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config.json

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+{
+  "_name_or_path": "BAAI/Matroyshka-ReRanker-document",
+  "architectures": [
+    "CostWiseMistralForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "BAAI/Matroyshka-ReRanker-document--mistral_config.CostWiseMistralConfig",
+    "AutoModel": "BAAI/Matroyshka-ReRanker-document--mistral_model.CostWiseMistralModel",
+    "AutoModelForCausalLM": "BAAI/Matroyshka-ReRanker-document--mistral_model.CostWiseMistralForCausalLM"
+  },
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "layer_sep": 1,
+  "layer_wise": true,
+  "max_position_embeddings": 32768,
+  "model_type": "cost_wise_mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "start_layer": 4,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.46.0"
+}

mistral_config.py

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+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Mistral model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+from transformers.models.mistral.configuration_mistral import MistralConfig
+
+logger = logging.get_logger(__name__)
+
+class CostWiseMistralConfig(MistralConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+    Mistral 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 Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+    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 Mistral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MistralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            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 `8`.
+        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 `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        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*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MistralModel, MistralConfig
+
+    >>> # Initializing a Mistral 7B style configuration
+    >>> configuration = MistralConfig()
+
+    >>> # Initializing a model from the Mistral 7B style configuration
+    >>> model = MistralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "cost_wise_mistral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        start_layer: int = 18,
+        layer_sep: int = 18,
+        layer_wise: bool = False,
+        **kwargs,
+    ):
+        self.start_layer = start_layer
+        self.layer_sep = layer_sep
+        self.layer_wise = layer_wise
+
+        super().__init__(
+            **kwargs,
+        )

mistral_model.py

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+# coding=utf-8
+# Copyright 2023 Mistral AI 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 Mistral model."""
+import inspect
+from dataclasses import dataclass
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+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, ModelOutput,
+)
+from .mistral_config import CostWiseMistralConfig
+
+from transformers.models.mistral.modeling_mistral import (
+    MistralRMSNorm,
+    MistralRotaryEmbedding,
+    rotate_half,
+    apply_rotary_pos_emb,
+    MistralMLP,
+    repeat_kv,
+    MistralAttention,
+    MistralFlashAttention2,
+    MistralSdpaAttention,
+    MISTRAL_ATTENTION_CLASSES,
+    MistralDecoderLayer,
+    MISTRAL_START_DOCSTRING,
+    MistralPreTrainedModel,
+    MISTRAL_INPUTS_DOCSTRING,
+
+)
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "CostWiseMistralConfig"
+
+@dataclass
+class CostWiseModelOutputWithPast(ModelOutput):
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    attention_masks: Optional[Tuple[torch.FloatTensor]] = None
+
+@dataclass
+class CostWiseCausalLMOutputWithPast(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    attention_masks: Optional[Tuple[torch.FloatTensor]] = None
+
+def token_compress(compress_ratio,
+                   hidden_states,
+                   attention_mask,
+                   query_lengths,
+                   prompt_lengths,
+                   weights: torch.Tensor = None):
+    # hidden_states = hidden_states.to('cpu')
+    # attention_mask = attention_mask.to('cpu')
+    # query_lengths = query_lengths.to('cpu')
+    # prompt_lengths = prompt_lengths.to('cpu')
+    # weights = weights.to('cpu')
+    # get some specific parameters
+    passage_lengths = torch.sum(attention_mask, dim=1, dtype=torch.int) - query_lengths - prompt_lengths # the raw passage lengths
+    retain_passage_lengths = (passage_lengths + compress_ratio - 1) // compress_ratio # the passage lengths need to be retained
+    final_useful_lengths = query_lengths + prompt_lengths + retain_passage_lengths # the final useful length after compress
+    max_passage_length = torch.max(passage_lengths) # the max passage lengths
+    max_final_lengths = torch.max(final_useful_lengths) # the max useful lengths after compress
+    # make new hidden states and new attention masks
+    new_hidden_states = torch.zeros((hidden_states.shape[0], max_final_lengths,
+                                     hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device)
+    new_attention_mask = torch.ones((hidden_states.shape[0], max_final_lengths), dtype=attention_mask.dtype).to(attention_mask.device)
+    # get new attention mask
+    mask_attention_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0) >= final_useful_lengths[:, None]
+    new_attention_mask[mask_attention_index] = 0
+    # get new hidden states
+    # add query into new hidden states
+    query_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
+    mask_query_index = query_index < query_lengths[:, None]
+    new_hidden_states[mask_query_index] = hidden_states[:, : max_final_lengths, :][mask_query_index]
+    # add prompt into new hidden states
+    # get the index of the prompt in new hidden states
+    new_prompt_start_length = query_lengths + retain_passage_lengths
+    new_prompt_end_length = new_prompt_start_length + prompt_lengths
+    new_prompt_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
+    new_mask_prompt_index_start = new_prompt_index >= new_prompt_start_length[:, None]
+    new_mask_prompt_index_end = new_prompt_index < new_prompt_end_length[:, None]
+    new_mask_prompt_index = new_mask_prompt_index_start & new_mask_prompt_index_end
+    # get the index of the prompt in hidden states
+    raw_prompt_start_length = query_lengths + passage_lengths
+    raw_prompt_end_length = raw_prompt_start_length + prompt_lengths
+    raw_prompt_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+    raw_mask_prompt_index_start = raw_prompt_index >= raw_prompt_start_length[:, None]
+    raw_mask_prompt_index_end = raw_prompt_index < raw_prompt_end_length[:, None]
+    raw_mask_prompt_index = raw_mask_prompt_index_start & raw_mask_prompt_index_end
+    # replace the prompt hidden states
+    new_hidden_states[new_mask_prompt_index] = hidden_states[raw_mask_prompt_index]
+    # 以上均没问题
+
+    # print(new_hidden_states.view(len(new_hidden_states), -1))
+    # print(new_attention_mask)
+
+    # get the index of the passage in new hidden states
+    new_passage_start_length = query_lengths
+    new_passage_end_length = new_passage_start_length + retain_passage_lengths
+    new_passage_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
+    new_mask_passage_index_start = new_passage_index >= new_passage_start_length[:, None]
+    new_mask_passage_index_end = new_passage_index < new_passage_end_length[:, None]
+    new_mask_passage_index = new_mask_passage_index_start & new_mask_passage_index_end
+    # print(query_lengths, prompt_lengths, retain_passage_lengths, final_useful_lengths)
+    # add passage into new hidden states
+    # get mask hidden states
+    psg_start_length = query_lengths
+    psg_end_length = query_lengths + passage_lengths
+    psg_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+    mask_psg_index_start = psg_index >= psg_start_length[:, None]
+    mask_psg_index_end = psg_index < psg_end_length[:, None]
+    mask_psg_index = mask_psg_index_start & mask_psg_index_end
+
+    hidden_states = hidden_states * mask_psg_index.unsqueeze(-1)
+    passage_hidden_states = torch.zeros((hidden_states.shape[0],
+                                         (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio,
+                                         hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device)
+    passage_end_length = passage_lengths
+    passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) # maybe exceed the max passage length
+    mask_passage_index = passage_index < passage_end_length[:, None]
+
+    raw_passage_end_length = query_lengths + passage_lengths
+    raw_passage_start_length = query_lengths
+    raw_passage_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+    raw_mask_passage_index_start = raw_passage_index >= raw_passage_start_length[:, None]
+    raw_mask_passage_index_end = raw_passage_index < raw_passage_end_length[:, None]
+    raw_mask_passage_index = raw_mask_passage_index_start & raw_mask_passage_index_end
+    passage_hidden_states[mask_passage_index] = hidden_states[raw_mask_passage_index]
+
+    passage_weights = torch.zeros((weights.shape[0],
+                                   (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio)
+                                  , dtype=weights.dtype).to(hidden_states.device)
+    weights = torch.sum(weights, dim=1)
+    passage_weights[mask_passage_index] = weights[raw_mask_passage_index]
+    passage_weights = passage_weights.view(passage_weights.shape[0], -1, compress_ratio)
+    passage_weights = passage_weights / torch.sum(passage_weights, dim=-1
+                                                  ).view(passage_weights.shape[0], -1, 1)
+    passage_weights = passage_weights.view(passage_weights.shape[0], -1)
+    # passage_weights = torch.where(passage_weights == torch.nan, 0, passage_weights)
+    passage_hidden_states = passage_hidden_states * passage_weights.unsqueeze(-1)
+    passage_hidden_states = passage_hidden_states.view(passage_hidden_states.shape[0], -1, compress_ratio,
+                                                       passage_hidden_states.shape[-1])
+    passage_hidden_states = torch.sum(passage_hidden_states, dim=2)
+    passage_end_length = retain_passage_lengths
+    passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+    mask_passage_index = passage_index < passage_end_length[:, None]
+    new_hidden_states[new_mask_passage_index] = passage_hidden_states[mask_passage_index]
+
+    return new_hidden_states, new_attention_mask
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class CostWiseMistralModel(MistralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
+
+    Args:
+        config: MistralConfig
+    """
+
+    def __init__(self, config: CostWiseMistralConfig):
+        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(
+            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    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(MISTRAL_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,
+        compress_layer: Optional[int] = None,
+        compress_ratio: Optional[int] = None,
+        cutoff_layers: Optional[List[int]] = None,
+        query_lengths: Optional[int] = None,
+        prompt_lengths: Optional[int] = None,
+    ) -> Union[Tuple, CostWiseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        compress_ratio = None if compress_ratio == 1 else compress_ratio
+        if compress_layer is not None and compress_ratio is not None:
+            output_attentions = True
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if self.config.layer_wise:
+            output_hidden_states = True
+
+        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 decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_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
+
+        if compress_layer is not None and compress_ratio is not None:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with reranker. 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).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            input_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "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.
+            input_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+        else:
+            # 4d mask is passed through the layers
+            input_attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_attention_masks = ()
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) and (
+                torch.sum(attention_mask) != attention_mask.shape[0] * attention_mask.shape[1])
+        query_lengths = [0] * hidden_states.shape[0] if query_lengths is None else query_lengths
+        prompt_lengths = [0] * hidden_states.shape[0] if prompt_lengths is None else prompt_lengths
+        if not isinstance(query_lengths, torch.Tensor):
+            query_lengths = torch.tensor(query_lengths, device=hidden_states.device)
+        if not isinstance(prompt_lengths, torch.Tensor):
+            prompt_lengths = torch.tensor(prompt_lengths, device=hidden_states.device)
+
+        if cutoff_layers is None:
+            max_layer = self.config.num_hidden_layers
+            cutoff_layers = [max_layer]
+        if isinstance(cutoff_layers, int):
+            max_layer = cutoff_layers
+            cutoff_layers = [cutoff_layers]
+        else:
+            max_layer = max(cutoff_layers)
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if self.config.layer_wise:
+                if idx in cutoff_layers and output_hidden_states:
+                    all_hidden_states += (self.norm(hidden_states),)
+                    all_attention_masks += (attention_mask,)
+                if idx == max_layer:
+                    break
+            elif output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if compress_layer is not None and compress_ratio is not None and idx in compress_layer and idx != 0:
+                # if all_self_attns is not None:
+                #     # weights = all_self_attns[-1][:, :, -1, :]
+                #     weights = all_self_attns
+                # else:
+                #     weights = None
+
+                if left_padding:
+                    raise ValueError('You must use right padding...')
+                hidden_states, attention_mask = token_compress(compress_ratio, hidden_states, attention_mask,
+                                                               query_lengths, prompt_lengths, all_self_attns)
+                torch.cuda.empty_cache()
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+                seq_length = hidden_states.shape[1]
+                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 self._attn_implementation == "flash_attention_2":
+                    # 2d mask is passed through the layers
+                    input_attention_mask = attention_mask if (
+                                attention_mask is not None and 0 in attention_mask) else None
+                elif self._attn_implementation == "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.
+                    input_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                        attention_mask,
+                        (batch_size, seq_length),
+                        inputs_embeds,
+                        past_key_values_length,
+                    )
+                else:
+                    # 4d mask is passed through the layers
+                    input_attention_mask = _prepare_4d_causal_attention_mask(
+                        attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+                    )
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    input_attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=input_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],)
+                all_self_attns = layer_outputs[1][:, :, -1, :]
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if not self.config.layer_wise:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+                all_attention_masks += (attention_mask,)
+        else:
+            if output_hidden_states and self.config.num_hidden_layers == max_layer:
+                all_hidden_states += (hidden_states,)
+                all_attention_masks += (attention_mask,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        torch.cuda.empty_cache()
+
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_attention_masks] if
+                v is not None)
+        return CostWiseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            attention_masks=all_attention_masks
+        )
+
+class CostWiseHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.linear_head = nn.Linear(input_size, output_size, bias=False)
+
+    def forward(self, **kwargs):
+        return self.linear_head(**kwargs)
+
+class CostWiseMistralForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = CostWiseMistralModel(config)
+        self.vocab_size = config.vocab_size
+        if not config.layer_wise:
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        else:
+            self.lm_head = nn.ModuleList(
+                [CostWiseHead(config.hidden_size, 1) for _ in range(
+                    config.start_layer, config.num_hidden_layers + 1, config.layer_sep
+                )]
+            )
+
+        # 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(MISTRAL_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,
+        compress_layer: Optional[int] = None,
+        compress_ratio: Optional[int] = None,
+        cutoff_layers: Optional[List[int]] = None,
+        query_lengths: Optional[int] = None,
+        prompt_lengths: Optional[int] = None,
+    ) -> Union[Tuple, CostWiseCausalLMOutputWithPast]:
+        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, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> 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
+
+        if compress_ratio is not None and compress_ratio == 1:
+            compress_ratio = None
+
+        if self.config.layer_wise:
+            if cutoff_layers is None:
+                cutoff_layers = [self.config.num_hidden_layers]
+            elif isinstance(cutoff_layers, int):
+                cutoff_layers = [cutoff_layers]
+            can_use_layers = list(range(self.config.start_layer, self.config.num_hidden_layers + 1, self.config.layer_sep))
+            remove_layers = [i for i in cutoff_layers if i not in can_use_layers]
+            if len(remove_layers) > 0:
+                logger.warning_once(
+                    f"layers {remove_layers} are incompatible with the setting. They will be removed..."
+                )
+            cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]
+            if len(cutoff_layers) == 0:
+                raise ValueError(f"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]")
+
+        # 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,
+            compress_layer=compress_layer,
+            compress_ratio=compress_ratio,
+            query_lengths=query_lengths,
+            prompt_lengths=prompt_lengths,
+            cutoff_layers=cutoff_layers
+        )
+
+        if not self.config.layer_wise:
+            hidden_states = outputs[0]
+            logits = self.lm_head(hidden_states)
+            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)
+        else:
+            hidden_states = outputs.hidden_states
+            logits = ()
+            for i in range(len(hidden_states)):
+                tmp_logits = self.lm_head[i].linear_head(hidden_states[i])
+                tmp_logits = tmp_logits.float()
+                tmp_logits = tmp_logits.reshape(hidden_states[i].shape[0], -1)
+                logits = logits + (tmp_logits,)
+            loss = None
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CostWiseCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            attention_masks=outputs[-1] if self.model.config.layer_wise else outputs[-1][-1]
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        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 exclusively 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

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