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"@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "_hOkqzsWvjDS" + }, + "outputs": [], + "source": [ + "!pip install ai-edge-litert" + ] + }, + { + "cell_type": "code", + "source": [ + "!huggingface-cli login" + ], + "metadata": { + "id": "G6MuXzv1wBM_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import hf_hub_download\n", + "\n", + "model_path = hf_hub_download(\n", + " repo_id=\"litert-community/SmolVLM-256M-Instruct\",\n", + " filename=\"smalvlm-256m-instruct_q8_ekv2048.tflite\",\n", + ")" + ], + "metadata": { + "id": "gadxhc94v2S8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from typing import Dict\n", + "from ai_edge_litert import interpreter as interpreter_lib\n", + "import numpy as np\n", + "import sys\n", + "from collections.abc import Sequence\n", + "from transformers import AutoProcessor\n", + "from PIL import Image\n", + "import requests\n", + "\n", + "\n", + "import torch\n", + "from transformers import AutoModelForVision2Seq\n", + "\n", + "\n", + "def _get_mask(shape: Sequence[int], k: int):\n", + " \"\"\"Gets the mask for the input to the model.\n", + "\n", + " Args:\n", + " shape: The shape of the mask input to the model.\n", + " k: all elements below the k-th diagonal are set to 0.\n", + "\n", + " Returns:\n", + " The mask for the input to the model. All the elements in the mask are set\n", + " to -inf except that all the elements below the k-th diagonal are set to 0.\n", + " \"\"\"\n", + " mask = np.ones(shape, dtype=np.float32) * float(\"-inf\")\n", + " mask = np.triu(mask, k=k)\n", + " return mask\n", + "\n", + "\n", + "class LiteRTLlmPipeline:\n", + "\n", + " def __init__(self, interpreter, processor):\n", + " \"\"\"Initializes the pipeline.\"\"\"\n", + " self._interpreter = interpreter\n", + " self._processor = processor\n", + "\n", + " self._prefill_runner = None\n", + " self._decode_runner = self._interpreter.get_signature_runner(\"decode\")\n", + "\n", + " def _init_prefill_runner(self, num_input_tokens: int):\n", + " \"\"\"Initializes all the variables related to the prefill runner.\n", + "\n", + " This method initializes the following variables:\n", + " - self._prefill_runner: The prefill runner based on the input size.\n", + " - self._max_seq_len: The maximum sequence length supported by the model.\n", + "\n", + " Args:\n", + " num_input_tokens: The number of input tokens.\n", + " \"\"\"\n", + " if not self._interpreter:\n", + " raise ValueError(\"Interpreter is not initialized.\")\n", + "\n", + " # Prefill runner related variables will be initialized in `predict_text` and\n", + " # `compute_log_likelihood`.\n", + " self._prefill_runner = self._get_prefill_runner(num_input_tokens)\n", + " # input_token_shape has shape (batch, max_seq_len)\n", + " input_token_shape = self._prefill_runner.get_input_details()[\"tokens\"][\n", + " \"shape\"\n", + " ]\n", + " if len(input_token_shape) == 1:\n", + " self._max_seq_len = input_token_shape[0]\n", + " else:\n", + " self._max_seq_len = input_token_shape[1]\n", + "\n", + " # SmolLM: kv cache input has shape [batch=1, cache_size, num_kv_heads, head_dim].\n", + " kv_cache_shape = self._prefill_runner.get_input_details()[\"kv_cache_k_0\"][\n", + " \"shape\"\n", + " ]\n", + " self._max_kv_cache_seq_len = kv_cache_shape[1]\n", + "\n", + " def _init_kv_cache(self) -> dict[str, np.ndarray]:\n", + " if self._prefill_runner is None:\n", + " raise ValueError(\"Prefill runner is not initialized.\")\n", + " kv_cache = {}\n", + " for input_key in self._prefill_runner.get_input_details().keys():\n", + " if \"kv_cache\" in input_key:\n", + " kv_cache[input_key] = np.zeros(\n", + " self._prefill_runner.get_input_details()[input_key][\"shape\"],\n", + " dtype=np.float32,\n", + " )\n", + " kv_cache[input_key] = np.zeros(\n", + " self._prefill_runner.get_input_details()[input_key][\"shape\"],\n", + " dtype=np.float32,\n", + " )\n", + " return kv_cache\n", + "\n", + " def _get_prefill_runner(self, num_input_tokens: int) :\n", + " \"\"\"Gets the prefill runner with the best suitable input size.\n", + "\n", + " Args:\n", + " num_input_tokens: The number of input tokens.\n", + "\n", + " Returns:\n", + " The prefill runner with the smallest input size.\n", + " \"\"\"\n", + " best_signature = None\n", + " delta = sys.maxsize\n", + " max_prefill_len = -1\n", + " for key in self._interpreter.get_signature_list().keys():\n", + " if \"prefill\" not in key or 'pixel' not in key:\n", + " continue\n", + " input_pos = self._interpreter.get_signature_runner(key).get_input_details()[\n", + " \"input_pos\"\n", + " ]\n", + " # input_pos[\"shape\"] has shape (max_seq_len, )\n", + " seq_size = input_pos[\"shape\"][0]\n", + " max_prefill_len = max(max_prefill_len, seq_size)\n", + " if num_input_tokens <= seq_size and seq_size - num_input_tokens < delta:\n", + " delta = seq_size - num_input_tokens\n", + " best_signature = key\n", + " if best_signature is None:\n", + " raise ValueError(\n", + " \"The largest prefill length supported is %d, but we have %d number of input tokens\"\n", + " %(max_prefill_len, num_input_tokens)\n", + " )\n", + " return self._interpreter.get_signature_runner(best_signature)\n", + "\n", + " def _run_prefill(\n", + " self, prefill_token_ids: Sequence[int], pixel_values: np.ndarray,\n", + " ) -> dict[str, np.ndarray]:\n", + " \"\"\"Runs prefill and returns the kv cache.\n", + "\n", + " Args:\n", + " prefill_token_ids: The token ids of the prefill input.\n", + "\n", + " Returns:\n", + " The updated kv cache.\n", + " \"\"\"\n", + " if not self._prefill_runner:\n", + " raise ValueError(\"Prefill runner is not initialized.\")\n", + " prefill_token_length = len(prefill_token_ids)\n", + " if prefill_token_length == 0:\n", + " return self._init_kv_cache()\n", + "\n", + " # Prepare the input to be [1, max_seq_len].\n", + " input_token_ids = [0] * self._max_seq_len\n", + " input_token_ids[:prefill_token_length] = prefill_token_ids\n", + " input_token_ids = np.asarray(input_token_ids, dtype=np.int32)\n", + " input_token_ids = np.expand_dims(input_token_ids, axis=0)\n", + "\n", + " # Prepare the input position to be [max_seq_len].\n", + " input_pos = [0] * self._max_seq_len\n", + " input_pos[:prefill_token_length] = range(prefill_token_length)\n", + " input_pos = np.asarray(input_pos, dtype=np.int32)\n", + "\n", + " # Initialize kv cache.\n", + " prefill_inputs = self._init_kv_cache()\n", + " # Prepare the tokens and input position inputs.\n", + " prefill_inputs.update({\n", + " \"tokens\": input_token_ids,\n", + " \"input_pos\": input_pos,\n", + " \"pixel_values\": pixel_values,\n", + " })\n", + " if \"mask\" in self._prefill_runner.get_input_details().keys():\n", + " # For prefill, mask has shape [batch=1, 1, seq_len, kv_cache_size].\n", + " # We want mask[0, 0, i, j] = 0 for j<=i and -inf otherwise.\n", + " prefill_inputs[\"mask\"] = _get_mask(\n", + " shape=self._prefill_runner.get_input_details()[\"mask\"][\"shape\"],\n", + " k=1,\n", + " )\n", + " prefill_outputs = self._prefill_runner(**prefill_inputs)\n", + " if \"logits\" in prefill_outputs:\n", + " # Prefill outputs includes logits and kv cache. We only output kv cache.\n", + " prefill_outputs.pop(\"logits\")\n", + "\n", + " return prefill_outputs\n", + "\n", + " def _greedy_sampler(self, logits: np.ndarray) -> int:\n", + " return int(np.argmax(logits))\n", + "\n", + "\n", + " def _run_decode(\n", + " self,\n", + " start_pos: int,\n", + " start_token_id: int,\n", + " kv_cache: dict[str, np.ndarray],\n", + " max_decode_steps: int,\n", + " ) -> str:\n", + " \"\"\"Runs decode and outputs the token ids from greedy sampler.\n", + "\n", + " Args:\n", + " start_pos: The position of the first token of the decode input.\n", + " start_token_id: The token id of the first token of the decode input.\n", + " kv_cache: The kv cache from the prefill.\n", + " max_decode_steps: The max decode steps.\n", + "\n", + " Returns:\n", + " The token ids from the greedy sampler.\n", + " \"\"\"\n", + " next_pos = start_pos\n", + " next_token = start_token_id\n", + " decode_text = []\n", + " decode_inputs = kv_cache\n", + "\n", + " for _ in range(max_decode_steps):\n", + " decode_inputs.update({\n", + " \"tokens\": np.array([[next_token]], dtype=np.int32),\n", + " \"input_pos\": np.array([next_pos], dtype=np.int32),\n", + " })\n", + " if \"mask\" in self._decode_runner.get_input_details().keys():\n", + " # For decode, mask has shape [batch=1, 1, 1, kv_cache_size].\n", + " # We want mask[0, 0, 0, j] = 0 for j<=next_pos and -inf otherwise.\n", + " decode_inputs[\"mask\"] = _get_mask(\n", + " shape=self._decode_runner.get_input_details()[\"mask\"][\"shape\"],\n", + " k=next_pos + 1,\n", + " )\n", + " decode_outputs = self._decode_runner(**decode_inputs)\n", + " # Output logits has shape (batch=1, 1, vocab_size). We only take the first\n", + " # element.\n", + " logits = decode_outputs.pop(\"logits\")[0][0]\n", + " next_token = self._greedy_sampler(logits)\n", + " if next_token == self._processor.tokenizer.eos_token_id:\n", + " break\n", + " decode_text.append(self._processor.tokenizer.decode(next_token, skip_special_tokens=True))\n", + " if len(decode_text[-1]) == 0:\n", + " # Break out the loop if we hit the special token.\n", + " break\n", + "\n", + " print(decode_text[-1], end='', flush=True)\n", + " # Decode outputs includes logits and kv cache. We already poped out\n", + " # logits, so the rest is kv cache. We pass the updated kv cache as input\n", + " # to the next decode step.\n", + " decode_inputs = decode_outputs\n", + " next_pos += 1\n", + "\n", + " print() # print a new line at the end.\n", + " return ''.join(decode_text)\n", + "\n", + " def generate(self, inputs: Dict, max_decode_steps: int | None = None) -> str:\n", + "\n", + " token_ids = inputs[\"input_ids\"][0]\n", + " pixel_values = inputs[\"pixel_values\"][0]\n", + "\n", + " # Initialize the prefill runner with the suitable input size.\n", + " self._init_prefill_runner(len(token_ids))\n", + "\n", + " # Run prefill.\n", + " # Prefill up to the seond to the last token of the prompt, because the last\n", + " # token of the prompt will be used to bootstrap decode.\n", + " prefill_token_length = len(token_ids) - 1\n", + "\n", + " print('Running prefill')\n", + " kv_cache = self._run_prefill(token_ids[:prefill_token_length], pixel_values)\n", + " # Run decode.\n", + " print('Running decode')\n", + " actual_max_decode_steps = self._max_kv_cache_seq_len - prefill_token_length - 1\n", + " if max_decode_steps is not None:\n", + " actual_max_decode_steps = min(actual_max_decode_steps, max_decode_steps)\n", + " decode_text = self._run_decode(\n", + " prefill_token_length,\n", + " token_ids[prefill_token_length],\n", + " kv_cache,\n", + " actual_max_decode_steps,\n", + " )\n", + " return decode_text\n", + "\n", + "\n", + "model_id = 'HuggingFaceTB/SmolVLM-256M-Instruct'\n", + "\n", + "interpreter = interpreter_lib.InterpreterWithCustomOps(\n", + " custom_op_registerers=[\"pywrap_genai_ops.GenAIOpsRegisterer\"],\n", + " model_path=model_path,\n", + " num_threads=2,\n", + " experimental_default_delegate_latest_features=True)\n", + "\n", + "processor = AutoProcessor.from_pretrained(model_id, do_image_splitting=True)\n", + "image_url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true\"\n", + "image = Image.open(requests.get(image_url, stream=True).raw)\n", + "# image = Image.open(\"/home/dragynir/ai_vlm/cats.jpg\")\n", + "# image = Image.open(\"/home/dragynir/ai_vlm/car.jpg\")\n", + "\n", + "messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"image\"},\n", + " {\"type\": \"text\", \"text\": \"What in the image?\"}\n", + " ]\n", + " },\n", + "]\n", + "prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n", + "print(prompt)\n", + "inputs = processor(text=prompt, images=[image], return_tensors=\"pt\")\n", + "\n", + "# Tflite model inference\n", + "pipeline = LiteRTLlmPipeline(interpreter, processor)\n", + "tflite_text = pipeline.generate(inputs, max_decode_steps=100)\n", + "\n", + "# HuggingFace model inference\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "inputs = inputs.to(DEVICE)\n", + "model = AutoModelForVision2Seq.from_pretrained(\n", + " model_id,\n", + " torch_dtype=torch.bfloat16,\n", + " _attn_implementation=\"eager\",\n", + ").to(DEVICE)\n", + "generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)\n", + "generated_texts = processor.batch_decode(\n", + " generated_ids,\n", + " skip_special_tokens=True,\n", + ")\n", + "\n", + "hf_text = generated_texts[0]\n", + "print(\"-\"*100)\n", + "print(\"Tflite:\\n\", tflite_text)\n", + "print(\"HF:\\n\", hf_text)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675, + "referenced_widgets": [ + "3fbc991222d04a5e90bfa27d0143f4b4", + "35c529800b754ae8bd6270118fef6109", + "1d8e8ca65a0b4deeb3dd945facfc44d2", + "9fe10ffe867e4f909268aaf44fc4a4a8", + "05d8a14e97194ee5888598c62ba7d0de", + "6d4f6409e6d34358a0746b0d4f4e99ef", + "fe5b221bdd254b10805b8e15a347cce8", + "26190b7243fa4089903a69bba9e856a4", + "8564958286f44a77b1b9e06c3a3d4027", + "7358ba73930340dbb08003ee0bdfeeca", + "e56b47750d92481e993ba39b217292d5", + "12f4b4bfe4584c12b9821991fba4979c", + 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"output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fb529548004b46ef95cfdc52e1be0d90" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/513M [00:00