init

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+  }

added_tokens.json

@@ -0,0 +1,10 @@
+{
+  "<|execute_end|>": 73444,
+  "<|execute_start|>": 73443,
+  "<|fim_middle|>": 73446,
+  "<|fim_prefix|>": 73445,
+  "<|fim_suffix|>": 73447,
+  "<|im_end|>": 73440,
+  "<|im_start|>": 73441,
+  "<|tool_call|>": 73442
+}

config.json

@@ -0,0 +1,110 @@
+{
+  "_name_or_path": "openbmb/UltraRAG-Embedding",
+  "adapt_mean_pooling": true,
+  "architectures": [
+    "MiniCPMModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_minicpm.MiniCPMConfig",
+    "AutoModel": "modeling_minicpm.MiniCPMModel",
+    "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
+    "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
+  },
+  "bos_token_id": 1,
+  "dim_model_base": 256,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.1,
+  "intermediate_size": 4096,
+  "is_causal": false,
+  "max_position_embeddings": 4096,
+  "model_type": "minicpm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "long_factor": [
+      1.0004360675811768,
+      1.0668443441390991,
+      1.1631425619125366,
+      1.3025742769241333,
+      1.5040205717086792,
+      1.7941505908966064,
+      2.2101221084594727,
+      2.802666664123535,
+      3.6389970779418945,
+      4.804192543029785,
+      6.39855432510376,
+      8.527148246765137,
+      11.277542114257812,
+      14.684998512268066,
+      18.69317054748535,
+      23.13019371032715,
+      27.72362518310547,
+      32.1606559753418,
+      36.168827056884766,
+      39.57627868652344,
+      42.32667541503906,
+      44.45526885986328,
+      46.04962921142578,
+      47.21482849121094,
+      48.05115509033203,
+      48.64370346069336,
+      49.05967712402344,
+      49.34980392456055,
+      49.551246643066406,
+      49.69068145751953,
+      49.78697967529297,
+      49.85338592529297
+    ],
+    "original_max_position_embeddings": 4096,
+    "short_factor": [
+      1.0004360675811768,
+      1.0668443441390991,
+      1.1631425619125366,
+      1.3025742769241333,
+      1.5040205717086792,
+      1.7941505908966064,
+      2.2101221084594727,
+      2.802666664123535,
+      3.6389970779418945,
+      4.804192543029785,
+      6.39855432510376,
+      8.527148246765137,
+      11.277542114257812,
+      14.684998512268066,
+      18.69317054748535,
+      23.13019371032715,
+      27.72362518310547,
+      32.1606559753418,
+      36.168827056884766,
+      39.57627868652344,
+      42.32667541503906,
+      44.45526885986328,
+      46.04962921142578,
+      47.21482849121094,
+      48.05115509033203,
+      48.64370346069336,
+      49.05967712402344,
+      49.34980392456055,
+      49.551246643066406,
+      49.69068145751953,
+      49.78697967529297,
+      49.85338592529297
+    ],
+    "type": "longrope"
+  },
+  "rope_theta": 10000.0,
+  "scale_depth": 1.4,
+  "scale_emb": 12,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": false,
+  "vocab_size": 73448
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+    "__version__": {
+      "sentence_transformers": "2.7.0",
+      "transformers": "4.37.2",
+      "pytorch": "2.0.1+cu121"
+    },
+    "prompts": {},
+    "default_prompt_name": null
+  }

configuration_minicpm.py

@@ -0,0 +1,206 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" MiniCPM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class MiniCPMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MiniCPM-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniCPMModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
+            MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MiniCPMModel, MiniCPMConfig
+
+    >>> # Initializing a MiniCPM minicpm-7b style configuration
+    >>> configuration = MiniCPMConfig()
+
+    >>> # Initializing a model from the minicpm-7b style configuration
+    >>> model = MiniCPMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minicpm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        scale_emb=1,
+        dim_model_base=1,
+        scale_depth=1,
+        is_causal=True,
+        adapt_mean_pooling=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        # self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.scale_emb = scale_emb
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+        self.is_causal = is_causal
+        self.adapt_mean_pooling = adapt_mean_pooling
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        # try:
+        #     import flash_attn
+        #     self._attn_implementation = "flash_attention_2"
+        # except:
+        #     pass
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:109f243eddb0ae63ec4bbcb16ddb127d65399184a4e01565a628911c9c4c6afc
+size 867773472

modeling_minicpm.py

@@ -0,0 +1,1806 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" PyTorch MiniCPM model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union, Dict
+import os
+from tqdm import tqdm
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+import numpy as np
+from copy import deepcopy
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers import AutoTokenizer
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+    _prepare_4d_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_minicpm import MiniCPMConfig
+import re
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+except:
+    pass
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MiniCPMConfig"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    warnings.warn(
+        "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+    )
+    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    warnings.warn(
+        "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
+    )
+    return AttentionMaskConverter._make_causal_mask(
+        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+    )
+
+# @torch.jit.script  # type: ignore
+def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
+    old_dtype = hidden.dtype
+    variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
+    hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
+    return hidden * weight
+
+
+class MiniCPMRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniCPMRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
+
+
+ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
+
+
+class MiniCPMRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+class MiniCPMLongRoPE(MiniCPMRotaryEmbedding):
+    """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, short_factor=None, long_factor=None, original_max_position_embeddings=None):
+        self.short_factor = short_factor
+        self.long_factor = long_factor
+        self.original_max_position_embeddings = original_max_position_embeddings
+        scale = (max_position_embeddings /
+                 self.original_max_position_embeddings)
+        self.scaling_factor = math.sqrt(
+                1 + math.log(scale) /
+                math.log(self.original_max_position_embeddings))
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        if seq_len > self.original_max_position_embeddings:
+            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=device)
+        else:
+            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=device)
+        
+        freqs = torch.mul(
+            torch.outer(t, 1.0 / ext_factors).to(device=device),
+            self.inv_freq.to(device=device).to(dtype)
+        )
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype) * self.scaling_factor, persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype) * self.scaling_factor, persistent=False)
+
+
+class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+    """MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+    """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    # cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    # sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    # q_embed = (q * cos) + (rotate_half(q) * sin)
+    # k_embed = (k * cos) + (rotate_half(k) * sin)
+    orig_dtype = k.dtype
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
+    q_fp32 = q.to(dtype=torch.float32, device=q.device)
+    k_fp32 = k.to(dtype=torch.float32, device=k.device)
+    q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
+    k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
+    return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
+
+class MiniCPMMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+
+class MiniCPMAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+                
+        self.is_causal = config.is_causal
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = MiniCPMRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            
+            if scaling_type == "linear":
+                scaling_factor = self.config.rope_scaling["factor"]
+                self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                scaling_factor = self.config.rope_scaling["factor"]
+                self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "longrope":
+                self.rotary_emb = MiniCPMLongRoPE(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    short_factor = self.config.rope_scaling["short_factor"],
+                    long_factor = self.config.rope_scaling["long_factor"],
+                    base=self.rope_theta,
+                    original_max_position_embeddings=self.config.rope_scaling["original_max_position_embeddings"]
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+        
+        return attn_output, attn_weights, past_key_value
+
+
+class MiniCPMFlashAttention2(MiniCPMAttention):
+    """
+    MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # MiniCPMFlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (MiniCPMRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+class MiniCPMSdpaAttention(MiniCPMAttention):
+    """
+    MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MiniCPMAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+MINICPM_ATTENTION_CLASSES = {
+    "eager": MiniCPMAttention,
+    "flash_attention_2": MiniCPMFlashAttention2,
+    "sdpa": MiniCPMSdpaAttention,
+}
+
+
+class MiniCPMDecoderLayer(nn.Module):
+    def __init__(self, config: MiniCPMConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = MiniCPMMLP(config)
+        self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.scale_depth = config.scale_depth
+        self.num_hidden_layers = config.num_hidden_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+MINICPM_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MiniCPMConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMPreTrainedModel(PreTrainedModel):
+    config_class = MiniCPMConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniCPMDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MINICPM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMModel(MiniCPMPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
+
+    Args:
+        config: MiniCPMConfig
+    """
+
+    def __init__(self, config: MiniCPMConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        self.is_causal = config.is_causal
+        self.adapt_mean_pooling = config.adapt_mean_pooling
+        
+        # Initialize weights and apply final processing
+        self.head = torch.nn.Linear(in_features=1024, out_features=1, bias=False).float()
+        self.post_init()
+        self.tokenizer = AutoTokenizer.from_pretrained(config._name_or_path, trust_remote_code=True)
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        adapt_mean_pooling: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
+        
+        # print(attention_mask)
+        _attention_mask = attention_mask
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            if self.is_causal:
+                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa (
+                    attention_mask,
+                    (batch_size, seq_length),
+                    inputs_embeds,
+                    past_key_values_length,
+                )
+            else:
+                attention_mask = _prepare_4d_attention_mask_for_sdpa(
+                    attention_mask,
+                    inputs_embeds.dtype,
+                )
+        else:
+            # 4d mask is passed through the layers
+            if self.is_causal:
+                attention_mask = _prepare_4d_causal_attention_mask (
+                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+                )
+            else:
+                attention_mask = _prepare_4d_attention_mask(
+                    attention_mask,
+                    inputs_embeds.dtype,
+                )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        
+        # gen weight before mean pooling
+        if adapt_mean_pooling is None:
+            adapt_mean_pooling = self.adapt_mean_pooling
+        if adapt_mean_pooling:
+            attention_mask_ = _attention_mask * _attention_mask.cumsum(dim=1)
+            s = hidden_states * attention_mask_.unsqueeze(-1).float()
+            d = attention_mask_.sum(dim=1, keepdim=True).unsqueeze(1).float() /_attention_mask.sum(dim=1, keepdim=True).unsqueeze(1).float()
+
+            hidden_states = s / d
+        
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        
+
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+    
+    @staticmethod
+    def wmean_pooling(hidden,attention_mask):
+        attention_mask_ = attention_mask * attention_mask.cumsum(dim=1)
+        hidden_masked = hidden * attention_mask_.unsqueeze(-1).float()
+        s = torch.sum(hidden_masked, dim=1)
+        d = attention_mask_.sum(dim=1, keepdim=True).float()
+        reps = s / d
+        return reps
+
+    
+    def sparse_pooling(self,items, hidden, attention_mask):
+        hidden = hidden * attention_mask.unsqueeze(-1).float()
+        max_hidden_norm = torch.max(torch.norm(hidden,dim=-1),dim = -1).values.detach()
+        token_weights = torch.relu(self.head(hidden.float()/max_hidden_norm.unsqueeze(-1).unsqueeze(-1)))
+        vocab_size = self.embed_tokens.weight.size(0)
+        input_ids = items["input_ids"]
+        sparse_embedding_chunks = []
+        mini_chunk_size = 1
+        mini_chunk_size = min(mini_chunk_size,hidden.shape[0])
+        for i in range(0, token_weights.size(0), mini_chunk_size):
+            now_chunk_size = min(mini_chunk_size, token_weights.size(0) - i)
+            sparse_embedding = torch.zeros(now_chunk_size , input_ids.size(1), vocab_size,
+                                       dtype=token_weights.dtype,
+                                       device=token_weights.device)
+            sparse_embedding_chunks.append(torch.max((torch.scatter(sparse_embedding, dim=-1, index=input_ids[i:i+now_chunk_size, :].unsqueeze(-1), src=token_weights[i:i+now_chunk_size, :])), dim=1).values)
+        sparse_embedding = torch.concat(sparse_embedding_chunks, dim=0)
+        unused_tokens = [self.tokenizer.unk_token_id, self.tokenizer.pad_token_id, self.tokenizer.eos_token_id, self.tokenizer.bos_token_id]
+        sparse_embedding[:,unused_tokens] = 0
+        return sparse_embedding
+    
+    @torch.no_grad()
+    def process_sparse_embedding(self, sparse_embeddings,input_ids):
+        results = []
+        unused_tokens = [self.tokenizer.unk_token_id, self.tokenizer.pad_token_id, self.tokenizer.eos_token_id, self.tokenizer.bos_token_id]
+        batch_size = sparse_embeddings.size(0)
+        for i in range(batch_size):
+            results.append({})
+        for i, (sparse_embedding, input_id) in enumerate(zip(sparse_embeddings, input_ids)):
+            for input_id_j in input_id.to(int).cpu().numpy().tolist():
+                if input_id_j in unused_tokens:
+                    continue
+                if sparse_embedding[input_id_j] == 0:
+                    continue
+                results[i][self.tokenizer.convert_ids_to_tokens(input_id_j)] = sparse_embedding[input_id_j].item()
+        return results
+    
+    def encode(self,
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: Optional[bool] = True,
+        convert_to_numpy: bool = True,
+        return_dense_vectors: bool = True,
+        return_sparse_vectors: bool = False,
+        max_length: int = 512,
+        dense_dim: int = 1024,
+    ):
+        if isinstance(sentences, str):
+            sentences = [sentences]
+        dense_vectors_list = []
+        sparse_vectors_list = []
+        for start_index in tqdm(range(0, len(sentences), batch_size), desc="Batches", disable=not show_progress_bar):
+            sentences_batch = sentences[start_index:start_index + batch_size]
+            batch_dict = self.tokenizer(sentences_batch, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
+            for key in batch_dict:
+                batch_dict[key] = batch_dict[key].to(self.device)
+            outputs = self.forward(**batch_dict,adapt_mean_pooling=False)
+            hidden_states = outputs.last_hidden_state
+            attention_mask = batch_dict["attention_mask"]
+            dense_vectors = None
+            sparse_vectors = None
+            if return_dense_vectors:
+                dense_vectors = self.wmean_pooling(hidden_states,attention_mask)
+                dense_vectors = F.normalize(dense_vectors[:,:dense_dim], p=2, dim=-1)
+                
+                if convert_to_numpy:
+                    dense_vectors = dense_vectors.cpu().numpy()
+                dense_vectors_list.append(dense_vectors)
+            if return_sparse_vectors:
+                sparse_vectors = self.sparse_pooling(batch_dict,hidden_states,attention_mask)
+                
+                if convert_to_numpy:
+                    sparse_vectors = self.process_sparse_embedding(sparse_vectors, batch_dict["input_ids"])
+                    sparse_vectors_list.extend(sparse_vectors)
+                else:
+                    sparse_vectors_list.append(sparse_vectors)
+               
+        if convert_to_numpy:
+            dense_vectors_list = np.concatenate(dense_vectors_list, axis=0)
+        else:
+            dense_vectors_list = torch.cat(dense_vectors_list, dim=0)
+            sparse_vectors_list = torch.cat(sparse_vectors_list, dim=0)
+        if len(sparse_vectors_list) == 0:
+            sparse_vectors_list = None
+        if len(dense_vectors_list) == 0:
+            dense_vectors_list = None
+        return dense_vectors_list, sparse_vectors_list
+    
+   
+    """Compute similarity scores between queries and documents using dense and/or sparse embeddings.
+
+    This method computes similarity scores between query-document pairs using a combination of dense and sparse embeddings.
+    It supports both single strings and lists of strings as input.
+
+    Args:
+        queries (Union[str, List[str]]): Query text or list of query texts
+        documents (Union[str, List[str]]): Document text or list of document texts
+        show_progress_bar (Optional[bool]): Whether to show progress bar during encoding. Defaults to True.
+        batch_size (int): Batch size for encoding. Defaults to 32.
+        query_instruction (str): Instruction prefix for query encoding. Defaults to "Query:".
+        return_dense_score (bool): Whether to compute and return dense embedding similarity scores. Defaults to True.
+        return_sparse_score (bool): Whether to compute and return sparse embedding similarity scores. Defaults to True.
+        weight_for_sparse_score (float): Weight factor for sparse scores when computing mixed scores. Defaults to 0.3.
+        max_length (int): Maximum sequence length for tokenization. Defaults to 512.
+        dense_dim (int): Dimension of dense embeddings. Defaults to 1024.
+
+    Returns:
+        Tuple containing:
+            dense_score (numpy.ndarray or None): Dense similarity scores if return_dense_score is True, else None
+            sparse_score (numpy.ndarray or None): Sparse similarity scores if return_sparse_score is True, else None
+            mix_score (numpy.ndarray or None): Weighted combination of dense and sparse scores if both are computed, else None
+
+    Note:
+        - Dense scores are computed using dot product between query and document embeddings
+        - Sparse scores are computed in chunks to handle memory efficiently
+        - Mix scores are computed as: weight_for_sparse_score * sparse_score + dense_score
+    """
+    @torch.no_grad()
+    def compute_score(self,
+                      queries: Union[str, List[str]],
+                      documents: Union[str, List[str]],
+                      show_progress_bar: Optional[bool] = True,
+                      batch_size: int = 32,
+                      query_instruction:str = "Query:",
+                      return_dense_score: bool = True,
+                      return_sparse_score: bool = True,
+                      weight_for_sparse_score: float = 0.3,
+                      max_length: int = 512,
+                      dense_dim: int = 1024):
+        query_embeddings_dense, query_embeddings_sparse = self.encode_query(queries, batch_size, show_progress_bar,
+                                                                            convert_to_numpy=False,
+                                                                            return_dense_vectors=return_dense_score,
+                                                                            return_sparse_vectors=return_sparse_score,
+                                                                            max_length=max_length,
+                                                                            dense_dim=dense_dim,
+                                                                            query_instruction=query_instruction,
+                                                                            )
+        corpus_embeddings_dense, corpus_embeddings_sparse = self.encode_corpus(documents, batch_size, show_progress_bar,
+                                                                                convert_to_numpy=False,
+                                                                                return_dense_vectors=return_dense_score,
+                                                                                return_sparse_vectors=return_sparse_score,
+                                                                                max_length=max_length,
+                                                                                dense_dim=dense_dim,
+                                                                                )
+        dense_score = None
+        sparse_score = None
+        mix_score = None
+        if return_dense_score:
+            dense_score = query_embeddings_dense @ corpus_embeddings_dense.T
+            dense_score = dense_score.cpu().numpy()
+        if return_sparse_score:
+            min_chunk_size = 1024
+            for i in range(0, query_embeddings_sparse.size(0), min_chunk_size):
+                now_chunk_size = min(min_chunk_size, query_embeddings_sparse.size(0) - i)
+                sparse_score_now_chunk = None
+                for j in range(0, corpus_embeddings_sparse.size(0), min_chunk_size):
+                    sparse_score_chunk = query_embeddings_sparse[i:i+now_chunk_size] @ corpus_embeddings_sparse[j:j+min_chunk_size].T
+                    if sparse_score_now_chunk is None:
+                        sparse_score_now_chunk = sparse_score_chunk
+                    else:
+                        sparse_score_now_chunk = torch.cat((sparse_score_now_chunk, sparse_score_chunk), dim=1)
+                if sparse_score is None:
+                    sparse_score = sparse_score_now_chunk
+                else:
+                    sparse_score = torch.cat((sparse_score, sparse_score_now_chunk), dim=0)
+            sparse_score = sparse_score.cpu().numpy()
+        if return_sparse_score and return_dense_score:
+            mix_score = weight_for_sparse_score * sparse_score + dense_score
+        return dense_score, sparse_score, mix_score
+    
+   
+    """
+    Encodes query sentences into vector representations.
+
+    Args:
+        sentences (Union[str, List[str]]): Input query sentence(s) to encode. Can be a single string or list of strings.
+        batch_size (int, optional): Batch size for processing. Defaults to 32.
+        show_progress_bar (Optional[bool], optional): Whether to display a progress bar. Defaults to True.
+        convert_to_numpy (bool, optional): Whether to convert outputs to numpy arrays. Defaults to True.
+        return_dense_vectors (bool, optional): Whether to return dense vector representations. Defaults to True.
+        return_sparse_vectors (bool, optional): Whether to return sparse vector representations. Defaults to False.
+        max_length (int, optional): Maximum sequence length for tokenization. Defaults to 512.
+        dense_dim (int, optional): Dimension of dense output vectors. Defaults to 1024.
+        query_instruction (str, optional): Instruction prefix to prepend to queries. Defaults to "Query:".
+
+    Returns:
+        Same output format as the encode() method, with vector representations of the input queries.
+
+    Notes:
+        This is a no-grad operation that wraps the encode() method by prepending a query instruction
+        to each input sentence before encoding.
+    """
+    @torch.no_grad()
+    def encode_query(self,
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: Optional[bool] = True,
+        convert_to_numpy: bool = True,
+        return_dense_vectors: bool = True,
+        return_sparse_vectors: bool = False,
+        max_length: int = 512,
+        dense_dim: int = 1024,
+        query_instruction:str = "Query:"
+    ):
+        new_sentences = [" ".join([query_instruction, sentence]) for sentence in sentences]
+        return self.encode(new_sentences, batch_size, show_progress_bar, convert_to_numpy, return_dense_vectors, return_sparse_vectors, max_length, dense_dim)
+    
+   
+    """Encodes a corpus of text sentences into vector representations.
+
+    This method provides a wrapper for the encode method, specifically designed for corpus encoding.
+    It processes text input into dense and/or sparse vector representations suitable for semantic search
+    and other NLP tasks.
+
+    Args:
+        sentences (Union[str, List[str]]): Input text or list of texts to encode.
+        batch_size (int, optional): Number of sentences to encode in each batch. Defaults to 32.
+        show_progress_bar (bool, optional): Whether to display a progress bar during encoding. 
+            Defaults to True.
+        convert_to_numpy (bool, optional): Whether to convert the output tensors to numpy arrays.
+            Defaults to True.
+        return_dense_vectors (bool, optional): Whether to return dense vector representations.
+            Defaults to True.
+        return_sparse_vectors (bool, optional): Whether to return sparse vector representations.
+            Defaults to False.
+        max_length (int, optional): Maximum length of input sequences. Texts will be truncated
+            to this length. Defaults to 512.
+        dense_dim (int, optional): Dimension of the dense output vectors. Defaults to 1024.
+
+    Returns:
+        The encoded representations as specified by return_dense_vectors and return_sparse_vectors
+        parameters. Output format matches that of the encode method.
+
+    Note:
+        This method is decorated with @torch.no_grad() for inference-only operation,
+        ensuring no gradients are computed during encoding.
+    """
+    @torch.no_grad()
+    def encode_corpus(self,
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: Optional[bool] = True,
+        convert_to_numpy: bool = True,
+        return_dense_vectors: bool = True,
+        return_sparse_vectors: bool = False,
+        max_length: int = 512,
+        dense_dim: int = 1024,
+    ):
+        return self.encode(sentences, batch_size, show_progress_bar, convert_to_numpy, return_dense_vectors, return_sparse_vectors, max_length, dense_dim)
+    
+    @staticmethod
+    def compute_sparse_score_dicts(dicts_query, dicts_corpus):
+        scores_list = []
+        for dict_q in dicts_query:
+            scores = []
+            for dict_d in dicts_corpus:
+                score = 0
+                for key in dict_q:
+                    if key in dict_d:
+                        score += dict_q[key] * dict_d[key]
+                scores.append(score)
+            scores_list.append(deepcopy(scores))      
+        return np.array(scores_list)
+        
+    
+
+class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniCPMModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniCPMForCausalLM
+
+        >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+    
+    @torch.inference_mode()
+    def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
+             max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
+             **kwargs):
+        if history is None:
+            history = []
+        if logits_processor:
+            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
+                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        else:
+            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
+                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        
+        history.append({"role": role, "content": query})
+        history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
+        inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
+        outputs = self.generate(**inputs, **gen_kwargs)
+        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
+        response = tokenizer.decode(outputs)
+        pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
+        matches = pattern.findall(response)
+        if len(matches) > 0:
+            response = matches[0]
+        history.append({"role": "assistant", "content": response})
+        return response, history
+
+
+@add_start_docstrings(
+    """
+    The MiniCPM Model transformer with a sequence classification head on top (linear layer).
+
+    [`MiniCPMForSequenceClassification`] uses the first token in order to do the classification, as other models
+    (e.g. Roberta) do.
+    """,
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MiniCPMModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        # logits = self.score(hidden_states)
+        logits = self.score(hidden_states[:,0,:])
+        pooled_logits = logits
+
+        # if input_ids is not None:
+        #     batch_size = input_ids.shape[0]
+        # else:
+        #     batch_size = inputs_embeds.shape[0]
+
+        # if self.config.pad_token_id is None and batch_size != 1:
+        #     raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        # if self.config.pad_token_id is None:
+        #     sequence_lengths = -1
+        # else:
+        #     if input_ids is not None:
+        #         sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+        #             logits.device
+        #         )
+        #     else:
+        #         sequence_lengths = -1
+
+        # pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        # if labels is not None:
+        #     labels = labels.to(logits.device)
+        #     if self.config.problem_type is None:
+        #         if self.num_labels == 1:
+        #             self.config.problem_type = "regression"
+        #         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+        #             self.config.problem_type = "single_label_classification"
+        #         else:
+        #             self.config.problem_type = "multi_label_classification"
+
+        #     if self.config.problem_type == "regression":
+        #         loss_fct = MSELoss()
+        #         if self.num_labels == 1:
+        #             loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+        #         else:
+        #             loss = loss_fct(pooled_logits, labels)
+        #     elif self.config.problem_type == "single_label_classification":
+        #         loss_fct = CrossEntropyLoss()
+        #         loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+        #     elif self.config.problem_type == "multi_label_classification":
+        #         loss_fct = BCEWithLogitsLoss()
+        #         loss = loss_fct(pooled_logits, labels)
+        # if not return_dict:
+        #     output = (pooled_logits,) + transformer_outputs[1:]
+        #     return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

modules.json

@@ -0,0 +1,20 @@
+[
+    {
+      "idx": 0,
+      "name": "0",
+      "path": "",
+      "type": "sentence_transformers.models.Transformer"
+    },
+    {
+      "idx": 1,
+      "name": "1",
+      "path": "1_Pooling",
+      "type": "sentence_transformers.models.Pooling"
+    },
+    {
+      "idx": 2,
+      "name": "2",
+      "path": "2_Normalize",
+      "type": "sentence_transformers.models.Normalize"
+    }
+  ]

scripts/flagembedding_demo.py

@@ -0,0 +1,27 @@
+from FlagEmbedding import FlagModel
+
+
+model = FlagModel("openbmb/UltraRAG-Embedding", 
+                          query_instruction_for_retrieval="Query: ",
+                          pooling_method="mean",
+                          trust_remote_code=True,
+                          normalize_embeddings=True,
+                          use_fp16=True)
+# You can hack the __init__() method of the FlagEmbedding BaseEmbedder class to use flash_attention_2 for faster inference
+#  self.model = AutoModel.from_pretrained(
+#             model_name_or_path,
+#             trust_remote_code=trust_remote_code,
+#             cache_dir=cache_dir,
+#             # torch_dtype=torch.float16, # we need to add this line to use fp16
+#             # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2
+#         )
+
+queries = ["中国的首都是哪里？"] # "What is the capital of China?"
+passages = ["beijing", "shanghai"] # "北京", "上海"
+
+
+embeddings_query = model.encode_queries(queries)
+embeddings_doc = model.encode_corpus(passages)
+
+scores = (embeddings_query @ embeddings_doc.T)
+print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]

scripts/infinity_demo.py

@@ -0,0 +1,24 @@
+import asyncio
+from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
+import numpy as np
+
+array = AsyncEngineArray.from_args([
+  EngineArgs(model_name_or_path = "OpenBMB/UltraRAG-Embedding", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True),
+])
+queries = ["中国的首都是哪里？"] # "What is the capital of China?"
+passages = ["beijing", "shanghai"] # "北京", "上海"
+
+INSTRUCTION = "Query:"
+queries = [f"{INSTRUCTION} {query}" for query in queries]
+
+
+async def embed_text(engine: AsyncEmbeddingEngine,sentences): 
+    async with engine: 
+        embeddings, usage = await engine.embed(sentences=sentences)
+    return embeddings
+
+queries_embedding = asyncio.run(embed_text(array[0],queries))
+passages_embedding = asyncio.run(embed_text(array[0],passages))
+
+scores = (np.array(queries_embedding) @ np.array(passages_embedding).T)
+print(scores.tolist())  # [[0.40356746315956116, 0.36183443665504456]]

scripts/sentence_transformers_demo.py

@@ -0,0 +1,20 @@
+import torch
+from sentence_transformers import SentenceTransformer
+
+
+model_name = "openbmb/UltraRAG-Embedding"
+model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"torch_dtype": torch.float16})
+
+# you can use flash_attention_2 for faster inference
+# model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": torch.float16})
+
+queries = ["中国的首都是哪里？"] # "What is the capital of China?"
+passages = ["beijing", "shanghai"] # "北京", "上海"
+
+INSTRUCTION = "Query: "
+
+embeddings_query = model.encode(queries, prompt=INSTRUCTION)
+embeddings_doc = model.encode(passages)
+
+scores = (embeddings_query @ embeddings_doc.T)
+print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]

scripts/test_mteb.py

@@ -0,0 +1,475 @@
+# copy from https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/blob/main/scripts/eval_mteb.py
+
+#### ATTENTION ####
+# To Reproduce the results of Sparse and Dense + Sparse, you need to hack the MTEB RetrievalEvaluator
+# in mteb/evaluation/evaluators/RetrievalEvaluator.py
+# class RetrievalEvaluator(Evaluator):
+    # def __init__(
+    #     self,
+    #     retriever=None,
+    #     task_name: str | None = None,
+    #     k_values: list[int] = [1, 3, 5, 10, 20, 100, 1000],
+    #     score_function: str = "cos_sim",
+    #     encode_kwargs: dict[str, Any] = {},
+    #     **kwargs,
+    # ):
+# you need to change default score_function to "dot" to reproduce the results of Sparse and Dense + Sparse
+MODE = "Dense" # "Dense" or "Sparse" or "Dense + Sparse"
+
+TASK_LIST_CLASSIFICATION = [
+    "AmazonCounterfactualClassification",
+    "AmazonPolarityClassification",
+    "AmazonReviewsClassification",
+    "Banking77Classification",
+    "EmotionClassification",
+    "ImdbClassification",
+    "MassiveIntentClassification",
+    "MassiveScenarioClassification",
+    "MTOPDomainClassification",
+    "MTOPIntentClassification",
+    "ToxicConversationsClassification",
+    "TweetSentimentExtractionClassification",
+]
+
+TASK_LIST_CLUSTERING = [
+    "ArxivClusteringP2P",
+    "ArxivClusteringS2S",
+    "BiorxivClusteringP2P",
+    "BiorxivClusteringS2S",
+    "MedrxivClusteringP2P",
+    "MedrxivClusteringS2S",
+    "RedditClustering",
+    "RedditClusteringP2P",
+    "StackExchangeClustering",
+    "StackExchangeClusteringP2P",
+    "TwentyNewsgroupsClustering",
+]
+
+TASK_LIST_PAIR_CLASSIFICATION = [
+    "SprintDuplicateQuestions",
+    "TwitterSemEval2015",
+    "TwitterURLCorpus",
+]
+
+TASK_LIST_RERANKING = [
+    "AskUbuntuDupQuestions",
+    "MindSmallReranking",
+    "SciDocsRR",
+    "StackOverflowDupQuestions",
+]
+
+TASK_LIST_RETRIEVAL = [
+    "ArguAna",
+    "FiQA2018",
+    "QuoraRetrieval",
+    "SCIDOCS",
+    "SciFact",
+    "Touche2020",
+    "TRECCOVID",
+    "NFCorpus",
+    "NQ",
+    "ClimateFEVER",
+    "CQADupstackAndroidRetrieval",
+    "CQADupstackEnglishRetrieval",
+    "CQADupstackGamingRetrieval",
+    "CQADupstackGisRetrieval",
+    "CQADupstackMathematicaRetrieval",
+    "CQADupstackPhysicsRetrieval",
+    "CQADupstackProgrammersRetrieval",
+    "CQADupstackStatsRetrieval",
+    "CQADupstackTexRetrieval",
+    "CQADupstackUnixRetrieval",
+    "CQADupstackWebmastersRetrieval",
+    "CQADupstackWordpressRetrieval",
+    "DBPedia",
+    "HotpotQA",
+    "MSMARCO",
+    "FEVER",
+]
+
+TASK_LIST_STS = [
+    "BIOSSES",
+    "SICK-R",
+    "STS12",
+    "STS13",
+    "STS14",
+    "STS15",
+    "STS16",
+    "STS17",
+    "STS22",
+    "STSBenchmark",
+    "SummEval",
+]
+
+MTEB_TASK_LIST = (
+     TASK_LIST_RETRIEVAL
+    + TASK_LIST_CLASSIFICATION
+    + TASK_LIST_CLUSTERING
+    + TASK_LIST_PAIR_CLASSIFICATION
+    + TASK_LIST_RERANKING
+    + TASK_LIST_STS
+)
+
+
+CMTEB_TASK_LIST = [
+    "TNews",
+    "IFlyTek",
+    "MultilingualSentiment",
+    "JDReview",
+    "OnlineShopping",
+    "Waimai",
+    "AmazonReviewsClassification",
+    "MassiveIntentClassification",
+    "MassiveScenarioClassification",
+    "MultilingualSentiment",
+    "CLSClusteringS2S",
+    "CLSClusteringP2P",
+    "ThuNewsClusteringS2S",
+    "ThuNewsClusteringP2P",
+    "Ocnli",
+    "Cmnli",
+    "T2Reranking",
+    "MMarcoReranking",
+    "CMedQAv1-reranking",
+    "CMedQAv2-reranking",
+    "T2Retrieval",
+    "MMarcoRetrieval",
+    "DuRetrieval",
+    "CovidRetrieval",
+    "CmedqaRetrieval",
+    "EcomRetrieval",
+    "MedicalRetrieval",
+    "VideoRetrieval",
+    "ATEC",
+    "BQ",
+    "LCQMC",
+    "PAWSX",
+    "STSB",
+    "AFQMC",
+    "QBQTC",
+    "STS22",
+]
+
+MTEB_TASK_LIST = CMTEB_TASK_LIST + MTEB_TASK_LIST
+
+import torch
+import torch.nn.functional as F
+import tqdm
+import numpy as np
+import math
+
+from functools import partial
+from torch.utils.data import DataLoader
+from datasets import Dataset
+from transformers import AutoModel, AutoTokenizer, DataCollatorWithPadding, PreTrainedTokenizerFast, BatchEncoding
+from transformers.modeling_outputs import BaseModelOutput
+from typing import List, Dict
+from mteb import MTEB
+
+def get_detailed_instruct(task_description: str) -> str:
+    if not task_description:
+        return ""
+
+    return "Instruction: {} Query: ".format(task_description)
+
+
+
+def get_task_def_by_task_name_and_type(
+    task_name: str,
+    task_type: str,
+    default_instruct="",
+):
+    if task_type in ["STS"]:
+        return None
+
+    if task_type in ["Summarization"]:
+        return "Given a news summary, retrieve other semantically similar summaries"
+
+    if task_type in ["Classification"]:
+        task_name_to_instruct: Dict[str, str] = {
+            "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not-counterfactual.",
+            "AmazonPolarityClassification": "Classify Amazon reviews into positive or negative sentiment.",
+            "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category.",
+            "Banking77Classification": "Given a online banking query, find the corresponding intents.",
+            "EmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise.",
+            "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset.",
+            "MassiveIntentClassification": "Given a user utterance as query, find the user intents.",
+            "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios.",
+            "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation.",
+            "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation.",
+            "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic.",
+            "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral.",
+            # C-MTEB eval instructions
+            "TNews": "根据标题确定新闻的类别。",
+            "IFlyTek": "根据描述确定APP的类别。",
+            "MultilingualSentiment": "将亚马逊评论分为积极、消极或中立情绪。",
+            "JDReview": "将商品评论分为积极或消极情绪。",
+            "OnlineShopping": "将商品评论分为积极或消极情绪。",
+            "Waimai": "将外卖评论分为积极或消极情绪。",
+        }
+        return task_name_to_instruct.get(task_name,None)
+
+    if task_type in ["Clustering"]:
+        task_name_to_instruct: Dict[str, str] = {
+            "ArxivClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts.",
+            "ArxivClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles.",
+            "BiorxivClusteringP2P": "Identify the main category of Biorxiv papers based on the titles and abstracts.",
+            "BiorxivClusteringS2S": "Identify the main category of Biorxiv papers based on the titles.",
+            "MedrxivClusteringP2P": "Identify the main category of Medrxiv papers based on the titles and abstracts.",
+            "MedrxivClusteringS2S": "Identify the main category of Medrxiv papers based on the titles.",
+            "RedditClustering": "Identify the topic or theme of Reddit posts based on the titles.",
+            "RedditClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts.",
+            "StackExchangeClustering": "Identify the topic or theme of StackExchange posts based on the titles.",
+            "StackExchangeClusteringP2P": "Identify the topic or theme of StackExchange posts based on the given paragraphs.",
+            "TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles.",
+            # C-MTEB eval instructions
+            "CLSClusteringS2S": "根据标题确定文章的类别。",
+            "CLSClusteringP2P": "根据标题和摘要确定文章的类别。",
+            "ThuNewsClusteringS2S": "根据标题确定新闻的类别。",
+            "ThuNewsClusteringP2P": "根据标题和摘要确定新闻的类别。",
+        }
+        return task_name_to_instruct.get(task_name,None)
+
+    if task_type in ["Reranking", "PairClassification"]:
+        task_name_to_instruct: Dict[str, str] = {
+            "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum.",
+            "MindSmallReranking": "Retrieve relevant news articles based on user browsing history.",
+            "SciDocsRR": "Given a title of a scientific paper, retrieve the titles of other relevant papers.",
+            "StackOverflowDupQuestions": "Retrieve duplicate questions from StackOverflow forum.",
+            "SprintDuplicateQuestions": "Retrieve duplicate questions from Sprint forum.",
+            "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet.",
+            "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet.",
+            # C-MTEB eval instructions
+            "T2Reranking": "为这个问题检索相关段落。",
+            "MMarcoReranking": "为这个查询检索相关段落。",
+            "CMedQAv1-reranking": "为这个医疗问题检索相关回答。",
+            "CMedQAv2-reranking": "为这个医疗问题检索相关回答。",
+        }
+
+        return task_name_to_instruct.get(task_name,None)
+
+    if task_type in ["Retrieval"]:
+        if task_name.lower().startswith("cqadupstack"):
+            return "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question"
+
+        task_name_to_instruct: Dict[str, str] = {
+            "ArguAna": "Given a claim, find documents that refute the claim.",
+            "ClimateFEVER": "Given a claim about climate change, retrieve documents that support or refute the claim.",
+            "DBPedia": "Given a query, retrieve relevant entity descriptions from DBPedia.",
+            "FEVER": "Given a claim, retrieve documents that support or refute the claim.",
+            "FiQA2018": "Given a financial question, retrieve user replies that best answer the question.",
+            "HotpotQA": "Given a multi-hop question, retrieve documents that can help answer the question.",
+            "MSMARCO": "Given a web search query, retrieve relevant passages that answer the query.",
+            "NFCorpus": "Given a question, retrieve relevant documents that best answer the question.",
+            "NQ": "Given a question, retrieve Wikipedia passages that answer the question.",
+            "QuoraRetrieval": "Given a question, retrieve questions that are semantically equivalent to the given question.",
+            "SCIDOCS": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.",
+            "SciFact": "Given a scientific claim, retrieve documents that support or refute the claim.",
+            "Touche2020": "Given a question, retrieve detailed and persuasive arguments that answer the question.",
+            "TRECCOVID": "Given a query on COVID-19, retrieve documents that answer the query.",
+            # C-MTEB eval instructions
+            "T2Retrieval": "为这个问题检索相关段落。",
+            "MMarcoRetrieval": "为这个查询检索相关段落。",
+            "DuRetrieval": "为这个问题检索相关百度知道回答。",
+            "CovidRetrieval": "为这个问题检索相关政策回答。",
+            "CmedqaRetrieval": "为这个医疗问题检索相关回答。",
+            "EcomRetrieval": "为这个查询检索相关商品标题。",
+            "MedicalRetrieval": "为这个医疗问题检索相关回答。",
+            "VideoRetrieval": "为这个电影标题检索相关段落。",
+        }
+
+        task_name_to_instruct.update({k.lower(): v for k, v in task_name_to_instruct.items()})
+
+        return task_name_to_instruct.get(task_name,None)
+    return default_instruct
+def _transform_func(tokenizer: PreTrainedTokenizerFast,
+                    examples: Dict[str, List]) -> BatchEncoding:
+    batch_dict = tokenizer(examples['input_texts'],
+                           max_length=1024,
+                           padding=True,
+                           truncation=True)
+
+    return batch_dict
+
+# def weighted_mean_pooling(hidden,attention_mask):
+#     # print(hidden.shape,attention_mask.shape)
+#     attention_mask_ = attention_mask * attention_mask.cumsum(dim=1)
+#     s = torch.sum(hidden * attention_mask_.unsqueeze(-1).float(), dim=1)
+#     d = attention_mask_.sum(dim=1, keepdim=True).float()
+#     reps = s / d
+#     return reps
+
+def mean_pooling(hidden,attention_mask):
+    # print(hidden.shape,attention_mask.shape)
+    s = torch.sum(hidden * attention_mask.unsqueeze(-1).float(), dim=1)
+    d = attention_mask.sum(dim=1, keepdim=True).float()
+    return s / d
+
+def wmean_pooling(hidden,attention_mask):
+    attention_mask_ = attention_mask * attention_mask.cumsum(dim=1)
+    hidden_masked = hidden * attention_mask_.unsqueeze(-1).float()
+    s = torch.sum(hidden_masked, dim=1)
+    d = attention_mask_.sum(dim=1, keepdim=True).float()
+    reps = s / d
+    return reps
+
+def reverse_wmean_pooling(hidden,attention_mask):
+    attention_mask_ = attention_mask * attention_mask.cumsum(dim=1)
+    d = attention_mask_.sum(dim=1, keepdim=True).unsqueeze(1).float() / attention_mask.sum(dim=1, keepdim=True).unsqueeze(1).float()
+    hidden = hidden.float() * d
+    return hidden / torch.clamp(attention_mask_.unsqueeze(-1).float(),min=1e-9)
+
+
+def sparse_pooling(head,model,items,hidden,attention_mask):
+    hidden = reverse_wmean_pooling(hidden,attention_mask) # reverse weighted mean pooling, beacuse the hidden states are modified in the model
+    max_hidden_norm = torch.max(torch.norm(hidden,dim=-1),dim = -1).values
+    token_weights = torch.relu(head(hidden.float()/max_hidden_norm.unsqueeze(-1).unsqueeze(-1)))
+    vocab_size = model.embed_tokens.weight.size(0)
+    input_ids = items["input_ids"]
+    sparse_embedding_chunks = []
+    mini_chunk_size = 1
+    mini_chunk_size = min(mini_chunk_size,hidden.shape[0])
+    for i in range(0, token_weights.size(0), mini_chunk_size):
+        now_chunk_size = min(mini_chunk_size, token_weights.size(0) - i)
+        sparse_embedding = torch.zeros(now_chunk_size , input_ids.size(1), vocab_size,
+                                   dtype=token_weights.dtype,
+                                   device=token_weights.device)
+        sparse_embedding_chunks.append(torch.max((torch.scatter(sparse_embedding, dim=-1, index=input_ids[i:i+now_chunk_size, :].unsqueeze(-1), src=token_weights[i:i+now_chunk_size, :])), dim=1).values)
+    sparse_embedding = torch.concat(sparse_embedding_chunks, dim=0)
+    unused_tokens = [0,1,2,73440]
+    sparse_embedding[:, unused_tokens] *= 0.
+    return sparse_embedding
+
+def concat_pooling(head,model,items,hidden,attention_mask):
+    mean_reps = mean_pooling(hidden,attention_mask)
+    mean_reps = F.normalize(mean_reps, p=2, dim=1)
+    sparse_reps = sparse_pooling(head,model,items,hidden,attention_mask) * math.sqrt(0.3)
+    return torch.cat([mean_reps,sparse_reps],dim=-1)
+
+#
+
+class DenseEncoder(torch.nn.Module):
+    def __init__(self, **kwargs):
+        super().__init__()
+        
+        model_path = "openbmb/UltraRAG-Embedding"
+        self.encoder = AutoModel.from_pretrained(model_path, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+        self.gpu_count = torch.cuda.device_count()
+        self.instruction = ""
+
+        self.encoder.eval()
+        self.encoder.cuda()
+
+        if self.gpu_count > 1:
+            self.encoder = torch.nn.DataParallel(self.encoder)
+    
+    @torch.no_grad()
+    def encode(self, sentences,is_query=None, **kwargs) -> np.ndarray:
+        """ Returns a list of embeddings for the given sentences.
+        Args:
+            sentences (`List[str]`): List of sentences to encode
+            batch_size (`int`): Batch size for the encoding
+
+        Returns:
+            `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences
+        """
+        if is_query is not False:
+            sentences = [self.instruction + s for s in sentences]
+        dataset: Dataset = Dataset.from_dict({'input_texts': sentences})
+        # dataset: Dataset = Dataset.from_dict({'input_texts': ["Query: " + s for s in sentences]})
+        
+        dataset.set_transform(partial(_transform_func, self.tokenizer))
+
+        data_collator = DataCollatorWithPadding(self.tokenizer, pad_to_multiple_of=8)
+        data_loader = DataLoader(
+            dataset,
+            batch_size=128* self.gpu_count,
+            shuffle=False,
+            drop_last=False,
+            num_workers=2,
+            collate_fn=data_collator,
+            pin_memory=True)
+
+        encoded_embeds = []
+        for batch_dict in tqdm.tqdm(data_loader, desc='encoding', mininterval=10):
+
+            with torch.cuda.amp.autocast() and torch.no_grad():
+                for key in batch_dict:
+                    batch_dict[key] = batch_dict[key].to("cuda")
+                outputs: BaseModelOutput = self.encoder(**batch_dict)
+                if MODE == "Dense":
+                    embeds = mean_pooling(outputs.last_hidden_state, batch_dict['attention_mask'])
+                    embeds = F.normalize(embeds, p=2, dim=1)
+                elif MODE == "Sparse":
+                    embeds = sparse_pooling(self.encoder.module.head,self.encoder.module, batch_dict, outputs.last_hidden_state, batch_dict['attention_mask'])
+                else:
+                    embeds = concat_pooling(self.encoder.module.head,self.encoder.module, batch_dict, outputs.last_hidden_state, batch_dict['attention_mask'])
+                encoded_embeds.append(embeds.cpu().numpy())
+
+        return np.concatenate(encoded_embeds, axis=0)
+    
+    @torch.no_grad()
+    def encode_queries(self, queries: list[str], **kwargs) -> list[np.ndarray] | list[torch.Tensor]:
+        """
+        Returns a list of embeddings for the given sentences.
+        Args:
+            queries: List of sentences to encode
+
+        Returns:
+            List of embeddings for the given sentences
+        """
+
+
+        queries = [query for query in queries]
+        return self.encode(queries, is_query=True, **kwargs)
+    
+    @torch.no_grad()
+    def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
+        # borrowed from mteb.abstasks.AbsTaskRetrieval.DRESModel
+        if type(corpus) is dict:
+            sentences = [
+                (corpus["title"][i] + " " + corpus["text"][i]).strip()
+                if "title" in corpus
+                else corpus["text"][i].strip()
+                for i in range(len(corpus["text"]))
+            ]
+        elif isinstance(corpus[0], dict):
+            sentences = [
+                (doc["title"] + " " + doc["text"]).strip()
+                if "title" in doc
+                else doc["text"].strip()
+                for doc in corpus
+            ]
+        else:
+            sentences = corpus
+        is_query = False
+        return self.encode(sentences, is_query=is_query, **kwargs)
+
+
+model = DenseEncoder()
+task_names = MTEB_TASK_LIST
+task_names = ["NFCorpus"]
+lang = ["en","zh", "zh-CN"]
+
+for task in task_names:
+    try:
+        evaluation = MTEB(tasks=[task], task_langs=lang)
+        task_cls = evaluation.tasks[0]
+        task_name: str = task_cls.metadata_dict["name"]
+        task_type: str = task_cls.metadata_dict["type"]
+        instruction = get_task_def_by_task_name_and_type(task_name, task_type)
+        model.instruction = get_detailed_instruct(instruction)
+        print(model.instruction)
+        if task == "MSMARCO":
+            eval_splits = ["dev"]
+        elif task in CMTEB_TASK_LIST:
+            eval_splits = task_cls.metadata_dict["eval_splits"]
+        else:
+            eval_splits = ["test"]
+        evaluation.run(model, eval_splits=eval_splits, overwrite_results=True)
+        
+    except Exception as e:
+        import traceback
+        print(traceback.format_exc())
+        continue

scripts/transformers_demo.py

@@ -0,0 +1,26 @@
+
+from transformers import AutoModel
+import torch
+
+model_name = "openbmb/UltraRAG-Embedding"
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")
+
+# you can use flash_attention_2 for faster inference
+# model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") 
+
+model.eval()
+
+queries = ["MiniCPM-o 2.6 A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone"]
+passages = ["MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming."]
+
+embeddings_query_dense, embeddings_query_sparse = model.encode_query(queries, return_sparse_vectors=True, max_length=8192, dense_dim=1024)
+embeddings_doc_dense, embeddings_doc_sparse = model.encode_corpus(passages, return_sparse_vectors=True)
+
+dense_scores = (embeddings_query_dense @ embeddings_doc_dense.T)
+print(dense_scores.tolist())  # [[0.6512398719787598]]
+print(model.compute_sparse_score_dicts(embeddings_query_sparse,  embeddings_doc_sparse)) # [[0.27202296]]
+
+dense_scores, sparse_scores, mixed_scores = model.compute_score(queries, passages)
+print(dense_scores) # [[0.65123993]]
+print(sparse_scores) # [[0.27202296]]
+print(mixed_scores) # [[0.73284686]]

sentence_bert_config.json

@@ -0,0 +1,3 @@
+{
+    "max_seq_length": 8192
+}

special_tokens_map.json

@@ -0,0 +1,40 @@
+{
+  "additional_special_tokens": [
+    "<|im_end|>",
+    "<|im_start|>",
+    "<|tool_call|>",
+    "<|execute_start|>",
+    "<|execute_end|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530
+size 1181204

tokenizer_config.json

@@ -0,0 +1,116 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73440": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73441": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73442": {
+      "content": "<|tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73443": {
+      "content": "<|execute_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73444": {
+      "content": "<|execute_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73445": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73446": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73447": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_end|>",
+    "<|im_start|>",
+    "<|tool_call|>",
+    "<|execute_start|>",
+    "<|execute_end|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>"
+  ],
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}