn.Module`): The module whose forward pass been executed just before this event. output (`Any`): The output of the module. Returns: `Any`: The processed `output`. """ return output def detach_hook(self, module): """ To be executed when the hook is detached from a module. Args: module (`torch.nn.Module`): The module detached from this hook. """ return module class SequentialHook(ModelHook): """ A hook that can contain several hooks and iterates through them at each event. """ def __init__(self, *hooks): self.hooks = hooks def init_hook(self, module): for hook in self.hooks: module = hook.init_hook(module) return module def pre_forward(self, module, *args, **kwargs): for hook in self.hooks: args, kwargs = hook.pre_forward(module, *args, **kwargs) return args, kwargs def post_forward(self, module, output): for hook in self.hooks: output = hook.post_forward(module, output) return output def detach_hook(self, module): for hook in self.hooks: module = hook.detach_hook(module) return module def add_hook_to_module( module: nn.Module, hook: ModelHook, append: bool = False, init_hook_kwargs: Optional[Dict] = None ): """ Adds a hook to a given module. This will rewrite the `forward` method of the module to include the hook, to remove this behavior and restore the original `forward` method, use `remove_hook_from_module`. <Tip warning={true}> If the module already contains a hook, this will replace it with the new hook passed by default. To chain two hooks together, pass `append=True`, so it chains the current and new hook into an instance of the `SequentialHook` class. </Tip> Args: module (`torch.nn.Module`): The module to attach a hook to. hook (`ModelHook`): The hook to attach. append (`bool`, *optional*, defaults to `False`): Whether the hook should be chained with an existing one (if module already contains a hook) or not. init_hook_kwargs (Optional[Dict], *optional*, defaults to `None`): Optional arguments to pass to the hook initialization. Returns: `torch.nn.Module`: The same module, with the hook attached (the module is modified in place, so the result can be discarded). """ if append and (getattr(module, "_hf_hook", None) is not None): old_hook = module._hf_hook remove_hook_from_module(module) hook = SequentialHook(old_hook, hook) if hasattr(module, "_hf_hook") and hasattr(module, "_old_forward"): # If we already put some hook on this module, we replace it with the new one. old_forward = module._old_forward else: old_forward = module.forward module._old_forward = old_forward if init_hook_kwargs is None: init_hook_kwargs = {} module = hook.init_hook(module, **init_hook_kwargs) module._hf_hook = hook def new_forward(module, *args, **kwargs): args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs) if module._hf_hook.no_grad: with torch.no_grad(): output = module._old_forward(*args, **kwargs) else: output = module._old_forward(*args, **kwargs) return module._hf_hook.post_forward(module, output) module.forward = functools.update_wrapper(functools.partial(new_forward, module), old_forward) return module def remove_hook_from_module(module: nn.Module, recurse=False): """ Removes any hook attached to a module via `add_hook_to_module`. Args: module (`torch.nn.Module`): The module to attach a hook to. recurse (`bool`, **optional**): Whether to remove the hooks recursively Returns: `torch.nn.Module`: The same module, with the hook detached (the module is modified in place, so the result can be discarded). """ if hasattr(module, "_hf_hook"): module._hf_hook.detach_hook(module) delattr(module, "_hf_hook") if hasattr(module, "_old_forward"): module.forward = module._old_forward delattr(module, "_old_forward") if recurse: for child in module.children(): remove_hook_from_module(child, recurse) return module class AlignDevicesHook(ModelHook): """ A generic `ModelHook` that ensures inputs and model weights are on the same device for the forward pass of the associated module, potentially offloading the weights after the forward pass. Args: execution_device (`torch.device`, *optional*): The device on which inputs and model weights should be placed before the forward pass. offload (`bool`, *optional*, defaults to `False`): Whether or not the weights should be offloaded after the forward pass. io_same_device (`bool`, *optional*, defaults to `False`): Whether or not the output should be placed on the same device as the input was. weights_map (`Mapping[str, torch.Tensor]`, *optional*): When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to include the associated module's buffers when offloading. place_submodules (`bool`, *optional*, defaults to `False`): Whether to place the submodules on `execution_device` during the `init_hook` event. """ def __init__( self, execution_device: Optional[Union[int, str, torch.device]] = None, offload: bool = False, io_same_device: bool = False, weights_map: Optional[Mapping] = None, offload_buffers: bool = False, place_submodules: bool = False, skip_keys: Optional[Union[str, List[str]]] = None, ): self.execution_device = execution_device self.offload = offload self.io_same_device = io_same_device self.weights_map = weights_map self.offload_buffers = offload_buffers self.place_submodules = place_submodules self.skip_keys = skip_keys # Will contain the input device when `io_same_device=True`. self.input_device = None self.param_original_devices = {} self.buffer_original_devices = {} self.tied_params_names = set() def __repr__(self): return ( f"AlignDevicesHook(execution_device={self.execution_device}, offload={self.offload}, " f"io_same_device={self.io_same_device}, offload_buffers={self.offload_buffers}, " f"place_submodules={self.place_submodules}, skip_keys={repr(self.skip_keys)})" ) def init_hook(self, module, tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None): # In case the AlignDevicesHook is on meta device, ignore tied weights as data_ptr() is then always zero. if self.execution_device == "meta" or self.execution_device == torch.device("meta"): tied_params_map = None if not self.offload and self.execution_device is not None: for name, _ in named_module_tensors(module, recurse=self.place_submodules): set_module_tensor_to_device(module, name, self.execution_device, tied_params_map=tied_params_map) elif self.offload: self.original_devices = { name: param.device for name, param in named_module_tensors(module, recurse=self.place_submodules) } if self.weights_map is None: self.weights_map = { name: param.to("cpu") for name, param in named_module_tensors( module, include_buffers=self.offload_buffers, recurse=self.place_submodules ) } for name, _ in named_module_tensors( module, include_buffers=self.offload_buffers, recurse=self.place_submodules, remove_non_persistent=True ): # When using disk offloading, we can not rely on `weights_map[name].data_ptr()` as the reference pointer, # as we have no guarantee that safetensors' `file.get_tensor()` will always give the same pointer. # As we have no reliable way to track the shared data pointer of tied weights in this case, we use tied_params_names: List[str] # to add on the fly pointers to `tied_params_map` in the pre_forward call. if tied_params_map is not None and recursive_getattr(module, name).data_ptr() in tied_params_map: self.tied_params_names.add(name) set_module_tensor_to_device(module, name, "meta") if not self.offload_buffers and self.execution_device is not None: for name, _ in module.named_buffers(recurse=self.place_submodules): set_module_tensor_to_device(module, name, self.execution_device, tied_params_map=tied_params_map) elif self.offload_buffers and self.execution_device is not None: for name in get_non_persistent_buffers(module, recurse=self.place_submodules): set_module_tensor_to_device(module, name, self.execution_device, tied_params_map=tied_params_map) # The hook pre_forward/post_forward need to have knowledge of this dictionary, as with offloading we want to avoid duplicating memory # for tied weights already loaded on the target execution device. self.tied_params_map = tied_params_map return module def pre_forward(self, module, *args, **kwargs): if self.io_same_device: self.input_device = find_device([args, kwargs]) if self.offload: self.tied_pointers_to_remove = set() for name, _ in named_module_tensors( module, include_buffers=self.offload_buffers, recurse=self.place_submodules, remove_non_persistent=True, ): fp16_statistics = None value = self.weights_map[name] if "weight" in name and name.replace("weight", "SCB") in self.weights_map.keys(): if value.dtype == torch.int8: fp16_statistics = self.weights_map[name.replace("weight", "SCB")] # In case we are using offloading with tied weights, we need to keep track of the offloaded weights # that are loaded on device at this point, as we will need to remove them as well from the dictionary # self.tied_params_map in order to allow to free memory. if name in self.tied_params_names and value.data_ptr() not in self.tied_params_map: self.tied_params_map[value.data_ptr()] = {} if ( value is not None and self.tied_params_map is not None and value.data_ptr() in self.tied_params_map and self.execution_device not in self.tied_params_map[value.data_ptr()] ): self.tied_pointers_to_remove.add((value.data_ptr(), self.execution_device)) set_module_tensor_to_device( module, name, self.execution_device, value=value, fp16_statistics=fp16_statistics, tied_params_map=self.tied_params_map, ) return send_to_device(args, self.execution_device), send_to_device( kwargs, self.execution_device, skip_keys=self.skip_keys ) def post_forward(self, module, output): if self.offload: for name, _ in named_module_tensors( module, include_buffers=self.offload_buffers, recurse=self.place_submodules, remove_non_persistent=True, ): set_module_tensor_to_device(module, name, "meta") if type(module).__name__ == "Linear8bitLt": module.state.SCB = None module.state.CxB = None # We may have loaded tied weights into self.tied_params_map (avoiding to load them several times in e.g. submodules): remove them from # this dictionary to allow the garbage collector to do its job. for value_pointer, device in self.tied_pointers_to_remove: del self.tied_params_map[value_pointer][device] self.tied_pointers_to_remove = None if self.io_same_device and self.input_device is not None: output = send_to_device(output, self.input_device, skip_keys=self.skip_keys) return output def detach_hook(self, module): if self.offload: for name, device in self.original_devices.items(): if device != torch.device("meta"): set_module_tensor_to_device(module, name, device, value=self.weights_map.get(name, None)) return module def attach_execution_device_hook( module: torch.nn.Module, execution_device: Union[int, str, torch.device], skip_keys: Optional[Union[str, List[str]]] = None, preload_module_classes: Optional[List[str]] = None, tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None, ): """ Recursively attaches `AlignDevicesHook` to all submodules of a given model to make sure they have the right execution device Args: module (`torch.nn.Module`): The module where we want to attach the hooks. execution_device (`int`, `str` or `torch.device`): The device on which inputs and model weights should be placed before the forward pass. skip_keys (`str` or `List[str]`, *optional*): A list of keys to ignore when moving inputs or outputs between devices. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`): A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution device, this parameter is useful to reuse the first available pointer of a shared weight for all others, instead of duplicating memory. """ if not hasattr(module, "_hf_hook") and len(module.state_dict()) > 0: add_hook_to_module( module, AlignDevicesHook(execution_device, skip_keys=skip_keys), init_hook_kwargs={"tied_params_map": tied_params_map}, ) # Break the recursion if we get to a preload module. if preload_module_classes is not None and module.__class__.__name__ in preload_module_classes: return for child in module.children(): attach_execution_device_hook(child, execution_device, tied_params_map=tied_params_map) def attach_align_device_hook( module: torch.nn.Module, execution_device: Optional[torch.device] = None, offload: bool = False, weights_map: Optional[Mapping] = None, offload_buffers: bool = False, module_name: str = "", skip_keys: Optional[Union[str, List[str]]] = None, preload_module_classes: Optional[List[str]] = None, tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None, ): """ Recursively attaches `AlignDevicesHook` to all submodules of a given model that have direct parameters and/or buffers. Args: module (`torch.nn.Module`): The module where we want to attach the hooks. execution_device (`torch.device`, *optional*): The device on which inputs and model weights should be placed before the forward pass. offload (`bool`, *optional*, defaults to `False`): Whether or not the weights should be offloaded after the forward pass. weights_map (`Mapping[str, torch.Tensor]`, *optional*): When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to include the associated module's buffers when offloading. module_name (`str`, *optional*, defaults to `""`): The name of the module. skip_keys (`str` or `List[str]`, *optional*): A list of keys to ignore when moving inputs or outputs between devices. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`): A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution device, this parameter is useful to reuse the first available pointer of a shared weight for all others, instead of duplicating memory. """ # Attach the hook on this module if it has any direct tensor. directs = named_module_tensors(module) full_offload = ( offload and preload_module_classes is not None and module.__class__.__name__ in preload_module_classes ) if len(list(directs)) > 0 or full_offload: if weights_map is not None: prefix = f"{module_name}." if len(module_name) > 0 else "" prefixed_weights_map = PrefixedDataset(weights_map, prefix) else: prefixed_weights_map = None hook = AlignDevicesHook( execution_device=execution_device, offload=offload, weights_map=prefixed_weights_map, offload_buffers=offload_buffers, place_submodules=full_offload, skip_keys=skip_keys, ) add_hook_to_module(module, hook, append=True, init_hook_kwargs={"tied_params_map": tied_params_map}) # We stop the recursion in case we hit the full offload. if full_offload: return # Recurse on all children of the module. for child_name, child in module.named_children(): child_name = f"{module_name}.{child_name}" if len(module_name) > 0 else child_name attach_align_device_hook( child, execution_device=execution_device, offload=offload, weights_map=weights_map, offload_buffers=offload_buffers, module_name=child_name, preload_module_classes=preload_module_classes, skip_keys=skip_keys, tied_params_map=tied_params_map, ) def remove_hook_from_submodules(module: nn.Module): """ Recursively removes all hooks attached on the submodules of a given model. Args: module (`torch.nn.Module`): The module on which to remove all hooks. """ remove_hook_from_module(module) for child in module.children(): remove_hook_from_submodules(child) def attach_align_device_hook_on_blocks( module: nn.Module, execution_device: Optional[Union[torch.device, Dict[str, torch.device]]] = None, offload: Union[bool, Dict[str, bool]] = False, weights_map: Mapping = None, offload_buffers: bool = False, module_name: str = "", skip_keys: Optional[Union[str, List[str]]] = None, preload_module_classes: Optional[List[str]] = None, tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None, ): """ Attaches `AlignDevicesHook` to all blocks of a given model as needed. Args: module (`torch.nn.Module`): The module where we want to attach the hooks. execution_device (`torch.device` or `Dict[str, torch.device]`, *optional*): The device on which inputs and model weights should be placed before the forward pass. It can be one device for the whole module, or a dictionary mapping module name to device. offload (`bool`, *optional*, defaults to `False`): Whether or not the weights should be offloaded after the forward pass. It can be one boolean for the whole module, or a dictionary mapping module name to boolean. weights_map (`Mapping[str, torch.Tensor]`, *optional*): When the model weights are offloaded, a (potentially lazy) map from param names to the tensor values. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to include the associated module's buffers when offloading. module_name (`str`, *optional*, defaults to `""`): The name of the module. skip_keys (`str` or `List[str]`, *optional*): A list of keys to ignore when moving inputs or outputs between devices. preload_module_classes (`List[str]`, *optional*): A list of classes whose instances should load all their weights (even in the submodules) at the beginning of the forward. This should only be used for classes that have submodules which are registered but not called directly during the forward, for instance if a `dense` linear layer is registered, but at forward, `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly. tied_params_map (Optional[Dict[int, Dict[torch.device, torch.Tensor]]], *optional*, defaults to `None`): A map of data pointers to dictionaries of devices to already dispatched tied weights. For a given execution device, this parameter is useful to reuse the first available pointer of a shared weight for all others, instead of duplicating memory. """ # If one device and one offload, we've got one hook. if not isinstance(execution_device, Mapping) and not isinstance(offload, dict): if not offload: hook = AlignDevicesHook( execution_device=execution_device, io_same_device=True, skip_keys=skip_keys, place_submodules=True ) add_hook_to_module(module, hook, init_hook_kwargs={"tied_params_map": tied_params_map}) else: attach_align_device_hook( module, execution_device=execution_device, offload=True, weights_map=weights_map, offload_buffers=offload_buffers, module_name=module_name, skip_keys=skip_keys, tied_params_map=tied_params_map, ) return if not isinstance(execution_device, Mapping): execution_device = {key: execution_device for key in offload.keys()} if not isinstance(offload, Mapping): offload = {key: offload for key in execution_device.keys()} if module_name in execution_device and module_name in offload and not offload[module_name]: hook = AlignDevicesHook( execution_device=execution_device[module_name], offload_buffers=offload_buffers, io_same_device=(module_name == ""), place_submodules=True, skip_keys=skip_keys, ) add_hook_to_module(module, hook, init_hook_kwargs={"tied_params_map": tied_params_map}) attach_execution_device_hook(module, execution_device[module_name], tied_params_map=tied_params_map) elif module_name in execution_device and module_name in offload: attach_align_device_hook( module, execution_device=execution_device[module_name], offload=True, weights_map=weights_map, offload_buffers=offload_buffers, module_name=module_name, skip_keys=skip_keys, preload_module_classes=preload_module_classes, tied_params_map=tied_params_map, ) if not hasattr(module, "_hf_hook"): hook = AlignDevicesHook( execution_device=execution_device[module_name], io_same_device=(module_name == ""), skip_keys=skip_keys ) add_hook_to_module(module, hook, init_hook_kwargs={"tied_params_map": tied_params_map}) attach_execution_device_hook( module, execution_device[module_name], preload_module_classes=preload_module_classes, skip_keys=skip_keys, tied_params_map=tied_params_map, ) elif module_name == "": hook = AlignDevicesHook(execution_device=execution_device.get(""), io_same_device=True, skip_keys=skip_keys) add_hook_to_module(module, hook, init_hook_kwargs={"tied_params_map": tied_params_map}) for child_name, child in module.named_children(): child_name = f"{module_name}.{child_name}" if len(module_name) > 0 else child_name attach_align_device_hook_on_blocks( child, execution_device=execution_device, offload=offload, weights_map=weights_map, offload_buffers=offload_buffers, module_name=child_name, preload_module_classes=preload_module_classes, skip_keys=skip_keys, tied_params_map=tied_params_map, ) class CpuOffload(ModelHook): """ Offloads a model on the CPU until its forward pass is called. The model will not be offloaded back to the CPU after the forward, the user needs to call the `init_hook` method again for this. Args: execution_device(`str`, `int` or `torch.device`, *optional*): The device on which the model should be executed. Will default to the MPS device if it's available, then GPU 0 if there is a GPU, and finally to the CPU. prev_module_hook (`UserCpuOffloadHook`, *optional*): The hook sent back by [`cpu_offload_with_hook`] for a previous model in the pipeline you are running. If passed, its offload method will be called just before the forward of the model to which this hook is attached. """ def __init__( self, execution_device: Optional[Union[str, int, torch.device]] = None, prev_module_hook: Optional["UserCpuOffloadHook"] = None, ): self.prev_module_hook = prev_module_hook self.execution_device = execution_device if execution_device is not None else PartialState().default_device def init_hook(self, module): return module.to("cpu") def pre_forward(self, module, *args, **kwargs): if self.prev_module_hook is not None: self.prev_module_hook.offload() module.to(self.execution_device) return send_to_device(args, self.execution_device), send_to_device(kwargs, self.execution_device) class UserCpuOffloadHook: """ A simple hook grouping a model and a `ModelHook`, which provides easy APIs for to call the init method of the hook or remove it entirely. """ def __init__(self, model, hook): self.model = model self.hook = hook def offload(self): self.hook.init_hook(self.model) def remove(self): remove_hook_from_module(self.model)

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/__init__.py

__version__ = "0.27.0.dev0" from .accelerator import Accelerator from .big_modeling import ( cpu_offload, cpu_offload_with_hook, disk_offload, dispatch_model, init_empty_weights, init_on_device, load_checkpoint_and_dispatch, ) from .data_loader import skip_first_batches from .launchers import debug_launcher, notebook_launcher from .state import PartialState from .utils import ( AutocastKwargs, DeepSpeedPlugin, DistributedDataParallelKwargs, DistributedType, FullyShardedDataParallelPlugin, GradScalerKwargs, InitProcessGroupKwargs, find_executable_batch_size, infer_auto_device_map, is_rich_available, load_checkpoint_in_model, synchronize_rng_states, ) if is_rich_available(): from .utils import rich

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/logging.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import logging import os from .state import PartialState class MultiProcessAdapter(logging.LoggerAdapter): """ An adapter to assist with logging in multiprocess. `log` takes in an additional `main_process_only` kwarg, which dictates whether it should be called on all processes or only the main executed one. Default is `main_process_only=True`. Does not require an `Accelerator` object to be created first. """ @staticmethod def _should_log(main_process_only): "Check if log should be performed" state = PartialState() return not main_process_only or (main_process_only and state.is_main_process) def log(self, level, msg, *args, **kwargs): """ Delegates logger call after checking if we should log. Accepts a new kwarg of `main_process_only`, which will dictate whether it will be logged across all processes or only the main executed one. Default is `True` if not passed Also accepts "in_order", which if `True` makes the processes log one by one, in order. This is much easier to read, but comes at the cost of sometimes needing to wait for the other processes. Default is `False` to not break with the previous behavior. `in_order` is ignored if `main_process_only` is passed. """ if PartialState._shared_state == {}: raise RuntimeError( "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility." ) main_process_only = kwargs.pop("main_process_only", True) in_order = kwargs.pop("in_order", False) if self.isEnabledFor(level): if self._should_log(main_process_only): msg, kwargs = self.process(msg, kwargs) self.logger.log(level, msg, *args, **kwargs) elif in_order: state = PartialState() for i in range(state.num_processes): if i == state.process_index: msg, kwargs = self.process(msg, kwargs) self.logger.log(level, msg, *args, **kwargs) state.wait_for_everyone() @functools.lru_cache(None) def warning_once(self, *args, **kwargs): """ This method is identical to `logger.warning()`, but will emit the warning with the same message only once Note: The cache is for the function arguments, so 2 different callers using the same arguments will hit the cache. The assumption here is that all warning messages are unique across the code. If they aren't then need to switch to another type of cache that includes the caller frame information in the hashing function. """ self.warning(*args, **kwargs) def get_logger(name: str, log_level: str = None): """ Returns a `logging.Logger` for `name` that can handle multiprocessing. If a log should be called on all processes, pass `main_process_only=False` If a log should be called on all processes and in order, also pass `in_order=True` Args: name (`str`): The name for the logger, such as `__file__` log_level (`str`, *optional*): The log level to use. If not passed, will default to the `LOG_LEVEL` environment variable, or `INFO` if not Example: ```python >>> from accelerate.logging import get_logger >>> from accelerate import Accelerator >>> logger = get_logger(__name__) >>> accelerator = Accelerator() >>> logger.info("My log", main_process_only=False) >>> logger.debug("My log", main_process_only=True) >>> logger = get_logger(__name__, log_level="DEBUG") >>> logger.info("My log") >>> logger.debug("My second log") >>> array = ["a", "b", "c", "d"] >>> letter_at_rank = array[accelerator.process_index] >>> logger.info(letter_at_rank, in_order=True) ``` """ if log_level is None: log_level = os.environ.get("ACCELERATE_LOG_LEVEL", None) logger = logging.getLogger(name) if log_level is not None: logger.setLevel(log_level.upper()) logger.root.setLevel(log_level.upper()) return MultiProcessAdapter(logger, {})

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/state.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import logging import math import os import threading import warnings from contextlib import contextmanager from functools import partial from typing import Any, Callable, Optional import torch from .utils import ( DistributedType, DynamoBackend, GradientAccumulationPlugin, check_cuda_p2p_ib_support, check_fp8_capability, get_ccl_version, get_int_from_env, is_ccl_available, is_deepspeed_available, is_fp8_available, is_ipex_available, is_mps_available, is_npu_available, is_tpu_available, is_xpu_available, parse_choice_from_env, parse_flag_from_env, ) from .utils.dataclasses import SageMakerDistributedType if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm if is_npu_available(check_device=False): import torch_npu # noqa: F401 logger = logging.getLogger(__name__) def is_initialized() -> bool: """ Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`, but works as a module method. """ return AcceleratorState._shared_state != {} # Lambda function that does nothing def do_nothing(*args, **kwargs): return None class ThreadLocalSharedDict(threading.local): """ Descriptor that holds a dict shared between instances of a class in the same thread. Note: Descriptors have slightly different semantics than just a dict field on its own. `PartialState(...)._shared_state` and `PartialState._shared_state` (instance vs class) give the same value: the underlying _storage dict. Likewise, `PartialState(...)._shared_state = {...}` overrides the _storage dict inside the descriptor as you would expect. However, `PartialState._shared_state = {}` actually replaces the descriptor object with a dict instead Thus, you should modify the _storage dict in-place (e.g. `_shared_state.clear()`). See Python documentation for an explanation of descriptors: https://docs.python.org/3/howto/descriptor.html This is required for using PyTorch/XLA with PJRT in multithreaded mode (required for TPU v2 and v3). See https://github.com/pytorch/xla/blob/r2.0/docs/pjrt.md#multithreading-on-tpu-v2v3 """ def __init__(self, thread_local: bool = False): self._storage = {} def __get__(self, obj, objtype=None): return self._storage def __set__(self, obj, value): self._storage = value # Prefer global shared dictionary, except when using TPU. SharedDict = dict if not is_tpu_available(check_device=False) else ThreadLocalSharedDict # Inspired by Alex Martelli's 'Borg'. class PartialState: """ Singleton class that has information about the current training environment and functions to help with process control. Designed to be used when only process control and device execution states are needed. Does *not* need to be initialized from `Accelerator`. **Available attributes:** - **device** (`torch.device`) -- The device to use. - **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently in use. - **local_process_index** (`int`) -- The index of the current process on the current server. - **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8'). - **num_processes** (`int`) -- The number of processes currently launched in parallel. - **process_index** (`int`) -- The index of the current process. - **is_last_process** (`bool`) -- Whether or not the current process is the last one. - **is_main_process** (`bool`) -- Whether or not the current process is the main one. - **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node. - **debug** (`bool`) -- Whether or not the current script is being run in debug mode. """ _shared_state = SharedDict() def __init__(self, cpu: bool = False, **kwargs): self.__dict__ = self._shared_state if not self.initialized: self._cpu = cpu self.backend = None env_device = os.environ.get("ACCELERATE_TORCH_DEVICE", None) self.device = torch.device(env_device) if env_device is not None else None self.debug = parse_flag_from_env("ACCELERATE_DEBUG_MODE") use_sagemaker_dp = kwargs.pop("_use_sagemaker_dp", None) if use_sagemaker_dp is None: use_sagemaker_dp = ( os.environ.get("ACCELERATE_USE_SAGEMAKER", "false") == "true" and os.environ.get("ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE") != SageMakerDistributedType.NO ) if use_sagemaker_dp and not cpu: if ( os.environ.get("ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE") == SageMakerDistributedType.DATA_PARALLEL ) or use_sagemaker_dp: self.distributed_type = DistributedType.MULTI_GPU import smdistributed.dataparallel.torch.torch_smddp # noqa if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="smddp") self.backend = "smddp" self.num_processes = torch.distributed.get_world_size() self.process_index = torch.distributed.get_rank() self.local_process_index = int(os.environ.get("LOCAL_RANK", -1)) if self.device is None: self.device = torch.device("cuda", self.local_process_index) torch.cuda.set_device(self.device) elif is_tpu_available() and not cpu: self.distributed_type = DistributedType.TPU self.num_processes = xm.xrt_world_size() self.process_index = xm.get_ordinal() self.local_process_index = xm.get_local_ordinal() self.device = xm.xla_device() elif ( os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" and int(os.environ.get("LOCAL_RANK", -1)) != -1 and not cpu ): assert ( is_deepspeed_available() ), "DeepSpeed is not available => install it using `pip3 install deepspeed` or build it from source" self.distributed_type = DistributedType.DEEPSPEED if not torch.distributed.is_initialized(): from deepspeed import comm as dist # DeepSpeed always uses nccl kwargs.pop("backend", None) if is_xpu_available and is_ccl_available(): # Set DeepSpeed backend to ccl for xpu self.backend = "ccl" elif is_npu_available(): self.backend = "hccl" else: self.backend = "nccl" dist.init_distributed(dist_backend=self.backend, auto_mpi_discovery=False, **kwargs) self.num_processes = torch.distributed.get_world_size() self.process_index = torch.distributed.get_rank() self.local_process_index = int(os.environ.get("LOCAL_RANK", -1)) if self.device is None: if is_xpu_available(): self.device = torch.device("xpu", self.local_process_index) if self.device is not None: torch.xpu.set_device(self.device) elif is_npu_available(): self.device = torch.device("npu", self.local_process_index) if self.device is not None: torch.npu.set_device(self.device) else: self.device = torch.device("cuda", self.local_process_index) if self.device is not None: torch.cuda.set_device(self.device) if self.device.type == "cuda" and not check_cuda_p2p_ib_support(): if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ: raise NotImplementedError( "Using RTX 4000 series doesn't support faster communication broadband via P2P or IB. " 'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which ' "will do this automatically." ) self._mixed_precision = "no" # deepspeed handles mixed_precision using deepspeed_config elif int(os.environ.get("LOCAL_RANK", -1)) != -1 and not cpu and torch.cuda.is_available(): self.distributed_type = DistributedType.MULTI_GPU if not torch.distributed.is_initialized(): self.backend = kwargs.pop("backend", "nccl") # Special case for `TrainingArguments`, where `backend` will be `None` if self.backend is None: self.backend = "nccl" torch.distributed.init_process_group(backend=self.backend, **kwargs) if not check_cuda_p2p_ib_support(): if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ: raise NotImplementedError( "Using RTX 4000 series doesn't support faster communication broadband via P2P or IB. " 'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which ' "will do this automatically." ) self.num_processes = torch.distributed.get_world_size() self.process_index = torch.distributed.get_rank() self.local_process_index = int(os.environ.get("LOCAL_RANK", -1)) if self.device is None: self.device = torch.device("cuda", self.local_process_index) torch.cuda.set_device(self.device) elif is_npu_available() and not cpu and int(os.environ.get("LOCAL_RANK", -1)) != -1: self.distributed_type = DistributedType.MULTI_NPU if not torch.distributed.is_initialized(): # Backend is not set by the user, we set it here kwargs.pop("backend", None) self.backend = "hccl" torch.distributed.init_process_group(backend=self.backend, **kwargs) self.num_processes = torch.distributed.get_world_size() self.process_index = torch.distributed.get_rank() self.local_process_index = int(os.environ.get("LOCAL_RANK", -1)) if self.device is None: self.device = torch.device("npu", self.local_process_index) torch.npu.set_device(self.device) elif get_int_from_env(["PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE", "WORLD_SIZE"], 1) > 1: if not cpu and is_xpu_available(): self.distributed_type = DistributedType.MULTI_XPU else: self.distributed_type = DistributedType.MULTI_CPU # Actually, CCL_WORKER_COUNT is a CPU only env var in CCL, no need to set it for XPU. if is_ccl_available() and ( get_int_from_env(["CCL_WORKER_COUNT"], 0) > 0 or self.distributed_type == DistributedType.MULTI_XPU ): if get_ccl_version() >= "1.12": import oneccl_bindings_for_pytorch # noqa: F401 else: import torch_ccl # noqa: F401 backend = "ccl" elif torch.distributed.is_mpi_available(): backend = "mpi" else: backend = "gloo" # Try to get launch configuration from environment variables set by MPI launcher - works for Intel MPI, OpenMPI and MVAPICH rank = get_int_from_env(["RANK", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK"], 0) size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE"], 1) local_rank = get_int_from_env( ["LOCAL_RANK", "MPI_LOCALRANKID", "OMPI_COMM_WORLD_LOCAL_RANK", "MV2_COMM_WORLD_LOCAL_RANK"], 0 ) local_size = get_int_from_env( ["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1 ) self.local_process_index = local_rank os.environ["RANK"] = str(rank) os.environ["WORLD_SIZE"] = str(size) os.environ["LOCAL_RANK"] = str(local_rank) if not os.environ.get("MASTER_PORT", None): os.environ["MASTER_PORT"] = "29500" if not os.environ.get("MASTER_ADDR", None): if local_size != size and backend != "mpi": raise ValueError( "Looks like distributed multinode run but MASTER_ADDR env not set, " "please try exporting rank 0's hostname as MASTER_ADDR" ) if ( self.distributed_type == DistributedType.MULTI_CPU and get_int_from_env(["OMP_NUM_THREADS", "MKL_NUM_THREADS"], 0) == 0 ): import psutil num_cpu_threads_per_process = int(psutil.cpu_count(logical=False) / local_size) if num_cpu_threads_per_process == 0: num_cpu_threads_per_process = 1 torch.set_num_threads(num_cpu_threads_per_process) warnings.warn( f"OMP_NUM_THREADS/MKL_NUM_THREADS unset, we set it at {num_cpu_threads_per_process} to improve oob" " performance." ) if not torch.distributed.is_initialized(): # Backend is not set by the user, we set it here kwargs.pop("backend", None) self.backend = backend torch.distributed.init_process_group(self.backend, rank=rank, world_size=size, **kwargs) self.num_processes = torch.distributed.get_world_size() self.process_index = torch.distributed.get_rank() if cpu: self.device = torch.device("cpu") elif is_xpu_available(): self.device = torch.device("xpu", self.local_process_index) torch.xpu.set_device(self.device) else: self.device = self.default_device else: self.distributed_type = ( DistributedType.NO if os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "false" else DistributedType.DEEPSPEED ) self.num_processes = 1 self.process_index = self.local_process_index = 0 if self.device is None: self.device = torch.device("cpu") if cpu else self.default_device self.fork_launched = parse_flag_from_env("FORK_LAUNCHED", 0) def __repr__(self) -> str: return ( f"Distributed environment: {self.distributed_type}{(' Backend: ' + self.backend) if self.backend else ''}\n" f"Num processes: {self.num_processes}\n" f"Process index: {self.process_index}\n" f"Local process index: {self.local_process_index}\n" f"Device: {self.device}\n" ) @staticmethod def _reset_state(): "Resets `_shared_state`, is used internally and should not be called" PartialState._shared_state.clear() @property def initialized(self) -> bool: "Returns whether the `PartialState` has been initialized" return self._shared_state != {} @property def use_distributed(self): """ Whether the Accelerator is configured for distributed training """ return self.distributed_type != DistributedType.NO and self.num_processes > 1 @property def is_last_process(self) -> bool: "Returns whether the current process is the last one" return self.process_index == self.num_processes - 1 @property def is_main_process(self) -> bool: "Returns whether the current process is the main process" return ( self.process_index == 0 if self.distributed_type != DistributedType.MEGATRON_LM else self.is_last_process ) @property def is_local_main_process(self) -> bool: "Returns whether the current process is the main process on the local node" return ( self.local_process_index == 0 if self.distributed_type != DistributedType.MEGATRON_LM else self.is_last_process ) def wait_for_everyone(self): """ Will stop the execution of the current process until every other process has reached that point (so this does nothing when the script is only run in one process). Useful to do before saving a model. Example: ```python >>> # Assuming two GPU processes >>> import time >>> from accelerate.state import PartialState >>> state = PartialState() >>> if state.is_main_process: ... time.sleep(2) >>> else: ... print("I'm waiting for the main process to finish its sleep...") >>> state.wait_for_everyone() >>> # Should print on every process at the same time >>> print("Everyone is here") ``` """ if self.distributed_type in ( DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, DistributedType.MULTI_CPU, DistributedType.DEEPSPEED, DistributedType.FSDP, ): torch.distributed.barrier() elif self.distributed_type == DistributedType.TPU: xm.rendezvous("accelerate.utils.wait_for_everyone") def _goes_first(self, is_main: bool): if not is_main: self.wait_for_everyone() yield if is_main: self.wait_for_everyone() @contextmanager def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False): """ Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing distributed inference, such as with different prompts. Note that when using a `dict`, all keys need to have the same number of elements. Args: inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`): The input to split between processes. apply_padding (`bool`, `optional`, defaults to `False`): Whether to apply padding by repeating the last element of the input so that all processes have the same number of elements. Useful when trying to perform actions such as `gather()` on the outputs or passing in less inputs than there are processes. If so, just remember to drop the padded elements afterwards. Example: ```python # Assume there are two processes from accelerate import PartialState state = PartialState() with state.split_between_processes(["A", "B", "C"]) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C"] with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C", "C"] ``` """ if self.num_processes == 1: yield inputs return length = len(inputs) # Nested dictionary of any types if isinstance(inputs, dict): length = len(inputs[list(inputs.keys())[0]]) if not all(len(v) == length for v in inputs.values()): raise ValueError("All values in the dictionary must have the same length") num_samples_per_process = math.ceil(length / self.num_processes) start_index = self.process_index * num_samples_per_process end_index = start_index + num_samples_per_process if (len(inputs) % self.num_processes != 0) and (self.process_index == self.num_processes - 1): end_index = length def _split_values(inputs, start_index, end_index): if isinstance(inputs, (list, tuple, torch.Tensor)): if start_index >= len(inputs): result = inputs[-1:] else: result = inputs[start_index:end_index] if apply_padding: if isinstance(result, torch.Tensor): from accelerate.utils import pad_across_processes, send_to_device # The tensor needs to be on the device before we can pad it tensorized_result = send_to_device(result, self.device) result = pad_across_processes(tensorized_result, pad_index=inputs[-1]) else: result += [result[-1]] * (num_samples_per_process - len(result)) return result elif isinstance(inputs, dict): for key in inputs.keys(): inputs[key] = _split_values(inputs[key], start_index, end_index) return inputs else: return inputs yield _split_values(inputs, start_index, end_index) @contextmanager def main_process_first(self): """ Lets the main process go first inside a with block. The other processes will enter the with block after the main process exits. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> with accelerator.main_process_first(): ... # This will be printed first by process 0 then in a seemingly ... # random order by the other processes. ... print(f"This will be printed by process {accelerator.process_index}") ``` """ yield from self._goes_first(self.is_main_process) @contextmanager def local_main_process_first(self): """ Lets the local main process go inside a with block. The other processes will enter the with block after the main process exits. Example: ```python >>> from accelerate.state import PartialState >>> state = PartialState() >>> with state.local_main_process_first(): ... # This will be printed first by local process 0 then in a seemingly ... # random order by the other processes. ... print(f"This will be printed by process {state.local_process_index}") ``` """ yield from self._goes_first(self.is_local_main_process) def on_main_process(self, function: Callable[..., Any] = None): """ Decorator that only runs the decorated function on the main process. Args: function (`Callable`): The function to decorate. Example: ```python >>> from accelerate.state import PartialState >>> state = PartialState() >>> @state.on_main_process ... def print_something(): ... print("This will be printed by process 0 only.") >>> print_something() "This will be printed by process 0 only" ``` """ if not self.initialized: raise ValueError("The `PartialState` or `Accelerator` must be initialized before calling this function.") if self.is_main_process or not self.use_distributed: return function return do_nothing def on_local_main_process(self, function: Callable[..., Any] = None): """ Decorator that only runs the decorated function on the local main process. Args: function (`Callable`): The function to decorate. Example: ```python # Assume we have 2 servers with 4 processes each. from accelerate.state import PartialState state = PartialState() @state.on_local_main_process def print_something(): print("This will be printed by process 0 only on each server.") print_something() # On server 1: "This will be printed by process 0 only" # On server 2: "This will be printed by process 0 only" ``` """ if self.is_local_main_process or not self.use_distributed: return function return do_nothing def on_last_process(self, function: Callable[..., Any]): """ Decorator that only runs the decorated function on the last process. Args: function (`Callable`): The function to decorate. Example: ```python # Assume we have 4 processes. from accelerate.state import PartialState state = PartialState() @state.on_last_process def print_something(): print(f"Printed on process {state.process_index}") print_something() "Printed on process 3" ``` """ if self.is_last_process or not self.use_distributed: return function return do_nothing def on_process(self, function: Callable[..., Any] = None, process_index: int = None): """ Decorator that only runs the decorated function on the process with the given index. Args: function (`Callable`, `optional`): The function to decorate. process_index (`int`, `optional`): The index of the process on which to run the function. Example: ```python # Assume we have 4 processes. from accelerate.state import PartialState state = PartialState() @state.on_process(process_index=2) def print_something(): print(f"Printed on process {state.process_index}") print_something() "Printed on process 2" ``` """ if function is None: return partial(self.on_process, process_index=process_index) if (self.process_index == process_index) or (not self.use_distributed): return function return do_nothing def on_local_process(self, function: Callable[..., Any] = None, local_process_index: int = None): """ Decorator that only runs the decorated function on the process with the given index on the current node. Args: function (`Callable`, *optional*): The function to decorate. local_process_index (`int`, *optional*): The index of the local process on which to run the function. Example: ```python # Assume we have 2 servers with 4 processes each. from accelerate import Accelerator accelerator = Accelerator() @accelerator.on_local_process(local_process_index=2) def print_something(): print(f"Printed on process {accelerator.local_process_index}") print_something() # On server 1: "Printed on process 2" # On server 2: "Printed on process 2" ``` """ if function is None: return partial(self.on_local_process, local_process_index=local_process_index) if (self.local_process_index == local_process_index) or (not self.use_distributed): return function return do_nothing def print(self, *args, **kwargs): if self.is_local_main_process: print(*args, **kwargs) @property def default_device(self) -> torch.device: """ Returns the default device which is: - MPS if `torch.backends.mps.is_available()` and `torch.backends.mps.is_built()` both return True. - CUDA if `torch.cuda.is_available()` - NPU if `is_npu_available()` - CPU otherwise """ if is_mps_available(): os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" return torch.device("mps") elif torch.cuda.is_available(): return torch.device("cuda") elif is_xpu_available(): return torch.device("xpu:0") elif is_npu_available(): return torch.device("npu") else: return torch.device("cpu") class AcceleratorState: """ Singleton class that has information about the current training environment. **Available attributes:** - **device** (`torch.device`) -- The device to use. - **distributed_type** ([`~accelerate.state.DistributedType`]) -- The type of distributed environment currently in use. - **initialized** (`bool`) -- Whether or not the `AcceleratorState` has been initialized from `Accelerator`. - **local_process_index** (`int`) -- The index of the current process on the current server. - **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8'). - **num_processes** (`int`) -- The number of processes currently launched in parallel. - **process_index** (`int`) -- The index of the current process. - **is_last_process** (`bool`) -- Whether or not the current process is the last one. - **is_main_process** (`bool`) -- Whether or not the current process is the main one. - **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node. - **debug** (`bool`) -- Whether or not the current script is being run in debug mode. """ _shared_state = SharedDict() def __init__( self, mixed_precision: str = None, cpu: bool = False, dynamo_plugin=None, deepspeed_plugin=None, fsdp_plugin=None, megatron_lm_plugin=None, _from_accelerator: bool = False, **kwargs, ): self.__dict__ = self._shared_state if parse_flag_from_env("ACCELERATE_USE_CPU"): cpu = True if PartialState._shared_state == {}: PartialState(cpu, **kwargs) self.__dict__.update(PartialState._shared_state) self._check_initialized(mixed_precision, cpu) if not self.initialized: self.deepspeed_plugin = None self.use_ipex = None mixed_precision = ( parse_choice_from_env("ACCELERATE_MIXED_PRECISION", "no") if mixed_precision is None else mixed_precision.lower() ) if mixed_precision == "fp8": if not is_fp8_available(): raise ValueError( "Using `fp8` precision requires `transformer_engine` or `MS-AMP` to be installed." ) elif not check_fp8_capability(): logger.warning( f"The current device has compute capability of {torch.cuda.get_device_capability()} which is " "insufficient for FP8 mixed precision training (requires a GPU Hopper/Ada Lovelace " "or higher, compute capability of 8.9 or higher). Will use FP16 instead." ) mixed_precision = "fp16" self.dynamo_plugin = dynamo_plugin if not _from_accelerator: raise ValueError( "Please make sure to properly initialize your accelerator via `accelerator = Accelerator()` " "before using any functionality from the `accelerate` library." ) # deepspeed handles mixed_precision using deepspeed_config self._mixed_precision = "no" if self.distributed_type == DistributedType.DEEPSPEED else mixed_precision if self.distributed_type == DistributedType.TPU: if mixed_precision == "bf16": if os.environ.get("ACCELERATE_DOWNCAST_BF16"): os.environ["XLA_USE_BF16"] = str(0) os.environ["XLA_DOWNCAST_BF16"] = str(1) self.downcast_bfloat = True else: os.environ["XLA_USE_BF16"] = str(1) os.environ["XLA_DOWNCAST_BF16"] = str(0) self.downcast_bfloat = False elif os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" and not cpu: self.deepspeed_plugin = deepspeed_plugin elif self.distributed_type == DistributedType.MULTI_GPU: if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true": self.distributed_type = DistributedType.FSDP if self._mixed_precision != "no": fsdp_plugin.set_mixed_precision(self._mixed_precision) self.fsdp_plugin = fsdp_plugin if os.environ.get("ACCELERATE_USE_MEGATRON_LM", "false") == "true": self.distributed_type = DistributedType.MEGATRON_LM megatron_lm_plugin.set_mixed_precision(self._mixed_precision) self.megatron_lm_plugin = megatron_lm_plugin elif self.distributed_type == DistributedType.MULTI_NPU: if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true": self.distributed_type = DistributedType.FSDP if self._mixed_precision != "no": fsdp_plugin.set_mixed_precision(self._mixed_precision) self.fsdp_plugin = fsdp_plugin elif self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]: if is_ipex_available(): "check if user disables it explicitly" self.use_ipex = parse_flag_from_env("ACCELERATE_USE_IPEX", default=True) else: self.use_ipex = False if self.distributed_type == DistributedType.MULTI_XPU: if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true": self.distributed_type = DistributedType.FSDP if self._mixed_precision != "no": fsdp_plugin.set_mixed_precision(self._mixed_precision) self.fsdp_plugin = fsdp_plugin if ( self.dynamo_plugin.backend != DynamoBackend.NO and self._mixed_precision == "no" and self.device.type == "cuda" ): torch.backends.cuda.matmul.allow_tf32 = True PartialState._shared_state["distributed_type"] = self.distributed_type @property def initialized(self) -> bool: return self._shared_state != PartialState._shared_state def __repr__(self): repr = PartialState().__repr__() + f"\nMixed precision type: {self.mixed_precision}\n" if self.distributed_type == DistributedType.DEEPSPEED: repr += f"ds_config: {self.deepspeed_plugin.deepspeed_config}\n" return repr def _check_initialized(self, mixed_precision=None, cpu=None): "Checks if a modification is trying to be made and the `AcceleratorState` has already been initialized" if self.initialized: err = "AcceleratorState has already been initialized and cannot be changed, restart your runtime completely and pass `{flag}` to `Accelerator()`." if cpu and self.device.type != "cpu": raise ValueError(err.format(flag="cpu=True")) if ( mixed_precision is not None and mixed_precision != self._mixed_precision and self.distributed_type != DistributedType.DEEPSPEED ): raise ValueError(err.format(flag=f"mixed_precision='{mixed_precision}'")) # For backward compatibility @property def use_fp16(self): warnings.warn( "The `use_fp16` property is deprecated and will be removed in version 1.0 of Accelerate use " "`AcceleratorState.mixed_precision == 'fp16'` instead.", FutureWarning, ) return self._mixed_precision != "no" @property def mixed_precision(self): if self.distributed_type == DistributedType.DEEPSPEED: config = self.deepspeed_plugin.deepspeed_config if config.get("fp16", {}).get("enabled", False): mixed_precision = "fp16" elif config.get("bf16", {}).get("enabled", False): mixed_precision = "bf16" else: mixed_precision = "no" else: mixed_precision = self._mixed_precision return mixed_precision @staticmethod def _reset_state(reset_partial_state: bool = False): "Resets `_shared_state`, is used internally and should not be called" AcceleratorState._shared_state.clear() if reset_partial_state: PartialState._reset_state() @property def use_distributed(self): """ Whether the Accelerator is configured for distributed training """ return PartialState().use_distributed @property def is_last_process(self) -> bool: "Returns whether the current process is the last one" return PartialState().is_last_process @property def is_main_process(self) -> bool: "Returns whether the current process is the main process" return PartialState().is_main_process @property def is_local_main_process(self) -> bool: "Returns whether the current process is the main process on the local node" return PartialState().is_local_main_process def wait_for_everyone(self): PartialState().wait_for_everyone() @contextmanager def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False): """ Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing distributed inference, such as with different prompts. Note that when using a `dict`, all keys need to have the same number of elements. Args: inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`): The input to split between processes. apply_padding (`bool`, `optional`, defaults to `False`): Whether to apply padding by repeating the last element of the input so that all processes have the same number of elements. Useful when trying to perform actions such as `gather()` on the outputs or passing in less inputs than there are processes. If so, just remember to drop the padded elements afterwards. Example: ```python # Assume there are two processes from accelerate.state import AcceleratorState state = AcceleratorState() with state.split_between_processes(["A", "B", "C"]) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C"] with state.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C", "C"] ``` """ with PartialState().split_between_processes(inputs, apply_padding=apply_padding) as inputs: yield inputs @contextmanager def main_process_first(self): """ Lets the main process go first inside a with block. The other processes will enter the with block after the main process exits. """ with PartialState().main_process_first(): yield @contextmanager def local_main_process_first(self): """ Lets the local main process go inside a with block. The other processes will enter the with block after the main process exits. """ with PartialState().local_main_process_first(): yield def print(self, *args, **kwargs): PartialState().print(*args, **kwargs) class GradientState: """ Singleton class that has information related to gradient synchronization for gradient accumulation **Available attributes:** - **end_of_dataloader** (`bool`) -- Whether we have reached the end the current dataloader - **remainder** (`int`) -- The number of extra samples that were added from padding the dataloader - **sync_gradients** (`bool`) -- Whether the gradients should be synced across all devices - **active_dataloader** (`Optional[DataLoader]`) -- The dataloader that is currently being iterated over - **dataloader_references** (`List[Optional[DataLoader]]`) -- A list of references to the dataloaders that are being iterated over - **num_steps** (`int`) -- The number of steps to accumulate over - **adjust_scheduler** (`bool`) -- Whether the scheduler should be adjusted to account for the gradient accumulation - **sync_with_dataloader** (`bool`) -- Whether the gradients should be synced at the end of the dataloader iteration and the number of total steps reset """ _shared_state = SharedDict() def __init__(self, gradient_accumulation_plugin: Optional[GradientAccumulationPlugin] = None): self.__dict__ = self._shared_state if not self.initialized: self.sync_gradients = True self.active_dataloader = None self.dataloader_references = [None] self.plugin_kwargs = ( gradient_accumulation_plugin.to_kwargs() if gradient_accumulation_plugin is not None else {} ) # Plugin args are different and can be updated if gradient_accumulation_plugin is not None and self.plugin_kwargs != gradient_accumulation_plugin.to_kwargs(): self.plugin_kwargs = gradient_accumulation_plugin.to_kwargs() @property def num_steps(self) -> int: "Returns the number of steps to accumulate over" return self.plugin_kwargs.get("num_steps", 1) @property def adjust_scheduler(self) -> bool: "Returns whether the scheduler should be adjusted" return self.plugin_kwargs.get("adjust_scheduler", False) @property def sync_with_dataloader(self) -> bool: "Returns whether the gradients should be synced at the end of the dataloader iteration and the number of total steps reset" return self.plugin_kwargs.get("sync_with_dataloader", True) @property def initialized(self) -> bool: "Returns whether the `GradientState` has been initialized" return GradientState._shared_state != {} @property def end_of_dataloader(self) -> bool: "Returns whether we have reached the end of the current dataloader" if not self.in_dataloader: return False return self.active_dataloader.end_of_dataloader @property def remainder(self) -> int: "Returns the number of extra samples that were added from padding the dataloader" if not self.in_dataloader: return -1 return self.active_dataloader.remainder def __repr__(self): return ( f"Sync Gradients: {self.sync_gradients}\n" f"At end of current dataloader: {self.end_of_dataloader}\n" f"Extra samples added: {self.remainder}\n" f"Gradient accumulation plugin: {self.plugin_kwargs}\n" ) def _set_sync_gradients(self, sync_gradients): "Private function that sets whether gradients should be synchronized. Users should not have to call this." self.sync_gradients = sync_gradients def _add_dataloader(self, dataloader): "Private function that adds a dataloader to `self.dataloader_references` and sets `in_dataloader` to `True`. Users should not have to call this." self.active_dataloader = dataloader self.dataloader_references.append(self.active_dataloader) def _remove_dataloader(self, dataloader): "Private function that removes a dataloader from `self.dataloader_references` and sets `in_dataloader` to `False` if there are no more dataloaders. Users should not have to call this." self.dataloader_references.remove(dataloader) self.active_dataloader = self.dataloader_references[-1] @property def in_dataloader(self) -> bool: "Returns whether the current process is in a dataloader" return self.active_dataloader is not None @staticmethod def _reset_state(): "Resets `_shared_state`, is used internally and should not be called" GradientState._shared_state.clear()

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/accelerator.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import contextlib import functools import json import math import os import re import shutil import sys import warnings from collections import OrderedDict from contextlib import contextmanager from functools import partial from types import MethodType from typing import Any, Callable, Union import torch import torch.utils.hooks as hooks from .checkpointing import load_accelerator_state, load_custom_state, save_accelerator_state, save_custom_state from .data_loader import DataLoaderDispatcher, prepare_data_loader, skip_first_batches from .hooks import AlignDevicesHook from .logging import get_logger from .optimizer import AcceleratedOptimizer from .scheduler import AcceleratedScheduler from .state import AcceleratorState, GradientState, PartialState from .tracking import LOGGER_TYPE_TO_CLASS, GeneralTracker, filter_trackers from .utils import ( MODEL_NAME, SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, AutocastKwargs, DeepSpeedPlugin, DistributedDataParallelKwargs, DistributedType, DynamoBackend, FP8RecipeKwargs, FullyShardedDataParallelPlugin, GradientAccumulationPlugin, GradScalerKwargs, InitProcessGroupKwargs, KwargsHandler, LoggerType, MegatronLMPlugin, PrecisionType, ProjectConfiguration, RNGType, TorchDynamoPlugin, check_os_kernel, clean_state_dict_for_safetensors, compare_versions, convert_model, convert_outputs_to_fp32, extract_model_from_parallel, gather, gather_object, get_mixed_precision_context_manager, get_pretty_name, has_transformer_engine_layers, is_bf16_available, is_deepspeed_available, is_fp8_available, is_ipex_available, is_megatron_lm_available, is_msamp_available, is_npu_available, is_torch_version, is_tpu_available, is_xpu_available, load_fsdp_model, load_fsdp_optimizer, pad_across_processes, parse_choice_from_env, recursively_apply, reduce, release_memory, save, save_fsdp_model, save_fsdp_optimizer, shard_checkpoint, wait_for_everyone, ) from .utils.constants import FSDP_PYTORCH_VERSION from .utils.modeling import get_state_dict_offloaded_model from .utils.other import is_compiled_module if is_deepspeed_available(): from .utils import ( DeepSpeedEngineWrapper, DeepSpeedOptimizerWrapper, DeepSpeedSchedulerWrapper, DummyOptim, DummyScheduler, ) if is_fp8_available(): import transformer_engine.common.recipe as te_recipe from transformer_engine.pytorch import fp8_autocast if is_megatron_lm_available(): from .utils import ( MegatronEngine, MegatronLMDummyDataLoader, MegatronLMDummyScheduler, MegatronLMOptimizerWrapper, MegatronLMSchedulerWrapper, megatron_lm_initialize, megatron_lm_prepare_data_loader, megatron_lm_prepare_model, megatron_lm_prepare_optimizer, megatron_lm_prepare_scheduler, ) from torch.distributed.algorithms.join import Join if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm import torch_xla.distributed.xla_multiprocessing as xmp if is_npu_available(check_device=False): import torch_npu # noqa: F401 try: from torch.optim.lr_scheduler import LRScheduler except ImportError: from torch.optim.lr_scheduler import _LRScheduler as LRScheduler logger = get_logger(__name__) class Accelerator: """ Creates an instance of an accelerator for distributed training (on multi-GPU, TPU) or mixed precision training. Args: device_placement (`bool`, *optional*, defaults to `True`): Whether or not the accelerator should put objects on device (tensors yielded by the dataloader, model, etc...). split_batches (`bool`, *optional*, defaults to `False`): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If `True` the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set in your script multiplied by the number of processes. mixed_precision (`str`, *optional*): Whether or not to use mixed precision training. Choose from 'no','fp16','bf16 or 'fp8'. Will default to the value in the environment variable `ACCELERATE_MIXED_PRECISION`, which will use the default value in the accelerate config of the current system or the flag passed with the `accelerate.launch` command. 'fp8' requires the installation of transformers-engine. gradient_accumulation_steps (`int`, *optional*, default to 1): The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with `Accelerator.accumulate`. If not passed, will default to the value in the environment variable `ACCELERATE_GRADIENT_ACCUMULATION_STEPS`. Can also be configured through a `GradientAccumulationPlugin`. cpu (`bool`, *optional*): Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force the execution on one process only. deepspeed_plugin (`DeepSpeedPlugin`, *optional*): Tweak your DeepSpeed related args using this argument. This argument is optional and can be configured directly using *accelerate config* fsdp_plugin (`FullyShardedDataParallelPlugin`, *optional*): Tweak your FSDP related args using this argument. This argument is optional and can be configured directly using *accelerate config* megatron_lm_plugin (`MegatronLMPlugin`, *optional*): Tweak your MegatronLM related args using this argument. This argument is optional and can be configured directly using *accelerate config* rng_types (list of `str` or [`~utils.RNGType`]): The list of random number generators to synchronize at the beginning of each iteration in your prepared dataloaders. Should be one or several of: - `"torch"`: the base torch random number generator - `"cuda"`: the CUDA random number generator (GPU only) - `"xla"`: the XLA random number generator (TPU only) - `"generator"`: the `torch.Generator` of the sampler (or batch sampler if there is no sampler in your dataloader) or of the iterable dataset (if it exists) if the underlying dataset is of that type. Will default to `["torch"]` for PyTorch versions <=1.5.1 and `["generator"]` for PyTorch versions >= 1.6. log_with (list of `str`, [`~utils.LoggerType`] or [`~tracking.GeneralTracker`], *optional*): A list of loggers to be setup for experiment tracking. Should be one or several of: - `"all"` - `"tensorboard"` - `"wandb"` - `"comet_ml"` If `"all"` is selected, will pick up all available trackers in the environment and initialize them. Can also accept implementations of `GeneralTracker` for custom trackers, and can be combined with `"all"`. project_config (`ProjectConfiguration`, *optional*): A configuration for how saving the state can be handled. project_dir (`str`, `os.PathLike`, *optional*): A path to a directory for storing data such as logs of locally-compatible loggers and potentially saved checkpoints. dispatch_batches (`bool`, *optional*): If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose underlying dataset is an `IterableDataset`, `False` otherwise. even_batches (`bool`, *optional*, defaults to `True`): If set to `True`, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers. use_seedable_sampler (`bool`, *optional*, defaults to `False`): Whether or not use a fully seedable random sampler ([`~data_loader.SeedableRandomSampler`]). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with [`~utils.set_seed`] for the best results. step_scheduler_with_optimizer (`bool`, *optional`, defaults to `True`): Set `True` if the learning rate scheduler is stepped at the same time as the optimizer, `False` if only done under certain circumstances (at the end of each epoch, for instance). kwargs_handlers (`list[KwargHandler]`, *optional*) A list of `KwargHandler` to customize how the objects related to distributed training or mixed precision are created. See [kwargs](kwargs) for more information. dynamo_backend (`str` or `DynamoBackend`, *optional*, defaults to `"no"`): Set to one of the possible dynamo backends to optimize your training with torch dynamo. gradient_accumulation_plugin (`GradientAccumulationPlugin`, *optional*): A configuration for how gradient accumulation should be handled, if more tweaking than just the `gradient_accumulation_steps` is needed. **Available attributes:** - **device** (`torch.device`) -- The device to use. - **distributed_type** ([`~utils.DistributedType`]) -- The distributed training configuration. - **local_process_index** (`int`) -- The process index on the current machine. - **mixed_precision** (`str`) -- The configured mixed precision mode. - **num_processes** (`int`) -- The total number of processes used for training. - **optimizer_step_was_skipped** (`bool`) -- Whether or not the optimizer update was skipped (because of gradient overflow in mixed precision), in which case the learning rate should not be changed. - **process_index** (`int`) -- The overall index of the current process among all processes. - **state** ([`~state.AcceleratorState`]) -- The distributed setup state. - **sync_gradients** (`bool`) -- Whether the gradients are currently being synced across all processes. - **use_distributed** (`bool`) -- Whether the current configuration is for distributed training. """ def __init__( self, device_placement: bool = True, split_batches: bool = False, mixed_precision: PrecisionType | str | None = None, gradient_accumulation_steps: int = 1, cpu: bool = False, deepspeed_plugin: DeepSpeedPlugin | None = None, fsdp_plugin: FullyShardedDataParallelPlugin | None = None, megatron_lm_plugin: MegatronLMPlugin | None = None, rng_types: list[str | RNGType] | None = None, log_with: str | LoggerType | GeneralTracker | list[str | LoggerType | GeneralTracker] | None = None, project_dir: str | os.PathLike | None = None, project_config: ProjectConfiguration | None = None, gradient_accumulation_plugin: GradientAccumulationPlugin | None = None, dispatch_batches: bool | None = None, even_batches: bool = True, use_seedable_sampler: bool = False, step_scheduler_with_optimizer: bool = True, kwargs_handlers: list[KwargsHandler] | None = None, dynamo_backend: DynamoBackend | str | None = None, ): self.trackers = [] if project_config is not None: self.project_configuration = project_config else: self.project_configuration = ProjectConfiguration(project_dir=project_dir) if project_dir is not None and self.project_dir is None: self.project_configuration.set_directories(project_dir) if mixed_precision is not None: mixed_precision = str(mixed_precision) if mixed_precision not in PrecisionType: raise ValueError( f"Unknown mixed_precision mode: {mixed_precision}. Choose between {PrecisionType.list()}" ) dynamo_plugin = TorchDynamoPlugin() if dynamo_backend is None else TorchDynamoPlugin(backend=dynamo_backend) if deepspeed_plugin is None: # init from env variables deepspeed_plugin = ( DeepSpeedPlugin() if os.environ.get("ACCELERATE_USE_DEEPSPEED", "false") == "true" else None ) else: assert isinstance( deepspeed_plugin, DeepSpeedPlugin ), "`deepspeed_plugin` must be an `accelerate.utils.DeepSpeedPlugin` object." os.environ["ACCELERATE_USE_DEEPSPEED"] = "true" # use DeepSpeed if plugin is provided if deepspeed_plugin: if not is_deepspeed_available(): raise ImportError("DeepSpeed is not installed => run `pip install deepspeed` or build it from source.") if compare_versions("deepspeed", "<", "0.9.3"): raise ImportError("DeepSpeed version must be >= 0.9.3. Please update DeepSpeed.") mixed_precision = ( os.environ.get("ACCELERATE_MIXED_PRECISION", "no") if mixed_precision is None else mixed_precision ) deepspeed_plugin.set_mixed_precision(mixed_precision) deepspeed_plugin.set_deepspeed_weakref() if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true" or isinstance( fsdp_plugin, FullyShardedDataParallelPlugin ): if is_torch_version("<", FSDP_PYTORCH_VERSION): raise ValueError(f"FSDP requires PyTorch >= {FSDP_PYTORCH_VERSION}") if fsdp_plugin is None: # init from env variables fsdp_plugin = ( FullyShardedDataParallelPlugin() if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true" else None ) else: if not isinstance(fsdp_plugin, FullyShardedDataParallelPlugin): raise TypeError("`fsdp_plugin` must be a FullyShardedDataParallelPlugin object.") os.environ["ACCELERATE_USE_FSDP"] = "true" # use FSDP if plugin is provided if megatron_lm_plugin is None: # init from env variables megatron_lm_plugin = ( MegatronLMPlugin() if os.environ.get("ACCELERATE_USE_MEGATRON_LM", "false") == "true" else None ) else: if not isinstance(megatron_lm_plugin, MegatronLMPlugin): raise TypeError("`megatron_lm_plugin` must be a MegatronLMPlugin object.") os.environ["ACCELERATE_USE_MEGATRON_LM"] = "true" # use MegatronLM if plugin is provided if megatron_lm_plugin: if not is_megatron_lm_available(): raise ImportError("Megatron is not installed. please build it from source.") # Kwargs handlers self.ddp_handler = None self.scaler_handler = None self.init_handler = None self.fp8_recipe_handler = None self.autocast_handler = None if kwargs_handlers is not None: for handler in kwargs_handlers: assert isinstance( handler, KwargsHandler ), f"Unsupported kwargs handler passed: {handler}, must be one that inherits `accelerate.utils.KwargsHandler`." if isinstance(handler, DistributedDataParallelKwargs): if self.ddp_handler is not None: raise ValueError("You can only pass one `DistributedDataParallelKwargs` in `kwargs_handler`.") else: self.ddp_handler = handler elif isinstance(handler, GradScalerKwargs): if self.scaler_handler is not None: raise ValueError("You can only pass one `GradScalerKwargs` in `kwargs_handler`.") else: self.scaler_handler = handler elif isinstance(handler, InitProcessGroupKwargs): if self.init_handler is not None: raise ValueError("You can only pass one `InitProcessGroupKwargs` in `kwargs_handler`.") else: self.init_handler = handler elif isinstance(handler, FP8RecipeKwargs): if self.fp8_recipe_handler is not None: raise ValueError("You can only pass one `FP8RecipeKwargs` in `kwargs_handler`.") else: self.fp8_recipe_handler = handler elif isinstance(handler, AutocastKwargs): if self.autocast_handler is not None: raise ValueError("You can only pass one `AutocastKwargs` in `kwargs_handler`.") else: self.autocast_handler = handler if self.fp8_recipe_handler is None and mixed_precision == "fp8": self.fp8_recipe_handler = FP8RecipeKwargs() kwargs = self.init_handler.to_kwargs() if self.init_handler is not None else {} self.state = AcceleratorState( mixed_precision=mixed_precision, cpu=cpu, dynamo_plugin=dynamo_plugin, deepspeed_plugin=deepspeed_plugin, fsdp_plugin=fsdp_plugin, megatron_lm_plugin=megatron_lm_plugin, _from_accelerator=True, **kwargs, ) trackers = filter_trackers(log_with, self.logging_dir) if len(trackers) < 1 and log_with is not None: warnings.warn(f"`log_with={log_with}` was passed but no supported trackers are currently installed.") self.log_with = trackers if ( (mixed_precision != "bf16") and getattr(self.state, "downcast_bfloat", False) and (self.state.distributedType != DistributedType.TPU) ): raise ValueError("Can only use `downcast_bf16` when using `mixed_precision='bf16'` and on a TPU") if gradient_accumulation_plugin is not None: if gradient_accumulation_steps != 1: raise ValueError( "You can only pass one of `gradient_accumulation_steps` and `gradient_accumulation_plugin`. Please only pass in the created `GradientAccumulationPlugin` object." ) else: gradient_accumulation_steps = int( parse_choice_from_env("ACCELERATE_GRADIENT_ACCUMULATION_STEPS", gradient_accumulation_steps) ) gradient_accumulation_plugin = GradientAccumulationPlugin(num_steps=gradient_accumulation_steps) self.gradient_state = GradientState( gradient_accumulation_plugin=gradient_accumulation_plugin, ) if self.state.distributed_type == DistributedType.TPU: if self.gradient_state.num_steps != 1: raise ValueError( "Gradient accumulation is not supported on TPU. Please set `gradient_accumulation_steps` to 1 and don't pass in a `GradientAccumulationPlugin` object." ) self.device_placement = device_placement self.split_batches = split_batches self.dispatch_batches = dispatch_batches self.even_batches = even_batches self.use_seedable_sampler = use_seedable_sampler self.step_scheduler_with_optimizer = step_scheduler_with_optimizer # Mixed precision attributes self.scaler = None self.native_amp = False err = "{mode} mixed precision requires {requirement}" if ( self.state.mixed_precision == "fp16" and self.device.type != "cpu" and self.distributed_type not in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM) ): self.native_amp = True if self.device.type not in ("xpu", "cuda", "mps", "npu"): raise ValueError(err.format(mode="fp16", requirement="a GPU")) kwargs = self.scaler_handler.to_kwargs() if self.scaler_handler is not None else {} if self.distributed_type == DistributedType.FSDP: from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler self.scaler = ShardedGradScaler(**kwargs) elif is_npu_available(): self.scaler = torch.npu.amp.GradScaler(**kwargs) else: self.scaler = torch.cuda.amp.GradScaler(**kwargs) elif self.state.mixed_precision == "bf16" and self.distributed_type not in ( DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM, ): if self.device.type in ["cpu", "xpu"]: self.native_amp = True else: self.native_amp = is_bf16_available(True) if mixed_precision == "bf16" and not self.native_amp and not is_tpu_available(): raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device.")) # Start of internal step tracking self.step = 0 # Internal references to the training objects self._optimizers = [] self._models = [] self._schedulers = [] self._dataloaders = [] self._custom_objects = [] # Hooks self._load_model_state_pre_hook = OrderedDict() self._save_model_state_pre_hook = OrderedDict() # RNG Types self.rng_types = rng_types if self.rng_types is None: self.rng_types = ["generator"] # Set a flag tensor for early stopping and other breakpoints self.flag_tensor = None check_os_kernel() @property def use_distributed(self): """ Whether the Accelerator is configured for distributed training """ return self.state.use_distributed @property def distributed_type(self): return self.state.distributed_type @property def num_processes(self): return self.state.num_processes @property def process_index(self): return self.state.process_index @property def local_process_index(self): return self.state.local_process_index @property def device(self): return self.state.device @property def project_dir(self): return self.project_configuration.project_dir @property def logging_dir(self): return self.project_configuration.logging_dir @property def save_iteration(self): return self.project_configuration.iteration @property def is_main_process(self): """True for one process only.""" return self.state.is_main_process @property def is_local_main_process(self): """True for one process per server.""" return self.state.is_local_main_process @property def use_fp16(self): warnings.warn( "The `use_fp16` property is deprecated and will be removed in version 1.0 of Accelerate use " "`Accelerator.mixed_precision == 'fp16'` instead.", FutureWarning, ) return self.mixed_precision != "no" @property def is_last_process(self): return self.process_index == self.num_processes - 1 @property def mixed_precision(self): return self.state.mixed_precision @contextmanager def split_between_processes(self, inputs: list | tuple | dict | torch.Tensor, apply_padding: bool = False): """ Splits `input` between `self.num_processes` quickly and can be then used on that process. Useful when doing distributed inference, such as with different prompts. Note that when using a `dict`, all keys need to have the same number of elements. Args: inputs (`list`, `tuple`, `torch.Tensor`, or `dict` of `list`/`tuple`/`torch.Tensor`): The input to split between processes. apply_padding (`bool`, `optional`, defaults to `False`): Whether to apply padding by repeating the last element of the input so that all processes have the same number of elements. Useful when trying to perform actions such as `Accelerator.gather()` on the outputs or passing in less inputs than there are processes. If so, just remember to drop the padded elements afterwards. Example: ```python # Assume there are two processes from accelerate import Accelerator accelerator = Accelerator() with accelerator.split_between_processes(["A", "B", "C"]) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C"] with accelerator.split_between_processes(["A", "B", "C"], apply_padding=True) as inputs: print(inputs) # Process 0 ["A", "B"] # Process 1 ["C", "C"] ``` """ with PartialState().split_between_processes(inputs, apply_padding=apply_padding) as inputs: yield inputs def on_main_process(self, function: Callable[..., Any] = None): """ A decorator that will run the decorated function on the main process only. Can also be called using the `PartialState` class. Args: function (`Callable`): The function to decorate. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> @accelerator.on_main_process ... def print_something(): ... print("This will be printed by process 0 only.") >>> print_something() "This will be printed by process 0 only" ``` """ # For times when the `Accelerator` object itself utilizes this decorator. if function is None: if "Accelerator." in self.__qualname__: function = self else: raise ValueError( "The `on_main_process` decorator must be called with a function on an instantiated `Accelerator` object." ) def _inner(*args, **kwargs): return PartialState().on_main_process(function)(*args, **kwargs) return _inner def on_local_main_process(self, function: Callable[..., Any] = None): """ A decorator that will run the decorated function on the local main process only. Can also be called using the `PartialState` class. Args: function (`Callable`): The function to decorate. Example: ```python # Assume we have 2 servers with 4 processes each. from accelerate import Accelerator accelerator = Accelerator() @accelerator.on_local_main_process def print_something(): print("This will be printed by process 0 only on each server.") print_something() # On server 1: "This will be printed by process 0 only" # On server 2: "This will be printed by process 0 only" ``` """ # For times when the `Accelerator` object itself utilizes this decorator. if function is None: if "Accelerator." in self.__qualname__: function = self else: raise ValueError( "The `on_local_main_process` decorator must be called with a function on an instantiated `Accelerator` object." ) def _inner(*args, **kwargs): return PartialState().on_local_main_process(function)(*args, **kwargs) return _inner def on_last_process(self, function: Callable[..., Any]): """ A decorator that will run the decorated function on the last process only. Can also be called using the `PartialState` class. Args: function (`Callable`): The function to decorate. Example: ```python # Assume we have 4 processes. from accelerate import Accelerator accelerator = Accelerator() @accelerator.on_last_process def print_something(): print(f"Printed on process {accelerator.process_index}") print_something() "Printed on process 3" ``` """ # For times when the `Accelerator` object itself utilizes this decorator. if function is None: if "Accelerator." in self.__qualname__: function = self else: raise ValueError( "The `on_last_process` decorator must be called with a function on an instantiated `Accelerator` object." ) def _inner(*args, **kwargs): return PartialState().on_last_process(function)(*args, **kwargs) return _inner def on_process(self, function: Callable[..., Any] = None, process_index: int = None): """ A decorator that will run the decorated function on a given process index only. Can also be called using the `PartialState` class. Args: function (`Callable`, `optional`): The function to decorate. process_index (`int`, `optional`): The index of the process on which to run the function. Example: ```python # Assume we have 4 processes. from accelerate import Accelerator accelerator = Accelerator() @accelerator.on_process(process_index=2) def print_something(): print(f"Printed on process {accelerator.process_index}") print_something() "Printed on process 2" ``` """ # Initial construction of the decorator. if (self is not None) and (process_index is not None) and (function is None): return partial(self.on_process, process_index=process_index) # For times when the `Accelerator` object itself utilizes this decorator. if function is None: if "Accelerator." in self.__qualname__: function = self else: raise ValueError( "The `on_main_process` decorator must be called with a function on an instantiated `Accelerator` object." ) def _inner(*args, **kwargs): return PartialState().on_process(function, process_index)(*args, **kwargs) return _inner def on_local_process(self, function: Callable[..., Any] = None, local_process_index: int = None): """ A decorator that will run the decorated function on a given local process index only. Can also be called using the `PartialState` class. Args: function (`Callable`, *optional*): The function to decorate. local_process_index (`int`, *optional*): The index of the local process on which to run the function. Example: ```python # Assume we have 2 servers with 4 processes each. from accelerate import Accelerator accelerator = Accelerator() @accelerator.on_local_process(local_process_index=2) def print_something(): print(f"Printed on process {accelerator.local_process_index}") print_something() # On server 1: "Printed on process 2" # On server 2: "Printed on process 2" ``` """ # Initial construction of the decorator. if (self is not None) and (local_process_index is not None) and (function is None): return partial(self.on_local_process, local_process_index=local_process_index) # For times when the `Accelerator` object itself utilizes this decorator. if function is None: if "Accelerator." in self.__qualname__: function = self else: raise ValueError( "The `on_main_process` decorator must be called with a function on an instantiated `Accelerator` object." ) def _inner(*args, **kwargs): return PartialState().on_local_process(function, local_process_index)(*args, **kwargs) return _inner @contextmanager def main_process_first(self): """ Lets the main process go first inside a with block. The other processes will enter the with block after the main process exits. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> with accelerator.main_process_first(): ... # This will be printed first by process 0 then in a seemingly ... # random order by the other processes. ... print(f"This will be printed by process {accelerator.process_index}") ``` """ with self.state.main_process_first(): yield @contextmanager def local_main_process_first(self): """ Lets the local main process go inside a with block. The other processes will enter the with block after the main process exits. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> with accelerator.local_main_process_first(): ... # This will be printed first by local process 0 then in a seemingly ... # random order by the other processes. ... print(f"This will be printed by process {accelerator.local_process_index}") ``` """ with self.state.local_main_process_first(): yield @contextmanager def no_sync(self, model): """ A context manager to disable gradient synchronizations across DDP processes by calling `torch.nn.parallel.DistributedDataParallel.no_sync`. If `model` is not in DDP, this context manager does nothing Args: model (`torch.nn.Module`): PyTorch Module that was prepared with `Accelerator.prepare` Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer) >>> input_a = next(iter(dataloader)) >>> input_b = next(iter(dataloader)) >>> with accelerator.no_sync(): ... outputs = model(input_a) ... loss = loss_func(outputs) ... accelerator.backward(loss) ... # No synchronization across processes, only accumulate gradients >>> outputs = model(input_b) >>> accelerator.backward(loss) >>> # Synchronization across all processes >>> optimizer.step() >>> optimizer.zero_grad() ``` """ context = contextlib.nullcontext if self.use_distributed: context = getattr(model, "no_sync", context) with context(): yield @staticmethod @contextmanager def trigger_sync_in_backward(model): """Trigger the sync of the gradients in the next backward pass of the model after multiple forward passes under `Accelerator.no_sync` (only applicable in multi-GPU scenarios). If the script is not launched in distributed mode, this context manager does nothing. Args: model (`torch.nn.Module`): The model for which to trigger the gradient synchronization. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer) >>> with accelerator.no_sync(): ... loss_a = loss_func(model(input_a)) # first forward pass ... loss_b = loss_func(model(input_b)) # second forward pass >>> accelerator.backward(loss_a) # No synchronization across processes, only accumulate gradients >>> with accelerator.trigger_sync_in_backward(model): ... accelerator.backward(loss_b) # Synchronization across all processes >>> optimizer.step() >>> optimizer.zero_grad() ``` """ if not isinstance(model, torch.nn.parallel.DistributedDataParallel): yield return old_require_backward_grad_sync = model.require_backward_grad_sync old_require_forward_param_sync = model.require_forward_param_sync # EXPERIMENTAL: This will force grad sync during `backward()`, but it is unknown if it breaks other DDP features. # https://github.com/pytorch/pytorch/blob/e1502c0cdbfd17548c612f25d5a65b1e4b86224d/torch/nn/parallel/distributed.py#L1453-L1466 model.require_backward_grad_sync = True model.require_forward_param_sync = True # https://github.com/pytorch/pytorch/blob/e1502c0cdbfd17548c612f25d5a65b1e4b86224d/torch/csrc/distributed/c10d/reducer.cpp#L1371-L1402 model.reducer.prepare_for_backward([]) try: yield finally: model.require_backward_grad_sync = old_require_backward_grad_sync model.require_forward_param_sync = old_require_forward_param_sync def _do_sync(self): "Sets the right `sync_gradients` context and either resets or increases `self.step`" if self.gradient_state.sync_with_dataloader and self.gradient_state.end_of_dataloader: self.step = 0 self.gradient_state._set_sync_gradients(True) else: self.step += 1 self.gradient_state._set_sync_gradients((self.step % self.gradient_state.num_steps) == 0) @property def sync_gradients(self): return self.gradient_state.sync_gradients @sync_gradients.setter def sync_gradients(self, sync_gradients): self.gradient_state.sync_gradients = sync_gradients @property def gradient_accumulation_steps(self): return self.gradient_state.num_steps @gradient_accumulation_steps.setter def gradient_accumulation_steps(self, gradient_accumulation_steps): self.gradient_state.plugin_kwargs.update({"num_steps": gradient_accumulation_steps}) @contextmanager def accumulate(self, *models): """ A context manager that will lightly wrap around and perform gradient accumulation automatically Args: *models (list of `torch.nn.Module`): PyTorch Modules that were prepared with `Accelerator.prepare`. Models passed to `accumulate()` will skip gradient syncing during backward pass in distributed training Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(gradient_accumulation_steps=1) >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler) >>> for input, output in dataloader: ... with accelerator.accumulate(model): ... outputs = model(input) ... loss = loss_func(outputs) ... loss.backward() ... optimizer.step() ... scheduler.step() ... optimizer.zero_grad() ``` """ self._do_sync() with contextlib.ExitStack() as cm_stack: for m in models: cm_stack.enter_context(contextlib.nullcontext() if self.sync_gradients else self.no_sync(m)) yield @contextmanager def join_uneven_inputs(self, joinables, even_batches=None): """ A context manager that facilitates distributed training or evaluation on uneven inputs, which acts as a wrapper around `torch.distributed.algorithms.join`. This is useful when the total batch size does not evenly divide the length of the dataset. Args: joinables (`list[torch.distributed.algorithms.Joinable]`): A list of models or optimizers that subclass `torch.distributed.algorithms.Joinable`. Most commonly, a PyTorch Module that was prepared with `Accelerator.prepare` for DistributedDataParallel training. even_batches (`bool`, *optional*) If set, this will override the value of `even_batches` set in the `Accelerator`. If it is not provided, the default `Accelerator` value wil be used. <Tip warning={true}> `join_uneven_inputs` is only supported for Distributed Data Parallel training on multiple GPUs. For any other configuration, this method will have no effect. </Tip> <Tip warning={true}> Overidding `even_batches` will not affect iterable-style data loaders. </Tip> Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(even_batches=True) >>> ddp_model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) >>> with accelerator.join_uneven_inputs([ddp_model], even_batches=False): ... for input, output in dataloader: ... outputs = model(input) ... loss = loss_func(outputs) ... loss.backward() ... optimizer.step() ... optimizer.zero_grad() ``` """ if self.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU): dl_even_batches_values = [] if even_batches is not None: iterable_dl_seen = False # override value in batch sampler for map-style datasets for dl_idx, dl in enumerate(self._dataloaders): if isinstance(dl, DataLoaderDispatcher): iterable_dl_seen = True continue dl_even_batches_values.append((dl_idx, dl.batch_sampler.even_batches)) dl.batch_sampler.even_batches = even_batches if iterable_dl_seen: warnings.warn( "Overridding even_batches is only supported for map-style datasets, yet some dataloaders given were iterable" ) else: even_batches = self.even_batches enable_join = False if even_batches else True try: with Join(joinables, enable=enable_join, throw_on_early_termination=False): yield finally: # reset any batch samplers that have been modified for dl_idx, even_batches_value in dl_even_batches_values: self._dataloaders[dl_idx].batch_sampler.even_batches = even_batches_value else: # Even when disabled, Join expects models to subclass Joinable, so skip entirely for single process runs if self.distributed_type != DistributedType.NO: warnings.warn( "Joining uneven inputs is only supported for multi-GPU training, as a result `join_uneven_inputs` will have no effect." ) with contextlib.nullcontext(joinables): yield def print(self, *args, **kwargs): """ Drop in replacement of `print()` to only print once per server. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> accelerator.print("Hello world!") ``` """ self.state.print(*args, **kwargs) def _prepare_one(self, obj, first_pass=False, device_placement=None): # First pass of preparation: DataLoader, model, optimizer if first_pass: if isinstance(obj, torch.utils.data.DataLoader): return self.prepare_data_loader(obj, device_placement=device_placement) elif isinstance(obj, torch.nn.Module): return self.prepare_model(obj, device_placement=device_placement) elif isinstance(obj, torch.optim.Optimizer): optimizer = self.prepare_optimizer(obj, device_placement=device_placement) return optimizer # Second pass of preparation: LR scheduler (which need the full list of optimizers) elif isinstance(obj, LRScheduler): scheduler = self.prepare_scheduler(obj) return scheduler # Return the unprocessed object if previous criteria was not met return obj def prepare(self, *args, device_placement=None): """ Prepare all objects passed in `args` for distributed training and mixed precision, then return them in the same order. Args: *args (list of objects): Any of the following type of objects: - `torch.utils.data.DataLoader`: PyTorch Dataloader - `torch.nn.Module`: PyTorch Module - `torch.optim.Optimizer`: PyTorch Optimizer - `torch.optim.lr_scheduler.LRScheduler`: PyTorch LR Scheduler device_placement (`list[bool]`, *optional*): Used to customize whether automatic device placement should be performed for each object passed. Needs to be a list of the same length as `args`. Not compatible with DeepSpeed or FSDP. <Tip> You don't need to prepare a model if you only use it for inference without any kind of mixed precision </Tip> Examples: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume a model, optimizer, data_loader and scheduler are defined >>> model, optimizer, data_loader, scheduler = accelerator.prepare(model, optimizer, data_loader, scheduler) ``` ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume a model, optimizer, data_loader and scheduler are defined >>> device_placement = [True, True, False, False] >>> # Will place the first to items passed in automatically to the right device but not the last two. >>> model, optimizer, data_loader, scheduler = accelerator.prepare( ... model, optimizer, data_loader, scheduler, device_placement=device_placement ... ) ``` """ if device_placement is None: device_placement = [None for _ in args] elif self.distributed_type in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM): raise ValueError("You can't customize device placements with DeepSpeed or Megatron-LM.") elif len(device_placement) != len(args): raise ValueError( f"`device_placement` should be a list with {len(args)} elements (the number of objects passed)." ) for obj in args: # TODO: Look at enabling native TP training directly with a proper config if ( isinstance(obj, torch.nn.Module) and self.verify_device_map(obj) and self.distributed_type != DistributedType.NO and os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true" ): raise ValueError( "You can't train a model that has been loaded with `device_map='auto'` in any distributed mode." " Please rerun your script specifying `--num_processes=1` or by launching with `python {{myscript.py}}`." ) if self.distributed_type == DistributedType.DEEPSPEED: model_count = 0 for obj in args: if isinstance(obj, torch.nn.Module): model_count += 1 if model_count > 1: raise AssertionError( "You can't use same `Accelerator()` instance with multiple models when using DeepSpeed" ) # On TPUs, putting the model on the XLA device will create new parameters, so the corresponding optimizer will # have parameters disconnected from the model (so no training :-( ). # If the model and optimizer have parameters on different devices we raise an error. if self.distributed_type == DistributedType.TPU: model_device, optimizer_device = self._get_devices() if model_device is not None and optimizer_device is not None and model_device != optimizer_device: raise ValueError( "The model and the optimizer parameters are not on the same device, which probably means you " "created an optimizer around your model **before** putting on the device. Make sure the line " "model.to(device) is before the optimizer creation in your script or remove it entirely and use " "the flag default value for `device_placement` in your `Accelerator` to let it handle that " "part for you." ) # If we're dealing with device placement, this deals with that by... tpu_should_fix_optimizer = self.device_placement and self.distributed_type == DistributedType.TPU if tpu_should_fix_optimizer or (self.mixed_precision == "fp8" and self.fp8_recipe_handler.backend == "TE"): # 1. grabbing old model parameters old_named_params = self._get_named_parameters(*args) if self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]: if self.device.type == "cpu" and self.state.use_ipex: args = self._prepare_ipex(*args) elif self.device.type == "xpu" and is_xpu_available(): args = self._prepare_ipex(*args) if self.distributed_type == DistributedType.DEEPSPEED: result = self._prepare_deepspeed(*args) elif self.distributed_type == DistributedType.MEGATRON_LM: result = self._prepare_megatron_lm(*args) else: if self.mixed_precision == "fp8" and self.fp8_recipe_handler.backend == "MSAMP": args = self._prepare_msamp(*args) # MS-AMP will handle the device placement device_placement = [False for _ in args] result = tuple( self._prepare_one(obj, first_pass=True, device_placement=d) for obj, d in zip(args, device_placement) ) result = tuple(self._prepare_one(obj, device_placement=d) for obj, d in zip(result, device_placement)) if tpu_should_fix_optimizer or (self.mixed_precision == "fp8" and self.fp8_recipe_handler.backend == "TE"): # 2. grabbing new model parameters new_named_params = self._get_named_parameters(*result) # 3. building a map from the first to the second mapping = {p: new_named_params[n] for n, p in old_named_params.items()} # 4. using that map to update the parameters of the optimizer for obj in result: if isinstance(obj, torch.optim.Optimizer): obj._switch_parameters(mapping) for item in result: if any( item in container for container in (self._dataloaders, self._models, self._optimizers, self._schedulers) ): setattr(item, "_is_accelerate_prepared", True) return result if len(result) > 1 else result[0] def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, evaluation_mode: bool = False): """ Prepares a PyTorch model for training in any distributed setup. It is recommended to use [`Accelerator.prepare`] instead. Args: model (`torch.nn.Module`): A PyTorch model to prepare. You don't need to prepare a model if it is used only for inference without any kind of mixed precision device_placement (`bool`, *optional*): Whether or not to place the model on the proper device. Will default to `self.device_placement`. evaluation_mode (`bool`, *optional*, defaults to `False`): Whether or not to set the model for evaluation only, by just applying mixed precision and `torch.compile` (if configured in the `Accelerator` object). Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume a model is defined >>> model = accelerator.prepare_model(model) ``` """ if device_placement is None: device_placement = self.device_placement and self.distributed_type != DistributedType.FSDP self._models.append(model) # TODO: Look at enabling native TP training directly with a proper config if ( self.verify_device_map(model) and self.distributed_type != DistributedType.NO and os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true" ): raise ValueError( "You can't train a model that has been loaded with `device_map='auto'` in any distributed mode." " Please rerun your script specifying `--num_processes=1` or by launching with `python {{myscript.py}}`." ) if self.native_amp: model._original_forward = model.forward model_forward_func = model.forward.__func__ if hasattr(model.forward, "__func__") else model.forward autocast_context = get_mixed_precision_context_manager(self.native_amp, self.autocast_handler) new_forward = autocast_context(model_forward_func) if hasattr(model.forward, "__func__"): model.forward = MethodType(new_forward, model) model.forward = MethodType(convert_outputs_to_fp32(model.forward.__func__), model) else: model.forward = convert_outputs_to_fp32(new_forward) elif self.mixed_precision == "fp8" and self.fp8_recipe_handler.backend == "TE": if not has_transformer_engine_layers(model): with torch.no_grad(): convert_model(model) model._converted_to_transformer_engine = True model._original_forward = model.forward kwargs = self.fp8_recipe_handler.to_kwargs() if self.fp8_recipe_handler is not None else {} if "fp8_format" in kwargs: kwargs["fp8_format"] = getattr(te_recipe.Format, kwargs["fp8_format"]) fp8_recipe = te_recipe.DelayedScaling(**kwargs) model.forward = fp8_autocast(enabled=True, fp8_recipe=fp8_recipe)(model.forward) if (getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)) and getattr( model, "hf_device_map", False ): model_devices = set(model.hf_device_map.values()) if len(model_devices) > 1 and self.distributed_type != DistributedType.NO: raise ValueError( "You can't train a model that has been loaded in 8-bit precision on multiple devices in any distributed mode." " In order to use 8-bit models that have been loaded across multiple GPUs the solution is to use Naive Pipeline Parallelism." " Therefore you should not specify that you are under any distributed regime in your accelerate config." ) current_device = list(model_devices)[0] current_device_index = current_device.index if isinstance(current_device, torch.device) else current_device if torch.device(current_device_index) != self.device: # if on the first device (GPU 0) we don't care if (self.device.index is not None) or (current_device_index != 0): raise ValueError( "You can't train a model that has been loaded in 8-bit precision on a different device than the one " "you're training on. Make sure you loaded the model on the correct device using for example `device_map={'':torch.cuda.current_device() or device_map={'':torch.xpu.current_device()}" ) if "cpu" in model_devices or "disk" in model_devices: raise ValueError( "You can't train a model that has been loaded in 8-bit precision with CPU or disk offload." ) elif device_placement and not self.verify_device_map(model): model = model.to(self.device) if not evaluation_mode: if self.distributed_type in ( DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, ): if any(p.requires_grad for p in model.parameters()): kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {} # TODO: Look at enabling native TP training directly with a proper config if os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true": device_ids, output_device = [self.local_process_index], self.local_process_index else: device_ids, output_device = None, None model = torch.nn.parallel.DistributedDataParallel( model, device_ids=device_ids, output_device=output_device, **kwargs ) elif self.distributed_type == DistributedType.FSDP: from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP # Check if the model is already a FSDP model due to `Manual Wrapping` and if so, # don't wrap it again # In case the model is already compiled using PyTorch 2.0 and the wrapped model in it # is a FSDP model, don't wrap it again is_type_fsdp = isinstance(model, FSDP) or ( is_compiled_module(model) and isinstance(model._orig_mod, FSDP) ) if not is_type_fsdp: self.state.fsdp_plugin.set_auto_wrap_policy(model) fsdp_plugin = self.state.fsdp_plugin kwargs = { "sharding_strategy": fsdp_plugin.sharding_strategy, "cpu_offload": fsdp_plugin.cpu_offload, "auto_wrap_policy": fsdp_plugin.auto_wrap_policy, "mixed_precision": fsdp_plugin.mixed_precision_policy, "sync_module_states": fsdp_plugin.sync_module_states, "backward_prefetch": fsdp_plugin.backward_prefetch, "forward_prefetch": fsdp_plugin.forward_prefetch, "use_orig_params": fsdp_plugin.use_orig_params, "param_init_fn": fsdp_plugin.param_init_fn, "ignored_modules": fsdp_plugin.ignored_modules, "limit_all_gathers": fsdp_plugin.limit_all_gathers, "device_id": self.device, } model = FSDP(model, **kwargs) if fsdp_plugin.activation_checkpointing: from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( CheckpointImpl, apply_activation_checkpointing, checkpoint_wrapper, ) apply_activation_checkpointing( model, checkpoint_wrapper_fn=functools.partial( checkpoint_wrapper, checkpoint_impl=CheckpointImpl.NO_REENTRANT, ), auto_wrap_policy=fsdp_plugin.auto_wrap_policy, ) # if the previous and current models are same, delete the previous one if len(self._models) > 1 and (self._models[-2] is self._models[-1]): del self._models[-2] self._models[-1] = model elif self.distributed_type == DistributedType.MULTI_CPU: kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {} model = torch.nn.parallel.DistributedDataParallel(model, **kwargs) elif self.distributed_type == DistributedType.TPU and self.state.fork_launched: model = xmp.MpModelWrapper(model).to(self.device) # torch.compile should be called last and only if the model isn't already compiled. if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model): if not is_torch_version(">=", "2.0"): raise ValueError("Using `torch.compile` requires PyTorch 2.0 or higher.") model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs()) return model def _prepare_deepspeed(self, *args): import deepspeed deepspeed_plugin = self.state.deepspeed_plugin is_dataloader_present = any(isinstance(obj, torch.utils.data.DataLoader) for obj in args) result = [ self._prepare_one(obj, first_pass=True) if isinstance(obj, torch.utils.data.DataLoader) else obj for obj in args ] if deepspeed_plugin.is_auto("train_micro_batch_size_per_gpu"): if is_dataloader_present: batch_sizes = [obj.batch_size for obj in args if hasattr(obj, "batch_size")] if any(bs is None for bs in batch_sizes): raise ValueError( "At least one of the dataloaders passed to `accelerate.prepare()` has `None` as batch size. " "Please set an integer value in `train_micro_batch_size_per_gpu` in the deepspeed config file " "or assign integer value to `AcceleratorState().deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu']`." ) if self.split_batches: batch_sizes = [batch_size // self.num_processes for batch_size in batch_sizes] batch_size_per_device = min(batch_sizes) if deepspeed_plugin.is_train_batch_min else max(batch_sizes) if len(batch_sizes) > 1: logger.info( "Since you passed both train and evaluation dataloader, `is_train_batch_min` (here " f"{deepspeed_plugin.is_train_batch_min} will decide the `train_batch_size` ({batch_size_per_device})." ) else: raise ValueError( "When using DeepSpeed, `accelerate.prepare()` requires you to pass at least one of training or evaluation dataloaders " "with `batch_size` attribute returning an integer value " "or alternatively set an integer value in `train_micro_batch_size_per_gpu` in the deepspeed config file " "or assign integer value to `AcceleratorState().deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu']`." ) else: batch_size_per_device = deepspeed_plugin.get_value("train_micro_batch_size_per_gpu") # handle `gradient_accumulation_steps` when the value is `auto` deepspeed_plugin.fill_match( "gradient_accumulation_steps", must_match=False, gradient_accumulation_steps=self.gradient_accumulation_steps, ) config_kwargs = { "train_micro_batch_size_per_gpu": batch_size_per_device, "train_batch_size": batch_size_per_device * deepspeed_plugin.get_value("gradient_accumulation_steps") * self.num_processes, "gradient_clipping": 1.0, "zero_optimization.stage3_gather_16bit_weights_on_model_save": False, } model = None optimizer = None scheduler = None for obj in result: if isinstance(obj, torch.nn.Module): model = obj elif isinstance(obj, (torch.optim.Optimizer, DummyOptim)): optimizer = obj elif (isinstance(obj, (LRScheduler, DummyScheduler))) or ( type(obj).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES ): scheduler = obj if optimizer is not None: if "optimizer" in deepspeed_plugin.deepspeed_config and not isinstance(optimizer, (DummyOptim)): raise ValueError( "You cannot specify an optimizer in the config file and in the code at the same time. " "Please remove the optimizer from the config file or " "create `accelerate.utils.DummyOptim` in the code." ) elif "optimizer" not in deepspeed_plugin.deepspeed_config and isinstance(optimizer, (DummyOptim)): raise ValueError( "You cannot create a `DummyOptim` without specifying an optimizer in the config file." ) if isinstance(optimizer, (torch.optim.Optimizer)): deepspeed_plugin.deepspeed_config["zero_allow_untested_optimizer"] = True if scheduler is not None: if "scheduler" in deepspeed_plugin.deepspeed_config and not isinstance(scheduler, (DummyScheduler)): raise ValueError( "You cannot specify a scheduler in the config file and in the code at the same time. " "Please remove the scheduler from the config file or " "create `accelerate.utils.DummyScheduler` in the code." ) elif ( "scheduler" not in deepspeed_plugin.deepspeed_config and isinstance(scheduler, (DummyScheduler)) and scheduler.lr_scheduler_callable is None ): raise ValueError( "Either specify a scheduler in the config file or " "pass in the `lr_scheduler_callable` parameter when using `accelerate.utils.DummyScheduler`." ) if optimizer is not None and scheduler is not None: if isinstance(optimizer, (DummyOptim)) and not isinstance(scheduler, (DummyScheduler)): raise ValueError( "You can only specify `accelerate.utils.DummyScheduler` in the code when using " "`accelerate.utils.DummyOptim`." ) if model is not None: # deal with config keys that use `auto` value and rely on model's hidden_size hidden_size_based_keys = [ "zero_optimization.reduce_bucket_size", "zero_optimization.stage3_prefetch_bucket_size", "zero_optimization.stage3_param_persistence_threshold", ] hidden_size_auto_keys = [x for x in hidden_size_based_keys if deepspeed_plugin.is_auto(x)] if len(hidden_size_auto_keys) > 0: reasoning = ( "therefore it's not possible to automatically fill out the following `auto` entries " + f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing " + "`auto` values for these keys with an integer value of your choice." ) if not hasattr(model, "config"): raise ValueError("Can't find `model.config` entry, " + reasoning) if hasattr(model.config, "hidden_size"): hidden_size = model.config.hidden_size elif hasattr(model.config, "hidden_sizes"): # if there are many hidden sizes pick the largest one hidden_size = max(model.config.hidden_sizes) else: raise ValueError( "Can find neither `model.config.hidden_size` nor `model.config.hidden_sizes`, " + reasoning ) config_kwargs.update( { "zero_optimization.reduce_bucket_size": hidden_size * hidden_size, "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size, "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size, } ) if isinstance(optimizer, (DummyOptim)): config_kwargs.update( {"optimizer.params.lr": optimizer.lr, "optimizer.params.weight_decay": optimizer.weight_decay} ) if isinstance(scheduler, (DummyScheduler)) and scheduler.lr_scheduler_callable is None: max_lr = ( getattr(scheduler.optimizer, "lr", None) if getattr(scheduler.optimizer, "defaults", None) is None else scheduler.optimizer.defaults["lr"] ) config_kwargs.update( { "scheduler.params.warmup_min_lr": 0, "scheduler.params.warmup_max_lr": max_lr, "scheduler.params.warmup_num_steps": scheduler.warmup_num_steps, } ) if scheduler.total_num_steps is not None: config_kwargs["scheduler.params.total_num_steps"] = ( math.ceil(scheduler.total_num_steps / self.num_processes) if not self.split_batches else scheduler.total_num_steps ) deepspeed_plugin.deepspeed_config_process(must_match=False, **config_kwargs) self.deepspeed_config = deepspeed_plugin.deepspeed_config kwargs = dict(model=model, config_params=self.deepspeed_config) if optimizer is not None: if isinstance(optimizer, (DummyOptim)): kwargs["model_parameters"] = optimizer.params if isinstance(scheduler, (DummyScheduler)) and scheduler.lr_scheduler_callable is not None: kwargs["lr_scheduler"] = scheduler.lr_scheduler_callable else: if self.deepspeed_config["zero_optimization"].get("offload_optimizer", {}).get( "device", "none" ) != "none" and self.deepspeed_config.get("zero_force_ds_cpu_optimizer", True): from deepspeed.ops.adam import DeepSpeedCPUAdam defaults = {k: v for k, v in optimizer.defaults.items() if k in ["lr", "weight_decay"]} optimizer = DeepSpeedCPUAdam(optimizer.param_groups, **defaults) kwargs["optimizer"] = optimizer if scheduler is not None: if type(scheduler).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES: kwargs["lr_scheduler"] = scheduler engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs) if optimizer is not None: optimizer = DeepSpeedOptimizerWrapper(optimizer) if scheduler is not None: if lr_scheduler is None: scheduler = AcceleratedScheduler( scheduler, optimizer, step_with_optimizer=self.step_scheduler_with_optimizer, split_batches=self.split_batches, ) else: scheduler = DeepSpeedSchedulerWrapper(lr_scheduler, optimizer) for i in range(len(result)): if isinstance(result[i], torch.nn.Module): result[i] = engine elif isinstance(result[i], (torch.optim.Optimizer, DummyOptim)): result[i] = optimizer elif (isinstance(result[i], (LRScheduler, DummyScheduler))) or ( type(result[i]).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES ): result[i] = scheduler # pointing for deepspeed_engine_wrapped.backward() self.deepspeed_engine_wrapped = DeepSpeedEngineWrapper(engine) self._models.append(engine) if optimizer is not None: self._optimizers.append(optimizer) if scheduler is not None: self._schedulers.append(scheduler) if len(self._models) > 1: raise AssertionError( "You can't use same `Accelerator()` instance with multiple models when using DeepSpeed" ) return tuple(result) def _prepare_megatron_lm(self, *args): megatron_lm_plugin = self.state.megatron_lm_plugin if not megatron_lm_plugin.megatron_dataset_flag: batch_sizes = [obj.batch_size for obj in args if hasattr(obj, "batch_size")] if len(batch_sizes) == 0: raise ValueError( "You must specify a training or evaluation dataloader in `accelerate.prepare()` when using Megatron-LM." ) micro_batch_size = min(batch_sizes) if megatron_lm_plugin.is_train_batch_min else max(batch_sizes) if len(batch_sizes) > 1: logger.info( "Since you passed both train and evaluation dataloader, `is_train_batch_min` (here " f"{megatron_lm_plugin.is_train_batch_min} will decide the `train_batch_size` ({micro_batch_size})." ) else: for obj in args: if isinstance(obj, MegatronLMDummyDataLoader): micro_batch_size = obj.dataset_args["micro_batch_size"] break dp_degree = self.num_processes // (megatron_lm_plugin.tp_degree * megatron_lm_plugin.pp_degree) megatron_lm_plugin.set_training_args(micro_batch_size, dp_degree) model = None optimizer = None scheduler = None is_dummy_scheduler = False batch_data = None for obj in args: if isinstance(obj, torch.utils.data.DataLoader) and batch_data is None: batch_data = next(iter(obj)) if isinstance(obj, torch.nn.Module): model = obj elif isinstance(obj, (torch.optim.Optimizer)): optimizer = obj elif isinstance(obj, (LRScheduler, MegatronLMDummyScheduler)): scheduler = obj if model is not None: megatron_lm_plugin.set_network_size_args(model, batch_data) if optimizer is not None: megatron_lm_plugin.set_optimizer_type(optimizer) if scheduler is not None: is_dummy_scheduler = isinstance(scheduler, MegatronLMDummyScheduler) if not is_dummy_scheduler: raise ValueError( "You can't use a custom scheduler with Megatron-LM. Please use the `accelerate.utils.MegatronLMDummyScheduler` instead." ) megatron_lm_plugin.set_scheduler_args(scheduler) # initialize megatron-lm megatron_lm_initialize(self, args_defaults=megatron_lm_plugin.megatron_lm_default_args) counter = 0 result = [] for obj in args: if isinstance(obj, torch.utils.data.DataLoader): result.append(megatron_lm_prepare_data_loader(self, obj)) counter += 1 elif isinstance(obj, MegatronLMDummyDataLoader): if counter == 0: obj.set_megatron_data_args() dataloaders = megatron_lm_prepare_data_loader(self, obj) result.append(dataloaders[counter]) counter += 1 else: result.append(obj) if model is not None: model = megatron_lm_prepare_model(self) if optimizer is not None: optimizer = megatron_lm_prepare_optimizer(self, model) if scheduler is not None: scheduler = megatron_lm_prepare_scheduler(self, optimizer, scheduler) if model is not None: model = MegatronEngine(self, model, optimizer, scheduler) if optimizer is not None: optimizer = MegatronLMOptimizerWrapper(optimizer) if scheduler is not None: scheduler = MegatronLMSchedulerWrapper(scheduler, optimizer) for i in range(len(result)): if isinstance(result[i], torch.nn.Module): result[i] = model elif isinstance(result[i], torch.optim.Optimizer): result[i] = optimizer elif isinstance(result[i], MegatronLMDummyScheduler): result[i] = scheduler if model is not None: self._models.append(model) if optimizer is not None: self._optimizers.append(optimizer) if scheduler is not None: self._schedulers.append(scheduler) if len(self._models) > 1: raise AssertionError( "You can't use same `Accelerator()` instance with multiple models when using Megatron-LM" ) return tuple(result) def _prepare_ipex(self, *args): if not is_ipex_available(): raise ImportError( "IPEX is not installed or IPEX's version does not match current PyTorch version. Please refer" " to https://github.com/intel/intel-extension-for-pytorch." ) else: import intel_extension_for_pytorch as ipex model = None optimizer = None result = [obj for obj in args] for obj in result: if isinstance(obj, torch.nn.Module): model = obj elif isinstance(obj, (torch.optim.Optimizer)): optimizer = obj if optimizer is not None and model is not None: dtype = torch.bfloat16 if self.state.mixed_precision == "bf16" else torch.float32 if self.device.type == "xpu" and is_xpu_available(): model = model.to(self.device) model, optimizer = torch.xpu.optimize( model, optimizer=optimizer, dtype=dtype, inplace=True, level="O1" ) else: model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=dtype, inplace=True, level="O1") for i in range(len(result)): if isinstance(result[i], torch.nn.Module): result[i] = model elif isinstance(result[i], (torch.optim.Optimizer)): result[i] = optimizer return tuple(result) def _prepare_msamp(self, *args): if not is_msamp_available(): raise ImportError( "MS-AMP was not found on your system. Please ensure that MS-AMP is available " " or choose `'te'` as the backend for FP8 mixed precision training." ) else: import msamp model, optimizer = None, None num_models, num_optimizers = 0, 0 result = [obj for obj in args] for obj in result: if isinstance(obj, torch.nn.Module): model = obj num_models += 1 elif isinstance(obj, (torch.optim.Optimizer)): optimizer = obj num_optimizers += 1 if optimizer is None or model is None: raise ValueError( "You must pass a model and an optimizer together to `accelerate.prepare()` when using MS-AMP." ) elif num_models > 1 or num_optimizers > 1: raise ValueError( f"You can't use multiple models ({num_models}) or optimizers {num_optimizers} with MS-AMP." ) else: model, optimizer = msamp.initialize(model, optimizer, opt_level=self.fp8_recipe_handler.opt_level) for i in range(len(result)): if isinstance(result[i], torch.nn.Module): result[i] = model elif isinstance(result[i], (torch.optim.Optimizer)): result[i] = optimizer return tuple(result) def prepare_data_loader( self, data_loader: torch.utils.data.DataLoader, device_placement=None, slice_fn_for_dispatch=None ): """ Prepares a PyTorch DataLoader for training in any distributed setup. It is recommended to use [`Accelerator.prepare`] instead. Args: data_loader (`torch.utils.data.DataLoader`): A vanilla PyTorch DataLoader to prepare device_placement (`bool`, *optional*): Whether or not to place the batches on the proper device in the prepared dataloader. Will default to `self.device_placement`. slice_fn_for_dispatch (`Callable`, *optional*`): If passed, this function will be used to slice tensors across `num_processes`. Will default to [`~utils.slice_tensors`]. This argument is used only when `dispatch_batches` is set to `True` and will be ignored otherwise. Example: ```python >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> data_loader = torch.utils.data.DataLoader(...) >>> data_loader = accelerator.prepare_data_loader(data_loader, device_placement=True) ``` """ # Ensure we can't double wrap a DataLoader due to `find_batch_size` if getattr(data_loader, "_is_accelerate_prepared", False): if data_loader not in self._dataloaders: self._dataloaders.append(data_loader) return data_loader if device_placement is None: device_placement = self.device_placement if self.distributed_type != DistributedType.TPU else False prepared_data_loader = prepare_data_loader( data_loader, self.device, num_processes=self.num_processes, process_index=self.process_index, split_batches=self.split_batches, put_on_device=device_placement, rng_types=self.rng_types.copy(), dispatch_batches=self.dispatch_batches, even_batches=self.even_batches, slice_fn_for_dispatch=slice_fn_for_dispatch, use_seedable_sampler=self.use_seedable_sampler, ) self._dataloaders.append(prepared_data_loader) return prepared_data_loader def prepare_optimizer(self, optimizer: torch.optim.Optimizer, device_placement=None): """ Prepares a PyTorch Optimizer for training in any distributed setup. It is recommended to use [`Accelerator.prepare`] instead. Args: optimizer (`torch.optim.Optimizer`): A vanilla PyTorch optimizer to prepare device_placement (`bool`, *optional*): Whether or not to place the optimizer on the proper device. Will default to `self.device_placement`. Example: ```python >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> optimizer = torch.optim.Adam(...) >>> optimizer = accelerator.prepare_optimizer(optimizer, device_placement=True) ``` """ # Ensure we can't double wrap an optimizer due to `find_batch_size` if getattr(optimizer, "_is_accelerate_prepared", False): if optimizer not in self._optimizers: self._optimizers.append(optimizer) return optimizer if device_placement is None: device_placement = self.device_placement optimizer = AcceleratedOptimizer(optimizer, device_placement=device_placement, scaler=self.scaler) self._optimizers.append(optimizer) return optimizer def prepare_scheduler(self, scheduler: LRScheduler): """ Prepares a PyTorch Scheduler for training in any distributed setup. It is recommended to use [`Accelerator.prepare`] instead. Args: scheduler (`torch.optim.lr_scheduler.LRScheduler`): A vanilla PyTorch scheduler to prepare Example: ```python >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> optimizer = torch.optim.Adam(...) >>> scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, ...) >>> scheduler = accelerator.prepare_scheduler(scheduler) ``` """ # Ensure we can't double wrap a scheduler due to `find_batch_size` if getattr(scheduler, "_is_accelerate_prepared", False): if scheduler not in self._schedulers: self._schedulers.append(scheduler) return scheduler # We try to find the optimizer associated with `scheduler`, the default is the full list. optimizer = self._optimizers for opt in self._optimizers: if getattr(scheduler, "optimizer", None) == opt.optimizer: optimizer = opt break scheduler = AcceleratedScheduler( scheduler, optimizer, step_with_optimizer=self.step_scheduler_with_optimizer, split_batches=self.split_batches, ) self._schedulers.append(scheduler) return scheduler def backward(self, loss, **kwargs): """ Scales the gradients in accordance to the `GradientAccumulationPlugin` and calls the correct `backward()` based on the configuration. Should be used in lieu of `loss.backward()`. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(gradient_accumulation_steps=2) >>> outputs = model(inputs) >>> loss = loss_fn(outputs, labels) >>> accelerator.backward(loss) ``` """ if self.distributed_type != DistributedType.DEEPSPEED: # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward` loss = loss / self.gradient_accumulation_steps if self.distributed_type == DistributedType.DEEPSPEED: self.deepspeed_engine_wrapped.backward(loss, **kwargs) elif self.distributed_type == DistributedType.MEGATRON_LM: return elif self.scaler is not None: self.scaler.scale(loss).backward(**kwargs) else: loss.backward(**kwargs) def set_trigger(self): """ Sets the internal trigger tensor to 1 on the current process. A latter check should follow using this which will check across all processes. Note: Does not require `wait_for_everyone()` Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume later in the training script >>> # `should_do_breakpoint` is a custom function to monitor when to break, >>> # e.g. when the loss is NaN >>> if should_do_breakpoint(loss): ... accelerator.set_trigger() >>> # Assume later in the training script >>> if accelerator.check_breakpoint(): ... break ``` """ self.flag_tensor = torch.tensor(1, device=self.device) def check_trigger(self): """ Checks if the internal trigger tensor has been set to 1 in any of the processes. If so, will return `True` and reset the trigger tensor to 0. Note: Does not require `wait_for_everyone()` Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume later in the training script >>> # `should_do_breakpoint` is a custom function to monitor when to break, >>> # e.g. when the loss is NaN >>> if should_do_breakpoint(loss): ... accelerator.set_trigger() >>> # Assume later in the training script >>> if accelerator.check_trigger(): ... break ``` """ # Now that we are outside `__init__`, we can initialize it if it is `None` on device if self.flag_tensor is None: self.flag_tensor = torch.tensor(0, device=self.device) flag_tensor = self.reduce(self.flag_tensor) if flag_tensor.item() >= 1: self.flag_tensor = torch.tensor(0, device=self.device) return True return False def unscale_gradients(self, optimizer=None): """ Unscale the gradients in mixed precision training with AMP. This is a noop in all other settings. Likely should be called through [`Accelerator.clip_grad_norm_`] or [`Accelerator.clip_grad_value_`] Args: optimizer (`torch.optim.Optimizer` or `list[torch.optim.Optimizer]`, *optional*): The optimizer(s) for which to unscale gradients. If not set, will unscale gradients on all optimizers that were passed to [`~Accelerator.prepare`]. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model, optimizer = accelerator.prepare(model, optimizer) >>> outputs = model(inputs) >>> loss = loss_fn(outputs, labels) >>> accelerator.backward(loss) >>> accelerator.unscale_gradients(optimizer=optimizer) ``` """ if self.native_amp and self.mixed_precision == "fp16": if optimizer is None: # TODO: this unscales all optimizers where we should only unscale the one where parameters are. optimizer = self._optimizers elif not isinstance(optimizer, (tuple, list)): optimizer = [optimizer] for opt in optimizer: while isinstance(opt, AcceleratedOptimizer): opt = opt.optimizer # Reduce gradients first for XLA if self.distributed_type == DistributedType.TPU: gradients = xm._fetch_gradients(opt) self.reduce(gradients, scale=1.0 / self.num_processes) self.scaler.unscale_(opt) def clip_grad_norm_(self, parameters, max_norm, norm_type=2): """ Should be used in place of `torch.nn.utils.clip_grad_norm_`. Returns: `torch.Tensor`: Total norm of the parameter gradients (viewed as a single vector). Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(gradient_accumulation_steps=2) >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler) >>> for input, target in dataloader: ... optimizer.zero_grad() ... output = model(input) ... loss = loss_func(output, target) ... accelerator.backward(loss) ... if accelerator.sync_gradients: ... accelerator.clip_grad_norm_(model.parameters(), max_grad_norm) ... optimizer.step() ``` """ if self.distributed_type == DistributedType.FSDP: self.unscale_gradients() parameters = [p for p in parameters] for model in self._models: if parameters == [p for p in model.parameters()]: return model.clip_grad_norm_(max_norm, norm_type) elif self.distributed_type == DistributedType.DEEPSPEED: # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed # We cannot return the gradient norm because DeepSpeed does it. return None self.unscale_gradients() return torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=norm_type) def clip_grad_value_(self, parameters, clip_value): """ Should be used in place of `torch.nn.utils.clip_grad_value_`. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(gradient_accumulation_steps=2) >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler) >>> for input, target in dataloader: ... optimizer.zero_grad() ... output = model(input) ... loss = loss_func(output, target) ... accelerator.backward(loss) ... if accelerator.sync_gradients: ... accelerator.clip_grad_value_(model.parameters(), clip_value) ... optimizer.step() ``` """ if self.distributed_type in [DistributedType.DEEPSPEED, DistributedType.FSDP]: raise Exception("DeepSpeed and FSDP do not support `clip_grad_value_`. Use `clip_grad_norm_` instead.") self.unscale_gradients() torch.nn.utils.clip_grad_value_(parameters, clip_value) def gather(self, tensor): """ Gather the values in *tensor* across all processes and concatenate them on the first dimension. Useful to regroup the predictions from all processes when doing evaluation. Note: This gather happens in all processes. Args: tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`): The tensors to gather across all processes. Returns: `torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`: The gathered tensor(s). Note that the first dimension of the result is *num_processes* multiplied by the first dimension of the input tensors. Example: ```python >>> # Assuming four processes >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> process_tensor = torch.tensor([accelerator.process_index]) >>> gathered_tensor = accelerator.gather(process_tensor) >>> gathered_tensor tensor([0, 1, 2, 3]) ``` """ return gather(tensor) def gather_for_metrics(self, input_data): """ Gathers `input_data` and potentially drops duplicates in the last batch if on a distributed system. Should be used for gathering the inputs and targets for metric calculation. Args: input (`torch.Tensor`, `object`, a nested tuple/list/dictionary of `torch.Tensor`, or a nested tuple/list/dictionary of `object`): The tensors or objects for calculating metrics across all processes Example: ```python >>> # Assuming two processes, with a batch size of 5 on a dataset with 9 samples >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> dataloader = torch.utils.data.DataLoader(range(9), batch_size=5) >>> dataloader = accelerator.prepare(dataloader) >>> batch = next(iter(dataloader)) >>> gathered_items = accelerator.gather_for_metrics(batch) >>> len(gathered_items) 9 ``` """ try: recursively_apply(lambda x: x, input_data, error_on_other_type=True) all_tensors = True except TypeError: all_tensors = False if not all_tensors: data = gather_object(input_data) else: data = self.gather(input_data) try: if self.gradient_state.end_of_dataloader: # at the end of a dataloader, `gather_for_metrics` regresses to # `gather` unless the dataset has a remainder so log. if self.gradient_state.remainder == -1: logger.info( "The used dataset had no length, returning gathered tensors. You should drop the remainder yourself." ) return data elif self.gradient_state.remainder > 0: # Last batch needs to be truncated on distributed systems as it contains additional samples def _adjust_samples(tensor): return tensor[: self.gradient_state.remainder] return recursively_apply(_adjust_samples, data) else: # remainder is 0 # no remainder even though at end of dataloader, so nothing to do. return data else: # Not at the end of the dataloader, no need to adjust the tensors return data except Exception: # Dataset had no length or raised an error return data def reduce(self, tensor, reduction="sum", scale=1.0): """ Reduce the values in *tensor* across all processes based on *reduction*. Note: All processes get the reduced value. Args: tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`): The tensors to reduce across all processes. reduction (`str`, *optional*, defaults to "sum"): A reduction type, can be one of 'sum', 'mean', or 'none'. If 'none', will not perform any operation. scale (`float`, *optional*, defaults to 1.0): A default scaling value to be applied after the reduce, only valied on XLA. Returns: `torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`: The reduced tensor(s). Example: ```python >>> # Assuming two processes >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> process_tensor = torch.arange(accelerator.num_processes) + 1 + (2 * accelerator.process_index) >>> process_tensor = process_tensor.to(accelerator.device) >>> reduced_tensor = accelerator.reduce(process_tensor, reduction="sum") >>> reduced_tensor tensor([4, 6]) ``` """ return reduce(tensor, reduction, scale) def pad_across_processes(self, tensor, dim=0, pad_index=0, pad_first=False): """ Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so they can safely be gathered. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather. dim (`int`, *optional*, defaults to 0): The dimension on which to pad. pad_index (`int`, *optional*, defaults to 0): The value with which to pad. pad_first (`bool`, *optional*, defaults to `False`): Whether to pad at the beginning or the end. Returns: `torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`: The padded tensor(s). Example: ```python >>> # Assuming two processes, with the first processes having a tensor of size 1 and the second of size 2 >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> process_tensor = torch.arange(accelerator.process_index + 1).to(accelerator.device) >>> padded_tensor = accelerator.pad_across_processes(process_tensor) >>> padded_tensor.shape torch.Size([2]) ``` """ return pad_across_processes(tensor, dim=dim, pad_index=pad_index, pad_first=pad_first) def unwrap_model(self, model, keep_fp32_wrapper: bool = True): """ Unwraps the `model` from the additional layer possible added by [`~Accelerator.prepare`]. Useful before saving the model. Args: model (`torch.nn.Module`): The model to unwrap. keep_fp32_wrapper (`bool`, *optional*, defaults to `True`): Whether to not remove the mixed precision hook if it was added. Returns: `torch.nn.Module`: The unwrapped model. Example: ```python >>> # Assuming two GPU processes >>> from torch.nn.parallel import DistributedDataParallel >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model = accelerator.prepare(MyModel()) >>> print(model.__class__.__name__) DistributedDataParallel >>> model = accelerator.unwrap_model(model) >>> print(model.__class__.__name__) MyModel ``` """ return extract_model_from_parallel(model, keep_fp32_wrapper) def wait_for_everyone(self): """ Will stop the execution of the current process until every other process has reached that point (so this does nothing when the script is only run in one process). Useful to do before saving a model. Example: ```python >>> # Assuming two GPU processes >>> import time >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> if accelerator.is_main_process: ... time.sleep(2) >>> else: ... print("I'm waiting for the main process to finish its sleep...") >>> accelerator.wait_for_everyone() >>> # Should print on every process at the same time >>> print("Everyone is here") ``` """ wait_for_everyone() @on_main_process def init_trackers(self, project_name: str, config: dict | None = None, init_kwargs: dict | None = {}): """ Initializes a run for all trackers stored in `self.log_with`, potentially with starting configurations Args: project_name (`str`): The name of the project. All trackers will save their data based on this config (`dict`, *optional*): Optional starting configuration to be logged. init_kwargs (`dict`, *optional*): A nested dictionary of kwargs to be passed to a specific tracker's `__init__` function. Should be formatted like so: ```python {"wandb": {"tags": ["tag_a", "tag_b"]}} ``` Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(log_with="tensorboard") >>> accelerator.init_trackers( ... project_name="my_project", ... config={"learning_rate": 0.001, "batch_size": 32}, ... init_kwargs={"tensorboard": {"flush_secs": 60}}, ... ) ``` """ for tracker in self.log_with: if issubclass(type(tracker), GeneralTracker): # Custom trackers are already initialized self.trackers.append(tracker) else: tracker_init = LOGGER_TYPE_TO_CLASS[str(tracker)] if getattr(tracker_init, "requires_logging_directory"): # We can skip this check since it was done in `__init__` self.trackers.append( tracker_init(project_name, self.logging_dir, **init_kwargs.get(str(tracker), {})) ) else: self.trackers.append(tracker_init(project_name, **init_kwargs.get(str(tracker), {}))) if config is not None: for tracker in self.trackers: tracker.store_init_configuration(config) def get_tracker(self, name: str, unwrap: bool = False): """ Returns a `tracker` from `self.trackers` based on `name` on the main process only. Args: name (`str`): The name of a tracker, corresponding to the `.name` property. unwrap (`bool`): Whether to return the internal tracking mechanism or to return the wrapped tracker instead (recommended). Returns: `GeneralTracker`: The tracker corresponding to `name` if it exists. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(log_with="tensorboard") >>> accelerator.init_trackers("my_project") >>> tensorboard_tracker = accelerator.get_tracker("tensorboard") ``` """ if len(self.trackers) > 0: for tracker in self.trackers: if tracker.name == name: return tracker.tracker if unwrap else tracker raise ValueError(f"{name} is not an available tracker stored inside the `Accelerator`.") # Handle tracker only made on main process return GeneralTracker(_blank=True) @on_main_process def log(self, values: dict, step: int | None = None, log_kwargs: dict | None = {}): """ Logs `values` to all stored trackers in `self.trackers` on the main process only. Args: values (`dict`): Values should be a dictionary-like object containing only types `int`, `float`, or `str`. step (`int`, *optional*): The run step. If included, the log will be affiliated with this step. log_kwargs (`dict`, *optional*): A nested dictionary of kwargs to be passed to a specific tracker's `log` function. Should be formatted like so: ```python {"wandb": {"tags": ["tag_a", "tag_b"]}} ``` Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(log_with="tensorboard") >>> accelerator.init_trackers("my_project") >>> accelerator.log({"loss": 0.5, "accuracy": 0.9}) ``` """ for tracker in self.trackers: tracker.log(values, step=step, **log_kwargs.get(tracker.name, {})) @on_main_process def end_training(self): """ Runs any special end training behaviors, such as stopping trackers on the main process only. Should always be called at the end of your script if using experiment tracking. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(log_with="tensorboard") >>> accelerator.init_trackers("my_project") >>> # Do training >>> accelerator.end_training() ``` """ for tracker in self.trackers: tracker.finish() def save(self, obj, f, safe_serialization=False): """ Save the object passed to disk once per machine. Use in place of `torch.save`. Args: obj (`object`): The object to save. f (`str` or `os.PathLike`): Where to save the content of `obj`. safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save `obj` using `safetensors` Note: If `save_on_each_node` was passed in as a `ProjectConfiguration`, will save the object once per node, rather than only once on the main node. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> arr = [0, 1, 2, 3] >>> accelerator.save(arr, "array.pkl") ``` """ save( obj, f, save_on_each_node=self.project_configuration.save_on_each_node, safe_serialization=safe_serialization, ) def save_model( self, model: torch.nn.Module, save_directory: Union[str, os.PathLike], max_shard_size: Union[int, str] = "10GB", safe_serialization: bool = True, ): """ Save a model so that it can be re-loaded using load_checkpoint_in_model Arguments: model: (`torch.nn.Module`): Model to be saved. The model can be wrapped or unwraped. save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`): The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). <Tip warning={true}> If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard which will be bigger than `max_shard_size`. </Tip> safe_serialization (`bool`, *optional*, defaults to `True`): Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model = ... >>> accelerator.save_model(model, save_directory) ``` """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) # get the state_dict of the model if any( [ module._hf_hook.offload for module in model.modules() if hasattr(module, "_hf_hook") and isinstance(module._hf_hook, AlignDevicesHook) ] ): state_dict = get_state_dict_offloaded_model(model) else: if any(param.device == torch.device("meta") for param in model.parameters()): raise RuntimeError("You can't save the model since some parameters are on the meta device.") state_dict = self.get_state_dict(model) if safe_serialization: state_dict = clean_state_dict_for_safetensors(state_dict) weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME # Shard the model if it is too big. shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name) # Clean the folder from a previous save for filename in os.listdir(save_directory): full_filename = os.path.join(save_directory, filename) # If we have a shard file that is not going to be replaced, we delete it, but only from the main process # in distributed settings to avoid race conditions. weights_no_suffix = weights_name.replace(".bin", "") # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005 filename_no_suffix = filename.replace(".bin", "") reg = re.compile(r"(.*?)-\d{5}-of-\d{5}") if ( filename.startswith(weights_no_suffix) and os.path.isfile(full_filename) and filename not in shards.keys() and reg.fullmatch(filename_no_suffix) is not None and PartialState().is_main_process ): os.remove(full_filename) # Save the model for shard_file, shard in shards.items(): self.save(shard, os.path.join(save_directory, shard_file), safe_serialization=safe_serialization) if index is None: path_to_weights = os.path.join(save_directory, WEIGHTS_NAME) logger.info(f"Model weights saved in {path_to_weights}") else: save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME save_index_file = os.path.join(save_directory, save_index_file) # Save the index as well with open(save_index_file, "w", encoding="utf-8") as f: content = json.dumps(index, indent=2, sort_keys=True) + "\n" f.write(content) logger.info( f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be " f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the " f"index located at {save_index_file}." ) def register_save_state_pre_hook(self, hook: Callable[..., None]) -> hooks.RemovableHandle: """ Registers a pre hook to be run before `save_checkpoint` is called in [`Accelerator.save_state`]. Args: hook (`Callable`): A function to be called in [`Accelerator.save_state`] before `save_checkpoint`. The hook should have the following signature: `hook(models: list[torch.nn.Module], weights: list[dict[str, torch.Tensor]], input_dir: str) -> None` The `models` argument are the models as saved in the accelerator state under `accelerator._models`, `weigths` argument are the state dicts of the `models`, and the `input_dir` argument is the `input_dir` argument passed to [`Accelerator.load_state`]. <Tip> Should only be used in conjunction with [`Accelerator.register_load_state_pre_hook`]. Can be useful to save configurations in addition to model weights. Can also be used to overwrite model saving with a customized method. In this case, make sure to remove already loaded weights from the weights list. </Tip> Returns: `torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling `handle.remove()` """ handle = hooks.RemovableHandle(self._save_model_state_pre_hook) self._save_model_state_pre_hook[handle.id] = hook return handle def save_state(self, output_dir: str = None, safe_serialization: bool = True, **save_model_func_kwargs): """ Saves the current states of the model, optimizer, scaler, RNG generators, and registered objects to a folder. If a `ProjectConfiguration` was passed to the `Accelerator` object with `automatic_checkpoint_naming` enabled then checkpoints will be saved to `self.project_dir/checkpoints`. If the number of current saves is greater than `total_limit` then the oldest save is deleted. Each checkpoint is saved in seperate folders named `checkpoint_<iteration>`. Otherwise they are just saved to `output_dir`. <Tip> Should only be used when wanting to save a checkpoint during training and restoring the state in the same environment. </Tip> Args: output_dir (`str` or `os.PathLike`): The name of the folder to save all relevant weights and states. safe_serialization (`bool`, *optional*, defaults to `True`): Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). save_model_func_kwargs (`dict`, *optional*): Additional keyword arguments for saving model which can be passed to the underlying save function, such as optional arguments for DeepSpeed's `save_checkpoint` function. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model, optimizer, lr_scheduler = ... >>> model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler) >>> accelerator.save_state(output_dir="my_checkpoint") ``` """ if self.project_configuration.automatic_checkpoint_naming: output_dir = os.path.join(self.project_dir, "checkpoints") os.makedirs(output_dir, exist_ok=True) if self.project_configuration.automatic_checkpoint_naming: folders = [os.path.join(output_dir, folder) for folder in os.listdir(output_dir)] if ( self.project_configuration.total_limit is not None and (len(folders) + 1 > self.project_configuration.total_limit) and self.is_main_process ): def _inner(folder): return list(map(int, re.findall(r"[\/]?([0-9]+)(?=[^\/]*$)", folder)))[0] folders.sort(key=_inner) logger.warning( f"Deleting {len(folders) + 1 - self.project_configuration.total_limit} checkpoints to make room for new checkpoint." ) for folder in folders[: len(folders) + 1 - self.project_configuration.total_limit]: shutil.rmtree(folder) output_dir = os.path.join(output_dir, f"checkpoint_{self.save_iteration}") if os.path.exists(output_dir): raise ValueError( f"Checkpoint directory {output_dir} ({self.save_iteration}) already exists. Please manually override `self.save_iteration` with what iteration to start with." ) self.wait_for_everyone() os.makedirs(output_dir, exist_ok=True) logger.info(f"Saving current state to {output_dir}") if self.distributed_type == DistributedType.TPU: # Finish running the previous step before checkpointing xm.mark_step() # Save the models taking care of FSDP and DeepSpeed nuances weights = [] for i, model in enumerate(self._models): if self.distributed_type == DistributedType.FSDP: logger.info("Saving FSDP model") save_fsdp_model(self.state.fsdp_plugin, self, model, output_dir, i) logger.info(f"FSDP Model saved to output dir {output_dir}") elif self.distributed_type == DistributedType.DEEPSPEED: logger.info("Saving DeepSpeed Model and Optimizer") ckpt_id = f"{MODEL_NAME}" if i == 0 else f"{MODEL_NAME}_{i}" model.save_checkpoint(output_dir, ckpt_id, **save_model_func_kwargs) logger.info(f"DeepSpeed Model and Optimizer saved to output dir {os.path.join(output_dir, ckpt_id)}") elif self.distributed_type == DistributedType.MEGATRON_LM: logger.info("Saving Megatron-LM Model, Optimizer and Scheduler") model.save_checkpoint(output_dir) logger.info(f"Megatron-LM Model , Optimizer and Scheduler saved to output dir {output_dir}") else: weights.append(self.get_state_dict(model, unwrap=False)) # Save the optimizers taking care of FSDP and DeepSpeed nuances optimizers = [] if self.distributed_type == DistributedType.FSDP: for i, opt in enumerate(self._optimizers): logger.info("Saving FSDP Optimizer") save_fsdp_optimizer(self.state.fsdp_plugin, self, opt, self._models[i], output_dir, i) logger.info(f"FSDP Optimizer saved to output dir {output_dir}") elif self.distributed_type not in [DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM]: optimizers = self._optimizers # Save the lr schedulers taking care of DeepSpeed nuances schedulers = [] if self.distributed_type == DistributedType.DEEPSPEED: for i, scheduler in enumerate(self._schedulers): if isinstance(scheduler, DeepSpeedSchedulerWrapper): continue schedulers.append(scheduler) elif self.distributed_type not in [DistributedType.MEGATRON_LM]: schedulers = self._schedulers # Save the samplers of the dataloaders dataloaders = self._dataloaders # Call model loading hooks that might have been registered with # accelerator.register_model_state_hook for hook in self._save_model_state_pre_hook.values(): hook(self._models, weights, output_dir) save_location = save_accelerator_state( output_dir, weights, optimizers, schedulers, dataloaders, self.state.process_index, self.scaler, save_on_each_node=self.project_configuration.save_on_each_node, safe_serialization=safe_serialization, ) for i, obj in enumerate(self._custom_objects): save_custom_state(obj, output_dir, i, save_on_each_node=self.project_configuration.save_on_each_node) self.project_configuration.iteration += 1 return save_location def register_load_state_pre_hook(self, hook: Callable[..., None]) -> hooks.RemovableHandle: """ Registers a pre hook to be run before [`load_checkpoint`] is called in [`Accelerator.load_state`]. Args: hook (`Callable`): A function to be called in [`Accelerator.load_state`] before `load_checkpoint`. The hook should have the following signature: `hook(models: list[torch.nn.Module], input_dir: str) -> None` The `models` argument are the models as saved in the accelerator state under `accelerator._models`, and the `input_dir` argument is the `input_dir` argument passed to [`Accelerator.load_state`]. <Tip> Should only be used in conjunction with [`Accelerator.register_save_state_pre_hook`]. Can be useful to load configurations in addition to model weights. Can also be used to overwrite model loading with a customized method. In this case, make sure to remove already loaded models from the models list. </Tip> Returns: `torch.utils.hooks.RemovableHandle`: a handle that can be used to remove the added hook by calling `handle.remove()` """ handle = hooks.RemovableHandle(self._load_model_state_pre_hook) self._load_model_state_pre_hook[handle.id] = hook return handle def load_state(self, input_dir: str = None, **load_model_func_kwargs): """ Loads the current states of the model, optimizer, scaler, RNG generators, and registered objects. <Tip> Should only be used in conjunction with [`Accelerator.save_state`]. If a file is not registered for checkpointing, it will not be loaded if stored in the directory. </Tip> Args: input_dir (`str` or `os.PathLike`): The name of the folder all relevant weights and states were saved in. Can be `None` if `automatic_checkpoint_naming` is used, and will pick up from the latest checkpoint. load_model_func_kwargs (`dict`, *optional*): Additional keyword arguments for loading model which can be passed to the underlying load function, such as optional arguments for DeepSpeed's `load_checkpoint` function or a `map_location` to load the model and optimizer on. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model, optimizer, lr_scheduler = ... >>> model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler) >>> accelerator.load_state("my_checkpoint") ``` """ if input_dir is not None: # Check if folder exists input_dir = os.path.expanduser(input_dir) if not os.path.isdir(input_dir): raise ValueError(f"Tried to find {input_dir} but folder does not exist") elif self.project_configuration.automatic_checkpoint_naming: # Pick up from automatic checkpoint naming input_dir = os.path.join(self.project_dir, "checkpoints") folders = [os.path.join(input_dir, folder) for folder in os.listdir(input_dir)] def _inner(folder): return list(map(int, re.findall(r"[\/]?([0-9]+)(?=[^\/]*$)", folder)))[0] folders.sort(key=_inner) input_dir = folders[-1] else: raise ValueError("No input_dir provided and automatic checkpoint naming is disabled.") logger.info(f"Loading states from {input_dir}") # Load the models taking care of FSDP and DeepSpeed nuances models = [] for i, model in enumerate(self._models): if self.distributed_type == DistributedType.FSDP: logger.info("Loading FSDP model") load_fsdp_model(self.state.fsdp_plugin, self, model, input_dir, i) logger.info(f"FSDP Model loaded from input dir {input_dir}") elif self.distributed_type == DistributedType.DEEPSPEED: logger.info("Loading DeepSpeed Model and Optimizer") ckpt_id = f"{MODEL_NAME}" if i == 0 else f"{MODEL_NAME}_{i}" model.load_checkpoint(input_dir, ckpt_id, **load_model_func_kwargs) logger.info(f"DeepSpeed Model and Optimizer loaded from input dir {os.path.join(input_dir, ckpt_id)}") elif self.distributed_type == DistributedType.MEGATRON_LM: logger.info("Loading Megatron-LM Model, Optimizer and Scheduler") model.load_checkpoint(input_dir) logger.info(f"Megatron-LM Model , Optimizer and Scheduler loaded from input dir {input_dir}") else: models.append(model) # Load the optimizers taking care of FSDP and DeepSpeed nuances optimizers = [] if self.distributed_type == DistributedType.FSDP: for i, opt in enumerate(self._optimizers): logger.info("Loading FSDP Optimizer") load_fsdp_optimizer(self.state.fsdp_plugin, self, opt, self._models[i], input_dir, i) logger.info(f"FSDP Optimizer loaded from input dir {input_dir}") elif self.distributed_type not in [DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM]: optimizers = self._optimizers # Load the lr schedulers taking care of DeepSpeed nuances schedulers = [] if self.distributed_type == DistributedType.DEEPSPEED: for i, scheduler in enumerate(self._schedulers): if isinstance(scheduler, DeepSpeedSchedulerWrapper): continue schedulers.append(scheduler) elif self.distributed_type not in [DistributedType.MEGATRON_LM]: schedulers = self._schedulers dataloaders = self._dataloaders # Call model loading hooks that might have been registered with # accelerator.register_model_state_hook for hook in self._load_model_state_pre_hook.values(): hook(models, input_dir) map_location = load_model_func_kwargs.pop("map_location", None) if map_location is None: if self.num_processes > 1 and self.distributed_type in ( DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, ): map_location = "on_device" else: map_location = "cpu" load_accelerator_state( input_dir, models, optimizers, schedulers, dataloaders, self.state.process_index, self.scaler, map_location, **load_model_func_kwargs, ) custom_checkpoints = [ f for f in os.listdir(input_dir) if re.search(r"^custom_checkpoint_\d+\.pkl$", f) is not None ] if len(custom_checkpoints) != len(self._custom_objects): err = "Number of custom checkpoints in folder {input_dir} does not match the number of registered objects:" err += f"\n\tFound checkpoints: {len(custom_checkpoints)}" err += f"\n\tRegistered objects: {len(self._custom_objects)}\n" err += "Please make sure to only load checkpoints from folders that were created with the same set of registered objects," err += "or avoid using `custom_checkpoint` in the filename for files in that same directory and load them in manually." raise RuntimeError(err) else: logger.info(f"Loading in {len(custom_checkpoints)} custom states") for index, obj in enumerate(self._custom_objects): load_custom_state(obj, input_dir, index) def free_memory(self): """ Will release all references to the internal objects stored and call the garbage collector. You should call this method between two trainings with different models/optimizers. Also will reset `Accelerator.step` to 0. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model, optimizer, scheduler = ... >>> model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler) >>> accelerator.free_memory() >>> del model, optimizer, scheduler ``` """ self._schedulers = [] self._optimizers = [] self._models = [] self._dataloaders = [] self.deepspeed_engine_wrapped = None self.step = 0 release_memory() def clear(self): """ Alias for [`Accelerate.free_memory`], releases all references to the internal objects stored and call the garbage collector. You should call this method between two trainings with different models/optimizers. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> model, optimizer, scheduler = ... >>> model, optimizer, scheduler = accelerator.prepare(model, optimizer, scheduler) >>> accelerator.free_memory() >>> del model, optimizer, scheduler ``` """ self.free_memory() def _get_named_parameters(self, *args): named_parameters = {} for obj in args: if isinstance(obj, torch.nn.Module): obj = extract_model_from_parallel(obj) named_parameters.update({n: p for n, p in obj.named_parameters()}) return named_parameters def _get_devices(self, *args): model_device = None optimizer_device = None for obj in args: # Loop through model parameters and stop at the first once we have its device. if isinstance(obj, torch.nn.Module): for param in obj.parameters(): model_device = param.device break # Loop through optimizer parameters groups and stop at the first once we have its device. if isinstance(obj, torch.optim.Optimizer): for param_group in obj.param_groups: if len(param_group["params"]) > 0: optimizer_device = param_group["params"][0].device break return (model_device, optimizer_device) def get_state_dict(self, model, unwrap=True): """ Returns the state dictionary of a model sent through [`Accelerator.prepare`] potentially without full precision. Args: model (`torch.nn.Module`): A PyTorch model sent through [`Accelerator.prepare`] unwrap (`bool`, *optional*, defaults to `True`): Whether to return the original underlying state_dict of `model` or to return the wrapped state_dict Returns: `dict`: The state dictionary of the model potentially without full precision. Example: ```python >>> import torch >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> net = torch.nn.Linear(2, 2) >>> net = accelerator.prepare(net) >>> state_dict = accelerator.get_state_dict(net) ``` """ if self.distributed_type == DistributedType.DEEPSPEED: if self.deepspeed_config["zero_optimization"]["stage"] == 3: if model.zero_gather_16bit_weights_on_model_save(): state_dict = model._zero3_consolidated_16bit_state_dict() else: raise ValueError( "Cannot get 16bit model weights because `stage3_gather_16bit_weights_on_model_save` in DeepSpeed config is False. " "To save the model weights in 16bit, set `stage3_gather_16bit_weights_on_model_save` to True in DeepSpeed config file or " "set `zero3_save_16bit_model` to True when using `accelerate config`. " "To save the full checkpoint, run `model.save_checkpoint(save_dir)` and use `zero_to_fp32.py` to recover weights." ) else: from deepspeed.checkpoint.utils import clone_tensors_for_torch_save state_dict = clone_tensors_for_torch_save(self.unwrap_model(model).state_dict()) elif self.distributed_type == DistributedType.FSDP: from torch.distributed.fsdp import FullStateDictConfig, StateDictType from torch.distributed.fsdp import FullyShardedDataParallel as FSDP full_state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, full_state_dict_config): state_dict = model.state_dict() else: if unwrap: model = self.unwrap_model(model) state_dict = model.state_dict() return state_dict def register_for_checkpointing(self, *objects): """ Makes note of `objects` and will save or load them in during `save_state` or `load_state`. These should be utilized when the state is being loaded or saved in the same script. It is not designed to be used in different scripts. <Tip> Every `object` must have a `load_state_dict` and `state_dict` function to be stored. </Tip> Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> # Assume `CustomObject` has a `state_dict` and `load_state_dict` function. >>> obj = CustomObject() >>> accelerator.register_for_checkpointing(obj) >>> accelerator.save_state("checkpoint.pt") ``` """ invalid_objects = [] for obj in objects: if not hasattr(obj, "state_dict") or not hasattr(obj, "load_state_dict"): invalid_objects.append(obj) if len(invalid_objects) > 0: err = "All `objects` must include a `state_dict` and `load_state_dict` function to be stored. The following inputs are invalid:" for index, obj in enumerate(invalid_objects): err += f"\n\t- Item at index {index}, `{get_pretty_name(obj)}`" raise ValueError(err) self._custom_objects.extend(objects) @contextmanager def autocast(self, cache_enabled: bool = False, autocast_handler: AutocastKwargs = None): """ Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing different will happen otherwise. A different `autocast_handler` can be passed in to override the one set in the `Accelerator` object. This is useful in blocks under `autocast` where you want to revert to fp32. Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator(mixed_precision="fp16") >>> with accelerator.autocast(): ... train() ``` """ if cache_enabled: warnings.warn( "Passing `cache_enabled=True` to `accelerator.autocast` is deprecated and will be removed in v0.23.0. " "Please use the `AutocastKwargs` class instead and pass it to the `Accelerator` as a `kwarg_handler`.", FutureWarning, ) if self.autocast_handler is not None: self.autocast_handler.cache_enabled = True else: self.autocast_handler = AutocastKwargs(cache_enabled=True) if autocast_handler is None: autocast_handler = self.autocast_handler autocast_context = get_mixed_precision_context_manager(self.native_amp, autocast_handler) autocast_context.__enter__() yield autocast_context.__exit__(*sys.exc_info()) @property def optimizer_step_was_skipped(self): """ Whether or not the optimizer update was skipped (because of gradient overflow in mixed precision), in which case the learning rate should not be changed. """ for optimizer in self._optimizers: if optimizer.step_was_skipped: return True return False def skip_first_batches(self, dataloader, num_batches: int = 0): """ Creates a new `torch.utils.data.DataLoader` that will efficiently skip the first `num_batches`. Args: dataloader (`torch.utils.data.DataLoader`): The data loader in which to skip batches. num_batches (`int`, *optional*, defaults to 0): The number of batches to skip Example: ```python >>> from accelerate import Accelerator >>> accelerator = Accelerator() >>> dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler) >>> skipped_dataloader = accelerator.skip_first_batches(dataloader, num_batches=2) >>> # for the first epoch only >>> for input, target in skipped_dataloader: ... optimizer.zero_grad() ... output = model(input) ... loss = loss_func(output, target) ... accelerator.backward(loss) ... optimizer.step() >>> # subsequent epochs >>> for input, target in dataloader: ... optimizer.zero_grad() ... ... ``` """ return skip_first_batches(dataloader, num_batches=num_batches) def __deepcopy__(self, memo): logger.info("Deep copying the `Accelerator` object, note that this will point to the same original object.") return self def verify_device_map(self, model: torch.nn.Module) -> bool: """ Verifies that `model` has not been prepared with big model inference with a device-map resembling `auto`. """ # Checks if any of the child modules has the attribute `hf_device_map` and this map has more than one entry. for m in model.modules(): if hasattr(m, "hf_device_map") and len(m.hf_device_map) > 1: return True return False

0

hf_public_repos/accelerate/src

hf_public_repos/accelerate/src/accelerate/scheduler.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation import warnings from .state import AcceleratorState, GradientState warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler") class AcceleratedScheduler: """ A wrapper around a learning rate scheduler that will only step when the optimizer(s) have a training step. Useful to avoid making a scheduler step too fast when gradients went overflow and there was no training step (in mixed precision training) When performing gradient accumulation scheduler lengths should not be changed accordingly, Accelerate will always step the scheduler to account for it. Args: scheduler (`torch.optim.lr_scheduler._LRScheduler`): The scheduler to wrap. optimizers (one or a list of `torch.optim.Optimizer`): The optimizers used. step_with_optimizer (`bool`, *optional*, defaults to `True`): Whether or not the scheduler should be stepped at each optimizer step. split_batches (`bool`, *optional*, defaults to `False`): Whether or not the dataloaders split one batch across the different processes (so batch size is the same regardless of the number of processes) or create batches on each process (so batch size is the original batch size multiplied by the number of processes). """ def __init__(self, scheduler, optimizers, step_with_optimizer: bool = True, split_batches: bool = False): self.scheduler = scheduler self.optimizers = optimizers if isinstance(optimizers, (list, tuple)) else [optimizers] self.split_batches = split_batches self.step_with_optimizer = step_with_optimizer self.gradient_state = GradientState() def step(self, *args, **kwargs): if not self.step_with_optimizer: # No link between scheduler and optimizer -> just step self.scheduler.step(*args, **kwargs) return # Otherwise, first make sure the optimizer was stepped. if not self.gradient_state.sync_gradients: if self.gradient_state.adjust_scheduler: self.scheduler._step_count += 1 return for opt in self.optimizers: if opt.step_was_skipped: return if self.split_batches: # Split batches -> the training dataloader batch size is not changed so one step per training step self.scheduler.step(*args, **kwargs) else: # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do # num_processes steps per training step num_processes = AcceleratorState().num_processes for _ in range(num_processes): # Special case when using OneCycle and `drop_last` was not used if hasattr(self.scheduler, "total_steps"): if self.scheduler._step_count <= self.scheduler.total_steps: self.scheduler.step(*args, **kwargs) else: self.scheduler.step(*args, **kwargs) # Passthroughs def get_last_lr(self): return self.scheduler.get_last_lr() def state_dict(self): return self.scheduler.state_dict() def load_state_dict(self, state_dict): self.scheduler.load_state_dict(state_dict) def get_lr(self): return self.scheduler.get_lr() def print_lr(self, *args, **kwargs): return self.scheduler.print_lr(*args, **kwargs)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/test_utils/examples.py

#!/usr/bin/env python # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A collection of utilities for comparing `examples/complete_*_example.py` scripts with the capabilities inside of each `examples/by_feature` example. `compare_against_test` is the main function that should be used when testing, while the others are used to either get the code that matters, or to preprocess them (such as stripping comments) """ import os from typing import List def get_function_contents_by_name(lines: List[str], name: str): """ Extracts a function from `lines` of segmented source code with the name `name`. Args: lines (`List[str]`): Source code of a script seperated by line. name (`str`): The name of the function to extract. Should be either `training_function` or `main` """ if name != "training_function" and name != "main": raise ValueError(f"Incorrect function name passed: {name}, choose either 'main' or 'training_function'") good_lines, found_start = [], False for line in lines: if not found_start and f"def {name}" in line: found_start = True good_lines.append(line) continue if found_start: if name == "training_function" and "def main" in line: return good_lines if name == "main" and "if __name__" in line: return good_lines good_lines.append(line) def clean_lines(lines: List[str]): """ Filters `lines` and removes any entries that start with a comment ('#') or is just a newline ('\n') Args: lines (`List[str]`): Source code of a script seperated by line. """ return [line for line in lines if not line.lstrip().startswith("#") and line != "\n"] def compare_against_test(base_filename: str, feature_filename: str, parser_only: bool, secondary_filename: str = None): """ Tests whether the additional code inside of `feature_filename` was implemented in `base_filename`. This should be used when testing to see if `complete_*_.py` examples have all of the implementations from each of the `examples/by_feature/*` scripts. It utilizes `nlp_example.py` to extract out all of the repeated training code, so that only the new additional code is examined and checked. If something *other* than `nlp_example.py` should be used, such as `cv_example.py` for the `complete_cv_example.py` script, it should be passed in for the `secondary_filename` parameter. Args: base_filename (`str` or `os.PathLike`): The filepath of a single "complete" example script to test, such as `examples/complete_cv_example.py` feature_filename (`str` or `os.PathLike`): The filepath of a single feature example script. The contents of this script are checked to see if they exist in `base_filename` parser_only (`bool`): Whether to compare only the `main()` sections in both files, or to compare the contents of `training_loop()` secondary_filename (`str`, *optional*): A potential secondary filepath that should be included in the check. This function extracts the base functionalities off of "examples/nlp_example.py", so if `base_filename` is a script other than `complete_nlp_example.py`, the template script should be included here. Such as `examples/cv_example.py` """ with open(base_filename, "r") as f: base_file_contents = f.readlines() with open(os.path.abspath(os.path.join("examples", "nlp_example.py")), "r") as f: full_file_contents = f.readlines() with open(feature_filename, "r") as f: feature_file_contents = f.readlines() if secondary_filename is not None: with open(secondary_filename, "r") as f: secondary_file_contents = f.readlines() # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content if parser_only: base_file_func = clean_lines(get_function_contents_by_name(base_file_contents, "main")) full_file_func = clean_lines(get_function_contents_by_name(full_file_contents, "main")) feature_file_func = clean_lines(get_function_contents_by_name(feature_file_contents, "main")) if secondary_filename is not None: secondary_file_func = clean_lines(get_function_contents_by_name(secondary_file_contents, "main")) else: base_file_func = clean_lines(get_function_contents_by_name(base_file_contents, "training_function")) full_file_func = clean_lines(get_function_contents_by_name(full_file_contents, "training_function")) feature_file_func = clean_lines(get_function_contents_by_name(feature_file_contents, "training_function")) if secondary_filename is not None: secondary_file_func = clean_lines( get_function_contents_by_name(secondary_file_contents, "training_function") ) _dl_line = "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n" # Specific code in our script that differs from the full version, aka what is new new_feature_code = [] passed_idxs = [] # We keep track of the idxs just in case it's a repeated statement it = iter(feature_file_func) for i in range(len(feature_file_func) - 1): if i not in passed_idxs: line = next(it) if (line not in full_file_func) and (line.lstrip() != _dl_line): if "TESTING_MOCKED_DATALOADERS" not in line: new_feature_code.append(line) passed_idxs.append(i) else: # Skip over the `config['num_epochs'] = 2` statement _ = next(it) # Extract out just the new parts from the full_file_training_func new_full_example_parts = [] passed_idxs = [] # We keep track of the idxs just in case it's a repeated statement for i, line in enumerate(base_file_func): if i not in passed_idxs: if (line not in full_file_func) and (line.lstrip() != _dl_line): if "TESTING_MOCKED_DATALOADERS" not in line: new_full_example_parts.append(line) passed_idxs.append(i) # Finally, get the overall diff diff_from_example = [line for line in new_feature_code if line not in new_full_example_parts] if secondary_filename is not None: diff_from_two = [line for line in full_file_contents if line not in secondary_file_func] diff_from_example = [line for line in diff_from_example if line not in diff_from_two] return diff_from_example

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/test_utils/__init__.py

from .testing import ( are_the_same_tensors, assert_exception, device_count, execute_subprocess_async, require_bnb, require_cpu, require_cuda, require_huggingface_suite, require_mps, require_multi_device, require_multi_gpu, require_multi_xpu, require_non_cpu, require_single_device, require_single_gpu, require_single_xpu, require_torch_min_version, require_tpu, require_xpu, skip, slow, torch_device, ) from .training import RegressionDataset, RegressionModel, RegressionModel4XPU from .scripts import test_script, test_sync, test_ops # isort: skip

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/test_utils/training.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import torch from torch.utils.data import DataLoader from accelerate.utils.dataclasses import DistributedType class RegressionDataset: def __init__(self, a=2, b=3, length=64, seed=None): rng = np.random.default_rng(seed) self.length = length self.x = rng.normal(size=(length,)).astype(np.float32) self.y = a * self.x + b + rng.normal(scale=0.1, size=(length,)).astype(np.float32) def __len__(self): return self.length def __getitem__(self, i): return {"x": self.x[i], "y": self.y[i]} class RegressionModel4XPU(torch.nn.Module): def __init__(self, a=0, b=0, double_output=False): super().__init__() self.a = torch.nn.Parameter(torch.tensor([2, 3]).float()) self.b = torch.nn.Parameter(torch.tensor([2, 3]).float()) self.first_batch = True def forward(self, x=None): if self.first_batch: print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}") self.first_batch = False return x * self.a[0] + self.b[0] class RegressionModel(torch.nn.Module): def __init__(self, a=0, b=0, double_output=False): super().__init__() self.a = torch.nn.Parameter(torch.tensor(a).float()) self.b = torch.nn.Parameter(torch.tensor(b).float()) self.first_batch = True def forward(self, x=None): if self.first_batch: print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}") self.first_batch = False return x * self.a + self.b def mocked_dataloaders(accelerator, batch_size: int = 16): from datasets import load_dataset from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") data_files = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"} datasets = load_dataset("csv", data_files=data_files) label_list = datasets["train"].unique("label") label_to_id = {v: i for i, v in enumerate(label_list)} def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer( examples["sentence1"], examples["sentence2"], truncation=True, max_length=None, padding="max_length" ) if "label" in examples: outputs["labels"] = [label_to_id[l] for l in examples["label"]] return outputs # Apply the method we just defined to all the examples in all the splits of the dataset tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["sentence1", "sentence2", "label"], ) def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader(tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=2) eval_dataloader = DataLoader(tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=1) return train_dataloader, eval_dataloader

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/test_utils/testing.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import os import shutil import subprocess import sys import tempfile import unittest from contextlib import contextmanager from functools import partial from pathlib import Path from typing import List, Union from unittest import mock import torch from ..state import AcceleratorState, PartialState from ..utils import ( gather, is_bnb_available, is_clearml_available, is_comet_ml_available, is_cuda_available, is_datasets_available, is_deepspeed_available, is_dvclive_available, is_mps_available, is_npu_available, is_pandas_available, is_tensorboard_available, is_timm_available, is_torch_version, is_tpu_available, is_transformers_available, is_wandb_available, is_xpu_available, str_to_bool, ) def get_backend(): if is_cuda_available(): return "cuda", torch.cuda.device_count() elif is_mps_available(): return "mps", 1 elif is_npu_available(): return "npu", torch.npu.device_count() elif is_xpu_available(): return "xpu", torch.xpu.device_count() else: return "cpu", 1 torch_device, device_count = get_backend() def parse_flag_from_env(key, default=False): try: value = os.environ[key] except KeyError: # KEY isn't set, default to `default`. _value = default else: # KEY is set, convert it to True or False. try: _value = str_to_bool(value) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(f"If set, {key} must be yes or no.") return _value _run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False) def skip(test_case): "Decorator that skips a test unconditionally" return unittest.skip("Test was skipped")(test_case) def slow(test_case): """ Decorator marking a test as slow. Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them. """ return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case) def require_cpu(test_case): """ Decorator marking a test that must be only ran on the CPU. These tests are skipped when a GPU is available. """ return unittest.skipUnless(torch_device == "cpu", "test requires only a CPU")(test_case) def require_non_cpu(test_case): """ Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no hardware accelerator available. """ return unittest.skipUnless(torch_device != "cpu", "test requires a GPU")(test_case) def require_cuda(test_case): """ Decorator marking a test that requires CUDA. These tests are skipped when there are no GPU available. """ return unittest.skipUnless(is_cuda_available(), "test requires a GPU")(test_case) def require_xpu(test_case): """ Decorator marking a test that requires XPU. These tests are skipped when there are no XPU available. """ return unittest.skipUnless(is_xpu_available(), "test requires a XPU")(test_case) def require_mps(test_case): """ Decorator marking a test that requires MPS backend. These tests are skipped when torch doesn't support `mps` backend. """ return unittest.skipUnless(is_mps_available(), "test requires a `mps` backend support in `torch`")(test_case) def require_huggingface_suite(test_case): """ Decorator marking a test that requires transformers and datasets. These tests are skipped when they are not. """ return unittest.skipUnless( is_transformers_available() and is_datasets_available(), "test requires the Hugging Face suite" )(test_case) def require_transformers(test_case): """ Decorator marking a test that requires transformers. These tests are skipped when they are not. """ return unittest.skipUnless(is_transformers_available(), "test requires the transformers library")(test_case) def require_timm(test_case): """ Decorator marking a test that requires transformers. These tests are skipped when they are not. """ return unittest.skipUnless(is_timm_available(), "test requires the timm library")(test_case) def require_bnb(test_case): """ Decorator marking a test that requires bitsandbytes. These tests are skipped when they are not. """ return unittest.skipUnless(is_bnb_available(), "test requires the bitsandbytes library")(test_case) def require_tpu(test_case): """ Decorator marking a test that requires TPUs. These tests are skipped when there are no TPUs available. """ return unittest.skipUnless(is_tpu_available(), "test requires TPU")(test_case) def require_single_device(test_case): """ Decorator marking a test that requires a single device. These tests are skipped when there is no hardware accelerator available or number of devices is more than one. """ return unittest.skipUnless(torch_device != "cpu" and device_count == 1, "test requires a hardware accelerator")( test_case ) def require_single_gpu(test_case): """ Decorator marking a test that requires CUDA on a single GPU. These tests are skipped when there are no GPU available or number of GPUs is more than one. """ return unittest.skipUnless(torch.cuda.device_count() == 1, "test requires a GPU")(test_case) def require_single_xpu(test_case): """ Decorator marking a test that requires CUDA on a single XPU. These tests are skipped when there are no XPU available or number of xPUs is more than one. """ return unittest.skipUnless(torch.xpu.device_count() == 1, "test requires a XPU")(test_case) def require_multi_device(test_case): """ Decorator marking a test that requires a multi-device setup. These tests are skipped on a machine without multiple devices. """ return unittest.skipUnless(device_count > 1, "test requires multiple hardware accelerators")(test_case) def require_multi_gpu(test_case): """ Decorator marking a test that requires a multi-GPU setup. These tests are skipped on a machine without multiple GPUs. """ return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case) def require_multi_xpu(test_case): """ Decorator marking a test that requires a multi-XPU setup. These tests are skipped on a machine without multiple XPUs. """ return unittest.skipUnless(torch.xpu.device_count() > 1, "test requires multiple XPUs")(test_case) def require_deepspeed(test_case): """ Decorator marking a test that requires DeepSpeed installed. These tests are skipped when DeepSpeed isn't installed """ return unittest.skipUnless(is_deepspeed_available(), "test requires DeepSpeed")(test_case) def require_fsdp(test_case): """ Decorator marking a test that requires FSDP installed. These tests are skipped when FSDP isn't installed """ return unittest.skipUnless(is_torch_version(">=", "1.12.0"), "test requires torch version >= 1.12.0")(test_case) def require_torch_min_version(test_case=None, version=None): """ Decorator marking that a test requires a particular torch version to be tested. These tests are skipped when an installed torch version is less than the required one. """ if test_case is None: return partial(require_torch_min_version, version=version) return unittest.skipUnless(is_torch_version(">=", version), f"test requires torch version >= {version}")(test_case) def require_tensorboard(test_case): """ Decorator marking a test that requires tensorboard installed. These tests are skipped when tensorboard isn't installed """ return unittest.skipUnless(is_tensorboard_available(), "test requires Tensorboard")(test_case) def require_wandb(test_case): """ Decorator marking a test that requires wandb installed. These tests are skipped when wandb isn't installed """ return unittest.skipUnless(is_wandb_available(), "test requires wandb")(test_case) def require_comet_ml(test_case): """ Decorator marking a test that requires comet_ml installed. These tests are skipped when comet_ml isn't installed """ return unittest.skipUnless(is_comet_ml_available(), "test requires comet_ml")(test_case) def require_clearml(test_case): """ Decorator marking a test that requires clearml installed. These tests are skipped when clearml isn't installed """ return unittest.skipUnless(is_clearml_available(), "test requires clearml")(test_case) def require_dvclive(test_case): """ Decorator marking a test that requires dvclive installed. These tests are skipped when dvclive isn't installed """ return unittest.skipUnless(is_dvclive_available(), "test requires dvclive")(test_case) def require_pandas(test_case): """ Decorator marking a test that requires pandas installed. These tests are skipped when pandas isn't installed """ return unittest.skipUnless(is_pandas_available(), "test requires pandas")(test_case) _atleast_one_tracker_available = ( any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available() ) def require_trackers(test_case): """ Decorator marking that a test requires at least one tracking library installed. These tests are skipped when none are installed """ return unittest.skipUnless( _atleast_one_tracker_available, "test requires at least one tracker to be available and for `comet_ml` to not be installed", )(test_case) class TempDirTestCase(unittest.TestCase): """ A TestCase class that keeps a single `tempfile.TemporaryDirectory` open for the duration of the class, wipes its data at the start of a test, and then destroyes it at the end of the TestCase. Useful for when a class or API requires a single constant folder throughout it's use, such as Weights and Biases The temporary directory location will be stored in `self.tmpdir` """ clear_on_setup = True @classmethod def setUpClass(cls): "Creates a `tempfile.TemporaryDirectory` and stores it in `cls.tmpdir`" cls.tmpdir = tempfile.mkdtemp() @classmethod def tearDownClass(cls): "Remove `cls.tmpdir` after test suite has finished" if os.path.exists(cls.tmpdir): shutil.rmtree(cls.tmpdir) def setUp(self): "Destroy all contents in `self.tmpdir`, but not `self.tmpdir`" if self.clear_on_setup: for path in Path(self.tmpdir).glob("**/*"): if path.is_file(): path.unlink() elif path.is_dir(): shutil.rmtree(path) class AccelerateTestCase(unittest.TestCase): """ A TestCase class that will reset the accelerator state at the end of every test. Every test that checks or utilizes the `AcceleratorState` class should inherit from this to avoid silent failures due to state being shared between tests. """ def tearDown(self): super().tearDown() # Reset the state of the AcceleratorState singleton. AcceleratorState._reset_state() PartialState._reset_state() class MockingTestCase(unittest.TestCase): """ A TestCase class designed to dynamically add various mockers that should be used in every test, mimicking the behavior of a class-wide mock when defining one normally will not do. Useful when a mock requires specific information available only initialized after `TestCase.setUpClass`, such as setting an environment variable with that information. The `add_mocks` function should be ran at the end of a `TestCase`'s `setUp` function, after a call to `super().setUp()` such as: ```python def setUp(self): super().setUp() mocks = mock.patch.dict(os.environ, {"SOME_ENV_VAR", "SOME_VALUE"}) self.add_mocks(mocks) ``` """ def add_mocks(self, mocks: Union[mock.Mock, List[mock.Mock]]): """ Add custom mocks for tests that should be repeated on each test. Should be called during `MockingTestCase.setUp`, after `super().setUp()`. Args: mocks (`mock.Mock` or list of `mock.Mock`): Mocks that should be added to the `TestCase` after `TestCase.setUpClass` has been run """ self.mocks = mocks if isinstance(mocks, (tuple, list)) else [mocks] for m in self.mocks: m.start() self.addCleanup(m.stop) def are_the_same_tensors(tensor): state = AcceleratorState() tensor = tensor[None].clone().to(state.device) tensors = gather(tensor).cpu() tensor = tensor[0].cpu() for i in range(tensors.shape[0]): if not torch.equal(tensors[i], tensor): return False return True class _RunOutput: def __init__(self, returncode, stdout, stderr): self.returncode = returncode self.stdout = stdout self.stderr = stderr async def _read_stream(stream, callback): while True: line = await stream.readline() if line: callback(line) else: break async def _stream_subprocess(cmd, env=None, stdin=None, timeout=None, quiet=False, echo=False) -> _RunOutput: if echo: print("\nRunning: ", " ".join(cmd)) p = await asyncio.create_subprocess_exec( cmd[0], *cmd[1:], stdin=stdin, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, env=env, ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) out = [] err = [] def tee(line, sink, pipe, label=""): line = line.decode("utf-8").rstrip() sink.append(line) if not quiet: print(label, line, file=pipe) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ asyncio.create_task(_read_stream(p.stdout, lambda l: tee(l, out, sys.stdout, label="stdout:"))), asyncio.create_task(_read_stream(p.stderr, lambda l: tee(l, err, sys.stderr, label="stderr:"))), ], timeout=timeout, ) return _RunOutput(await p.wait(), out, err) def execute_subprocess_async(cmd, env=None, stdin=None, timeout=180, quiet=False, echo=True) -> _RunOutput: loop = asyncio.get_event_loop() result = loop.run_until_complete( _stream_subprocess(cmd, env=env, stdin=stdin, timeout=timeout, quiet=quiet, echo=echo) ) cmd_str = " ".join(cmd) if result.returncode > 0: stderr = "\n".join(result.stderr) raise RuntimeError( f"'{cmd_str}' failed with returncode {result.returncode}\n\n" f"The combined stderr from workers follows:\n{stderr}" ) return result class SubprocessCallException(Exception): pass def run_command(command: List[str], return_stdout=False, env=None): """ Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture if an error occured while running `command` """ if env is None: env = os.environ.copy() try: output = subprocess.check_output(command, stderr=subprocess.STDOUT, env=env) if return_stdout: if hasattr(output, "decode"): output = output.decode("utf-8") return output except subprocess.CalledProcessError as e: raise SubprocessCallException( f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}" ) from e @contextmanager def assert_exception(exception_class: Exception, msg: str = None) -> bool: """ Context manager to assert that the right `Exception` class was raised. If `msg` is provided, will check that the message is contained in the raised exception. """ was_ran = False try: yield was_ran = True except Exception as e: assert isinstance(e, exception_class), f"Expected exception of type {exception_class} but got {type(e)}" if msg is not None: assert msg in str(e), f"Expected message '{msg}' to be in exception but got '{str(e)}'" if was_ran: raise AssertionError(f"Expected exception of type {exception_class} but ran without issue.")

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_cli.py

import torch def main(): if torch.cuda.is_available(): num_gpus = torch.cuda.device_count() else: num_gpus = 0 print(f"Successfully ran on {num_gpus} GPUs") if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_notebook.py

# Test file to ensure that in general certain situational setups for notebooks work. import os from pytest import raises from accelerate import PartialState, notebook_launcher from accelerate.test_utils import require_bnb from accelerate.utils import is_bnb_available def basic_function(): # Just prints the PartialState print(f"PartialState:\n{PartialState()}") NUM_PROCESSES = int(os.environ.get("ACCELERATE_NUM_PROCESSES", 1)) def test_can_initialize(): notebook_launcher(basic_function, (), num_processes=NUM_PROCESSES) @require_bnb def test_problematic_imports(): with raises(RuntimeError, match="Please keep these imports"): import bitsandbytes as bnb # noqa: F401 notebook_launcher(basic_function, (), num_processes=NUM_PROCESSES) def main(): print("Test basic notebook can be ran") test_can_initialize() if is_bnb_available(): print("Test problematic imports (bnb)") test_problematic_imports() if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_ops.py

#!/usr/bin/env python # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from accelerate import PartialState from accelerate.test_utils.testing import assert_exception from accelerate.utils.dataclasses import DistributedType from accelerate.utils.operations import ( DistributedOperationException, broadcast, gather, gather_object, pad_across_processes, reduce, ) def create_tensor(state): return (torch.arange(state.num_processes) + 1.0 + (state.num_processes * state.process_index)).to(state.device) def test_gather(state): tensor = create_tensor(state) gathered_tensor = gather(tensor) assert gathered_tensor.tolist() == list(range(1, state.num_processes**2 + 1)) def test_gather_object(state): obj = [state.process_index] gathered_obj = gather_object(obj) assert len(gathered_obj) == state.num_processes, f"{gathered_obj}, {len(gathered_obj)} != {state.num_processes}" assert gathered_obj == list(range(state.num_processes)), f"{gathered_obj} != {list(range(state.num_processes))}" def test_gather_non_contigous(state): # Create a non-contiguous tensor tensor = torch.arange(12).view(4, 3).t().to(state.device) assert not tensor.is_contiguous() # Shouldn't error out _ = gather(tensor) def test_broadcast(state): tensor = create_tensor(state) broadcasted_tensor = broadcast(tensor) assert broadcasted_tensor.shape == torch.Size([state.num_processes]) assert broadcasted_tensor.tolist() == list(range(1, state.num_processes + 1)) def test_pad_across_processes(state): # We need to pad the tensor with one more element if we are the main process # to ensure that we can pad if state.is_main_process: tensor = torch.arange(state.num_processes + 1).to(state.device) else: tensor = torch.arange(state.num_processes).to(state.device) padded_tensor = pad_across_processes(tensor) assert padded_tensor.shape == torch.Size([state.num_processes + 1]) if not state.is_main_process: assert padded_tensor.tolist() == list(range(0, state.num_processes)) + [0] def test_reduce_sum(state): # For now runs on only two processes if state.num_processes != 2: return tensor = create_tensor(state) reduced_tensor = reduce(tensor, "sum") truth_tensor = torch.tensor([4.0, 6]).to(state.device) assert torch.allclose(reduced_tensor, truth_tensor), f"{reduced_tensor} != {truth_tensor}" def test_reduce_mean(state): # For now runs on only two processes if state.num_processes != 2: return tensor = create_tensor(state) reduced_tensor = reduce(tensor, "mean") truth_tensor = torch.tensor([2.0, 3]).to(state.device) assert torch.allclose(reduced_tensor, truth_tensor), f"{reduced_tensor} != {truth_tensor}" def test_op_checker(state): # Must be in a distributed state if state.distributed_type == DistributedType.NO: return state.debug = True # `pad_across_processes` if state.process_index == 0: data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)} else: data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4, 5]]]).to(state.device)} with assert_exception(DistributedOperationException): pad_across_processes(data, dim=0) # `reduce` if state.process_index == 0: data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)} else: data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)} with assert_exception(DistributedOperationException): reduce(data) # `broadcast` if state.process_index == 0: data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)} else: data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)} with assert_exception(DistributedOperationException): broadcast(data) state.debug = False def _mp_fn(index): # For xla_spawn (TPUs) main() def main(): state = PartialState() state.print(f"State: {state}") state.print("testing gather") test_gather(state) state.print("testing gather_object") test_gather_object(state) state.print("testing gather non-contigous") test_gather_non_contigous(state) state.print("testing broadcast") test_broadcast(state) state.print("testing pad_across_processes") test_pad_across_processes(state) state.print("testing reduce_sum") test_reduce_sum(state) state.print("testing reduce_mean") test_reduce_mean(state) state.print("testing op_checker") test_op_checker(state) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_script.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import io import math import time from copy import deepcopy from pathlib import Path import numpy as np import torch from torch.utils.data import DataLoader, Dataset from accelerate import Accelerator from accelerate.data_loader import SeedableRandomSampler, prepare_data_loader from accelerate.state import AcceleratorState from accelerate.test_utils import RegressionDataset, are_the_same_tensors from accelerate.utils import ( DistributedType, gather, is_bf16_available, is_ipex_available, is_npu_available, is_xpu_available, set_seed, synchronize_rng_states, ) # TODO: remove RegressionModel4XPU once ccl support empty buffer in broadcasting. if is_xpu_available(): from accelerate.test_utils import RegressionModel4XPU as RegressionModel else: from accelerate.test_utils import RegressionModel def print_main(state): print(f"Printing from the main process {state.process_index}") def print_local_main(state): print(f"Printing from the local main process {state.local_process_index}") def print_last(state): print(f"Printing from the last process {state.process_index}") def print_on(state, process_idx): print(f"Printing from process {process_idx}: {state.process_index}") def process_execution_check(): accelerator = Accelerator() num_processes = accelerator.num_processes # Test main_process_first context manager path = Path("check_main_process_first.txt") with accelerator.main_process_first(): if accelerator.is_main_process: time.sleep(0.1) # ensure main process takes longest with open(path, "a+") as f: f.write("Currently in the main process\n") else: with open(path, "a+") as f: f.write("Now on another process\n") accelerator.wait_for_everyone() if accelerator.is_main_process: with open(path, "r") as f: text = "".join(f.readlines()) try: assert text.startswith("Currently in the main process\n"), "Main process was not first" if num_processes > 1: assert text.endswith("Now on another process\n"), "Main process was not first" assert ( text.count("Now on another process\n") == accelerator.num_processes - 1 ), f"Only wrote to file {text.count('Now on another process') + 1} times, not {accelerator.num_processes}" except AssertionError: path.unlink() raise if accelerator.is_main_process and path.exists(): path.unlink() accelerator.wait_for_everyone() # Test the decorators f = io.StringIO() with contextlib.redirect_stdout(f): accelerator.on_main_process(print_main)(accelerator.state) result = f.getvalue().rstrip() if accelerator.is_main_process: assert result == "Printing from the main process 0", f"{result} != Printing from the main process 0" else: assert f.getvalue().rstrip() == "", f'{result} != ""' f.truncate(0) f.seek(0) with contextlib.redirect_stdout(f): accelerator.on_local_main_process(print_local_main)(accelerator.state) if accelerator.is_local_main_process: assert f.getvalue().rstrip() == "Printing from the local main process 0" else: assert f.getvalue().rstrip() == "" f.truncate(0) f.seek(0) with contextlib.redirect_stdout(f): accelerator.on_last_process(print_last)(accelerator.state) if accelerator.is_last_process: assert f.getvalue().rstrip() == f"Printing from the last process {accelerator.state.num_processes - 1}" else: assert f.getvalue().rstrip() == "" f.truncate(0) f.seek(0) for process_idx in range(num_processes): with contextlib.redirect_stdout(f): accelerator.on_process(print_on, process_index=process_idx)(accelerator.state, process_idx) if accelerator.process_index == process_idx: assert f.getvalue().rstrip() == f"Printing from process {process_idx}: {accelerator.process_index}" else: assert f.getvalue().rstrip() == "" f.truncate(0) f.seek(0) def init_state_check(): # Test we can instantiate this twice in a row. state = AcceleratorState() if state.local_process_index == 0: print("Testing, testing. 1, 2, 3.") print(state) def rng_sync_check(): state = AcceleratorState() synchronize_rng_states(["torch"]) assert are_the_same_tensors(torch.get_rng_state()), "RNG states improperly synchronized on CPU." if state.distributed_type == DistributedType.MULTI_GPU: synchronize_rng_states(["cuda"]) assert are_the_same_tensors(torch.cuda.get_rng_state()), "RNG states improperly synchronized on GPU." elif state.distributed_type == DistributedType.MULTI_XPU: synchronize_rng_states(["xpu"]) assert are_the_same_tensors(torch.xpu.get_rng_state()), "RNG states improperly synchronized on XPU." generator = torch.Generator() synchronize_rng_states(["generator"], generator=generator) assert are_the_same_tensors(generator.get_state()), "RNG states improperly synchronized in generator." if state.local_process_index == 0: print("All rng are properly synched.") def dl_preparation_check(): state = AcceleratorState() length = 32 * state.num_processes dl = DataLoader(range(length), batch_size=8) dl = prepare_data_loader(dl, state.device, state.num_processes, state.process_index, put_on_device=True) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result) print(state.process_index, result, type(dl)) assert torch.equal(result.cpu(), torch.arange(0, length).long()), "Wrong non-shuffled dataloader result." dl = DataLoader(range(length), batch_size=8) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, split_batches=True, ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result) assert torch.equal(result.cpu(), torch.arange(0, length).long()), "Wrong non-shuffled dataloader result." if state.process_index == 0: print("Non-shuffled dataloader passing.") dl = DataLoader(range(length), batch_size=8, shuffle=True) dl = prepare_data_loader(dl, state.device, state.num_processes, state.process_index, put_on_device=True) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result).tolist() result.sort() assert result == list(range(length)), "Wrong shuffled dataloader result." dl = DataLoader(range(length), batch_size=8, shuffle=True) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, split_batches=True, ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result).tolist() result.sort() assert result == list(range(length)), "Wrong shuffled dataloader result." if state.local_process_index == 0: print("Shuffled dataloader passing.") def central_dl_preparation_check(): state = AcceleratorState() length = 32 * state.num_processes dl = DataLoader(range(length), batch_size=8) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, dispatch_batches=True ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result) assert torch.equal(result.cpu(), torch.arange(0, length).long()), "Wrong non-shuffled dataloader result." dl = DataLoader(range(length), batch_size=8) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, split_batches=True, dispatch_batches=True, ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result) assert torch.equal(result.cpu(), torch.arange(0, length).long()), "Wrong non-shuffled dataloader result." if state.process_index == 0: print("Non-shuffled central dataloader passing.") dl = DataLoader(range(length), batch_size=8, shuffle=True) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, dispatch_batches=True ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result).tolist() result.sort() assert result == list(range(length)), "Wrong shuffled dataloader result." dl = DataLoader(range(length), batch_size=8, shuffle=True) dl = prepare_data_loader( dl, state.device, state.num_processes, state.process_index, put_on_device=True, split_batches=True, dispatch_batches=True, ) result = [] for batch in dl: result.append(gather(batch)) result = torch.cat(result).tolist() result.sort() assert result == list(range(length)), "Wrong shuffled dataloader result." if state.local_process_index == 0: print("Shuffled central dataloader passing.") def custom_sampler_check(): state = AcceleratorState() class CustomDataset(Dataset): def __init__(self, data): self.data = data def __len__(self): return len(self.data) def __getitem__(self, index): return self.data[index] class CustomBatchSampler: def __init__(self, dataset_length: int, batch_size: int, shuffle: bool = True): self.batch_size = batch_size self.data_index = np.arange(dataset_length) self.shuffle = shuffle def __iter__(self): num_batches = len(self) if self.shuffle: index = np.random.permutation(self.data_index) else: index = self.data_index output = np.array_split(index, num_batches) yield from output def __len__(self): return math.ceil(len(self.data_index) / self.batch_size) dataset = CustomDataset(range(32 * state.num_processes)) sampler = CustomBatchSampler(len(dataset), batch_size=8) dl = DataLoader(dataset, batch_sampler=sampler) dl = prepare_data_loader(dl, state.device, state.num_processes, state.process_index) # We need just ensure that `dl.batch_sampler` (or `dl.batch_sampler.batch_sampler` is indeed the old batch sampler if hasattr(dl.batch_sampler, "batch_sampler"): assert isinstance( dl.batch_sampler.batch_sampler, CustomBatchSampler ), "Custom sampler was changed after calling `prepare_data_loader`" else: assert isinstance( dl.batch_sampler, CustomBatchSampler ), "Custom sampler was changed after calling `prepare_data_loader`" def mock_training(length, batch_size, generator, use_seedable_sampler=False): set_seed(42) generator.manual_seed(42) train_set = RegressionDataset(length=length, seed=42) if use_seedable_sampler: # The SeedableRandomSampler is needed during distributed setups # for full reproducability across processes with the `DataLoader` sampler = SeedableRandomSampler( generator=generator, data_source=train_set, num_samples=len(train_set), ) train_dl = DataLoader(train_set, batch_size=batch_size, sampler=sampler) else: train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) for epoch in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) loss.backward() optimizer.step() return train_set, model def training_check(use_seedable_sampler=False): state = AcceleratorState() generator = torch.Generator() batch_size = 8 length = batch_size * 4 * state.num_processes train_set, old_model = mock_training(length, batch_size * state.num_processes, generator, use_seedable_sampler) assert are_the_same_tensors(old_model.a), "Did not obtain the same model on both processes." assert are_the_same_tensors(old_model.b), "Did not obtain the same model on both processes." accelerator = Accelerator() if use_seedable_sampler: # The SeedableRandomSampler is needed during distributed setups # for full reproducability across processes with the `DataLoader` sampler = SeedableRandomSampler( generator=generator, data_source=train_set, num_samples=len(train_set), ) train_dl = DataLoader(train_set, batch_size=batch_size, sampler=sampler) else: train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for epoch in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training." accelerator.print("Training yielded the same results on one CPU or distributed setup with no batch split.") accelerator = Accelerator(split_batches=True, use_seedable_sampler=use_seedable_sampler) train_dl = DataLoader(train_set, batch_size=batch_size * state.num_processes, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for _ in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training." accelerator.print("Training yielded the same results on one CPU or distributes setup with batch split.") if torch.cuda.is_available() or is_npu_available(): # Mostly a test that FP16 doesn't crash as the operation inside the model is not converted to FP16 print("FP16 training check.") AcceleratorState._reset_state() accelerator = Accelerator(mixed_precision="fp16", use_seedable_sampler=use_seedable_sampler) train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for _ in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training." if torch.cuda.is_available(): # Mostly a test that model.forward will have autocast when running unwrap_model(model, keep_fp32_wrapper=True) print("Keep fp32 wrapper check.") AcceleratorState._reset_state() accelerator = Accelerator(mixed_precision="fp16") model = torch.nn.Linear(2, 4) model = accelerator.prepare(model) model_with_fp32_wrapper = accelerator.unwrap_model(model, keep_fp32_wrapper=True) # Run forward with fp16 as input. # When the model is with mixed precision wrapper, no error will be raised. input_tensor = torch.Tensor([1, 2]).to(dtype=torch.float16, device=accelerator.device) output = model_with_fp32_wrapper(input_tensor) # BF16 support is only for CPU + TPU, and some GPU if is_bf16_available(): # Mostly a test that BF16 doesn't crash as the operation inside the model is not converted to BF16 print("BF16 training check.") AcceleratorState._reset_state() accelerator = Accelerator(mixed_precision="bf16", use_seedable_sampler=use_seedable_sampler) train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for _ in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training." # IPEX support is only for CPU if is_ipex_available(): print("ipex BF16 training check.") AcceleratorState._reset_state() accelerator = Accelerator(mixed_precision="bf16", cpu=True, use_seedable_sampler=use_seedable_sampler) train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for _ in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training." # XPU support is only for XPU if is_xpu_available(): print("xpu BF16 training check.") AcceleratorState._reset_state() accelerator = Accelerator(mixed_precision="bf16", cpu=False, use_seedable_sampler=use_seedable_sampler) train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator) model = RegressionModel() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer) set_seed(42) generator.manual_seed(42) for _ in range(3): for batch in train_dl: model.zero_grad() output = model(batch["x"]) loss = torch.nn.functional.mse_loss(output, batch["y"]) accelerator.backward(loss) optimizer.step() model = accelerator.unwrap_model(model).cpu() assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on XPU or distributed training." assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on XPU or distributed training." def test_split_between_processes_list(): state = AcceleratorState() data = list(range(0, 2 * state.num_processes)) with state.split_between_processes(data) as results: assert ( len(results) == 2 ), f"Each process did not have two items. Process index: {state.process_index}; Length: {len(results)}" data = list(range(0, (3 * state.num_processes) - 1)) with state.split_between_processes(data, apply_padding=True) as results: if state.is_last_process: # Test that the last process gets the extra item(s) num_samples_per_device = math.ceil(len(data) / state.num_processes) assert ( len(results) == num_samples_per_device ), f"Last process did not get the extra item(s). Process index: {state.process_index}; Length: {len(results)}" state.wait_for_everyone() def test_split_between_processes_nested_dict(): state = AcceleratorState() a = [1, 2, 3, 4, 5, 6, 7, 8] b = ["a", "b", "c", "d", "e", "f", "g", "h"] c = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]) if state.num_processes in (1, 2, 4): data = {"a": a, "b": b, "c": c} data_copy = deepcopy(data) with state.split_between_processes(data) as results: if state.process_index == 0: assert results["a"] == data_copy["a"][: 8 // state.num_processes] elif state.num_processes == 2: assert results["a"] == data_copy["a"][4:] elif state.process_index == 3: # We return a list each time assert results["a"] == data_copy["a"][-2:], f'Expected: {data_copy["a"][-2]}, Actual: {results["a"]}' if state.process_index == 0: assert results["b"] == data_copy["b"][: 8 // state.num_processes] elif state.num_processes == 2: assert results["b"] == data_copy["b"][4:] elif state.process_index == 3: assert results["b"] == data_copy["b"][-2:] if state.process_index == 0: assert torch.allclose( results["c"], data_copy["c"][: 8 // state.num_processes] ), f"Did not obtain expected values on process 0, expected `{data['c'][:8 // state.num_processes]}`, received: {results['c']}" elif state.num_processes == 2: assert torch.allclose( results["c"], data_copy["c"][4:] ), f"Did not obtain expected values on process 2, expected `{data['c'][4:]}`, received: {results['c']}" elif state.process_index == 3: assert torch.allclose( results["c"], data_copy["c"][-2:] ), f"Did not obtain expected values on process 4, expected `{data['c'][-2:]}`, received: {results['c']}" state.wait_for_everyone() def test_split_between_processes_tensor(): state = AcceleratorState() if state.num_processes > 1: data = torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]]).to(state.device) with state.split_between_processes(data) as results: if state.process_index == 0: assert torch.allclose(results, torch.tensor([0, 1, 2, 3]).to(state.device)) else: assert torch.allclose(results, torch.tensor([4, 5, 6, 7]).to(state.device)) state.wait_for_everyone() def test_trigger(): accelerator = Accelerator() # should start with being false assert accelerator.check_trigger() is False # set a breakpoint on the main process if accelerator.is_main_process: accelerator.set_trigger() # check it's been activated across all processes # calls `all_reduce` and triggers a sync assert accelerator.check_trigger() is True # check it's been reset after the sync assert accelerator.check_trigger() is False def main(): accelerator = Accelerator() state = accelerator.state if state.local_process_index == 0: print("**Initialization**") init_state_check() state.wait_for_everyone() if state.distributed_type == DistributedType.MULTI_GPU: num_processes_per_node = torch.cuda.device_count() else: num_processes_per_node = state.num_processes # We only run this test on non-multinode if num_processes_per_node == state.num_processes: if state.process_index == 0: print("\n**Test process execution**") process_execution_check() if state.process_index == 0: print("\n**Test split between processes as a list**") test_split_between_processes_list() if state.process_index == 0: print("\n**Test split between processes as a dict**") test_split_between_processes_nested_dict() if state.process_index == 0: print("\n**Test split between processes as a tensor**") test_split_between_processes_tensor() if state.local_process_index == 0: print("\n**Test random number generator synchronization**") rng_sync_check() if state.local_process_index == 0: print("\n**DataLoader integration test**") dl_preparation_check() if state.distributed_type != DistributedType.TPU: central_dl_preparation_check() custom_sampler_check() # Trainings are not exactly the same in DeepSpeed and CPU mode if state.distributed_type == DistributedType.DEEPSPEED: return if state.local_process_index == 0: print("\n**Training integration test**") training_check(use_seedable_sampler=False) training_check(use_seedable_sampler=True) if state.local_process_index == 0: print("\n**Breakpoint trigger test**") test_trigger() if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_distributed_data_loop.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from typing import List from unittest.mock import Mock import torch from torch.utils.data import DataLoader, IterableDataset, TensorDataset from accelerate.accelerator import Accelerator from accelerate.utils.dataclasses import DistributedType class DummyIterableDataset(IterableDataset): def __init__(self, data): self.data = data def __iter__(self): for element in self.data: yield element def create_accelerator(even_batches=True): accelerator = Accelerator(even_batches=even_batches) assert accelerator.num_processes == 2, "this script expects that two GPUs are available" return accelerator def create_dataloader(accelerator: Accelerator, dataset_size: int, batch_size: int, iterable: bool = False): """ Create a simple DataLoader to use during the test cases """ if iterable: dataset = DummyIterableDataset(torch.as_tensor(range(dataset_size))) else: dataset = TensorDataset(torch.as_tensor(range(dataset_size))) dl = DataLoader(dataset, batch_size=batch_size) dl = accelerator.prepare(dl) return dl def verify_dataloader_batch_sizes( accelerator: Accelerator, dataset_size: int, batch_size: int, process_0_expected_batch_sizes: List[int], process_1_expected_batch_sizes: List[int], ): """ A helper function for verifying the batch sizes coming from a prepared dataloader in each process """ dl = create_dataloader(accelerator=accelerator, dataset_size=dataset_size, batch_size=batch_size) batch_sizes = [len(batch[0]) for batch in dl] if accelerator.process_index == 0: assert batch_sizes == process_0_expected_batch_sizes elif accelerator.process_index == 1: assert batch_sizes == process_1_expected_batch_sizes def test_default_ensures_even_batch_sizes(): accelerator = create_accelerator() # without padding, we would expect a different number of batches verify_dataloader_batch_sizes( accelerator, dataset_size=3, batch_size=1, process_0_expected_batch_sizes=[1, 1], process_1_expected_batch_sizes=[1, 1], ) # without padding, we would expect the same number of batches, but different sizes verify_dataloader_batch_sizes( accelerator, dataset_size=7, batch_size=2, process_0_expected_batch_sizes=[2, 2], process_1_expected_batch_sizes=[2, 2], ) def test_can_disable_even_batches(): accelerator = create_accelerator(even_batches=False) verify_dataloader_batch_sizes( accelerator, dataset_size=3, batch_size=1, process_0_expected_batch_sizes=[1, 1], process_1_expected_batch_sizes=[1], ) verify_dataloader_batch_sizes( accelerator, dataset_size=7, batch_size=2, process_0_expected_batch_sizes=[2, 2], process_1_expected_batch_sizes=[2, 1], ) def test_can_join_uneven_inputs(): accelerator = create_accelerator(even_batches=False) model = torch.nn.Linear(1, 1) ddp_model = accelerator.prepare(model) dl = create_dataloader(accelerator, dataset_size=3, batch_size=1) batch_idxs = [] with accelerator.join_uneven_inputs([ddp_model]): for batch_idx, batch in enumerate(dl): output = ddp_model(batch[0].float()) loss = output.sum() loss.backward() batch_idxs.append(batch_idx) accelerator.wait_for_everyone() if accelerator.process_index == 0: assert batch_idxs == [0, 1] elif accelerator.process_index == 1: assert batch_idxs == [0] def test_join_raises_warning_for_non_ddp_distributed(accelerator): with warnings.catch_warnings(record=True) as w: with accelerator.join_uneven_inputs([Mock()]): pass assert issubclass(w[-1].category, UserWarning) assert "only supported for multi-GPU" in str(w[-1].message) def test_join_can_override_even_batches(): default_even_batches = True overridden_even_batches = False accelerator = create_accelerator(even_batches=default_even_batches) model = torch.nn.Linear(1, 1) ddp_model = accelerator.prepare(model) train_dl = create_dataloader(accelerator, dataset_size=3, batch_size=1) valid_dl = create_dataloader(accelerator, dataset_size=3, batch_size=1) with accelerator.join_uneven_inputs([ddp_model], even_batches=overridden_even_batches): train_dl_overridden_value = train_dl.batch_sampler.even_batches valid_dl_overridden_value = valid_dl.batch_sampler.even_batches assert train_dl_overridden_value == overridden_even_batches assert valid_dl_overridden_value == overridden_even_batches assert train_dl.batch_sampler.even_batches == default_even_batches assert valid_dl.batch_sampler.even_batches == default_even_batches def test_join_can_override_for_mixed_type_dataloaders(): default_even_batches = True overridden_even_batches = False accelerator = create_accelerator(even_batches=default_even_batches) model = torch.nn.Linear(1, 1) ddp_model = accelerator.prepare(model) create_dataloader(accelerator, dataset_size=3, batch_size=1, iterable=True) batch_dl = create_dataloader(accelerator, dataset_size=3, batch_size=1) with warnings.catch_warnings(): warnings.filterwarnings("ignore") try: with accelerator.join_uneven_inputs([ddp_model], even_batches=overridden_even_batches): batch_dl_overridden_value = batch_dl.batch_sampler.even_batches except AttributeError: # ensure attribute error is not raised when processing iterable dl raise AssertionError assert batch_dl_overridden_value == overridden_even_batches assert batch_dl.batch_sampler.even_batches == default_even_batches def test_join_raises_warning_for_iterable_when_overriding_even_batches(): accelerator = create_accelerator() model = torch.nn.Linear(1, 1) ddp_model = accelerator.prepare(model) create_dataloader(accelerator, dataset_size=3, batch_size=1, iterable=True) with warnings.catch_warnings(record=True) as w: with accelerator.join_uneven_inputs([ddp_model], even_batches=False): pass assert issubclass(w[-1].category, UserWarning) assert "only supported for map-style datasets" in str(w[-1].message) def main(): accelerator = create_accelerator() accelerator.print("Test that even_batches variable ensures uniform batches across processes") test_default_ensures_even_batch_sizes() accelerator.print("Run tests with even_batches disabled") test_can_disable_even_batches() accelerator.print("Test joining uneven inputs") test_can_join_uneven_inputs() accelerator.print("Test overriding even_batches when joining uneven inputs") test_join_can_override_even_batches() accelerator.print("Test overriding even_batches for mixed dataloader types") test_join_can_override_for_mixed_type_dataloaders() accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders") test_join_raises_warning_for_iterable_when_overriding_even_batches() accelerator.print("Test join with non DDP distributed raises warning") original_state = accelerator.state.distributed_type accelerator.state.distributed_type = DistributedType.FSDP test_join_raises_warning_for_non_ddp_distributed(accelerator) accelerator.state.distributed_type = original_state if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/test_sync.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from copy import deepcopy import torch import torch.nn.functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from accelerate.accelerator import Accelerator from accelerate.state import GradientState from accelerate.test_utils import RegressionDataset, RegressionModel from accelerate.utils import DistributedType, is_torch_version, set_seed def check_model_parameters(model_a, model_b, did_step, iteration): for param, grad_param in zip(model_a.parameters(), model_b.parameters()): if not param.requires_grad: continue if not did_step: # Grads should not be in sync assert ( torch.allclose(param.grad, grad_param.grad) is False ), f"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})" else: # Grads should be in sync assert ( torch.allclose(param.grad, grad_param.grad) is True ), f"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})" def step_model(model, input, target, accelerator, do_backward=True): model.train() output = model(input) loss = F.mse_loss(output, target.to(output.device)) if not do_backward: loss /= accelerator.gradient_accumulation_steps loss.backward() else: accelerator.backward(loss) def get_training_setup(accelerator, sched=False): "Returns everything needed to perform basic training" set_seed(42) model = RegressionModel() ddp_model = deepcopy(model) dset = RegressionDataset(length=80) dataloader = DataLoader(dset, batch_size=16) model.to(accelerator.device) if sched: opt = AdamW(params=model.parameters(), lr=1e-3) ddp_opt = AdamW(params=ddp_model.parameters(), lr=1e-3) sched = LambdaLR(opt, lr_lambda=lambda epoch: epoch**0.65) ddp_sched = LambdaLR(ddp_opt, lr_lambda=lambda epoch: epoch**0.65) # Make a copy of `model` if sched: ddp_model, ddp_opt, ddp_sched, dataloader = accelerator.prepare(ddp_model, ddp_opt, ddp_sched, dataloader) else: ddp_model, dataloader = accelerator.prepare(ddp_model, dataloader) if sched: return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched) return model, ddp_model, dataloader def test_noop_sync(accelerator): # Test when on a single CPU or GPU that the context manager does nothing model, ddp_model, dataloader = get_training_setup(accelerator) # Use a single batch ddp_input, ddp_target = next(iter(dataloader)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model input, target = accelerator.gather((ddp_input, ddp_target)) input, target = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(model, input, target, accelerator) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(ddp_model): step_model(ddp_model, ddp_input, ddp_target, accelerator) else: # Sync grads step_model(ddp_model, ddp_input, ddp_target, accelerator) # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync check_model_parameters(model, ddp_model, True, iteration) for param, ddp_param in zip(model.parameters(), ddp_model.parameters()): if not param.requires_grad: continue assert torch.allclose( param.grad, ddp_param.grad ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})" # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) ddp_input = ddp_input[torch.randperm(len(ddp_input))] def test_distributed_sync(accelerator): # Test on distributed setup that context manager behaves properly model, ddp_model, dataloader = get_training_setup(accelerator) # Use a single batch ddp_input, ddp_target = next(iter(dataloader)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model input, target = accelerator.gather((ddp_input, ddp_target)) input, target = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(model, input, target, accelerator) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(ddp_model): step_model(ddp_model, ddp_input, ddp_target, accelerator) else: # Sync grads step_model(ddp_model, ddp_input, ddp_target, accelerator) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters(), ddp_model.parameters()): if not param.requires_grad: continue if iteration % 2 == 0: # Grads should not be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is False ), f"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})" else: # Grads should be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is True ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})" # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) ddp_input = ddp_input[torch.randperm(len(ddp_input))] def test_distributed_sync_multiple_fwd(accelerator): # Test on distributed setup that context manager behaves properly when used with multiple forwards followed by multiple backwards model, ddp_model, dataloader = get_training_setup(accelerator) # Do multiple forwards losses = [] num_iterations = 3 for iteration in range(num_iterations): ddp_input, ddp_target = next(iter(dataloader)).values() # Gather the distributed inputs and targs for the base model input, target = accelerator.gather((ddp_input, ddp_target)) input, target = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(model, input, target, accelerator) # Accumulate grads locally with accelerator.no_sync(ddp_model): ddp_output = ddp_model(ddp_input) loss = F.mse_loss(ddp_output, ddp_target.to(ddp_output.device)) losses.append(loss) # Do multiple backwards and sync only at the last backward for iteration in range(num_iterations): loss = losses[iteration] if iteration < num_iterations - 1: # Accumulate grads locally accelerator.backward(loss) # DDP model and model should only be in sync after last backward for param, ddp_param in zip(model.parameters(), ddp_model.parameters()): if not param.requires_grad: continue # Grads should not be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is False ), f"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})" else: # Sync grads if last backward with accelerator.trigger_sync_in_backward(ddp_model): accelerator.backward(loss) # DDP model and model should only be in sync after last backward for param, ddp_param in zip(model.parameters(), ddp_model.parameters()): if not param.requires_grad: continue # Grads should be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is True ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})" def test_gradient_accumulation(split_batches=False, dispatch_batches=False): accelerator = Accelerator( split_batches=split_batches, dispatch_batches=dispatch_batches, gradient_accumulation_steps=2 ) # Test that context manager behaves properly model, ddp_model, dataloader = get_training_setup(accelerator) for iteration, batch in enumerate(dataloader): ddp_input, ddp_target = batch.values() # Gather the distributed inputs and targs for the base model input, target = accelerator.gather((ddp_input, ddp_target)) input, target = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(model, input, target, accelerator, False) # Do "gradient accumulation" (noop) with accelerator.accumulate(ddp_model): step_model(ddp_model, ddp_input, ddp_target, accelerator) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters(), ddp_model.parameters()): if not param.requires_grad: continue if ((iteration + 1) % 2 == 0) or (iteration == len(dataloader) - 1): # Grads should be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is True ), f"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})" else: # Grads should not be in sync assert ( torch.allclose(param.grad, ddp_param.grad) is False ), f"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})" # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) ddp_input = ddp_input[torch.randperm(len(ddp_input))] GradientState._reset_state() def test_gradient_accumulation_with_opt_and_scheduler(split_batches=False, dispatch_batches=False): accelerator = Accelerator( split_batches=split_batches, dispatch_batches=dispatch_batches, gradient_accumulation_steps=2 ) # Test that context manager behaves properly model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched = get_training_setup(accelerator, True) for iteration, batch in enumerate(dataloader): ddp_input, ddp_target = batch.values() # Gather the distributed inputs and targs for the base model input, target = accelerator.gather((ddp_input, ddp_target)) input, target = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" model.train() ddp_model.train() step_model(model, input, target, accelerator, False) opt.step() if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(dataloader)): if split_batches: sched.step() else: for _ in range(accelerator.num_processes): sched.step() opt.zero_grad() # Perform gradient accumulation under wrapper with accelerator.accumulate(ddp_model): step_model(ddp_model, ddp_input, ddp_target, accelerator) ddp_opt.step() ddp_sched.step() ddp_opt.zero_grad() # Learning rates should be the same assert ( opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"] ), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n' did_step = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(dataloader)) if accelerator.num_processes > 1: check_model_parameters(model, ddp_model, did_step, iteration) # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) GradientState._reset_state() def test_dataloader_break(): accelerator = Accelerator() first_dset = RegressionDataset(length=80) first_dataloader = DataLoader(first_dset, batch_size=16) second_dset = RegressionDataset(length=96) second_dataloader = DataLoader(second_dset, batch_size=16) first_dataloader, second_dataloader = accelerator.prepare(first_dataloader, second_dataloader) assert accelerator.gradient_state.active_dataloader is None for iteration, _ in enumerate(first_dataloader): assert id(accelerator.gradient_state.active_dataloader) == id(first_dataloader) if iteration < len(first_dataloader) - 1: assert not accelerator.gradient_state.end_of_dataloader if iteration == 1: for batch_num, _ in enumerate(second_dataloader): assert id(accelerator.gradient_state.active_dataloader) == id(second_dataloader) if batch_num < len(second_dataloader) - 1: assert not accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader assert accelerator.gradient_state.active_dataloader is None def main(): accelerator = Accelerator() state = accelerator.state if state.local_process_index == 0: print("**Test `accumulate` gradient accumulation with dataloader break**") test_dataloader_break() if state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print("**Test NOOP `no_sync` context manager**") test_noop_sync(accelerator) if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_CPU): if state.local_process_index == 0: print("**Test Distributed `no_sync` context manager**") test_distributed_sync(accelerator) if state.local_process_index == 0: print("**Test Distributed `no_sync` context manager with multiple forwards**") test_distributed_sync_multiple_fwd(accelerator) if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU): for split_batch in [True, False]: for dispatch_batches in [True, False]: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation, ", f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**", ) test_gradient_accumulation(split_batch, dispatch_batches) # Currently will break on torch 2.0 +, need to investigate why if is_torch_version("<", "2.0") or state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, ", "`split_batches=False`, `dispatch_batches=False`**", ) test_gradient_accumulation_with_opt_and_scheduler() if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU): for split_batch in [True, False]: for dispatch_batches in [True, False]: if not split_batch and not dispatch_batches: continue if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, ", f"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**", ) test_gradient_accumulation_with_opt_and_scheduler(split_batch, dispatch_batches) def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils/scripts

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/external_deps/test_performance.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.utils.deepspeed import DummyOptim, DummyScheduler MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 def get_dataloaders(accelerator: Accelerator, batch_size: int = 16, model_name: str = "bert-base-cased"): """ Creates a set of `DataLoader`s for the `glue` dataset. Args: accelerator (`Accelerator`): An `Accelerator` object batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. model_name (`str`, *optional*): """ tokenizer = AutoTokenizer.from_pretrained(model_name) datasets = load_dataset("glue", "mrpc") def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=False ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader( tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size ) eval_dataloader = DataLoader( tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) return train_dataloader, eval_dataloader def training_function(config, args): # Initialize accelerator accelerator = Accelerator() # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) model_name = args.model_name_or_path set_seed(seed) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size, model_name) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=True) # Instantiate optimizer optimizer_cls = ( AdamW if accelerator.state.deepspeed_plugin is None or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) optimizer = optimizer_cls(params=model.parameters(), lr=lr) max_training_steps = len(train_dataloader) * num_epochs # Instantiate scheduler linear_decay_scheduler = False if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=0, num_training_steps=max_training_steps, ) linear_decay_scheduler = True else: lr_scheduler = DummyScheduler(optimizer, total_num_steps=max_training_steps, warmup_num_steps=0) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We also need to keep track of the stating epoch so files are named properly starting_epoch = 0 # Now we train the model metric = evaluate.load("glue", "mrpc") best_performance = 0 performance_metric = {} expected_lr_after_first_optim_step = lr * ( 1 - 1 / (max_training_steps / accelerator.num_processes / accelerator.gradient_accumulation_steps) ) lr_scheduler_check_completed = False for epoch in range(starting_epoch, num_epochs): model.train() for step, batch in enumerate(train_dataloader): with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # assert the learning rate after first optimizer step if ( accelerator.sync_gradients and not lr_scheduler_check_completed and linear_decay_scheduler and accelerator.state.mixed_precision == "no" ): assert ( lr_scheduler.get_last_lr()[0] == expected_lr_after_first_optim_step ), f"Wrong lr found at second step, expected {expected_lr_after_first_optim_step}, got {lr_scheduler.get_last_lr()[0]}" lr_scheduler_check_completed = True model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) # It is slightly faster to call this once, than multiple times predictions, references = accelerator.gather( (predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.use_distributed: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(f"epoch {epoch}:", eval_metric) performance_metric[f"epoch-{epoch}"] = eval_metric["accuracy"] if best_performance < eval_metric["accuracy"]: best_performance = eval_metric["accuracy"] # check that the LR is 0 if linear_decay_scheduler and accelerator.state.mixed_precision == "no": assert ( lr_scheduler.get_last_lr()[0] == 0 ), f"Wrong lr found at last step, expected 0, got {lr_scheduler.get_last_lr()[0]}" if args.performance_lower_bound is not None: assert ( args.performance_lower_bound <= best_performance ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}" accelerator.wait_for_everyone() if accelerator.is_main_process: with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(performance_metric, f) def main(): parser = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.") parser.add_argument( "--model_name_or_path", type=str, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--output_dir", type=str, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", ) parser.add_argument( "--performance_lower_bound", type=float, default=None, help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", ) parser.add_argument( "--num_epochs", type=int, default=3, help="Number of train epochs.", ) args = parser.parse_args() config = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16} training_function(config, args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils/scripts

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/external_deps/test_peak_memory_usage.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import gc import json import os import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.utils import is_npu_available, is_xpu_available from accelerate.utils.deepspeed import DummyOptim, DummyScheduler MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 # Converting Bytes to Megabytes def b2mb(x): return int(x / 2**20) # This context manager is used to track the peak memory usage of the process class TorchTracemalloc: def __enter__(self): gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero self.begin = torch.cuda.memory_allocated() elif is_npu_available(): torch.npu.empty_cache() torch.npu.reset_max_memory_allocated() # reset the peak gauge to zero self.begin = torch.npu.memory_allocated() elif is_xpu_available(): torch.xpu.empty_cache() torch.xpu.reset_max_memory_allocated() # reset the peak gauge to zero self.begin = torch.xpu.memory_allocated() return self def __exit__(self, *exc): gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() self.end = torch.cuda.memory_allocated() self.peak = torch.cuda.max_memory_allocated() elif is_npu_available(): torch.npu.empty_cache() self.end = torch.npu.memory_allocated() self.peak = torch.npu.max_memory_allocated() elif is_xpu_available(): torch.xpu.empty_cache() self.end = torch.xpu.memory_allocated() self.peak = torch.xpu.max_memory_allocated() self.used = b2mb(self.end - self.begin) self.peaked = b2mb(self.peak - self.begin) # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}") def get_dataloaders( accelerator: Accelerator, batch_size: int = 16, model_name: str = "bert-base-cased", n_train: int = 320, n_val: int = 160, ): """ Creates a set of `DataLoader`s for the `glue` dataset. Args: accelerator (`Accelerator`): An `Accelerator` object batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. model_name (`str`, *optional*): The name of the model to use. n_train (`int`, *optional*): The number of training examples to use. n_val (`int`, *optional*): The number of validation examples to use. """ tokenizer = AutoTokenizer.from_pretrained(model_name) datasets = load_dataset( "glue", "mrpc", split={"train": f"train[:{n_train}]", "validation": f"validation[:{n_val}]"} ) def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=False ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader( tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size ) eval_dataloader = DataLoader( tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) return train_dataloader, eval_dataloader def training_function(config, args): # Initialize accelerator accelerator = Accelerator() # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) model_name = args.model_name_or_path set_seed(seed) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size, model_name, args.n_train, args.n_val) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=True) # Instantiate optimizer optimizer_cls = ( AdamW if accelerator.state.deepspeed_plugin is None or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) optimizer = optimizer_cls(params=model.parameters(), lr=lr) if accelerator.state.deepspeed_plugin is not None: gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[ "gradient_accumulation_steps" ] else: gradient_accumulation_steps = 1 max_training_steps = (len(train_dataloader) * num_epochs) // gradient_accumulation_steps # Instantiate scheduler if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=0, num_training_steps=max_training_steps, ) else: lr_scheduler = DummyScheduler(optimizer, total_num_steps=max_training_steps, warmup_num_steps=0) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to keep track of how many total steps we have iterated over overall_step = 0 # We also need to keep track of the stating epoch so files are named properly starting_epoch = 0 # Now we train the model train_total_peak_memory = {} for epoch in range(starting_epoch, num_epochs): with TorchTracemalloc() as tracemalloc: model.train() for step, batch in enumerate(train_dataloader): outputs = model(**batch) loss = outputs.loss loss = loss / gradient_accumulation_steps accelerator.backward(loss) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() overall_step += 1 # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage accelerator.print("Memory before entering the train : {}".format(b2mb(tracemalloc.begin))) accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used)) accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked)) accelerator.print( "Total Peak Memory consumed during the train (max): {}".format( tracemalloc.peaked + b2mb(tracemalloc.begin) ) ) train_total_peak_memory[f"epoch-{epoch}"] = tracemalloc.peaked + b2mb(tracemalloc.begin) if args.peak_memory_upper_bound is not None: assert ( train_total_peak_memory[f"epoch-{epoch}"] <= args.peak_memory_upper_bound ), "Peak memory usage exceeded the upper bound" accelerator.wait_for_everyone() if accelerator.is_main_process: with open(os.path.join(args.output_dir, "peak_memory_utilization.json"), "w") as f: json.dump(train_total_peak_memory, f) def main(): parser = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.") parser.add_argument( "--model_name_or_path", type=str, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--output_dir", type=str, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", ) parser.add_argument( "--peak_memory_upper_bound", type=float, default=None, help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.", ) parser.add_argument( "--n_train", type=int, default=320, help="Number of training examples to use.", ) parser.add_argument( "--n_val", type=int, default=160, help="Number of validation examples to use.", ) parser.add_argument( "--num_epochs", type=int, default=1, help="Number of train epochs.", ) args = parser.parse_args() config = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16} training_function(config, args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils/scripts

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/external_deps/test_checkpointing.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.utils.deepspeed import DummyOptim, DummyScheduler MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 def get_dataloaders(accelerator: Accelerator, batch_size: int = 16, model_name: str = "bert-base-cased"): """ Creates a set of `DataLoader`s for the `glue` dataset. Args: accelerator (`Accelerator`): An `Accelerator` object batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. model_name (`str`, *optional*): """ tokenizer = AutoTokenizer.from_pretrained(model_name) datasets = load_dataset("glue", "mrpc") def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=False ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader( tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size ) eval_dataloader = DataLoader( tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) return train_dataloader, eval_dataloader def evaluation_loop(accelerator, model, eval_dataloader, metric): model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) # It is slightly faster to call this once, than multiple times predictions, references = accelerator.gather( (predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.use_distributed: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() return eval_metric["accuracy"] def training_function(config, args): # Initialize accelerator accelerator = Accelerator() # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) model_name = args.model_name_or_path set_seed(seed) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size, model_name) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = AutoModelForSequenceClassification.from_pretrained(model_name, return_dict=True) # Instantiate optimizer optimizer_cls = ( AdamW if accelerator.state.deepspeed_plugin is None or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) optimizer = optimizer_cls(params=model.parameters(), lr=lr) if accelerator.state.deepspeed_plugin is not None: gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[ "gradient_accumulation_steps" ] else: gradient_accumulation_steps = 1 max_training_steps = (len(train_dataloader) * num_epochs) // gradient_accumulation_steps # Instantiate scheduler if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=0, num_training_steps=max_training_steps, ) else: lr_scheduler = DummyScheduler(optimizer, total_num_steps=max_training_steps, warmup_num_steps=0) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to keep track of how many total steps we have iterated over overall_step = 0 # We also need to keep track of the stating epoch so files are named properly starting_epoch = 0 metric = evaluate.load("glue", "mrpc") ending_epoch = num_epochs if args.partial_train_epoch is not None: ending_epoch = args.partial_train_epoch if args.resume_from_checkpoint: accelerator.load_state(args.resume_from_checkpoint) epoch_string = args.resume_from_checkpoint.split("epoch_")[1] state_epoch_num = "" for char in epoch_string: if char.isdigit(): state_epoch_num += char else: break starting_epoch = int(state_epoch_num) + 1 accuracy = evaluation_loop(accelerator, model, eval_dataloader, metric) accelerator.print("resumed checkpoint performance:", accuracy) accelerator.print("resumed checkpoint's scheduler's lr:", lr_scheduler.get_lr()[0]) accelerator.print("resumed optimizers's lr:", optimizer.param_groups[0]["lr"]) with open(os.path.join(args.output_dir, f"state_{starting_epoch-1}.json"), "r") as f: resumed_state = json.load(f) assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed" assert ( resumed_state["lr"] == lr_scheduler.get_lr()[0] ), "Scheduler learning rate mismatch, loading from checkpoint failed" assert ( resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"] ), "Optimizer learning rate mismatch, loading from checkpoint failed" assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed" return # Now we train the model state = {} for epoch in range(starting_epoch, ending_epoch): model.train() for step, batch in enumerate(train_dataloader): outputs = model(**batch) loss = outputs.loss loss = loss / gradient_accumulation_steps accelerator.backward(loss) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() overall_step += 1 output_dir = f"epoch_{epoch}" output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) accuracy = evaluation_loop(accelerator, model, eval_dataloader, metric) state["accuracy"] = accuracy state["lr"] = lr_scheduler.get_lr()[0] state["optimizer_lr"] = optimizer.param_groups[0]["lr"] state["epoch"] = epoch state["step"] = overall_step accelerator.print(f"epoch {epoch}:", state) accelerator.wait_for_everyone() if accelerator.is_main_process: with open(os.path.join(args.output_dir, f"state_{epoch}.json"), "w") as f: json.dump(state, f) def main(): parser = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage.") parser.add_argument( "--model_name_or_path", type=str, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--output_dir", type=str, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--partial_train_epoch", type=int, default=None, help="If passed, the training will stop after this number of epochs.", ) parser.add_argument( "--num_epochs", type=int, default=2, help="Number of train epochs.", ) args = parser.parse_args() config = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16} training_function(config, args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/test_utils/scripts

hf_public_repos/accelerate/src/accelerate/test_utils/scripts/external_deps/test_metrics.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import math import os from copy import deepcopy import datasets import evaluate import torch import transformers from datasets import load_dataset from torch.utils.data import DataLoader, IterableDataset from transformers import AutoModelForSequenceClassification, AutoTokenizer from accelerate import Accelerator from accelerate.data_loader import DataLoaderDispatcher from accelerate.test_utils import RegressionDataset, RegressionModel, torch_device from accelerate.utils import set_seed os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true" class ListHandler(logging.Handler): def __init__(self, *args, **kwargs): super(ListHandler, self).__init__(*args, **kwargs) self.logs = [] def emit(self, record): self.logs.append(record) def get_basic_setup(accelerator, num_samples=82, batch_size=16): "Returns everything needed to perform basic training" set_seed(42) model = RegressionModel() ddp_model = deepcopy(model) dset = RegressionDataset(length=num_samples) dataloader = DataLoader(dset, batch_size=batch_size) model.to(accelerator.device) ddp_model, dataloader = accelerator.prepare(ddp_model, dataloader) return model, ddp_model, dataloader def get_dataloader(accelerator: Accelerator, use_longest=False): tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased") dataset = load_dataset("glue", "mrpc", split="validation") def tokenize_function(examples): outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs with accelerator.main_process_first(): tokenized_datasets = dataset.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], ) tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): if use_longest: return tokenizer.pad(examples, padding="longest", return_tensors="pt") return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return DataLoader(tokenized_datasets, shuffle=False, collate_fn=collate_fn, batch_size=16) def get_mrpc_setup(dispatch_batches, split_batches): accelerator = Accelerator(dispatch_batches=dispatch_batches, split_batches=split_batches) dataloader = get_dataloader(accelerator, not dispatch_batches) model = AutoModelForSequenceClassification.from_pretrained( "hf-internal-testing/mrpc-bert-base-cased", return_dict=True ) ddp_model, ddp_dataloader = accelerator.prepare(model, dataloader) return { "ddp": [ddp_model, ddp_dataloader, torch_device], "no": [model, dataloader, accelerator.device], }, accelerator def generate_predictions(model, dataloader, accelerator): logits_and_targets = [] for batch in dataloader: input, target = batch.values() with torch.no_grad(): logit = model(input) logit, target = accelerator.gather_for_metrics((logit, target)) logits_and_targets.append((logit, target)) logits, targs = [], [] for logit, targ in logits_and_targets: logits.append(logit) targs.append(targ) logits, targs = torch.cat(logits), torch.cat(targs) return logits, targs def test_torch_metrics( accelerator: Accelerator, num_samples=82, dispatch_batches=False, split_batches=False, batch_size=16 ): model, ddp_model, dataloader = get_basic_setup(accelerator, num_samples, batch_size) logits, targs = generate_predictions(ddp_model, dataloader, accelerator) assert ( len(logits) == num_samples ), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(logits)}" def test_mrpc(dispatch_batches: bool = False, split_batches: bool = False): metric = evaluate.load("glue", "mrpc") setup, accelerator = get_mrpc_setup(dispatch_batches, split_batches) # First do baseline model, dataloader, device = setup["no"] model.to(device) model.eval() for batch in dataloader: batch.to(device) with torch.inference_mode(): outputs = model(**batch) preds = outputs.logits.argmax(dim=-1) metric.add_batch(predictions=preds, references=batch["labels"]) baseline = metric.compute() # Then do distributed model, dataloader, device = setup["ddp"] model.eval() for batch in dataloader: with torch.inference_mode(): outputs = model(**batch) preds = outputs.logits.argmax(dim=-1) references = batch["labels"] preds, references = accelerator.gather_for_metrics((preds, references)) metric.add_batch(predictions=preds, references=references) distributed = metric.compute() for key in "accuracy f1".split(): assert math.isclose( baseline[key], distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n" def test_gather_for_metrics_with_non_tensor_objects_iterable_dataset(): class DummyIterableDataset(IterableDataset): def __init__(self, data): self.data = data def __len__(self): return len(self.data) def __iter__(self): for element in self.data: yield element iterable_dataset = DummyIterableDataset([n for n in range(30)]) dataloader = DataLoader(iterable_dataset, batch_size=4) accelerator = Accelerator() prepared_dataloader = accelerator.prepare(dataloader) if accelerator.is_main_process: logger = logging.root.manager.loggerDict["accelerate.accelerator"] list_handler = ListHandler() logger.addHandler(list_handler) batches_for_metrics = [] for batch in prepared_dataloader: batches_for_metrics.append(accelerator.gather_for_metrics(batch)) assert torch.cat(batches_for_metrics).size(0) == 30 if accelerator.is_main_process: assert len(list_handler.logs) == 0 logger.removeHandler(list_handler) def test_gather_for_metrics_with_iterable_dataset(): class DummyIterableDataset(IterableDataset): def __init__(self, data): self.data = data def __len__(self): return len(self.data) def __iter__(self): for element in self.data: yield element iterable_dataset = DummyIterableDataset(torch.as_tensor(range(30))) dataloader = DataLoader(iterable_dataset, batch_size=4) accelerator = Accelerator() prepared_dataloader = accelerator.prepare(dataloader) assert isinstance(prepared_dataloader, DataLoaderDispatcher) if accelerator.is_main_process: logger = logging.root.manager.loggerDict["accelerate.accelerator"] list_handler = ListHandler() logger.addHandler(list_handler) batches_for_metrics = [] for batch in prepared_dataloader: batches_for_metrics.append(accelerator.gather_for_metrics(batch)) assert torch.cat(batches_for_metrics).size(0) == 30 if accelerator.is_main_process: assert len(list_handler.logs) == 0 logger.removeHandler(list_handler) def test_gather_for_metrics_drop_last(): accelerator = Accelerator() per_device_batch_size = 5 num_items = (10 * accelerator.num_processes) + 1 dataloader = DataLoader(range(num_items), batch_size=per_device_batch_size, drop_last=True) dataloader = accelerator.prepare(dataloader) iterator = iter(dataloader) next(iterator) # Skip first batch tensor([0, 1, 2, 3, 4], device='cuda:0') batch = next(iterator) gathered_items = accelerator.gather_for_metrics(batch) # Should return a full set of complete batches from each GPU num_expected_items = per_device_batch_size * accelerator.num_processes assert gathered_items.size(0) == ( num_expected_items ), f"Expected number of items: {num_expected_items}, Actual: {gathered_items.size(0)}" def main(): accelerator = Accelerator(split_batches=False, dispatch_batches=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_warning() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # These are a bit slower so they should only be ran on the GPU or TPU if accelerator.device.type != "cpu": if accelerator.is_local_main_process: print("**Testing gather_for_metrics**") for split_batches in [True, False]: for dispatch_batches in [True, False]: if accelerator.is_local_main_process: print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`") test_mrpc(dispatch_batches, split_batches) accelerator.state._reset_state() print("test_gather_for_metrics_with_iterable_dataset") test_gather_for_metrics_with_iterable_dataset() print("test gather_for_metrics_with_non_tensor_objects_iterable_dataset") test_gather_for_metrics_with_non_tensor_objects_iterable_dataset() if accelerator.is_local_main_process: print("**Test torch metrics**") for split_batches in [True, False]: for dispatch_batches in [True, False]: accelerator = Accelerator(split_batches=split_batches, dispatch_batches=dispatch_batches) if accelerator.is_local_main_process: print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99") test_torch_metrics(accelerator, 99) accelerator.state._reset_state() if accelerator.is_local_main_process: print("**Test last batch is not dropped when perfectly divisible**") accelerator = Accelerator() test_torch_metrics(accelerator, 512) accelerator.state._reset_state() if accelerator.is_local_main_process: print("**Test that `drop_last` is taken into account**") test_gather_for_metrics_drop_last() accelerator.state._reset_state() def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/other.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import os import platform import re import socket from contextlib import contextmanager from functools import partial, reduce from types import MethodType from typing import OrderedDict import torch from packaging.version import Version from safetensors.torch import save_file as safe_save_file from ..commands.config.default import write_basic_config # noqa: F401 from ..logging import get_logger from ..state import PartialState from .constants import FSDP_PYTORCH_VERSION from .dataclasses import DistributedType from .imports import is_deepspeed_available, is_torch_distributed_available, is_tpu_available from .modeling import id_tensor_storage from .transformer_engine import convert_model from .versions import is_torch_version logger = get_logger(__name__) if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm def is_compiled_module(module): """ Check whether the module was compiled with torch.compile() """ if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"): return False return isinstance(module, torch._dynamo.eval_frame.OptimizedModule) def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True): """ Extract a model from its distributed containers. Args: model (`torch.nn.Module`): The model to extract. keep_fp32_wrapper (`bool`, *optional*): Whether to remove mixed precision hooks from the model. Returns: `torch.nn.Module`: The extracted model. """ options = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel) is_compiled = is_compiled_module(model) if is_compiled: compiled_model = model model = model._orig_mod if is_deepspeed_available(): from deepspeed import DeepSpeedEngine options += (DeepSpeedEngine,) if is_torch_version(">=", FSDP_PYTORCH_VERSION) and is_torch_distributed_available(): from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP options += (FSDP,) while isinstance(model, options): model = model.module if not keep_fp32_wrapper: forward = getattr(model, "forward") original_forward = model.__dict__.pop("_original_forward", None) if original_forward is not None: while hasattr(forward, "__wrapped__"): forward = forward.__wrapped__ if forward == original_forward: break model.forward = MethodType(forward, model) if getattr(model, "_converted_to_transformer_engine", False): convert_model(model, to_transformer_engine=False) if is_compiled: compiled_model._orig_mod = model model = compiled_model return model def wait_for_everyone(): """ Introduces a blocking point in the script, making sure all processes have reached this point before continuing. <Tip warning={true}> Make sure all processes will reach this instruction otherwise one of your processes will hang forever. </Tip> """ PartialState().wait_for_everyone() def clean_state_dict_for_safetensors(state_dict: dict): """ Cleans the state dictionary from a model and removes tensor aliasing if present. Args: state_dict (`dict`): The state dictionary from a model """ ptrs = collections.defaultdict(list) # When bnb serialization is used, weights in state dict can be strings for name, tensor in state_dict.items(): if not isinstance(tensor, str): ptrs[id_tensor_storage(tensor)].append(name) # These are all pointers of tensors with shared memory shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1} warn_names = set() for names in shared_ptrs.values(): # When not all duplicates have been cleaned, we still remove those keys but put a clear warning. # If the link between tensors was done at runtime then `from_pretrained` will not get # the key back leading to random tensor. A proper warning will be shown # during reload (if applicable), but since the file is not necessarily compatible with # the config, better show a proper warning. found_names = [name for name in names if name in state_dict] warn_names.update(found_names[1:]) for name in found_names[1:]: del state_dict[name] if len(warn_names) > 0: logger.warning( f"Removed shared tensor {warn_names} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading", ) state_dict = {k: v.contiguous() if isinstance(v, torch.Tensor) else v for k, v in state_dict.items()} return state_dict def save(obj, f, save_on_each_node: bool = False, safe_serialization: bool = False): """ Save the data to disk. Use in place of `torch.save()`. Args: obj: The data to save f: The file (or file-like object) to use to save the data save_on_each_node (`bool`, *optional*, defaults to `False`): Whether to only save on the global main process safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save `obj` using `safetensors` or the traditional PyTorch way (that uses `pickle`). """ # Check if it's a model and remove duplicates if safe_serialization: save_func = partial(safe_save_file, metadata={"format": "pt"}) if isinstance(obj, OrderedDict): obj = clean_state_dict_for_safetensors(obj) else: save_func = torch.save if PartialState().distributed_type == DistributedType.TPU: xm.save(obj, f) elif PartialState().is_main_process and not save_on_each_node: save_func(obj, f) elif PartialState().is_local_main_process and save_on_each_node: save_func(obj, f) @contextmanager def clear_environment(): """ A context manager that will cache origin `os.environ` and replace it with a empty dictionary in this context. When this context exits, the cached `os.environ` will be back. Example: ```python >>> import os >>> from accelerate.utils import clear_environment >>> os.environ["FOO"] = "bar" >>> with clear_environment(): ... print(os.environ) ... os.environ["FOO"] = "new_bar" ... print(os.environ["FOO"]) {} new_bar >>> print(os.environ["FOO"]) bar ``` """ _old_os_environ = os.environ os.environ = dict() yield os.environ = _old_os_environ @contextmanager def patch_environment(**kwargs): """ A context manager that will add each keyword argument passed to `os.environ` and remove them when exiting. Will convert the values in `kwargs` to strings and upper-case all the keys. Example: ```python >>> import os >>> from accelerate.utils import patch_environment >>> with patch_environment(FOO="bar"): ... print(os.environ["FOO"]) # prints "bar" >>> print(os.environ["FOO"]) # raises KeyError ``` """ existing_vars = {} for key, value in kwargs.items(): key = key.upper() if key in os.environ: existing_vars[key] = os.environ[key] os.environ[key] = str(value) yield for key in kwargs: key = key.upper() if key in existing_vars: # restore previous value os.environ[key] = existing_vars[key] else: os.environ.pop(key, None) def get_pretty_name(obj): """ Gets a pretty name from `obj`. """ if not hasattr(obj, "__qualname__") and not hasattr(obj, "__name__"): obj = getattr(obj, "__class__", obj) if hasattr(obj, "__qualname__"): return obj.__qualname__ if hasattr(obj, "__name__"): return obj.__name__ return str(obj) def merge_dicts(source, destination): """ Recursively merges two dictionaries. Args: source (`dict`): The dictionary to merge into `destination`. destination (`dict`): The dictionary to merge `source` into. """ for key, value in source.items(): if isinstance(value, dict): node = destination.setdefault(key, {}) merge_dicts(value, node) else: destination[key] = value return destination def is_port_in_use(port: int = None) -> bool: """ Checks if a port is in use on `localhost`. Useful for checking if multiple `accelerate launch` commands have been run and need to see if the port is already in use. """ if port is None: port = 29500 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: return s.connect_ex(("localhost", port)) == 0 def convert_bytes(size): "Converts `size` from bytes to the largest possible unit" for x in ["bytes", "KB", "MB", "GB", "TB"]: if size < 1024.0: return f"{round(size, 2)} {x}" size /= 1024.0 return f"{round(size, 2)} PB" def check_os_kernel(): """Warns if the kernel version is below the recommended minimum on Linux.""" # see issue #1929 info = platform.uname() system = info.system if system != "Linux": return _, version, *_ = re.split(r"(\d+\.\d+\.\d+)", info.release) min_version = "5.5.0" if Version(version) < Version(min_version): msg = ( f"Detected kernel version {version}, which is below the recommended minimum of {min_version}; this can " "cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher." ) logger.warning(msg, main_process_only=True) def recursive_getattr(obj, attr: str): """ Recursive `getattr`. Args: obj: A class instance holding the attribute. attr (`str`): The attribute that is to be retrieved, e.g. 'attribute1.attribute2'. """ def _getattr(obj, attr): return getattr(obj, attr) return reduce(_getattr, [obj] + attr.split("."))

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/bnb.py

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from copy import deepcopy from typing import Dict, List, Optional, Union import torch import torch.nn as nn from accelerate.utils.imports import ( is_4bit_bnb_available, is_8bit_bnb_available, ) from ..big_modeling import dispatch_model, init_empty_weights from .dataclasses import BnbQuantizationConfig from .modeling import ( find_tied_parameters, get_balanced_memory, infer_auto_device_map, load_checkpoint_in_model, offload_weight, set_module_tensor_to_device, ) logger = logging.getLogger(__name__) def load_and_quantize_model( model: torch.nn.Module, bnb_quantization_config: BnbQuantizationConfig, weights_location: Union[str, os.PathLike] = None, device_map: Optional[Dict[str, Union[int, str, torch.device]]] = None, no_split_module_classes: Optional[List[str]] = None, max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None, offload_folder: Optional[Union[str, os.PathLike]] = None, offload_state_dict: bool = False, ): """ This function will quantize the input model with the associated config passed in `bnb_quantization_config`. If the model is in the meta device, we will load and dispatch the weights according to the `device_map` passed. If the model is already loaded, we will quantize the model and put the model on the GPU, Args: model (`torch.nn.Module`): Input model. The model can be already loaded or on the meta device bnb_quantization_config (`BnbQuantizationConfig`): The bitsandbytes quantization parameters weights_location (`str` or `os.PathLike`): The folder weights_location to load. It can be: - a path to a file containing a whole model state dict - a path to a `.json` file containing the index to a sharded checkpoint - a path to a folder containing a unique `.index.json` file and the shards of a checkpoint. - a path to a folder containing a unique pytorch_model.bin file. device_map (`Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. no_split_module_classes (`List[str]`, *optional*): A list of layer class names that should never be split across device (for instance any layer that has a residual connection). max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available if unset. offload_folder (`str` or `os.PathLike`, *optional*): If the `device_map` contains any value `"disk"`, the folder where we will offload weights. offload_state_dict (`bool`, *optional*, defaults to `False`): If `True`, will temporarily offload the CPU state dict on the hard drive to avoid getting out of CPU RAM if the weight of the CPU state dict + the biggest shard does not fit. Returns: `torch.nn.Module`: The quantized model """ load_in_4bit = bnb_quantization_config.load_in_4bit load_in_8bit = bnb_quantization_config.load_in_8bit if load_in_8bit and not is_8bit_bnb_available(): raise ImportError( "You have a version of `bitsandbytes` that is not compatible with 8bit quantization," " make sure you have the latest version of `bitsandbytes` installed." ) if load_in_4bit and not is_4bit_bnb_available(): raise ValueError( "You have a version of `bitsandbytes` that is not compatible with 4bit quantization," "make sure you have the latest version of `bitsandbytes` installed." ) modules_on_cpu = [] # custom device map if isinstance(device_map, dict) and len(device_map.keys()) > 1: modules_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]] # We keep some modules such as the lm_head in their original dtype for numerical stability reasons if bnb_quantization_config.skip_modules is None: bnb_quantization_config.skip_modules = get_keys_to_not_convert(model) # add cpu modules to skip modules only for 4-bit modules if load_in_4bit: bnb_quantization_config.skip_modules.extend(modules_on_cpu) modules_to_not_convert = bnb_quantization_config.skip_modules # We add the modules we want to keep in full precision if bnb_quantization_config.keep_in_fp32_modules is None: bnb_quantization_config.keep_in_fp32_modules = [] keep_in_fp32_modules = bnb_quantization_config.keep_in_fp32_modules modules_to_not_convert.extend(keep_in_fp32_modules) # compatibility with peft model.is_loaded_in_4bit = load_in_4bit model.is_loaded_in_8bit = load_in_8bit model_device = get_parameter_device(model) if model_device.type != "meta": # quantization of an already loaded model logger.warning( "It is not recommended to quantize a loaded model. " "The model should be instantiated under the `init_empty_weights` context manager." ) model = replace_with_bnb_layers(model, bnb_quantization_config, modules_to_not_convert=modules_to_not_convert) # convert param to the right dtype dtype = bnb_quantization_config.torch_dtype for name, param in model.state_dict().items(): if any(module_to_keep_in_fp32 in name for module_to_keep_in_fp32 in keep_in_fp32_modules): param.to(torch.float32) if param.dtype != torch.float32: name = name.replace(".weight", "").replace(".bias", "") param = getattr(model, name, None) if param is not None: param.to(torch.float32) elif torch.is_floating_point(param): param.to(dtype) if model_device.type == "cuda": # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda model.cuda(torch.cuda.current_device()) torch.cuda.empty_cache() elif torch.cuda.is_available(): model.to(torch.cuda.current_device()) else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info( f"The model device type is {model_device.type}. However, cuda is needed for quantization." "We move the model to cuda." ) return model elif weights_location is None: raise RuntimeError( f"`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} " ) else: with init_empty_weights(): model = replace_with_bnb_layers( model, bnb_quantization_config, modules_to_not_convert=modules_to_not_convert ) device_map = get_quantized_model_device_map( model, bnb_quantization_config, device_map, max_memory=max_memory, no_split_module_classes=no_split_module_classes, ) if offload_state_dict is None and device_map is not None and "disk" in device_map.values(): offload_state_dict = True offload = any(x in list(device_map.values()) for x in ["cpu", "disk"]) load_checkpoint_in_model( model, weights_location, device_map, dtype=bnb_quantization_config.torch_dtype, offload_folder=offload_folder, offload_state_dict=offload_state_dict, keep_in_fp32_modules=bnb_quantization_config.keep_in_fp32_modules, offload_8bit_bnb=load_in_8bit and offload, ) return dispatch_model(model, device_map=device_map, offload_dir=offload_folder) def get_quantized_model_device_map( model, bnb_quantization_config, device_map=None, max_memory=None, no_split_module_classes=None ): if device_map is None: if torch.cuda.is_available(): device_map = {"": torch.cuda.current_device()} else: raise RuntimeError("No GPU found. A GPU is needed for quantization.") logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`.") if isinstance(device_map, str): if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: raise ValueError( "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or " "'sequential'." ) special_dtypes = {} special_dtypes.update( { name: bnb_quantization_config.torch_dtype for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.skip_modules) } ) special_dtypes.update( { name: torch.float32 for name, _ in model.named_parameters() if any(m in name for m in bnb_quantization_config.keep_in_fp32_modules) } ) kwargs = {} kwargs["special_dtypes"] = special_dtypes kwargs["no_split_module_classes"] = no_split_module_classes kwargs["dtype"] = bnb_quantization_config.target_dtype # get max_memory for each device. if device_map != "sequential": max_memory = get_balanced_memory( model, low_zero=(device_map == "balanced_low_0"), max_memory=max_memory, **kwargs, ) kwargs["max_memory"] = max_memory device_map = infer_auto_device_map(model, **kwargs) if isinstance(device_map, dict): # check if don't have any quantized module on the cpu modules_not_to_convert = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fp32_modules device_map_without_some_modules = { key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert } for device in ["cpu", "disk"]: if device in device_map_without_some_modules.values(): if bnb_quantization_config.load_in_4bit: raise ValueError( """ Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in `torch_dtype`, you need to pass a custom `device_map` to `load_and_quantize_model`. Check https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk for more details. """ ) else: logger.info( "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit" ) del device_map_without_some_modules return device_map def replace_with_bnb_layers(model, bnb_quantization_config, modules_to_not_convert=None, current_key_name=None): """ A helper function to replace all `torch.nn.Linear` modules by `bnb.nn.Linear8bit` modules or by `bnb.nn.Linear4bit` modules from the `bitsandbytes`library. The function will be run recursively and replace `torch.nn.Linear` modules. Parameters: model (`torch.nn.Module`): Input model or `torch.nn.Module` as the function is run recursively. modules_to_not_convert (`List[str]`): Names of the modules to not quantize convert. In practice we keep the `lm_head` in full precision for numerical stability reasons. current_key_name (`List[str]`, *optional*): An array to track the current key of the recursion. This is used to check whether the current key (part of it) is not in the list of modules to not convert. """ if modules_to_not_convert is None: modules_to_not_convert = [] model, has_been_replaced = _replace_with_bnb_layers( model, bnb_quantization_config, modules_to_not_convert, current_key_name ) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug." ) return model def _replace_with_bnb_layers( model, bnb_quantization_config, modules_to_not_convert=None, current_key_name=None, ): """ Private method that wraps the recursion for module replacement. Returns the converted model and a boolean that indicates if the conversion has been successfull or not. """ # bitsandbytes will initialize CUDA on import, so it needs to be imported lazily import bitsandbytes as bnb has_been_replaced = False for name, module in model.named_children(): if current_key_name is None: current_key_name = [] current_key_name.append(name) if isinstance(module, nn.Linear) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` current_key_name_str = ".".join(current_key_name) proceed = True for key in modules_to_not_convert: if ( (key in current_key_name_str) and (key + "." in current_key_name_str) ) or key == current_key_name_str: proceed = False break if proceed: # Load bnb module with empty weight and replace ``nn.Linear` module if bnb_quantization_config.load_in_8bit: bnb_module = bnb.nn.Linear8bitLt( module.in_features, module.out_features, module.bias is not None, has_fp16_weights=False, threshold=bnb_quantization_config.llm_int8_threshold, ) elif bnb_quantization_config.load_in_4bit: bnb_module = bnb.nn.Linear4bit( module.in_features, module.out_features, module.bias is not None, bnb_quantization_config.bnb_4bit_compute_dtype, compress_statistics=bnb_quantization_config.bnb_4bit_use_double_quant, quant_type=bnb_quantization_config.bnb_4bit_quant_type, ) else: raise ValueError("load_in_8bit and load_in_4bit can't be both False") bnb_module.weight.data = module.weight.data if module.bias is not None: bnb_module.bias.data = module.bias.data bnb_module.requires_grad_(False) setattr(model, name, bnb_module) has_been_replaced = True if len(list(module.children())) > 0: _, _has_been_replaced = _replace_with_bnb_layers( module, bnb_quantization_config, modules_to_not_convert, current_key_name ) has_been_replaced = has_been_replaced | _has_been_replaced # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def get_keys_to_not_convert(model): r""" An utility function to get the key of the module to keep in full precision if any For example for CausalLM modules we may want to keep the lm_head in full precision for numerical stability reasons. For other architectures, we want to keep the tied weights of the model. The function will return a list of the keys of the modules to not convert in int8. Parameters: model (`torch.nn.Module`): Input model """ # Create a copy of the model with init_empty_weights(): tied_model = deepcopy(model) # this has 0 cost since it is done inside `init_empty_weights` context manager` tied_params = find_tied_parameters(tied_model) # For compatibility with Accelerate < 0.18 if isinstance(tied_params, dict): tied_keys = sum(list(tied_params.values()), []) + list(tied_params.keys()) else: tied_keys = sum(tied_params, []) has_tied_params = len(tied_keys) > 0 # Check if it is a base model is_base_model = False if hasattr(model, "base_model_prefix"): is_base_model = not hasattr(model, model.base_model_prefix) # Ignore this for base models (BertModel, GPT2Model, etc.) if (not has_tied_params) and is_base_model: return [] # otherwise they have an attached head list_modules = list(model.named_children()) list_last_module = [list_modules[-1][0]] # add last module together with tied weights intersection = set(list_last_module) - set(tied_keys) list_untouched = list(set(tied_keys)) + list(intersection) # remove ".weight" from the keys names_to_remove = [".weight", ".bias"] filtered_module_names = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: name = name.replace(name_to_remove, "") filtered_module_names.append(name) return filtered_module_names def has_4bit_bnb_layers(model): """Check if we have `bnb.nn.Linear4bit` or `bnb.nn.Linear8bitLt` layers inside our model""" # bitsandbytes will initialize CUDA on import, so it needs to be imported lazily import bitsandbytes as bnb for m in model.modules(): if isinstance(m, bnb.nn.Linear4bit): return True return False def get_parameter_device(parameter: nn.Module): return next(parameter.parameters()).device def quantize_and_offload_8bit(model, param, param_name, new_dtype, offload_folder, offload_index, fp16_statistics): # if it is not quantized, we quantize and offload the quantized weights and the SCB stats if fp16_statistics is None: set_module_tensor_to_device(model, param_name, 0, dtype=new_dtype, value=param) tensor_name = param_name module = model if "." in tensor_name: splits = tensor_name.split(".") for split in splits[:-1]: new_module = getattr(module, split) if new_module is None: raise ValueError(f"{module} has no attribute {split}.") module = new_module tensor_name = splits[-1] # offload weights module._parameters[tensor_name].requires_grad = False offload_weight(module._parameters[tensor_name], param_name, offload_folder, index=offload_index) if hasattr(module._parameters[tensor_name], "SCB"): offload_weight( module._parameters[tensor_name].SCB, param_name.replace("weight", "SCB"), offload_folder, index=offload_index, ) else: offload_weight(param, param_name, offload_folder, index=offload_index) offload_weight(fp16_statistics, param_name.replace("weight", "SCB"), offload_folder, index=offload_index) set_module_tensor_to_device(model, param_name, "meta", dtype=new_dtype, value=torch.empty(*param.size()))

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hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/memory.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A collection of utilities for ensuring that training can always occur. Heavily influenced by the [toma](https://github.com/BlackHC/toma) library. """ import functools import gc import inspect import torch from .imports import is_npu_available, is_xpu_available def release_memory(*objects): """ Releases memory from `objects` by setting them to `None` and calls `gc.collect()` and `torch.cuda.empty_cache()`. Returned objects should be reassigned to the same variables. Args: objects (`Iterable`): An iterable of objects Returns: A list of `None` objects to replace `objects` Example: ```python >>> import torch >>> from accelerate.utils import release_memory >>> a = torch.ones(1000, 1000).cuda() >>> b = torch.ones(1000, 1000).cuda() >>> a, b = release_memory(a, b) ``` """ if not isinstance(objects, list): objects = list(objects) for i in range(len(objects)): objects[i] = None gc.collect() if is_xpu_available(): torch.xpu.empty_cache() elif is_npu_available(): torch.npu.empty_cache() else: torch.cuda.empty_cache() return objects def should_reduce_batch_size(exception: Exception) -> bool: """ Checks if `exception` relates to CUDA out-of-memory, CUDNN not supported, or CPU out-of-memory Args: exception (`Exception`): An exception """ _statements = [ "CUDA out of memory.", # CUDA OOM "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.", # CUDNN SNAFU "DefaultCPUAllocator: can't allocate memory", # CPU OOM ] if isinstance(exception, RuntimeError) and len(exception.args) == 1: return any(err in exception.args[0] for err in _statements) return False def find_executable_batch_size(function: callable = None, starting_batch_size: int = 128): """ A basic decorator that will try to execute `function`. If it fails from exceptions related to out-of-memory or CUDNN, the batch size is cut in half and passed to `function` `function` must take in a `batch_size` parameter as its first argument. Args: function (`callable`, *optional*): A function to wrap starting_batch_size (`int`, *optional*): The batch size to try and fit into memory Example: ```python >>> from accelerate.utils import find_executable_batch_size >>> @find_executable_batch_size(starting_batch_size=128) ... def train(batch_size, model, optimizer): ... ... >>> train(model, optimizer) ``` """ if function is None: return functools.partial(find_executable_batch_size, starting_batch_size=starting_batch_size) batch_size = starting_batch_size def decorator(*args, **kwargs): nonlocal batch_size gc.collect() if is_xpu_available(): torch.xpu.empty_cache() elif is_npu_available(): torch.npu.empty_cache() else: torch.cuda.empty_cache() params = list(inspect.signature(function).parameters.keys()) # Guard against user error if len(params) < (len(args) + 1): arg_str = ", ".join([f"{arg}={value}" for arg, value in zip(params[1:], args[1:])]) raise TypeError( f"Batch size was passed into `{function.__name__}` as the first argument when called." f"Remove this as the decorator already does so: `{function.__name__}({arg_str})`" ) while True: if batch_size == 0: raise RuntimeError("No executable batch size found, reached zero.") try: return function(batch_size, *args, **kwargs) except Exception as e: if should_reduce_batch_size(e): gc.collect() if is_xpu_available(): torch.xpu.empty_cache() elif is_npu_available(): torch.npu.empty_cache() else: torch.cuda.empty_cache() batch_size //= 2 else: raise return decorator

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/random.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random from typing import List, Optional, Union import numpy as np import torch from ..state import AcceleratorState from .constants import CUDA_DISTRIBUTED_TYPES from .dataclasses import DistributedType, RNGType from .imports import is_npu_available, is_tpu_available, is_xpu_available if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm def set_seed(seed: int, device_specific: bool = False): """ Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`. Args: seed (`int`): The seed to set. device_specific (`bool`, *optional*, defaults to `False`): Whether to differ the seed on each device slightly with `self.process_index`. """ if device_specific: seed += AcceleratorState().process_index random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if is_xpu_available(): torch.xpu.manual_seed_all(seed) elif is_npu_available(): torch.npu.manual_seed_all(seed) else: torch.cuda.manual_seed_all(seed) # ^^ safe to call this function even if cuda is not available if is_tpu_available(): xm.set_rng_state(seed) def synchronize_rng_state(rng_type: Optional[RNGType] = None, generator: Optional[torch.Generator] = None): # Get the proper rng state if rng_type == RNGType.TORCH: rng_state = torch.get_rng_state() elif rng_type == RNGType.CUDA: rng_state = torch.cuda.get_rng_state() elif rng_type == RNGType.XLA: assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs." rng_state = torch.tensor(xm.get_rng_state()) elif rng_type == RNGType.NPU: assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs." rng_state = torch.npu.get_rng_state() elif rng_type == RNGType.XPU: assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs." rng_state = torch.xpu.get_rng_state() elif rng_type == RNGType.GENERATOR: assert generator is not None, "Need a generator to synchronize its seed." rng_state = generator.get_state() # Broadcast the rng state from device 0 to other devices state = AcceleratorState() if state.distributed_type == DistributedType.TPU: rng_state = rng_state.to(xm.xla_device()) xm.collective_broadcast([rng_state]) xm.mark_step() rng_state = rng_state.cpu() elif ( state.distributed_type in CUDA_DISTRIBUTED_TYPES or state.distributed_type == DistributedType.MULTI_NPU or state.distributed_type == DistributedType.MULTI_XPU ): rng_state = rng_state.to(state.device) torch.distributed.broadcast(rng_state, 0) rng_state = rng_state.cpu() elif state.distributed_type == DistributedType.MULTI_CPU: torch.distributed.broadcast(rng_state, 0) # Set the broadcast rng state if rng_type == RNGType.TORCH: torch.set_rng_state(rng_state) elif rng_type == RNGType.CUDA: torch.cuda.set_rng_state(rng_state) elif rng_type == RNGType.NPU: torch.npu.set_rng_state(rng_state) elif rng_type == RNGType.XPU: torch.xpu.set_rng_state(rng_state) elif rng_type == RNGType.XLA: xm.set_rng_state(rng_state.item()) elif rng_type == RNGType.GENERATOR: generator.set_state(rng_state) def synchronize_rng_states(rng_types: List[Union[str, RNGType]], generator: Optional[torch.Generator] = None): for rng_type in rng_types: synchronize_rng_state(RNGType(rng_type), generator=generator)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/versions.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib.metadata from typing import Union from packaging.version import Version, parse from .constants import STR_OPERATION_TO_FUNC torch_version = parse(importlib.metadata.version("torch")) def compare_versions(library_or_version: Union[str, Version], operation: str, requirement_version: str): """ Compares a library version to some requirement using a given operation. Args: library_or_version (`str` or `packaging.version.Version`): A library name or a version to check. operation (`str`): A string representation of an operator, such as `">"` or `"<="`. requirement_version (`str`): The version to compare the library version against """ if operation not in STR_OPERATION_TO_FUNC.keys(): raise ValueError(f"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}") operation = STR_OPERATION_TO_FUNC[operation] if isinstance(library_or_version, str): library_or_version = parse(importlib.metadata.version(library_or_version)) return operation(library_or_version, parse(requirement_version)) def is_torch_version(operation: str, version: str): """ Compares the current PyTorch version to a given reference with an operation. Args: operation (`str`): A string representation of an operator, such as `">"` or `"<="` version (`str`): A string version of PyTorch """ return compare_versions(torch_version, operation, version)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/tqdm.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .imports import is_tqdm_available if is_tqdm_available(): from tqdm.auto import tqdm as _tqdm from ..state import PartialState def tqdm(main_process_only: bool = True, *args, **kwargs): """ Wrapper around `tqdm.tqdm` that optionally displays only on the main process. Args: main_process_only (`bool`, *optional*): Whether to display the progress bar only on the main process """ if not is_tqdm_available(): raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.") disable = False if main_process_only: disable = PartialState().local_process_index != 0 return _tqdm(*args, **kwargs, disable=disable)

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hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/dataclasses.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ General namespace and dataclass related classes """ import argparse import copy import enum import functools import os import typing import warnings from contextlib import contextmanager from dataclasses import dataclass, field from datetime import timedelta from typing import Any, Callable, Dict, Iterable, List, Literal, Optional, Tuple, get_args import torch from .constants import FSDP_AUTO_WRAP_POLICY, FSDP_BACKWARD_PREFETCH, FSDP_SHARDING_STRATEGY, FSDP_STATE_DICT_TYPE from .environment import str_to_bool from .imports import is_cuda_available, is_npu_available, is_xpu_available from .versions import compare_versions class KwargsHandler: """ Internal mixin that implements a `to_kwargs()` method for a dataclass. """ def to_dict(self): return copy.deepcopy(self.__dict__) def to_kwargs(self): """ Returns a dictionary containing the attributes with values different from the default of this class. """ # import clear_environment here to avoid circular import problem from .other import clear_environment with clear_environment(): default_dict = self.__class__().to_dict() this_dict = self.to_dict() return {k: v for k, v in this_dict.items() if default_dict[k] != v} @dataclass class AutocastKwargs(KwargsHandler): """ Use this object in your [`Accelerator`] to customize how `torch.autocast` behaves. Please refer to the documentation of this [context manager](https://pytorch.org/docs/stable/amp.html#torch.autocast) for more information on each argument. Example: ```python from accelerate import Accelerator from accelerate.utils import AutocastKwargs kwargs = AutocastKwargs(cache_enabled=True) accelerator = Accelerator(kwargs_handlers=[kwargs]) ``` """ enabled: bool = True cache_enabled: bool = None @dataclass class DistributedDataParallelKwargs(KwargsHandler): """ Use this object in your [`Accelerator`] to customize how your model is wrapped in a `torch.nn.parallel.DistributedDataParallel`. Please refer to the documentation of this [wrapper](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) for more information on each argument. <Tip warning={true}> `gradient_as_bucket_view` is only available in PyTorch 1.7.0 and later versions. `static_graph` is only available in PyTorch 1.11.0 and later versions. </Tip> Example: ```python from accelerate import Accelerator from accelerate.utils import DistributedDataParallelKwargs kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) accelerator = Accelerator(kwargs_handlers=[kwargs]) ``` """ dim: int = 0 broadcast_buffers: bool = True bucket_cap_mb: int = 25 find_unused_parameters: bool = False check_reduction: bool = False gradient_as_bucket_view: bool = False static_graph: bool = False @dataclass class GradScalerKwargs(KwargsHandler): """ Use this object in your [`Accelerator`] to customize the behavior of mixed precision, specifically how the `torch.cuda.amp.GradScaler` used is created. Please refer to the documentation of this [scaler](https://pytorch.org/docs/stable/amp.html?highlight=gradscaler) for more information on each argument. <Tip warning={true}> `GradScaler` is only available in PyTorch 1.5.0 and later versions. </Tip> Example: ```python from accelerate import Accelerator from accelerate.utils import GradScalerKwargs kwargs = GradScalerKwargs(backoff_filter=0.25) accelerator = Accelerator(kwargs_handlers=[kwargs]) ``` """ init_scale: float = 65536.0 growth_factor: float = 2.0 backoff_factor: float = 0.5 growth_interval: int = 2000 enabled: bool = True @dataclass class InitProcessGroupKwargs(KwargsHandler): """ Use this object in your [`Accelerator`] to customize the initialization of the distributed processes. Please refer to the documentation of this [method](https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information on each argument. ```python from datetime import timedelta from accelerate import Accelerator from accelerate.utils import InitProcessGroupKwargs kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=800)) accelerator = Accelerator(kwargs_handlers=[kwargs]) ``` """ backend: Optional[str] = "nccl" init_method: Optional[str] = None timeout: timedelta = timedelta(seconds=1800) # Literals Backend = Literal["msamp", "te"] OptLevel = Literal["O1", "O2"] FP8Format = Literal["E4M3", "HYBRID"] AmaxComputeAlgorithm = Literal["max", "most_recent"] @dataclass class FP8RecipeKwargs(KwargsHandler): """ Use this object in your [`Accelerator`] to customize the initialization of the recipe for FP8 mixed precision training with `transformer-engine` or `ms-amp`. <Tip> For more information on `transformer-engine` args, please refer to the API [documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html). For more information on the `ms-amp` args, please refer to the Optimization Level [documentation](https://azure.github.io/MS-AMP/docs/user-tutorial/optimization-level). </Tip> ```python from accelerate import Accelerator from accelerate.utils import FP8RecipeKwargs kwargs = FP8RecipeKwargs(backend="te", fp8_format="HYBRID") accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=[kwargs]) ``` To use MS-AMP as an engine, pass `backend="msamp"` and the `optimization_level`: ```python kwargs = FP8RecipeKwargs(backend="msamp", optimization_level="02") ``` Args: backend (`str`, *optional*, defaults to "msamp"): Which FP8 engine to use. Must be one of `"msamp"` (MS-AMP) or `"te"` (TransformerEngine). margin (`int`, *optional*, default to 0): The margin to use for the gradient scaling. interval (`int`, *optional*, default to 1): The interval to use for how often the scaling factor is recomputed. fp8_format (`str`, *optional*, default to "E4M3"): The format to use for the FP8 recipe. Must be one of `E4M3` or `HYBRID`. amax_history_len (`int`, *optional*, default to 1024): The length of the history to use for the scaling factor computation amax_compute_algo (`str`, *optional*, default to "most_recent"): The algorithm to use for the scaling factor computation. Must be one of `max` or `most_recent`. override_linear_precision (`tuple` of three `bool`, *optional*, default to `(False, False, False)`): Whether or not to execute `fprop`, `dgrad`, and `wgrad` GEMMS in higher precision. optimization_level (`str`), one of `O1`, `O2`. (default is `O2`): What level of 8-bit collective communication should be used with MS-AMP. In general: * O1: Weight gradients and `all_reduce` communications are done in fp8, reducing GPU memory usage and communication bandwidth * O2: First-order optimizer states are in 8-bit, and second order states are in FP16. Only available when using Adam or AdamW. This maintains accuracy and can potentially save the highest memory. * 03: Specifically for DeepSpeed, implements capabilities so weights and master weights of models are stored in FP8. If `fp8` is selected and deepspeed is enabled, will be used by default. (Not available currently). """ backend: Backend = "msamp" opt_level: OptLevel = "O2" margin: int = 0 interval: int = 1 fp8_format: FP8Format = "E4M3" amax_history_len: int = 1 amax_compute_algo: AmaxComputeAlgorithm = "most_recent" override_linear_precision: Tuple[bool, bool, bool] = (False, False, False) def __post_init__(self): self.backend = self.backend.upper() if self.backend not in get_args(Backend): raise ValueError("`backend` must be 'MSAMP' or 'TE' (TransformerEngine).") # Check TE args if self.backend == "TE": self.fp8_format = self.fp8_format.upper() if self.fp8_format not in get_args(FP8Format): raise ValueError(f"`fp8_format` must be one of {' or '.join(get_args(FP8Format))}.") if self.amax_compute_algo not in get_args(AmaxComputeAlgorithm): raise ValueError(f"`amax_compute_algo` must be one of {' or '.join(get_args(AmaxComputeAlgorithm))}") elif self.backend == "MSAMP": if self.opt_level not in get_args(OptLevel): raise ValueError(f"`optimization_level` must be one of {' or '.join(get_args(OptLevel))}") class EnumWithContains(enum.EnumMeta): "A metaclass that adds the ability to check if `self` contains an item with the `in` operator" def __contains__(cls, item): try: cls(item) except ValueError: return False return True class BaseEnum(enum.Enum, metaclass=EnumWithContains): "An enum class that can get the value of an item with `str(Enum.key)`" def __str__(self): return self.value @classmethod def list(cls): "Method to list all the possible items in `cls`" return list(map(str, cls)) class DistributedType(str, enum.Enum): """ Represents a type of distributed environment. Values: - **NO** -- Not a distributed environment, just a single process. - **MULTI_CPU** -- Distributed on multiple CPU nodes. - **MULTI_GPU** -- Distributed on multiple GPUs. - **MULTI_NPU** -- Distributed on multiple NPUs. - **MULTI_XPU** -- Distributed on multiple XPUs. - **DEEPSPEED** -- Using DeepSpeed. - **TPU** -- Distributed on TPUs. """ # Subclassing str as well as Enum allows the `DistributedType` to be JSON-serializable out of the box. NO = "NO" MULTI_CPU = "MULTI_CPU" MULTI_GPU = "MULTI_GPU" MULTI_NPU = "MULTI_NPU" MULTI_XPU = "MULTI_XPU" DEEPSPEED = "DEEPSPEED" FSDP = "FSDP" TPU = "TPU" MEGATRON_LM = "MEGATRON_LM" class SageMakerDistributedType(str, enum.Enum): """ Represents a type of distributed environment. Values: - **NO** -- Not a distributed environment, just a single process. - **DATA_PARALLEL** -- using sagemaker distributed data parallelism. - **MODEL_PARALLEL** -- using sagemaker distributed model parallelism. """ # Subclassing str as well as Enum allows the `SageMakerDistributedType` to be JSON-serializable out of the box. NO = "NO" DATA_PARALLEL = "DATA_PARALLEL" MODEL_PARALLEL = "MODEL_PARALLEL" class ComputeEnvironment(str, enum.Enum): """ Represents a type of the compute environment. Values: - **LOCAL_MACHINE** -- private/custom cluster hardware. - **AMAZON_SAGEMAKER** -- Amazon SageMaker as compute environment. """ # Subclassing str as well as Enum allows the `ComputeEnvironment` to be JSON-serializable out of the box. LOCAL_MACHINE = "LOCAL_MACHINE" AMAZON_SAGEMAKER = "AMAZON_SAGEMAKER" class DynamoBackend(str, BaseEnum): """ Represents a dynamo backend (see https://github.com/pytorch/torchdynamo). Values: - **NO** -- Do not use torch dynamo. - **EAGER** -- Uses PyTorch to run the extracted GraphModule. This is quite useful in debugging TorchDynamo issues. - **AOT_EAGER** -- Uses AotAutograd with no compiler, i.e, just using PyTorch eager for the AotAutograd's extracted forward and backward graphs. This is useful for debugging, and unlikely to give speedups. - **INDUCTOR** -- Uses TorchInductor backend with AotAutograd and cudagraphs by leveraging codegened Triton kernels. [Read more](https://dev-discuss.pytorch.org/t/torchinductor-a-pytorch-native-compiler-with-define-by-run-ir-and-symbolic-shapes/747) - **AOT_TS_NVFUSER** -- nvFuser with AotAutograd/TorchScript. [Read more](https://dev-discuss.pytorch.org/t/tracing-with-primitives-update-1-nvfuser-and-its-primitives/593) - **NVPRIMS_NVFUSER** -- nvFuser with PrimTorch. [Read more](https://dev-discuss.pytorch.org/t/tracing-with-primitives-update-1-nvfuser-and-its-primitives/593) - **CUDAGRAPHS** -- cudagraphs with AotAutograd. [Read more](https://github.com/pytorch/torchdynamo/pull/757) - **OFI** -- Uses Torchscript optimize_for_inference. Inference only. [Read more](https://pytorch.org/docs/stable/generated/torch.jit.optimize_for_inference.html) - **FX2TRT** -- Uses Nvidia TensorRT for inference optimizations. Inference only. [Read more](https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst) - **ONNXRT** -- Uses ONNXRT for inference on CPU/GPU. Inference only. [Read more](https://onnxruntime.ai/) - **TENSORRT** -- Uses ONNXRT to run TensorRT for inference optimizations. [Read more](https://github.com/onnx/onnx-tensorrt) - **IPEX** -- Uses IPEX for inference on CPU. Inference only. [Read more](https://github.com/intel/intel-extension-for-pytorch). - **TVM** -- Uses Apach TVM for inference optimizations. [Read more](https://tvm.apache.org/) """ # Subclassing str as well as Enum allows the `SageMakerDistributedType` to be JSON-serializable out of the box. NO = "NO" EAGER = "EAGER" AOT_EAGER = "AOT_EAGER" INDUCTOR = "INDUCTOR" AOT_TS_NVFUSER = "AOT_TS_NVFUSER" NVPRIMS_NVFUSER = "NVPRIMS_NVFUSER" CUDAGRAPHS = "CUDAGRAPHS" OFI = "OFI" FX2TRT = "FX2TRT" ONNXRT = "ONNXRT" TENSORRT = "TENSORRT" IPEX = "IPEX" TVM = "TVM" class LoggerType(BaseEnum): """Represents a type of supported experiment tracker Values: - **ALL** -- all available trackers in the environment that are supported - **TENSORBOARD** -- TensorBoard as an experiment tracker - **WANDB** -- wandb as an experiment tracker - **COMETML** -- comet_ml as an experiment tracker - **DVCLIVE** -- dvclive as an experiment tracker """ ALL = "all" AIM = "aim" TENSORBOARD = "tensorboard" WANDB = "wandb" COMETML = "comet_ml" MLFLOW = "mlflow" CLEARML = "clearml" DVCLIVE = "dvclive" class PrecisionType(BaseEnum): """Represents a type of precision used on floating point values Values: - **NO** -- using full precision (FP32) - **FP16** -- using half precision - **BF16** -- using brain floating point precision """ NO = "no" FP8 = "fp8" FP16 = "fp16" BF16 = "bf16" class RNGType(BaseEnum): TORCH = "torch" CUDA = "cuda" NPU = "npu" XLA = "xla" XPU = "xpu" GENERATOR = "generator" class CustomDtype(enum.Enum): r""" An enum that contains multiple custom dtypes that can be used for `infer_auto_device_map`. """ FP8 = "fp8" INT4 = "int4" # data classes @dataclass class TensorInformation: shape: torch.Size dtype: torch.dtype @dataclass class ProjectConfiguration: """ Configuration for the Accelerator object based on inner-project needs. """ project_dir: str = field(default=None, metadata={"help": "A path to a directory for storing data."}) logging_dir: str = field( default=None, metadata={ "help": "A path to a directory for storing logs of locally-compatible loggers. If None, defaults to `project_dir`." }, ) automatic_checkpoint_naming: bool = field( default=False, metadata={"help": "Whether saved states should be automatically iteratively named."}, ) total_limit: int = field( default=None, metadata={"help": "The maximum number of total saved states to keep."}, ) iteration: int = field( default=0, metadata={"help": "The current save iteration."}, ) save_on_each_node: bool = field( default=False, metadata={ "help": ( "When doing multi-node distributed training, whether to save models and checkpoints on each node, or" " only on the main one" ) }, ) def set_directories(self, project_dir: str = None): "Sets `self.project_dir` and `self.logging_dir` to the appropriate values." self.project_dir = project_dir if self.logging_dir is None: self.logging_dir = project_dir def __post_init__(self): self.set_directories(self.project_dir) @dataclass class GradientAccumulationPlugin(KwargsHandler): """ A plugin to configure gradient accumulation behavior. """ num_steps: int = field(default=None, metadata={"help": "The number of steps to accumulate gradients for."}) adjust_scheduler: bool = field( default=True, metadata={ "help": "Whether to adjust the scheduler steps to account for the number of steps being accumulated. Should be `True` if the used scheduler was not adjusted for gradient accumulation." }, ) sync_with_dataloader: bool = field( default=True, metadata={ "help": "Whether to synchronize setting the gradients when at the end of the dataloader. Should only be set to `False` if you know what you're doing." }, ) @dataclass class TorchDynamoPlugin(KwargsHandler): """ This plugin is used to compile a model with PyTorch 2.0 """ backend: DynamoBackend = field( default=None, metadata={"help": f"Possible options are {[b.value.lower() for b in DynamoBackend]}"}, ) mode: str = field( default=None, metadata={"help": "Possible options are 'default', 'reduce-overhead' or 'max-autotune'"} ) fullgraph: bool = field(default=None, metadata={"help": "Whether it is ok to break model into several subgraphs"}) dynamic: bool = field(default=None, metadata={"help": "Whether to use dynamic shape for tracing"}) options: Any = field(default=None, metadata={"help": "A dictionary of options to pass to the backend."}) disable: bool = field(default=False, metadata={"help": "Turn torch.compile() into a no-op for testing"}) def __post_init__(self): prefix = "ACCELERATE_DYNAMO_" if self.backend is None: self.backend = os.environ.get(prefix + "BACKEND", "no") self.backend = DynamoBackend(self.backend.upper()) if self.mode is None: self.mode = os.environ.get(prefix + "MODE", "default") if self.fullgraph is None: self.fullgraph = str_to_bool(os.environ.get(prefix + "USE_FULLGRAPH", "False")) == 1 if self.dynamic is None: self.dynamic = str_to_bool(os.environ.get(prefix + "USE_DYNAMIC", "False")) == 1 def to_dict(self): dynamo_config = copy.deepcopy(self.__dict__) dynamo_config["backend"] = dynamo_config["backend"].value.lower() return dynamo_config @dataclass class DeepSpeedPlugin: """ This plugin is used to integrate DeepSpeed. """ hf_ds_config: Any = field( default=None, metadata={ "help": "path to DeepSpeed config file or dict or an object of class `accelerate.utils.deepspeed.HfDeepSpeedConfig`." }, ) gradient_accumulation_steps: int = field( default=None, metadata={ "help": "Number of steps to accumulate gradients before updating optimizer states. If not set, will use the value from the `Accelerator` directly." }, ) gradient_clipping: float = field(default=None, metadata={"help": "Enable gradient clipping with value"}) zero_stage: int = field( default=None, metadata={"help": "Possible options are 0,1,2,3; Default will be taken from environment variable"}, ) is_train_batch_min: str = field( default=True, metadata={"help": "If both train & eval dataloaders are specified, this will decide the train_batch_size"}, ) offload_optimizer_device: bool = field( default=None, metadata={"help": "Possible options are none|cpu|nvme. Only applicable with ZeRO Stages 2 and 3."}, ) offload_param_device: bool = field( default=None, metadata={"help": "Possible options are none|cpu|nvme. Only applicable with ZeRO Stage 3."}, ) offload_optimizer_nvme_path: str = field( default=None, metadata={"help": "Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3."}, ) offload_param_nvme_path: str = field( default=None, metadata={"help": "Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3."}, ) zero3_init_flag: bool = field( default=None, metadata={ "help": "Flag to indicate whether to enable `deepspeed.zero.Init` for constructing massive models." "Only applicable with ZeRO Stage-3." }, ) zero3_save_16bit_model: bool = field( default=None, metadata={"help": "Flag to indicate whether to save 16-bit model. Only applicable with ZeRO Stage-3."}, ) def __post_init__(self): from .deepspeed import HfDeepSpeedConfig if self.gradient_accumulation_steps is None: gas = os.environ.get("ACCELERATE_GRADIENT_ACCUMULATION_STEPS", "auto") self.gradient_accumulation_steps = int(gas) if gas.isdigit() else gas if self.gradient_clipping is None: gradient_clipping = os.environ.get("ACCELERATE_GRADIENT_CLIPPING", "none") if gradient_clipping != "none": self.gradient_clipping = float(gradient_clipping) if self.zero_stage is None: self.zero_stage = int(os.environ.get("ACCELERATE_DEEPSPEED_ZERO_STAGE", 2)) if self.offload_optimizer_device is None: self.offload_optimizer_device = os.environ.get("ACCELERATE_DEEPSPEED_OFFLOAD_OPTIMIZER_DEVICE", "none") if self.offload_param_device is None: self.offload_param_device = os.environ.get("ACCELERATE_DEEPSPEED_OFFLOAD_PARAM_DEVICE", "none") if self.offload_optimizer_nvme_path is None: self.offload_optimizer_nvme_path = os.environ.get( "ACCELERATE_DEEPSPEED_OFFLOAD_OPTIMIZER_NVME_PATH", "none" ) if self.offload_param_nvme_path is None: self.offload_param_nvme_path = os.environ.get("ACCELERATE_DEEPSPEED_OFFLOAD_PARAM_NVME_PATH", "none") if self.zero3_save_16bit_model is None: self.zero3_save_16bit_model = ( os.environ.get("ACCELERATE_DEEPSPEED_ZERO3_SAVE_16BIT_MODEL", "false") == "true" ) if self.hf_ds_config is None: self.hf_ds_config = os.environ.get("ACCELERATE_DEEPSPEED_CONFIG_FILE", "none") if ( isinstance(self.hf_ds_config, dict) or (isinstance(self.hf_ds_config, str) and self.hf_ds_config != "none") or isinstance(self.hf_ds_config, HfDeepSpeedConfig) ): if not isinstance(self.hf_ds_config, HfDeepSpeedConfig): self.hf_ds_config = HfDeepSpeedConfig(self.hf_ds_config) if "gradient_accumulation_steps" not in self.hf_ds_config.config: self.hf_ds_config.config["gradient_accumulation_steps"] = 1 if "zero_optimization" not in self.hf_ds_config.config: raise ValueError("Please specify the ZeRO optimization config in the DeepSpeed config.") self._deepspeed_config_checks() plugin_to_config_mapping = { "gradient_accumulation_steps": "gradient_accumulation_steps", "gradient_clipping": "gradient_clipping", "zero_stage": "zero_optimization.stage", "offload_optimizer_device": "zero_optimization.offload_optimizer.device", "offload_param_device": "zero_optimization.offload_param.device", "offload_param_nvme_path": "zero_optimization.offload_param.nvme_path", "offload_optimizer_nvme_path": "zero_optimization.offload_optimizer.nvme_path", "zero3_save_16bit_model": "zero_optimization.stage3_gather_16bit_weights_on_model_save", } kwargs = {v: getattr(self, k) for k, v in plugin_to_config_mapping.items() if getattr(self, k) is not None} for key in kwargs.keys(): self.fill_match(key, **kwargs, must_match=False) self.hf_ds_config.set_stage_and_offload() # filling the missing values in the class attributes from the DeepSpeed config # when using the DeepSpeed config file. for key, value in plugin_to_config_mapping.items(): config_value = self.hf_ds_config.get_value(value) if config_value is not None and config_value != "auto": setattr(self, key, config_value) else: config = { "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": self.gradient_accumulation_steps, "zero_optimization": { "stage": self.zero_stage, "offload_optimizer": { "device": self.offload_optimizer_device, "nvme_path": self.offload_optimizer_nvme_path if self.offload_optimizer_device == "nvme" else None, }, "offload_param": { "device": self.offload_param_device, "nvme_path": self.offload_param_nvme_path if self.offload_param_device == "nvme" else None, }, "stage3_gather_16bit_weights_on_model_save": self.zero3_save_16bit_model, }, } if self.gradient_clipping: config["gradient_clipping"] = self.gradient_clipping self.hf_ds_config = HfDeepSpeedConfig(config) self.deepspeed_config = self.hf_ds_config.config self.deepspeed_config["steps_per_print"] = float("inf") # this will stop deepspeed from logging @ stdout if self.zero3_init_flag is None: self.zero3_init_flag = ( str_to_bool(os.environ.get("ACCELERATE_DEEPSPEED_ZERO3_INIT", str(self.hf_ds_config.is_zero3()))) == 1 ) if self.zero3_init_flag and not self.hf_ds_config.is_zero3(): warnings.warn("DeepSpeed Zero3 Init flag is only applicable for ZeRO Stage 3. Setting it to False.") self.zero3_init_flag = False def fill_match(self, ds_key_long, mismatches=None, must_match=True, **kwargs): mismatches = [] if mismatches is None else mismatches config, ds_key = self.hf_ds_config.find_config_node(ds_key_long) if config is None: return if config.get(ds_key) == "auto": if ds_key_long in kwargs: config[ds_key] = kwargs[ds_key_long] return else: raise ValueError( f"`{ds_key_long}` not found in kwargs. " f"Please specify `{ds_key_long}` without `auto` (set to correct value) in the DeepSpeed config file or " "pass it in kwargs." ) if not must_match: return ds_val = config.get(ds_key) if ds_val is not None and ds_key_long in kwargs: if ds_val != kwargs[ds_key_long]: mismatches.append(f"- ds {ds_key_long}={ds_val} vs arg {ds_key_long}={kwargs[ds_key_long]}") def is_auto(self, ds_key_long): val = self.hf_ds_config.get_value(ds_key_long) if val is None: return False else: return val == "auto" def get_value(self, ds_key_long, default=None): return self.hf_ds_config.get_value(ds_key_long, default) def deepspeed_config_process(self, prefix="", mismatches=None, config=None, must_match=True, **kwargs): """Process the DeepSpeed config with the values from the kwargs.""" mismatches = [] if mismatches is None else mismatches if config is None: config = self.deepspeed_config for key, value in config.items(): if isinstance(value, dict): self.deepspeed_config_process( prefix=prefix + key + ".", mismatches=mismatches, config=value, must_match=must_match, **kwargs ) else: self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs) if len(mismatches) > 0 and prefix == "": mismatches_msg = "\n".join(mismatches) raise ValueError( "Please correct the following DeepSpeed config values that mismatch kwargs " f" values:\n{mismatches_msg}\nThe easiest method is to set these DeepSpeed config values to 'auto'." ) def set_mixed_precision(self, mixed_precision): ds_config = self.deepspeed_config kwargs = { "fp16.enabled": mixed_precision == "fp16", "bf16.enabled": mixed_precision == "bf16", } if mixed_precision == "fp16": if "fp16" not in ds_config: ds_config["fp16"] = {"enabled": True, "auto_cast": True} elif mixed_precision == "bf16": if "bf16" not in ds_config: ds_config["bf16"] = {"enabled": True} if mixed_precision != "no": diff_dtype = "bf16" if mixed_precision == "fp16" else "fp16" if str(ds_config.get(diff_dtype, {}).get("enabled", "False")).lower() == "true": raise ValueError( f"`--mixed_precision` arg cannot be set to `{mixed_precision}` when `{diff_dtype}` is set in the DeepSpeed config file." ) for dtype in ["fp16", "bf16"]: if dtype not in ds_config: ds_config[dtype] = {"enabled": False} self.fill_match("fp16.enabled", must_match=False, **kwargs) self.fill_match("bf16.enabled", must_match=False, **kwargs) def set_deepspeed_weakref(self): from .imports import is_transformers_available if self.zero3_init_flag: if not is_transformers_available(): raise Exception( "When `zero3_init_flag` is set, it requires Transformers to be installed. " "Please run `pip install transformers`." ) ds_config = copy.deepcopy(self.deepspeed_config) if "gradient_accumulation_steps" not in ds_config or ds_config["gradient_accumulation_steps"] == "auto": ds_config["gradient_accumulation_steps"] = 1 if ( "train_micro_batch_size_per_gpu" not in ds_config or ds_config["train_micro_batch_size_per_gpu"] == "auto" ): ds_config["train_micro_batch_size_per_gpu"] = 1 if ds_config.get("train_batch_size", None) == "auto": del ds_config["train_batch_size"] if compare_versions("transformers", "<", "4.33"): from transformers.deepspeed import HfDeepSpeedConfig else: from transformers.integrations import HfDeepSpeedConfig self.dschf = HfDeepSpeedConfig(ds_config) # keep this object alive # noqa def is_zero3_init_enabled(self): return self.zero3_init_flag @contextmanager def zero3_init_context_manager(self, enable=False): old = self.zero3_init_flag if old == enable: yield else: self.zero3_init_flag = enable self.dschf = None self.set_deepspeed_weakref() yield self.zero3_init_flag = old self.dschf = None self.set_deepspeed_weakref() def _deepspeed_config_checks(self): env_variable_names_to_ignore = [ "ACCELERATE_GRADIENT_ACCUMULATION_STEPS", "ACCELERATE_GRADIENT_CLIPPING", "ACCELERATE_DEEPSPEED_ZERO_STAGE", "ACCELERATE_DEEPSPEED_OFFLOAD_OPTIMIZER_DEVICE", "ACCELERATE_DEEPSPEED_OFFLOAD_PARAM_DEVICE", "ACCELERATE_DEEPSPEED_OFFLOAD_PARAM_NVME_PATH", "ACCELERATE_DEEPSPEED_OFFLOAD_OPTIMIZER_NVME_PATH", "ACCELERATE_DEEPSPEED_ZERO3_SAVE_16BIT_MODEL", "ACCELERATE_MIXED_PRECISION", ] env_variable_names_to_ignore = [ name.replace("ACCELERATE_", "").replace("DEEPSPEED_", "").lower() for name in env_variable_names_to_ignore ] deepspeed_fields_from_accelerate_config = os.environ.get("ACCELERATE_CONFIG_DS_FIELDS", "").split(",") if any(name in env_variable_names_to_ignore for name in deepspeed_fields_from_accelerate_config): raise ValueError( f"When using `deepspeed_config_file`, the following accelerate config variables will be ignored: {env_variable_names_to_ignore}.\n" "Please specify them appropriately in the DeepSpeed config file.\n" "If you are using an accelerate config file, remove others config variables mentioned in the above specified list.\n" "The easiest method is to create a new config following the questionnaire via `accelerate config`.\n" "It will only ask for the necessary config variables when using `deepspeed_config_file`." ) @dataclass class FullyShardedDataParallelPlugin: """ This plugin is used to enable fully sharded data parallelism. """ sharding_strategy: "typing.Any" = field( default=None, metadata={ "help": "FSDP Sharding Strategy of type `torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy`" }, ) backward_prefetch: "typing.Any" = field( default=None, metadata={ "help": "FSDP Backward Prefetch of type `torch.distributed.fsdp.fully_sharded_data_parallel.BackwardPrefetch`" }, ) mixed_precision_policy: "typing.Any" = field( default=None, metadata={ "help": "A config to enable mixed precision training with FullyShardedDataParallel. " "The 3 flags that are set are `param_dtype`, `reduce_dtype`, `buffer_dtype`. " "Each flag expects `torch.dtype` as the value. " "It is of type `torch.distributed.fsdp.fully_sharded_data_parallel.MixedPrecision`." }, ) auto_wrap_policy: Optional[Callable] = field( default=None, metadata={"help": "A callable specifying a policy to recursively wrap layers with FSDP"}, ) cpu_offload: "typing.Any" = field( default=None, metadata={ "help": "Decides Whether to offload parameters and gradients to CPU. " "It is of type `torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffload`." }, ) ignored_modules: Optional[Iterable[torch.nn.Module]] = field( default=None, metadata={"help": "A list of modules to ignore for FSDP."}, ) state_dict_type: "typing.Any" = field( default=None, metadata={ "help": "FSDP State Dict Type of type `torch.distributed.fsdp.fully_sharded_data_parallel.StateDictType`" }, ) state_dict_config: "typing.Any" = field( default=None, metadata={ "help": "FSDP State Dict Config of type `torch.distributed.fsdp.fully_sharded_data_parallel.StateDictConfig`" }, ) optim_state_dict_config: "typing.Any" = field( default=None, metadata={ "help": "FSDP Optimizer State Dict Config of type `torch.distributed.fsdp.fully_sharded_data_parallel.OptimStateDictConfig`" }, ) limit_all_gathers: bool = field( default=True, metadata={ "help": "If False, then FSDP allows the CPU thread to schedule all-gathers " "without any extra synchronization. If True, then FSDP explicitly synchronizes the CPU thread to prevent " "too many in-flight all-gathers. This bool only affects the sharded strategies that schedule all-gathers. " "Enabling this can help lower the number of CUDA malloc retries." }, ) use_orig_params: bool = field( default=True, metadata={ "help": "If `True`, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable parameters. " "Useful in cases such as parameter-efficient fine-tuning. " "Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019). " "This also enables multiple optimizer param groups. This should be `True` when creating an optimizer object before preparing/wrapping the model with FSDP." }, ) param_init_fn: Optional[Callable[[torch.nn.Module], None]] = field( default=None, metadata={ "help": "A Callable[torch.nn.Module] -> None that specifies how modules " "that are currently on the meta device should be initialized onto an actual device." }, ) sync_module_states: bool = field( default=True, metadata={ "help": "If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0 " "to ensure they are the same across all ranks after initialization" }, ) forward_prefetch: bool = field( default=False, metadata={ "help": "If True, then FSDP explicitly prefetches the next upcoming " "all-gather while executing in the forward pass. only use with Static graphs." }, ) activation_checkpointing: bool = field( default=False, metadata={ "help": "If True, activation checkpointing is a technique to reduce memory usage by clearing activations of " "certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time " "for reduced memory usage." }, ) def __post_init__(self): from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, CPUOffload, ShardingStrategy prefix = "FSDP_" if self.sharding_strategy is None: sharding_strategy = os.environ.get(prefix + "SHARDING_STRATEGY", "FULL_SHARD") sharding_strategy = ( FSDP_SHARDING_STRATEGY.index(sharding_strategy) + 1 if not sharding_strategy.isdigit() else int(sharding_strategy) ) self.sharding_strategy = ShardingStrategy(sharding_strategy) if self.cpu_offload is None: if str_to_bool(os.environ.get(prefix + "OFFLOAD_PARAMS", "False")) == 1: self.cpu_offload = CPUOffload(offload_params=True) else: self.cpu_offload = CPUOffload(offload_params=False) if self.backward_prefetch is None: prefetch_policy = os.environ.get(prefix + "BACKWARD_PREFETCH", "NO_PREFETCH") if prefetch_policy != FSDP_BACKWARD_PREFETCH[-1]: self.backward_prefetch = BackwardPrefetch(FSDP_BACKWARD_PREFETCH.index(prefetch_policy) + 1) if self.state_dict_type is None: state_dict_type_policy = os.environ.get(prefix + "STATE_DICT_TYPE", "FULL_STATE_DICT") self.set_state_dict_type(state_dict_type_policy) self.use_orig_params = str_to_bool(os.environ.get(prefix + "USE_ORIG_PARAMS", "False")) == 1 self.sync_module_states = str_to_bool(os.environ.get(prefix + "SYNC_MODULE_STATES", "True")) == 1 self.forward_prefetch = str_to_bool(os.environ.get(prefix + "FORWARD_PREFETCH", "False")) == 1 self.activation_checkpointing = str_to_bool(os.environ.get(prefix + "ACTIVATION_CHECKPOINTING", "False")) == 1 if self.sync_module_states: if is_npu_available(): device = torch.npu.current_device() elif is_cuda_available(): device = torch.cuda.current_device() elif is_xpu_available(): device = torch.xpu.current_device() else: raise RuntimeError( "There are currently no available devices found, must be one of 'XPU', 'CUDA', or 'NPU'." ) self.param_init_fn = lambda x: x.to_empty(device=device, recurse=False) @staticmethod def get_module_class_from_name(module, name): """ Gets a class from a module by its name. Args: module (`torch.nn.Module`): The module to get the class from. name (`str`): The name of the class. """ modules_children = list(module.children()) if module.__class__.__name__ == name: return module.__class__ elif len(modules_children) == 0: return else: for child_module in modules_children: module_class = FullyShardedDataParallelPlugin.get_module_class_from_name(child_module, name) if module_class is not None: return module_class def set_auto_wrap_policy(self, model): from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy default_transformer_cls_names_to_wrap = ( ",".join(model._no_split_modules) if getattr(model, "_no_split_modules", None) is not None else "" ) if self.auto_wrap_policy is None: auto_wrap_policy = os.environ.get("FSDP_AUTO_WRAP_POLICY", "NO_WRAP") if auto_wrap_policy == FSDP_AUTO_WRAP_POLICY[0]: transformer_cls_names_to_wrap = os.environ.get( "FSDP_TRANSFORMER_CLS_TO_WRAP", default_transformer_cls_names_to_wrap ).split(",") transformer_cls_to_wrap = set() for layer_class in transformer_cls_names_to_wrap: transformer_cls = FullyShardedDataParallelPlugin.get_module_class_from_name(model, layer_class) if transformer_cls is None: raise Exception("Could not find the transformer layer class to wrap in the model.") else: transformer_cls_to_wrap.add(transformer_cls) self.auto_wrap_policy = functools.partial( transformer_auto_wrap_policy, # Transformer layer class to wrap transformer_layer_cls=transformer_cls_to_wrap, ) elif auto_wrap_policy == FSDP_AUTO_WRAP_POLICY[1]: min_num_params = int(os.environ.get("FSDP_MIN_NUM_PARAMS", 0)) if min_num_params > 0: self.auto_wrap_policy = functools.partial( size_based_auto_wrap_policy, min_num_params=min_num_params ) def set_mixed_precision(self, mixed_precision): if mixed_precision == "fp16": dtype = torch.float16 elif mixed_precision == "bf16": dtype = torch.bfloat16 else: raise ValueError(f"Unknown mixed precision value: {mixed_precision}") from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision if self.mixed_precision_policy is None: self.mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype) def set_state_dict_type(self, state_dict_type_policy): from torch.distributed.fsdp.fully_sharded_data_parallel import ( FullOptimStateDictConfig, FullStateDictConfig, StateDictType, ) self.state_dict_type = StateDictType(FSDP_STATE_DICT_TYPE.index(state_dict_type_policy) + 1) if self.state_dict_type == StateDictType.FULL_STATE_DICT: if self.state_dict_config is None: self.state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) if self.optim_state_dict_config is None: self.optim_state_dict_config = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True) @dataclass class MegatronLMPlugin: """ Plugin for Megatron-LM to enable tensor, pipeline, sequence and data parallelism. Also to enable selective activation recomputation and optimized fused kernels. """ tp_degree: int = field(default=None, metadata={"help": "tensor parallelism degree."}) pp_degree: int = field(default=None, metadata={"help": "pipeline parallelism degree."}) num_micro_batches: int = field(default=None, metadata={"help": "number of micro-batches."}) gradient_clipping: float = field( default=None, metadata={"help": "gradient clipping value based on global L2 Norm (0 to disable)"} ) sequence_parallelism: bool = field( default=None, metadata={"help": "enable sequence parallelism"}, ) recompute_activations: bool = field( default=None, metadata={"help": "enable selective activation recomputation"}, ) use_distributed_optimizer: bool = field( default=None, metadata={"help": "enable distributed optimizer"}, ) pipeline_model_parallel_split_rank: int = field( default=None, metadata={"help": "Rank where encoder and decoder should be split."} ) num_layers_per_virtual_pipeline_stage: int = field( default=None, metadata={"help": "Number of layers per virtual pipeline stage."} ) is_train_batch_min: str = field( default=True, metadata={"help": "If both train & eval dataloaders are specified, this will decide the micro_batch_size"}, ) train_iters: int = field( default=None, metadata={ "help": "Total number of iterations to train over all training runs. " "Note that either train-iters or train-samples should be provided when using `MegatronLMDummyScheduler`" }, ) train_samples: int = field( default=None, metadata={ "help": "Total number of samples to train over all training runs. " "Note that either train-iters or train-samples should be provided when using `MegatronLMDummyScheduler`" }, ) weight_decay_incr_style: str = field( default="constant", metadata={"help": 'Weight decay increment function. choices=["constant", "linear", "cosine"]. '}, ) start_weight_decay: float = field( default=None, metadata={"help": "Initial weight decay coefficient for L2 regularization."}, ) end_weight_decay: float = field( default=None, metadata={"help": "End of run weight decay coefficient for L2 regularization."}, ) lr_decay_style: str = field( default="linear", metadata={"help": "Learning rate decay function. choices=['constant', 'linear', 'cosine']."}, ) lr_decay_iters: int = field( default=None, metadata={"help": "Number of iterations for learning rate decay. If None defaults to `train_iters`."}, ) lr_decay_samples: int = field( default=None, metadata={"help": "Number of samples for learning rate decay. If None defaults to `train_samples`."}, ) lr_warmup_iters: int = field( default=None, metadata={"help": "number of iterations to linearly warmup learning rate over."}, ) lr_warmup_samples: int = field( default=None, metadata={"help": "number of samples to linearly warmup learning rate over."}, ) lr_warmup_fraction: float = field( default=None, metadata={"help": "fraction of lr-warmup-(iters/samples) to linearly warmup learning rate over."}, ) min_lr: float = field( default=0, metadata={"help": "Minumum value for learning rate. The scheduler clip values below this threshold."}, ) consumed_samples: List[int] = field( default=None, metadata={ "help": "Number of samples consumed in the same order as the dataloaders to `accelerator.prepare` call." }, ) no_wd_decay_cond: Optional[Callable] = field(default=None, metadata={"help": "Condition to disable weight decay."}) scale_lr_cond: Optional[Callable] = field(default=None, metadata={"help": "Condition to scale learning rate."}) lr_mult: float = field(default=1.0, metadata={"help": "Learning rate multiplier."}) megatron_dataset_flag: bool = field( default=False, metadata={"help": "Whether the format of dataset follows Megatron-LM Indexed/Cached/MemoryMapped format."}, ) seq_length: int = field( default=None, metadata={"help": "Maximum sequence length to process."}, ) encoder_seq_length: int = field( default=None, metadata={"help": "Maximum sequence length to process for the encoder."}, ) decoder_seq_length: int = field( default=None, metadata={"help": "Maximum sequence length to process for the decoder."}, ) tensorboard_dir: str = field( default=None, metadata={"help": "Path to save tensorboard logs."}, ) set_all_logging_options: bool = field( default=False, metadata={"help": "Whether to set all logging options."}, ) eval_iters: int = field( default=100, metadata={"help": "Number of iterations to run for evaluation validation/test for."} ) eval_interval: int = field( default=1000, metadata={"help": "Interval between running evaluation on validation set."} ) return_logits: bool = field( default=False, metadata={"help": "Whether to return logits from the model."}, ) # custom train step args custom_train_step_class: Optional[Any] = field( default=None, metadata={"help": "Custom train step class."}, ) custom_train_step_kwargs: Optional[Dict[str, Any]] = field( default=None, metadata={"help": "Custom train step kwargs."}, ) # custom model args custom_model_provider_function: Optional[Callable] = field( default=None, metadata={"help": "Custom model provider function."}, ) custom_prepare_model_function: Optional[Callable] = field( default=None, metadata={"help": "Custom prepare model function."}, ) # remaining args such as enabling Alibi/ROPE positional embeddings, # wandb logging, Multi-Query Attention, etc. other_megatron_args: Optional[Dict[str, Any]] = field( default=None, metadata={"help": "Other Megatron-LM arguments. Please refer Megatron-LM"}, ) def __post_init__(self): prefix = "MEGATRON_LM_" if self.tp_degree is None: self.tp_degree = int(os.environ.get(prefix + "TP_DEGREE", 1)) if self.pp_degree is None: self.pp_degree = int(os.environ.get(prefix + "PP_DEGREE", 1)) if self.num_micro_batches is None: self.num_micro_batches = int(os.environ.get(prefix + "NUM_MICRO_BATCHES", 1)) if self.gradient_clipping is None: self.gradient_clipping = float(os.environ.get(prefix + "GRADIENT_CLIPPING", 1.0)) if self.recompute_activations is None: self.recompute_activations = str_to_bool(os.environ.get(prefix + "RECOMPUTE_ACTIVATIONS", "False")) == 1 if self.use_distributed_optimizer is None: self.use_distributed_optimizer = ( str_to_bool(os.environ.get(prefix + "USE_DISTRIBUTED_OPTIMIZER", "False")) == 1 ) if self.sequence_parallelism is None: self.sequence_parallelism = str_to_bool(os.environ.get(prefix + "SEQUENCE_PARALLELISM", "False")) == 1 if self.pp_degree > 1 or self.use_distributed_optimizer: self.DDP_impl = "local" else: self.DDP_impl = "torch" if self.consumed_samples is not None: if len(self.consumed_samples) == 1: self.consumed_samples.extend([0, 0]) elif len(self.consumed_samples) == 2: self.consumed_samples.append(0) self.megatron_lm_default_args = { "tensor_model_parallel_size": self.tp_degree, "pipeline_model_parallel_size": self.pp_degree, "pipeline_model_parallel_split_rank": self.pipeline_model_parallel_split_rank, "num_layers_per_virtual_pipeline_stage": self.num_layers_per_virtual_pipeline_stage, "DDP_impl": self.DDP_impl, "use_distributed_optimizer": self.use_distributed_optimizer, "sequence_parallel": self.sequence_parallelism, "clip_grad": self.gradient_clipping, "num_micro_batches": self.num_micro_batches, "consumed_samples": self.consumed_samples, "no_wd_decay_cond": self.no_wd_decay_cond, "scale_lr_cond": self.scale_lr_cond, "lr_mult": self.lr_mult, "megatron_dataset_flag": self.megatron_dataset_flag, "eval_iters": self.eval_iters, "eval_interval": self.eval_interval, } if self.recompute_activations: self.megatron_lm_default_args["recompute_granularity"] = "selective" if self.tensorboard_dir is not None: self.megatron_lm_default_args["tensorboard_dir"] = self.tensorboard_dir if self.set_all_logging_options: self.set_tensorboard_logging_options() if self.other_megatron_args is not None: self.megatron_lm_default_args.update(self.other_megatron_args) def set_network_size_args(self, model, batch_data=None): # Check if the model is either BERT, GPT or T5 else raise error # set 'num_layers', 'hidden_size', 'num_attention_heads', 'max_position_embeddings' if "megatron-bert" in model.config.model_type.lower(): model_type_name = "bert" num_layers = model.config.num_hidden_layers hidden_size = model.config.hidden_size num_attention_heads = model.config.num_attention_heads max_position_embeddings = model.config.max_position_embeddings num_labels = model.config.num_labels orig_vocab_size = model.config.vocab_size if "maskedlm" in model.__class__.__name__.lower(): pretraining_flag = True if self.seq_length is not None: if self.encoder_seq_length is not None: warnings.warn("Both `seq_length` and `encoder_seq_length` are set. Using `encoder_seq_length`.") self.seq_length = self.encoder_seq_length elif self.encoder_seq_length is not None: self.seq_length = self.encoder_seq_length elif batch_data is not None: self.seq_length = batch_data["input_ids"].shape[1] else: self.seq_length = max_position_embeddings self.megatron_lm_default_args["seq_length"] = self.seq_length elif "gpt2" in model.config.model_type.lower(): model_type_name = "gpt" num_layers = model.config.n_layer hidden_size = model.config.n_embd num_attention_heads = model.config.n_head max_position_embeddings = model.config.n_positions orig_vocab_size = model.config.vocab_size pretraining_flag = True if self.seq_length is not None: if self.decoder_seq_length is not None: warnings.warn("Both `seq_length` and `decoder_seq_length` are set. Using `decoder_seq_length`.") self.seq_length = self.decoder_seq_length elif self.decoder_seq_length is not None: self.seq_length = self.decoder_seq_length elif batch_data is not None: self.seq_length = batch_data["input_ids"].shape[1] else: self.seq_length = max_position_embeddings self.megatron_lm_default_args["seq_length"] = self.seq_length self.megatron_lm_default_args["return_logits"] = self.return_logits self.megatron_lm_default_args["tokenizer_type"] = "GPT2BPETokenizer" elif "t5" in model.config.model_type.lower(): model_type_name = "t5" num_layers = model.config.num_layers hidden_size = model.config.d_model num_attention_heads = model.config.num_heads max_position_embeddings = model.config.n_positions if hasattr(model.config, "n_positions") else 1024 orig_vocab_size = model.config.vocab_size pretraining_flag = True if self.encoder_seq_length is None: if batch_data is not None: self.encoder_seq_length = batch_data["input_ids"].shape[1] else: self.encoder_seq_length = max_position_embeddings if self.decoder_seq_length is None: if batch_data is not None: self.decoder_seq_length = batch_data["labels"].shape[1] else: self.decoder_seq_length = max_position_embeddings self.megatron_lm_default_args["encoder_seq_length"] = self.encoder_seq_length self.megatron_lm_default_args["decoder_seq_length"] = self.decoder_seq_length else: raise ValueError( "🤗 Accelerate Megatron-LM integration supports only BERT, GPT and T5 model. " "Please check the model you are using is one of those." ) self.megatron_lm_default_args["model_type_name"] = model_type_name self.megatron_lm_default_args["num_layers"] = num_layers self.megatron_lm_default_args["hidden_size"] = hidden_size self.megatron_lm_default_args["num_attention_heads"] = num_attention_heads self.megatron_lm_default_args["max_position_embeddings"] = max_position_embeddings self.megatron_lm_default_args["pretraining_flag"] = pretraining_flag self.megatron_lm_default_args["orig_vocab_size"] = orig_vocab_size self.megatron_lm_default_args["model_return_dict"] = model.config.return_dict if model_type_name == "bert": self.megatron_lm_default_args["num_labels"] = num_labels def set_mixed_precision(self, mixed_precision): if mixed_precision == "fp16": self.megatron_lm_default_args["fp16"] = True elif mixed_precision == "bf16": self.megatron_lm_default_args["bf16"] = True self.DDP_impl = "local" self.megatron_lm_default_args["DDP_impl"] = self.DDP_impl def set_training_args(self, micro_batch_size, dp_degree): self.data_parallel_size = dp_degree self.micro_batch_size = micro_batch_size self.global_batch_size = dp_degree * micro_batch_size * self.num_micro_batches self.megatron_lm_default_args["data_parallel_size"] = self.data_parallel_size self.megatron_lm_default_args["micro_batch_size"] = self.micro_batch_size self.megatron_lm_default_args["global_batch_size"] = self.global_batch_size def set_optimizer_type(self, optimizer): optimizer_name = optimizer.__class__.__name__.lower() if "adam" in optimizer_name: self.megatron_lm_default_args["optimizer"] = "adam" self.megatron_lm_default_args["adam_beta1"] = optimizer.defaults["betas"][0] self.megatron_lm_default_args["adam_beta2"] = optimizer.defaults["betas"][1] self.megatron_lm_default_args["adam_eps"] = optimizer.defaults["eps"] elif "sgd" in optimizer_name: self.megatron_lm_default_args["optimizer"] = "sgd" self.megatron_lm_default_args["sgd_momentum"] = optimizer.defaults["momentum"] else: raise ValueError(f"Optimizer {optimizer_name} is not supported by Megatron-LM") self.megatron_lm_default_args["lr"] = optimizer.defaults["lr"] self.megatron_lm_default_args["weight_decay"] = optimizer.defaults["weight_decay"] def set_scheduler_args(self, scheduler): if self.train_iters is None: self.train_iters = scheduler.total_num_steps // self.megatron_lm_default_args["data_parallel_size"] if self.train_samples is not None: self.train_samples = None warnings.warn( "Ignoring `train_samples` as `train_iters` based on scheduler is being used for training." ) if self.lr_warmup_iters is None: self.lr_warmup_iters = scheduler.warmup_num_steps // self.megatron_lm_default_args["data_parallel_size"] if self.lr_warmup_samples is not None: warnings.warn( "Ignoring `lr_warmup_samples` as `lr_warmup_iters` based on scheduler is being used for training." ) self.lr_warmup_samples = 0 self.megatron_lm_default_args["train_iters"] = self.train_iters self.megatron_lm_default_args["lr_warmup_iters"] = self.lr_warmup_iters self.megatron_lm_default_args["train_samples"] = self.train_samples self.megatron_lm_default_args["lr_warmup_samples"] = self.lr_warmup_samples self.megatron_lm_default_args["lr_decay_iters"] = self.lr_decay_iters self.megatron_lm_default_args["lr_decay_samples"] = self.lr_decay_samples self.megatron_lm_default_args["lr_warmup_fraction"] = self.lr_warmup_fraction self.megatron_lm_default_args["lr_decay_style"] = self.lr_decay_style self.megatron_lm_default_args["weight_decay_incr_style"] = self.weight_decay_incr_style self.megatron_lm_default_args["start_weight_decay"] = self.start_weight_decay self.megatron_lm_default_args["end_weight_decay"] = self.end_weight_decay self.megatron_lm_default_args["min_lr"] = self.min_lr def set_tensorboard_logging_options(self): from megatron.arguments import _add_logging_args parser = argparse.ArgumentParser() parser = _add_logging_args(parser) logging_args = parser.parse_known_args() self.dataset_args = vars(logging_args[0]) for key, value in self.dataset_args.items(): if key.startswith("log_"): self.megatron_lm_default_args[key] = True elif key.startswith("no_log_"): self.megatron_lm_default_args[key.replace("no_", "")] = True @dataclass class BnbQuantizationConfig: """ A plugin to enable BitsAndBytes 4bit and 8bit quantization """ load_in_8bit: bool = field(default=False, metadata={"help": "enable 8bit quantization."}) llm_int8_threshold: float = field( default=6.0, metadata={"help": "value of the outliner threshold. only relevant when load_in_8bit=True"} ) load_in_4bit: bool = field(default=False, metadata={"help": "enable 4bit quantization."}) bnb_4bit_quant_type: str = field( default="fp4", metadata={ "help": "set the quantization data type in the `bnb.nn.Linear4Bit` layers. Options are {'fp4','np4'}." }, ) bnb_4bit_use_double_quant: bool = field( default=False, metadata={ "help": "enable nested quantization where the quantization constants from the first quantization are quantized again." }, ) bnb_4bit_compute_dtype: bool = field( default="fp16", metadata={ "help": "This sets the computational type which might be different than the input time. For example, inputs might be " "fp32, but computation can be set to bf16 for speedups. Options are {'fp32','fp16','bf16'}." }, ) torch_dtype: torch.dtype = field( default=None, metadata={ "help": "this sets the dtype of the remaining non quantized layers. `bitsandbytes` library suggests to set the value" "to `torch.float16` for 8 bit model and use the same dtype as the compute dtype for 4 bit model " }, ) skip_modules: List[str] = field( default=None, metadata={ "help": "an explicit list of the modules that we don't quantize. The dtype of these modules will be `torch_dtype`." }, ) keep_in_fp32_modules: List[str] = field( default=None, metadata={"help": "an explicit list of the modules that we don't quantize. We keep them in `torch.float32`."}, ) def __post_init__(self): """ Safety checker that arguments are correct - also replaces some NoneType arguments with their default values. """ if not isinstance(self.load_in_8bit, bool): raise ValueError("load_in_8bit must be a boolean") if not isinstance(self.load_in_4bit, bool): raise ValueError("load_in_4bit must be a boolean") if self.load_in_4bit and self.load_in_8bit: raise ValueError("load_in_4bit and load_in_8 can't be both True") if not self.load_in_4bit and not self.load_in_8bit: raise ValueError("load_in_4bit and load_in_8 can't be both False") if not isinstance(self.llm_int8_threshold, (int, float)): raise ValueError("llm_int8_threshold must be a float or an int") if not isinstance(self.bnb_4bit_quant_type, str): raise ValueError("bnb_4bit_quant_type must be a string") elif self.bnb_4bit_quant_type not in ["fp4", "nf4"]: raise ValueError(f"bnb_4bit_quant_type must be in ['fp4','nf4'] but found {self.bnb_4bit_quant_type}") if not isinstance(self.bnb_4bit_use_double_quant, bool): raise ValueError("bnb_4bit_use_double_quant must be a boolean") if isinstance(self.bnb_4bit_compute_dtype, str): if self.bnb_4bit_compute_dtype == "fp32": self.bnb_4bit_compute_dtype = torch.float32 elif self.bnb_4bit_compute_dtype == "fp16": self.bnb_4bit_compute_dtype = torch.float16 elif self.bnb_4bit_compute_dtype == "bf16": self.bnb_4bit_compute_dtype = torch.bfloat16 else: raise ValueError( f"bnb_4bit_compute_dtype must be in ['fp32','fp16','bf16'] but found {self.bnb_4bit_compute_dtype}" ) elif not isinstance(self.bnb_4bit_compute_dtype, torch.dtype): raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype") if self.skip_modules is not None and not isinstance(self.skip_modules, list): raise ValueError("skip_modules must be a list of strings") if self.keep_in_fp32_modules is not None and not isinstance(self.keep_in_fp32_modules, list): raise ValueError("keep_in_fp_32_modules must be a list of strings") if self.load_in_4bit: self.target_dtype = CustomDtype.INT4 if self.load_in_8bit: self.target_dtype = torch.int8 if self.load_in_4bit and self.llm_int8_threshold != 6.0: warnings.warn("llm_int8_threshold can only be used for model loaded in 8bit") if isinstance(self.torch_dtype, str): if self.torch_dtype == "fp32": self.torch_dtype = torch.float32 elif self.torch_dtype == "fp16": self.torch_dtype = torch.float16 elif self.torch_dtype == "bf16": self.torch_dtype = torch.bfloat16 else: raise ValueError(f"torch_dtype must be in ['fp32','fp16','bf16'] but found {self.torch_dtype}") if self.load_in_8bit and self.torch_dtype is None: self.torch_dtype = torch.float16 if self.load_in_4bit and self.torch_dtype is None: self.torch_dtype = self.bnb_4bit_compute_dtype if not isinstance(self.torch_dtype, torch.dtype): raise ValueError("torch_dtype must be a torch.dtype")

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/operations.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A set of basic tensor ops compatible with tpu, gpu, and multigpu """ import pickle import warnings from functools import update_wrapper, wraps from typing import Any, Mapping import torch from ..state import PartialState from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES from .dataclasses import DistributedType, TensorInformation from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm if is_torch_distributed_available(): from torch.distributed import ReduceOp def is_torch_tensor(tensor): return isinstance(tensor, torch.Tensor) def is_torch_xpu_tensor(tensor): return isinstance( tensor, torch.xpu.FloatTensor, torch.xpu.ByteTensor, torch.xpu.IntTensor, torch.xpu.LongTensor, torch.xpu.HalfTensor, torch.xpu.DoubleTensor, torch.xpu.BFloat16Tensor, ) def is_tensor_information(tensor_info): return isinstance(tensor_info, TensorInformation) def is_namedtuple(data): """ Checks if `x` is a `namedtuple` or not. Can have false positives, but only if a user is trying to mimic a `namedtuple` perfectly. """ data_type = type(data) bases = data_type.__bases__ if len(bases) != 1 or bases[0] != tuple: return False fields = getattr(data_type, "_fields", None) if not isinstance(fields, tuple): return False return all(isinstance(member, str) for member in fields) def honor_type(obj, generator): """ Cast a generator to the same type as obj (list, tuple, or namedtuple) """ # Some objects may not be able to instantiate from a generator directly if is_namedtuple(obj): return type(obj)(*list(generator)) else: return type(obj)(generator) def recursively_apply(func, data, *args, test_type=is_torch_tensor, error_on_other_type=False, **kwargs): """ Recursively apply a function on a data structure that is a nested list/tuple/dictionary of a given base type. Args: func (`callable`): The function to recursively apply. data (nested list/tuple/dictionary of `main_type`): The data on which to apply `func` *args: Positional arguments that will be passed to `func` when applied on the unpacked data. main_type (`type`, *optional*, defaults to `torch.Tensor`): The base type of the objects to which apply `func`. error_on_other_type (`bool`, *optional*, defaults to `False`): Whether to return an error or not if after unpacking `data`, we get on an object that is not of type `main_type`. If `False`, the function will leave objects of types different than `main_type` unchanged. **kwargs: Keyword arguments that will be passed to `func` when applied on the unpacked data. Returns: The same data structure as `data` with `func` applied to every object of type `main_type`. """ if isinstance(data, (tuple, list)): return honor_type( data, ( recursively_apply( func, o, *args, test_type=test_type, error_on_other_type=error_on_other_type, **kwargs ) for o in data ), ) elif isinstance(data, Mapping): return type(data)( { k: recursively_apply( func, v, *args, test_type=test_type, error_on_other_type=error_on_other_type, **kwargs ) for k, v in data.items() } ) elif test_type(data): return func(data, *args, **kwargs) elif error_on_other_type: raise TypeError( f"Unsupported types ({type(data)}) passed to `{func.__name__}`. Only nested list/tuple/dicts of " f"objects that are valid for `{test_type.__name__}` should be passed." ) return data def send_to_device(tensor, device, non_blocking=False, skip_keys=None): """ Recursively sends the elements in a nested list/tuple/dictionary of tensors to a given device. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to send to a given device. device (`torch.device`): The device to send the data to. Returns: The same data structure as `tensor` with all tensors sent to the proper device. """ if isinstance(tensor, (tuple, list)): return honor_type( tensor, (send_to_device(t, device, non_blocking=non_blocking, skip_keys=skip_keys) for t in tensor) ) elif isinstance(tensor, Mapping): if isinstance(skip_keys, str): skip_keys = [skip_keys] elif skip_keys is None: skip_keys = [] return type(tensor)( { k: t if k in skip_keys else send_to_device(t, device, non_blocking=non_blocking, skip_keys=skip_keys) for k, t in tensor.items() } ) elif hasattr(tensor, "to"): # `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)). if is_npu_available() and isinstance(device, int): device = f"npu:{device}" try: return tensor.to(device, non_blocking=non_blocking) except TypeError: # .to() doesn't accept non_blocking as kwarg return tensor.to(device) else: return tensor def get_data_structure(data): """ Recursively gathers the information needed to rebuild a nested list/tuple/dictionary of tensors. Args: data (nested list/tuple/dictionary of `torch.Tensor`): The data to send to analyze. Returns: The same data structure as `data` with [`~utils.TensorInformation`] instead of tensors. """ def _get_data_structure(tensor): return TensorInformation(shape=tensor.shape, dtype=tensor.dtype) return recursively_apply(_get_data_structure, data) def get_shape(data): """ Recursively gathers the shape of a nested list/tuple/dictionary of tensors as a list. Args: data (nested list/tuple/dictionary of `torch.Tensor`): The data to send to analyze. Returns: The same data structure as `data` with lists of tensor shapes instead of tensors. """ def _get_shape(tensor): return list(tensor.shape) return recursively_apply(_get_shape, data) def initialize_tensors(data_structure): """ Recursively initializes tensors from a nested list/tuple/dictionary of [`~utils.TensorInformation`]. Returns: The same data structure as `data` with tensors instead of [`~utils.TensorInformation`]. """ def _initialize_tensor(tensor_info): return torch.empty(*tensor_info.shape, dtype=tensor_info.dtype) return recursively_apply(_initialize_tensor, data_structure, test_type=is_tensor_information) def find_batch_size(data): """ Recursively finds the batch size in a nested list/tuple/dictionary of lists of tensors. Args: data (nested list/tuple/dictionary of `torch.Tensor`): The data from which to find the batch size. Returns: `int`: The batch size. """ if isinstance(data, (tuple, list, Mapping)) and (len(data) == 0): raise ValueError(f"Cannot find the batch size from empty {type(data)}.") if isinstance(data, (tuple, list)): return find_batch_size(data[0]) elif isinstance(data, Mapping): for k in data.keys(): return find_batch_size(data[k]) elif not isinstance(data, torch.Tensor): raise TypeError(f"Can only find the batch size of tensors but got {type(data)}.") return data.shape[0] def listify(data): """ Recursively finds tensors in a nested list/tuple/dictionary and converts them to a list of numbers. Args: data (nested list/tuple/dictionary of `torch.Tensor`): The data from which to convert to regular numbers. Returns: The same data structure as `data` with lists of numbers instead of `torch.Tensor`. """ def _convert_to_list(tensor): tensor = tensor.detach().cpu() if tensor.dtype == torch.bfloat16: # As of Numpy 1.21.4, NumPy does not support bfloat16 (see # https://github.com/numpy/numpy/blob/a47ecdea856986cd60eabbd53265c2ca5916ad5d/doc/source/user/basics.types.rst ). # Until Numpy adds bfloat16, we must convert float32. tensor = tensor.to(torch.float32) return tensor.tolist() return recursively_apply(_convert_to_list, data) def _tpu_gather(tensor): def _tpu_gather_one(tensor): if tensor.ndim == 0: tensor = tensor.clone()[None] # Can only gather contiguous tensors if not tensor.is_contiguous(): tensor = tensor.contiguous() return xm.all_gather(tensor) res = recursively_apply(_tpu_gather_one, tensor, error_on_other_type=True) xm.mark_step() return res def _gpu_gather(tensor): state = PartialState() if is_torch_version(">=", "1.13"): gather_op = torch.distributed.all_gather_into_tensor else: gather_op = torch.distributed._all_gather_base def _gpu_gather_one(tensor): if tensor.ndim == 0: tensor = tensor.clone()[None] # Can only gather contiguous tensors if not tensor.is_contiguous(): tensor = tensor.contiguous() if state.backend is not None and state.backend != "gloo": # We use `empty` as `all_gather_into_tensor` slightly # differs from `all_gather` for better efficiency, # and we rely on the number of items in the tensor # rather than its direct shape output_tensors = torch.empty( state.num_processes * tensor.numel(), dtype=tensor.dtype, device=state.device, ) gather_op(output_tensors, tensor) return output_tensors.view(-1, *tensor.size()[1:]) else: # a backend of `None` is always CPU # also gloo does not support `all_gather_into_tensor`, # which will result in a larger memory overhead for the op output_tensors = [torch.empty_like(tensor) for _ in range(state.num_processes)] torch.distributed.all_gather(output_tensors, tensor) return torch.cat(output_tensors, dim=0) return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True) class DistributedOperationException(Exception): """ An exception class for distributed operations. Raised if the operation cannot be performed due to the shape of the tensors. """ pass def verify_operation(function): """ Verifies that `tensor` is the same shape across all processes. Only ran if `PartialState().debug` is `True`. """ @wraps(function) def wrapper(*args, **kwargs): if PartialState().distributed_type == DistributedType.NO or not PartialState().debug: return function(*args, **kwargs) operation = f"{function.__module__}.{function.__name__}" if "tensor" in kwargs: tensor = kwargs["tensor"] else: tensor = args[0] if PartialState().device.type != find_device(tensor).type: raise DistributedOperationException( f"One or more of the tensors passed to {operation} were not on the {tensor.device.type} while the `Accelerator` is configured for {PartialState().device.type}. " f"Please move it to the {PartialState().device.type} before calling {operation}." ) shapes = get_shape(tensor) output = gather_object([shapes]) if output[0] is not None: are_same = output.count(output[0]) == len(output) if not are_same: process_shape_str = "\n - ".join([f"Process {i}: {shape}" for i, shape in enumerate(output)]) raise DistributedOperationException( f"Cannot apply desired operation due to shape mismatches. " "All shapes across devices must be valid." f"\n\nOperation: `{operation}`\nInput shapes:\n - {process_shape_str}" ) return function(*args, **kwargs) return wrapper def chained_operation(function): """ Checks that `verify_operation` failed and if so reports a more helpful error chaining the existing `DistributedOperationException`. """ @wraps(function) def wrapper(*args, **kwargs): try: return function(*args, **kwargs) except DistributedOperationException as e: operation = f"{function.__module__}.{function.__name__}" raise DistributedOperationException( f"Error found while calling `{operation}`. Please see the earlier error for more details." ) from e return wrapper @verify_operation def gather(tensor): """ Recursively gather tensor in a nested list/tuple/dictionary of tensors from all devices. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather. Returns: The same data structure as `tensor` with all tensors sent to the proper device. """ if PartialState().distributed_type == DistributedType.TPU: return _tpu_gather(tensor) elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES: return _gpu_gather(tensor) else: return tensor def _gpu_gather_object(object: Any): output_objects = [None for _ in range(PartialState().num_processes)] torch.distributed.all_gather_object(output_objects, object) # all_gather_object returns a list of lists, so we need to flatten it return [x for y in output_objects for x in y] def gather_object(object: Any): """ Recursively gather object in a nested list/tuple/dictionary of objects from all devices. Args: object (nested list/tuple/dictionary of picklable object): The data to gather. Returns: The same data structure as `object` with all the objects sent to every device. """ if PartialState().distributed_type == DistributedType.TPU: raise NotImplementedError("gather objects in TPU is not supported") elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES: return _gpu_gather_object(object) else: return object def _gpu_broadcast(data, src=0): def _gpu_broadcast_one(tensor, src=0): torch.distributed.broadcast(tensor, src=src) return tensor return recursively_apply(_gpu_broadcast_one, data, error_on_other_type=True, src=src) def _tpu_broadcast(tensor, src=0, name="broadcast tensor"): if isinstance(tensor, (list, tuple)): return honor_type(tensor, (_tpu_broadcast(t, name=f"{name}_{i}") for i, t in enumerate(tensor))) elif isinstance(tensor, Mapping): return type(tensor)({k: _tpu_broadcast(v, name=f"{name}_{k}") for k, v in tensor.items()}) return xm.mesh_reduce(name, tensor, lambda x: x[src]) @verify_operation def broadcast(tensor, from_process: int = 0): """ Recursively broadcast tensor in a nested list/tuple/dictionary of tensors to all devices. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather. from_process (`int`, *optional*, defaults to 0): The process from which to send the data Returns: The same data structure as `tensor` with all tensors broadcasted to the proper device. """ if PartialState().distributed_type == DistributedType.TPU: return _tpu_broadcast(tensor, src=from_process, name="accelerate.utils.broadcast") elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES: return _gpu_broadcast(tensor, src=from_process) else: return tensor def broadcast_object_list(object_list, from_process: int = 0): """ Broadcast a list of picklable objects form one process to the others. Args: object_list (list of picklable objects): The list of objects to broadcast. This list will be modified inplace. from_process (`int`, *optional*, defaults to 0): The process from which to send the data. Returns: The same list containing the objects from process 0. """ if PartialState().distributed_type == DistributedType.TPU: for i, obj in enumerate(object_list): object_list[i] = xm.mesh_reduce("accelerate.utils.broadcast_object_list", obj, lambda x: x[from_process]) elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES: torch.distributed.broadcast_object_list(object_list, src=from_process) return object_list def slice_tensors(data, tensor_slice, process_index=None, num_processes=None): """ Recursively takes a slice in a nested list/tuple/dictionary of tensors. Args: data (nested list/tuple/dictionary of `torch.Tensor`): The data to slice. tensor_slice (`slice`): The slice to take. Returns: The same data structure as `data` with all the tensors slices. """ def _slice_tensor(tensor, tensor_slice): return tensor[tensor_slice] return recursively_apply(_slice_tensor, data, tensor_slice) def concatenate(data, dim=0): """ Recursively concatenate the tensors in a nested list/tuple/dictionary of lists of tensors with the same shape. Args: data (nested list/tuple/dictionary of lists of tensors `torch.Tensor`): The data to concatenate. dim (`int`, *optional*, defaults to 0): The dimension on which to concatenate. Returns: The same data structure as `data` with all the tensors concatenated. """ if isinstance(data[0], (tuple, list)): return honor_type(data[0], (concatenate([d[i] for d in data], dim=dim) for i in range(len(data[0])))) elif isinstance(data[0], Mapping): return type(data[0])({k: concatenate([d[k] for d in data], dim=dim) for k in data[0].keys()}) elif not isinstance(data[0], torch.Tensor): raise TypeError(f"Can only concatenate tensors but got {type(data[0])}") return torch.cat(data, dim=dim) class CannotPadNestedTensorWarning(UserWarning): pass @chained_operation def pad_across_processes(tensor, dim=0, pad_index=0, pad_first=False): """ Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so they can safely be gathered. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather. dim (`int`, *optional*, defaults to 0): The dimension on which to pad. pad_index (`int`, *optional*, defaults to 0): The value with which to pad. pad_first (`bool`, *optional*, defaults to `False`): Whether to pad at the beginning or the end. """ def _pad_across_processes(tensor, dim=0, pad_index=0, pad_first=False): if getattr(tensor, "is_nested", False): warnings.warn( "Cannot pad nested tensors without more information. Leaving unprocessed.", CannotPadNestedTensorWarning, ) return tensor if dim >= len(tensor.shape): return tensor # Gather all sizes size = torch.tensor(tensor.shape, device=tensor.device)[None] sizes = gather(size).cpu() # Then pad to the maximum size max_size = max(s[dim] for s in sizes) if max_size == tensor.shape[dim]: return tensor old_size = tensor.shape new_size = list(old_size) new_size[dim] = max_size new_tensor = tensor.new_zeros(tuple(new_size)) + pad_index if pad_first: indices = tuple( slice(max_size - old_size[dim], max_size) if i == dim else slice(None) for i in range(len(new_size)) ) else: indices = tuple(slice(0, old_size[dim]) if i == dim else slice(None) for i in range(len(new_size))) new_tensor[indices] = tensor return new_tensor return recursively_apply( _pad_across_processes, tensor, error_on_other_type=True, dim=dim, pad_index=pad_index, pad_first=pad_first ) @verify_operation def reduce(tensor, reduction="mean", scale=1.0): """ Recursively reduce the tensors in a nested list/tuple/dictionary of lists of tensors across all processes by the mean of a given operation. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to reduce. reduction (`str`, *optional*, defaults to `"mean"`): A reduction method. Can be of "mean", "sum", or "none" scale (`float`, *optional*): A default scaling value to be applied after the reduce, only valied on XLA. Returns: The same data structure as `data` with all the tensors reduced. """ def _reduce_across_processes(tensor, reduction="mean", scale=1.0): state = PartialState() cloned_tensor = tensor.clone() if state.distributed_type == DistributedType.NO: return cloned_tensor if state.distributed_type == DistributedType.TPU: # Some processes may have different HLO graphs than other # processes, for example in the breakpoint API # accelerator.set_trigger(). Use mark_step to make HLOs # the same on all processes. xm.mark_step() xm.all_reduce(xm.REDUCE_SUM, [cloned_tensor], scale) xm.mark_step() elif state.distributed_type.value in TORCH_DISTRIBUTED_OPERATION_TYPES: torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM) if reduction == "mean": cloned_tensor /= state.num_processes return cloned_tensor return recursively_apply( _reduce_across_processes, tensor, error_on_other_type=True, reduction=reduction, scale=scale ) def convert_to_fp32(tensor): """ Recursively converts the elements nested list/tuple/dictionary of tensors in FP16/BF16 precision to FP32. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to convert from FP16/BF16 to FP32. Returns: The same data structure as `tensor` with all tensors that were in FP16/BF16 precision converted to FP32. """ def _convert_to_fp32(tensor): return tensor.float() def _is_fp16_bf16_tensor(tensor): return hasattr(tensor, "dtype") and tensor.dtype in (torch.float16, torch.bfloat16) return recursively_apply(_convert_to_fp32, tensor, test_type=_is_fp16_bf16_tensor) class ConvertOutputsToFp32: """ Decorator to apply to a function outputing tensors (like a model forward pass) that ensures the outputs in FP16 precision will be convert back to FP32. Args: model_forward (`Callable`): The function which outputs we want to treat. Returns: The same function as `model_forward` but with converted outputs. """ def __init__(self, model_forward): self.model_forward = model_forward update_wrapper(self, model_forward) def __call__(self, *args, **kwargs): return convert_to_fp32(self.model_forward(*args, **kwargs)) def __getstate__(self): raise pickle.PicklingError( "Cannot pickle a prepared model with automatic mixed precision, please unwrap the model with `Accelerator.unwrap_model(model)` before pickling it." ) def convert_outputs_to_fp32(model_forward): model_forward = ConvertOutputsToFp32(model_forward) def forward(*args, **kwargs): return model_forward(*args, **kwargs) # To act like a decorator so that it can be popped when doing `extract_model_from_parallel` forward.__wrapped__ = model_forward return forward def find_device(data): """ Finds the device on which a nested dict/list/tuple of tensors lies (assuming they are all on the same device). Args: (nested list/tuple/dictionary of `torch.Tensor`): The data we want to know the device of. """ if isinstance(data, Mapping): for obj in data.values(): device = find_device(obj) if device is not None: return device elif isinstance(data, (tuple, list)): for obj in data: device = find_device(obj) if device is not None: return device elif isinstance(data, torch.Tensor): return data.device

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/torch_xla.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib.metadata import subprocess import sys def install_xla(upgrade: bool = False): """ Helper function to install appropriate xla wheels based on the `torch` version in Google Colaboratory. Args: upgrade (`bool`, *optional*, defaults to `False`): Whether to upgrade `torch` and install the latest `torch_xla` wheels. Example: ```python >>> from accelerate.utils import install_xla >>> install_xla(upgrade=True) ``` """ in_colab = False if "IPython" in sys.modules: in_colab = "google.colab" in str(sys.modules["IPython"].get_ipython()) if in_colab: if upgrade: torch_install_cmd = ["pip", "install", "-U", "torch"] subprocess.run(torch_install_cmd, check=True) # get the current version of torch torch_version = importlib.metadata.version("torch") torch_version_trunc = torch_version[: torch_version.rindex(".")] xla_wheel = f"https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl" xla_install_cmd = ["pip", "install", xla_wheel] subprocess.run(xla_install_cmd, check=True) else: raise RuntimeError("`install_xla` utility works only on google colab.")

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/__init__.py

from .constants import ( MODEL_NAME, OPTIMIZER_NAME, RNG_STATE_NAME, SAFE_MODEL_NAME, SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, SAMPLER_NAME, SCALER_NAME, SCHEDULER_NAME, TORCH_DISTRIBUTED_OPERATION_TYPES, TORCH_LAUNCH_PARAMS, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ) from .dataclasses import ( AutocastKwargs, BnbQuantizationConfig, ComputeEnvironment, CustomDtype, DeepSpeedPlugin, DistributedDataParallelKwargs, DistributedType, DynamoBackend, FP8RecipeKwargs, FullyShardedDataParallelPlugin, GradientAccumulationPlugin, GradScalerKwargs, InitProcessGroupKwargs, KwargsHandler, LoggerType, MegatronLMPlugin, PrecisionType, ProjectConfiguration, RNGType, SageMakerDistributedType, TensorInformation, TorchDynamoPlugin, ) from .environment import ( are_libraries_initialized, check_cuda_p2p_ib_support, check_fp8_capability, get_int_from_env, parse_choice_from_env, parse_flag_from_env, str_to_bool, ) from .imports import ( get_ccl_version, is_4bit_bnb_available, is_8bit_bnb_available, is_aim_available, is_bf16_available, is_bnb_available, is_boto3_available, is_ccl_available, is_clearml_available, is_comet_ml_available, is_cuda_available, is_datasets_available, is_deepspeed_available, is_dvclive_available, is_fp8_available, is_ipex_available, is_megatron_lm_available, is_mlflow_available, is_mps_available, is_msamp_available, is_npu_available, is_pandas_available, is_rich_available, is_sagemaker_available, is_tensorboard_available, is_timm_available, is_tpu_available, is_transformer_engine_available, is_transformers_available, is_wandb_available, is_xpu_available, ) from .modeling import ( calculate_maximum_sizes, check_device_map, check_tied_parameters_in_config, check_tied_parameters_on_same_device, compute_module_sizes, convert_file_size_to_int, dtype_byte_size, find_tied_parameters, get_balanced_memory, get_max_layer_size, get_max_memory, get_mixed_precision_context_manager, id_tensor_storage, infer_auto_device_map, load_checkpoint_in_model, load_offloaded_weights, load_state_dict, named_module_tensors, retie_parameters, set_module_tensor_to_device, shard_checkpoint, ) from .offload import ( OffloadedWeightsLoader, PrefixedDataset, extract_submodules_state_dict, load_offloaded_weight, offload_state_dict, offload_weight, save_offload_index, ) from .operations import ( CannotPadNestedTensorWarning, broadcast, broadcast_object_list, concatenate, convert_outputs_to_fp32, convert_to_fp32, find_batch_size, find_device, gather, gather_object, get_data_structure, honor_type, initialize_tensors, is_namedtuple, is_tensor_information, is_torch_tensor, listify, pad_across_processes, recursively_apply, reduce, send_to_device, slice_tensors, ) from .versions import compare_versions, is_torch_version if is_deepspeed_available(): from .deepspeed import ( DeepSpeedEngineWrapper, DeepSpeedOptimizerWrapper, DeepSpeedSchedulerWrapper, DummyOptim, DummyScheduler, HfDeepSpeedConfig, ) from .bnb import has_4bit_bnb_layers, load_and_quantize_model from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer from .launch import ( PrepareForLaunch, _filter_args, prepare_deepspeed_cmd_env, prepare_multi_gpu_env, prepare_sagemager_args_inputs, prepare_simple_launcher_cmd_env, prepare_tpu, ) from .megatron_lm import ( AbstractTrainStep, BertTrainStep, GPTTrainStep, MegatronEngine, MegatronLMDummyDataLoader, MegatronLMDummyScheduler, MegatronLMOptimizerWrapper, MegatronLMSchedulerWrapper, T5TrainStep, avg_losses_across_data_parallel_group, gather_across_data_parallel_groups, ) from .megatron_lm import initialize as megatron_lm_initialize from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader from .megatron_lm import prepare_model as megatron_lm_prepare_model from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler from .memory import find_executable_batch_size, release_memory from .other import ( check_os_kernel, clean_state_dict_for_safetensors, clear_environment, convert_bytes, extract_model_from_parallel, get_pretty_name, is_port_in_use, merge_dicts, patch_environment, recursive_getattr, save, wait_for_everyone, write_basic_config, ) from .random import set_seed, synchronize_rng_state, synchronize_rng_states from .torch_xla import install_xla from .tqdm import tqdm from .transformer_engine import convert_model, has_transformer_engine_layers

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/deepspeed.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import io import json import os from copy import deepcopy from ..optimizer import AcceleratedOptimizer from ..scheduler import AcceleratedScheduler class HfDeepSpeedConfig: """ This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage. A `weakref` of this object is stored in the module's globals to be able to access the config from areas where things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore it's important that this object remains alive while the program is still running. [`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic the DeepSpeed configuration is not modified in any way. Args: config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict. """ def __init__(self, config_file_or_dict): if isinstance(config_file_or_dict, dict): # Don't modify user's data should they want to reuse it (e.g. in tests), because once we # modified it, it will not be accepted here again, since `auto` values would have been overridden config = deepcopy(config_file_or_dict) elif os.path.exists(config_file_or_dict): with io.open(config_file_or_dict, "r", encoding="utf-8") as f: config = json.load(f) else: try: config_decoded = base64.urlsafe_b64decode(config_file_or_dict).decode("utf-8") config = json.loads(config_decoded) except (UnicodeDecodeError, AttributeError, ValueError): raise ValueError( f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}" ) self.config = config self.set_stage_and_offload() def set_stage_and_offload(self): # zero stage - this is done as early as possible, before model is created, to allow # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object # during ``zero.Init()`` which needs to know the dtype, and some other hparams. self._stage = self.get_value("zero_optimization.stage", -1) # offload self._offload = False if self.is_zero2() or self.is_zero3(): offload_devices_valid = set(["cpu", "nvme"]) offload_devices = set( [ self.get_value("zero_optimization.offload_optimizer.device"), self.get_value("zero_optimization.offload_param.device"), ] ) if len(offload_devices & offload_devices_valid) > 0: self._offload = True def find_config_node(self, ds_key_long): config = self.config # find the config node of interest if it exists nodes = ds_key_long.split(".") ds_key = nodes.pop() for node in nodes: config = config.get(node) if config is None: return None, ds_key return config, ds_key def get_value(self, ds_key_long, default=None): """ Returns the set value or `default` if no value is set """ config, ds_key = self.find_config_node(ds_key_long) if config is None: return default return config.get(ds_key, default) def del_config_sub_tree(self, ds_key_long, must_exist=False): """ Deletes a sub-section of the config file if it's found. Unless `must_exist` is `True` the section doesn't have to exist. """ config = self.config # find the config node of interest if it exists nodes = ds_key_long.split(".") for node in nodes: parent_config = config config = config.get(node) if config is None: if must_exist: raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}") else: return # if found remove it if parent_config is not None: parent_config.pop(node) def is_true(self, ds_key_long): """ Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very specific question of whether the value is set to `True` (and it's not set to `False`` or isn't set). """ value = self.get_value(ds_key_long) return False if value is None else bool(value) def is_false(self, ds_key_long): """ Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set). """ value = self.get_value(ds_key_long) return False if value is None else not bool(value) def is_zero2(self): return self._stage == 2 def is_zero3(self): return self._stage == 3 def is_offload(self): return self._offload class DeepSpeedEngineWrapper: """ Internal wrapper for deepspeed.runtime.engine.DeepSpeedEngine. This is used to follow conventional training loop. Args: engine (deepspeed.runtime.engine.DeepSpeedEngine): deepspeed engine to wrap """ def __init__(self, engine): self.engine = engine def backward(self, loss, **kwargs): # runs backpropagation and handles mixed precision self.engine.backward(loss, **kwargs) # Deepspeed's `engine.step` performs the following operations: # - gradient accumulation check # - gradient clipping # - optimizer step # - zero grad # - checking overflow # - lr_scheduler step (only if engine.lr_scheduler is not None) self.engine.step() # and this plugin overrides the above calls with no-ops when Accelerate runs under # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple # training loop that works transparently under many training regimes. class DeepSpeedOptimizerWrapper(AcceleratedOptimizer): """ Internal wrapper around a deepspeed optimizer. Args: optimizer (`torch.optim.optimizer.Optimizer`): The optimizer to wrap. """ def __init__(self, optimizer): super().__init__(optimizer, device_placement=False, scaler=None) self.__has_overflow__ = hasattr(self.optimizer, "overflow") def zero_grad(self, set_to_none=None): pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed def step(self): pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed @property def step_was_skipped(self): """Whether or not the optimizer step was done, or skipped because of gradient overflow.""" if self.__has_overflow__: return self.optimizer.overflow return False class DeepSpeedSchedulerWrapper(AcceleratedScheduler): """ Internal wrapper around a deepspeed scheduler. Args: scheduler (`torch.optim.lr_scheduler.LambdaLR`): The scheduler to wrap. optimizers (one or a list of `torch.optim.Optimizer`): """ def __init__(self, scheduler, optimizers): super().__init__(scheduler, optimizers) def step(self): pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed class DummyOptim: """ Dummy optimizer presents model parameters or param groups, this is primarily used to follow conventional training loop when optimizer config is specified in the deepspeed config file. Args: lr (float): Learning rate. params (iterable): iterable of parameters to optimize or dicts defining parameter groups weight_decay (float): Weight decay. **kwargs: Other arguments. """ def __init__(self, params, lr=0.001, weight_decay=0, **kwargs): self.params = params self.lr = lr self.weight_decay = weight_decay self.kwargs = kwargs class DummyScheduler: """ Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training loop when scheduler config is specified in the deepspeed config file. Args: optimizer (`torch.optim.optimizer.Optimizer`): The optimizer to wrap. total_num_steps (int, *optional*): Total number of steps. warmup_num_steps (int, *optional*): Number of steps for warmup. lr_scheduler_callable (callable, *optional*): A callable function that creates an LR Scheduler. It accepts only one argument `optimizer`. **kwargs: Other arguments. """ def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, lr_scheduler_callable=None, **kwargs): self.optimizer = optimizer self.total_num_steps = total_num_steps self.warmup_num_steps = warmup_num_steps self.lr_scheduler_callable = lr_scheduler_callable self.kwargs = kwargs

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/megatron_lm.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import math from abc import ABC from functools import partial import torch import torch.nn.functional as F from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from ..optimizer import AcceleratedOptimizer from ..scheduler import AcceleratedScheduler from .imports import is_megatron_lm_available, is_transformers_available from .operations import recursively_apply, send_to_device if is_transformers_available(): from transformers.modeling_outputs import ( CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, SequenceClassifierOutput, ) if is_megatron_lm_available(): from megatron import ( get_args, get_num_microbatches, get_tensorboard_writer, get_timers, get_tokenizer, mpu, print_rank_0, print_rank_last, ) from megatron.arguments import _add_data_args, _add_validation_args, parse_args, validate_args from megatron.checkpointing import load_args_from_checkpoint, load_checkpoint, save_checkpoint from megatron.data.data_samplers import MegatronPretrainingRandomSampler, MegatronPretrainingSampler from megatron.global_vars import set_global_variables from megatron.initialize import ( _compile_dependencies, _init_autoresume, _set_random_seed, set_jit_fusion_options, write_args_to_tensorboard, ) from megatron.model import BertModel, Float16Module, GPTModel, ModelType, T5Model from megatron.model import DistributedDataParallel as LocalDDP from megatron.model.classification import Classification from megatron.optimizer import get_megatron_optimizer from megatron.schedules import get_forward_backward_func from megatron.text_generation.communication import broadcast_int_list, broadcast_tensor from megatron.text_generation.generation import ( beam_search_and_return_on_first_stage, generate_tokens_probs_and_return_on_first_stage, ) from megatron.tokenizer.tokenizer import _vocab_size_with_padding from megatron.training import get_model, get_optimizer_param_scheduler, training_log from megatron.utils import ( average_losses_across_data_parallel_group, calc_params_l2_norm, get_ltor_masks_and_position_ids, unwrap_model, ) # model utilities def model_provider_func(pre_process=True, post_process=True, add_encoder=True, add_decoder=True): """Build the model.""" args = get_args() mode = "pre-training" if args.pretraining_flag else "fine-tuning" if args.rank == 0: print(f"Building {args.model_type_name} model in the {mode} mode.") print( "The Megatron LM model weights are initialized at random in `accelerator.prepare`. " "Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup." ) if args.model_type_name == "bert": if args.pretraining_flag: num_tokentypes = 2 if args.bert_binary_head else 0 model = BertModel( num_tokentypes=num_tokentypes, add_binary_head=args.bert_binary_head, parallel_output=True, pre_process=pre_process, post_process=post_process, ) else: model = Classification( num_classes=args.num_labels, num_tokentypes=2, pre_process=pre_process, post_process=post_process ) elif args.model_type_name == "gpt": model = GPTModel(num_tokentypes=0, parallel_output=True, pre_process=pre_process, post_process=post_process) elif args.model_type_name == "t5": model = T5Model( num_tokentypes=0, parallel_output=True, pre_process=pre_process, post_process=post_process, add_encoder=add_encoder, add_decoder=add_decoder, ) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") return model def prepare_model(accelerator): accelerator.print("Preparing model") args = get_args() if accelerator.state.megatron_lm_plugin.custom_prepare_model_function is not None: if accelerator.state.megatron_lm_plugin.custom_model_provider_function is None: raise ValueError( "You must provide a `custom_model_provider_function` when using a `custom_prepare_model_function`." ) custom_model_provider_func = accelerator.state.megatron_lm_plugin.custom_model_provider_function model = accelerator.state.megatron_lm_plugin.custom_prepare_model_function(custom_model_provider_func) else: if args.model_type_name in ("bert", "gpt"): model_type = ModelType.encoder_or_decoder elif args.model_type_name == "t5": model_type = ModelType.encoder_and_decoder if args.pipeline_model_parallel_split_rank is None and args.pipeline_model_parallel_size > 1: args.pipeline_model_parallel_split_rank = args.pipeline_model_parallel_size // 2 model = get_model(model_provider_func, model_type) return model # dataloader utilities class MegatronLMDummyDataLoader: """ Dummy dataloader presents model parameters or param groups, this is primarily used to follow conventional training Args: **dataset_kwargs: Megatron data arguments. """ def __init__(self, **dataset_kwargs): parser = argparse.ArgumentParser() parser = _add_data_args(parser) parser = _add_validation_args(parser) data_args = parser.parse_known_args() self.dataset_args = vars(data_args[0]) self.dataset_args.update(dataset_kwargs) self.dataset_args["megatron_dataset_flag"] = True def set_megatron_data_args(self): args = get_args() for key, value in self.dataset_args.items(): setattr(args, key, value) def get_train_valid_test_datasets_provider(self): def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() dataset_args = { "data_prefix": args.data_path, "data_impl": args.data_impl, "splits_string": args.split, "train_valid_test_num_samples": train_val_test_num_samples, "skip_warmup": (not args.mmap_warmup), "seed": args.seed, } if args.model_type_name == "bert": dataset_args.update( { "max_seq_length": args.seq_length, "masked_lm_prob": args.mask_prob, "short_seq_prob": args.short_seq_prob, "binary_head": args.bert_binary_head, } ) elif args.model_type_name == "gpt": dataset_args.update( { "seq_length": args.seq_length, } ) elif args.model_type_name == "t5": dataset_args.update( { "max_seq_length": args.encoder_seq_length, "max_seq_length_dec": args.decoder_seq_length, "masked_lm_prob": args.mask_prob, "short_seq_prob": args.short_seq_prob, "dataset_type": "t5", } ) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") if args.model_type_name == "gpt": from megatron.data.gpt_dataset import build_train_valid_test_datasets else: from megatron.data.dataset_utils import build_train_valid_test_datasets train_ds, valid_ds, test_ds = build_train_valid_test_datasets(**dataset_args) return train_ds, valid_ds, test_ds return train_valid_test_datasets_provider def build_pretraining_data_loader(self, dataset, consumed_samples): if dataset is None: return None args = get_args() micro_batch_size = args.micro_batch_size * args.num_micro_batches # Megatron sampler if args.dataloader_type == "single": batch_sampler = MegatronPretrainingSampler( total_samples=len(dataset), consumed_samples=consumed_samples, micro_batch_size=micro_batch_size, data_parallel_rank=mpu.get_data_parallel_rank(), data_parallel_size=mpu.get_data_parallel_world_size(), ) elif args.dataloader_type == "cyclic": batch_sampler = MegatronPretrainingRandomSampler( dataset, total_samples=len(dataset), consumed_samples=consumed_samples, micro_batch_size=micro_batch_size, data_parallel_rank=mpu.get_data_parallel_rank(), data_parallel_size=mpu.get_data_parallel_world_size(), data_sharding=args.data_sharding, ) else: raise Exception("{} dataloader type is not supported.".format(args.dataloader_type)) # Torch dataloader. return torch.utils.data.DataLoader( dataset, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True ) def build_train_valid_test_data_iterators(self): def cyclic_iter(iter): while True: for x in iter: yield x args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0("> building train, validation, and test datasets ...") # Backward compatibility, assume fixed batch size. if args.iteration > 0 and args.consumed_train_samples == 0: assert args.train_samples is None, "only backward compatiblity support for iteration-based training" args.consumed_train_samples = args.iteration * args.global_batch_size if args.iteration > 0 and args.consumed_valid_samples == 0: if args.train_samples is None: args.consumed_valid_samples = ( (args.iteration // args.eval_interval) * args.eval_iters * args.global_batch_size ) # Data loader only on rank 0 of each model parallel group. if mpu.get_tensor_model_parallel_rank() == 0: # Number of train/valid/test samples. if args.train_samples: train_samples = args.train_samples else: train_samples = args.train_iters * args.global_batch_size eval_iters = (args.train_iters // args.eval_interval + 1) * args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [ train_samples, eval_iters * args.global_batch_size, test_iters * args.global_batch_size, ] print_rank_0(" > datasets target sizes (minimum size):") print_rank_0(" train: {}".format(train_val_test_num_samples[0])) print_rank_0(" validation: {}".format(train_val_test_num_samples[1])) print_rank_0(" test: {}".format(train_val_test_num_samples[2])) # Build the datasets. train_valid_test_datasets_provider = self.get_train_valid_test_datasets_provider() train_ds, valid_ds, test_ds = train_valid_test_datasets_provider(train_val_test_num_samples) # Build dataloders. train_dataloader = self.build_pretraining_data_loader(train_ds, args.consumed_train_samples) valid_dataloader = self.build_pretraining_data_loader(valid_ds, args.consumed_valid_samples) test_dataloader = self.build_pretraining_data_loader(test_ds, 0) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor([int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast( flags, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group() ) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Build iterators. dl_type = args.dataloader_type assert dl_type in ["single", "cyclic"] if train_dataloader is not None: train_data_iterator = ( iter(train_dataloader) if dl_type == "single" else iter(cyclic_iter(train_dataloader)) ) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = ( iter(valid_dataloader) if dl_type == "single" else iter(cyclic_iter(valid_dataloader)) ) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) if dl_type == "single" else iter(cyclic_iter(test_dataloader)) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator def prepare_data_loader(accelerator, dataloader): accelerator.print("Preparing dataloader") args = get_args() if not args.megatron_dataset_flag: from ..data_loader import _PYTORCH_DATALOADER_KWARGS, prepare_data_loader args = get_args() micro_batch_size = args.micro_batch_size * args.num_micro_batches kwargs = {k: getattr(dataloader, k, _PYTORCH_DATALOADER_KWARGS[k]) for k in _PYTORCH_DATALOADER_KWARGS} if kwargs["batch_size"] is None: if isinstance(kwargs["sampler"], torch.utils.data.BatchSampler): kwargs["sampler"].batch_size = micro_batch_size else: del kwargs["sampler"] del kwargs["shuffle"] del kwargs["batch_size"] kwargs["batch_sampler"].batch_size = micro_batch_size else: del kwargs["batch_sampler"] kwargs["batch_size"] = micro_batch_size dataloader = torch.utils.data.DataLoader(dataloader.dataset, **kwargs) return prepare_data_loader( dataloader, accelerator.device, num_processes=mpu.get_data_parallel_world_size(), process_index=mpu.get_data_parallel_rank(), split_batches=accelerator.split_batches, put_on_device=True, rng_types=accelerator.rng_types.copy(), dispatch_batches=accelerator.dispatch_batches, ) else: if args.consumed_samples is not None: ( args.consumed_train_samples, args.consumed_valid_samples, args.consumed_test_samples, ) = args.consumed_samples else: args.consumed_train_samples, args.consumed_valid_samples, args.consumed_test_samples = 0, 0, 0 ( train_data_iterator, valid_data_iterator, test_data_iterator, ) = dataloader.build_train_valid_test_data_iterators() return train_data_iterator, valid_data_iterator, test_data_iterator # optimizer utilities class MegatronLMOptimizerWrapper(AcceleratedOptimizer): def __init__(self, optimizer): super().__init__(optimizer, device_placement=False, scaler=None) def zero_grad(self, set_to_none=None): pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed def step(self): pass # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed @property def step_was_skipped(self): """Whether or not the optimizer step was done, or skipped because of gradient overflow.""" return self.optimizer.skipped_iter def prepare_optimizer(accelerator, model): accelerator.print("Preparing optimizer") args = get_args() optimizer = get_megatron_optimizer(model, args.no_wd_decay_cond, args.scale_lr_cond, args.lr_mult) return optimizer # scheduler utilities class MegatronLMDummyScheduler: """ Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training loop when scheduler config is specified in the deepspeed config file. Args: optimizer (`torch.optim.optimizer.Optimizer`): The optimizer to wrap. total_num_steps (int): Total number of steps. warmup_num_steps (int): Number of steps for warmup. **kwargs: Other arguments. """ def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, **kwargs): self.optimizer = optimizer self.total_num_steps = total_num_steps self.warmup_num_steps = warmup_num_steps self.kwargs = kwargs class MegatronLMSchedulerWrapper(AcceleratedScheduler): def __init__(self, scheduler, optimizers): super().__init__(scheduler, optimizers) def step(self, *args, **kwargs): return # `model(**batch)` is doing that automatically. Therefore, it's implementation is not needed def prepare_scheduler(accelerator, optimizer, scheduler): accelerator.print("Preparing scheduler") scheduler = get_optimizer_param_scheduler(optimizer) return scheduler class AbstractTrainStep(ABC): """Abstract class for batching, forward pass and loss handler.""" def __init__(self, name): super().__init__() self.name = name def get_batch_func(self): pass def get_forward_step_func(self): pass def get_loss_func(self): pass class BertTrainStep(AbstractTrainStep): """ Bert train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("BertTrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func(args.pretraining_flag, args.num_labels) self.forward_step = self.get_forward_step_func(args.pretraining_flag, args.bert_binary_head) if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = SequenceClassifierOutput def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Build the batch.""" # Items and their type. keys = ["text", "types", "labels", "is_random", "loss_mask", "padding_mask"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens = data_b["text"].long() types = data_b["types"].long() sentence_order = data_b["is_random"].long() loss_mask = data_b["loss_mask"].float() lm_labels = data_b["labels"].long() padding_mask = data_b["padding_mask"].long() return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask def get_batch_transformer(data_iterator): """Build the batch.""" data = next(data_iterator) data = send_to_device(data, torch.cuda.current_device()) # Unpack. tokens = data["input_ids"].long() padding_mask = data["attention_mask"].long() if "token_type_ids" in data: types = data["token_type_ids"].long() else: types = None if "labels" in data: lm_labels = data["labels"].long() loss_mask = (data["labels"] != -100).to(torch.float) else: lm_labels = None loss_mask = None if "next_sentence_label" in data: sentence_order = data["next_sentence_label"].long() else: sentence_order = None return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self, pretraining_flag, num_labels): def loss_func_pretrain(loss_mask, sentence_order, output_tensor): lm_loss_, sop_logits = output_tensor lm_loss_ = lm_loss_.float() loss_mask = loss_mask.float() lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() if sop_logits is not None: sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(), sentence_order.view(-1), ignore_index=-1) sop_loss = sop_loss.float() loss = lm_loss + sop_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss, sop_loss]) return loss, {"lm loss": averaged_losses[0], "sop loss": averaged_losses[1]} else: loss = lm_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss]) return loss, {"lm loss": averaged_losses[0]} def loss_func_finetune(labels, logits): if num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)): loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, num_labels), labels.view(-1)) else: loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) averaged_losses = average_losses_across_data_parallel_group([loss]) return loss, {"loss": averaged_losses[0]} if pretraining_flag: return loss_func_pretrain else: return loss_func_finetune def get_forward_step_func(self, pretraining_flag, bert_binary_head): def forward_step(data_iterator, model): """Forward step.""" tokens, types, sentence_order, loss_mask, labels, padding_mask = self.get_batch(data_iterator) if not bert_binary_head: types = None # Forward pass through the model. if pretraining_flag: output_tensor = model(tokens, padding_mask, tokentype_ids=types, lm_labels=labels) return output_tensor, partial(self.loss_func, loss_mask, sentence_order) else: logits = model(tokens, padding_mask, tokentype_ids=types) return logits, partial(self.loss_func, labels) return forward_step class GPTTrainStep(AbstractTrainStep): """ GPT train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("GPTTrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func() self.forward_step = self.get_forward_step_func() self.eod_token = args.padded_vocab_size - 1 if args.vocab_file is not None: tokenizer = get_tokenizer() self.eod_token = tokenizer.eod self.reset_position_ids = args.reset_position_ids self.reset_attention_mask = args.reset_attention_mask self.eod_mask_loss = args.eod_mask_loss if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = CausalLMOutputWithCrossAttentions def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Generate a batch""" # Items and their type. keys = ["text"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b["text"].long() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, self.eod_mask_loss ) return tokens, labels, loss_mask, attention_mask, position_ids def get_batch_transformer(data_iterator): data = next(data_iterator) data = {"input_ids": data["input_ids"]} data = send_to_device(data, torch.cuda.current_device()) tokens_ = data["input_ids"].long() padding = torch.zeros((tokens_.shape[0], 1), dtype=tokens_.dtype, device=tokens_.device) + self.eod_token tokens_ = torch.concat([tokens_, padding], dim=1) labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, self.eod_token, self.reset_position_ids, self.reset_attention_mask, True ) return tokens, labels, loss_mask, attention_mask, position_ids if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self): args = get_args() def loss_func(loss_mask, output_tensor): if args.return_logits: losses, logits = output_tensor else: losses = output_tensor losses = losses.float() loss_mask = loss_mask.view(-1).float() loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # Reduce loss for logging. averaged_loss = average_losses_across_data_parallel_group([loss]) output_dict = {"lm loss": averaged_loss[0]} if args.return_logits: output_dict.update({"logits": logits}) return loss, output_dict return loss_func def get_forward_step_func(self): def forward_step(data_iterator, model): """Forward step.""" # Get the batch. tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator) output_tensor = model(tokens, position_ids, attention_mask, labels=labels) return output_tensor, partial(self.loss_func, loss_mask) return forward_step class T5TrainStep(AbstractTrainStep): """ T5 train step class. Args: args (`argparse.Namespace`): Megatron-LM arguments. """ def __init__(self, args): super().__init__("T5TrainStep") self.get_batch = self.get_batch_func(args.megatron_dataset_flag) self.loss_func = self.get_loss_func() self.forward_step = self.get_forward_step_func() if not args.model_return_dict: self.model_output_class = None else: self.model_output_class = Seq2SeqLMOutput @staticmethod def attn_mask_postprocess(attention_mask): # We create a 3D attention mask from a 2D tensor mask. # [b, 1, s] attention_mask_b1s = attention_mask.unsqueeze(1) # [b, s, 1] attention_mask_bs1 = attention_mask.unsqueeze(2) # [b, s, s] attention_mask_bss = attention_mask_b1s * attention_mask_bs1 # Convert attention mask to binary: extended_attention_mask = attention_mask_bss < 0.5 return extended_attention_mask @staticmethod def get_decoder_mask(seq_length, device): attention_mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device)) attention_mask = attention_mask < 0.5 return attention_mask @staticmethod def get_enc_dec_mask(attention_mask, dec_seq_length, device): batch_size, _ = attention_mask.shape # We create a 3D attention mask from a 2D tensor mask. # [b, 1, s] attention_mask_b1s = attention_mask.unsqueeze(1) # [b, s, 1] attention_mask_bs1 = torch.ones((batch_size, dec_seq_length, 1), device=device) attention_mask_bss = attention_mask_bs1 * attention_mask_b1s extended_attention_mask = attention_mask_bss < 0.5 return extended_attention_mask def get_batch_func(self, megatron_dataset_flag): def get_batch_megatron(data_iterator): """Build the batch.""" keys = ["text_enc", "text_dec", "labels", "loss_mask", "enc_mask", "dec_mask", "enc_dec_mask"] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_enc = data_b["text_enc"].long() tokens_dec = data_b["text_dec"].long() labels = data_b["labels"].long() loss_mask = data_b["loss_mask"].float() enc_mask = data_b["enc_mask"] < 0.5 dec_mask = data_b["dec_mask"] < 0.5 enc_dec_mask = data_b["enc_dec_mask"] < 0.5 return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask def get_batch_transformer(data_iterator): """Build the batch.""" data = next(data_iterator) data = send_to_device(data, torch.cuda.current_device()) tokens_enc = data["input_ids"].long() labels = data["labels"].long() loss_mask = (labels != -100).to(torch.float) if "decoder_input_ids" in data: tokens_dec = data["decoder_input_ids"].long() else: tokens_dec = labels.new_zeros(labels.shape, device=labels.device, dtype=torch.long) tokens_dec[..., 1:] = labels[..., :-1].clone() tokens_dec[..., 0] = 0 tokens_dec.masked_fill_(tokens_dec == -100, 0) enc_mask = T5TrainStep.attn_mask_postprocess(data["attention_mask"].long()) dec_mask = T5TrainStep.get_decoder_mask(tokens_dec.shape[1], tokens_dec.device) enc_dec_mask = T5TrainStep.get_enc_dec_mask( data["attention_mask"].long(), tokens_dec.shape[1], tokens_dec.device ) return tokens_enc, tokens_dec, loss_mask, labels, enc_mask, dec_mask, enc_dec_mask if megatron_dataset_flag: return get_batch_megatron else: return get_batch_transformer def get_loss_func(self): def loss_func(loss_mask, output_tensor): lm_loss_ = output_tensor.float() lm_loss = torch.sum(lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() loss = lm_loss averaged_losses = average_losses_across_data_parallel_group([lm_loss]) return loss, {"lm loss": averaged_losses[0]} return loss_func def get_forward_step_func(self): def forward_step(data_iterator, model): """Forward step.""" # Get the batch. tokens_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask = self.get_batch( data_iterator ) # Forward model lm_labels output_tensor = model( tokens_enc, tokens_dec, enc_mask, dec_mask, enc_dec_mask, tokentype_ids=None, lm_labels=lm_labels ) return output_tensor, partial(self.loss_func, loss_mask) return forward_step # intialize megatron setup def initialize(accelerator, extra_args_provider=None, args_defaults={}): accelerator.print("Initializing Megatron-LM") assert torch.cuda.is_available(), "Megatron requires CUDA." # Parse arguments args = parse_args(extra_args_provider, ignore_unknown_args=True) # Set defaults for key, value in args_defaults.items(): if getattr(args, key, None) is not None: if args.rank == 0: print( "WARNING: overriding default arguments for {key}:{v} \ with {key}:{v2}".format( key=key, v=getattr(args, key), v2=value ), flush=True, ) setattr(args, key, value) if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False): assert args.load is not None, "--use-checkpoints-args requires --load argument" load_args_from_checkpoint(args) validate_args(args) # set global args, build tokenizer, and set adlr-autoresume, # tensorboard-writer, and timers. set_global_variables(args) # torch.distributed initialization def finish_mpu_init(): args = get_args() # Pytorch distributed. device_count = torch.cuda.device_count() args.rank = torch.distributed.get_rank() args.world_size = torch.distributed.get_world_size() if device_count > 0: device = args.rank % device_count if args.local_rank is not None: assert args.local_rank == device, "expected local-rank to be the same as rank % device-count." else: args.local_rank = device # Set the tensor model-parallel, pipeline model-parallel, and # data-parallel communicators. if mpu.model_parallel_is_initialized(): print("model parallel is already initialized") else: mpu.initialize_model_parallel( args.tensor_model_parallel_size, args.pipeline_model_parallel_size, args.virtual_pipeline_model_parallel_size, args.pipeline_model_parallel_split_rank, ) # Random seeds for reproducibility. if args.rank == 0: print("> setting random seeds to {} ...".format(args.seed)) _set_random_seed(args.seed, args.data_parallel_random_init) args = get_args() # Megatron's MPU is the master. Complete initialization right away. finish_mpu_init() # Autoresume. _init_autoresume() # Compile dependencies. _compile_dependencies() # Set pytorch JIT layer fusion options and warmup JIT functions. set_jit_fusion_options() args = get_args() args.padded_vocab_size = _vocab_size_with_padding(args.orig_vocab_size, args) if args.model_type_name == "bert" and args.pretraining_flag and args.num_labels == 2: args.bert_binary_head = True else: args.bert_binary_head = False args.iteration = 0 class MegatronEngine(torch.nn.Module): """ Megatron-LM model wrapper Args: accelerator (:class:`~accelerate.Accelerator`): The accelerator object to use. model: Megatron-LM model optimizer: Megatron-LM optimizer lr_scheduler: Megatron-LM lr scheduler """ def __init__(self, accelerator, model, optimizer, scheduler): super(MegatronEngine, self).__init__() self.module = model self.base_model = model[0] self.optimizer = optimizer self.scheduler = scheduler args = get_args() if accelerator.state.megatron_lm_plugin.custom_train_step_class is not None: self.train_step_handler = accelerator.state.megatron_lm_plugin.custom_train_step_class( args, **accelerator.state.megatron_lm_plugin.custom_train_step_kwargs ) elif args.model_type_name == "bert": self.train_step_handler = BertTrainStep(args) elif args.model_type_name == "gpt": self.train_step_handler = GPTTrainStep(args) elif args.model_type_name == "t5": self.train_step_handler = T5TrainStep(args) else: raise ValueError(f"Unsupported model type: {args.model_type_name}") self.optimizer.skipped_iter = False # Tracking loss. self.total_loss_dict = {} self.eval_total_loss_dict = {} self.iteration = 0 self.report_memory_flag = True if args.tensorboard_dir is not None: write_args_to_tensorboard() def train(self): for model_module in self.module: model_module.train() self.log_eval_results() def eval(self): for model_module in self.module: model_module.eval() def train_step(self, **batch_data): """ Training step for Megatron-LM Args: batch_data (:obj:`dict`): The batch data to train on. """ args = get_args() timers = get_timers() if len(batch_data) > 0: data_chunks = [] if args.num_micro_batches > 1: for i in range(0, args.num_micro_batches): data_chunks.append( { k: v[i * args.micro_batch_size : (i + 1) * args.micro_batch_size] for k, v in batch_data.items() } ) else: data_chunks = [batch_data] if len(self.module) > 1: batch_data_iterator = ( [iter(data_chunks) for _ in range(len(self.module))] if len(batch_data) > 0 else [None] * len(self.module) ) else: batch_data_iterator = iter(data_chunks) if len(batch_data) > 0 else None # Set grad to zero. if args.DDP_impl == "local" and args.use_contiguous_buffers_in_local_ddp: for partition in self.module: partition.zero_grad_buffer() self.optimizer.zero_grad() # Forward pass. forward_backward_func = get_forward_backward_func() losses_reduced = forward_backward_func( self.train_step_handler.forward_step, batch_data_iterator, self.module, self.optimizer, None, forward_only=False, ) # Empty unused memory. if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() # Reduce gradients. timers("backward-reduce-model-grads").start() self.optimizer.reduce_model_grads(args, timers) timers("backward-reduce-model-grads").stop() # Update parameters. timers("optimizer").start() update_successful, grad_norm, num_zeros_in_grad = self.optimizer.step(args, timers) timers("optimizer").stop() # Gather params. if update_successful: timers("backward-gather-model-params").start() self.optimizer.gather_model_params(args, timers) timers("backward-gather-model-params").stop() # Update learning rate. if update_successful: if self.scheduler is not None: increment = get_num_microbatches() * args.micro_batch_size * args.data_parallel_size self.scheduler.step(increment=increment) skipped_iter = 0 else: skipped_iter = 1 self.optimizer.skipped_iter = not update_successful # Empty unused memory. if args.empty_unused_memory_level >= 2: torch.cuda.empty_cache() args.consumed_train_samples += ( mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches() ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # Average loss across microbatches. loss_reduced = {} for key in losses_reduced[0]: losses_reduced_for_key = [x[key] for x in losses_reduced] if len(losses_reduced_for_key[0].shape) == 0: loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) else: loss_reduced[key] = torch.concat(losses_reduced_for_key) return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad return {}, skipped_iter, grad_norm, num_zeros_in_grad def eval_step(self, **batch_data): """ Evaluation step for Megatron-LM Args: batch_data (:obj:`dict`): The batch data to evaluate on. """ args = get_args() data_chunks = [] if args.num_micro_batches > 1: for i in range(0, args.num_micro_batches): data_chunks.append( {k: v[i * args.micro_batch_size : (i + 1) * args.micro_batch_size] for k, v in batch_data.items()} ) else: data_chunks = [batch_data] if len(self.module) > 1: batch_data_iterator = [iter(data_chunks) for _ in range(len(self.module))] else: batch_data_iterator = iter(data_chunks) forward_backward_func = get_forward_backward_func() loss_dicts = forward_backward_func( self.train_step_handler.forward_step, batch_data_iterator, self.module, optimizer=None, timers=None, forward_only=True, ) # Empty unused memory if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() args.consumed_valid_samples += ( mpu.get_data_parallel_world_size() * args.micro_batch_size * get_num_microbatches() ) if mpu.is_pipeline_last_stage(ignore_virtual=True): # Average loss across microbatches. loss_reduced = {} for key in loss_dicts[0]: losses_reduced_for_key = [x[key] for x in loss_dicts] if len(losses_reduced_for_key[0].shape) == 0: loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) else: loss_reduced[key] = torch.concat(losses_reduced_for_key) return loss_reduced else: return {} def forward(self, **batch_data): # During training, we use train_step() # model(**batch_data) performs following operations by delegating it to `self.train_step`: # 1. Prepare **batch_data for Tendor, Pipeline and Model Parallelism # 2. Set grad to zero. # 3. forward pass and backward pass using Pipeline Parallelism # 4. Empty unused memory. # 5. Reduce gradients. # 6. Update parameters. # 7. Gather params when using Distributed Optimizer (Data Parallelism). # 8. Update learning rate if scheduler is specified. # 9. Empty unused memory. # 10. Average loss across microbatches and across DP ranks. # # During evaluation, we use eval_step() args = get_args() if self.module[0].training: loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = self.train_step(**batch_data) self.iteration += 1 if args.tensorboard_dir is not None: # Logging. loss_scale = self.optimizer.get_loss_scale().item() params_norm = None if args.log_params_norm: params_norm = calc_params_l2_norm(self.model) self.report_memory_flag = training_log( loss_dict, self.total_loss_dict, self.optimizer.param_groups[0]["lr"], self.iteration, loss_scale, self.report_memory_flag, skipped_iter, grad_norm, params_norm, num_zeros_in_grad, ) else: loss_dict = self.eval_step(**batch_data) if args.tensorboard_dir is not None: for key in loss_dict: self.eval_total_loss_dict[key] = ( self.eval_total_loss_dict.get(key, torch.cuda.FloatTensor([0.0])) + loss_dict[key] ) self.eval_total_loss_dict[key + "_num_iters"] = self.eval_total_loss_dict.get( key + "_num_iters", torch.cuda.FloatTensor([0.0]) ) + torch.cuda.FloatTensor([1.0]) loss = torch.tensor(0.0, device=args.local_rank) for key in loss_dict: if len(loss_dict[key].shape) == 0: loss += loss_dict[key] logits = None if "logits" in loss_dict: logits = loss_dict["logits"] # loss = reduce(loss) if self.train_step_handler.model_output_class is not None: return self.train_step_handler.model_output_class(loss=loss, logits=logits) return loss def log_eval_results(self): args = get_args() if args.tensorboard_dir is None or self.iteration == 0: return args = get_args() writer = get_tensorboard_writer() string = f"validation loss at iteration {self.iteration} | " for key in self.eval_total_loss_dict: if key.endswith("_num_iters"): continue value = self.eval_total_loss_dict[key] / self.eval_total_loss_dict[key + "_num_iters"] string += f"{key} value: {value} | " ppl = math.exp(min(20, value.item())) if args.pretraining_flag: string += f"{key} PPL: {ppl} | " if writer: writer.add_scalar(f"{key} validation", value.item(), self.iteration) if args.pretraining_flag: writer.add_scalar(f"{key} validation ppl", ppl, self.iteration) length = len(string) + 1 print_rank_last("-" * length) print_rank_last(string) print_rank_last("-" * length) self.eval_total_loss_dict = {} def save_checkpoint(self, output_dir): self.log_eval_results() args = get_args() args.save = output_dir torch.distributed.barrier() save_checkpoint(self.iteration, self.module, self.optimizer, self.scheduler) torch.distributed.barrier() def load_checkpoint(self, input_dir): args = get_args() args.load = input_dir args.consumed_train_samples = 0 args.consumed_valid_samples = 0 torch.distributed.barrier() iteration = load_checkpoint(self.module, self.optimizer, self.scheduler) torch.distributed.barrier() self.iteration = iteration if args.fp16 and self.iteration == 0: self.optimizer.reload_model_params() def megatron_generate( self, inputs, attention_mask=None, max_length=None, max_new_tokens=None, num_beams=None, temperature=None, top_k=None, top_p=None, length_penalty=None, **kwargs, ): """ Generate method for GPT2 model. This method is used for inference. Supports both greedy and beam search along with sampling. Refer the Megatron-LM repo for more details Args: inputs (torch.Tensor): input ids attention_mask (torch.Tensor, optional): attention mask. Defaults to None. max_length (int, optional): max length of the generated sequence. Defaults to None. Either this or max_new_tokens should be provided. max_new_tokens (int, optional): max number of tokens to be generated. Defaults to None. Either this or max_length should be provided. num_beams (int, optional): number of beams to use for beam search. Defaults to None. temperature (float, optional): temperature for sampling. Defaults to 1.0. top_k (int, optional): top k tokens to consider for sampling. Defaults to 0.0. top_p (float, optional): tokens in top p probability are considered for sampling. Defaults to 0.0. length_penalty (float, optional): length penalty for beam search. Defaults to None. kwargs: additional key-value arguments """ # checking if required arguments are passed args = get_args() if args.model_type_name != "gpt": raise NotImplementedError("Generate method is not implemented for this model") if args.data_parallel_size > 1: raise ValueError("Generate method requires data parallelism to be 1") if args.sequence_parallel: raise ValueError("Generate method requires sequence parallelism to be False") if args.recompute_granularity is not None: raise ValueError("Checkpoint activations cannot be set for inference") if args.vocab_file is None: raise ValueError("Vocab file is required for inference") # Prepare inputs if max_length is None and max_new_tokens is None: raise ValueError("`max_length` or `max_new_tokens` are required for inference") if temperature is None: temperature = 1.0 elif not (0.0 < temperature <= 100.0): raise ValueError("temperature must be a positive number less than or equal to 100.0") if top_k is None: top_k = 0 elif not (0 <= top_k <= 1000): raise ValueError("top_k must be a positive number less than or equal to 1000") if top_p is None: top_p = 0.0 elif top_p > 0.0 and top_k > 0.0: raise ValueError("top_p and top_k sampling cannot be set together") else: if not (0.0 <= top_p <= 1.0): raise ValueError("top_p must be less than or equal to 1.0") top_p_decay = kwargs.get("top_p_decay", 0.0) if not (0.0 <= top_p_decay <= 1.0): raise ValueError("top_p_decay must be less than or equal to 1.0") top_p_bound = kwargs.get("top_p_bound", 0.0) if not (0.0 <= top_p_bound <= 1.0): raise ValueError("top_p_bound must be less than or equal to 1.0") add_BOS = kwargs.get("add_BOS", False) if not (isinstance(add_BOS, bool)): raise ValueError("add_BOS must be a boolean") beam_width = num_beams if beam_width is not None: if not isinstance(beam_width, int): raise ValueError("beam_width must be an integer") if beam_width < 1: raise ValueError("beam_width must be greater than 0") if inputs.shape[0] > 1: return "When doing beam_search, batch size must be 1" tokenizer = get_tokenizer() stop_token = kwargs.get("stop_token", tokenizer.eod) if stop_token is not None: if not isinstance(stop_token, int): raise ValueError("stop_token must be an integer") if length_penalty is None: length_penalty = 1.0 sizes_list = None prompts_tokens_tensor = None prompts_length_tensor = None if torch.distributed.get_rank() == 0: # Get the prompts length. if attention_mask is None: prompts_length_tensor = torch.cuda.LongTensor([inputs.shape[1]] * inputs.shape[0]) else: prompts_length_tensor = attention_mask.sum(axis=-1).cuda() if max_new_tokens is None: max_new_tokens = max_length - inputs.shape[1] if max_new_tokens <= 0: raise ValueError("max_new_tokens must be greater than 0") if add_BOS: max_length = max_new_tokens + inputs.shape[1] + 1 # making sure that `max_length` is a multiple of 4 to leverage fused kernels max_length = 4 * math.ceil(max_length / 4) max_new_tokens = max_length - (inputs.shape[1] + 1) padding = torch.cuda.LongTensor([[tokenizer.eod] * max_new_tokens] * inputs.shape[0]) prompts_tokens_tensor = torch.concat( [torch.unsqueeze(padding[:, 0], axis=-1), inputs.cuda(), padding], axis=-1 ) else: # making sure that `max_length` is a multiple of 4 to leverage fused kernels max_length = max_new_tokens + inputs.shape[1] max_length = 4 * math.ceil(max_length / 4) max_new_tokens = max_length - inputs.shape[1] padding = torch.cuda.LongTensor([[tokenizer.eod] * max_new_tokens] * inputs.shape[0]) prompts_tokens_tensor = torch.concat([inputs.cuda(), padding], axis=-1) # We need the sizes of these tensors for the boradcast sizes_list = [ prompts_tokens_tensor.size(0), # Batch size prompts_tokens_tensor.size(1), ] # Sequence lenght # First, broadcast the sizes. sizes_tensor = broadcast_int_list(2, int_list=sizes_list, rank=0) # Now that we have the sizes, we can boradcast the tokens # and length tensors. sizes = sizes_tensor.tolist() context_tokens_tensor = broadcast_tensor(sizes, torch.int64, tensor=prompts_tokens_tensor, rank=0) context_length_tensor = broadcast_tensor(sizes[0], torch.int64, tensor=prompts_length_tensor, rank=0) # Run the inference random_seed = kwargs.get("random_seed", 0) torch.random.manual_seed(random_seed) unwrapped_model = unwrap_model(self.base_model, (torchDDP, LocalDDP, Float16Module)) if beam_width is not None: tokens, _ = beam_search_and_return_on_first_stage( unwrapped_model, context_tokens_tensor, context_length_tensor, beam_width, stop_token=stop_token, num_return_gen=1, length_penalty=length_penalty, ) else: tokens, _, _ = generate_tokens_probs_and_return_on_first_stage( unwrapped_model, context_tokens_tensor, context_length_tensor, return_output_log_probs=False, top_k=top_k, top_p=top_p, top_p_decay=top_p_decay, top_p_bound=top_p_bound, temperature=temperature, use_eod_token_for_early_termination=True, ) return tokens # other utilities def avg_losses_across_data_parallel_group(losses): """ Average losses across data parallel group. Args: losses (List[Tensor]): List of losses to average across data parallel group. """ return average_losses_across_data_parallel_group(losses) def gather_across_data_parallel_groups(tensor): """ Recursively gather tensor in a nested list/tuple/dictionary of tensors from data parallel ranks. Args: tensor (nested list/tuple/dictionary of `torch.Tensor`): The data to gather across data parallel ranks. """ def _gpu_gather_one(tensor): if tensor.ndim == 0: tensor = tensor.clone()[None] output_tensors = [ torch.empty_like(tensor) for _ in range(torch.distributed.get_world_size(group=mpu.get_data_parallel_group())) ] torch.distributed.all_gather(output_tensors, tensor, group=mpu.get_data_parallel_group()) return torch.cat(output_tensors, dim=0) return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/imports.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import importlib.metadata import os import warnings from functools import lru_cache import torch from packaging import version from packaging.version import parse from .environment import parse_flag_from_env, str_to_bool from .versions import compare_versions, is_torch_version try: import torch_xla.core.xla_model as xm # noqa: F401 _tpu_available = True except ImportError: _tpu_available = False # Cache this result has it's a C FFI call which can be pretty time-consuming _torch_distributed_available = torch.distributed.is_available() def _is_package_available(pkg_name): # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version package_exists = importlib.util.find_spec(pkg_name) is not None if package_exists: try: _ = importlib.metadata.metadata(pkg_name) return True except importlib.metadata.PackageNotFoundError: return False def is_torch_distributed_available() -> bool: return _torch_distributed_available def is_ccl_available(): try: pass except ImportError: print( "Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not" " detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL" " Bindings for PyTorch*." ) return ( importlib.util.find_spec("torch_ccl") is not None or importlib.util.find_spec("oneccl_bindings_for_pytorch") is not None ) def get_ccl_version(): return importlib.metadata.version("oneccl_bind_pt") def is_msamp_available(): package_exists = importlib.util.find_spec("msamp") is not None if package_exists: try: # MS-AMP has a different metadata name _ = importlib.metadata.metadata("ms-amp") return True except importlib.metadata.PackageNotFoundError: return False return False def is_transformer_engine_available(): return _is_package_available("transformer_engine") def is_fp8_available(): return is_msamp_available() or is_transformer_engine_available() def is_cuda_available(): """ Checks if `cuda` is available via an `nvml-based` check which won't trigger the drivers and leave cuda uninitialized. """ try: os.environ["PYTORCH_NVML_BASED_CUDA_CHECK"] = str(1) available = torch.cuda.is_available() finally: os.environ.pop("PYTORCH_NVML_BASED_CUDA_CHECK", None) return available @lru_cache def is_tpu_available(check_device=True): "Checks if `torch_xla` is installed and potentially if a TPU is in the environment" # Due to bugs on the amp series GPUs, we disable torch-xla on them if is_cuda_available(): return False if check_device: if _tpu_available: try: # Will raise a RuntimeError if no XLA configuration is found _ = xm.xla_device() return True except RuntimeError: return False return _tpu_available def is_deepspeed_available(): return _is_package_available("deepspeed") def is_bf16_available(ignore_tpu=False): "Checks if bf16 is supported, optionally ignoring the TPU" if is_tpu_available(): return not ignore_tpu if is_cuda_available(): return torch.cuda.is_bf16_supported() return True def is_4bit_bnb_available(): package_exists = _is_package_available("bitsandbytes") if package_exists: bnb_version = version.parse(importlib.metadata.version("bitsandbytes")) return compare_versions(bnb_version, ">=", "0.39.0") return False def is_8bit_bnb_available(): package_exists = _is_package_available("bitsandbytes") if package_exists: bnb_version = version.parse(importlib.metadata.version("bitsandbytes")) return compare_versions(bnb_version, ">=", "0.37.2") return False def is_bnb_available(): return _is_package_available("bitsandbytes") def is_megatron_lm_available(): if str_to_bool(os.environ.get("ACCELERATE_USE_MEGATRON_LM", "False")) == 1: package_exists = importlib.util.find_spec("megatron") is not None if package_exists: try: megatron_version = parse(importlib.metadata.version("megatron-lm")) return compare_versions(megatron_version, ">=", "2.2.0") except Exception as e: warnings.warn(f"Parse Megatron version failed. Exception:{e}") return False def is_transformers_available(): return _is_package_available("transformers") def is_datasets_available(): return _is_package_available("datasets") def is_timm_available(): return _is_package_available("timm") def is_aim_available(): package_exists = _is_package_available("aim") if package_exists: aim_version = version.parse(importlib.metadata.version("aim")) return compare_versions(aim_version, "<", "4.0.0") return False def is_tensorboard_available(): return _is_package_available("tensorboard") or _is_package_available("tensorboardX") def is_wandb_available(): return _is_package_available("wandb") def is_comet_ml_available(): return _is_package_available("comet_ml") def is_boto3_available(): return _is_package_available("boto3") def is_rich_available(): if _is_package_available("rich"): if "ACCELERATE_DISABLE_RICH" in os.environ: warnings.warn( "`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`." ) return not parse_flag_from_env("ACCELERATE_DISABLE_RICH", False) return parse_flag_from_env("ACCELERATE_ENABLE_RICH", False) return False def is_sagemaker_available(): return _is_package_available("sagemaker") def is_tqdm_available(): return _is_package_available("tqdm") def is_clearml_available(): return _is_package_available("clearml") def is_pandas_available(): return _is_package_available("pandas") def is_mlflow_available(): if _is_package_available("mlflow"): return True if importlib.util.find_spec("mlflow") is not None: try: _ = importlib.metadata.metadata("mlflow-skinny") return True except importlib.metadata.PackageNotFoundError: return False return False def is_mps_available(): return is_torch_version(">=", "1.12") and torch.backends.mps.is_available() and torch.backends.mps.is_built() def is_ipex_available(): def get_major_and_minor_from_version(full_version): return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor) _torch_version = importlib.metadata.version("torch") if importlib.util.find_spec("intel_extension_for_pytorch") is None: return False _ipex_version = "N/A" try: _ipex_version = importlib.metadata.version("intel_extension_for_pytorch") except importlib.metadata.PackageNotFoundError: return False torch_major_and_minor = get_major_and_minor_from_version(_torch_version) ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version) if torch_major_and_minor != ipex_major_and_minor: warnings.warn( f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*," f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again." ) return False return True @lru_cache def is_npu_available(check_device=False): "Checks if `torch_npu` is installed and potentially if a NPU is in the environment" if importlib.util.find_spec("torch") is None or importlib.util.find_spec("torch_npu") is None: return False import torch import torch_npu # noqa: F401 if check_device: try: # Will raise a RuntimeError if no NPU is found _ = torch.npu.device_count() return torch.npu.is_available() except RuntimeError: return False return hasattr(torch, "npu") and torch.npu.is_available() @lru_cache def is_xpu_available(check_device=False): "check if user disables it explicitly" if not parse_flag_from_env("ACCELERATE_USE_XPU", default=True): return False "Checks if `intel_extension_for_pytorch` is installed and potentially if a XPU is in the environment" if is_ipex_available(): import torch if is_torch_version("<=", "1.12"): return False else: return False import intel_extension_for_pytorch # noqa: F401 if check_device: try: # Will raise a RuntimeError if no XPU is found _ = torch.xpu.device_count() return torch.xpu.is_available() except RuntimeError: return False return hasattr(torch, "xpu") and torch.xpu.is_available() def is_dvclive_available(): return _is_package_available("dvclive")

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/environment.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import platform import subprocess import sys from distutils import spawn from typing import Dict import torch def str_to_bool(value) -> int: """ Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`; """ value = value.lower() if value in ("y", "yes", "t", "true", "on", "1"): return 1 elif value in ("n", "no", "f", "false", "off", "0"): return 0 else: raise ValueError(f"invalid truth value {value}") def get_int_from_env(env_keys, default): """Returns the first positive env value found in the `env_keys` list or the default.""" for e in env_keys: val = int(os.environ.get(e, -1)) if val >= 0: return val return default def parse_flag_from_env(key, default=False): """Returns truthy value for `key` from the env if available else the default.""" value = os.environ.get(key, str(default)) return str_to_bool(value) == 1 # As its name indicates `str_to_bool` actually returns an int... def parse_choice_from_env(key, default="no"): value = os.environ.get(key, str(default)) return value def are_libraries_initialized(*library_names: str) -> Dict[str, bool]: """ Checks if any of `library_names` are imported in the environment. Will return results as a `key:bool` pair. """ return [lib_name for lib_name in library_names if lib_name in sys.modules.keys()] def get_gpu_info(): """ Gets GPU count and names using `nvidia-smi` instead of torch to not initialize CUDA. Largely based on the `gputil` library. """ if platform.system() == "Windows": # If platform is Windows and nvidia-smi can't be found in path # try from systemd rive with default installation path command = spawn.find_executable("nvidia-smi") if command is None: command = "%s\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi.exe" % os.environ["systemdrive"] else: command = "nvidia-smi" # Returns as list of `n` GPUs and their names output = subprocess.check_output( [command, "--query-gpu=count,name", "--format=csv,noheader"], universal_newlines=True ) output = output.strip() gpus = output.split(os.linesep) # Get names from output gpu_count = len(gpus) gpu_names = [gpu.split(",")[1].strip() for gpu in gpus] return gpu_names, gpu_count def check_cuda_p2p_ib_support(): """ Checks if the devices being used have issues with P2P and IB communications, namely any consumer GPU hardware after the 3090. Noteably uses `nvidia-smi` instead of torch to not initialize CUDA. """ try: device_names, device_count = get_gpu_info() # As new consumer GPUs get released, add them to `unsupported_devices`` unsupported_devices = {"RTX 40"} if device_count > 1: if any( unsupported_device in device_name for device_name in device_names for unsupported_device in unsupported_devices ): return False except Exception: pass return True def check_fp8_capability(): """ Checks if all the current GPUs available support FP8. Notably must initialize `torch.cuda` to check. """ cuda_device_capacity = torch.cuda.get_device_capability() return cuda_device_capacity >= (8, 9)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/fsdp_utils.py

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch from ..logging import get_logger from .constants import FSDP_MODEL_NAME, FSDP_PYTORCH_VERSION, OPTIMIZER_NAME from .imports import is_torch_distributed_available from .versions import is_torch_version if is_torch_version(">=", FSDP_PYTORCH_VERSION) and is_torch_distributed_available(): import torch.distributed.checkpoint as dist_cp from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType logger = get_logger(__name__) def save_fsdp_model(fsdp_plugin, accelerator, model, output_dir, model_index=0): os.makedirs(output_dir, exist_ok=True) if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: # FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT # so, only enable it when num_processes>1 is_multi_process = accelerator.num_processes > 1 fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process fsdp_plugin.state_dict_config.rank0_only = is_multi_process with FSDP.state_dict_type( model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): state_dict = model.state_dict() if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: weights_name = f"{FSDP_MODEL_NAME}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}.bin" output_model_file = os.path.join(output_dir, weights_name) if accelerator.process_index == 0: logger.info(f"Saving model to {output_model_file}") torch.save(state_dict, output_model_file) logger.info(f"Model saved to {output_model_file}") elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT: weights_name = ( f"{FSDP_MODEL_NAME}_rank{accelerator.process_index}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin" ) output_model_file = os.path.join(output_dir, weights_name) logger.info(f"Saving model to {output_model_file}") torch.save(state_dict, output_model_file) logger.info(f"Model saved to {output_model_file}") elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT: ckpt_dir = os.path.join(output_dir, f"{FSDP_MODEL_NAME}_{model_index}") os.makedirs(ckpt_dir, exist_ok=True) logger.info(f"Saving model to {ckpt_dir}") state_dict = {"model": state_dict} dist_cp.save_state_dict( state_dict=state_dict, storage_writer=dist_cp.FileSystemWriter(ckpt_dir), planner=DefaultSavePlanner(), ) logger.info(f"Model saved to {ckpt_dir}") def load_fsdp_model(fsdp_plugin, accelerator, model, input_dir, model_index=0): accelerator.wait_for_everyone() if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: # FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT # so, only enable it when num_processes>1 is_multi_process = accelerator.num_processes > 1 fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process fsdp_plugin.state_dict_config.rank0_only = is_multi_process with FSDP.state_dict_type( model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: if type(model) != FSDP and accelerator.process_index != 0: if not fsdp_plugin.sync_module_states: raise ValueError( "Set the `sync_module_states` flag to `True` so that model states are synced across processes when " "initializing FSDP object" ) return weights_name = f"{FSDP_MODEL_NAME}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}.bin" input_model_file = os.path.join(input_dir, weights_name) logger.info(f"Loading model from {input_model_file}") state_dict = torch.load(input_model_file) logger.info(f"Model loaded from {input_model_file}") elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT: weights_name = ( f"{FSDP_MODEL_NAME}_rank{accelerator.process_index}.bin" if model_index == 0 else f"{FSDP_MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin" ) input_model_file = os.path.join(input_dir, weights_name) logger.info(f"Loading model from {input_model_file}") state_dict = torch.load(input_model_file) logger.info(f"Model loaded from {input_model_file}") elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT: ckpt_dir = ( os.path.join(input_dir, f"{FSDP_MODEL_NAME}_{model_index}") if f"{FSDP_MODEL_NAME}" not in input_dir else input_dir ) logger.info(f"Loading model from {ckpt_dir}") state_dict = {"model": model.state_dict()} dist_cp.load_state_dict( state_dict=state_dict, storage_reader=dist_cp.FileSystemReader(ckpt_dir), planner=DefaultLoadPlanner(), ) state_dict = state_dict["model"] logger.info(f"Model loaded from {ckpt_dir}") load_result = model.load_state_dict(state_dict) return load_result def save_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, output_dir, optimizer_index=0): os.makedirs(output_dir, exist_ok=True) with FSDP.state_dict_type( model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): optim_state = FSDP.optim_state_dict(model, optimizer) if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: if accelerator.process_index == 0: optim_state_name = ( f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin" ) output_optimizer_file = os.path.join(output_dir, optim_state_name) logger.info(f"Saving Optimizer state to {output_optimizer_file}") torch.save(optim_state, output_optimizer_file) logger.info(f"Optimizer state saved in {output_optimizer_file}") else: ckpt_dir = os.path.join(output_dir, f"{OPTIMIZER_NAME}_{optimizer_index}") os.makedirs(ckpt_dir, exist_ok=True) logger.info(f"Saving Optimizer state to {ckpt_dir}") dist_cp.save_state_dict( state_dict={"optimizer": optim_state}, storage_writer=dist_cp.FileSystemWriter(ckpt_dir), planner=DefaultSavePlanner(), ) logger.info(f"Optimizer state saved in {ckpt_dir}") def load_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, input_dir, optimizer_index=0): accelerator.wait_for_everyone() with FSDP.state_dict_type( model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: optim_state = None if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only: optimizer_name = ( f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin" ) input_optimizer_file = os.path.join(input_dir, optimizer_name) logger.info(f"Loading Optimizer state from {input_optimizer_file}") optim_state = torch.load(input_optimizer_file) logger.info(f"Optimizer state loaded from {input_optimizer_file}") else: ckpt_dir = ( os.path.join(input_dir, f"{OPTIMIZER_NAME}_{optimizer_index}") if f"{OPTIMIZER_NAME}" not in input_dir else input_dir ) logger.info(f"Loading Optimizer from {ckpt_dir}") optim_state = load_sharded_optimizer_state_dict( model_state_dict=model.state_dict(), optimizer_key="optimizer", storage_reader=dist_cp.FileSystemReader(ckpt_dir), ) optim_state = optim_state["optimizer"] logger.info(f"Optimizer loaded from {ckpt_dir}") flattened_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=optim_state) optimizer.load_state_dict(flattened_osd)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/transformer_engine.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch.nn as nn from .imports import is_fp8_available if is_fp8_available(): import transformer_engine.pytorch as te def convert_model(model, to_transformer_engine=True, _convert_linear=True, _convert_ln=True): """ Recursively converts the linear and layernorm layers of a model to their `transformers_engine` counterpart. """ if not is_fp8_available(): raise ImportError("Using `convert_model` requires transformer_engine to be installed.") for name, module in model.named_children(): if isinstance(module, nn.Linear) and to_transformer_engine and _convert_linear: # Return early if the linear layer weights are not multiples of 16 if any(p % 16 != 0 for p in module.weight.shape): return has_bias = module.bias is not None te_module = te.Linear( module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype ) te_module.weight.copy_(module.weight) if has_bias: te_module.bias.copy_(module.bias) setattr(model, name, te_module) elif isinstance(module, nn.LayerNorm) and to_transformer_engine and _convert_ln: te_module = te.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype) te_module.weight.copy_(module.weight) te_module.bias.copy_(module.bias) setattr(model, name, te_module) elif isinstance(module, te.Linear) and not to_transformer_engine and _convert_linear: has_bias = module.bias is not None new_module = nn.Linear( module.in_features, module.out_features, bias=has_bias, params_dtype=module.weight.dtype ) new_module.weight.copy_(module.weight) if has_bias: new_module.bias.copy_(module.bias) setattr(model, name, new_module) elif isinstance(module, te.LayerNorm) and not to_transformer_engine and _convert_ln: new_module = nn.LayerNorm(module.normalized_shape[0], eps=module.eps, params_dtype=module.weight.dtype) new_module.weight.copy_(module.weight) new_module.bias.copy_(module.bias) setattr(model, name, new_module) else: convert_model( module, to_transformer_engine=to_transformer_engine, _convert_linear=_convert_linear, _convert_ln=_convert_ln, ) def has_transformer_engine_layers(model): """ Returns whether a given model has some `transformer_engine` layer or not. """ if not is_fp8_available(): raise ImportError("Using `has_transformer_engine_layers` requires transformer_engine to be installed.") for m in model.modules(): if isinstance(m, (te.LayerNorm, te.Linear, te.TransformerLayer)): return True return False

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/constants.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import operator as op SCALER_NAME = "scaler.pt" MODEL_NAME = "pytorch_model" SAFE_MODEL_NAME = "model" RNG_STATE_NAME = "random_states" OPTIMIZER_NAME = "optimizer" SCHEDULER_NAME = "scheduler" SAMPLER_NAME = "sampler" WEIGHTS_NAME = f"{MODEL_NAME}.bin" WEIGHTS_INDEX_NAME = f"{WEIGHTS_NAME}.index.json" SAFE_WEIGHTS_NAME = f"{SAFE_MODEL_NAME}.safetensors" SAFE_WEIGHTS_INDEX_NAME = f"{SAFE_WEIGHTS_NAME}.index.json" SAGEMAKER_PYTORCH_VERSION = "1.10.2" SAGEMAKER_PYTHON_VERSION = "py38" SAGEMAKER_TRANSFORMERS_VERSION = "4.17.0" SAGEMAKER_PARALLEL_EC2_INSTANCES = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"] FSDP_SHARDING_STRATEGY = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"] FSDP_AUTO_WRAP_POLICY = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"] FSDP_BACKWARD_PREFETCH = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"] FSDP_STATE_DICT_TYPE = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"] FSDP_PYTORCH_VERSION = "2.1.0" FSDP_MODEL_NAME = "pytorch_model_fsdp" DEEPSPEED_MULTINODE_LAUNCHERS = ["pdsh", "standard", "openmpi", "mvapich", "mpich"] TORCH_DYNAMO_MODES = ["default", "reduce-overhead", "max-autotune"] STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt} # These are the args for `torch.distributed.launch` for pytorch < 1.9 TORCH_LAUNCH_PARAMS = [ "nnodes", "nproc_per_node", "rdzv_backend", "rdzv_endpoint", "rdzv_id", "rdzv_conf", "standalone", "max_restarts", "monitor_interval", "start_method", "role", "module", "m", "no_python", "run_path", "log_dir", "r", "redirects", "t", "tee", "node_rank", "master_addr", "master_port", ] CUDA_DISTRIBUTED_TYPES = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"] TORCH_DISTRIBUTED_OPERATION_TYPES = CUDA_DISTRIBUTED_TYPES + ["MULTI_NPU", "MULTI_XPU", "MULTI_CPU"]

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hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/offload.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from collections.abc import Mapping from typing import Dict, List, Optional, Union import numpy as np import torch from safetensors import safe_open def offload_weight(weight, weight_name, offload_folder, index=None): dtype = None # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16. if str(weight.dtype) == "torch.bfloat16": # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s. weight = weight.view(torch.int16) dtype = "bfloat16" array = weight.cpu().numpy() tensor_file = os.path.join(offload_folder, f"{weight_name}.dat") if index is not None: if dtype is None: dtype = str(array.dtype) index[weight_name] = {"dtype": dtype, "shape": list(array.shape)} if array.ndim == 0: array = array[None] file_array = np.memmap(tensor_file, dtype=array.dtype, mode="w+", shape=array.shape) file_array[:] = array[:] file_array.flush() return index def load_offloaded_weight(weight_file, weight_info): shape = tuple(weight_info["shape"]) if shape == (): # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor shape = (1,) dtype = weight_info["dtype"] if dtype == "bfloat16": # NumPy does not support bfloat16 so this was saved as a int16 dtype = "int16" weight = np.memmap(weight_file, dtype=dtype, shape=shape, mode="r") if len(weight_info["shape"]) == 0: weight = weight[0] weight = torch.tensor(weight) if weight_info["dtype"] == "bfloat16": weight = weight.view(torch.bfloat16) return weight def save_offload_index(index, offload_folder): if index is None or len(index) == 0: # Nothing to save return offload_index_file = os.path.join(offload_folder, "index.json") if os.path.isfile(offload_index_file): with open(offload_index_file, "r", encoding="utf-8") as f: current_index = json.load(f) else: current_index = {} current_index.update(index) with open(offload_index_file, "w", encoding="utf-8") as f: json.dump(current_index, f, indent=2) def offload_state_dict(save_dir: Union[str, os.PathLike], state_dict: Dict[str, torch.Tensor]): """ Offload a state dict in a given folder. Args: save_dir (`str` or `os.PathLike`): The directory in which to offload the state dict. state_dict (`Dict[str, torch.Tensor]`): The dictionary of tensors to offload. """ os.makedirs(save_dir, exist_ok=True) index = {} for name, parameter in state_dict.items(): index = offload_weight(parameter, name, save_dir, index=index) # Update index save_offload_index(index, save_dir) class PrefixedDataset(Mapping): """ Will access keys in a given dataset by adding a prefix. Args: dataset (`Mapping`): Any map with string keys. prefix (`str`): A prefix to add when trying to access any element in the underlying dataset. """ def __init__(self, dataset: Mapping, prefix: str): self.dataset = dataset self.prefix = prefix def __getitem__(self, key): return self.dataset[f"{self.prefix}{key}"] def __iter__(self): return iter([key for key in self.dataset if key.startswith(self.prefix)]) def __len__(self): return len(self.dataset) class OffloadedWeightsLoader(Mapping): """ A collection that loads weights stored in a given state dict or memory-mapped on disk. Args: state_dict (`Dict[str, torch.Tensor]`, *optional*): A dictionary parameter name to tensor. save_folder (`str` or `os.PathLike`, *optional*): The directory in which the weights are stored (by `offload_state_dict` for instance). index (`Dict`, *optional*): A dictionary from weight name to their information (`dtype`/ `shape` or safetensors filename). Will default to the index saved in `save_folder`. """ def __init__( self, state_dict: Dict[str, torch.Tensor] = None, save_folder: Optional[Union[str, os.PathLike]] = None, index: Mapping = None, device=None, ): if state_dict is None and save_folder is None and index is None: raise ValueError("Need either a `state_dict`, a `save_folder` or an `index` containing offloaded weights.") self.state_dict = {} if state_dict is None else state_dict self.save_folder = save_folder if index is None and save_folder is not None: with open(os.path.join(save_folder, "index.json")) as f: index = json.load(f) self.index = {} if index is None else index self.all_keys = list(self.state_dict.keys()) self.all_keys.extend([key for key in self.index if key not in self.all_keys]) self.device = device def __getitem__(self, key: str): # State dict gets priority if key in self.state_dict: return self.state_dict[key] weight_info = self.index[key] if weight_info.get("safetensors_file") is not None: device = "cpu" if self.device is None else self.device tensor = None try: with safe_open(weight_info["safetensors_file"], framework="pt", device=device) as f: tensor = f.get_tensor(weight_info.get("weight_name", key)) except TypeError: # if failed to get_tensor on the device, such as bf16 on mps, try to load it on CPU first with safe_open(weight_info["safetensors_file"], framework="pt", device="cpu") as f: tensor = f.get_tensor(weight_info.get("weight_name", key)) if "dtype" in weight_info: tensor = tensor.to(getattr(torch, weight_info["dtype"])) if tensor.device != torch.device(device): tensor = tensor.to(device) return tensor weight_file = os.path.join(self.save_folder, f"{key}.dat") return load_offloaded_weight(weight_file, weight_info) def __iter__(self): return iter(self.all_keys) def __len__(self): return len(self.all_keys) def extract_submodules_state_dict(state_dict: Dict[str, torch.Tensor], submodule_names: List[str]): """ Extract the sub state-dict corresponding to a list of given submodules. Args: state_dict (`Dict[str, torch.Tensor]`): The state dict to extract from. submodule_names (`List[str]`): The list of submodule names we want to extract. """ result = {} for module_name in submodule_names: # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance) result.update( { key: param for key, param in state_dict.items() if key == module_name or key.startswith(module_name + ".") } ) return result

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/modeling.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import gc import inspect import json import logging import os import re import shutil import tempfile from collections import OrderedDict, defaultdict from typing import Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from ..state import AcceleratorState from .constants import SAFE_WEIGHTS_NAME, WEIGHTS_NAME from .dataclasses import AutocastKwargs, CustomDtype, DistributedType from .imports import is_mps_available, is_npu_available, is_xpu_available from .offload import load_offloaded_weight, offload_weight, save_offload_index from .tqdm import is_tqdm_available, tqdm if is_npu_available(check_device=False): import torch_npu # noqa: F401 from safetensors import safe_open from safetensors.torch import load_file as safe_load_file WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json" logger = logging.getLogger(__name__) def check_device_same(first_device, second_device): """ Utility method to check if two `torch` devices are similar. When dealing with CUDA devices, torch throws `False` for `torch.device("cuda") == torch.device("cuda:0")` whereas they should be the same Args: first_device (`torch.device`): First device to check second_device (`torch.device`): Second device to check """ if first_device.type != second_device.type: return False if first_device.type == "cuda" and first_device.index is None: # In case the first_device is a cuda device and have # the index attribute set to `None`, default it to `0` first_device = torch.device("cuda", index=0) if second_device.type == "cuda" and second_device.index is None: # In case the second_device is a cuda device and have # the index attribute set to `None`, default it to `0` second_device = torch.device("cuda", index=0) return first_device == second_device def convert_file_size_to_int(size: Union[int, str]): """ Converts a size expressed as a string with digits an unit (like `"5MB"`) to an integer (in bytes). Args: size (`int` or `str`): The size to convert. Will be directly returned if an `int`. Example: ```py >>> convert_file_size_to_int("1MiB") 1048576 ``` """ mem_size = 0 err_msg = ( f"`size` {size} is not in a valid format. Use an integer for bytes, or a string with an unit (like '5.0GB')." ) try: if isinstance(size, int): mem_size = size elif size.upper().endswith("GIB"): mem_size = int(float(size[:-3]) * (2**30)) elif size.upper().endswith("MIB"): mem_size = int(float(size[:-3]) * (2**20)) elif size.upper().endswith("KIB"): mem_size = int(float(size[:-3]) * (2**10)) elif size.upper().endswith("GB"): int_size = int(float(size[:-2]) * (10**9)) mem_size = int_size // 8 if size.endswith("b") else int_size elif size.upper().endswith("MB"): int_size = int(float(size[:-2]) * (10**6)) mem_size = int_size // 8 if size.endswith("b") else int_size elif size.upper().endswith("KB"): int_size = int(float(size[:-2]) * (10**3)) mem_size = int_size // 8 if size.endswith("b") else int_size except ValueError: raise ValueError(err_msg) if mem_size <= 0: raise ValueError(err_msg) return mem_size def dtype_byte_size(dtype: torch.dtype): """ Returns the size (in bytes) occupied by one parameter of type `dtype`. Example: ```py >>> dtype_byte_size(torch.float32) 4 ``` """ if dtype == torch.bool: return 1 / 8 elif dtype == CustomDtype.INT4: return 1 / 2 elif dtype == CustomDtype.FP8: return 1 bit_search = re.search(r"[^\d](\d+)$", str(dtype)) if bit_search is None: raise ValueError(f"`dtype` is not a valid dtype: {dtype}.") bit_size = int(bit_search.groups()[0]) return bit_size // 8 def id_tensor_storage(tensor: torch.Tensor) -> Tuple[torch.device, int, int]: """ Unique identifier to a tensor storage. Multiple different tensors can share the same underlying storage. For example, "meta" tensors all share the same storage, and thus their identifier will all be equal. This identifier is guaranteed to be unique and constant for this tensor's storage during its lifetime. Two tensor storages with non-overlapping lifetimes may have the same id. """ _SIZE = { torch.int64: 8, torch.float32: 4, torch.int32: 4, torch.bfloat16: 2, torch.float16: 2, torch.int16: 2, torch.uint8: 1, torch.int8: 1, torch.bool: 1, torch.float64: 8, } try: storage_ptr = tensor.untyped_storage().data_ptr() storage_size = tensor.untyped_storage().nbytes() except Exception: # Fallback for torch==1.10 try: storage_ptr = tensor.storage().data_ptr() storage_size = tensor.storage().size() * _SIZE[tensor.dtype] except NotImplementedError: # Fallback for meta storage storage_ptr = 0 # On torch >=2.0 this is the tensor size storage_size = tensor.nelement() * _SIZE[tensor.dtype] return tensor.device, storage_ptr, storage_size def shard_checkpoint( state_dict: Dict[str, torch.Tensor], max_shard_size: Union[int, str] = "10GB", weights_name: str = WEIGHTS_NAME ): """ Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB]. <Tip warning={true}> If one of the model's weight is bigger that `max_sahrd_size`, it will end up in its own sub-checkpoint which will have a size greater than `max_shard_size`. </Tip> Args: state_dict (`Dict[str, torch.Tensor]`): The state dictionary of a model to save. max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`): The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). weights_name (`str`, *optional*, defaults to `"pytorch_model.bin"`): The name of the model save file. """ max_shard_size = convert_file_size_to_int(max_shard_size) sharded_state_dicts = [{}] last_block_size = 0 total_size = 0 storage_id_to_block = {} for key, weight in state_dict.items(): # when bnb serialization is used the weights in the state dict can be strings # check: https://github.com/huggingface/transformers/pull/24416 for more details if isinstance(weight, str): continue else: storage_id = id_tensor_storage(weight) # If a `weight` shares the same underlying storage as another tensor, we put `weight` in the same `block` if storage_id in storage_id_to_block: block_id = storage_id_to_block[storage_id] sharded_state_dicts[block_id][key] = weight continue weight_size = weight.numel() * dtype_byte_size(weight.dtype) # If this weight is going to tip up over the maximal size, we split. if last_block_size + weight_size > max_shard_size: sharded_state_dicts.append({}) last_block_size = 0 sharded_state_dicts[-1][key] = weight last_block_size += weight_size total_size += weight_size storage_id_to_block[storage_id] = len(sharded_state_dicts) - 1 # If we only have one shard, we return it if len(sharded_state_dicts) == 1: return {weights_name: sharded_state_dicts[0]}, None # Otherwise, let's build the index weight_map = {} shards = {} for idx, shard in enumerate(sharded_state_dicts): shard_file = weights_name.replace(".bin", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.bin") shard_file = shard_file.replace( ".safetensors", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.safetensors" ) shards[shard_file] = shard for key in shard.keys(): weight_map[key] = shard_file # Add the metadata metadata = {"total_size": total_size} index = {"metadata": metadata, "weight_map": weight_map} return shards, index def set_module_tensor_to_device( module: nn.Module, tensor_name: str, device: Union[int, str, torch.device], value: Optional[torch.Tensor] = None, dtype: Optional[Union[str, torch.dtype]] = None, fp16_statistics: Optional[torch.HalfTensor] = None, tied_params_map: Optional[Dict[int, Dict[torch.device, torch.Tensor]]] = None, ): """ A helper function to set a given tensor (parameter of buffer) of a module on a specific device (note that doing `param.to(device)` creates a new tensor not linked to the parameter, which is why we need this function). Args: module (`torch.nn.Module`): The module in which the tensor we want to move lives. tensor_name (`str`): The full name of the parameter/buffer. device (`int`, `str` or `torch.device`): The device on which to set the tensor. value (`torch.Tensor`, *optional*): The value of the tensor (useful when going from the meta device to any other device). dtype (`torch.dtype`, *optional*): If passed along the value of the parameter will be cast to this `dtype`. Otherwise, `value` will be cast to the dtype of the existing parameter in the model. fp16_statistics (`torch.HalfTensor`, *optional*): The list of fp16 statistics to set on the module, used for 8 bit model serialization. tied_params_map (Dict[int, Dict[torch.device, torch.Tensor]], *optional*, defaults to `None`): A map of current data pointers to dictionaries of devices to already dispatched tied weights. For a given execution device, this parameter is useful to reuse the first available pointer of a shared weight on the device for all others, instead of duplicating memory. """ # Recurse if needed if "." in tensor_name: splits = tensor_name.split(".") for split in splits[:-1]: new_module = getattr(module, split) if new_module is None: raise ValueError(f"{module} has no attribute {split}.") module = new_module tensor_name = splits[-1] if tensor_name not in module._parameters and tensor_name not in module._buffers: raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.") is_buffer = tensor_name in module._buffers old_value = getattr(module, tensor_name) # Treat the case where old_value (or a custom `value`, typically offloaded to RAM/disk) belongs to a tied group, and one of the weight # in the tied group has already been dispatched to the device, by avoiding reallocating memory on the device and just copying the pointer. if ( value is not None and tied_params_map is not None and value.data_ptr() in tied_params_map and device in tied_params_map[value.data_ptr()] ): module._parameters[tensor_name] = tied_params_map[value.data_ptr()][device] return elif ( tied_params_map is not None and old_value.data_ptr() in tied_params_map and device in tied_params_map[old_value.data_ptr()] ): module._parameters[tensor_name] = tied_params_map[old_value.data_ptr()][device] return if old_value.device == torch.device("meta") and device not in ["meta", torch.device("meta")] and value is None: raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}.") if value is not None: if old_value.shape != value.shape: raise ValueError( f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this look incorrect.' ) if dtype is None: # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model value = value.to(old_value.dtype) elif not str(value.dtype).startswith(("torch.uint", "torch.int", "torch.bool")): value = value.to(dtype) param = module._parameters[tensor_name] if tensor_name in module._parameters else None param_cls = type(param) device_quantization = None with torch.no_grad(): # leave it on cpu first before moving them to cuda # # fix the case where the device is meta, we don't want to put it on cpu because there is no data =0 if ( param is not None and param.device.type != "cuda" and torch.device(device).type == "cuda" and param_cls.__name__ in ["Int8Params", "FP4Params", "Params4bit"] ): device_quantization = device device = "cpu" # `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)). if is_npu_available() and isinstance(device, int): device = f"npu:{device}" if value is None: new_value = old_value.to(device) if dtype is not None and device in ["meta", torch.device("meta")]: if not str(old_value.dtype).startswith(("torch.uint", "torch.int", "torch.bool")): new_value = new_value.to(dtype) if not is_buffer: module._parameters[tensor_name] = param_cls(new_value, requires_grad=old_value.requires_grad) elif isinstance(value, torch.Tensor): new_value = value.to(device) else: new_value = torch.tensor(value, device=device) if device_quantization is not None: device = device_quantization if is_buffer: module._buffers[tensor_name] = new_value elif value is not None or not check_device_same(torch.device(device), module._parameters[tensor_name].device): param_cls = type(module._parameters[tensor_name]) kwargs = module._parameters[tensor_name].__dict__ if param_cls.__name__ in ["Int8Params", "FP4Params"]: if param_cls.__name__ == "Int8Params" and new_value.dtype == torch.float32: # downcast to fp16 if any - needed for 8bit serialization new_value = new_value.to(torch.float16) # quantize module that are going to stay on the cpu so that we offload quantized weights if device == "cpu" and param_cls.__name__ == "Int8Params": new_value = param_cls(new_value, requires_grad=old_value.requires_grad, **kwargs).to(0).to("cpu") new_value.CB = new_value.CB.to("cpu") new_value.SCB = new_value.SCB.to("cpu") else: new_value = param_cls(new_value, requires_grad=old_value.requires_grad, **kwargs).to(device) else: new_value = param_cls(new_value, requires_grad=old_value.requires_grad).to(device) module._parameters[tensor_name] = new_value if fp16_statistics is not None: setattr(module._parameters[tensor_name], "SCB", fp16_statistics.to(device)) del fp16_statistics # as we put the weight to meta, it doesn't have SCB attr anymore. make sure that it is not a meta weight if ( module.__class__.__name__ == "Linear8bitLt" and getattr(module.weight, "SCB", None) is None and str(module.weight.device) != "meta" ): # quantize only if necessary device_index = torch.device(device).index if torch.device(device).type == "cuda" else None if not getattr(module.weight, "SCB", None) and device_index is not None: if module.bias is not None and module.bias.device.type != "meta": # if a bias exists, we need to wait until the bias is set on the correct device module = module.cuda(device_index) elif module.bias is None: # if no bias exists, we can quantize right away module = module.cuda(device_index) elif module.__class__.__name__ == "Linear4bit" and getattr(module.weight, "quant_state", None) is None: # quantize only if necessary device_index = torch.device(device).index if torch.device(device).type == "cuda" else None if not getattr(module.weight, "quant_state", None) and device_index is not None: module.weight = module.weight.cuda(device_index) # clean pre and post foward hook if is_npu_available(): torch.npu.empty_cache() else: torch.cuda.empty_cache() # When handling tied weights, we update tied_params_map to keep track of the tied weights that have already been allocated on the device in # order to avoid duplicating memory, see above. if ( tied_params_map is not None and old_value.data_ptr() in tied_params_map and device not in tied_params_map[old_value.data_ptr()] ): tied_params_map[old_value.data_ptr()][device] = new_value elif ( value is not None and tied_params_map is not None and value.data_ptr() in tied_params_map and device not in tied_params_map[value.data_ptr()] ): tied_params_map[value.data_ptr()][device] = new_value def named_module_tensors( module: nn.Module, include_buffers: bool = True, recurse: bool = False, remove_non_persistent: bool = False ): """ A helper function that gathers all the tensors (parameters + buffers) of a given module. If `include_buffers=True` it's the same as doing `module.named_parameters(recurse=recurse) + module.named_buffers(recurse=recurse)`. Args: module (`torch.nn.Module`): The module we want the tensors on. include_buffer (`bool`, *optional*, defaults to `True`): Whether or not to include the buffers in the result. recurse (`bool`, *optional`, defaults to `False`): Whether or not to go look in every submodule or just return the direct parameters and buffers. remove_non_persistent (`bool`, *optional*, defaults to `False`): Whether or not to remove the non persistent buffer from the buffers. Useful only when include_buffers = True """ for named_parameter in module.named_parameters(recurse=recurse): yield named_parameter if include_buffers: non_persistent_buffers = set() if remove_non_persistent: non_persistent_buffers = get_non_persistent_buffers(module, recurse=recurse) for named_buffer in module.named_buffers(recurse=recurse): name, _ = named_buffer if name not in non_persistent_buffers: yield named_buffer def get_non_persistent_buffers(module: nn.Module, recurse: bool = False): """ Gather all non persistent buffers of a given modules into a set Args: module (`nn.Module`): The module we want the non persistent buffers on. recurse (`bool`, *optional*, defaults to `False`): Whether or not to go look in every submodule or just return the direct non persistent buffers. """ non_persistent_buffers_set = module._non_persistent_buffers_set if recurse: for _, m in module.named_modules(): non_persistent_buffers_set |= m._non_persistent_buffers_set return non_persistent_buffers_set class FindTiedParametersResult(list): """ This is a subclass of a list to handle backward compatibility for Transformers. Do not rely on the fact this is not a list or on the `values` method as in the future this will be removed. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def values(self): # TODO: at the next Transformers release (4.28.0) issue a deprecation warning here. return sum([x[1:] for x in self], []) def check_tied_parameters_in_config(model: nn.Module): """ Check if there is any indication in the given model that some weights should be tied. Args: model (`torch.nn.Module`): The model to inspect Returns: bool: True if the model needs to have tied weights """ # based on model.tie_weights() method has_tied_word_embedding = False has_tied_encoder_decoder = False has_tied_module = False if "PreTrainedModel" in [c.__name__ for c in inspect.getmro(model.__class__)]: has_tied_word_embedding = ( hasattr(model, "config") and getattr(model.config, "tie_word_embeddings", False) and model.get_output_embeddings() ) has_tied_encoder_decoder = ( hasattr(model, "config") and getattr(model.config, "is_encoder_decoder", False) and getattr(model.config, "tie_encoder_decoder", False) ) has_tied_module = any(hasattr(module, "_tie_weights") for module in model.modules()) return any([has_tied_word_embedding, has_tied_encoder_decoder, has_tied_module]) def _get_param_device(param, device_map): if param in device_map: return device_map[param] parent_param = ".".join(param.split(".")[:-1]) if parent_param == param: raise ValueError(f"The `device_map` does not contain the module {param}.") else: return _get_param_device(parent_param, device_map) def check_tied_parameters_on_same_device(tied_params, device_map): """ Check if tied parameters are on the same device Args: tied_params (`List[List[str]]`): A list of lists of parameter names being all tied together. device_map (`Dict[str, Union[int, str, torch.device]]`): A map that specifies where each submodule should go. """ for tie_param in tied_params: tie_param_devices = {} for param in tie_param: tie_param_devices[param] = _get_param_device(param, device_map) if len(set(tie_param_devices.values())) > 1: logger.warn( f"Tied parameters are on different devices: {tie_param_devices}. " "Please modify your custom device map or set `device_map='auto'`. " ) def find_tied_parameters(model: nn.Module, **kwargs): """ Find the tied parameters in a given model. <Tip warning={true}> The signature accepts keyword arguments, but they are for the recursive part of this function and you should ignore them. </Tip> Args: model (`torch.nn.Module`): The model to inspect. Returns: List[List[str]]: A list of lists of parameter names being all tied together. Example: ```py >>> from collections import OrderedDict >>> import torch.nn as nn >>> model = nn.Sequential(OrderedDict([("linear1", nn.Linear(4, 4)), ("linear2", nn.Linear(4, 4))])) >>> model.linear2.weight = model.linear1.weight >>> find_tied_parameters(model) [['linear1.weight', 'linear2.weight']] ``` """ # Initialize result and named_parameters before recursing. named_parameters = kwargs.get("named_parameters", None) prefix = kwargs.get("prefix", "") result = kwargs.get("result", {}) if named_parameters is None: named_parameters = {n: p for n, p in model.named_parameters()} else: # A tied parameter will not be in the full `named_parameters` seen above but will be in the `named_parameters` # of the submodule it belongs to. So while recursing we track the names that are not in the initial # `named_parameters`. for name, parameter in model.named_parameters(): full_name = name if prefix == "" else f"{prefix}.{name}" if full_name not in named_parameters: # When we find one, it has to be one of the existing parameters. for new_name, new_param in named_parameters.items(): if new_param is parameter: if new_name not in result: result[new_name] = [] result[new_name].append(full_name) # Once we have treated direct parameters, we move to the child modules. for name, child in model.named_children(): child_name = name if prefix == "" else f"{prefix}.{name}" find_tied_parameters(child, named_parameters=named_parameters, prefix=child_name, result=result) return FindTiedParametersResult([sorted([weight] + list(set(tied))) for weight, tied in result.items()]) def retie_parameters(model, tied_params): """ Reties tied parameters in a given model if the link was broken (for instance when adding hooks). Args: model (`torch.nn.Module`): The model in which to retie parameters. tied_params (`List[List[str]]`): A mapping parameter name to tied parameter name as obtained by `find_tied_parameters`. """ for tied_group in tied_params: param_to_tie = None # two loops : the first one to set param_to_tie , the second one to change the values of tied_group for param_name in tied_group: module = model splits = param_name.split(".") for split in splits[:-1]: module = getattr(module, split) param = getattr(module, splits[-1]) if param_to_tie is None and param.device != torch.device("meta"): param_to_tie = param break if param_to_tie is not None: for param_name in tied_group: module = model splits = param_name.split(".") for split in splits[:-1]: module = getattr(module, split) setattr(module, splits[-1], param_to_tie) def _get_proper_dtype(dtype: Union[str, torch.device]) -> torch.dtype: """ Just does torch.dtype(dtype) if necessary. """ if isinstance(dtype, str): # We accept "torch.float16" or just "float16" dtype = dtype.replace("torch.", "") dtype = getattr(torch, dtype) return dtype def compute_module_sizes( model: nn.Module, dtype: Optional[Union[str, torch.device]] = None, special_dtypes: Optional[Dict[str, Union[str, torch.device]]] = None, ): """ Compute the size of each submodule of a given model. """ if dtype is not None: dtype = _get_proper_dtype(dtype) dtype_size = dtype_byte_size(dtype) if special_dtypes is not None: special_dtypes = {key: _get_proper_dtype(dtyp) for key, dtyp in special_dtypes.items()} special_dtypes_size = {key: dtype_byte_size(dtyp) for key, dtyp in special_dtypes.items()} module_sizes = defaultdict(int) for name, tensor in named_module_tensors(model, recurse=True): if special_dtypes is not None and name in special_dtypes: size = tensor.numel() * special_dtypes_size[name] elif dtype is None: size = tensor.numel() * dtype_byte_size(tensor.dtype) else: size = tensor.numel() * min(dtype_size, dtype_byte_size(tensor.dtype)) name_parts = name.split(".") for idx in range(len(name_parts) + 1): module_sizes[".".join(name_parts[:idx])] += size return module_sizes def get_max_layer_size( modules: List[Tuple[str, torch.nn.Module]], module_sizes: Dict[str, int], no_split_module_classes: List[str] ): """ Utility function that will scan a list of named modules and return the maximum size used by one full layer. The definition of a layer being: - a module with no direct children (just parameters and buffers) - a module whose class name is in the list `no_split_module_classes` Args: modules (`List[Tuple[str, torch.nn.Module]]`): The list of named modules where we want to determine the maximum layer size. module_sizes (`Dict[str, int]`): A dictionary mapping each layer name to its size (as generated by `compute_module_sizes`). no_split_module_classes (`List[str]`): A list of class names for layers we don't want to be split. Returns: `Tuple[int, List[str]]`: The maximum size of a layer with the list of layer names realizing that maximum size. """ max_size = 0 layer_names = [] modules_to_treat = modules.copy() while len(modules_to_treat) > 0: module_name, module = modules_to_treat.pop(0) modules_children = list(module.named_children()) if isinstance(module, torch.nn.Module) else [] if len(modules_children) == 0 or module.__class__.__name__ in no_split_module_classes: # No splitting this one so we compare to the max_size size = module_sizes[module_name] if size > max_size: max_size = size layer_names = [module_name] elif size == max_size: layer_names.append(module_name) else: modules_to_treat = [(f"{module_name}.{n}", v) for n, v in modules_children] + modules_to_treat return max_size, layer_names def get_max_memory(max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None): """ Get the maximum memory available if nothing is passed, converts string to int otherwise. """ import psutil if max_memory is None: if not (torch.cuda.is_available() or is_npu_available() or is_xpu_available()): max_memory = {} else: # Make sure CUDA is initialized on each GPU to have the right memory info. if is_npu_available(): for i in range(torch.npu.device_count()): _ = torch.tensor(0, device=torch.device("npu", i)) max_memory = {i: torch.npu.mem_get_info(i)[0] for i in range(torch.npu.device_count())} elif is_xpu_available(): for i in range(torch.xpu.device_count()): _ = torch.tensor(0, device=torch.device("xpu", i)) max_memory = {i: torch.xpu.max_memory_allocated(i) for i in range(torch.xpu.device_count())} else: for i in range(torch.cuda.device_count()): _ = torch.tensor([0], device=i) max_memory = {i: torch.cuda.mem_get_info(i)[0] for i in range(torch.cuda.device_count())} # allocate everything in the mps device as the RAM is shared if is_mps_available(): max_memory["mps"] = psutil.virtual_memory().available else: max_memory["cpu"] = psutil.virtual_memory().available return max_memory for key in max_memory: if isinstance(max_memory[key], str): max_memory[key] = convert_file_size_to_int(max_memory[key]) # Need to sort the device by type to make sure that we allocate the gpu first. # As gpu/npu/xpu are represented by int, we need to sort them first. gpu_devices = [k for k in max_memory.keys() if isinstance(k, int)] gpu_devices.sort() # check if gpu/npu/xpu devices are available and if not, throw a warning if is_npu_available(): num_devices = torch.npu.device_count() elif is_xpu_available(): num_devices = torch.xpu.device_count() else: num_devices = torch.cuda.device_count() for device in gpu_devices: if device >= num_devices or device < 0: logger.warning(f"Device {device} is not available, available devices are {list(range(num_devices))}") # Add the other devices in the preset order if they are available all_devices = gpu_devices + [k for k in ["mps", "cpu", "disk"] if k in max_memory.keys()] # Raise an error if a device is not recognized for k in max_memory.keys(): if k not in all_devices: raise ValueError( f"Device {k} is not recognized, available devices are integers(for GPU/XPU), 'mps', 'cpu' and 'disk'" ) max_memory = {k: max_memory[k] for k in all_devices} return max_memory def clean_device_map(device_map: Dict[str, Union[int, str, torch.device]], module_name: str = ""): """ Cleans a device_map by grouping all submodules that go on the same device together. """ # Get the value of the current module and if there is only one split across several keys, regroup it. prefix = "" if module_name == "" else f"{module_name}." values = [v for k, v in device_map.items() if k.startswith(prefix)] if len(set(values)) == 1 and len(values) > 1: for k in [k for k in device_map if k.startswith(prefix)]: del device_map[k] device_map[module_name] = values[0] # Recurse over the children children_modules = [k for k in device_map.keys() if k.startswith(prefix) and len(k) > len(module_name)] idx = len(module_name.split(".")) + 1 if len(module_name) > 0 else 1 children_modules = set(".".join(k.split(".")[:idx]) for k in children_modules) for child in children_modules: clean_device_map(device_map, module_name=child) return device_map def load_offloaded_weights(model, index, offload_folder): """ Loads the weights from the offload folder into the model. Args: model (`torch.nn.Module`): The model to load the weights into. index (`dict`): A dictionary containing the parameter name and its metadata for each parameter that was offloaded from the model. offload_folder (`str`): The folder where the offloaded weights are stored. """ if index is None or len(index) == 0: # Nothing to do return for param_name, metadata in index.items(): if "SCB" in param_name: continue fp16_statistics = None if "weight" in param_name and param_name.replace("weight", "SCB") in index.keys(): weight_name = param_name.replace("weight", "SCB") fp16_statistics = load_offloaded_weight( os.path.join(offload_folder, f"{weight_name}.dat"), index[weight_name] ) tensor_file = os.path.join(offload_folder, f"{param_name}.dat") weight = load_offloaded_weight(tensor_file, metadata) set_module_tensor_to_device(model, param_name, "cpu", value=weight, fp16_statistics=fp16_statistics) def get_balanced_memory( model: nn.Module, max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None, no_split_module_classes: Optional[List[str]] = None, dtype: Optional[Union[str, torch.dtype]] = None, special_dtypes: Optional[Dict[str, Union[str, torch.device]]] = None, low_zero: bool = False, ): """ Compute a `max_memory` dictionary for [`infer_auto_device_map`] that will balance the use of each available GPU. <Tip> All computation is done analyzing sizes and dtypes of the model parameters. As a result, the model can be on the meta device (as it would if initialized within the `init_empty_weights` context manager). </Tip> Args: model (`torch.nn.Module`): The model to analyze. max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available if unset. Example: `max_memory={0: "1GB"}`. no_split_module_classes (`List[str]`, *optional*): A list of layer class names that should never be split across device (for instance any layer that has a residual connection). dtype (`str` or `torch.dtype`, *optional*): If provided, the weights will be converted to that type when loaded. special_dtypes (`Dict[str, Union[str, torch.device]]`, *optional*): If provided, special dtypes to consider for some specific weights (will override dtype used as default for all weights). low_zero (`bool`, *optional*): Minimizes the number of weights on GPU 0, which is convenient when it's used for other operations (like the Transformers generate function). """ # Get default / clean up max_memory user_not_set_max_memory = max_memory is None max_memory = get_max_memory(max_memory) if is_npu_available(): num_devices = len([d for d in max_memory if torch.device(d).type == "npu" and max_memory[d] > 0]) elif is_xpu_available(): num_devices = len( [ d for d in max_memory if ( d != "cpu" and (torch.device(d).type == "xpu" or torch.xpu.get_device_properties(d).dev_type == "gpu") ) and max_memory[d] > 0 ] ) else: num_devices = len([d for d in max_memory if torch.device(d).type == "cuda" and max_memory[d] > 0]) if num_devices == 0: return max_memory if num_devices == 1: # We cannot do low_zero on just one GPU, but we will still reserve some memory for the buffer low_zero = False # If user just asked us to handle memory usage, we should avoid OOM if user_not_set_max_memory: for key in max_memory.keys(): if isinstance(key, int): max_memory[key] *= 0.9 # 90% is a good compromise logger.info( f"We will use 90% of the memory on device {key} for storing the model, and 10% for the buffer to avoid OOM. " "You can set `max_memory` in to a higher value to use more memory (at your own risk)." ) break # only one device module_sizes = compute_module_sizes(model, dtype=dtype, special_dtypes=special_dtypes) per_gpu = module_sizes[""] // (num_devices - 1 if low_zero else num_devices) # We can't just set the memory to model_size // num_devices as it will end being too small: each GPU will get # slightly less layers and some layers will end up offload at the end. So this function computes a buffer size to # add which is the biggest of: # - the size of no split block (if applicable) # - the mean of the layer sizes if no_split_module_classes is None: no_split_module_classes = [] elif not isinstance(no_split_module_classes, (list, tuple)): no_split_module_classes = [no_split_module_classes] # Identify the size of the no_split_block modules if len(no_split_module_classes) > 0: no_split_children = {} for name, size in module_sizes.items(): if name == "": continue submodule = model for submodule_name in name.split("."): submodule = getattr(submodule, submodule_name) class_name = submodule.__class__.__name__ if class_name in no_split_module_classes and class_name not in no_split_children: no_split_children[class_name] = size if set(no_split_children.keys()) == set(no_split_module_classes): break buffer = max(no_split_children.values()) if len(no_split_children) > 0 else 0 else: buffer = 0 # Compute mean of final modules. In the first dict of module sizes, leaves are the parameters leaves = [n for n in module_sizes if len([p for p in module_sizes if n == "" or p.startswith(n + ".")]) == 0] module_sizes = {n: v for n, v in module_sizes.items() if n not in leaves} # Once removed, leaves are the final modules. leaves = [n for n in module_sizes if len([p for p in module_sizes if n == "" or p.startswith(n + ".")]) == 0] mean_leaves = int(sum([module_sizes[n] for n in leaves]) / max(len(leaves), 1)) buffer = int(1.25 * max(buffer, mean_leaves)) per_gpu += buffer # Sorted list of GPUs id (we may have some gpu ids not included in the our max_memory list - let's ignore them) gpus_idx_list = list( sorted( device_id for device_id, device_mem in max_memory.items() if isinstance(device_id, int) and device_mem > 0 ) ) # The last device is left with max_memory just in case the buffer is not enough. for idx in gpus_idx_list[:-1]: max_memory[idx] = min(max_memory[0] if low_zero and idx == 0 else per_gpu, max_memory[idx]) if low_zero: min_zero = max(0, module_sizes[""] - sum([max_memory[i] for i in range(1, num_devices)])) max_memory[0] = min(min_zero, max_memory[0]) return max_memory def calculate_maximum_sizes(model: torch.nn.Module): "Computes the total size of the model and its largest layer" sizes = compute_module_sizes(model) # `transformers` models store this information for us no_split_modules = getattr(model, "_no_split_modules", None) if no_split_modules is None: no_split_modules = [] modules_to_treat = ( list(model.named_parameters(recurse=False)) + list(model.named_children()) + list(model.named_buffers(recurse=False)) ) largest_layer = get_max_layer_size(modules_to_treat, sizes, no_split_modules) total_size = sizes[""] return total_size, largest_layer def infer_auto_device_map( model: nn.Module, max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None, no_split_module_classes: Optional[List[str]] = None, dtype: Optional[Union[str, torch.dtype]] = None, special_dtypes: Optional[Dict[str, Union[str, torch.dtype]]] = None, verbose: bool = False, clean_result: bool = True, ): """ Compute a device map for a given model giving priority to GPUs, then offload on CPU and finally offload to disk, such that: - we don't exceed the memory available of any of the GPU. - if offload to the CPU is needed, there is always room left on GPU 0 to put back the layer offloaded on CPU that has the largest size. - if offload to the CPU is needed,we don't exceed the RAM available on the CPU. - if offload to the disk is needed, there is always room left on the CPU to put back the layer offloaded on disk that has the largest size. <Tip> All computation is done analyzing sizes and dtypes of the model parameters. As a result, the model can be on the meta device (as it would if initialized within the `init_empty_weights` context manager). </Tip> Args: model (`torch.nn.Module`): The model to analyze. max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available if unset. Example: `max_memory={0: "1GB"}`. no_split_module_classes (`List[str]`, *optional*): A list of layer class names that should never be split across device (for instance any layer that has a residual connection). dtype (`str` or `torch.dtype`, *optional*): If provided, the weights will be converted to that type when loaded. special_dtypes (`Dict[str, Union[str, torch.device]]`, *optional*): If provided, special dtypes to consider for some specific weights (will override dtype used as default for all weights). verbose (`bool`, *optional*, defaults to `False`): Whether or not to provide debugging statements as the function builds the device_map. clean_result (`bool`, *optional*, defaults to `True`): Clean the resulting device_map by grouping all submodules that go on the same device together. """ # Get default / clean up max_memory max_memory = get_max_memory(max_memory) if no_split_module_classes is None: no_split_module_classes = [] elif not isinstance(no_split_module_classes, (list, tuple)): no_split_module_classes = [no_split_module_classes] devices = list(max_memory.keys()) if "disk" not in devices: devices.append("disk") gpus = [device for device in devices if device not in ["cpu", "disk"]] # Devices that need to keep space for a potential offloaded layer. if "mps" in gpus: main_devices = ["mps"] elif len(gpus) > 0: main_devices = [gpus[0], "cpu"] else: main_devices = ["cpu"] module_sizes = compute_module_sizes(model, dtype=dtype, special_dtypes=special_dtypes) tied_parameters = find_tied_parameters(model) if check_tied_parameters_in_config(model) and len(tied_parameters) == 0: logger.warn( "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function." ) device_map = OrderedDict() current_device = 0 current_memory_used = 0 # Direct submodules and parameters modules_to_treat = ( list(model.named_parameters(recurse=False)) + list(model.named_children()) + list(model.named_buffers(recurse=False)) ) # Initialize maximum largest layer, to know which space to keep in memory max_layer_size, max_layer_names = get_max_layer_size(modules_to_treat, module_sizes, no_split_module_classes) # Ready ? This is going to be a bit messy. while len(modules_to_treat) > 0: name, module = modules_to_treat.pop(0) if verbose: print(f"\nTreating module {name}.") # Max size in the remaining layers may have changed since we took one, so we maybe update it. max_layer_names = [n for n in max_layer_names if n != name and not n.startswith(name + ".")] if len(max_layer_names) == 0: max_layer_size, max_layer_names = get_max_layer_size( [(n, m) for n, m in modules_to_treat if isinstance(m, torch.nn.Module)], module_sizes, no_split_module_classes, ) # Assess size needed module_size = module_sizes[name] # We keep relevant tied parameters only: one of the tied parameters in the group is inside the current module # and the other is not. # Note: If we are currently processing the name `compute.weight`, an other parameter named e.g. `compute.weight_submodule.parameter` # needs to be considered outside the current module, hence the check with additional dots. tied_param_goups = [ tied_group for tied_group in tied_parameters if any(name + "." in k + "." for k in tied_group) and not all(name + "." in k + "." for k in tied_group) ] if verbose and len(tied_param_goups) > 0: print(f" Found the relevant tied param groups {tied_param_goups}") # Then we keep track of all the parameters that are tied to the current module, but not in the current module tied_params = sum( [[p for p in tied_group if name + "." not in p + "."] for tied_group in tied_param_goups], [] ) if verbose and len(tied_params) > 0: print(f" So those parameters need to be taken into account {tied_params}") device = devices[current_device] current_max_size = max_memory[device] if device != "disk" else None # Reduce max size available by the largest layer. if devices[current_device] in main_devices: current_max_size = current_max_size - max_layer_size # Case 1 -> We're too big! if current_max_size is not None and current_memory_used + module_size > current_max_size: # Split or not split? modules_children = [] if isinstance(module, nn.Parameter) else list(module.named_children()) if verbose: print( f"Not enough space on {devices[current_device]} to put {name} (space available " f"{current_max_size - current_memory_used}, module size {module_size})." ) if len(modules_children) == 0 or module.__class__.__name__ in no_split_module_classes: # -> no split, we go to the next device if verbose: print("This module cannot be split, going to the next device.") current_device += 1 modules_to_treat = [(name, module)] + modules_to_treat current_memory_used = 0 else: # -> split, we replace the module studied by its children + parameters if verbose: print(f"Splitting {name}.") modules_children = list(module.named_parameters(recurse=False)) + modules_children modules_to_treat = [(f"{name}.{n}", v) for n, v in modules_children] + modules_to_treat # Update the max layer size. max_layer_size, max_layer_names = get_max_layer_size( [(n, m) for n, m in modules_to_treat if isinstance(m, torch.nn.Module)], module_sizes, no_split_module_classes, ) # Case 2, it fits! We're not entirely out of the wood though, because we may have some tied parameters. elif len(tied_params) > 0: # First locate all tied modules tied_module_names = [] tied_modules = [] for tied_param in tied_params: tied_module_index = [i for i, (n, _) in enumerate(modules_to_treat) if n in tied_param][0] tied_module_names.append(modules_to_treat[tied_module_index][0]) tied_modules.append(modules_to_treat[tied_module_index][1]) if verbose: print( f" It looks like {name} is going to fit on {devices[current_device]} but we have tied " f"parameters to account for.\n - Names {tied_params}\n - Module names {tied_module_names}" ) # Let's see if it all fits first module_size_with_ties = module_size for tied_param, tied_module_name in zip(tied_params, tied_module_names): module_size_with_ties += module_sizes[tied_module_name] - module_sizes[tied_param] if current_max_size is None or current_memory_used + module_size_with_ties <= current_max_size: # We really really fit! if verbose: print(f"Putting {name} and {tied_module_names} on {devices[current_device]}.") current_memory_used += module_size_with_ties device_map[name] = devices[current_device] for tied_module_name in tied_module_names: if tied_module_name in [m[0] for m in modules_to_treat]: # The module may have been removed by a previous iteration of this loop. tied_module_index = [i for i, (n, _) in enumerate(modules_to_treat) if n == tied_module_name][ 0 ] modules_to_treat.pop(tied_module_index) device_map[tied_module_name] = devices[current_device] else: # We don't fit with the tied modules. Next question is: can we split one of the tied modules to make it # smaller or do we need to go on the next device? if verbose: print( f"Not enough space on {devices[current_device]} to put {name} and {tied_module_names} (space " f"available {current_max_size - current_memory_used}, needed size {module_size_with_ties})." ) split_happened = False for tied_module_name, tied_module in zip(tied_module_names, tied_modules): tied_module_children = list(tied_module.named_children()) if len(tied_module_children) == 0 or tied_module.__class__.__name__ in no_split_module_classes: # can't break this one. continue if verbose: print(f"Splitting {tied_module_name}.") tied_module_children = list(tied_module.named_parameters(recurse=False)) + tied_module_children tied_module_children = [(f"{tied_module_name}.{n}", v) for n, v in tied_module_children] tied_module_index = [i for i, (n, _) in enumerate(modules_to_treat) if n == tied_module_name][0] modules_to_treat = ( [(name, module)] + modules_to_treat[:tied_module_index] + tied_module_children + modules_to_treat[tied_module_index + 1 :] ) # Update the max layer size. max_layer_size, max_layer_names = get_max_layer_size( [(n, m) for n, m in modules_to_treat if isinstance(m, torch.nn.Module)], module_sizes, no_split_module_classes, ) split_happened = True break if not split_happened: # If the tied module is not split, we go to the next device if verbose: print("None of the tied module can be split, going to the next device.") current_device += 1 modules_to_treat = [(name, module)] + modules_to_treat current_memory_used = 0 else: if verbose: if current_max_size is None: print(f"Putting {name} (size={module_size}) on {devices[current_device]}.") else: print( f"Putting {name} (size={module_size}) on {devices[current_device]} " f"(available={current_max_size - current_memory_used})." ) current_memory_used += module_size device_map[name] = devices[current_device] if clean_result: device_map = clean_device_map(device_map) return device_map def check_device_map(model: nn.Module, device_map: Dict[str, Union[int, str, torch.device]]): """ Checks a device map covers everything in a given model. Args: model (`torch.nn.Module`): The model to check the device map against. device_map (`Dict[str, Union[int, str, torch.device]]`): The device map to check. """ all_model_tensors = [name for name, _ in model.state_dict().items()] for module_name in device_map.keys(): if module_name == "": all_model_tensors.clear() break else: all_model_tensors = [ name for name in all_model_tensors if not name == module_name and not name.startswith(module_name + ".") ] if len(all_model_tensors) > 0: non_covered_params = ", ".join(all_model_tensors) raise ValueError( f"The device_map provided does not give any device for the following parameters: {non_covered_params}" ) def load_state_dict(checkpoint_file, device_map=None): """ Load a checkpoint from a given file. If the checkpoint is in the safetensors format and a device map is passed, the weights can be fast-loaded directly on the GPU. Args: checkpoint_file (`str`): The path to the checkpoint to load. device_map (`Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. """ if checkpoint_file.endswith(".safetensors"): with safe_open(checkpoint_file, framework="pt") as f: metadata = f.metadata() weight_names = f.keys() if metadata is None: logger.warn( f"The safetensors archive passed at {checkpoint_file} does not contain metadata. " "Make sure to save your model with the `save_pretrained` method. Defaulting to 'pt' metadata." ) metadata = {"format": "pt"} if metadata.get("format") not in ["pt", "tf", "flax"]: raise OSError( f"The safetensors archive passed at {checkpoint_file} does not contain the valid metadata. Make sure " "you save your model with the `save_pretrained` method." ) elif metadata["format"] != "pt": raise ValueError(f"The checkpoint passed was saved with {metadata['format']}, we need a the pt format.") if device_map is None: return safe_load_file(checkpoint_file) else: # if we only have one device we can load everything directly if len(set(device_map.values())) == 1: return safe_load_file(checkpoint_file, device=list(device_map.values())[0]) devices = list(set(device_map.values()) - {"disk"}) # cpu device should always exist as fallback option if "cpu" not in devices: devices.append("cpu") # For each device, get the weights that go there device_weights = {device: [] for device in devices} for module_name, device in device_map.items(): if device in devices: device_weights[device].extend( [k for k in weight_names if k == module_name or k.startswith(module_name + ".")] ) # all weights that haven't defined a device should be loaded on CPU device_weights["cpu"].extend([k for k in weight_names if k not in sum(device_weights.values(), [])]) tensors = {} if is_tqdm_available(): progress_bar = tqdm( main_process_only=False, total=sum([len(device_weights[device]) for device in devices]), unit="w", smoothing=0, leave=False, ) else: progress_bar = None for device in devices: with safe_open(checkpoint_file, framework="pt", device=device) as f: for key in device_weights[device]: if progress_bar is not None: progress_bar.set_postfix(dev=device, refresh=False) progress_bar.set_description(key) tensors[key] = f.get_tensor(key) if progress_bar is not None: progress_bar.update() if progress_bar is not None: progress_bar.close() return tensors else: return torch.load(checkpoint_file, map_location=torch.device("cpu")) def get_state_dict_offloaded_model(model: nn.Module): """ Returns the state dictionary for an offloaded model via iterative onloading Args: model (`torch.nn.Module`): The offloaded model we want to save """ from ..hooks import AlignDevicesHook state_dict = {} placeholders = set() for name, module in model.named_modules(): if name == "": continue if hasattr(module, "_hf_hook") and isinstance(module._hf_hook, AlignDevicesHook) and module._hf_hook.offload: original_device = module._hf_hook.execution_device # assign hook execution device to cpu module._hf_hook.execution_device = "cpu" # onload meta tensors to execution device try: module._hf_hook.pre_forward(module) except MemoryError: raise MemoryError("Offloaded module must fit in CPU memory to call save_model!") from None module_state_dict = module.state_dict() # offload meta tensors from cpu module._hf_hook.post_forward(module, torch.tensor([])) # re-assign hook to original execution device module._hf_hook.execution_device = original_device else: module_state_dict = module.state_dict() for key in module_state_dict: # ignore placeholder parameters that are still on the meta device if module_state_dict[key].device == torch.device("meta"): placeholders.add(name + f".{key}") continue params = module_state_dict[key] state_dict[name + f".{key}"] = params for key in placeholders.copy(): if key in state_dict: placeholders.remove(key) if placeholders: logger.warning(f"The following tensors were not saved because they were still on meta device: {placeholders}") return state_dict def load_checkpoint_in_model( model: nn.Module, checkpoint: Union[str, os.PathLike], device_map: Optional[Dict[str, Union[int, str, torch.device]]] = None, offload_folder: Optional[Union[str, os.PathLike]] = None, dtype: Optional[Union[str, torch.dtype]] = None, offload_state_dict: bool = False, offload_buffers: bool = False, keep_in_fp32_modules: List[str] = None, offload_8bit_bnb: bool = False, ): """ Loads a (potentially sharded) checkpoint inside a model, potentially sending weights to a given device as they are loaded. <Tip warning={true}> Once loaded across devices, you still need to call [`dispatch_model`] on your model to make it able to run. To group the checkpoint loading and dispatch in one single call, use [`load_checkpoint_and_dispatch`]. </Tip> Args: model (`torch.nn.Module`): The model in which we want to load a checkpoint. checkpoint (`str` or `os.PathLike`): The folder checkpoint to load. It can be: - a path to a file containing a whole model state dict - a path to a `.json` file containing the index to a sharded checkpoint - a path to a folder containing a unique `.index.json` file and the shards of a checkpoint. - a path to a folder containing a unique pytorch_model.bin or a model.safetensors file. device_map (`Dict[str, Union[int, str, torch.device]]`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. offload_folder (`str` or `os.PathLike`, *optional*): If the `device_map` contains any value `"disk"`, the folder where we will offload weights. dtype (`str` or `torch.dtype`, *optional*): If provided, the weights will be converted to that type when loaded. offload_state_dict (`bool`, *optional*, defaults to `False`): If `True`, will temporarily offload the CPU state dict on the hard drive to avoid getting out of CPU RAM if the weight of the CPU state dict + the biggest shard does not fit. offload_buffers (`bool`, *optional*, defaults to `False`): Whether or not to include the buffers in the weights offloaded to disk. keep_in_fp32_modules(`List[str]`, *optional*): A list of the modules that we keep in `torch.float32` dtype. offload_8bit_bnb (`bool`, *optional*): Whether or not to enable offload of 8-bit modules on cpu/disk. """ if offload_8bit_bnb: from .bnb import quantize_and_offload_8bit tied_params = find_tied_parameters(model) if check_tied_parameters_in_config(model) and len(tied_params) == 0: logger.warn( "The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function." ) if device_map is not None: check_tied_parameters_on_same_device(tied_params, device_map) if offload_folder is None and device_map is not None and "disk" in device_map.values(): raise ValueError( "At least one of the model submodule will be offloaded to disk, please pass along an `offload_folder`." ) elif offload_folder is not None and device_map is not None and "disk" in device_map.values(): os.makedirs(offload_folder, exist_ok=True) if isinstance(dtype, str): # We accept "torch.float16" or just "float16" dtype = dtype.replace("torch.", "") dtype = getattr(torch, dtype) checkpoint_files = None index_filename = None if os.path.isfile(checkpoint): if str(checkpoint).endswith(".json"): index_filename = checkpoint else: checkpoint_files = [checkpoint] elif os.path.isdir(checkpoint): # check if the whole state dict is present potential_state_bin = [f for f in os.listdir(checkpoint) if f == WEIGHTS_NAME] potential_state_safetensor = [f for f in os.listdir(checkpoint) if f == SAFE_WEIGHTS_NAME] if len(potential_state_bin) == 1: checkpoint_files = [os.path.join(checkpoint, potential_state_bin[0])] elif len(potential_state_safetensor) == 1: checkpoint_files = [os.path.join(checkpoint, potential_state_safetensor[0])] else: # otherwise check for sharded checkpoints potential_index = [f for f in os.listdir(checkpoint) if f.endswith(".index.json")] if len(potential_index) == 0: raise ValueError( f"{checkpoint} is not a folder containing a `.index.json` file or a {WEIGHTS_NAME} or a {SAFE_WEIGHTS_NAME} file" ) elif len(potential_index) == 1: index_filename = os.path.join(checkpoint, potential_index[0]) else: raise ValueError( f"{checkpoint} containing more than one `.index.json` file, delete the irrelevant ones." ) else: raise ValueError( "`checkpoint` should be the path to a file containing a whole state dict, or the index of a sharded " f"checkpoint, or a folder containing a sharded checkpoint or the whole state dict, but got {checkpoint}." ) if index_filename is not None: checkpoint_folder = os.path.split(index_filename)[0] with open(index_filename, "r") as f: index = json.loads(f.read()) if "weight_map" in index: index = index["weight_map"] checkpoint_files = sorted(list(set(index.values()))) checkpoint_files = [os.path.join(checkpoint_folder, f) for f in checkpoint_files] # Logic for missing/unexepected keys goes here. offload_index = {} if offload_state_dict: state_dict_folder = tempfile.mkdtemp() state_dict_index = {} buffer_names = [name for name, _ in model.named_buffers()] for checkpoint_file in checkpoint_files: checkpoint = load_state_dict(checkpoint_file, device_map=device_map) if device_map is None: model.load_state_dict(checkpoint, strict=False) else: for param_name, param in checkpoint.items(): # skip SCB parameter (for 8-bit serialization) if "SCB" in param_name: continue module_name = param_name while len(module_name) > 0 and module_name not in device_map: module_name = ".".join(module_name.split(".")[:-1]) if module_name == "" and "" not in device_map: # TODO: group all errors and raise at the end. raise ValueError(f"{param_name} doesn't have any device set.") param_device = device_map[module_name] new_dtype = dtype if dtype is not None and torch.is_floating_point(param): if keep_in_fp32_modules is not None and dtype == torch.float16: proceed = False for key in keep_in_fp32_modules: if ((key in param_name) and (key + "." in param_name)) or key == param_name: proceed = True break if proceed: new_dtype = torch.float32 if "weight" in param_name and param_name.replace("weight", "SCB") in checkpoint.keys(): if param.dtype == torch.int8: fp16_statistics = checkpoint[param_name.replace("weight", "SCB")] else: fp16_statistics = None if param_device == "disk": if offload_buffers or param_name not in buffer_names: if new_dtype is None: new_dtype = param.dtype if offload_8bit_bnb: quantize_and_offload_8bit( model, param, param_name, new_dtype, offload_folder, offload_index, fp16_statistics ) continue else: set_module_tensor_to_device(model, param_name, "meta", dtype=new_dtype) offload_weight(param, param_name, offload_folder, index=offload_index) elif param_device == "cpu" and offload_state_dict: if new_dtype is None: new_dtype = param.dtype if offload_8bit_bnb: quantize_and_offload_8bit( model, param, param_name, new_dtype, state_dict_folder, state_dict_index, fp16_statistics ) else: set_module_tensor_to_device(model, param_name, "meta", dtype=new_dtype) offload_weight(param, param_name, state_dict_folder, index=state_dict_index) else: set_module_tensor_to_device( model, param_name, param_device, value=param, dtype=new_dtype, fp16_statistics=fp16_statistics, ) # Force Python to clean up. del checkpoint gc.collect() save_offload_index(offload_index, offload_folder) # Load back offloaded state dict on CPU if offload_state_dict: load_offloaded_weights(model, state_dict_index, state_dict_folder) shutil.rmtree(state_dict_folder) retie_parameters(model, tied_params) def get_mixed_precision_context_manager(native_amp: bool = False, autocast_kwargs: AutocastKwargs = None): """ Return a context manager for autocasting mixed precision Args: native_amp (`bool`, *optional*, defaults to False): Whether mixed precision is actually enabled. cache_enabled (`bool`, *optional*, defaults to True): Whether the weight cache inside autocast should be enabled. """ state = AcceleratorState() if autocast_kwargs is None: autocast_kwargs = {} else: autocast_kwargs = autocast_kwargs.to_kwargs() if native_amp: if state.mixed_precision == "fp16": return torch.autocast(device_type=state.device.type, dtype=torch.float16, **autocast_kwargs) elif state.mixed_precision == "bf16" and state.distributed_type in [ DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, DistributedType.FSDP, ]: return torch.autocast(device_type=state.device.type, dtype=torch.bfloat16, **autocast_kwargs) else: return torch.autocast(device_type=state.device.type, **autocast_kwargs) else: return contextlib.nullcontext()

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/rich.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .imports import is_rich_available if is_rich_available(): from rich.traceback import install install(show_locals=False) else: raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/utils/launch.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import sys import warnings from ast import literal_eval from typing import Any, Dict, List, Tuple import torch from ..commands.config.config_args import SageMakerConfig from ..utils import ( DynamoBackend, PrecisionType, is_ipex_available, is_npu_available, is_xpu_available, ) from ..utils.constants import DEEPSPEED_MULTINODE_LAUNCHERS from ..utils.other import is_port_in_use, merge_dicts from .dataclasses import DistributedType, SageMakerDistributedType def _filter_args(args, parser, default_args=[]): """ Filters out all `accelerate` specific args """ new_args, _ = parser.parse_known_args(default_args) for key, value in vars(args).items(): if key in vars(new_args).keys(): setattr(new_args, key, value) return new_args def prepare_simple_launcher_cmd_env(args: argparse.Namespace) -> Tuple[List[str], Dict[str, str]]: """ Prepares and returns the command list and an environment with the correct simple launcher environment variables. """ cmd = [] if args.no_python and args.module: raise ValueError("--module and --no_python cannot be used together") if not args.no_python: cmd.append(sys.executable) if args.module: cmd.append("-m") cmd.append(args.training_script) cmd.extend(args.training_script_args) current_env = os.environ.copy() current_env["ACCELERATE_USE_CPU"] = str(args.cpu or args.use_cpu) if args.debug: current_env["ACCELERATE_DEBUG_MODE"] = "true" if args.gpu_ids != "all" and args.gpu_ids is not None: if is_xpu_available(): current_env["ZE_AFFINITY_MASK"] = args.gpu_ids elif is_npu_available(): current_env["ASCEND_RT_VISIBLE_DEVICES"] = args.gpu_ids else: current_env["CUDA_VISIBLE_DEVICES"] = args.gpu_ids if args.num_machines > 1: current_env["MASTER_ADDR"] = args.main_process_ip current_env["MASTER_PORT"] = str(args.main_process_port) elif args.num_processes > 1: current_env["MASTER_ADDR"] = args.main_process_ip if args.main_process_ip is not None else "127.0.0.1" current_env["MASTER_PORT"] = str(args.main_process_port) if args.main_process_port is not None else "29500" try: mixed_precision = PrecisionType(args.mixed_precision.lower()) except ValueError: raise ValueError( f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." ) current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision) try: dynamo_backend = DynamoBackend(args.dynamo_backend.upper()) except ValueError: raise ValueError( f"Unknown dynamo backend: {args.dynamo_backend.upper()}. Choose between {DynamoBackend.list()}." ) current_env["ACCELERATE_DYNAMO_BACKEND"] = dynamo_backend.value current_env["ACCELERATE_DYNAMO_MODE"] = args.dynamo_mode current_env["ACCELERATE_DYNAMO_USE_FULLGRAPH"] = str(args.dynamo_use_fullgraph) current_env["ACCELERATE_DYNAMO_USE_DYNAMIC"] = str(args.dynamo_use_dynamic) current_env["OMP_NUM_THREADS"] = str(args.num_cpu_threads_per_process) if is_ipex_available(): current_env["ACCELERATE_USE_IPEX"] = str(args.ipex).lower() current_env["ACCELERATE_USE_XPU"] = str(args.use_xpu).lower() return cmd, current_env def prepare_multi_gpu_env(args: argparse.Namespace) -> Dict[str, str]: """ Prepares and returns an environment with the correct multi-GPU environment variables. """ num_processes = getattr(args, "num_processes") num_machines = getattr(args, "num_machines") main_process_ip = getattr(args, "main_process_ip") main_process_port = getattr(args, "main_process_port") if num_machines > 1: setattr(args, "nproc_per_node", str(num_processes // num_machines)) setattr(args, "nnodes", str(num_machines)) setattr(args, "node_rank", int(args.machine_rank)) if getattr(args, "same_network", False): setattr(args, "master_addr", str(main_process_ip)) setattr(args, "master_port", str(main_process_port)) else: setattr(args, "rdzv_endpoint", f"{main_process_ip}:{main_process_port}") else: setattr(args, "nproc_per_node", str(num_processes)) if main_process_port is not None: setattr(args, "master_port", str(main_process_port)) if main_process_port is None: main_process_port = 29500 # only need to check port availability in main process, in case we have to start multiple launchers on the same machine # for some reasons like splitting log files. need_port_check = num_machines <= 1 or int(args.machine_rank) == 0 if need_port_check and is_port_in_use(main_process_port): raise ConnectionError( f"Tried to launch distributed communication on port `{main_process_port}`, but another process is utilizing it. " "Please specify a different port (such as using the `----main_process_port` flag or specifying a different `main_process_port` in your config file)" " and rerun your script. To automatically use the next open port (on a single node), you can set this to `0`." ) if args.module and args.no_python: raise ValueError("--module and --no_python cannot be used together") elif args.module: setattr(args, "module", True) elif args.no_python: setattr(args, "no_python", True) current_env = os.environ.copy() if args.debug: current_env["ACCELERATE_DEBUG_MODE"] = "true" gpu_ids = getattr(args, "gpu_ids", "all") if gpu_ids != "all" and args.gpu_ids is not None: if is_xpu_available(): current_env["ZE_AFFINITY_MASK"] = gpu_ids elif is_npu_available(): current_env["ASCEND_RT_VISIBLE_DEVICES"] = gpu_ids else: current_env["CUDA_VISIBLE_DEVICES"] = gpu_ids mixed_precision = args.mixed_precision.lower() try: mixed_precision = PrecisionType(mixed_precision) except ValueError: raise ValueError(f"Unknown mixed_precision mode: {mixed_precision}. Choose between {PrecisionType.list()}.") current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision) try: dynamo_backend = DynamoBackend(args.dynamo_backend.upper()) except ValueError: raise ValueError( f"Unknown dynamo backend: {args.dynamo_backend.upper()}. Choose between {DynamoBackend.list()}." ) current_env["ACCELERATE_DYNAMO_BACKEND"] = dynamo_backend.value current_env["ACCELERATE_DYNAMO_MODE"] = args.dynamo_mode current_env["ACCELERATE_DYNAMO_USE_FULLGRAPH"] = str(args.dynamo_use_fullgraph) current_env["ACCELERATE_DYNAMO_USE_DYNAMIC"] = str(args.dynamo_use_dynamic) if args.use_fsdp: current_env["ACCELERATE_USE_FSDP"] = "true" if args.fsdp_cpu_ram_efficient_loading and not args.fsdp_sync_module_states: raise ValueError("When using `--fsdp_cpu_ram_efficient_loading` set `--fsdp_sync_module_states` to `True`") current_env["FSDP_SHARDING_STRATEGY"] = str(args.fsdp_sharding_strategy) current_env["FSDP_OFFLOAD_PARAMS"] = str(args.fsdp_offload_params).lower() current_env["FSDP_MIN_NUM_PARAMS"] = str(args.fsdp_min_num_params) if args.fsdp_auto_wrap_policy is not None: current_env["FSDP_AUTO_WRAP_POLICY"] = str(args.fsdp_auto_wrap_policy) if args.fsdp_transformer_layer_cls_to_wrap is not None: current_env["FSDP_TRANSFORMER_CLS_TO_WRAP"] = str(args.fsdp_transformer_layer_cls_to_wrap) if args.fsdp_backward_prefetch_policy is not None: warnings.warn( "`fsdp_backward_prefetch_policy` is deprecated and will be removed in version 0.27.0 of 🤗 Accelerate. Use" " `fsdp_backward_prefetch` instead", FutureWarning, ) args.fsdp_backward_prefetch = args.fsdp_backward_prefetch_policy if args.fsdp_backward_prefetch is not None: current_env["FSDP_BACKWARD_PREFETCH"] = str(args.fsdp_backward_prefetch) if args.fsdp_state_dict_type is not None: current_env["FSDP_STATE_DICT_TYPE"] = str(args.fsdp_state_dict_type) current_env["FSDP_FORWARD_PREFETCH"] = str(args.fsdp_forward_prefetch).lower() current_env["FSDP_USE_ORIG_PARAMS"] = str(args.fsdp_use_orig_params).lower() current_env["FSDP_CPU_RAM_EFFICIENT_LOADING"] = str(args.fsdp_cpu_ram_efficient_loading).lower() current_env["FSDP_SYNC_MODULE_STATES"] = str(args.fsdp_sync_module_states).lower() if args.use_megatron_lm: prefix = "MEGATRON_LM_" current_env["ACCELERATE_USE_MEGATRON_LM"] = "true" current_env[prefix + "TP_DEGREE"] = str(args.megatron_lm_tp_degree) current_env[prefix + "PP_DEGREE"] = str(args.megatron_lm_pp_degree) current_env[prefix + "GRADIENT_CLIPPING"] = str(args.megatron_lm_gradient_clipping) if args.megatron_lm_num_micro_batches is not None: current_env[prefix + "NUM_MICRO_BATCHES"] = str(args.megatron_lm_num_micro_batches) if args.megatron_lm_sequence_parallelism is not None: current_env[prefix + "SEQUENCE_PARALLELISM"] = str(args.megatron_lm_sequence_parallelism) if args.megatron_lm_recompute_activations is not None: current_env[prefix + "RECOMPUTE_ACTIVATIONS"] = str(args.megatron_lm_recompute_activations) if args.megatron_lm_use_distributed_optimizer is not None: current_env[prefix + "USE_DISTRIBUTED_OPTIMIZER"] = str(args.megatron_lm_use_distributed_optimizer) current_env["OMP_NUM_THREADS"] = str(args.num_cpu_threads_per_process) return current_env def prepare_deepspeed_cmd_env(args: argparse.Namespace) -> Tuple[List[str], Dict[str, str]]: """ Prepares and returns the command list and an environment with the correct DeepSpeed environment variables. """ num_processes = getattr(args, "num_processes") num_machines = getattr(args, "num_machines") main_process_ip = getattr(args, "main_process_ip") main_process_port = getattr(args, "main_process_port") cmd = None # make sure launcher is not None if args.deepspeed_multinode_launcher is None: # set to default pdsh setattr(args, "deepspeed_multinode_launcher", DEEPSPEED_MULTINODE_LAUNCHERS[0]) if num_machines > 1 and args.deepspeed_multinode_launcher != DEEPSPEED_MULTINODE_LAUNCHERS[1]: cmd = ["deepspeed", "--no_local_rank"] cmd.extend(["--hostfile", str(args.deepspeed_hostfile), "--launcher", str(args.deepspeed_multinode_launcher)]) if args.deepspeed_exclusion_filter is not None: cmd.extend( [ "--exclude", str(args.deepspeed_exclusion_filter), ] ) elif args.deepspeed_inclusion_filter is not None: cmd.extend( [ "--include", str(args.deepspeed_inclusion_filter), ] ) else: cmd.extend(["--num_gpus", str(args.num_processes // args.num_machines)]) cmd.extend(["--master_port", str(main_process_port)]) if args.module and args.no_python: raise ValueError("--module and --no_python cannot be used together") elif args.module: cmd.append("--module") elif args.no_python: cmd.append("--no_python") cmd.append(args.training_script) cmd.extend(args.training_script_args) elif num_machines > 1 and args.deepspeed_multinode_launcher == DEEPSPEED_MULTINODE_LAUNCHERS[1]: setattr(args, "nproc_per_node", str(num_processes // num_machines)) setattr(args, "nnodes", str(num_machines)) setattr(args, "node_rank", int(args.machine_rank)) if getattr(args, "same_network", False): setattr(args, "master_addr", str(main_process_ip)) setattr(args, "master_port", str(main_process_port)) else: setattr(args, "rdzv_endpoint", f"{main_process_ip}:{main_process_port}") else: setattr(args, "nproc_per_node", str(num_processes)) if main_process_port is not None: setattr(args, "master_port", str(main_process_port)) if main_process_port is None: main_process_port = 29500 # only need to check port availability in main process, in case we have to start multiple launchers on the same machine # for some reasons like splitting log files. need_port_check = num_machines <= 1 or int(args.machine_rank) == 0 if need_port_check and is_port_in_use(main_process_port): raise ConnectionError( f"Tried to launch distributed communication on port `{main_process_port}`, but another process is utilizing it. " "Please specify a different port (such as using the `----main_process_port` flag or specifying a different `main_process_port` in your config file)" " and rerun your script. To automatically use the next open port (on a single node), you can set this to `0`." ) if args.module and args.no_python: raise ValueError("--module and --no_python cannot be used together") elif args.module: setattr(args, "module", True) elif args.no_python: setattr(args, "no_python", True) current_env = os.environ.copy() if args.debug: current_env["ACCELERATE_DEBUG_MODE"] = "true" gpu_ids = getattr(args, "gpu_ids", "all") if gpu_ids != "all" and args.gpu_ids is not None: if is_xpu_available(): current_env["ZE_AFFINITY_MASK"] = gpu_ids elif is_npu_available(): current_env["ASCEND_RT_VISIBLE_DEVICES"] = gpu_ids else: current_env["CUDA_VISIBLE_DEVICES"] = gpu_ids try: mixed_precision = PrecisionType(args.mixed_precision.lower()) except ValueError: raise ValueError( f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." ) current_env["PYTHONPATH"] = env_var_path_add("PYTHONPATH", os.path.abspath(".")) current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision) current_env["ACCELERATE_CONFIG_DS_FIELDS"] = str(args.deepspeed_fields_from_accelerate_config).lower() current_env["ACCELERATE_USE_DEEPSPEED"] = "true" if args.zero_stage is not None: current_env["ACCELERATE_DEEPSPEED_ZERO_STAGE"] = str(args.zero_stage) if args.gradient_accumulation_steps is not None: current_env["ACCELERATE_GRADIENT_ACCUMULATION_STEPS"] = str(args.gradient_accumulation_steps) if args.gradient_clipping is not None: current_env["ACCELERATE_GRADIENT_CLIPPING"] = str(args.gradient_clipping).lower() if args.offload_optimizer_device is not None: current_env["ACCELERATE_DEEPSPEED_OFFLOAD_OPTIMIZER_DEVICE"] = str(args.offload_optimizer_device).lower() if args.offload_param_device is not None: current_env["ACCELERATE_DEEPSPEED_OFFLOAD_PARAM_DEVICE"] = str(args.offload_param_device).lower() if args.zero3_init_flag is not None: current_env["ACCELERATE_DEEPSPEED_ZERO3_INIT"] = str(args.zero3_init_flag).lower() if args.zero3_save_16bit_model is not None: current_env["ACCELERATE_DEEPSPEED_ZERO3_SAVE_16BIT_MODEL"] = str(args.zero3_save_16bit_model).lower() if args.deepspeed_config_file is not None: current_env["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = str(args.deepspeed_config_file) return cmd, current_env def prepare_tpu( args: argparse.Namespace, current_env: Dict[str, str], pod: bool = False ) -> Tuple[argparse.Namespace, Dict[str, str]]: """ Prepares and returns an environment with the correct TPU environment variables. """ if args.mixed_precision == "bf16": if args.downcast_bf16: current_env["XLA_DOWNCAST_BF16"] = "1" else: current_env["XLA_USE_BF16"] = "1" if args.debug: current_env["ACCELERATE_DEBUG_MODE"] = "true" if pod: # Take explicit args and set them up for XLA args.vm = args.tpu_vm args.tpu = args.tpu_name return args, current_env def _convert_nargs_to_dict(nargs: List[str]) -> Dict[str, str]: if len(nargs) < 0: return {} # helper function to infer type for argsparser def _infer_type(s): try: s = float(s) if s // 1 == s: return int(s) return s except ValueError: return s parser = argparse.ArgumentParser() _, unknown = parser.parse_known_args(nargs) for index, argument in enumerate(unknown): if argument.startswith(("-", "--")): action = None if index + 1 < len(unknown): # checks if next index would be in list if unknown[index + 1].startswith(("-", "--")): # checks if next element is an key # raise an error if element is store_true or store_false raise ValueError( "SageMaker doesn’t support argparse actions for `store_true` or `store_false`. Please define explicit types" ) else: # raise an error if last element is store_true or store_false raise ValueError( "SageMaker doesn’t support argparse actions for `store_true` or `store_false`. Please define explicit types" ) # adds argument to parser based on action_store true if action is None: parser.add_argument(argument, type=_infer_type) else: parser.add_argument(argument, action=action) return { key: (literal_eval(value) if value in ("True", "False") else value) for key, value in parser.parse_args(nargs).__dict__.items() } def prepare_sagemager_args_inputs( sagemaker_config: SageMakerConfig, args: argparse.Namespace ) -> Tuple[argparse.Namespace, Dict[str, Any]]: # configure environment print("Configuring Amazon SageMaker environment") os.environ["AWS_DEFAULT_REGION"] = sagemaker_config.region # configure credentials if sagemaker_config.profile is not None: os.environ["AWS_PROFILE"] = sagemaker_config.profile elif args.aws_access_key_id is not None and args.aws_secret_access_key is not None: os.environ["AWS_ACCESS_KEY_ID"] = args.aws_access_key_id os.environ["AWS_SECRET_ACCESS_KEY"] = args.aws_secret_access_key else: raise EnvironmentError( "You need to provide an aws_access_key_id and aws_secret_access_key when not using aws_profile" ) # extract needed arguments source_dir = os.path.dirname(args.training_script) if not source_dir: # checks if string is empty source_dir = "." entry_point = os.path.basename(args.training_script) if not entry_point.endswith(".py"): raise ValueError(f'Your training script should be a python script and not "{entry_point}"') print("Converting Arguments to Hyperparameters") hyperparameters = _convert_nargs_to_dict(args.training_script_args) try: mixed_precision = PrecisionType(args.mixed_precision.lower()) except ValueError: raise ValueError( f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." ) try: dynamo_backend = DynamoBackend(args.dynamo_backend.upper()) except ValueError: raise ValueError( f"Unknown dynamo backend: {args.dynamo_backend.upper()}. Choose between {DynamoBackend.list()}." ) # Environment variables to be set for use during training job environment = { "ACCELERATE_USE_SAGEMAKER": "true", "ACCELERATE_MIXED_PRECISION": str(mixed_precision), "ACCELERATE_DYNAMO_BACKEND": dynamo_backend.value, "ACCELERATE_DYNAMO_MODE": args.dynamo_mode, "ACCELERATE_DYNAMO_USE_FULLGRAPH": str(args.dynamo_use_fullgraph), "ACCELERATE_DYNAMO_USE_DYNAMIC": str(args.dynamo_use_dynamic), "ACCELERATE_SAGEMAKER_DISTRIBUTED_TYPE": sagemaker_config.distributed_type.value, } # configure distribution set up distribution = None if sagemaker_config.distributed_type == SageMakerDistributedType.DATA_PARALLEL: distribution = {"smdistributed": {"dataparallel": {"enabled": True}}} # configure sagemaker inputs sagemaker_inputs = None if sagemaker_config.sagemaker_inputs_file is not None: print(f"Loading SageMaker Inputs from {sagemaker_config.sagemaker_inputs_file} file") sagemaker_inputs = {} with open(sagemaker_config.sagemaker_inputs_file) as file: for i, line in enumerate(file): if i == 0: continue l = line.split("\t") sagemaker_inputs[l[0]] = l[1].strip() print(f"Loaded SageMaker Inputs: {sagemaker_inputs}") # configure sagemaker metrics sagemaker_metrics = None if sagemaker_config.sagemaker_metrics_file is not None: print(f"Loading SageMaker Metrics from {sagemaker_config.sagemaker_metrics_file} file") sagemaker_metrics = [] with open(sagemaker_config.sagemaker_metrics_file) as file: for i, line in enumerate(file): if i == 0: continue l = line.split("\t") metric_dict = { "Name": l[0], "Regex": l[1].strip(), } sagemaker_metrics.append(metric_dict) print(f"Loaded SageMaker Metrics: {sagemaker_metrics}") # configure session print("Creating Estimator") args = { "image_uri": sagemaker_config.image_uri, "entry_point": entry_point, "source_dir": source_dir, "role": sagemaker_config.iam_role_name, "transformers_version": sagemaker_config.transformers_version, "pytorch_version": sagemaker_config.pytorch_version, "py_version": sagemaker_config.py_version, "base_job_name": sagemaker_config.base_job_name, "instance_count": sagemaker_config.num_machines, "instance_type": sagemaker_config.ec2_instance_type, "debugger_hook_config": False, "distribution": distribution, "hyperparameters": hyperparameters, "environment": environment, "metric_definitions": sagemaker_metrics, } if sagemaker_config.additional_args is not None: args = merge_dicts(sagemaker_config.additional_args, args) return args, sagemaker_inputs def env_var_path_add(env_var_name, path_to_add): """ Extends a path-based environment variable's value with a new path and returns the updated value. It's up to the caller to set it in os.environ. """ paths = [p for p in os.environ.get(env_var_name, "").split(":") if len(p) > 0] paths.append(str(path_to_add)) return ":".join(paths) class PrepareForLaunch: """ Prepare a function that will launched in a distributed setup. Args: launcher (`Callable`): The function to launch. distributed_type ([`~state.DistributedType`]): The distributed type to prepare for. debug (`bool`, *optional*, defaults to `False`): Whether or not this is a debug launch. """ def __init__(self, launcher, distributed_type="NO", debug=False): self.launcher = launcher self.distributed_type = DistributedType(distributed_type) self.debug = debug def __call__(self, index, *args): if self.debug: world_size = int(os.environ.get("WORLD_SIZE")) rdv_file = os.environ.get("ACCELERATE_DEBUG_RDV_FILE") torch.distributed.init_process_group( "gloo", rank=index, store=torch.distributed.FileStore(rdv_file, world_size), world_size=world_size, ) elif self.distributed_type in ( DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, DistributedType.MULTI_CPU, ): # Prepare the environment for torch.distributed os.environ["LOCAL_RANK"] = str(index) nproc = int(os.environ.get("NPROC", 1)) node_rank = int(os.environ.get("NODE_RANK", 0)) os.environ["RANK"] = str(nproc * node_rank + index) os.environ["FORK_LAUNCHED"] = str(1) self.launcher(*args)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/tpu.py

#!/usr/bin/env python # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import subprocess from packaging.version import Version, parse from accelerate.commands.config.config_args import default_config_file, load_config_from_file _description = "Run commands across TPU VMs for initial setup before running `accelerate launch`." def tpu_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("tpu-config", description=_description) else: parser = argparse.ArgumentParser("Accelerate tpu-config command", description=_description) # Core arguments config_args = parser.add_argument_group( "Config Arguments", "Arguments that can be configured through `accelerate config`." ) config_args.add_argument( "--config_file", type=str, default=None, help="Path to the config file to use for accelerate.", ) config_args.add_argument( "--tpu_name", default=None, help="The name of the TPU to use. If not specified, will use the TPU specified in the config file.", ) config_args.add_argument( "--tpu_zone", default=None, help="The zone of the TPU to use. If not specified, will use the zone specified in the config file.", ) pod_args = parser.add_argument_group("TPU Arguments", "Arguments for options ran inside the TPU.") pod_args.add_argument( "--use_alpha", action="store_true", help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.", ) pod_args.add_argument( "--command_file", default=None, help="The path to the file containing the commands to run on the pod on startup.", ) pod_args.add_argument( "--command", action="append", nargs="+", help="A command to run on the pod. Can be passed multiple times.", ) pod_args.add_argument( "--install_accelerate", action="store_true", help="Whether to install accelerate on the pod. Defaults to False.", ) pod_args.add_argument( "--accelerate_version", default="latest", help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.", ) pod_args.add_argument( "--debug", action="store_true", help="If set, will print the command that would be run instead of running it." ) if subparsers is not None: parser.set_defaults(func=tpu_command_launcher) return parser def tpu_command_launcher(args): defaults = None # Get the default from the config file if it exists. if args.config_file is not None or os.path.isfile(default_config_file): defaults = load_config_from_file(args.config_file) if not args.command_file and defaults.command_file is not None and not args.command: args.command_file = defaults.command_file if not args.command and defaults.commands is not None: args.command = defaults.commands if not args.tpu_name: args.tpu_name = defaults.tpu_name if not args.tpu_zone: args.tpu_zone = defaults.tpu_zone if args.accelerate_version == "dev": args.accelerate_version = "git+https://github.com/huggingface/accelerate.git" elif args.accelerate_version == "latest": args.accelerate_version = "accelerate -U" elif isinstance(parse(args.accelerate_version), Version): args.accelerate_version = f"accelerate=={args.accelerate_version}" if not args.command_file and not args.command: raise ValueError("You must specify either a command file or a command to run on the pod.") if args.command_file: with open(args.command_file, "r") as f: args.command = [f.read().splitlines()] # To turn list of lists into list of strings if isinstance(args.command[0], list): args.command = [line for cmd in args.command for line in cmd] # Default to the shared folder and install accelerate new_cmd = ["cd /usr/share"] if args.install_accelerate: new_cmd += [f"pip install {args.accelerate_version}"] new_cmd += args.command args.command = "; ".join(new_cmd) # Then send it to gcloud # Eventually try to use google-api-core to do this instead of subprocess cmd = ["gcloud"] if args.use_alpha: cmd += ["alpha"] cmd += [ "compute", "tpus", "tpu-vm", "ssh", args.tpu_name, "--zone", args.tpu_zone, "--command", args.command, "--worker", "all", ] if args.debug: print(f"Running {' '.join(cmd)}") return subprocess.run(cmd) print("Successfully setup pod.") def main(): parser = tpu_command_parser() args = parser.parse_args() tpu_command_launcher(args)

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/test.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os from accelerate.test_utils import execute_subprocess_async def test_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("test") else: parser = argparse.ArgumentParser("Accelerate test command") parser.add_argument( "--config_file", default=None, help=( "The path to use to store the config file. Will default to a file named default_config.yaml in the cache " "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " "with 'huggingface'." ), ) if subparsers is not None: parser.set_defaults(func=test_command) return parser def test_command(args): script_name = os.path.sep.join(__file__.split(os.path.sep)[:-2] + ["test_utils", "scripts", "test_script.py"]) if args.config_file is None: test_args = script_name else: test_args = f"--config_file={args.config_file} {script_name}" cmd = ["accelerate-launch"] + test_args.split() result = execute_subprocess_async(cmd, env=os.environ.copy()) if result.returncode == 0: print("Test is a success! You are ready for your distributed training!") def main(): parser = test_command_parser() args = parser.parse_args() test_command(args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/estimate.py

#!/usr/bin/env python # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse from huggingface_hub import model_info from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError from accelerate import init_empty_weights from accelerate.utils import ( calculate_maximum_sizes, convert_bytes, is_timm_available, is_transformers_available, ) if is_transformers_available(): import transformers from transformers import AutoConfig, AutoModel if is_timm_available(): import timm def verify_on_hub(repo: str, token: str = None): "Verifies that the model is on the hub and returns the model info." try: return model_info(repo, token=token) except GatedRepoError: return "gated" except RepositoryNotFoundError: return "repo" def check_has_model(error): """ Checks what library spawned `error` when a model is not found """ if is_timm_available() and isinstance(error, RuntimeError) and "Unknown model" in error.args[0]: return "timm" elif ( is_transformers_available() and isinstance(error, OSError) and "does not appear to have a file named" in error.args[0] ): return "transformers" else: return "unknown" def create_empty_model(model_name: str, library_name: str, trust_remote_code: bool = False, access_token: str = None): """ Creates an empty model from its parent library on the `Hub` to calculate the overall memory consumption. Args: model_name (`str`): The model name on the Hub library_name (`str`): The library the model has an integration with, such as `transformers`. Will be used if `model_name` has no metadata on the Hub to determine the library. trust_remote_code (`bool`, `optional`, defaults to `False`): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. access_token (`str`, `optional`, defaults to `None`): The access token to use to access private or gated models on the Hub. (for use on the Gradio app) Returns: `torch.nn.Module`: The torch model that has been initialized on the `meta` device. """ model_info = verify_on_hub(model_name, access_token) # Simplified errors if model_info == "gated": raise GatedRepoError( f"Repo for model `{model_name}` is gated. You must be authenticated to access it. Please run `huggingface-cli login`." ) elif model_info == "repo": raise RepositoryNotFoundError( f"Repo for model `{model_name}` does not exist on the Hub. If you are trying to access a private repo," " make sure you are authenticated via `huggingface-cli login` and have access." ) if library_name is None: library_name = getattr(model_info, "library_name", False) if not library_name: raise ValueError( f"Model `{model_name}` does not have any library metadata on the Hub, please manually pass in a `--library_name` to use (such as `transformers`)" ) if library_name == "transformers": if not is_transformers_available(): raise ImportError( f"To check `{model_name}`, `transformers` must be installed. Please install it via `pip install transformers`" ) print(f"Loading pretrained config for `{model_name}` from `transformers`...") auto_map = model_info.config.get("auto_map", False) config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code, token=access_token) with init_empty_weights(): # remote code could specify a specific `AutoModel` class in the `auto_map` constructor = AutoModel if isinstance(auto_map, dict): value = None for key in auto_map.keys(): if key.startswith("AutoModelFor"): value = key break if value is not None: constructor = getattr(transformers, value) model = constructor.from_config(config, trust_remote_code=trust_remote_code) elif library_name == "timm": if not is_timm_available(): raise ImportError( f"To check `{model_name}`, `timm` must be installed. Please install it via `pip install timm`" ) print(f"Loading pretrained config for `{model_name}` from `timm`...") with init_empty_weights(): model = timm.create_model(model_name, pretrained=False) else: raise ValueError( f"Library `{library_name}` is not supported yet, please open an issue on GitHub for us to add support." ) return model def create_ascii_table(headers: list, rows: list, title: str): "Creates a pretty table from a list of rows, minimal version of `tabulate`." sep_char, in_between = "│", "─" column_widths = [] for i in range(len(headers)): column_values = [row[i] for row in rows] + [headers[i]] max_column_width = max(len(value) for value in column_values) column_widths.append(max_column_width) formats = [f"%{column_widths[i]}s" for i in range(len(rows[0]))] pattern = f"{sep_char}{sep_char.join(formats)}{sep_char}" diff = 0 def make_row(left_char, middle_char, right_char): return f"{left_char}{middle_char.join([in_between * n for n in column_widths])}{in_between * diff}{right_char}" separator = make_row("├", "┼", "┤") if len(title) > sum(column_widths): diff = abs(len(title) - len(separator)) column_widths[-1] += diff # Update with diff separator = make_row("├", "┼", "┤") initial_rows = [ make_row("┌", in_between, "┐"), f"{sep_char}{title.center(len(separator) - 2)}{sep_char}", make_row("├", "┬", "┤"), ] table = "\n".join(initial_rows) + "\n" column_widths[-1] += diff centered_line = [text.center(column_widths[i]) for i, text in enumerate(headers)] table += f"{pattern % tuple(centered_line)}\n{separator}\n" for i, line in enumerate(rows): centered_line = [t.center(column_widths[i]) for i, t in enumerate(line)] table += f"{pattern % tuple(centered_line)}\n" table += f'└{"┴".join([in_between * n for n in column_widths])}┘' return table def estimate_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("estimate-memory") else: parser = argparse.ArgumentParser(description="Model size estimator for fitting a model onto CUDA memory.") parser.add_argument("model_name", type=str, help="The model name on the Hugging Face Hub.") parser.add_argument( "--library_name", type=str, help="The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub.", choices=["timm", "transformers"], ) parser.add_argument( "--dtypes", type=str, nargs="+", default=["float32", "float16", "int8", "int4"], help="The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4`", choices=["float32", "float16", "int8", "int4"], ) parser.add_argument( "--trust_remote_code", action="store_true", help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. This flag should only be used for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.""", ) if subparsers is not None: parser.set_defaults(func=estimate_command) return parser def gather_data(args): "Creates an empty model and gathers the data for the sizes" try: model = create_empty_model( args.model_name, library_name=args.library_name, trust_remote_code=args.trust_remote_code ) except (RuntimeError, OSError) as e: library = check_has_model(e) if library != "unknown": raise RuntimeError( f"Tried to load `{args.model_name}` with `{library}` but a possible model to load was not found inside the repo." ) raise e total_size, largest_layer = calculate_maximum_sizes(model) data = [] for dtype in args.dtypes: dtype_total_size = total_size dtype_largest_layer = largest_layer[0] if dtype == "float16": dtype_total_size /= 2 dtype_largest_layer /= 2 elif dtype == "int8": dtype_total_size /= 4 dtype_largest_layer /= 4 elif dtype == "int4": dtype_total_size /= 8 dtype_largest_layer /= 8 dtype_training_size = dtype_total_size * 4 data.append([dtype, dtype_largest_layer, dtype_total_size, dtype_training_size]) return data def estimate_command(args): data = gather_data(args) for row in data: for i, item in enumerate(row): if isinstance(item, (int, float)): row[i] = convert_bytes(item) headers = ["dtype", "Largest Layer", "Total Size", "Training using Adam"] title = f"Memory Usage for loading `{args.model_name}`" table = create_ascii_table(headers, data, title) print(table) def main(): parser = estimate_command_parser() args = parser.parse_args() estimate_command(args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/env.py

#!/usr/bin/env python # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import platform import numpy as np import psutil import torch from accelerate import __version__ as version from accelerate.commands.config import default_config_file, load_config_from_file from ..utils import is_npu_available, is_xpu_available def env_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("env") else: parser = argparse.ArgumentParser("Accelerate env command") parser.add_argument( "--config_file", default=None, help="The config file to use for the default values in the launching script." ) if subparsers is not None: parser.set_defaults(func=env_command) return parser def env_command(args): pt_version = torch.__version__ pt_cuda_available = torch.cuda.is_available() pt_xpu_available = is_xpu_available() pt_npu_available = is_npu_available() accelerate_config = "Not found" # Get the default from the config file. if args.config_file is not None or os.path.isfile(default_config_file): accelerate_config = load_config_from_file(args.config_file).to_dict() info = { "`Accelerate` version": version, "Platform": platform.platform(), "Python version": platform.python_version(), "Numpy version": np.__version__, "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})", "PyTorch XPU available": str(pt_xpu_available), "PyTorch NPU available": str(pt_npu_available), "System RAM": f"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB", } if pt_cuda_available: info["GPU type"] = torch.cuda.get_device_name() print("\nCopy-and-paste the text below in your GitHub issue\n") print("\n".join([f"- {prop}: {val}" for prop, val in info.items()])) print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:") accelerate_config_str = ( "\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()]) if isinstance(accelerate_config, dict) else f"\t{accelerate_config}" ) print(accelerate_config_str) info["`Accelerate` configs"] = accelerate_config return info def main() -> int: parser = env_command_parser() args = parser.parse_args() env_command(args) return 0 if __name__ == "__main__": raise SystemExit(main())

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/accelerate_cli.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from accelerate.commands.config import get_config_parser from accelerate.commands.env import env_command_parser from accelerate.commands.estimate import estimate_command_parser from accelerate.commands.launch import launch_command_parser from accelerate.commands.test import test_command_parser from accelerate.commands.tpu import tpu_command_parser def main(): parser = ArgumentParser("Accelerate CLI tool", usage="accelerate <command> [<args>]", allow_abbrev=False) subparsers = parser.add_subparsers(help="accelerate command helpers") # Register commands get_config_parser(subparsers=subparsers) estimate_command_parser(subparsers=subparsers) env_command_parser(subparsers=subparsers) launch_command_parser(subparsers=subparsers) tpu_command_parser(subparsers=subparsers) test_command_parser(subparsers=subparsers) # Let's go args = parser.parse_args() if not hasattr(args, "func"): parser.print_help() exit(1) # Run args.func(args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate

hf_public_repos/accelerate/src/accelerate/commands/launch.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import importlib import logging import os import subprocess import sys from pathlib import Path import psutil import torch from accelerate.commands.config import default_config_file, load_config_from_file from accelerate.commands.config.config_args import SageMakerConfig from accelerate.commands.config.config_utils import DYNAMO_BACKENDS from accelerate.state import get_int_from_env from accelerate.utils import ( ComputeEnvironment, DistributedType, PrepareForLaunch, _filter_args, check_cuda_p2p_ib_support, is_bf16_available, is_deepspeed_available, is_npu_available, is_rich_available, is_sagemaker_available, is_torch_version, is_tpu_available, is_xpu_available, patch_environment, prepare_deepspeed_cmd_env, prepare_multi_gpu_env, prepare_sagemager_args_inputs, prepare_simple_launcher_cmd_env, prepare_tpu, ) from accelerate.utils.constants import DEEPSPEED_MULTINODE_LAUNCHERS, TORCH_DYNAMO_MODES if is_rich_available(): from rich import get_console from rich.logging import RichHandler FORMAT = "%(message)s" logging.basicConfig(format=FORMAT, datefmt="[%X]", handlers=[RichHandler()]) logger = logging.getLogger(__name__) options_to_group = { "--multi-gpu": "Distributed GPUs", "--tpu": "TPU", "--use_deepspeed": "DeepSpeed Arguments", "--use_fsdp": "FSDP Arguments", "--use_megatron_lm": "Megatron-LM Arguments", } def clean_option(option): "Finds all cases of - after the first two characters and changes them to _" if option.startswith("--"): return option[:3] + option[3:].replace("-", "_") class _CustomHelpAction(argparse._HelpAction): """ This is a custom help action that will hide all arguments that are not used in the command line when the help is called. This is useful for the case where the user is using a specific platform and only wants to see the arguments for that platform. """ def __call__(self, parser, namespace, values, option_string=None): if "accelerate" in sys.argv[0] and "launch" in sys.argv[1:]: args = sys.argv[2:] else: args = sys.argv[1:] opts = parser._actions titles = [ "Hardware Selection Arguments", "Resource Selection Arguments", "Training Paradigm Arguments", "positional arguments", "optional arguments", ] if len(args) > 1: used_platforms = [arg for arg in args if arg in options_to_group.keys()] args = list(map(clean_option, args)) used_titles = [options_to_group[o] for o in used_platforms] for i, arg in enumerate(opts): # If the argument's container is outside of the used titles, hide it if arg.container.title not in titles + used_titles: setattr(opts[i], "help", argparse.SUPPRESS) # If the argument is hardware selection, but not being passed, hide it elif arg.container.title == "Hardware Selection Arguments": if set(arg.option_strings).isdisjoint(set(args)): setattr(opts[i], "help", argparse.SUPPRESS) else: setattr(opts[i], "help", arg.help + " (currently selected)") # If the argument is a training paradigm, but not being passed, hide it elif arg.container.title == "Training Paradigm Arguments": if set(arg.option_strings).isdisjoint(set(used_platforms)): setattr(opts[i], "help", argparse.SUPPRESS) else: setattr(opts[i], "help", arg.help + " (currently selected)") for i, group in enumerate(list(parser._action_groups)): # If all arguments in the group are hidden, hide the group if all([arg.help == argparse.SUPPRESS for arg in group._group_actions]): parser._action_groups.remove(group) super().__call__(parser, namespace, values, option_string) def launch_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("launch", add_help=False, allow_abbrev=False) else: parser = argparse.ArgumentParser("Accelerate launch command", add_help=False, allow_abbrev=False) parser.register("action", "help", _CustomHelpAction) parser.add_argument("-h", "--help", action="help", help="Show this help message and exit.") parser.add_argument( "--config_file", default=None, help="The config file to use for the default values in the launching script." ) parser.add_argument( "--quiet", "-q", action="store_true", help="Silence subprocess errors from the launch stack trace and only show the relevant tracebacks. (Only applicable to DeepSpeed and single-process configurations)", ) # Hardware selection arguments hardware_args = parser.add_argument_group( "Hardware Selection Arguments", "Arguments for selecting the hardware to be used." ) hardware_args.add_argument( "--cpu", default=False, action="store_true", help="Whether or not to force the training on the CPU." ) hardware_args.add_argument( "--multi_gpu", default=False, action="store_true", help="Whether or not this should launch a distributed GPU training.", ) hardware_args.add_argument( "--tpu", default=False, action="store_true", help="Whether or not this should launch a TPU training." ) hardware_args.add_argument( "--ipex", default=False, action="store_true", help="Whether or not this should launch a Intel PyTorch Extension (IPEX) training.", ) # Resource selection arguments resource_args = parser.add_argument_group( "Resource Selection Arguments", "Arguments for fine-tuning how available hardware should be used." ) resource_args.add_argument( "--mixed_precision", type=str, choices=["no", "fp16", "bf16", "fp8"], help="Whether or not to use mixed precision training. " "Choose between FP16 and BF16 (bfloat16) training. " "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", ) resource_args.add_argument( "--num_processes", type=int, default=None, help="The total number of processes to be launched in parallel." ) resource_args.add_argument( "--num_machines", type=int, default=None, help="The total number of machines used in this training." ) resource_args.add_argument( "--num_cpu_threads_per_process", type=int, default=None, help="The number of CPU threads per process. Can be tuned for optimal performance.", ) # Dynamo arguments resource_args.add_argument( "--dynamo_backend", type=str, choices=["no"] + [b.lower() for b in DYNAMO_BACKENDS], help="Choose a backend to optimize your training with dynamo, see more at " "https://github.com/pytorch/torchdynamo.", ) resource_args.add_argument( "--dynamo_mode", type=str, default="default", choices=TORCH_DYNAMO_MODES, help="Choose a mode to optimize your training with dynamo.", ) resource_args.add_argument( "--dynamo_use_fullgraph", default=False, action="store_true", help="Whether to use full graph mode for dynamo or it is ok to break model into several subgraphs", ) resource_args.add_argument( "--dynamo_use_dynamic", default=False, action="store_true", help="Whether to enable dynamic shape tracing.", ) # Training Paradigm arguments paradigm_args = parser.add_argument_group( "Training Paradigm Arguments", "Arguments for selecting which training paradigm to be used." ) paradigm_args.add_argument( "--use_deepspeed", default=False, action="store_true", help="Whether to use deepspeed.", ) paradigm_args.add_argument( "--use_fsdp", default=False, action="store_true", help="Whether to use fsdp.", ) paradigm_args.add_argument( "--use_megatron_lm", default=False, action="store_true", help="Whether to use Megatron-LM.", ) paradigm_args.add_argument( "--use_xpu", default=False, action="store_true", help="Whether to use IPEX plugin to speed up training on XPU specifically.", ) # distributed GPU training arguments distributed_args = parser.add_argument_group("Distributed GPUs", "Arguments related to distributed GPU training.") distributed_args.add_argument( "--gpu_ids", default=None, help="What GPUs (by id) should be used for training on this machine as a comma-seperated list", ) distributed_args.add_argument( "--same_network", default=False, action="store_true", help="Whether all machines used for multinode training exist on the same local network.", ) distributed_args.add_argument( "--machine_rank", type=int, default=None, help="The rank of the machine on which this script is launched." ) distributed_args.add_argument( "--main_process_ip", type=str, default=None, help="The IP address of the machine of rank 0." ) distributed_args.add_argument( "--main_process_port", type=int, default=None, help="The port to use to communicate with the machine of rank 0.", ) distributed_args.add_argument( "-t", "--tee", default="0", type=str, help="Tee std streams into a log file and also to console.", ) distributed_args.add_argument( "--role", type=str, default="default", help="User-defined role for the workers.", ) # Rendezvous related arguments distributed_args.add_argument( "--rdzv_backend", type=str, default="static", help="The rendezvous method to use, such as 'static' (the default) or 'c10d'", ) distributed_args.add_argument( "--rdzv_conf", type=str, default="", help="Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...).", ) distributed_args.add_argument( "--max_restarts", type=int, default=0, help="Maximum number of worker group restarts before failing.", ) distributed_args.add_argument( "--monitor_interval", type=float, default=5, help="Interval, in seconds, to monitor the state of workers.", ) parser.add_argument( "-m", "--module", action="store_true", help="Change each process to interpret the launch script as a Python module, executing with the same behavior as 'python -m'.", ) parser.add_argument( "--no_python", action="store_true", help="Skip prepending the training script with 'python' - just execute it directly. Useful when the script is not a Python script.", ) # TPU arguments tpu_args = parser.add_argument_group("TPU", "Arguments related to TPU.") tpu_args.add_argument( "--tpu_cluster", action="store_true", dest="tpu_use_cluster", help="Whether to use a GCP TPU pod for training.", ) tpu_args.add_argument( "--no_tpu_cluster", action="store_false", dest="tpu_use_cluster", help="Should not be passed explicitly, this is for internal use only.", ) tpu_args.add_argument( "--tpu_use_sudo", action="store_true", help="Whether to use `sudo` when running the TPU training script in each pod.", ) tpu_args.add_argument( "--vm", type=str, action="append", help=( "List of single Compute VM instance names. " "If not provided we assume usage of instance groups. For TPU pods." ), ) tpu_args.add_argument( "--env", type=str, action="append", help="List of environment variables to set on the Compute VM instances. For TPU pods.", ) tpu_args.add_argument( "--main_training_function", type=str, default=None, help="The name of the main function to be executed in your script (only for TPU training).", ) tpu_args.add_argument( "--downcast_bf16", action="store_true", help="Whether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32.", ) # DeepSpeed arguments deepspeed_args = parser.add_argument_group("DeepSpeed Arguments", "Arguments related to DeepSpeed.") deepspeed_args.add_argument( "--deepspeed_config_file", default=None, type=str, help="DeepSpeed config file.", ) deepspeed_args.add_argument( "--zero_stage", default=None, type=int, help="DeepSpeed's ZeRO optimization stage (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to `2`.", ) deepspeed_args.add_argument( "--offload_optimizer_device", default=None, type=str, help="Decides where (none|cpu|nvme) to offload optimizer states (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to 'none'.", ) deepspeed_args.add_argument( "--offload_param_device", default=None, type=str, help="Decides where (none|cpu|nvme) to offload parameters (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to 'none'.", ) deepspeed_args.add_argument( "--offload_optimizer_nvme_path", default=None, type=str, help="Decides Nvme Path to offload optimizer states (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to 'none'.", ) deepspeed_args.add_argument( "--offload_param_nvme_path", default=None, type=str, help="Decides Nvme Path to offload parameters (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to 'none'.", ) deepspeed_args.add_argument( "--gradient_accumulation_steps", default=None, type=int, help="No of gradient_accumulation_steps used in your training script (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to `1`.", ) deepspeed_args.add_argument( "--gradient_clipping", default=None, type=float, help="gradient clipping value used in your training script (useful only when `use_deepspeed` flag is passed). " "If unspecified, will default to `1.0`.", ) deepspeed_args.add_argument( "--zero3_init_flag", default=None, type=str, help="Decides Whether (true|false) to enable `deepspeed.zero.Init` for constructing massive models. " "Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `true`.", ) deepspeed_args.add_argument( "--zero3_save_16bit_model", default=None, type=str, help="Decides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. " "Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `false`.", ) deepspeed_args.add_argument( "--deepspeed_hostfile", default=None, type=str, help="DeepSpeed hostfile for configuring multi-node compute resources.", ) deepspeed_args.add_argument( "--deepspeed_exclusion_filter", default=None, type=str, help="DeepSpeed exclusion filter string when using mutli-node setup.", ) deepspeed_args.add_argument( "--deepspeed_inclusion_filter", default=None, type=str, help="DeepSpeed inclusion filter string when using mutli-node setup.", ) deepspeed_args.add_argument( "--deepspeed_multinode_launcher", default=None, type=str, help="DeepSpeed multi-node launcher to use. If unspecified, will default to `pdsh`.", ) # fsdp arguments fsdp_args = parser.add_argument_group("FSDP Arguments", "Arguments related to Fully Shared Data Parallelism.") fsdp_args.add_argument( "--fsdp_offload_params", default="false", type=str, help="Decides Whether (true|false) to offload parameters and gradients to CPU. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_min_num_params", type=int, default=1e8, help="FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_sharding_strategy", type=str, default="FULL_SHARD", help="FSDP's Sharding Strategy. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_auto_wrap_policy", type=str, default=None, help="FSDP's auto wrap policy. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_transformer_layer_cls_to_wrap", default=None, type=str, help="Transformer layer class name (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... " "(useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_backward_prefetch_policy", default=None, type=str, help="This argument is deprecated and will be removed in version 0.27.0 of 🤗 Accelerate. Use `fsdp_backward_prefetch` instead.", ) fsdp_args.add_argument( "--fsdp_backward_prefetch", default=None, type=str, help="FSDP's backward prefetch policy. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_state_dict_type", default=None, type=str, help="FSDP's state dict type. (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_forward_prefetch", default="false", type=str, help="If True, then FSDP explicitly prefetches the next upcoming " "all-gather while executing in the forward pass (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_use_orig_params", default="true", type=str, help="If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres." " (useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_cpu_ram_efficient_loading", default="true", type=str, help="If True, only the first process loads the pretrained model checkoint while all other processes have empty weights. " "Only applicable for 🤗 Transformers. When using this, `--fsdp_sync_module_states` needs to True. " "(useful only when `use_fsdp` flag is passed).", ) fsdp_args.add_argument( "--fsdp_sync_module_states", default="true", type=str, help="If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0." " (useful only when `use_fsdp` flag is passed).", ) # megatron_lm args megatron_lm_args = parser.add_argument_group("Megatron-LM Arguments", "Arguments related to Megatron-LM.") megatron_lm_args.add_argument( "--megatron_lm_tp_degree", type=int, default=1, help="Megatron-LM's Tensor Parallelism (TP) degree. (useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_pp_degree", type=int, default=1, help="Megatron-LM's Pipeline Parallelism (PP) degree. (useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_num_micro_batches", type=int, default=None, help="Megatron-LM's number of micro batches when PP degree > 1. (useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_sequence_parallelism", default=None, type=str, help="Decides Whether (true|false) to enable Sequence Parallelism when TP degree > 1. " "(useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_recompute_activations", default=None, type=str, help="Decides Whether (true|false) to enable Selective Activation Recomputation. " "(useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_use_distributed_optimizer", default=None, type=str, help="Decides Whether (true|false) to use distributed optimizer " "which shards optimizer state and gradients across Data Pralellel (DP) ranks. " "(useful only when `use_megatron_lm` flag is passed).", ) megatron_lm_args.add_argument( "--megatron_lm_gradient_clipping", default=1.0, type=float, help="Megatron-LM's gradient clipping value based on global L2 Norm (0 to disable). " "(useful only when `use_megatron_lm` flag is passed).", ) # AWS arguments aws_args = parser.add_argument_group("AWS Arguments", "Arguments related to AWS.") aws_args.add_argument( "--aws_access_key_id", type=str, default=None, help="The AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training job", ) aws_args.add_argument( "--aws_secret_access_key", type=str, default=None, help="The AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training job.", ) parser.add_argument( "--debug", action="store_true", help="Whether to print out the torch.distributed stack trace when something fails.", ) parser.add_argument( "training_script", type=str, help=( "The full path to the script to be launched in parallel, followed by all the arguments for the training " "script." ), ) # Other arguments of the training scripts parser.add_argument("training_script_args", nargs=argparse.REMAINDER, help="Arguments of the training script.") if subparsers is not None: parser.set_defaults(func=launch_command) return parser def simple_launcher(args): cmd, current_env = prepare_simple_launcher_cmd_env(args) process = subprocess.Popen(cmd, env=current_env) process.wait() if process.returncode != 0: if not args.quiet: raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) else: sys.exit(1) def multi_gpu_launcher(args): import torch.distributed.run as distrib_run current_env = prepare_multi_gpu_env(args) if not check_cuda_p2p_ib_support(): message = "Using RTX 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled." warn = False if "NCCL_P2P_DISABLE" not in current_env: current_env["NCCL_P2P_DISABLE"] = "1" warn = True if "NCCL_IB_DISABLE" not in current_env: current_env["NCCL_IB_DISABLE"] = "1" warn = True if warn: logger.warning(message) debug = getattr(args, "debug", False) args = _filter_args( args, distrib_run.get_args_parser(), ["--training_script", args.training_script, "--training_script_args", args.training_script_args], ) with patch_environment(**current_env): try: distrib_run.run(args) except Exception: if is_rich_available() and debug: console = get_console() console.print("\n[bold red]Using --debug, `torch.distributed` Stack Trace:[/bold red]") console.print_exception(suppress=[__file__], show_locals=False) else: raise def deepspeed_launcher(args): import torch.distributed.run as distrib_run if not is_deepspeed_available(): raise ImportError("DeepSpeed is not installed => run `pip3 install deepspeed` or build it from source.") cmd, current_env = prepare_deepspeed_cmd_env(args) if not check_cuda_p2p_ib_support(): message = "Using RTX 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled." warn = False if "NCCL_P2P_DISABLE" not in current_env: current_env["NCCL_P2P_DISABLE"] = "1" warn = True if "NCCL_IB_DISABLE" not in current_env: current_env["NCCL_IB_DISABLE"] = "1" warn = True if warn: logger.warning(message) if args.num_machines > 1 and args.deepspeed_multinode_launcher != DEEPSPEED_MULTINODE_LAUNCHERS[1]: with open(".deepspeed_env", "a") as f: for key, value in current_env.items(): if ";" in value or " " in value: continue f.write(f"{key}={value}\n") process = subprocess.Popen(cmd, env=current_env) process.wait() if process.returncode != 0: if not args.quiet: raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) else: sys.exit(1) else: debug = getattr(args, "debug", False) args = _filter_args( args, distrib_run.get_args_parser(), ["--training_script", args.training_script, "--training_script_args", args.training_script_args], ) with patch_environment(**current_env): try: distrib_run.run(args) except Exception: if is_rich_available() and debug: console = get_console() console.print("\n[bold red]Using --debug, `torch.distributed` Stack Trace:[/bold red]") console.print_exception(suppress=[__file__], show_locals=False) else: raise def tpu_launcher(args): import torch_xla.distributed.xla_multiprocessing as xmp if args.no_python: raise ValueError("--no_python cannot be used with TPU launcher") args, current_env = prepare_tpu(args, {}) if args.module: mod_name = args.training_script else: # Import training_script as a module script_path = Path(args.training_script) sys.path.append(str(script_path.parent.resolve())) mod_name = script_path.stem mod = importlib.import_module(mod_name) if not hasattr(mod, args.main_training_function): raise ValueError( f"Your training script should have a function named {args.main_training_function}, or you should pass a " "different value to `--main_training_function`." ) # Patch sys.argv sys.argv = [mod.__file__] + args.training_script_args main_function = getattr(mod, args.main_training_function) with patch_environment(**current_env): xmp.spawn(PrepareForLaunch(main_function), args=(), nprocs=args.num_processes) def tpu_pod_launcher(args): from torch_xla.distributed import xla_dist current_env = {} args, current_env = prepare_tpu(args, current_env, True) debug = getattr(args, "debug", False) training_script = args.training_script training_script_args = args.training_script_args new_args = _filter_args( args, xla_dist.get_args_parser(), ["--tpu", args.tpu_name, "--positional", "", "--restart-tpuvm-pod-server"] ) if args.tpu_use_sudo: new_cmd = ["sudo"] else: new_cmd = [] new_cmd += [ "accelerate-launch", "--tpu", "--no_tpu_cluster", "--num_machines", "1", "--mixed_precision", "no", "--dynamo_backend", "no", "--num_processes", str(args.num_processes), "--main_training_function", str(args.main_training_function), training_script, ] + training_script_args new_args.positional = new_cmd bad_flags = "" for arg in vars(new_args): if arg.startswith("docker_"): value = getattr(new_args, arg) if value != "" and value is not None: bad_flags += f'{arg}="{value}"\n' if bad_flags != "": raise ValueError( f"Docker containers are not supported for TPU pod launcher currently, please remove the following flags:\n{bad_flags}" ) new_args.env = [f"{k}={v}" for k, v in current_env.items()] new_args.env.append("ACCELERATE_IN_TPU_POD=1") try: xla_dist.resolve_and_execute(new_args) except Exception: if is_rich_available() and debug: console = get_console() console.print("\n[bold red]Using --debug, `torch_xla.xla_dist` Stack Trace:[/bold red]") console.print_exception(suppress=[__file__], show_locals=False) else: raise def sagemaker_launcher(sagemaker_config: SageMakerConfig, args): if not is_sagemaker_available(): raise ImportError( "Please install sagemaker to be able to launch training on Amazon SageMaker with `pip install accelerate[sagemaker]`" ) if args.module or args.no_python: raise ValueError( "SageMaker requires a python training script file and cannot be used with --module or --no_python" ) from sagemaker.huggingface import HuggingFace args, sagemaker_inputs = prepare_sagemager_args_inputs(sagemaker_config, args) huggingface_estimator = HuggingFace(**args) huggingface_estimator.fit(inputs=sagemaker_inputs) print(f"You can find your model data at: {huggingface_estimator.model_data}") def _validate_launch_command(args): # Sanity checks if sum([args.multi_gpu, args.cpu, args.tpu, args.use_deepspeed, args.use_fsdp]) > 1: raise ValueError( "You can only use one of `--cpu`, `--multi_gpu`, `--tpu`, `--use_deepspeed`, `--use_fsdp` at a time." ) if args.multi_gpu and (args.num_processes is not None) and (args.num_processes < 2): raise ValueError("You need to use at least 2 processes to use `--multi_gpu`.") defaults = None warned = [] mp_from_config_flag = False # Get the default from the config file. if args.config_file is not None or os.path.isfile(default_config_file) and not args.cpu: defaults = load_config_from_file(args.config_file) if ( not args.multi_gpu and not args.tpu and not args.tpu_use_cluster and not args.use_deepspeed and not args.use_fsdp and not args.use_megatron_lm ): args.use_deepspeed = defaults.distributed_type == DistributedType.DEEPSPEED args.multi_gpu = ( True if defaults.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU) else False ) args.tpu = defaults.distributed_type == DistributedType.TPU args.use_fsdp = defaults.distributed_type == DistributedType.FSDP args.use_megatron_lm = defaults.distributed_type == DistributedType.MEGATRON_LM args.tpu_use_cluster = defaults.tpu_use_cluster if args.tpu else False if args.gpu_ids is None: if defaults.gpu_ids is not None: args.gpu_ids = defaults.gpu_ids else: args.gpu_ids = "all" if args.multi_gpu and args.num_machines is None: args.num_machines = defaults.num_machines if len(args.gpu_ids.split(",")) < 2 and (args.gpu_ids != "all") and args.multi_gpu and args.num_machines <= 1: raise ValueError( "Less than two GPU ids were configured and tried to run on on multiple GPUs. " "Please ensure at least two are specified for `--gpu_ids`, or use `--gpu_ids='all'`." ) if defaults.compute_environment == ComputeEnvironment.LOCAL_MACHINE: # Update args with the defaults for name, attr in defaults.__dict__.items(): if isinstance(attr, dict): for k in defaults.deepspeed_config: setattr(args, k, defaults.deepspeed_config[k]) for k in defaults.fsdp_config: arg_to_set = k if "fsdp" not in arg_to_set: arg_to_set = "fsdp_" + arg_to_set setattr(args, arg_to_set, defaults.fsdp_config[k]) for k in defaults.megatron_lm_config: setattr(args, k, defaults.megatron_lm_config[k]) for k in defaults.dynamo_config: setattr(args, k, defaults.dynamo_config[k]) for k in defaults.ipex_config: setattr(args, k, defaults.ipex_config[k]) continue # Those args are handled separately if ( name not in ["compute_environment", "mixed_precision", "distributed_type"] and getattr(args, name, None) is None ): setattr(args, name, attr) if not args.debug: args.debug = defaults.debug if not args.mixed_precision: if defaults.mixed_precision is None: args.mixed_precision = "no" else: args.mixed_precision = defaults.mixed_precision mp_from_config_flag = True else: native_amp = False err = "{mode} mixed precision requires {requirement}" if args.use_cpu or (args.use_xpu and torch.xpu.is_available()): native_amp = is_torch_version(">=", "1.10") else: native_amp = is_bf16_available(True) if args.mixed_precision == "bf16" and not native_amp and not (args.tpu and is_tpu_available()): raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device.")) # Silently set the default here if args.dynamo_backend is None: args.dynamo_backend = "no" else: if args.num_processes is None: if args.use_xpu and is_xpu_available(): args.num_processes = torch.xpu.device_count() elif is_npu_available(): args.num_processes = torch.npu.device_count() else: args.num_processes = torch.cuda.device_count() warned.append(f"\t`--num_processes` was set to a value of `{args.num_processes}`") if args.debug is None: args.debug = False if not args.multi_gpu and ( (args.use_xpu and is_xpu_available() and torch.xpu.device_count() > 1) or (is_npu_available() and torch.npu.device_count() > 1) or (torch.cuda.device_count() > 1) ): warned.append( "\t\tMore than one GPU was found, enabling multi-GPU training.\n" "\t\tIf this was unintended please pass in `--num_processes=1`." ) args.multi_gpu = True if args.num_machines is None: warned.append("\t`--num_machines` was set to a value of `1`") args.num_machines = 1 if args.mixed_precision is None: warned.append("\t`--mixed_precision` was set to a value of `'no'`") args.mixed_precision = "no" if not hasattr(args, "use_cpu"): args.use_cpu = args.cpu if args.dynamo_backend is None: warned.append("\t`--dynamo_backend` was set to a value of `'no'`") args.dynamo_backend = "no" if args.debug: logger.debug("Running script in debug mode, expect distributed operations to be slightly slower.") is_aws_env_disabled = defaults is None or ( defaults is not None and defaults.compute_environment != ComputeEnvironment.AMAZON_SAGEMAKER ) if is_aws_env_disabled and args.num_cpu_threads_per_process is None: args.num_cpu_threads_per_process = 1 if args.use_cpu and args.num_processes >= 1: local_size = get_int_from_env( ["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1 ) threads_per_process = int(psutil.cpu_count(logical=False) / local_size) if threads_per_process > 1: args.num_cpu_threads_per_process = threads_per_process warned.append( f"\t`--num_cpu_threads_per_process` was set to `{args.num_cpu_threads_per_process}` to improve out-of-box performance when training on CPUs" ) if any(warned): message = "The following values were not passed to `accelerate launch` and had defaults used instead:\n" message += "\n".join(warned) message += ( "\nTo avoid this warning pass in values for each of the problematic parameters or run `accelerate config`." ) logger.warning(message) return args, defaults, mp_from_config_flag def launch_command(args): args, defaults, mp_from_config_flag = _validate_launch_command(args) # Use the proper launcher if args.use_deepspeed and not args.cpu: args.deepspeed_fields_from_accelerate_config = list(defaults.deepspeed_config.keys()) if defaults else [] if mp_from_config_flag: args.deepspeed_fields_from_accelerate_config.append("mixed_precision") args.deepspeed_fields_from_accelerate_config = ",".join(args.deepspeed_fields_from_accelerate_config) deepspeed_launcher(args) elif args.use_fsdp and not args.cpu: multi_gpu_launcher(args) elif args.use_megatron_lm and not args.cpu: multi_gpu_launcher(args) elif args.multi_gpu and not args.cpu: multi_gpu_launcher(args) elif args.tpu and not args.cpu: if args.tpu_use_cluster: tpu_pod_launcher(args) else: tpu_launcher(args) elif defaults is not None and defaults.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER: sagemaker_launcher(defaults, args) else: simple_launcher(args) def main(): parser = launch_command_parser() args = parser.parse_args() launch_command(args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/update.py

#!/usr/bin/env python # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path from .config_args import default_config_file, load_config_from_file from .config_utils import SubcommandHelpFormatter description = "Update an existing config file with the latest defaults while maintaining the old configuration." def update_config(args): """ Update an existing config file with the latest defaults while maintaining the old configuration. """ config_file = args.config_file if config_file is None and Path(default_config_file).exists(): config_file = default_config_file elif not Path(config_file).exists(): raise ValueError(f"The passed config file located at {config_file} doesn't exist.") config = load_config_from_file(config_file) if config_file.endswith(".json"): config.to_json_file(config_file) else: config.to_yaml_file(config_file) return config_file def update_command_parser(parser, parents): parser = parser.add_parser("update", parents=parents, help=description, formatter_class=SubcommandHelpFormatter) parser.add_argument( "--config_file", default=None, help=( "The path to the config file to update. Will default to a file named default_config.yaml in the cache " "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " "with 'huggingface'." ), ) parser.set_defaults(func=update_config_command) return parser def update_config_command(args): config_file = update_config(args) print(f"Sucessfully updated the configuration file at {config_file}.")

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/config_args.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import yaml from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION hf_cache_home = os.path.expanduser( os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface")) ) cache_dir = os.path.join(hf_cache_home, "accelerate") default_json_config_file = os.path.join(cache_dir, "default_config.yaml") default_yaml_config_file = os.path.join(cache_dir, "default_config.yaml") # For backward compatibility: the default config is the json one if it's the only existing file. if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file): default_config_file = default_yaml_config_file else: default_config_file = default_json_config_file def load_config_from_file(config_file): if config_file is not None: if not os.path.isfile(config_file): raise FileNotFoundError( f"The passed configuration file `{config_file}` does not exist. " "Please pass an existing file to `accelerate launch`, or use the the default one " "created through `accelerate config` and run `accelerate launch` " "without the `--config_file` argument." ) else: config_file = default_config_file with open(config_file, "r", encoding="utf-8") as f: if config_file.endswith(".json"): if ( json.load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE) == ComputeEnvironment.LOCAL_MACHINE ): config_class = ClusterConfig else: config_class = SageMakerConfig return config_class.from_json_file(json_file=config_file) else: if ( yaml.safe_load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE) == ComputeEnvironment.LOCAL_MACHINE ): config_class = ClusterConfig else: config_class = SageMakerConfig return config_class.from_yaml_file(yaml_file=config_file) @dataclass class BaseConfig: compute_environment: ComputeEnvironment distributed_type: Union[DistributedType, SageMakerDistributedType] mixed_precision: str use_cpu: bool debug: bool def to_dict(self): result = self.__dict__ # For serialization, it's best to convert Enums to strings (or their underlying value type). for key, value in result.items(): if isinstance(value, Enum): result[key] = value.value if isinstance(value, dict) and not bool(value): result[key] = None result = {k: v for k, v in result.items() if v is not None} return result @classmethod def from_json_file(cls, json_file=None): json_file = default_json_config_file if json_file is None else json_file with open(json_file, "r", encoding="utf-8") as f: config_dict = json.load(f) if "compute_environment" not in config_dict: config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE if "mixed_precision" not in config_dict: config_dict["mixed_precision"] = "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None if "fp16" in config_dict: # Convert the config to the new format. del config_dict["fp16"] if "dynamo_backend" in config_dict: # Convert the config to the new format. dynamo_backend = config_dict.pop("dynamo_backend") config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend} if "use_cpu" not in config_dict: config_dict["use_cpu"] = False if "debug" not in config_dict: config_dict["debug"] = False extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys())) if len(extra_keys) > 0: raise ValueError( f"The config file at {json_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`" " version or fix (and potentially remove) these keys from your config file." ) return cls(**config_dict) def to_json_file(self, json_file): with open(json_file, "w", encoding="utf-8") as f: content = json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" f.write(content) @classmethod def from_yaml_file(cls, yaml_file=None): yaml_file = default_yaml_config_file if yaml_file is None else yaml_file with open(yaml_file, "r", encoding="utf-8") as f: config_dict = yaml.safe_load(f) if "compute_environment" not in config_dict: config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE if "mixed_precision" not in config_dict: config_dict["mixed_precision"] = "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None if isinstance(config_dict["mixed_precision"], bool) and not config_dict["mixed_precision"]: config_dict["mixed_precision"] = "no" if "fp16" in config_dict: # Convert the config to the new format. del config_dict["fp16"] if "dynamo_backend" in config_dict: # Convert the config to the new format. dynamo_backend = config_dict.pop("dynamo_backend") config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend} if "use_cpu" not in config_dict: config_dict["use_cpu"] = False if "debug" not in config_dict: config_dict["debug"] = False extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys())) if len(extra_keys) > 0: raise ValueError( f"The config file at {yaml_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`" " version or fix (and potentially remove) these keys from your config file." ) return cls(**config_dict) def to_yaml_file(self, yaml_file): with open(yaml_file, "w", encoding="utf-8") as f: yaml.safe_dump(self.to_dict(), f) def __post_init__(self): if isinstance(self.compute_environment, str): self.compute_environment = ComputeEnvironment(self.compute_environment) if isinstance(self.distributed_type, str): if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER: self.distributed_type = SageMakerDistributedType(self.distributed_type) else: self.distributed_type = DistributedType(self.distributed_type) if self.dynamo_config is None: self.dynamo_config = {} @dataclass class ClusterConfig(BaseConfig): num_processes: int machine_rank: int = 0 num_machines: int = 1 gpu_ids: Optional[str] = None main_process_ip: Optional[str] = None main_process_port: Optional[int] = None rdzv_backend: Optional[str] = "static" same_network: Optional[bool] = False main_training_function: str = "main" # args for deepspeed_plugin deepspeed_config: dict = None # args for fsdp fsdp_config: dict = None # args for megatron_lm megatron_lm_config: dict = None # args for ipex ipex_config: dict = None # args for TPU downcast_bf16: bool = False # args for TPU pods tpu_name: str = None tpu_zone: str = None tpu_use_cluster: bool = False tpu_use_sudo: bool = False command_file: str = None commands: List[str] = None tpu_vm: List[str] = None tpu_env: List[str] = None # args for dynamo dynamo_config: dict = None def __post_init__(self): if self.deepspeed_config is None: self.deepspeed_config = {} if self.fsdp_config is None: self.fsdp_config = {} if self.megatron_lm_config is None: self.megatron_lm_config = {} if self.ipex_config is None: self.ipex_config = {} return super().__post_init__() @dataclass class SageMakerConfig(BaseConfig): ec2_instance_type: str iam_role_name: str image_uri: Optional[str] = None profile: Optional[str] = None region: str = "us-east-1" num_machines: int = 1 gpu_ids: str = "all" base_job_name: str = f"accelerate-sagemaker-{num_machines}" pytorch_version: str = SAGEMAKER_PYTORCH_VERSION transformers_version: str = SAGEMAKER_TRANSFORMERS_VERSION py_version: str = SAGEMAKER_PYTHON_VERSION sagemaker_inputs_file: str = None sagemaker_metrics_file: str = None additional_args: dict = None dynamo_config: dict = None

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hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/cluster.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from ...utils import ( ComputeEnvironment, DistributedType, is_deepspeed_available, is_mps_available, is_npu_available, is_transformers_available, is_xpu_available, ) from ...utils.constants import ( DEEPSPEED_MULTINODE_LAUNCHERS, FSDP_AUTO_WRAP_POLICY, FSDP_BACKWARD_PREFETCH, FSDP_SHARDING_STRATEGY, FSDP_STATE_DICT_TYPE, TORCH_DYNAMO_MODES, ) from .config_args import ClusterConfig from .config_utils import ( DYNAMO_BACKENDS, _ask_field, _ask_options, _convert_distributed_mode, _convert_dynamo_backend, _convert_mixed_precision, _convert_yes_no_to_bool, ) def get_cluster_input(): distributed_type = _ask_options( "Which type of machine are you using?", ["No distributed training", "multi-CPU", "multi-XPU", "multi-GPU", "multi-NPU", "TPU"], _convert_distributed_mode, ) machine_rank = 0 num_machines = 1 num_processes = 1 gpu_ids = None main_process_ip = None main_process_port = None rdzv_backend = "static" same_network = True debug = False if distributed_type in [ DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, DistributedType.MULTI_CPU, ]: num_machines = _ask_field( "How many different machines will you use (use more than 1 for multi-node training)? [1]: ", int, default=1, ) if num_machines > 1: machine_rank = _ask_options( "What is the rank of this machine?", list(range(num_machines)), int, ) main_process_ip = _ask_field( "What is the IP address of the machine that will host the main process? ", ) main_process_port = _ask_field( "What is the port you will use to communicate with the main process? ", int, ) same_network = _ask_field( "Are all the machines on the same local network? Answer `no` if nodes are on the cloud and/or on different network hosts [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) if not same_network: rdzv_backend = _ask_field( "What rendezvous backend will you use? ('static', 'c10d', ...): ", default="static" ) debug = _ask_field( "Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if distributed_type == DistributedType.NO: use_cpu = _ask_field( "Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)? [yes/NO]:", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) elif distributed_type == DistributedType.MULTI_CPU: use_cpu = True else: use_cpu = False ipex_config = {} if use_cpu: ipex_config["ipex"] = _ask_field( "Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if ( not use_cpu and is_xpu_available() and distributed_type not in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.TPU] ): ipex_config["use_xpu"] = _ask_field( "Do you want to use XPU plugin to speed up training on XPU? [yes/NO]:", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) dynamo_config = {} use_dynamo = _ask_field( "Do you wish to optimize your script with torch dynamo?[yes/NO]:", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_dynamo: prefix = "dynamo_" dynamo_config[prefix + "backend"] = _ask_options( "Which dynamo backend would you like to use?", [x.lower() for x in DYNAMO_BACKENDS], _convert_dynamo_backend, default=2, ) use_custom_options = _ask_field( "Do you want to customize the defaults sent to torch.compile? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_custom_options: dynamo_config[prefix + "mode"] = _ask_options( "Which mode do you want to use?", TORCH_DYNAMO_MODES, lambda x: TORCH_DYNAMO_MODES[int(x)], default=0, ) dynamo_config[prefix + "use_fullgraph"] = _ask_field( "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) dynamo_config[prefix + "use_dynamic"] = _ask_field( "Do you want to enable dynamic shape tracing? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) use_mps = not use_cpu and is_mps_available() deepspeed_config = {} if distributed_type in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.NO] and not use_mps: use_deepspeed = _ask_field( "Do you want to use DeepSpeed? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_deepspeed: distributed_type = DistributedType.DEEPSPEED assert ( is_deepspeed_available() ), "DeepSpeed is not installed => run `pip3 install deepspeed` or build it from source" if distributed_type == DistributedType.DEEPSPEED: use_deepspeed_config = _ask_field( "Do you want to specify a json file to a DeepSpeed config? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_deepspeed_config: deepspeed_config["deepspeed_config_file"] = _ask_field( "Please enter the path to the json DeepSpeed config file: ", str, default="none", ) else: deepspeed_config["zero_stage"] = _ask_options( "What should be your DeepSpeed's ZeRO optimization stage?", [0, 1, 2, 3], int, default=2, ) deepspeed_devices = ["none", "cpu", "nvme"] if deepspeed_config["zero_stage"] >= 2: deepspeed_config["offload_optimizer_device"] = _ask_options( "Where to offload optimizer states?", deepspeed_devices, lambda x: deepspeed_devices[int(x)] ) deepspeed_config["offload_param_device"] = _ask_options( "Where to offload parameters?", deepspeed_devices, lambda x: deepspeed_devices[int(x)] ) if deepspeed_config["offload_param_device"] == "nvme": deepspeed_config["offload_param_nvme_path"] = _ask_field( "Nvme Path to offload parameters?", str, default="/nvme", ) if deepspeed_config["offload_optimizer_device"] == "nvme": deepspeed_config["offload_optimizer_nvme_path"] = _ask_field( "Nvme Path to offload optimizer states?", str, default="/nvme", ) deepspeed_config["gradient_accumulation_steps"] = _ask_field( "How many gradient accumulation steps you're passing in your script? [1]: ", int, default=1, ) use_gradient_clipping = _ask_field( "Do you want to use gradient clipping? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_gradient_clipping: deepspeed_config["gradient_clipping"] = _ask_field( "What is the gradient clipping value? [1.0]: ", float, default=1.0, ) if deepspeed_config["zero_stage"] == 3: deepspeed_config["zero3_save_16bit_model"] = _ask_field( "Do you want to save 16-bit model weights when using ZeRO Stage-3? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) deepspeed_config["zero3_init_flag"] = _ask_field( "Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if deepspeed_config["zero3_init_flag"]: if not is_transformers_available(): raise Exception( "When `zero3_init_flag` is set, it requires Transformers to be installed. " "Please run `pip3 install transformers`." ) if num_machines > 1: launcher_query = "Which Type of launcher do you want to use?" deepspeed_config["deepspeed_multinode_launcher"] = _ask_options( launcher_query, DEEPSPEED_MULTINODE_LAUNCHERS, lambda x: DEEPSPEED_MULTINODE_LAUNCHERS[int(x)], ) if deepspeed_config["deepspeed_multinode_launcher"] != DEEPSPEED_MULTINODE_LAUNCHERS[1]: deepspeed_config["deepspeed_hostfile"] = _ask_field( "DeepSpeed configures multi-node compute resources with hostfile. " "Each row is of the format `hostname slots=[num_gpus]`, e.g., `localhost slots=2`; " "for more information please refer official [documentation]" "(https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node). " "Please specify the location of hostfile: ", str, ) is_exclusion_filter = _ask_field( "Do you want to specify exclusion filter string? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if is_exclusion_filter: deepspeed_config["deepspeed_exclusion_filter"] = _ask_field( "DeepSpeed exclusion filter string: ", str, ) is_inclusion_filter = _ask_field( "Do you want to specify inclusion filter string? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if is_inclusion_filter: deepspeed_config["deepspeed_inclusion_filter"] = _ask_field( "DeepSpeed inclusion filter string: ", str, ) fsdp_config = {} if distributed_type in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU]: use_fsdp = _ask_field( "Do you want to use FullyShardedDataParallel? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_fsdp: distributed_type = DistributedType.FSDP if distributed_type == DistributedType.FSDP: sharding_strategy_query = "What should be your sharding strategy?" fsdp_config["fsdp_sharding_strategy"] = _ask_options( sharding_strategy_query, FSDP_SHARDING_STRATEGY, lambda x: FSDP_SHARDING_STRATEGY[int(x)], ) fsdp_config["fsdp_offload_params"] = _ask_field( "Do you want to offload parameters and gradients to CPU? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) fsdp_wrap_query = "What should be your auto wrap policy?" fsdp_config["fsdp_auto_wrap_policy"] = _ask_options( fsdp_wrap_query, FSDP_AUTO_WRAP_POLICY, lambda x: FSDP_AUTO_WRAP_POLICY[int(x)], ) if fsdp_config["fsdp_auto_wrap_policy"] == FSDP_AUTO_WRAP_POLICY[0]: use_no_split_modules = _ask_field( "Do you want to use the model's `_no_split_modules` to wrap. Only applicable for 🤗 Transformers [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if not use_no_split_modules: fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = _ask_field( "Specify the comma-separated list of transformer layer class names (case-sensitive) to wrap ,e.g, :" "`BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput` ...? : ", str, ) elif fsdp_config["fsdp_auto_wrap_policy"] == FSDP_AUTO_WRAP_POLICY[1]: fsdp_config["fsdp_min_num_params"] = _ask_field( "What should be your FSDP's minimum number of parameters for Default Auto Wrapping Policy? [1e8]: ", int, default=100000000, ) fsdp_backward_prefetch_query = "What should be your FSDP's backward prefetch policy?" fsdp_config["fsdp_backward_prefetch"] = _ask_options( fsdp_backward_prefetch_query, FSDP_BACKWARD_PREFETCH, lambda x: FSDP_BACKWARD_PREFETCH[int(x)], ) fsdp_state_dict_type_query = "What should be your FSDP's state dict type?" fsdp_config["fsdp_state_dict_type"] = _ask_options( fsdp_state_dict_type_query, FSDP_STATE_DICT_TYPE, lambda x: FSDP_STATE_DICT_TYPE[int(x)], default=2, ) fsdp_config["fsdp_forward_prefetch"] = _ask_field( "Do you want to enable FSDP's forward prefetch policy? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) fsdp_config["fsdp_use_orig_params"] = _ask_field( "Do you want to enable FSDP's `use_orig_params` feature? [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) fsdp_config["fsdp_cpu_ram_efficient_loading"] = _ask_field( "Do you want to enable CPU RAM efficient model loading? Only applicable for 🤗 Transformers models. [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) if fsdp_config["fsdp_cpu_ram_efficient_loading"]: fsdp_config["fsdp_sync_module_states"] = True else: fsdp_config["fsdp_sync_module_states"] = _ask_field( "Do you want each individually wrapped FSDP unit to broadcast module parameters from rank 0 at the start? [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) megatron_lm_config = {} if distributed_type in [DistributedType.MULTI_GPU]: use_megatron_lm = _ask_field( "Do you want to use Megatron-LM ? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_megatron_lm: distributed_type = DistributedType.MEGATRON_LM if distributed_type == DistributedType.MEGATRON_LM: prefix = "megatron_lm_" megatron_lm_config[prefix + "tp_degree"] = _ask_field( "What is the Tensor Parallelism degree/size? [1]:", int, default=1, error_message="Please enter an integer.", ) if megatron_lm_config[prefix + "tp_degree"] > 1: megatron_lm_config[prefix + "sequence_parallelism"] = _ask_field( "Do you want to enable Sequence Parallelism? [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) megatron_lm_config[prefix + "pp_degree"] = _ask_field( "What is the Pipeline Parallelism degree/size? [1]:", int, default=1, error_message="Please enter an integer.", ) if megatron_lm_config[prefix + "pp_degree"] > 1: megatron_lm_config[prefix + "num_micro_batches"] = _ask_field( "What is the number of micro-batches? [1]:", int, default=1, error_message="Please enter an integer.", ) megatron_lm_config[prefix + "recompute_activations"] = _ask_field( "Do you want to enable selective activation recomputation? [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) megatron_lm_config[prefix + "use_distributed_optimizer"] = _ask_field( "Do you want to use distributed optimizer " "which shards optimizer state and gradients across data parallel ranks? [YES/no]: ", _convert_yes_no_to_bool, default=True, error_message="Please enter yes or no.", ) megatron_lm_config[prefix + "gradient_clipping"] = _ask_field( "What is the gradient clipping value based on global L2 Norm (0 to disable)? [1.0]: ", float, default=1.0, ) # TPU specific defaults tpu_commands = None tpu_command_file = None tpu_downcast_bf16 = "no" tpu_env = [] tpu_name = None tpu_vm = None tpu_zone = None tpu_use_sudo = False tpu_use_cluster = False if distributed_type in [ DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.TPU, ]: machine_type = str(distributed_type).split(".")[1].replace("MULTI_", "") if machine_type == "TPU": machine_type += " cores" else: machine_type += "(s)" num_processes = _ask_field( f"How many {machine_type} should be used for distributed training? [1]:", int, default=1, error_message="Please enter an integer.", ) elif distributed_type in [DistributedType.FSDP, DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM]: num_processes = _ask_field( "How many GPU(s) should be used for distributed training? [1]:", int, default=1, error_message="Please enter an integer.", ) else: num_processes = 1 if (distributed_type == DistributedType.MULTI_GPU) and (num_machines == 1) and (num_processes == 1): raise ValueError( f"Specified distributed type {distributed_type} but only using 1 GPU on a single machine. Please select `No distributed training` for the type of machine you are using." ) if ( distributed_type in [ DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU, DistributedType.NO, ] and not use_cpu and not use_mps ): if is_npu_available(): machine_type = "NPU(s)" else: machine_type = "GPU(s)" gpu_ids = _ask_field( f"What {machine_type} (by id) should be used for training on this machine as a comma-seperated list? [all]:", default="all", ) if distributed_type == DistributedType.TPU: mixed_precision = "no" main_training_function = _ask_field( "What is the name of the function in your script that should be launched in all parallel scripts? [main]: ", default="main", ) tpu_use_cluster = _ask_field( "Are you using a TPU cluster? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if tpu_use_cluster: tpu_name = _ask_field( "What is the name of your TPU cluster? ", default=None, error_message="Please enter the name of your TPU cluster.", ) tpu_zone = _ask_field( "What is the zone of your TPU cluster? ", default=None, error_message="Please enter the zone of your TPU cluster.", ) tpu_use_sudo = _ask_field( "To run a python script in a TPU pod, should `sudo` be used? [yes/NO]: ", default=False, error_message="Please enter yes or no.", ) run_commands = _ask_field( "Do you have code you wish to run on startup in each pod? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if run_commands: use_command_file = _ask_field( "Is this code located in a bash script? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_command_file: tpu_command_file = _ask_field( "What is the path to your bash script? ", default=None, error_message="Please enter the path to your bash script.", ) tpu_command_file = os.path.abspath(tpu_command_file) else: print("Please enter each command seperately you wish to run on startup in each pod.") tpu_commands = [] another_command = True while another_command: tpu_commands.append( _ask_field( "Please enter a single command to be ran ", default=None, error_message="Please enter the commands you wish to run on startup in each pod as a single string.", ) ) another_command = _ask_field( "Do you wish to add another command? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) tpu_vm = _ask_field( "If not using an instance group, what are the names of the Compute VM instances to be used, seperated by a comma: ", default="", ).split(",") tpu_env = _ask_field( "What environment variables do you wish to set in each pod, seperated by a comma: ", default="", ).split(",") else: main_training_function = "main" if distributed_type == DistributedType.DEEPSPEED and use_deepspeed_config: mixed_precision = None else: mixed_precision = _ask_options( "Do you wish to use FP16 or BF16 (mixed precision)?", ["no", "fp16", "bf16", "fp8"], _convert_mixed_precision, ) if use_dynamo and mixed_precision == "no" and not use_cpu: print( "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." ) if distributed_type == DistributedType.TPU and mixed_precision == "bf16": tpu_downcast_bf16 = _ask_field( "Should `torch.float` be cast as `bfloat16` and `torch.double` remain `float32` on TPUs?", default="no" ) return ClusterConfig( compute_environment=ComputeEnvironment.LOCAL_MACHINE, distributed_type=distributed_type, num_processes=num_processes, gpu_ids=gpu_ids, mixed_precision=mixed_precision, downcast_bf16=tpu_downcast_bf16, machine_rank=machine_rank, num_machines=num_machines, main_process_ip=main_process_ip, main_process_port=main_process_port, main_training_function=main_training_function, deepspeed_config=deepspeed_config, fsdp_config=fsdp_config, megatron_lm_config=megatron_lm_config, ipex_config=ipex_config, use_cpu=use_cpu, rdzv_backend=rdzv_backend, same_network=same_network, commands=tpu_commands, command_file=tpu_command_file, tpu_env=tpu_env, tpu_name=tpu_name, tpu_vm=tpu_vm, tpu_zone=tpu_zone, tpu_use_sudo=tpu_use_sudo, tpu_use_cluster=tpu_use_cluster, dynamo_config=dynamo_config, debug=debug, )

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/config.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os from accelerate.utils import ComputeEnvironment from .cluster import get_cluster_input from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401 from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401 from .sagemaker import get_sagemaker_input description = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine" def get_user_input(): compute_environment = _ask_options( "In which compute environment are you running?", ["This machine", "AWS (Amazon SageMaker)"], _convert_compute_environment, ) if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER: config = get_sagemaker_input() else: config = get_cluster_input() return config def config_command_parser(subparsers=None): if subparsers is not None: parser = subparsers.add_parser("config", description=description) else: parser = argparse.ArgumentParser("Accelerate config command", description=description) parser.add_argument( "--config_file", default=None, help=( "The path to use to store the config file. Will default to a file named default_config.yaml in the cache " "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " "with 'huggingface'." ), ) if subparsers is not None: parser.set_defaults(func=config_command) return parser def config_command(args): config = get_user_input() if args.config_file is not None: config_file = args.config_file else: if not os.path.isdir(cache_dir): os.makedirs(cache_dir) config_file = default_yaml_config_file if config_file.endswith(".json"): config.to_json_file(config_file) else: config.to_yaml_file(config_file) print(f"accelerate configuration saved at {config_file}") def main(): parser = config_command_parser() args = parser.parse_args() config_command(args) if __name__ == "__main__": main()

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hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/config_utils.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse from ...utils.dataclasses import ( ComputeEnvironment, DistributedType, DynamoBackend, PrecisionType, SageMakerDistributedType, ) from ..menu import BulletMenu DYNAMO_BACKENDS = [ "EAGER", "AOT_EAGER", "INDUCTOR", "AOT_TS_NVFUSER", "NVPRIMS_NVFUSER", "CUDAGRAPHS", "OFI", "FX2TRT", "ONNXRT", "TENSORRT", "IPEX", "TVM", ] def _ask_field(input_text, convert_value=None, default=None, error_message=None): ask_again = True while ask_again: result = input(input_text) try: if default is not None and len(result) == 0: return default return convert_value(result) if convert_value is not None else result except Exception: if error_message is not None: print(error_message) def _ask_options(input_text, options=[], convert_value=None, default=0): menu = BulletMenu(input_text, options) result = menu.run(default_choice=default) return convert_value(result) if convert_value is not None else result def _convert_compute_environment(value): value = int(value) return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]) def _convert_distributed_mode(value): value = int(value) return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]) def _convert_dynamo_backend(value): value = int(value) return DynamoBackend(DYNAMO_BACKENDS[value]).value def _convert_mixed_precision(value): value = int(value) return PrecisionType(["no", "fp16", "bf16", "fp8"][value]) def _convert_sagemaker_distributed_mode(value): value = int(value) return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]) def _convert_yes_no_to_bool(value): return {"yes": True, "no": False}[value.lower()] class SubcommandHelpFormatter(argparse.RawDescriptionHelpFormatter): """ A custom formatter that will remove the usage line from the help message for subcommands. """ def _format_usage(self, usage, actions, groups, prefix): usage = super()._format_usage(usage, actions, groups, prefix) usage = usage.replace("<command> [<args>] ", "") return usage

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/__init__.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse from .config import config_command_parser from .config_args import default_config_file, load_config_from_file # noqa: F401 from .default import default_command_parser from .update import update_command_parser def get_config_parser(subparsers=None): parent_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) # The main config parser config_parser = config_command_parser(subparsers) # The subparser to add commands to subcommands = config_parser.add_subparsers(title="subcommands", dest="subcommand") # Then add other parsers with the parent parser default_command_parser(subcommands, parents=[parent_parser]) update_command_parser(subcommands, parents=[parent_parser]) return config_parser def main(): config_parser = get_config_parser() args = config_parser.parse_args() if not hasattr(args, "func"): config_parser.print_help() exit(1) # Run args.func(args) if __name__ == "__main__": main()

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/default.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path import torch from ...utils import is_npu_available, is_xpu_available from .config_args import ClusterConfig, default_json_config_file from .config_utils import SubcommandHelpFormatter description = "Create a default config file for Accelerate with only a few flags set." def write_basic_config(mixed_precision="no", save_location: str = default_json_config_file, use_xpu: bool = False): """ Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also set CPU if it is a CPU-only machine. Args: mixed_precision (`str`, *optional*, defaults to "no"): Mixed Precision to use. Should be one of "no", "fp16", or "bf16" save_location (`str`, *optional*, defaults to `default_json_config_file`): Optional custom save location. Should be passed to `--config_file` when using `accelerate launch`. Default location is inside the huggingface cache folder (`~/.cache/huggingface`) but can be overriden by setting the `HF_HOME` environmental variable, followed by `accelerate/default_config.yaml`. use_xpu (`bool`, *optional*, defaults to `False`): Whether to use XPU if available. """ path = Path(save_location) path.parent.mkdir(parents=True, exist_ok=True) if path.exists(): print( f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`." ) return False mixed_precision = mixed_precision.lower() if mixed_precision not in ["no", "fp16", "bf16", "fp8"]: raise ValueError( f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}" ) config = { "compute_environment": "LOCAL_MACHINE", "mixed_precision": mixed_precision, } if torch.cuda.is_available(): num_gpus = torch.cuda.device_count() config["num_processes"] = num_gpus config["use_cpu"] = False if num_gpus > 1: config["distributed_type"] = "MULTI_GPU" else: config["distributed_type"] = "NO" elif is_xpu_available() and use_xpu: num_xpus = torch.xpu.device_count() config["num_processes"] = num_xpus config["use_cpu"] = False if num_xpus > 1: config["distributed_type"] = "MULTI_XPU" else: config["distributed_type"] = "NO" elif is_npu_available(): num_npus = torch.npu.device_count() config["num_processes"] = num_npus config["use_cpu"] = False if num_npus > 1: config["distributed_type"] = "MULTI_NPU" else: config["distributed_type"] = "NO" else: num_xpus = 0 config["use_cpu"] = True config["num_processes"] = 1 config["distributed_type"] = "NO" config["debug"] = False config = ClusterConfig(**config) config.to_json_file(path) return path def default_command_parser(parser, parents): parser = parser.add_parser("default", parents=parents, help=description, formatter_class=SubcommandHelpFormatter) parser.add_argument( "--config_file", default=default_json_config_file, help=( "The path to use to store the config file. Will default to a file named default_config.yaml in the cache " "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " "with 'huggingface'." ), dest="save_location", ) parser.add_argument( "--mixed_precision", choices=["no", "fp16", "bf16"], type=str, help="Whether or not to use mixed precision training. " "Choose between FP16 and BF16 (bfloat16) training. " "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", ) parser.set_defaults(func=default_config_command) return parser def default_config_command(args): config_file = write_basic_config(args.mixed_precision, args.save_location) if config_file: print(f"accelerate configuration saved at {config_file}")

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hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/config/sagemaker.py

#!/usr/bin/env python # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType from ...utils.imports import is_boto3_available from .config_args import SageMakerConfig from .config_utils import ( DYNAMO_BACKENDS, _ask_field, _ask_options, _convert_dynamo_backend, _convert_mixed_precision, _convert_sagemaker_distributed_mode, _convert_yes_no_to_bool, ) if is_boto3_available(): import boto3 # noqa: F401 def _create_iam_role_for_sagemaker(role_name): iam_client = boto3.client("iam") sagemaker_trust_policy = { "Version": "2012-10-17", "Statement": [ {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"} ], } try: # create the role, associated with the chosen trust policy iam_client.create_role( RoleName=role_name, AssumeRolePolicyDocument=json.dumps(sagemaker_trust_policy, indent=2) ) policy_document = { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "sagemaker:*", "ecr:GetDownloadUrlForLayer", "ecr:BatchGetImage", "ecr:BatchCheckLayerAvailability", "ecr:GetAuthorizationToken", "cloudwatch:PutMetricData", "cloudwatch:GetMetricData", "cloudwatch:GetMetricStatistics", "cloudwatch:ListMetrics", "logs:CreateLogGroup", "logs:CreateLogStream", "logs:DescribeLogStreams", "logs:PutLogEvents", "logs:GetLogEvents", "s3:CreateBucket", "s3:ListBucket", "s3:GetBucketLocation", "s3:GetObject", "s3:PutObject", ], "Resource": "*", } ], } # attach policy to role iam_client.put_role_policy( RoleName=role_name, PolicyName=f"{role_name}_policy_permission", PolicyDocument=json.dumps(policy_document, indent=2), ) except iam_client.exceptions.EntityAlreadyExistsException: print(f"role {role_name} already exists. Using existing one") def _get_iam_role_arn(role_name): iam_client = boto3.client("iam") return iam_client.get_role(RoleName=role_name)["Role"]["Arn"] def get_sagemaker_input(): credentials_configuration = _ask_options( "How do you want to authorize?", ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "], int, ) aws_profile = None if credentials_configuration == 0: aws_profile = _ask_field("Enter your AWS Profile name: [default] ", default="default") os.environ["AWS_PROFILE"] = aws_profile else: print( "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with," "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" ) aws_access_key_id = _ask_field("AWS Access Key ID: ") os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id aws_secret_access_key = _ask_field("AWS Secret Access Key: ") os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key aws_region = _ask_field("Enter your AWS Region: [us-east-1]", default="us-east-1") os.environ["AWS_DEFAULT_REGION"] = aws_region role_management = _ask_options( "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?", ["Provide IAM Role name", "Create new IAM role using credentials"], int, ) if role_management == 0: iam_role_name = _ask_field("Enter your IAM role name: ") else: iam_role_name = "accelerate_sagemaker_execution_role" print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials') _create_iam_role_for_sagemaker(iam_role_name) is_custom_docker_image = _ask_field( "Do you want to use custom Docker image? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) docker_image = None if is_custom_docker_image: docker_image = _ask_field("Enter your Docker image: ", lambda x: str(x).lower()) is_sagemaker_inputs_enabled = _ask_field( "Do you want to provide SageMaker input channels with data locations? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) sagemaker_inputs_file = None if is_sagemaker_inputs_enabled: sagemaker_inputs_file = _ask_field( "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ", lambda x: str(x).lower(), ) is_sagemaker_metrics_enabled = _ask_field( "Do you want to enable SageMaker metrics? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) sagemaker_metrics_file = None if is_sagemaker_metrics_enabled: sagemaker_metrics_file = _ask_field( "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ", lambda x: str(x).lower(), ) distributed_type = _ask_options( "What is the distributed mode?", ["No distributed training", "Data parallelism"], _convert_sagemaker_distributed_mode, ) dynamo_config = {} use_dynamo = _ask_field( "Do you wish to optimize your script with torch dynamo?[yes/NO]:", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_dynamo: prefix = "dynamo_" dynamo_config[prefix + "backend"] = _ask_options( "Which dynamo backend would you like to use?", [x.lower() for x in DYNAMO_BACKENDS], _convert_dynamo_backend, default=2, ) use_custom_options = _ask_field( "Do you want to customize the defaults sent to torch.compile? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) if use_custom_options: dynamo_config[prefix + "mode"] = _ask_options( "Which mode do you want to use?", TORCH_DYNAMO_MODES, lambda x: TORCH_DYNAMO_MODES[int(x)], default="default", ) dynamo_config[prefix + "use_fullgraph"] = _ask_field( "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) dynamo_config[prefix + "use_dynamic"] = _ask_field( "Do you want to enable dynamic shape tracing? [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) ec2_instance_query = "Which EC2 instance type you want to use for your training?" if distributed_type != SageMakerDistributedType.NO: ec2_instance_type = _ask_options( ec2_instance_query, SAGEMAKER_PARALLEL_EC2_INSTANCES, lambda x: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(x)] ) else: ec2_instance_query += "? [ml.p3.2xlarge]:" ec2_instance_type = _ask_field(ec2_instance_query, lambda x: str(x).lower(), default="ml.p3.2xlarge") debug = False if distributed_type != SageMakerDistributedType.NO: debug = _ask_field( "Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]: ", _convert_yes_no_to_bool, default=False, error_message="Please enter yes or no.", ) num_machines = 1 if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL): num_machines = _ask_field( "How many machines do you want use? [1]: ", int, default=1, ) mixed_precision = _ask_options( "Do you wish to use FP16 or BF16 (mixed precision)?", ["no", "fp16", "bf16", "fp8"], _convert_mixed_precision, ) if use_dynamo and mixed_precision == "no": print( "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." ) return SageMakerConfig( image_uri=docker_image, compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER, distributed_type=distributed_type, use_cpu=False, dynamo_config=dynamo_config, ec2_instance_type=ec2_instance_type, profile=aws_profile, region=aws_region, iam_role_name=iam_role_name, mixed_precision=mixed_precision, num_machines=num_machines, sagemaker_inputs_file=sagemaker_inputs_file, sagemaker_metrics_file=sagemaker_metrics_file, debug=debug, )

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/menu/helpers.py

# Copyright 2022 The HuggingFace Team and Brian Chao. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A variety of helper functions and constants when dealing with terminal menu choices, based on https://github.com/bchao1/bullet """ import enum import shutil import sys TERMINAL_WIDTH, _ = shutil.get_terminal_size() CURSOR_TO_CHAR = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"} class Direction(enum.Enum): UP = 0 DOWN = 1 def forceWrite(content, end=""): sys.stdout.write(str(content) + end) sys.stdout.flush() def writeColor(content, color, end=""): forceWrite(f"\u001b[{color}m{content}\u001b[0m", end) def reset_cursor(): forceWrite("\r") def move_cursor(num_lines: int, direction: str): forceWrite(f"\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}") def clear_line(): forceWrite(" " * TERMINAL_WIDTH) reset_cursor() def linebreak(): reset_cursor() forceWrite("-" * TERMINAL_WIDTH)

0

hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/menu/input.py

# Copyright 2022 The HuggingFace Team and Brian Chao. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This file contains utilities for handling input from the user and registering specific keys to specific functions, based on https://github.com/bchao1/bullet """ from typing import List from .keymap import KEYMAP, get_character def mark(key: str): """ Mark the function with the key code so it can be handled in the register """ def decorator(func): handle = getattr(func, "handle_key", []) handle += [key] setattr(func, "handle_key", handle) return func return decorator def mark_multiple(*keys: List[str]): """ Mark the function with the key codes so it can be handled in the register """ def decorator(func): handle = getattr(func, "handle_key", []) handle += keys setattr(func, "handle_key", handle) return func return decorator class KeyHandler(type): """ Metaclass that adds the key handlers to the class """ def __new__(cls, name, bases, attrs): new_cls = super().__new__(cls, name, bases, attrs) if not hasattr(new_cls, "key_handler"): setattr(new_cls, "key_handler", {}) setattr(new_cls, "handle_input", KeyHandler.handle_input) for value in attrs.values(): handled_keys = getattr(value, "handle_key", []) for key in handled_keys: new_cls.key_handler[key] = value return new_cls @staticmethod def handle_input(cls): "Finds and returns the selected character if it exists in the handler" char = get_character() if char != KEYMAP["undefined"]: char = ord(char) handler = cls.key_handler.get(char) if handler: cls.current_selection = char return handler(cls) else: return None def register(cls): """Adds KeyHandler metaclass to the class""" return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy())

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hf_public_repos/accelerate/src/accelerate/commands

hf_public_repos/accelerate/src/accelerate/commands/menu/selection_menu.py

# Copyright 2022 The HuggingFace Team and Brian Chao. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Main driver for the selection menu, based on https://github.com/bchao1/bullet """ import builtins import sys from ...utils.imports import _is_package_available from . import cursor, input from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor from .keymap import KEYMAP in_colab = False try: in_colab = _is_package_available("google.colab") except ModuleNotFoundError: pass @input.register class BulletMenu: """ A CLI menu to select a choice from a list of choices using the keyboard. """ def __init__(self, prompt: str = None, choices: list = []): self.position = 0 self.choices = choices self.prompt = prompt if sys.platform == "win32": self.arrow_char = "*" else: self.arrow_char = "➔ " def write_choice(self, index, end: str = ""): if sys.platform != "win32": writeColor(self.choices[index], 32, end) else: forceWrite(self.choices[index], end) def print_choice(self, index: int): "Prints the choice at the given index" if index == self.position: forceWrite(f" {self.arrow_char} ") self.write_choice(index) else: forceWrite(f" {self.choices[index]}") reset_cursor() def move_direction(self, direction: Direction, num_spaces: int = 1): "Should not be directly called, used to move a direction of either up or down" old_position = self.position if direction == Direction.DOWN: if self.position + 1 >= len(self.choices): return self.position += num_spaces else: if self.position - 1 < 0: return self.position -= num_spaces clear_line() self.print_choice(old_position) move_cursor(num_spaces, direction.name) self.print_choice(self.position) @input.mark(KEYMAP["up"]) def move_up(self): self.move_direction(Direction.UP) @input.mark(KEYMAP["down"]) def move_down(self): self.move_direction(Direction.DOWN) @input.mark(KEYMAP["newline"]) def select(self): move_cursor(len(self.choices) - self.position, "DOWN") return self.position @input.mark(KEYMAP["interrupt"]) def interrupt(self): move_cursor(len(self.choices) - self.position, "DOWN") raise KeyboardInterrupt @input.mark_multiple(*[KEYMAP[str(number)] for number in range(10)]) def select_row(self): index = int(chr(self.current_selection)) movement = index - self.position if index == self.position: return if index < len(self.choices): if self.position > index: self.move_direction(Direction.UP, -movement) elif self.position < index: self.move_direction(Direction.DOWN, movement) else: return else: return def run(self, default_choice: int = 0): "Start the menu and return the selected choice" if self.prompt: linebreak() forceWrite(self.prompt, "\n") if in_colab: forceWrite("Please input a choice index (starting from 0), and press enter", "\n") else: forceWrite("Please select a choice using the arrow or number keys, and selecting with enter", "\n") self.position = default_choice for i in range(len(self.choices)): self.print_choice(i) forceWrite("\n") move_cursor(len(self.choices) - self.position, "UP") with cursor.hide(): while True: if in_colab: try: choice = int(builtins.input()) except ValueError: choice = default_choice else: choice = self.handle_input() if choice is not None: reset_cursor() for _ in range(len(self.choices) + 1): move_cursor(1, "UP") clear_line() self.write_choice(choice, "\n") return choice

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