Delete Junk

.ipynb_checkpoints/README-checkpoint.md

@@ -1,122 +0,0 @@
----
-pipeline_tag: image-text-to-text
-language:
-- multilingual
-tags:
-- deepseek
-- vision-language
-- ocr
-- custom_code
-license: mit
----
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek AI" />
-</div>
-<hr>
-<div align="center">
-  <a href="https://www.deepseek.com/" target="_blank">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" />
-  </a>
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR" target="_blank">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
-  </a>
-
-</div>
-
-<div align="center">
-
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" />
-  </a>
-
-</div>
-
-
-
-<p align="center">
-  <a href="https://github.com/deepseek-ai/DeepSeek-OCR"><b>🌟 Github</b></a> |
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 Model Download</b></a> |
-  <a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 Paper Link</b></a> |
-  <a href=""><b>📄 Arxiv Paper Link</b></a> |
-</p>
-<h2>
-<p align="center">
-  <a href="">DeepSeek-OCR: Contexts Optical Compression</a>
-</p>
-</h2>
-<p align="center">
-<img src="assets/fig1.png" style="width: 1000px" align=center>
-</p>
-<p align="center">
-<a href="">Explore the boundaries of visual-text compression.</a>       
-</p>
-
-## Usage
-Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：
-
-```
-torch==2.6.0
-transformers==4.46.3
-tokenizers==0.20.3
-einops
-addict 
-easydict
-pip install flash-attn==2.7.3 --no-build-isolation
-```
-
-```python
-from transformers import AutoModel, AutoTokenizer
-import torch
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = '0'
-model_name = 'deepseek-ai/DeepSeek-OCR'
-
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)
-model = model.eval().cuda().to(torch.bfloat16)
-
-# prompt = "<image>\nFree OCR. "
-prompt = "<image>\n<|grounding|>Convert the document to markdown. "
-image_file = 'your_image.jpg'
-output_path = 'your/output/dir'
-
-# infer(self, tokenizer, prompt='', image_file='', output_path = ' ', base_size = 1024, image_size = 640, crop_mode = True, test_compress = False, save_results = False):
-
-# Tiny: base_size = 512, image_size = 512, crop_mode = False
-# Small: base_size = 640, image_size = 640, crop_mode = False
-# Base: base_size = 1024, image_size = 1024, crop_mode = False
-# Large: base_size = 1280, image_size = 1280, crop_mode = False
-
-# Gundam: base_size = 1024, image_size = 640, crop_mode = True
-
-res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = True)
-```
-
-## vLLM
-Refer to [🌟GitHub](https://github.com/deepseek-ai/DeepSeek-OCR/) for guidance on model inference acceleration and PDF processing, etc.<!--  -->
-
-## Visualizations
-<table>
-<tr>
-<td><img src="assets/show1.jpg" style="width: 500px"></td>
-<td><img src="assets/show2.jpg" style="width: 500px"></td>
-</tr>
-<tr>
-<td><img src="assets/show3.jpg" style="width: 500px"></td>
-<td><img src="assets/show4.jpg" style="width: 500px"></td>
-</tr>
-</table>
-
-
-## Acknowledgement
-
-We would like to thank [Vary](https://github.com/Ucas-HaoranWei/Vary/), [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [OneChart](https://github.com/LingyvKong/OneChart), [Slow Perception](https://github.com/Ucas-HaoranWei/Slow-Perception) for their valuable models and ideas.
-
-We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench).
-
-
-## Citation
-Coming soon!

.ipynb_checkpoints/configuration_deepseek_v2-checkpoint.py

@@ -1,210 +0,0 @@
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-class DeepseekV2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
-    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 DeepSeek-V2 with multi-latent attention.
-
-    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 102400):
-            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`DeepseekV2Model`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 11008):
-            Dimension of the MLP representations.
-        moe_intermediate_size (`int`, *optional*, defaults to 1407):
-            Dimension of the MoE 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.
-        n_shared_experts (`int`, *optional*, defaults to None):
-            Number of shared experts, None means dense model.
-        n_routed_experts (`int`, *optional*, defaults to None):
-            Number of routed experts, None means dense model.
-        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
-            Scaling factor or routed experts.
-        topk_method (`str`, *optional*, defaults to `gready`):
-            Topk method used in routed gate.
-        n_group (`int`, *optional*, defaults to None):
-            Number of groups for routed experts.
-        topk_group (`int`, *optional*, defaults to None):
-            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
-        num_experts_per_tok (`int`, *optional*, defaults to None):
-            Number of selected experts, None means dense model.
-        moe_layer_freq (`int`, *optional*, defaults to 1):
-            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
-        first_k_dense_replace (`int`, *optional*, defaults to 0):
-            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
-                                                            \--k dense layers--/
-        norm_topk_prob (`bool`, *optional*, defaults to False):
-            Whether to normalize the weights of the routed experts.
-        scoring_func (`str`, *optional*, defaults to 'softmax'):
-            Method of computing expert weights.
-        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
-            Auxiliary loss weight coefficient.
-        seq_aux = (`bool`, *optional*, defaults to True):
-            Whether to compute the auxiliary loss for each individual sample.
-        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.
-        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.
-        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.
-        use_mla (`bool`, *optional*, defaults to `True`): Use multi-latent attention or multi-head attention. If True,
-            the model will use multi-latent attention, otherwise, it will use multi-head attention.
-
-    ```python
-    >>> from transformers import DeepseekV2Model, DeepseekV2Config
-
-    >>> # Initializing a Deepseek-V2 style configuration
-    >>> configuration = DeepseekV2Config()
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "deepseek_v2"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=102400,
-        hidden_size=4096,
-        intermediate_size=11008,
-        moe_intermediate_size = 1407,
-        num_hidden_layers=30,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        n_shared_experts = None,
-        n_routed_experts = None,
-        ep_size = 1,
-        routed_scaling_factor = 1.0,
-        kv_lora_rank = 512,
-        q_lora_rank = 1536,
-        qk_rope_head_dim = 64,
-        v_head_dim = 128,
-        qk_nope_head_dim = 128,
-        topk_method = 'gready',
-        n_group = None,
-        topk_group = None,
-        num_experts_per_tok = None,
-        moe_layer_freq = 1,
-        first_k_dense_replace = 0,
-        norm_topk_prob = False,
-        scoring_func = 'softmax',
-        aux_loss_alpha = 0.001,
-        seq_aux = True,
-        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=100000,
-        eos_token_id=100001,
-        pretraining_tp=1,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        use_mla=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.moe_intermediate_size = moe_intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.n_shared_experts = n_shared_experts
-        self.n_routed_experts = n_routed_experts
-        self.ep_size = ep_size
-        self.routed_scaling_factor = routed_scaling_factor
-        self.kv_lora_rank = kv_lora_rank
-        self.q_lora_rank = q_lora_rank
-        self.qk_rope_head_dim = qk_rope_head_dim
-        self.v_head_dim = v_head_dim
-        self.qk_nope_head_dim = qk_nope_head_dim
-        self.topk_method = topk_method
-        self.n_group = n_group
-        self.topk_group = topk_group
-        self.num_experts_per_tok = num_experts_per_tok
-        self.moe_layer_freq = moe_layer_freq
-        self.first_k_dense_replace = first_k_dense_replace
-        self.norm_topk_prob = norm_topk_prob
-        self.scoring_func = scoring_func
-        self.aux_loss_alpha = aux_loss_alpha
-        self.seq_aux = seq_aux
-        # 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 = float(rms_norm_eps)
-        self.pretraining_tp = pretraining_tp
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.use_mla = use_mla
-
-        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,
-        )

.ipynb_checkpoints/conversation-checkpoint.py

@@ -1,280 +0,0 @@
-"""
-From https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-"""
-
-import dataclasses
-from enum import IntEnum, auto
-from typing import Any, Dict, List
-
-
-class SeparatorStyle(IntEnum):
-    """Separator styles."""
-
-    DeepSeek = auto()
-    DeepSeekV2 = auto()
-    PLAIN = auto()
-    ALIGNMENT = auto()
-
-
-@dataclasses.dataclass
-class Conversation:
-    """A class that manages prompt templates and keeps all conversation history."""
-
-    # The name of this template
-    name: str
-    # The template of the system prompt
-    system_template: str = "{system_message}"
-    # The system message
-    system_message: str = ""
-    # The names of two roles
-    roles: List[str] = (("USER", "ASSISTANT"),)
-    # All messages. Each item is (role, message).
-    messages: List[List[str]] = ()
-    # The number of few shot examples
-    offset: int = 0
-    # The separator style and configurations
-    sep_style: SeparatorStyle = SeparatorStyle.DeepSeek
-    sep: str = "\n"
-    sep2: str = None
-    # Stop criteria (the default one is EOS token)
-    stop_str: str = None
-    # Stops generation if meeting any token in this list
-    stop_token_ids: List[int] = None
-
-    def get_prompt(self) -> str:
-        """Get the prompt for generation."""
-        system_prompt = self.system_template.format(system_message=self.system_message)
-        if self.sep_style == SeparatorStyle.DeepSeek:
-            seps = [self.sep, self.sep2]
-            if system_prompt == "" or system_prompt is None:
-                ret = ""
-            else:
-                ret = system_prompt + seps[0]
-            for i, (role, message) in enumerate(self.messages):
-                if message:
-                    ret += role + ": " + message + seps[i % 2]
-                else:
-                    ret += role + ":"
-            return ret
-        elif self.sep_style == SeparatorStyle.DeepSeekV2:
-            seps = [self.sep, self.sep2]
-            if system_prompt == "" or system_prompt is None:
-                ret = ""
-            else:
-                ret = system_prompt + seps[0]
-            for i, (role, message) in enumerate(self.messages):
-                if message:
-                    if role == "User":
-                        ret += "<｜sft▁begin｜>\n" + message + self.sep #<｜sft▁begin｜>User Input<｜sft▁end｜>\nResponse<｜end▁of▁sentence｜>
-                    else:
-                        ret += message + self.sep2
-                else:
-                    ret = ret
-            return ret
-
-        elif self.sep_style == SeparatorStyle.PLAIN:
-            seps = [self.sep, self.sep2]
-            ret = ""
-            for i, (role, message) in enumerate(self.messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    if i % 2 == 0:
-                        ret += message + seps[i % 2]
-                    else:
-                        ret += message + seps[i % 2]
-                else:
-                    ret += ""
-            return ret
-        elif self.sep_style == SeparatorStyle.ALIGNMENT:
-            seps = [self.sep, self.sep2]
-            ret = ""
-            for i, (role, message) in enumerate(self.messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    if i % 2 == 0:
-                        ret += '<image>\n' + seps[i % 2]
-                    else:
-                        ret += message + seps[i % 2]
-                else:
-                    ret += ""
-            return ret
-        else:
-            raise ValueError(f"Invalid style: {self.sep_style}")
-
-    def set_system_message(self, system_message: str):
-        """Set the system message."""
-        self.system_message = system_message
-
-    def append_message(self, role: str, message: str):
-        """Append a new message."""
-        self.messages.append([role, message])
-
-    def update_last_message(self, message: str):
-        """Update the last output.
-
-        The last message is typically set to be None when constructing the prompt,
-        so we need to update it in-place after getting the response from a model.
-        """
-        self.messages[-1][1] = message
-
-    def reset_message(self):
-        """Reset a new message."""
-        self.messages = []
-
-    def to_gradio_chatbot(self):
-        """Convert the conversation to gradio chatbot format."""
-        ret = []
-        for i, (role, msg) in enumerate(self.messages[self.offset :]):
-            if i % 2 == 0:
-                ret.append([msg, None])
-            else:
-                ret[-1][-1] = msg
-        return ret
-
-    def to_openai_api_messages(self):
-        """Convert the conversation to OpenAI chat completion format."""
-        system_prompt = self.system_template.format(system_message=self.system_message)
-        ret = [{"role": "system", "content": system_prompt}]
-
-        for i, (_, msg) in enumerate(self.messages[self.offset :]):
-            if i % 2 == 0:
-                ret.append({"role": "user", "content": msg})
-            else:
-                if msg is not None:
-                    ret.append({"role": "assistant", "content": msg})
-        return ret
-
-    def copy(self):
-        return Conversation(
-            name=self.name,
-            system_template=self.system_template,
-            system_message=self.system_message,
-            roles=self.roles,
-            messages=[[x, y] for x, y in self.messages],
-            offset=self.offset,
-            sep_style=self.sep_style,
-            sep=self.sep,
-            sep2=self.sep2,
-            stop_str=self.stop_str,
-            stop_token_ids=self.stop_token_ids,
-        )
-
-    def dict(self):
-        return {
-            "template_name": self.name,
-            "system_message": self.system_message,
-            "roles": self.roles,
-            "messages": self.messages,
-            "offset": self.offset,
-        }
-
-
-# A global registry for all conversation templates
-conv_templates: Dict[str, Conversation] = {}
-
-
-def register_conv_template(template: Conversation, override: bool = False):
-    """Register a new conversation template."""
-    if not override:
-        assert template.name not in conv_templates, f"{template.name} has been registered."
-
-    conv_templates[template.name] = template
-
-
-def get_conv_template(name: str) -> Conversation:
-    """Get a conversation template."""
-    return conv_templates[name].copy()
-
-
-register_conv_template(
-    Conversation(
-        name="deepseek",
-        system_template="{system_message}",
-        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
-        # "thinking step by step to be sure you get the right answer.",
-        system_message="",
-        roles=("<|User|>", "<|Assistant|>"),
-        messages=(),
-        offset=0,
-        sep_style=SeparatorStyle.DeepSeek,
-        sep="\n\n",
-        sep2="<｜end▁of▁sentence｜>",
-        stop_token_ids=[100001],
-        stop_str=["User:", "<｜end▁of▁sentence｜>"]
-    )
-)
-register_conv_template(
-    Conversation(
-        name="deepseekv2",
-        system_template="{system_message}",
-        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
-        # "thinking step by step to be sure you get the right answer.",
-        system_message="",
-        roles=("<｜User｜>", "<｜Assistant｜>"),
-        messages=(),
-        offset=0,
-        sep_style=SeparatorStyle.DeepSeek,
-        sep="",
-        sep2="<｜end▁of▁sentence｜>",
-        stop_token_ids=[100001],
-        stop_str=["User:", "<｜end▁of▁sentence｜>"]
-    )
-)
-
-
-register_conv_template(
-    Conversation(
-        name="plain",
-        system_template="",
-        system_message="",
-        roles=("", ""),
-        messages=(),
-        offset=0,
-        sep_style=SeparatorStyle.PLAIN,
-        sep="",
-        sep2="",
-        stop_token_ids=[100001],
-        stop_str=['</s>'],
-    )
-)
-
-
-register_conv_template(
-    Conversation(
-        name="alignment",
-        system_template="",
-        system_message="",
-        roles=("", ""),
-        messages=(),
-        offset=0,
-        sep_style=SeparatorStyle.ALIGNMENT,
-        sep="",
-        sep2="",
-        stop_token_ids=[100001],
-        stop_str=['</s>'],
-    )
-)
-
-
-if __name__ == "__main__":
-    print("deepseek template:")
-    conv = get_conv_template("deepseek")
-    conv.append_message(conv.roles[0], "Hello!")
-    conv.append_message(conv.roles[1], "Hi! This is Tony.")
-    conv.append_message(conv.roles[0], "Who are you?")
-    conv.append_message(conv.roles[1], "I am a helpful assistant.")
-    conv.append_message(conv.roles[0], "How are you?")
-    conv.append_message(conv.roles[1], None)
-    print(conv.get_prompt())
-
-    print("deepseekv2 template:")
-    conv = get_conv_template("deepseekv2")
-    conv.append_message(conv.roles[0], "Hello!")
-    conv.append_message(conv.roles[1], "Hi! This is Tony.")
-    conv.append_message(conv.roles[0], "Who are you?")
-    conv.append_message(conv.roles[1], "I am a helpful assistant.")
-    conv.append_message(conv.roles[0], "How are you?")
-    conv.append_message(conv.roles[1], None)
-    print(conv.get_prompt())

.ipynb_checkpoints/deepencoder-checkpoint.py

@@ -1,1058 +0,0 @@
-import torch.nn as nn
-import torch
-import torch.nn.functional as F
-import copy
-
-from contextlib import nullcontext
-import math
-from typing import Optional, Tuple
-# from megatron.model import LayerNorm
-
-from einops import rearrange
-from easydict import EasyDict as adict
-
-
-from typing import Optional, Tuple, Type
-from functools import partial
-
-
-
-class MlpProjector(nn.Module):
-
-    def __init__(self, cfg):
-
-        super().__init__()
-
-        self.cfg = cfg
-
-        if cfg.projector_type == "identity":
-            modules = nn.Identity()
-
-        elif cfg.projector_type == "linear":
-            modules = nn.Linear(cfg.input_dim, cfg.n_embed)
-
-        elif cfg.projector_type == "mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
-            for _ in range(1, mlp_depth):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
-            modules = nn.Sequential(*modules)
-        
-        elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            mlp_ratio = cfg.get("mlp_ratio", 1)
-            modules = [
-                nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
-                nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
-            ]
-            for _ in range(1, mlp_depth - 1):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
-            modules.append(nn.GELU())
-            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
-            modules = nn.Sequential(*modules)
-        
-        elif cfg.projector_type == "downsample_mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            mlp_ratio = cfg.get("mlp_ratio", 1)
-            modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
-            for _ in range(1, mlp_depth - 1):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
-            modules.append(nn.GELU())
-            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
-            modules = nn.Sequential(*modules)
-
-        elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
-            self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
-
-            modules = []
-            for _ in range(1, mlp_depth):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
-            modules = nn.Sequential(*modules)
-
-        elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            channel_div = cfg.get("channel_div", 0.5)
-            self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
-            self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))
-
-            modules = []
-            for _ in range(1, mlp_depth):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
-            modules = nn.Sequential(*modules)
-
-        elif cfg.projector_type == "low_high_split_mlp_gelu":
-            mlp_depth = cfg.get("depth", 1)
-            modules = []
-            for _ in range(1, mlp_depth):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
-            modules = nn.Sequential(*modules)
-            self.high_layers = nn.Sequential(*modules)
-            self.low_layers = copy.deepcopy(modules)
-
-        else:
-            raise ValueError(f"Unknown projector type: {cfg.projector_type}")
-
-        if cfg.get("token_pooling", False):
-            self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
-
-        if cfg.get("conv_fusion_high_low_features", False):
-            self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
-        self.layers = modules
-
-    def forward(self, x):
-        if self.cfg.get("token_pooling", False):
-            batch_size, wxh, channels = x.shape
-            w = h = int(wxh**0.5)
-            x = x.view(batch_size, w, h, channels)
-            x = x.permute(0, 3, 1, 2)
-            # import ipdb; ipdb.set_trace()
-            patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
-            batch_size, channels, h_patches, w_patches, _, _ = patches.size()
-            # 在通道维度上拼接
-            patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)
-
-            # 通过线性层
-            patches = patches.permute(0, 2, 1, 3).contiguous()
-            patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
-
-            x = self.token_pooling_layer(patches)
-        
-        if self.cfg.get("conv_fusion_high_low_features", False):
-            x = self.fusion_layer(x[:, 0]) + x[:, 1]
-
-        if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
-            high_x, low_x = x[0], x[1]
-            high_x = self.high_up_proj(high_x)
-            low_x = self.low_up_proj(low_x)
-            x = torch.concat([high_x, low_x], dim=-1)
-        
-        if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
-            high_x = x[...,:self.cfg.input_dim[0]]
-            low_x = x[...,self.cfg.input_dim[0]:]
-            high_x = self.high_up_proj(high_x)
-            low_x = self.low_up_proj(low_x)
-            x = torch.concat([high_x, low_x], dim=-1)
-        
-        if self.cfg.projector_type == 'low_high_split_mlp_gelu':
-            high_x, low_x = x[0], x[1]
-            high_x = self.high_layers(high_x)
-            low_x = self.low_layers(low_x)
-            x = torch.concat([high_x, low_x], dim=-1)
-            return x
-        
-        if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
-            bs, hw, input_dim = x.shape
-            h = w = int((hw) ** 0.5)
-
-            """compute padding"""
-            if h % self.cfg.downsample_ratio:
-                pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
-            else:
-                pad = 0
-            x = x.reshape(bs, h, w, input_dim)
-            if pad > 0:
-                x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
-
-            """4 to 1 concat"""
-            x = x.permute(0, 3, 1, 2)  # B, C, H, W
-            x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
-            x = x.permute(0, 2, 1)
-            
-        return self.layers(x)
-
-    @staticmethod
-    def get_flops_per_sample(cfg):
-        if cfg.projector_type == "linear":
-            fwd = 2 * cfg.input_dim * cfg.n_embed
-
-        elif "mlp_gelu" in cfg.projector_type :
-            mlp_depth = cfg.get("depth", 1)
-            downsample_ratio = cfg.get("downsample_ratio", 1)
-            input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
-            input_dim = input_dim * downsample_ratio * downsample_ratio
-            fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
-        else:
-            fwd = 0
-
-        return fwd * 3
-    
-
-#===================clip============================================================
-
-class LayerNormfp32(torch.nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-def get_abs_pos(abs_pos, tgt_size):
-    # abs_pos: L, C
-    # tgt_size: M
-    # return: M, C
-
-    # print(tgt_size)
-    # print(abs_pos.shape)
-    # exit()
-    dim = abs_pos.size(-1)
-    # print(dim)
-    abs_pos_new = abs_pos.squeeze(0)
-    cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]
-
-
-
-    src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
-    tgt_size = int(math.sqrt(tgt_size))
-    dtype = abs_pos.dtype
-
-    if src_size != tgt_size:
-        old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1,
-                                                                                    2).contiguous()
-        old_pos_embed = old_pos_embed.to(torch.float32)
-        new_pos_embed = F.interpolate(
-            old_pos_embed,
-            size=(tgt_size, tgt_size),
-            mode='bicubic',
-            antialias=True,
-            align_corners=False,
-        ).to(dtype)
-        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
-        new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
-        vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
-        vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
-        return vision_pos_embed
-    else:
-        return abs_pos
-
-@torch.jit.script
-def quick_gelu(x):
-    return x * torch.sigmoid(1.702 * x)
-
-
-
-class CLIPVisionEmbeddings(nn.Module):
-    def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
-        super().__init__()
-        self.embed_dim = hidden_size
-        self.image_size = image_size
-        self.patch_size = patch_size
-
-        self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))
-
-        self.patch_embedding = torch.nn.Conv2d(
-            in_channels=num_channels,
-            out_channels=self.embed_dim,
-            kernel_size=self.patch_size,
-            stride=self.patch_size,
-            bias=False,
-        )
-
-        self.num_patches = (self.image_size // self.patch_size) ** 2
-        self.num_positions = self.num_patches + 1
-        self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)
-        self.register_buffer(
-            "position_ids", torch.arange(self.num_positions).expand((1, -1))
-        )
-
-    def forward(self, pixel_values, patch_embeds):
-        batch_size = pixel_values.shape[0]
-        # patch_embeds = self.patch_embedding(
-        #     pixel_values
-        # )  # shape = [*, width, grid, grid]
-
-
-        if patch_embeds is not None:
-            patch_embeds = patch_embeds
-            # print(patch_embeds.shape)
-        else:
-            patch_embeds = self.patch_embedding(pixel_values)  
-            # print(111111)
-        # shape = [*, width, grid, grid]
-        # patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-
-        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-
-        # x = torch.cat([cls_token, x], dim=1)
-        embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1))
-        # embeddings = embeddings + self.position_embedding(self.position_ids)
-        return embeddings
-
-
-class NoTPFeedForward(nn.Module):
-    def __init__(
-            self,
-            cfg,
-            dim: int,
-            hidden_dim: int,
-    ):
-        super().__init__()
-
-        self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
-        self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)
-
-    def forward(self, x):
-        output = self.fc2(quick_gelu(self.fc1(x)))
-        return output
-
-
-
-
-class NoTPAttention(torch.nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.num_heads = cfg.num_attention_heads
-        self.n_local_heads = cfg.num_attention_heads
-        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
-        self.max_seq_len = cfg.seq_length
-        self.use_flash_attention = cfg.use_flash_attn
-
-        self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
-        self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)
-
-        # self.core_attention = CoreAttention(cfg, AttnType.self_attn)
-
-        self.attn_drop = cfg.attention_dropout
-
-    def forward(
-            self,
-            x: torch.Tensor,
-    ):
-        bsz, seqlen, _ = x.shape
-        xqkv = self.qkv_proj(x)
-        xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
-
-        if self.use_flash_attention:
-
-            xq, xk, xv = torch.split(xqkv, 1, dim=2)
-            xq = xq.squeeze(2)
-            xk = xk.squeeze(2)
-            xv = xv.squeeze(2)
-            # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
-
-            # （B, num_head, S, head_size)
-            xq = xq.permute(0, 2, 1, 3)
-            xk = xk.permute(0, 2, 1, 3)
-            xv = xv.permute(0, 2, 1, 3)
-            # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
-            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
-            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
-                # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
-        else:
-            # print(22222)
-            xq, xk, xv = torch.split(xqkv, 1, dim=2)
-            xq = xq.squeeze(2)
-            xk = xk.squeeze(2)
-            xv = xv.squeeze(2)
-            # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
-
-            # （B, num_head, S, head_size)
-            xq = xq.permute(0, 2, 1, 3)
-            xk = xk.permute(0, 2, 1, 3)
-            xv = xv.permute(0, 2, 1, 3)
-            # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
-            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
-            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
-            # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
-        output = self.out_proj(output)
-        return output
-
-class NoTPTransformerBlock(nn.Module):
-    def __init__(self, cfg, layer_id: int, multiple_of=256):
-        super().__init__()
-
-        self.n_heads = cfg.num_attention_heads
-        self.dim = cfg.hidden_size
-        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
-        self.self_attn = NoTPAttention(cfg)
-        self.mlp = NoTPFeedForward(
-            cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size
-        )
-        self.layer_id = layer_id
-        self.layer_norm1 = torch.nn.LayerNorm(
-            cfg.hidden_size, eps=cfg.layernorm_epsilon
-        )
-        self.layer_norm2 = torch.nn.LayerNorm(
-            cfg.hidden_size, eps=cfg.layernorm_epsilon
-        )
-
-    def forward(self, x: torch.Tensor):
-        residual = self.self_attn.forward(self.layer_norm1(x))
-        h = x + residual
-        out = h + self.mlp.forward(self.layer_norm2(h))
-        return out
-
-
-class NoTPTransformer(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-
-        self.cfg = cfg
-        # self.recompute_list = self.cfg.get("recompute_list", [])
-        self.num_layers = cfg.num_layers  # _get_num_layers(cfg)
-
-        self.layers = torch.nn.ModuleList()
-        for layer_id in range(self.num_layers):
-            self.layers.append(
-                NoTPTransformerBlock(
-                    cfg,
-                    layer_id + 1,
-                )
-            )
-
-    def forward(
-            self,
-            hidden_states,
-    ):
-
-        for lid, layer in enumerate(self.layers):
-            # if lid in self.recompute_list:
-            #     def custom(layer_id):
-            #         def custom_forward(*args, **kwargs):
-            #             x_ = self.layers[layer_id](*args, **kwargs)
-            #             return x_
-
-            #         return custom_forward
-
-            #     assert hidden_states.requires_grad == True, logger.warning(
-            #         "When using recalculation, the input must have grad fn"
-            #     )
-            #     hidden_states = tensor_parallel.checkpoint(
-            #         custom(lid),
-            #         False,
-            #         hidden_states.contiguous()
-            #     )
-            # else:
-            hidden_states = layer(hidden_states)
-
-        return hidden_states
-
-
-# from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter
-
-class VitModel(nn.Module):
-    def __init__(
-            self,
-            cfg,
-            freeze_embed=False,
-            freeze_pre_norm=False
-    ) -> None:
-        super().__init__()
-
-        self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size)
-
-        if freeze_embed:
-            for name, param in self.embeddings.named_parameters():
-                param.requires_grad = False
-
-        self.transformer = NoTPTransformer(cfg=cfg)
-
-        if cfg.get("fp32norm", False):
-            logger.info("Load fp32 layernorm for ViT.")
-            self.pre_layrnorm = LayerNormfp32(
-                cfg.hidden_size,
-                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
-            )
-        else:
-            self.pre_layrnorm = torch.nn.LayerNorm(
-                cfg.hidden_size,
-                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
-            )
-
-        # self.pre_layrnorm = RMSNorm(
-        #     cfg.hidden_size,
-        #     eps=cfg.get("pre_layernorm_epsilon", 1e-5),
-        #     sequence_parallel=False,
-        #     use_fp32=True,
-        #     use_optimus=True,
-        # )
-
-        if freeze_pre_norm:
-            for name, param in self.pre_layrnorm.named_parameters():
-                param.requires_grad = False
-
-        for p in self.parameters():
-            p.micro_dp = True
-
-    def set_input_tensor(self, input_tensor):
-        if not isinstance(input_tensor, list):
-            input_tensor = [input_tensor]
-        self.transformer.set_input_tensor(input_tensor[0])
-
-    def __str__(self) -> str:
-        return "open_clip"
-
-    def forward(
-            self,
-            x,
-            patch_embeds
-    ):
-        x = self.embeddings(x, patch_embeds)
-        hidden_states = self.pre_layrnorm(x)
-
-        # hidden_states, dis = local_dp_scatter(hidden_states)
-        output = self.transformer(hidden_states)
-
-        # output = local_dp_reduce(output, dis)
-
-        return output
-
-
-vit_model_cfg = adict(
-    num_layers=24,
-    hidden_size=1024,
-    num_heads = 16,
-    num_attention_heads=16,
-    ffn_hidden_size=4096,
-    seq_length=256,
-    max_position_embeddings=256,
-    use_flash_attn=False,
-    understand_projector_stride=2,
-    hidden_dropout = 0.0,
-    attention_dropout = 0.0,
-    no_persist_layer_norm = False,
-    layernorm_epsilon = 1e-5,
-    pre_layernorm_epsilon = 1e-5,
-    image_size = 224,
-    patch_size = 14,
-    recompute_list = []
-)
-
-def build_clip_l():
-    return VitModel(
-        cfg=vit_model_cfg,
-        freeze_embed=False,
-        freeze_pre_norm=False,
-    )
-
-
-
-
-
-#=========================Sam-Vary=================================
-
-
-def get_abs_pos_sam(abs_pos, tgt_size):
-
-    dtype = abs_pos.dtype
-
-    src_size = abs_pos.size(1)
-
-    if src_size != tgt_size:
-        old_pos_embed = abs_pos.permute(0, 3, 1, 2)
-        old_pos_embed = old_pos_embed.to(torch.float32)
-        new_pos_embed = F.interpolate(
-            old_pos_embed,
-            size=(tgt_size, tgt_size),
-            mode='bicubic',
-            antialias=True,
-            align_corners=False,
-        ).to(dtype)
-        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
-        return new_pos_embed
-    else:
-        return abs_pos
-
-
-
-
-class MLPBlock(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        mlp_dim: int,
-        act: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        super().__init__()
-        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
-        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
-        self.act = act()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
-class LayerNorm2d(nn.Module):
-    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        u = x.mean(1, keepdim=True)
-        s = (x - u).pow(2).mean(1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.eps)
-        x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
-    def __init__(
-        self,
-        img_size: int = 1024,
-        patch_size: int = 16,
-        in_chans: int = 3,
-        embed_dim: int = 768,
-        depth: int = 12,
-        num_heads: int = 12,
-        mlp_ratio: float = 4.0,
-        out_chans: int = 256,
-        qkv_bias: bool = True,
-        norm_layer: Type[nn.Module] = nn.LayerNorm,
-        act_layer: Type[nn.Module] = nn.GELU,
-        use_abs_pos: bool = True,
-        use_rel_pos: bool = False,
-        rel_pos_zero_init: bool = True,
-        window_size: int = 0,
-        global_attn_indexes: Tuple[int, ...] = (),
-    ) -> None:
-        """
-        Args:
-            img_size (int): Input image size.
-            patch_size (int): Patch size.
-            in_chans (int): Number of input image channels.
-            embed_dim (int): Patch embedding dimension.
-            depth (int): Depth of ViT.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_abs_pos (bool): If True, use absolute positional embeddings.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks.
-            global_attn_indexes (list): Indexes for blocks using global attention.
-        """
-        super().__init__()
-        self.img_size = img_size
-
-        self.patch_embed = PatchEmbed(
-            kernel_size=(patch_size, patch_size),
-            stride=(patch_size, patch_size),
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-
-        self.pos_embed: Optional[nn.Parameter] = None
-        if use_abs_pos:
-            # Initialize absolute positional embedding with pretrain image size.
-            self.pos_embed = nn.Parameter(
-                torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
-            )
-
-        self.blocks = nn.ModuleList()
-        for i in range(depth):
-            block = Block(
-                dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                norm_layer=norm_layer,
-                act_layer=act_layer,
-                use_rel_pos=use_rel_pos,
-                rel_pos_zero_init=rel_pos_zero_init,
-                window_size=window_size if i not in global_attn_indexes else 0,
-                input_size=(img_size // patch_size, img_size // patch_size),
-            )
-            self.blocks.append(block)
-
-        self.neck = nn.Sequential(
-            nn.Conv2d(
-                embed_dim,
-                out_chans,
-                kernel_size=1,
-                bias=False,
-            ),
-            LayerNorm2d(out_chans),
-            nn.Conv2d(
-                out_chans,
-                out_chans,
-                kernel_size=3,
-                padding=1,
-                bias=False,
-            ),
-            LayerNorm2d(out_chans),
-        )
-
-        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
-        self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.patch_embed(x)
-        if self.pos_embed is not None:
-            # x = x + self.pos_embed
-            x = x + get_abs_pos_sam(self.pos_embed, x.size(1))
-
-        for blk in self.blocks:
-            x = blk(x)
-
-        x = self.neck(x.permute(0, 3, 1, 2))
-        x2 = self.net_2(x)
-        x3 = self.net_3(x2.clone())
-
-        return x3
-
-
-class Block(nn.Module):
-    """Transformer blocks with support of window attention and residual propagation blocks"""
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qkv_bias: bool = True,
-        norm_layer: Type[nn.Module] = nn.LayerNorm,
-        act_layer: Type[nn.Module] = nn.GELU,
-        use_rel_pos: bool = False,
-        rel_pos_zero_init: bool = True,
-        window_size: int = 0,
-        input_size: Optional[Tuple[int, int]] = None,
-    ) -> None:
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks. If it equals 0, then
-                use global attention.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative
-                positional parameter size.
-        """
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            use_rel_pos=use_rel_pos,
-            rel_pos_zero_init=rel_pos_zero_init,
-            input_size=input_size if window_size == 0 else (window_size, window_size),
-        )
-
-        self.norm2 = norm_layer(dim)
-        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
-        self.window_size = window_size
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        shortcut = x
-        x = self.norm1(x)
-        # Window partition
-        if self.window_size > 0:
-            H, W = x.shape[1], x.shape[2]
-            x, pad_hw = window_partition(x, self.window_size)
-
-        x = self.attn(x)
-        # Reverse window partition
-        if self.window_size > 0:
-            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
-        x = shortcut + x
-        x = x + self.mlp(self.norm2(x))
-
-        return x
-
-
-class Attention(nn.Module):
-    """Multi-head Attention block with relative position embeddings."""
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int = 8,
-        qkv_bias: bool = True,
-        use_rel_pos: bool = False,
-        rel_pos_zero_init: bool = True,
-        input_size: Optional[Tuple[int, int]] = None,
-    ) -> None:
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads.
-            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
-            rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative
-                positional parameter size.
-        """
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim**-0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.proj = nn.Linear(dim, dim)
-
-        self.use_rel_pos = use_rel_pos
-        if self.use_rel_pos:
-            assert (
-                input_size is not None
-            ), "Input size must be provided if using relative positional encoding."
-            # initialize relative positional embeddings
-            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
-            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        B, H, W, _ = x.shape
-        # qkv with shape (3, B, nHead, H * W, C)
-        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        # q, k, v with shape (B * nHead, H * W, C)
-        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
-        rel_h, rel_w = None, None
-        if self.use_rel_pos:
-            rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
-        q = q.view(B, self.num_heads, H * W, -1)
-        k = k.view(B, self.num_heads, H * W, -1)
-        v = v.view(B, self.num_heads, H * W, -1)
-
-        if self.use_rel_pos:
-            rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3))
-            rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3))
-            attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4))
-            x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias)
-            # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
-        else:
-            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
-
-        x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
-
-        x = self.proj(x)
-
-        return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
-    """
-    Partition into non-overlapping windows with padding if needed.
-    Args:
-        x (tensor): input tokens with [B, H, W, C].
-        window_size (int): window size.
-
-    Returns:
-        windows: windows after partition with [B * num_windows, window_size, window_size, C].
-        (Hp, Wp): padded height and width before partition
-    """
-    B, H, W, C = x.shape
-
-    pad_h = (window_size - H % window_size) % window_size
-    pad_w = (window_size - W % window_size) % window_size
-    if pad_h > 0 or pad_w > 0:
-        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
-    Hp, Wp = H + pad_h, W + pad_w
-
-    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows, (Hp, Wp)
-
-
-def window_unpartition(
-    windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
-    """
-    Window unpartition into original sequences and removing padding.
-    Args:
-        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
-        window_size (int): window size.
-        pad_hw (Tuple): padded height and width (Hp, Wp).
-        hw (Tuple): original height and width (H, W) before padding.
-
-    Returns:
-        x: unpartitioned sequences with [B, H, W, C].
-    """
-    Hp, Wp = pad_hw
-    H, W = hw
-    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
-    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
-    if Hp > H or Wp > W:
-        x = x[:, :H, :W, :].contiguous()
-    return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
-    """
-    Get relative positional embeddings according to the relative positions of
-        query and key sizes.
-    Args:
-        q_size (int): size of query q.
-        k_size (int): size of key k.
-        rel_pos (Tensor): relative position embeddings (L, C).
-
-    Returns:
-        Extracted positional embeddings according to relative positions.
-    """
-    max_rel_dist = int(2 * max(q_size, k_size) - 1)
-    # Interpolate rel pos if needed.
-    if rel_pos.shape[0] != max_rel_dist:
-        # Interpolate rel pos.
-        dtype = rel_pos.dtype
-        rel_pos = rel_pos.to(torch.float32)
-        rel_pos_resized = F.interpolate(
-            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
-            size=max_rel_dist,
-            mode="linear",
-        ).to(dtype)
-        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
-    else:
-        rel_pos_resized = rel_pos
-
-    # Scale the coords with short length if shapes for q and k are different.
-    q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0)
-    k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0)
-    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
-    return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
-    q: torch.Tensor,
-    rel_pos_h: torch.Tensor,
-    rel_pos_w: torch.Tensor,
-    q_size: Tuple[int, int],
-    k_size: Tuple[int, int],
-) -> torch.Tensor:
-    """
-    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
-    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
-    Args:
-        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
-        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
-        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
-        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
-        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
-    Returns:
-        attn (Tensor): attention map with added relative positional embeddings.
-    """
-    q_h, q_w = q_size
-    k_h, k_w = k_size
-    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
-    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
-    B, _, dim = q.shape
-    r_q = q.reshape(B, q_h, q_w, dim)
-    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
-    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-    rel_h = rel_h.unsqueeze(-1)
-    rel_w = rel_w.unsqueeze(-2)
-    rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
-    rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)
-
-    return rel_h, rel_w
-
-
-class PatchEmbed(nn.Module):
-    """
-    Image to Patch Embedding.
-    """
-
-    def __init__(
-        self,
-        kernel_size: Tuple[int, int] = (16, 16),
-        stride: Tuple[int, int] = (16, 16),
-        padding: Tuple[int, int] = (0, 0),
-        in_chans: int = 3,
-        embed_dim: int = 768,
-    ) -> None:
-        """
-        Args:
-            kernel_size (Tuple): kernel size of the projection layer.
-            stride (Tuple): stride of the projection layer.
-            padding (Tuple): padding size of the projection layer.
-            in_chans (int): Number of input image channels.
-            embed_dim (int): Patch embedding dimension.
-        """
-        super().__init__()
-
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.proj(x)
-        # B C H W -> B H W C
-        x = x.permute(0, 2, 3, 1)
-        return x
-
-
-def build_sam_vit_b(checkpoint=None):
-    return _build_sam(
-        encoder_embed_dim=768,
-        encoder_depth=12,
-        encoder_num_heads=12,
-        encoder_global_attn_indexes=[2, 5, 8, 11],
-        checkpoint=checkpoint,
-    )
-
-def build_sam_fast_vit_b(checkpoint=None, compile_mode='max-autotune', dtype=torch.bfloat16):
-    image_encoder = build_sam_vit_b(checkpoint).eval().to(dtype)
-    # sam = _apply_eval_dtype_sam(sam, dtype)
-    image_encoder = torch.compile(image_encoder, mode=compile_mode)
-    return image_encoder
-
-
-def _build_sam(
-    encoder_embed_dim,
-    encoder_depth,
-    encoder_num_heads,
-    encoder_global_attn_indexes,
-    checkpoint=None,
-):
-    prompt_embed_dim = 256
-    image_size = 1024
-    vit_patch_size = 16
-    image_embedding_size = image_size // vit_patch_size
-    image_encoder=ImageEncoderViT(
-            depth=encoder_depth,
-            embed_dim=encoder_embed_dim,
-            img_size=image_size,
-            mlp_ratio=4,
-            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
-            num_heads=encoder_num_heads,
-            patch_size=vit_patch_size,
-            qkv_bias=True,
-            use_rel_pos=True,
-            global_attn_indexes=encoder_global_attn_indexes,
-            window_size=14,
-            out_chans=prompt_embed_dim,
-        )
-    image_encoder.eval()
-    if checkpoint is not None:
-        # with open(checkpoint, "rb") as f:
-        state_dict = torch.load(checkpoint)
-        # print(state_dict.keys())
-        # for key in state_dict:
-        # image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False)
-        # ocr-anyting
-        # image_encoder.load_state_dict(state_dict, strict=True)
-        # tob
-        image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True)
-        print(checkpoint)
-    return image_encoder

.ipynb_checkpoints/modeling_deepseekocr-checkpoint.py

@@ -1,1043 +0,0 @@
-# MIT License modified from prithivMLmods/DeepSeek-OCR-Latest-BF16.I64
-import os
-import math
-import re
-from tqdm import tqdm
-from abc import ABC
-from typing import List, Optional, Tuple, Union
-
-from addict import Dict
-from PIL import Image, ImageOps, ImageDraw, ImageFont
-import numpy as np
-
-import torch
-import torch.nn as nn
-from torch.nn import CrossEntropyLoss
-from torchvision import transforms
-
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers import DeepseekV2Model, DeepseekV2ForCausalLM
-from transformers import DeepseekV2Config
-from transformers.models.deepseek_v2.modeling_deepseek_v2 import (
-    DeepseekV2Attention, DeepseekV2MLP, DeepseekV2MoE, DeepseekV2RMSNorm, DeepseekV2DecoderLayer)
-from transformers.models.llama.modeling_llama import LlamaAttention, LlamaRotaryEmbedding
-from transformers import TextStreamer
-from .deepencoder import build_sam_vit_b, build_clip_l, MlpProjector
-from .conversation import get_conv_template
-
-torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
-
-def load_image(image_path):
-
-    try:
-        image = Image.open(image_path)
-        
-        corrected_image = ImageOps.exif_transpose(image)
-        
-        return corrected_image
-        
-    except Exception as e:
-        print(f"error: {e}")
-        try:
-            return Image.open(image_path)
-        except:
-            return None
-
-
-def re_match(text):
-    pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
-    matches = re.findall(pattern, text, re.DOTALL)
-
-    # pattern1 = r'<\|ref\|>.*?<\|/ref\|>\n'
-    # new_text1 = re.sub(pattern1, '', text, flags=re.DOTALL)
-
-    mathes_image = []
-    mathes_other = []
-    for a_match in matches:
-        if '<|ref|>image<|/ref|>' in a_match[0]:
-            mathes_image.append(a_match[0])
-        else:
-            mathes_other.append(a_match[0])
-    return matches, mathes_image, mathes_other
-
-
-def extract_coordinates_and_label(ref_text, image_width, image_height):
-
-    try:
-        label_type = ref_text[1]
-        cor_list = eval(ref_text[2])
-    except Exception as e:
-        print(e)
-        return None
-
-    return (label_type, cor_list)
-
-
-def draw_bounding_boxes(image, refs, ouput_path):
-
-    image_width, image_height = image.size
-    
-    img_draw = image.copy()
-    draw = ImageDraw.Draw(img_draw)
-
-    overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
-    draw2 = ImageDraw.Draw(overlay)
-    
-    # try:
-    # except IOError:
-    #     try:
-    #         font = ImageFont.truetype("DejaVuSans.ttf", 20) 
-    #     except IOError:
-    font = ImageFont.load_default()
-
-    img_idx = 0
-    
-    for i, ref in enumerate(refs):
-        try:
-            result = extract_coordinates_and_label(ref, image_width, image_height)
-            if result:
-                label_type, points_list = result
-                
-                color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
-
-                color_a = color + (20, )
-                for points in points_list:
-                    x1, y1, x2, y2 = points
-
-                    x1 = int(x1 / 999 * image_width)
-                    y1 = int(y1 / 999 * image_height)
-
-                    x2 = int(x2 / 999 * image_width)
-                    y2 = int(y2 / 999 * image_height)
-
-                    if label_type == 'image':
-                        try:
-                            cropped = image.crop((x1, y1, x2, y2))
-                            cropped.save(f"{ouput_path}/images/{img_idx}.jpg")
-                        except Exception as e:
-                            print(e)
-                            pass
-                        img_idx += 1
-                        
-                    try:
-                        if label_type == 'title':
-                            draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
-                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
-                        else:
-                            draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
-                            draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
-                        text_x = x1
-                        text_y = max(0, y1 - 15)
-                            
-                        
-                        text_bbox = draw.textbbox((0, 0), label_type, font=font)
-                        text_width = text_bbox[2] - text_bbox[0]
-                        text_height = text_bbox[3] - text_bbox[1]
-                        draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height], 
-                                    fill=(255, 255, 255, 30))
-                        
-                        draw.text((text_x, text_y), label_type, font=font, fill=color)
-                    except:
-                        pass
-        except:
-            continue
-    img_draw.paste(overlay, (0, 0), overlay)
-    return img_draw
-
-
-def process_image_with_refs(image, ref_texts, output_path):
-
-    result_image = draw_bounding_boxes(image, ref_texts, output_path)
-    
-    return result_image
-
-
-
-
-
-def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
-    best_ratio_diff = float('inf')
-    best_ratio = (1, 1)
-    area = width * height
-    for ratio in target_ratios:
-        target_aspect_ratio = ratio[0] / ratio[1]
-        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-        if ratio_diff < best_ratio_diff:
-            best_ratio_diff = ratio_diff
-            best_ratio = ratio
-        elif ratio_diff == best_ratio_diff:
-            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
-                best_ratio = ratio
-    # print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
-    return best_ratio
-
-
-def dynamic_preprocess(image, min_num=2, max_num=9, image_size=640, use_thumbnail=False):
-    orig_width, orig_height = image.size
-    aspect_ratio = orig_width / orig_height
-
-    # calculate the existing image aspect ratio
-    target_ratios = set(
-        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
-        i * j <= max_num and i * j >= min_num)
-    # print(target_ratios)
-    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-    # find the closest aspect ratio to the target
-    target_aspect_ratio = find_closest_aspect_ratio(
-        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
-
-    # print(target_aspect_ratio)
-    # calculate the target width and height
-    target_width = image_size * target_aspect_ratio[0]
-    target_height = image_size * target_aspect_ratio[1]
-    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-
-    # resize the image
-    resized_img = image.resize((target_width, target_height))
-    processed_images = []
-    for i in range(blocks):
-        box = (
-            (i % (target_width // image_size)) * image_size,
-            (i // (target_width // image_size)) * image_size,
-            ((i % (target_width // image_size)) + 1) * image_size,
-            ((i // (target_width // image_size)) + 1) * image_size
-        )
-        # split the image
-        split_img = resized_img.crop(box)
-        processed_images.append(split_img)
-    assert len(processed_images) == blocks
-    if use_thumbnail and len(processed_images) != 1:
-        thumbnail_img = image.resize((image_size, image_size))
-        processed_images.append(thumbnail_img)
-    return processed_images, target_aspect_ratio
-
-
-
-def normalize_transform(mean, std):
-    if mean is None and std is None:
-        transform = None
-    elif mean is None and std is not None:
-        mean = [0.] * len(std)
-        transform = transforms.Normalize(mean=mean, std=std)
-    elif mean is not None and std is None:
-        std = [1.] * len(mean)
-        transform = transforms.Normalize(mean=mean, std=std)
-    else:
-        transform = transforms.Normalize(mean=mean, std=std)
-
-    return transform
-
-
-
-def format_messages(
-        conversations: List[Dict[str, str]],
-        sft_format: str = "deepseek",
-        system_prompt: str = "",
-):
-    """
-    Applies the SFT template to conversation.
-
-    Args:
-        conversations (List[Dict]): A List of messages.
-        sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
-        system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
-
-    Returns:
-        sft_prompt (str): The formatted text.
-    """
-
-    conv = get_conv_template(sft_format)
-    conv.set_system_message(system_prompt)
-    for message in conversations:
-        conv.append_message(message["role"], message["content"].strip())
-    sft_prompt = conv.get_prompt().strip()
-
-    return sft_prompt
-
-
-def text_encode(tokenizer, text: str, bos: bool = True, eos: bool = False):
-    t = tokenizer.encode(text, add_special_tokens=False)
-    bos_id = 0
-    eos_id = 1
-    if bos:
-        t = [bos_id] + t
-    if eos:
-        t = t + [eos_id]
-
-    return t
-
-def load_pil_images(conversations: List[Dict[str, str]]) -> List[Image.Image]:
-    """
-
-    Args:
-        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
-            [
-                {
-                    "role": "User",
-                    "content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
-                    "images": ["./examples/table_datasets.png"]
-                },
-                {"role": "Assistant", "content": ""},
-            ]
-
-    Returns:
-        pil_images (List[PIL.Image.Image]): the list of PIL images.
-
-    """
-
-    pil_images = []
-
-    for message in conversations:
-        if "images" not in message:
-            continue
-
-        for image_path in message["images"]:
-            # print('----------------')
-            # print(image_path)
-            # print('----------------')
-            # exit()
-            
-            # pil_img = Image.open(image_path)
-            pil_img = load_image(image_path)
-            pil_img = pil_img.convert("RGB")
-            pil_images.append(pil_img)
-
-    return pil_images
-
-
-class BaseTransform(ABC):
-
-    def set_rng(self, *args, **kwargs):
-        pass
-
-    def __call__(self, *args, **kwargs) -> torch.Tensor:
-        pass
-
-    @property
-    def default_shape(self):
-        raise NotImplementedError
-
-
-class BasicImageTransform(BaseTransform):
-    def __init__(
-        self, 
-        mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
-        std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
-        normalize: bool = True
-    ):
-        self.mean = mean
-        self.std = std
-    
-        transform_pipelines = [
-            transforms.ToTensor()
-        ]
-
-        normalize = normalize_transform(mean, std) if normalize else nn.Identity()
-        if normalize is not None:
-            transform_pipelines.append(normalize)
-
-        self.transform = transforms.Compose(transform_pipelines)
-    
-    def __call__(self, x):
-        x = self.transform(x)
-        return x
-
-class NoEOSTextStreamer(TextStreamer):
-    def on_finalized_text(self, text: str, stream_end: bool = False):
-
-        eos_text = self.tokenizer.decode([self.tokenizer.eos_token_id], skip_special_tokens=False)
-        text = text.replace(eos_text, "\n")
-        print(text, flush=True, end="")
-
-
-def decoder_layer_init(self, config: DeepseekV2Config, layer_idx: int):
-    nn.Module.__init__(self)
-    self.hidden_size = config.hidden_size
-
-    if config.use_mla:
-        self.self_attn = DeepseekV2Attention(config=config, layer_idx=layer_idx)
-    else:
-        config.head_dim = config.hidden_size // config.num_attention_heads
-        self.self_attn = LlamaAttention(config, layer_idx)
-    self.mlp = DeepseekV2MoE(config) if layer_idx >= config.first_k_dense_replace else DeepseekV2MLP(config)
-
-    self.input_layernorm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-    self.post_attention_layernorm = DeepseekV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-
-DeepseekV2DecoderLayer.__init__ = decoder_layer_init
-
-class DeepseekOCRConfig(DeepseekV2Config):
-    model_type = "DeepseekOCR"
-
-class DeepseekOCRModel(DeepseekV2Model):
-    config_class = DeepseekOCRConfig
-
-    def __init__(self, config: DeepseekV2Config):
-        super(DeepseekOCRModel, self).__init__(config)
-
-        self.sam_model = build_sam_vit_b()
-        self.vision_model = build_clip_l()
-        # self.conv_2 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=2, stride=2)
-        n_embed = 1280
-        self.projector =  MlpProjector(Dict(projector_type="linear", input_dim=2048, n_embed=n_embed))
-        embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
-        self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
-        self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
-
-        self.rotary_emb = LlamaRotaryEmbedding(config=config)
-    
-    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,
-        images: Optional[torch.FloatTensor] = None,
-        images_seq_mask: Optional[torch.FloatTensor] = None,
-        images_spatial_crop: Optional[torch.FloatTensor] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-
-
-
-        if inputs_embeds is None:
-            # inputs_embeds = self.embed_tokens(input_ids)
-            inputs_embeds = self.get_input_embeddings()(input_ids)
-
-        inputs_embeds = inputs_embeds.clone()
-
-        sam_model = getattr(self, 'sam_model', None)
-        # sam_model = self.sam_model
-        vision_model = getattr(self, 'vision_model', None)
-
-
-
-        if sam_model is not None and (input_ids.shape[1] != 1 or self.training) and torch.sum(images[0][1]).item() != 0:
-
-            idx = 0
-            
-            # sam_model = torch.jit.script(sam_model)
-            
-            # start_time = time.time()
-            for image, crop_shape in zip(images, images_spatial_crop):
-                images_in_this_batch = []
-
-                patches = image[0]
-                image_ori = image[1]
-
-                with torch.no_grad():
-                # with torch.inference_mode(): 
-                    
-                    if torch.sum(patches).item() != 0:
-                        # P, C, H, W = patches.shape
-                        crop_flag = 1
-                        local_features_1 = sam_model(patches)
-
-                        local_features_2 = vision_model(patches, local_features_1)  
-                        # vit_time = time.time()
-                        local_features = torch.cat((local_features_2[:, 1:], local_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
-                        local_features = self.projector(local_features)
-
-
-                        global_features_1 = sam_model(image_ori)
-                        global_features_2 = vision_model(image_ori, global_features_1) 
-                        global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
-                        global_features = self.projector(global_features)
-
-                        # print('=====================')
-                        # print('BASE: ', global_features.shape)
-                        # print('PATCHES: ', local_features.shape)
-                        # print('=====================')
-
-                        _, hw, n_dim = global_features.shape
-                        h = w = int(hw ** 0.5)
-
-                        _2, hw2, n_dim2 = local_features.shape
-                        h2 = w2 = int(hw2 ** 0.5)
-
-                        width_crop_num, height_crop_num = crop_shape[0], crop_shape[1]
-
-                        global_features = global_features.view(h, w, n_dim)
-
-                        global_features = torch.cat(
-                            [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
-                        )
-
-                        global_features = global_features.view(-1, n_dim)
-
-
-                        local_features = local_features.view(height_crop_num, width_crop_num, h2, w2, n_dim2).permute(0, 2, 1, 3, 4).reshape(height_crop_num*h2, width_crop_num*w2, n_dim2)
-                        local_features = torch.cat(
-                            [local_features, self.image_newline[None, None, :].expand(height_crop_num * h2, 1, n_dim2)], dim=1
-                        )
-                        local_features = local_features.view(-1, n_dim2)
-
-                        global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0)
-
-                        # end_time = time.time()
-
-                        # print('sam: ', sam_time - start_time)
-                        # print('vit: ', vit_time - sam_time)
-                        # print('all: ', end_time - start_time)
-
-                        # exit()
-                   
-                    else:
-                        global_features_1 = sam_model(image_ori)
-                        global_features_2 = vision_model(image_ori, global_features_1) 
-                        global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) 
-                        global_features = self.projector(global_features)
-                        _, hw, n_dim = global_features.shape
-                        h = w = int(hw ** 0.5)
-
-
-                        global_features = global_features.view(h, w, n_dim)
-
-                        global_features = torch.cat(
-                            [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1
-                        )
-
-                        global_features = global_features.view(-1, n_dim)
-
-                        global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0)
-
-                    images_in_this_batch.append(global_local_features)
-                
-
-                if images_in_this_batch:
-                    images_in_this_batch = torch.cat(images_in_this_batch, dim=0)
-                    images_in_this_batch = images_in_this_batch.to(
-                        device=inputs_embeds.device, dtype=inputs_embeds.dtype
-                    )
-                    mask = images_seq_mask[idx].unsqueeze(-1).to(inputs_embeds.device)   # bool [T, 1]
-                    updated_row = inputs_embeds[idx].masked_scatter(mask, images_in_this_batch)
-                    inputs_embeds[idx] = updated_row
-
-                idx += 1
-            
-        return super(DeepseekOCRModel, self).forward(
-            input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids,
-            output_attentions=output_attentions, output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-    
-
-class DeepseekOCRForCausalLM(DeepseekV2ForCausalLM):
-
-    config_class = DeepseekOCRConfig
-    # supports_gradient_checkpointing = True
-
-    def __init__(self, config):
-        super(DeepseekV2ForCausalLM, self).__init__(config)
-        self.model = DeepseekOCRModel(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_model(self):
-        return self.model
-
-
-    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,
-        images: Optional[torch.FloatTensor] = None,
-        images_seq_mask: Optional[torch.FloatTensor] = None,
-        images_spatial_crop: Optional[torch.FloatTensor] = None,
-        return_dict: Optional[bool] = None,
-        
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        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
-
-
-
-        outputs  = self.model(
-            input_ids=input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            images=images,
-            images_seq_mask = images_seq_mask,
-            images_spatial_crop = images_spatial_crop,
-            return_dict=return_dict
-            
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-
-        # logits
-
-        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
-    ):
-        # Omit tokens covered by past_key_values
-        past_length = 0
-        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.get_seq_length()
-                max_cache_length = None
-            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 self.generation_config.cache_implementation == "static":
-        #     # generation with static cache
-        #     cache_position = kwargs.get("cache_position", None)
-        #     if cache_position is None:
-        #         past_length = 0
-        #     else:
-        #         past_length = cache_position[-1] + 1
-        #     input_ids = input_ids[:, past_length:]
-        #     position_ids = position_ids[:, past_length:]
-
-        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
-        # same goes for position ids. Could also help with continued generation.
-        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
-
-        # 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,
-                "images": kwargs.get("images", None),
-                "images_seq_mask": kwargs.get("images_seq_mask", None),
-                "images_spatial_crop": kwargs.get("images_spatial_crop", None),
-            }
-        )
-        return model_inputs
-    
-
-    def disable_torch_init(self):
-        """
-        Disable the redundant torch default initialization to accelerate model creation.
-        """
-        import torch
-        setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
-        setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
-
-
-
-    def infer(self, tokenizer, prompt='', image_file='', output_path = '', base_size=1024, image_size=640, crop_mode=True, test_compress=False, save_results=False, eval_mode=False):
-        self.disable_torch_init()
-
-        os.makedirs(output_path, exist_ok=True)
-        os.makedirs(f'{output_path}/images', exist_ok=True)
-
-        if prompt and image_file:
-            conversation = [
-                {
-                    "role": "<|User|>",
-                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
-                    "content": f'{prompt}',
-                    # "content": "君不见黄河之水天上来的下一句是什么？",
-                    # "content": "<image>\nFree OCR. ",
-                    # "content": "<image>\nParse the figure. ",
-                    # "content": "<image>\nExtract the text in the image. ",
-                    "images": [f'{image_file}'],
-                },
-                {"role": "<|Assistant|>", "content": ""},
-            ]
-        
-        elif prompt:
-            conversation = [
-                {
-                    "role": "<|User|>",
-                    # "content": "<image>\n<|grounding|>Given the layout of the image. ",
-                    "content": f'{prompt}',
-                    # "content": "君不见黄河之水天上来的下一句是什么？",
-                    # "content": "<image>\nFree OCR. ",
-                    # "content": "<image>\nParse the figure. ",
-                    # "content": "<image>\nExtract the text in the image. ",
-                    # "images": [f'{image_file}'],
-                },
-                {"role": "<|Assistant|>", "content": ""},
-            ]
-        else:
-            assert False, f'prompt is none!'
-        
-        prompt = format_messages(conversations=conversation, sft_format='plain', system_prompt='')
-
-        patch_size = 16
-        downsample_ratio = 4
-        images = load_pil_images(conversation)
-
-        valid_img_tokens = 0
-        ratio = 1
-
-        image_draw = images[0].copy()
-
-        w,h = image_draw.size
-        # print(w, h)
-        ratio = 1 - ((max(w, h) - min(w, h)) / (max(w, h)))
-    
-
-        image_transform=BasicImageTransform(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), normalize=True)
-        images_seq_mask = []
-
-        image_token = '<image>'
-        image_token_id = 128815
-        text_splits = prompt.split(image_token)
-
-        images_list, images_crop_list, images_seq_mask = [], [], []
-        tokenized_str = []
-        images_spatial_crop = []
-        for text_sep, image in zip(text_splits, images):
-
-            tokenized_sep = text_encode(tokenizer, text_sep, bos=False, eos=False)
-            tokenized_str += tokenized_sep
-            images_seq_mask += [False] * len(tokenized_sep)
-
-            if crop_mode:
-
-                if image.size[0] <= 640 and image.size[1] <= 640:
-                    crop_ratio = [1, 1]
-
-                else:
-                    if crop_mode:
-                        # best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
-                        images_crop_raw, crop_ratio = dynamic_preprocess(image)
-                    else:
-                        # best_width, best_height = self.image_size, self.image_size
-                        crop_ratio = [1, 1]
-                
-                """process the global view"""
-                # image = image.resize((base_size, base_size))
-                global_view = ImageOps.pad(image, (base_size, base_size),
-                                        color=tuple(int(x * 255) for x in image_transform.mean))
-                
-                if base_size == 1024:
-                    valid_img_tokens += int(256 * ratio)
-                elif base_size == 1280:
-                    valid_img_tokens += int(400 * ratio)
-                # elif base_size == 640:
-                #     valid_img_tokens += int(100 * ratio)
-                
-
-
-
-                
-                images_list.append(image_transform(global_view).to(torch_dtype))
-
-                # global_view_tensor = image_transform(global_view).to(torch_dtype)
-
-                width_crop_num, height_crop_num = crop_ratio
-
-                images_spatial_crop.append([width_crop_num, height_crop_num])
-                
-                
-                if width_crop_num > 1 or height_crop_num > 1:
-                    """process the local views"""
-                    
-                    for i in range(len(images_crop_raw)):
-                        images_crop_list.append(image_transform(images_crop_raw[i]).to(torch_dtype))
-                
-                if image_size == 640:
-                    valid_img_tokens += len(images_crop_list) * 100
-
-                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
-                num_queries_base = math.ceil((base_size // patch_size) / downsample_ratio)
-
-
-
-                """add image tokens"""
-
-                
-
-                tokenized_image = ([image_token_id] * num_queries_base + [image_token_id]) * num_queries_base
-                tokenized_image += [image_token_id]
-                if width_crop_num > 1 or height_crop_num > 1:
-                    tokenized_image += ([image_token_id] * (num_queries * width_crop_num) + [image_token_id]) * (
-                                num_queries * height_crop_num)
-                tokenized_str += tokenized_image
-                images_seq_mask += [True] * len(tokenized_image)
-                # num_image_tokens.append(len(tokenized_image))
-
-            else:
-                # best_width, best_height = self.image_size, self.image_size
-                # print(image.size, (best_width, best_height)) # check the select_best_resolutions func
-
-                """process the global view"""
-                if image_size <= 640:
-                    print('directly resize')
-                    image = image.resize((image_size, image_size))
-                # else:
-                global_view = ImageOps.pad(image, (image_size, image_size),
-                                        color=tuple(int(x * 255) for x in image_transform.mean))
-                images_list.append(image_transform(global_view).to(torch_dtype))
-
-                if base_size == 1024:
-                    valid_img_tokens += int(256 * ratio)
-                elif base_size == 1280:
-                    valid_img_tokens += int(400 * ratio)
-                elif base_size == 640:
-                    valid_img_tokens += int(100 * 1)
-                elif base_size == 512:
-                    valid_img_tokens += int(64 * 1)
-
-                width_crop_num, height_crop_num = 1, 1
-
-                images_spatial_crop.append([width_crop_num, height_crop_num])
-
-
-                """add image tokens"""
-                num_queries = math.ceil((image_size // patch_size) / downsample_ratio)
-
-                tokenized_image = ([image_token_id] * num_queries + [image_token_id]) * num_queries
-                tokenized_image += [image_token_id]
-                # tokenized_image += ([self.image_token_id] * (num_queries * width_crop_num) + [self.image_token_id]) * (
-                #             num_queries * height_crop_num)
-                tokenized_str += tokenized_image
-                images_seq_mask += [True] * len(tokenized_image)
-                # num_image_tokens.append(len(tokenized_image))
-        
-
-        """process the last text split"""
-        tokenized_sep = text_encode(tokenizer, text_splits[-1], bos=False, eos=False)
-        tokenized_str += tokenized_sep
-        images_seq_mask += [False] * len(tokenized_sep)
-
-        """add the bos tokens"""
-        bos_id = 0
-        tokenized_str = [bos_id] + tokenized_str 
-        images_seq_mask = [False] + images_seq_mask
-
-
-
-        input_ids = torch.LongTensor(tokenized_str)
-
-        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
-
-
-        if len(images_list) == 0:
-            images_ori = torch.zeros((1, 3, image_size, image_size))
-            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
-            images_crop = torch.zeros((1, 3, base_size, base_size))
-
-        else:
-            images_ori = torch.stack(images_list, dim=0)
-            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
-            if images_crop_list:
-                images_crop = torch.stack(images_crop_list, dim=0)
-            else:
-                images_crop = torch.zeros((1, 3, base_size, base_size))
-
-
-
-        if not eval_mode:
-            streamer = NoEOSTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
-            with torch.autocast("cuda", dtype=torch_dtype):
-                with torch.no_grad():
-                    output_ids = self.generate(
-                        input_ids.unsqueeze(0).cuda(),
-                        images=[(images_crop.cuda(), images_ori.cuda())],
-                        images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
-                        images_spatial_crop = images_spatial_crop,
-                        # do_sample=False,
-                        # num_beams = 1,
-                        temperature=0.0,
-                        eos_token_id=tokenizer.eos_token_id,
-                        streamer=streamer,
-                        max_new_tokens=8192,
-                        no_repeat_ngram_size = 20,
-                        use_cache = True
-                        )
-
-        else:
-            with torch.autocast("cuda", dtype=torch_dtype):
-                with torch.no_grad():
-                    output_ids = self.generate(
-                        input_ids.unsqueeze(0).cuda(),
-                        images=[(images_crop.cuda(), images_ori.cuda())],
-                        images_seq_mask = images_seq_mask.unsqueeze(0).cuda(),
-                        images_spatial_crop = images_spatial_crop,
-                        # do_sample=False,
-                        # num_beams = 1,
-                        temperature=0.0,
-                        eos_token_id=tokenizer.eos_token_id,
-                        max_new_tokens=8192,
-                        no_repeat_ngram_size = 35,
-                        use_cache = True
-                        )
-                
-
-        if '<image>' in conversation[0]['content'] and eval_mode:
-                outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
-                stop_str = '<｜end▁of▁sentence｜>'
-                if outputs.endswith(stop_str):
-                    outputs = outputs[:-len(stop_str)]
-                # re_match
-                outputs = outputs.strip()
-
-                return outputs
-        
-        if '<image>' in conversation[0]['content'] and test_compress:
-            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
-            pure_texts_outputs_token_length = len(text_encode(tokenizer, outputs, bos=False, eos=False))
-            print('='*50)
-            print('image size: ', (w, h))
-            print('valid image tokens: ', int(valid_img_tokens))
-            print('output texts tokens (valid): ', pure_texts_outputs_token_length)
-            print('compression ratio: ', round(pure_texts_outputs_token_length/valid_img_tokens, 2))
-            print('='*50)
-
-
-        if '<image>' in conversation[0]['content'] and save_results:
-            outputs = tokenizer.decode(output_ids[0, input_ids.unsqueeze(0).cuda().shape[1]:])
-            stop_str = '<｜end▁of▁sentence｜>'
-
-            print('='*15 + 'save results:' + '='*15)
-            
-            # # # # conv.messages[-1][-1] = outputs
-            if outputs.endswith(stop_str):
-                outputs = outputs[:-len(stop_str)]
-            outputs = outputs.strip()
-
-            matches_ref, matches_images, mathes_other = re_match(outputs)
-            # print(matches_ref)
-            result = process_image_with_refs(image_draw, matches_ref, output_path)
-
-
-            for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
-                outputs = outputs.replace(a_match_image, '![](images/' + str(idx) + '.jpg)\n')
-            
-            for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
-                outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
-
-
-            # if 'structural formula' in conversation[0]['content']:
-            #     outputs = '<smiles>' + outputs + '</smiles>'
-            with open(f'{output_path}/result.mmd', 'w', encoding = 'utf-8') as afile:
-                afile.write(outputs)
-
-            if 'line_type' in outputs:
-                import matplotlib.pyplot as plt
-                lines = eval(outputs)['Line']['line']
-
-                line_type = eval(outputs)['Line']['line_type']
-                # print(lines)
-
-                endpoints = eval(outputs)['Line']['line_endpoint']
-
-                fig, ax = plt.subplots(figsize=(3,3), dpi=200)
-                ax.set_xlim(-15, 15)
-                ax.set_ylim(-15, 15)
-
-                for idx, line in enumerate(lines):
-                    try:
-                        p0 = eval(line.split(' -- ')[0])
-                        p1 = eval(line.split(' -- ')[-1])
-
-                        if line_type[idx] == '--':
-                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
-                        else:
-                            ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
-
-                        ax.scatter(p0[0], p0[1], s=5, color = 'k')
-                        ax.scatter(p1[0], p1[1], s=5, color = 'k')
-                    except:
-                        pass
-
-                for endpoint in endpoints:
-
-                    label = endpoint.split(': ')[0]
-                    (x, y) = eval(endpoint.split(': ')[1])
-                    ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points', 
-                                fontsize=5, fontweight='light')
-                
-
-                plt.savefig(f'{output_path}/geo.jpg')
-                plt.close()
-
-            result.save(f"{output_path}/result_with_boxes.jpg")

.ipynb_checkpoints/modeling_deepseekv2-checkpoint.py

@@ -1,1996 +0,0 @@
-# coding=utf-8
-# Copyright 2023 DeepSeek-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 DeepSeek model and compatible with both DeepSeekV2 and DeepSeekV3"""
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-import numpy as np
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import torch.distributed as dist
-from einops import repeat
-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
-try:
-    from transformers.models.llama.modeling_llama import LlamaAttention
-except:
-    LlamaAttention = None
-try:
-    from transformers.models.llama.modeling_llama import LlamaFlashAttention2
-except:
-    LlamaFlashAttention2 = None
-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_deepseek_v2 import DeepseekV2Config
-
-if is_flash_attn_2_available():
-    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
-
-# 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 = "DeepseekV2Config"
-
-
-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,
-    )
-
-
-class DeepseekV2RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        DeepseekV2RMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
-
-
-
-
-class DeepseekV2RotaryEmbedding(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(),
-        )
-        self.max_seq_len_cached = None
-
-    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.to(t.device))
-        # 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 self.max_seq_len_cached is None or 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),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding 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)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding 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)
-
-
-# Inverse dim formula to find dim based on number of rotations
-def yarn_find_correction_dim(
-    num_rotations, dim, base=10000, max_position_embeddings=2048
-):
-    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
-        2 * math.log(base)
-    )
-
-
-# Find dim range bounds based on rotations
-def yarn_find_correction_range(
-    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
-):
-    low = math.floor(
-        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
-    )
-    high = math.ceil(
-        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
-    )
-    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
-
-
-def yarn_get_mscale(scale=1, mscale=1):
-    if scale <= 1:
-        return 1.0
-    return 0.1 * mscale * math.log(scale) + 1.0
-
-
-def yarn_linear_ramp_mask(min, max, dim):
-    if min == max:
-        max += 0.001  # Prevent singularity
-
-    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
-    ramp_func = torch.clamp(linear_func, 0, 1)
-    return ramp_func
-
-
-class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
-
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-        original_max_position_embeddings=4096,
-        beta_fast=32,
-        beta_slow=1,
-        mscale=1,
-        mscale_all_dim=0,
-    ):
-        self.scaling_factor = scaling_factor
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.beta_fast = beta_fast
-        self.beta_slow = beta_slow
-        self.mscale = mscale
-        self.mscale_all_dim = mscale_all_dim
-        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
-        dim = self.dim
-
-        freq_extra = 1.0 / (
-            self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-        freq_inter = 1.0 / (
-            self.scaling_factor
-            * self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-
-        low, high = yarn_find_correction_range(
-            self.beta_fast,
-            self.beta_slow,
-            dim,
-            self.base,
-            self.original_max_position_embeddings,
-        )
-        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
-            device=device, dtype=torch.float32
-        )
-        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(seq_len, device=device, dtype=torch.float32)
-
-        freqs = torch.outer(t, inv_freq)
-
-        _mscale = float(
-            yarn_get_mscale(self.scaling_factor, self.mscale)
-            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
-        )
-
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer(
-            "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
-        )
-        self.register_buffer(
-            "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-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)
-
-
-    # print()
-
-    b, h, s, d = q.shape
-    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    b, h, s, d = k.shape
-    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-
-
-    return q_embed, k_embed
-
-
-class DeepseekV2MLP(nn.Module):
-    def __init__(self, config, hidden_size=None, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
-        self.intermediate_size = (
-            config.intermediate_size if intermediate_size is None else 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):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-
-class MoEGate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.top_k = config.num_experts_per_tok
-        self.n_routed_experts = config.n_routed_experts
-        self.routed_scaling_factor = config.routed_scaling_factor
-        self.scoring_func = config.scoring_func
-        self.alpha = config.aux_loss_alpha
-        self.seq_aux = config.seq_aux
-        self.topk_method = config.topk_method
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
-
-        # topk selection algorithm
-        self.norm_topk_prob = config.norm_topk_prob
-        self.gating_dim = config.hidden_size
-        self.weight = nn.Parameter(
-            torch.empty((self.n_routed_experts, self.gating_dim))
-        )
-        if self.topk_method == "noaux_tc":
-            self.e_score_correction_bias = nn.Parameter(
-                torch.empty((self.n_routed_experts))
-            )
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        import torch.nn.init as init
-
-        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
-    def forward(self, hidden_states):
-        bsz, seq_len, h = hidden_states.shape
-        ### compute gating score
-        hidden_states = hidden_states.view(-1, h)
-        logits = F.linear(
-            hidden_states.type(torch.float32), self.weight.type(torch.float32), None
-        )
-        if self.scoring_func == "softmax":
-            scores = logits.softmax(dim=-1, dtype=torch.float32)
-        elif self.scoring_func == "sigmoid":
-            scores = logits.sigmoid()
-        else:
-            raise NotImplementedError(
-                f"insupportable scoring function for MoE gating: {self.scoring_func}"
-            )
-
-        ### select top-k experts
-        if self.topk_method == "greedy":
-            topk_weight, topk_idx = torch.topk(
-                scores, k=self.top_k, dim=-1, sorted=False
-            )
-        elif self.topk_method == "group_limited_greedy":
-            group_scores = (
-                scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
-            )  # [n, n_group]
-            group_idx = torch.topk(
-                group_scores, k=self.topk_group, dim=-1, sorted=False
-            )[
-                1
-            ]  # [n, top_k_group]
-            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
-            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
-            score_mask = (
-                group_mask.unsqueeze(-1)
-                .expand(
-                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
-                )
-                .reshape(bsz * seq_len, -1)
-            )  # [n, e]
-            tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-            topk_weight, topk_idx = torch.topk(
-                tmp_scores, k=self.top_k, dim=-1, sorted=False
-            )
-        elif self.topk_method == "noaux_tc":
-            assert not self.training
-            scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0)
-            group_scores = (
-                scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1)
-            )  # [n, n_group]
-            group_idx = torch.topk(
-                group_scores, k=self.topk_group, dim=-1, sorted=False
-            )[
-                1
-            ]  # [n, top_k_group]
-            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
-            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
-            score_mask = (
-                group_mask.unsqueeze(-1)
-                .expand(
-                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
-                )
-                .reshape(bsz * seq_len, -1)
-            )  # [n, e]
-            tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-            _, topk_idx = torch.topk(
-                tmp_scores, k=self.top_k, dim=-1, sorted=False
-            )
-            topk_weight = scores.gather(1, topk_idx)
-
-        ### norm gate to sum 1
-        if self.top_k > 1 and self.norm_topk_prob:
-            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
-            topk_weight = topk_weight / denominator * self.routed_scaling_factor
-        else:
-            topk_weight = topk_weight * self.routed_scaling_factor
-        ### expert-level computation auxiliary loss
-        if self.training and self.alpha > 0.0:
-            scores_for_aux = scores
-            aux_topk = self.top_k
-            # always compute aux loss based on the naive greedy topk method
-            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
-            if self.seq_aux:
-                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
-                ce = torch.zeros(
-                    bsz, self.n_routed_experts, device=hidden_states.device
-                )
-                ce.scatter_add_(
-                    1,
-                    topk_idx_for_aux_loss,
-                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
-                ).div_(seq_len * aux_topk / self.n_routed_experts)
-                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
-                    dim=1
-                ).mean() * self.alpha
-            else:
-                mask_ce = F.one_hot(
-                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
-                )
-                ce = mask_ce.float().mean(0)
-                Pi = scores_for_aux.mean(0)
-                fi = ce * self.n_routed_experts
-                aux_loss = (Pi * fi).sum() * self.alpha
-        else:
-            aux_loss = None
-        return topk_idx, topk_weight, aux_loss
-
-
-class AddAuxiliaryLoss(torch.autograd.Function):
-    """
-    The trick function of adding auxiliary (aux) loss,
-    which includes the gradient of the aux loss during backpropagation.
-    """
-
-    @staticmethod
-    def forward(ctx, x, loss):
-        assert loss.numel() == 1
-        ctx.dtype = loss.dtype
-        ctx.required_aux_loss = loss.requires_grad
-        return x
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        grad_loss = None
-        if ctx.required_aux_loss:
-            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
-        return grad_output, grad_loss
-
-
-class DeepseekV2MoE(nn.Module):
-    """
-    A mixed expert module containing shared experts.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.num_experts_per_tok = config.num_experts_per_tok
-
-        if hasattr(config, "ep_size") and config.ep_size > 1:
-            assert config.ep_size == dist.get_world_size()
-            self.ep_size = config.ep_size
-            self.experts_per_rank = config.n_routed_experts // config.ep_size
-            self.ep_rank = dist.get_rank()
-            self.experts = nn.ModuleList(
-                [
-                    (
-                        DeepseekV2MLP(
-                            config, intermediate_size=config.moe_intermediate_size
-                        )
-                        if i >= self.ep_rank * self.experts_per_rank
-                        and i < (self.ep_rank + 1) * self.experts_per_rank
-                        else None
-                    )
-                    for i in range(config.n_routed_experts)
-                ]
-            )
-        else:
-            self.ep_size = 1
-            self.experts_per_rank = config.n_routed_experts
-            self.ep_rank = 0
-            self.experts = nn.ModuleList(
-                [
-                    DeepseekV2MLP(
-                        config, intermediate_size=config.moe_intermediate_size
-                    )
-                    for i in range(config.n_routed_experts)
-                ]
-            )
-        self.gate = MoEGate(config)
-        if config.n_shared_experts is not None:
-            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
-            self.shared_experts = DeepseekV2MLP(
-                config=config, intermediate_size=intermediate_size
-            )
-
-    def forward(self, hidden_states):
-        identity = hidden_states
-        orig_shape = hidden_states.shape
-        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        flat_topk_idx = topk_idx.view(-1)
-        if self.training:
-            hidden_states = hidden_states.repeat_interleave(
-                self.num_experts_per_tok, dim=0
-            )
-            y = torch.empty_like(hidden_states)
-            for i, expert in enumerate(self.experts):
-                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
-            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-            y = y.to(hidden_states.dtype).view(*orig_shape)
-            y = AddAuxiliaryLoss.apply(y, aux_loss)
-        else:
-            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
-        if self.config.n_shared_experts is not None:
-            y = y + self.shared_experts(identity)
-        return y
-
-    @torch.no_grad()
-    def moe_infer(self, x, topk_ids, topk_weight):
-        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
-        cnts.scatter_(1, topk_ids, 1)
-        tokens_per_expert = cnts.sum(dim=0)
-        idxs = topk_ids.view(-1).argsort()
-        sorted_tokens = x[idxs // topk_ids.shape[1]]
-        sorted_tokens_shape = sorted_tokens.shape
-        if self.ep_size > 1:
-            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
-            tokens_per_expert_group = tokens_per_expert.new_empty(
-                tokens_per_expert.shape[0]
-            )
-            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
-            output_splits = (
-                tokens_per_expert_group.view(self.ep_size, -1)
-                .sum(1)
-                .cpu()
-                .numpy()
-                .tolist()
-            )
-            gathered_tokens = sorted_tokens.new_empty(
-                tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
-            )
-            input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
-            dist.all_to_all(
-                list(gathered_tokens.split(output_splits)),
-                list(sorted_tokens.split(input_split_sizes)),
-            )
-            tokens_per_expert_post_gather = tokens_per_expert_group.view(
-                self.ep_size, self.experts_per_rank
-            ).sum(dim=0)
-            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
-            s = 0
-            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
-                gatherd_idxs[s : s + k] = i % self.experts_per_rank
-                s += k
-            gatherd_idxs = gatherd_idxs.argsort()
-            sorted_tokens = gathered_tokens[gatherd_idxs]
-            tokens_per_expert = tokens_per_expert_post_gather
-        tokens_per_expert = tokens_per_expert.cpu().numpy()
-
-        outputs = []
-        start_idx = 0
-        for i, num_tokens in enumerate(tokens_per_expert):
-            end_idx = start_idx + num_tokens
-            if num_tokens == 0:
-                continue
-            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
-            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
-            expert_out = expert(tokens_for_this_expert)
-            outputs.append(expert_out)
-            start_idx = end_idx
-
-        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
-        if self.ep_size > 1:
-            new_x = torch.empty_like(outs)
-            new_x[gatherd_idxs] = outs
-            gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
-            dist.all_to_all(
-                list(gathered_tokens.split(input_split_sizes)),
-                list(new_x.split(output_splits)),
-            )
-            outs = gathered_tokens
-
-        new_x = torch.empty_like(outs)
-        new_x[idxs] = outs
-        final_out = (
-            new_x.view(*topk_ids.shape, -1)
-            .type(topk_weight.dtype)
-            .mul_(topk_weight.unsqueeze(dim=-1))
-            .sum(dim=1)
-            .type(new_x.dtype)
-        )
-        return final_out
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-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)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
-class DeepseekV2Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: DeepseekV2Config, 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.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.q_lora_rank = config.q_lora_rank
-        self.qk_rope_head_dim = config.qk_rope_head_dim
-        self.kv_lora_rank = config.kv_lora_rank
-        self.v_head_dim = config.v_head_dim
-        self.qk_nope_head_dim = config.qk_nope_head_dim
-        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
-
-        self.is_causal = True
-
-        if self.q_lora_rank is None:
-            self.q_proj = nn.Linear(
-                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
-            )
-        else:
-            self.q_a_proj = nn.Linear(
-                self.hidden_size, config.q_lora_rank, bias=config.attention_bias
-            )
-            self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
-            self.q_b_proj = nn.Linear(
-                config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
-            )
-        # config.kv_lora_rank + config.qk_rope_head_dim,
-        self.kv_a_proj_with_mqa = nn.Linear(
-            self.hidden_size,
-            config.kv_lora_rank + config.qk_rope_head_dim,
-            bias=config.attention_bias,
-        )
-        self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
-        self.kv_b_proj = nn.Linear(
-            config.kv_lora_rank,
-            self.num_heads
-            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
-            bias=False,
-        )
-
-        self.o_proj = nn.Linear(
-            self.num_heads * self.v_head_dim,
-            self.hidden_size,
-            bias=config.attention_bias,
-        )
-        self._init_rope()
-
-        self.softmax_scale = self.q_head_dim ** (-0.5)
-        if self.config.rope_scaling is not None:
-            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
-            scaling_factor = self.config.rope_scaling["factor"]
-            if mscale_all_dim:
-                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
-                self.softmax_scale = self.softmax_scale * mscale * mscale
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = DeepseekV2RotaryEmbedding(
-                self.qk_rope_head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-            # self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
-            #     self.qk_rope_head_dim,
-            #     max_position_embeddings=self.max_position_embeddings,
-            #     scaling_factor=scaling_factor,
-            #     base=self.rope_theta,
-            # )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "yarn":
-                kwargs = {
-                    key: self.config.rope_scaling[key]
-                    for key in [
-                        "original_max_position_embeddings",
-                        "beta_fast",
-                        "beta_slow",
-                        "mscale",
-                        "mscale_all_dim",
-                    ]
-                    if key in self.config.rope_scaling
-                }
-                self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                    **kwargs,
-                )
-            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.v_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.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
-        else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-
-
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
-
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        compressed_kv = self.kv_a_layernorm(compressed_kv)
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-
-        kv_seq_len = k_pe.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(q_pe, seq_len=kv_seq_len)
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            compressed_kv = compressed_kv.unsqueeze(1)
-            k_pe, compressed_kv = past_key_value.update(k_pe, compressed_kv, self.layer_idx, cache_kwargs)
-            compressed_kv = compressed_kv.squeeze(1)
-
-        kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
-        q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :]
-        out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :]
-
-        q_nope = torch.matmul(q_nope, q_absorb)
-        attn_weights = (torch.matmul(q_pe, k_pe.mT) +
-                        torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)) * self.softmax_scale
-        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()}"
-            )
-        assert attention_mask is not None
-        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(q_pe.dtype)
-        attn_weights = nn.functional.dropout(
-            attn_weights, p=self.attention_dropout, training=self.training
-        )
-        attn_output = torch.einsum('bhql,blc->bhqc', attn_weights, compressed_kv)
-
-        attn_output = torch.matmul(attn_output, out_absorb.mT)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
-class DeepseekV2FlashAttention2(DeepseekV2Attention):
-    """
-    DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` 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]]]:
-        # DeepseekV2FlashAttention2 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()
-
-        if self.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
-        else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
-
-        # 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
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-        kv = (
-            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
-            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
-            .transpose(1, 2)
-        )
-
-        k_nope, value_states = torch.split(
-            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
-        )
-        kv_seq_len = value_states.shape[-2]
-
-        kv_seq_len = value_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)
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
-        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
-
-        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
-        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
-
-        if self.q_head_dim != self.v_head_dim:
-            value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
-
-        # TODO: support compressed_kv for kv_cache (instead of key_states, value_states) in flash_attention version
-        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. (DeepseekV2RMSNorm 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
-            elif torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            else:
-                target_dtype = (
-                    self.q_proj.weight.dtype
-                    if self.q_lora_rank is None
-                    else self.q_a_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,
-            softmax_scale=self.softmax_scale,
-        )
-        if self.q_head_dim != self.v_head_dim:
-            attn_output = attn_output[:, :, :, : self.v_head_dim]
-
-        attn_output = attn_output.reshape(
-            bsz, q_len, self.num_heads * self.v_head_dim
-        ).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 DeepseekV2FlashAttention2 __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),
-        )
-
-
-ATTENTION_CLASSES = {
-    "eager": DeepseekV2Attention,
-    "flash_attention_2": DeepseekV2FlashAttention2,
-
-    "mla_eager": DeepseekV2Attention,
-    "mla_flash_attention_2": DeepseekV2FlashAttention2,
-
-    "mha_eager": LlamaAttention,
-    "mha_flash_attention_2": LlamaFlashAttention2
-}
-
-
-class DeepseekV2DecoderLayer(nn.Module):
-    def __init__(self, config: DeepseekV2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-
-        if config.use_mla:
-            attn_implementation = "mla_" + config._attn_implementation
-        else:
-            attn_implementation = "mha_" + config._attn_implementation
-
-        self.self_attn = ATTENTION_CLASSES[attn_implementation](
-            config=config, layer_idx=layer_idx
-        )
-
-        self.mlp = (
-            DeepseekV2MoE(config)
-            if (
-                config.n_routed_experts is not None
-                and layer_idx >= config.first_k_dense_replace
-                and layer_idx % config.moe_layer_freq == 0
-            )
-            else DeepseekV2MLP(config)
-        )
-        self.input_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-        self.post_attention_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-
-    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
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-DeepseekV2_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 ([`DeepseekV2Config`]):
-            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 DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2PreTrainedModel(PreTrainedModel):
-    config_class = DeepseekV2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DeepseekV2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = 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_()
-
-
-DeepseekV2_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 DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2Model(DeepseekV2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
-
-    Args:
-        config: DeepseekV2Config
-    """
-
-    def __init__(self, config: DeepseekV2Config):
-        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(
-            [
-                DeepseekV2DecoderLayer(config, layer_idx)
-                for layer_idx in range(config.num_hidden_layers)
-            ]
-        )
-        # print(config._attn_implementation)
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self.norm = DeepseekV2RMSNorm(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(DeepseekV2_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,
-        cache_position: Optional[torch.LongTensor] = 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`transformers."
-                )
-                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)
-
-        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
-            )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-
-        # 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
-        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,
-        )
-
-
-class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = DeepseekV2Model(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(DeepseekV2_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,
-            cache_position: Optional[torch.LongTensor] = 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, transformers.,
-                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, transformers., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
-
-        >>> model = DeepseekV2ForCausalLM.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,
-            cache_position=cache_position
-        )
-
-        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)
-
-        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,
-    ):
-        past_length = 0
-        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 self.generation_config.cache_implementation == "static":
-            # generation with static cache
-            cache_position = kwargs.get("cache_position", None)
-            if cache_position is None:
-                past_length = 0
-            else:
-                past_length = cache_position[-1] + 1
-            input_ids = input_ids[:, past_length:]
-            position_ids = position_ids[:, past_length:]
-
-        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
-        # same goes for position ids. Could also help with continued generation.
-        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)
-
-        # 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:
-            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
-            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
-            # TODO: use `next_tokens` directly instead.
-            model_inputs = {"input_ids": input_ids.contiguous()}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids.contiguous(),
-                "cache_position": cache_position,
-                "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
-
-
-@add_start_docstrings(
-    """
-    The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
-
-    [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = DeepseekV2Model(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(DeepseekV2_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, transformers.,
-            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)
-
-        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,
-        )