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configuration_deepseek.py

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+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,
+        )

conversation.py

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+"""
+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="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="",
+#         roles=("User", "Assistant"),
+#         messages=(),
+#         offset=0,
+#         sep_style=SeparatorStyle.DeepSeekV2,
+#         sep="\n<｜sft▁end｜>",
+#         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="",
+        roles=("|<User>|", "|<Assistant>|"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.DeepSeekV2,
+        sep="\n<｜sft▁end｜>",
+        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())

modeling_deepseek.py

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+# 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
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    LlamaFlashAttention2
+)
+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 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)
+
+    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
+            )
+
+        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,
+            )
+        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)
+            ]
+        )
+        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,
+        )

modeling_deepseek_vl_v2.py

@@ -0,0 +1,697 @@
+from attrdict import AttrDict
+from dataclasses import dataclass
+import logging
+import gc
+
+from einops import rearrange, repeat
+from typing import Optional, List, Tuple, Callable, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+from transformers.modeling_outputs import ModelOutput
+from transformers.configuration_utils import PretrainedConfig
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    PreTrainedModel
+)
+from transformers.utils import logging
+
+from .siglip_vit import VisionTransformer
+from .configuration_deepseek import DeepseekV2Config
+from .modeling_deepseek import DeepseekV2ForCausalLM
+
+
+logger = logging.get_logger(__name__)
+
+
+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.depth
+            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 == "downsample_mlp_gelu":
+            mlp_depth = cfg.depth
+            mlp_ratio = cfg.mlp_ratio
+            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)
+
+        else:
+            raise ValueError(f"Unknown projector type: {cfg.projector_type}")
+
+        if cfg.token_pooling:
+            self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
+
+        self.layers = modules
+
+    def forward(self, x):
+        if self.cfg.token_pooling:
+            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)
+
+        elif self.cfg.projector_type == '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)
+
+
+class VisionEncoderConfig(PretrainedConfig):
+    model_type: str = "vision"
+
+    model_name: str = "siglip_large_patch16_384"
+    image_size: int = 384
+    patch_size: int = 16
+    width: int = 1024
+    layers: int = 24
+    heads: int = 16
+    mlp_ratio: int = 4
+    global_pool: str = "map"
+    ignore_head: bool = True
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+    weight_init: str = "skip"
+    deterministic: bool = False
+    num_recomputing_layers: int = 0
+
+    def __init__(
+            self,
+            model_name: str = "siglip_large_patch16_384",
+            image_size: int = 384,
+            patch_size: int = 16,
+            width: int = 1024,
+            layers: int = 24,
+            heads: int = 16,
+            mlp_ratio: int = 4,
+            global_pool: str = "map",
+            ignore_head: bool = True,
+            class_token: bool = False,
+            num_classes: int = 0,
+            use_checkpoint: bool = False,
+            **kwargs
+    ):
+        self.model_name = model_name
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.width = width
+        self.layers = layers
+        self.heads = heads
+        self.mlp_ratio = mlp_ratio
+        self.global_pool = global_pool
+        self.ignore_head = ignore_head
+        self.class_token = class_token
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+
+        super().__init__(**kwargs)
+
+
+class MlpProjectorConfig(PretrainedConfig):
+    model_type = "mlp_projector"
+    projector_type: str = "downsample_mlp_gelu"
+    input_dim: int = 1152
+    n_embed: int = 2048
+    depth: int = 2
+    mlp_ratio: int = 1
+    downsample_ratio: int = 2
+    token_pooling: bool = False
+
+    def __init__(
+            self,
+            projector_type: str = "downsample_mlp_gelu",
+            input_dim: int = 1152,
+            n_embed: int = 2048,
+            depth: int = 2,
+            mlp_ratio: int = 1,
+            downsample_ratio: int = 2,
+            **kwargs
+    ):
+        self.projector_type = projector_type
+        self.input_dim = input_dim
+        self.n_embed = n_embed
+        self.depth = depth
+        self.mlp_ratio = mlp_ratio
+        self.downsample_ratio = downsample_ratio
+
+        super().__init__(**kwargs)
+
+
+@dataclass
+class DeepSeekVLV2CausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for DeepSeek-VL2 causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            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)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    rope_deltas: Optional[torch.LongTensor] = None
+
+
+class DeepseekVLV2Config(PretrainedConfig):
+    model_type = "deepseek_vl_v2"
+    vision_config: VisionEncoderConfig
+    projector_config: MlpProjectorConfig
+    language_config: DeepseekV2Config
+
+    tile_tag: str = "2D"
+    global_view_pos: str = "head"
+    candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384),)
+
+    def __init__(
+            self,
+            tile_tag: str = "tile_tag",
+            global_view_pos: str = "head",
+            candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384),),
+            **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        vision_config = kwargs.get("vision_config", {})
+        self.vision_config = VisionEncoderConfig(**vision_config)
+
+        projector_config = kwargs.get("projector_config", {})
+        self.projector_config = MlpProjectorConfig(**projector_config)
+
+        language_config = kwargs.get("language_config", {})
+        if isinstance(language_config, DeepseekV2Config):
+            self.language_config = language_config
+        else:
+            self.language_config = DeepseekV2Config(**language_config)
+
+        self.tile_tag = tile_tag
+        self.global_view_pos = global_view_pos
+        self.candidate_resolutions = candidate_resolutions
+
+
+class DeepseekVLV2PreTrainedModel(PreTrainedModel):
+    config_class = DeepseekVLV2Config
+    base_model_prefix = "deepseek_vl_v2"
+    _no_split_modules = []
+    _skip_keys_device_placement = "past_key_values"
+
+
+class DeepseekVLV2ForCausalLM(DeepseekVLV2PreTrainedModel):
+
+    def __init__(self, config: DeepseekVLV2Config):
+        super().__init__(config)
+
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        # ----------- vision encoder ------------
+        vision_config = config.vision_config
+        self.vision = VisionTransformer(
+            img_size=vision_config.image_size,
+            patch_size=vision_config.patch_size,
+            embed_dim=vision_config.width,
+            depth=vision_config.layers,
+            num_heads=vision_config.heads,
+            mlp_ratio=vision_config.mlp_ratio,
+            class_token=vision_config.class_token,
+            global_pool=vision_config.global_pool,
+            ignore_head=vision_config.ignore_head,
+            weight_init=vision_config.weight_init,
+            num_classes=0,
+            deterministic=vision_config.deterministic,
+            num_recomputing_layers=vision_config.num_recomputing_layers
+        )
+
+        # ----------- vl projector ------------
+        projector_config = config.projector_config
+        self.projector = MlpProjector(projector_config)
+
+        # image token format 形式
+        # FIXME 目前tile tag & global_view_pos的默认取值都是之前的实验策略；后续应当去掉默认取值，改为没有取值就raise error
+        self.tile_tag = config.tile_tag
+        self.global_view_pos = config.global_view_pos
+
+        # 用于format image token sequence的特殊token
+        embed_std = 1 / torch.sqrt(torch.tensor(projector_config.n_embed, dtype=torch.float32))
+        if self.tile_tag == "2D":
+            # <|view_separator|>, <|\n|>
+            self.image_newline = nn.Parameter(torch.randn(projector_config.n_embed) * embed_std)
+            # fix the typo: view_seperater
+            self.view_seperator = nn.Parameter(torch.randn(projector_config.n_embed) * embed_std)
+        elif self.tile_tag == "1D":
+            # <|tile_x|>, <|tile_global|>
+            candidate_resolutions = config.candidate_resolutions
+            if len(candidate_resolutions) == 0:
+                raise ValueError(
+                    f"len(candidate_resolutions) should be larger than 0, but got {len(candidate_resolutions)}")
+            tile_variants_num = len(candidate_resolutions)
+            self.tile_indicators = nn.Parameter(
+                torch.randn(size=(tile_variants_num + 1, config.aligner.params.n_embed)) * embed_std
+            )
+        else:
+            raise ValueError(f"tile tag should be either 1D or 2D, but got {self.tile_tag}")
+
+        # ----------- language model ------------
+        language_config = config.language_config
+        self.language = DeepseekV2ForCausalLM(language_config)
+
+    def prepare_inputs_embeds(
+            self,
+            input_ids: torch.LongTensor,
+            images: Optional[torch.FloatTensor] = None,
+            images_seq_mask: Optional[torch.LongTensor] = None,
+            images_spatial_crop: Optional[torch.LongTensor] = None,
+            **ignore_kwargs
+    ):
+        """
+
+        Args:
+            input_ids (torch.LongTensor): [b, T]
+            images (torch.FloatTensor): [b, max_n_images, 3, height, width]
+            images_seq_mask (torch.BoolTensor): [b, T]
+            images_spatial_crop (torch.LongTensor): [b, max_n_images, 2]
+
+        Returns:
+            input_embeds (torch.Tensor): [b, T, D]
+        """
+
+        if images is None or images_spatial_crop.sum() == 0:
+            return self.language.get_input_embeddings()(input_ids)
+
+        bs, max_n_images, _ = images_spatial_crop.shape
+        batch_num_tiles = [0 for _ in range(bs)]
+        total_tiles = []
+        for idx in range(bs):
+            for jdx in range(max_n_images):
+                num_width_tiles, num_height_tiles = images_spatial_crop[idx, jdx]
+                if num_width_tiles == 0 or num_height_tiles == 0:
+                    break
+                batch_num_tiles[idx] += (1 + num_width_tiles * num_height_tiles)
+
+            total_tiles.append(images[idx, :batch_num_tiles[idx]])
+
+        # [batch_all_tiles, 3, height, width]
+        total_tiles = torch.cat(total_tiles, dim=0)
+        assert total_tiles.shape[0] == sum(batch_num_tiles)
+        if total_tiles.shape[0] == 0:
+            return self.language.get_input_embeddings()(input_ids)
+
+        # [batch_all_tiles, vit_seq_len, c]
+        images_feature = self.vision(total_tiles)
+
+        # [batch_all_tiles, hw, D]
+        images_embeds = self.projector(images_feature)
+        _, hw, n_dim = images_embeds.shape
+        h = w = int(hw ** 0.5)
+
+        # put image tokens into the input_embeds, [b, T, D]
+        input_embeds = self.language.get_input_embeddings()(input_ids)
+
+        # 根据self.tile_tag & self.global_view_pos填充image token sequence
+        tile_index = 0
+        for idx in range(images_spatial_crop.shape[0]):
+            images_in_this_batch = []
+            for jdx in range(images_spatial_crop.shape[1]):
+
+                # extra global & local features
+                num_width_tiles, num_height_tiles = images_spatial_crop[idx, jdx]
+                if num_width_tiles == 0 or num_height_tiles == 0:
+                    break
+
+                num_tiles_in_image = num_width_tiles * num_height_tiles
+
+                # [hw, D]
+                global_features = images_embeds[tile_index]
+
+                # [num_height_tiles * num_width_tiles, hw, D]
+                local_features = images_embeds[tile_index + 1: tile_index + 1 + num_tiles_in_image]
+
+                tile_index += num_tiles_in_image + 1
+
+                # format global and local features
+                if self.tile_tag == "2D":
+
+                    # ----------------- global view add newline -----------------
+                    # [hw, D] -> [h, w, D]
+                    global_features = global_features.view(h, w, n_dim)
+                    # [D]     -> [h, 1, D]
+                    new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)
+                    # cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
+                    global_features = torch.cat([global_features, new_lines_in_global], dim=1)
+                    # [h, w + 1, D] -> [h * (w + 1), D]
+                    global_features = global_features.view(-1, n_dim)
+
+                    # ----------------- local view add newline -----------------
+                    # [num_height_tiles * num_width_tiles, h * w, D] -> [num_height_tiles * h, num_width_tiles * w, D]
+                    local_features = rearrange(
+                        local_features,
+                        "(th tw) (h w) d -> (th h) (tw w) d",
+                        th=num_height_tiles,
+                        tw=num_width_tiles,
+                        h=h,
+                        w=w
+                    )
+
+                    # [D] -> [num_height_tiles * h, 1, D]
+                    new_lines_in_local = repeat(
+                        self.image_newline,
+                        "d -> (th h) 1 d",
+                        th=num_height_tiles,
+                        h=h
+                    )
+
+                    # [num_height_tiles * h, num_width_tiles * w + 1, D]
+                    local_features = torch.cat([local_features, new_lines_in_local], dim=1)
+
+                    # [num_height_tiles * h, num_width_tiles * w + 1, D]
+                    #   --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
+                    local_features = local_features.view(-1, n_dim)
+
+                    # ----------------- merge global and local tiles -----------------
+                    if self.global_view_pos == "head":
+                        global_local_features = torch.cat(
+                            [global_features, self.view_seperator[None, :], local_features], dim=0)
+                    else:
+                        global_local_features = torch.cat(
+                            [local_features, self.view_seperator[None, :], global_features], dim=0)
+
+                else:
+                    # abandoned，实际上不会走这个逻辑
+                    global_features = torch.cat(
+                        [self.tile_indicators[0:1], global_features], dim=0
+                    )
+                    local_features = torch.cat(
+                        [self.tile_indicators[1:num_tiles_in_image + 1].unsqueeze(1), local_features], dim=1
+                    )
+                    local_features = rearrange(local_features, 'crop_num hw d -> (crop_num hw) d')
+
+                    if self.global_view_pos == "head":
+                        global_local_features = torch.cat([global_features, local_features], dim=0)
+                    else:
+                        global_local_features = torch.cat([local_features, global_features], dim=0)
+
+                images_in_this_batch.append(global_local_features)
+
+            if len(images_in_this_batch) > 0:
+                images_in_this_batch = torch.cat(images_in_this_batch, dim=0)
+                input_embeds[idx].masked_scatter_(images_seq_mask[idx].unsqueeze(-1), images_in_this_batch)
+
+        return input_embeds
+
+    @torch.no_grad()
+    def incremental_prefilling(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+
+            images: Optional[torch.FloatTensor] = None,
+            images_seq_mask: Optional[torch.LongTensor] = None,
+            images_spatial_crop: Optional[torch.LongTensor] = None,
+            chunk_size: int = 1024
+    ):
+        if inputs_embeds is None:
+            inputs_embeds = self.prepare_inputs_embeds(
+                input_ids=input_ids,
+                images=images,
+                images_seq_mask=images_seq_mask,
+                images_spatial_crop=images_spatial_crop,
+            )
+
+            del images
+            del images_seq_mask
+            del images_spatial_crop
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+            self._clear_cuda_cache()
+
+        bzs, seq_len, _ = inputs_embeds.shape
+        past_key_values = None
+
+        # remain the last token for the next forward
+        prefilling_len = seq_len - 1
+        for i in range(0, prefilling_len, chunk_size):
+            chunk_start = i
+            chunk_end = min(i + chunk_size, prefilling_len)
+            chunk_inputs_embeds = inputs_embeds[:, chunk_start: chunk_end]
+            chunk_attention_mask = attention_mask[:, 0: chunk_end]
+            # print(f"start = {chunk_start}, end = {chunk_end}, prefilling_len = {prefilling_len}, seq_len = {seq_len}")
+
+            # compute position_ids
+            if past_key_values is not None:
+                position_ids = torch.arange(
+                    chunk_start,
+                    chunk_end,
+                    dtype=torch.long,
+                    device=inputs_embeds.device
+                ).unsqueeze(0)
+                past_key_values = self._move_past_key_values_to_gpu(past_key_values, inputs_embeds.device)
+            else:
+                position_ids = None
+
+            # chunk-forward
+            with torch.no_grad():
+                outputs = self.forward(
+                    inputs_embeds=chunk_inputs_embeds,
+                    attention_mask=chunk_attention_mask,
+                    past_key_values=past_key_values,
+                    position_ids=position_ids,
+                    use_cache=True,
+                )
+                # update past_key_values
+                past_key_values = outputs.past_key_values
+                past_key_values = self._move_past_key_values_to_cpu(past_key_values)
+
+                del outputs, position_ids
+                self._clear_cuda_cache()
+
+        prefilling_key_values = []
+        for layer_past in past_key_values:
+            prefilling_key_values.append(
+                (
+                    layer_past[0][:, :, 0: prefilling_len, ...].to(inputs_embeds.device),
+                    layer_past[1][:, :, 0: prefilling_len, ...].to(inputs_embeds.device),
+                )
+            )
+
+        return inputs_embeds, prefilling_key_values
+
+    def forward(
+            self,
+            input_ids: Optional[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,
+
+            images: Optional[torch.FloatTensor] = None,
+            images_seq_mask: Optional[torch.LongTensor] = None,
+            images_spatial_crop: Optional[torch.LongTensor] = 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,
+    ):
+
+        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
+        )
+        if inputs_embeds is None:
+            inputs_embeds = self.prepare_inputs_embeds(
+                input_ids=input_ids,
+                images=images,
+                images_seq_mask=images_seq_mask,
+                images_spatial_crop=images_spatial_crop,
+            )
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        # print(inputs_embeds.shape)
+        outputs = self.language.forward(
+            input_ids=None,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position
+        )
+
+        return outputs
+
+    def _clear_cuda_cache(self):
+        """clear CUDA memory cache"""
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()
+
+    def _move_past_key_values_to_cpu(self, past_key_values):
+        # print(f"past_key_values -> cpu")
+        if past_key_values is None:
+            return None
+        return tuple(tuple(t.cpu() for t in layer) for layer in past_key_values)
+
+    def _move_past_key_values_to_gpu(self, past_key_values, device="cuda:0"):
+        # print(f"past_key_values -> gpu")
+        if past_key_values is None:
+            return None
+        return tuple(tuple(t.to(device) for t in layer) for layer in past_key_values)
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            inputs_embeds=None,
+
+            images: Optional[torch.FloatTensor] = None,
+            images_seq_mask: Optional[torch.LongTensor] = None,
+            images_spatial_crop: Optional[torch.LongTensor] = None,
+
+            attention_mask=None,
+            cache_position=None,
+
+            pixel_values=None,
+            image_sizes=None,
+            num_logits_to_keep=None,
+            **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+        model_inputs = self.language.prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            num_logits_to_keep=num_logits_to_keep,
+            **kwargs,
+        )
+
+        # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+        # Otherwise we need pixel values to be passed to model
+        cache_position = model_inputs["cache_position"]
+        if cache_position[0] == 0:
+            model_inputs["images"] = images
+            model_inputs["images_seq_mask"] = images_seq_mask
+            model_inputs["images_spatial_crop"] = images_spatial_crop
+
+        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
+
+
+AutoConfig.register("vision", VisionEncoderConfig)
+AutoConfig.register("mlp_projector", MlpProjectorConfig)
+AutoConfig.register("deepseek_vl_v2", DeepseekVLV2Config)
+AutoModelForCausalLM.register(DeepseekVLV2Config, DeepseekVLV2ForCausalLM)

processing_deepseek_vl_v2.py

@@ -0,0 +1,675 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from dataclasses import dataclass
+from typing import Dict, Tuple, List, Literal, Optional
+import math
+
+import torch
+from torch.nn.utils.rnn import pad_sequence
+import torchvision.transforms as T
+from transformers import LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+from PIL import Image, ImageOps
+
+from .conversation import get_conv_template
+
+
+def select_best_resolution(image_size, candidate_resolutions):
+    # used for cropping
+    original_width, original_height = image_size
+    best_fit = None
+    max_effective_resolution = 0
+    min_wasted_resolution = float("inf")
+
+    for width, height in candidate_resolutions:
+        scale = min(width / original_width, height / original_height)
+        downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)
+        effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
+        wasted_resolution = (width * height) - effective_resolution
+
+        if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
+            max_effective_resolution = effective_resolution
+            min_wasted_resolution = wasted_resolution
+            best_fit = (width, height)
+
+    return best_fit
+
+
+class DictOutput(object):
+    def keys(self):
+        return self.__dict__.keys()
+
+    def __getitem__(self, item):
+        return self.__dict__[item]
+
+    def __setitem__(self, key, value):
+        self.__dict__[key] = value
+
+
+# 对于inference sample也可以维护input_ids，反正最后不会用到
+@dataclass
+class VLChatProcessorOutput(DictOutput):
+    sft_format: str
+    input_ids: torch.LongTensor
+    target_ids: torch.LongTensor
+    images: torch.Tensor
+    images_seq_mask: torch.BoolTensor
+    images_spatial_crop: torch.LongTensor
+    num_image_tokens: List[int]
+
+    def __len__(self):
+        return len(self.input_ids)
+
+
+@dataclass
+class BatchCollateOutput(DictOutput):
+    sft_format: List[str]
+    input_ids: torch.LongTensor
+    labels: torch.LongTensor
+    images: torch.Tensor
+    attention_mask: torch.Tensor
+    images_seq_mask: torch.BoolTensor
+    images_spatial_crop: torch.LongTensor
+    seq_lens: List[int]
+
+    def to(self, device, dtype=torch.bfloat16):
+        self.input_ids = self.input_ids.to(device)
+        self.labels = self.labels.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.images_seq_mask = self.images_seq_mask.to(device)
+        self.images_spatial_crop = self.images_spatial_crop.to(device)
+        self.images = self.images.to(device=device, dtype=dtype)
+        return self
+
+
+class ImageTransform(object):
+    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
+        self.normalize = normalize
+
+        transform_pipelines = [
+            T.ToTensor()
+        ]
+
+        if normalize:
+            transform_pipelines.append(T.Normalize(mean, std))
+
+        self.transform = T.Compose(transform_pipelines)
+
+    def __call__(self, pil_img: Image.Image):
+        x = self.transform(pil_img)
+        return x
+
+
+
+class DeepseekVLV2Processor(ProcessorMixin):
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    attributes = ["tokenizer"]
+
+    def __init__(
+            self,
+            tokenizer: LlamaTokenizerFast,
+            candidate_resolutions: Tuple[Tuple[int, int]],
+            patch_size: int,
+            downsample_ratio: int,
+            image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+            image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+            normalize: bool = True,
+            image_token: str = "<image>",
+            pad_token: str = "<｜▁pad▁｜>",
+            add_special_token: bool = False,
+            sft_format: str = "deepseek",
+            mask_prompt: bool = True,
+            ignore_id: int = -100,
+            **kwargs,
+    ):
+
+        self.candidate_resolutions = candidate_resolutions
+        self.image_size = candidate_resolutions[0][0]
+        self.patch_size = patch_size
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.normalize = normalize
+        self.downsample_ratio = downsample_ratio
+
+        self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize)
+        self.tokenizer = tokenizer
+        self.tokenizer.padding_side = 'left'  # must set this，padding side with make a difference in batch inference
+
+        # add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
+        if tokenizer.pad_token is None:
+            self.tokenizer.add_special_tokens({'pad_token': pad_token})
+        print(f"Add pad token = ['{pad_token}'] to the tokenizer\n"
+              f"{pad_token}:{tokenizer.encode(pad_token, add_special_tokens=False)[0]}")
+
+        # add image token
+        image_token_id = self.tokenizer.vocab.get(image_token)
+        if image_token_id is None:
+            special_tokens = [image_token]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+        self.image_token_id = self.tokenizer.vocab.get(image_token)
+        print(f"Add image token = ['{image_token}'] to the tokenizer\n"
+              f"{image_token}:{tokenizer.encode(image_token, add_special_tokens=False)[0]}")
+
+        # add five special tokens for grounding-related tasks
+        # <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
+        special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>']
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+        print(f"Add grounding-related tokens = {special_tokens} to the tokenizer with input_ids\n"
+              f"<|ref|>:{tokenizer.encode('<|ref|>', add_special_tokens=False)[0]}\n"
+              f"<|/ref|>:{tokenizer.encode('<|/ref|>', add_special_tokens=False)[0]}\n"
+              f"<|det|>:{tokenizer.encode('<|det|>', add_special_tokens=False)[0]}\n"
+              f"<|/det|>:{tokenizer.encode('<|/det|>', add_special_tokens=False)[0]}\n"
+              f"<|grounding|>:{tokenizer.encode('<|grounding|>', add_special_tokens=False)[0]}")
+
+        # add special tokens for SFT data
+        special_tokens = ["<|User|>", "<|Assistant|>"]
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+        print(f"Add chat tokens = {special_tokens} to the tokenizer with input_ids\n"
+              f"<|User|>:{tokenizer.encode('<|User|>', add_special_tokens=False)[0]}\n"
+              f"<|Assistant|>:{tokenizer.encode('<|Assistant|>', add_special_tokens=False)[0]}\n")
+
+        self.image_token = image_token
+        self.pad_token = pad_token
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            tokenizer,
+            **kwargs,
+        )
+
+    def new_chat_template(self):
+        conv = get_conv_template(self.sft_format)
+        return conv
+
+    def format_messages(
+            self,
+            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 format_messages_v2(self, messages, pil_images, systems=None):
+        """play the role of format_messages_v2 and get_images_info in the last version"""
+        tokenized_data = []
+        masked_tokenized_data = []  # labels
+        images_list = []
+        images_seq_mask = []
+        images_spatial_crop = []
+        num_image_tokens = []
+
+        image_index = 0
+
+        conv = get_conv_template(self.sft_format)
+        conv_system_message = conv.system_message
+
+        for idx, message in enumerate(messages):
+            if idx == 0:
+                tokenized_data += [self.bos_id]
+                masked_tokenized_data += [self.bos_id]
+                images_seq_mask += [False]
+                conv.system_message = conv_system_message
+            else:
+                conv.system_message = ''
+
+            if message['role'] == conv.roles[0] or message['role'] == "user":
+                conv.reset_message()
+                conv.append_message(conv.roles[0], str(message['content']).strip())
+                conv.append_message(conv.roles[1], '')
+                formatted_question = conv.get_prompt()
+                tokenized_str, images, seq_mask, spatial_crop, n_image_tokens = self.tokenize_with_images(
+                    formatted_question,
+                    pil_images[image_index: image_index + formatted_question.count(self.image_token)],
+                    bos=False,
+                    eos=False,
+                    cropping=len(pil_images) <= 2
+                )
+                image_index += formatted_question.count(self.image_token)
+
+                tokenized_data += tokenized_str
+                if self.mask_prompt:
+                    masked_tokenized_data += [self.ignore_id] * len(tokenized_str)
+                else:
+                    masked_tokenized_data += tokenized_str
+                images_list += images
+                images_seq_mask += seq_mask
+                images_spatial_crop += spatial_crop
+                num_image_tokens += n_image_tokens
+
+            elif message['role'] == conv.roles[1] or message['role'] == "assistant":
+                formatted_answer = message['content'].strip()
+                assert formatted_answer.count(
+                    self.image_token) == 0, f"there should be no {self.image_token} in the assistant's reply, but got {messages}"
+                tokenized_str, images, seq_mask, spatial_crop, n_image_tokens = self.tokenize_with_images(
+                    formatted_answer,
+                    [],
+                    bos=False,
+                    eos=True,
+                    cropping=len(pil_images) <= 2)
+
+                tokenized_data += tokenized_str
+                masked_tokenized_data += tokenized_str
+                images_seq_mask += seq_mask
+
+            elif message['role'] == 'system' or message['role'] == 'deepseekapi-sys':
+                # 如果message里面有system，那就只允许出现在message的第一句，同时conv原本的system就会失效
+                assert idx == 0, 'system information should only exist in the begining of the conversation'
+                formatted_system = message['content'].strip()
+                tokenized_str = self.encode(formatted_system, bos=False, eos=False)
+                tokenized_data += tokenized_str
+                if self.mask_prompt:
+                    masked_tokenized_data += [self.ignore_id] * len(tokenized_str)
+                else:
+                    masked_tokenized_data += tokenized_str
+                seq_mask = [False] * len(tokenized_str)
+                images_seq_mask += seq_mask
+
+            else:
+                assert False, f"Unknown role: {message['role']}"
+
+        assert len(tokenized_data) == len(
+            images_seq_mask), f"format_messages_v2: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
+        assert len(images_spatial_crop) == len(num_image_tokens), f"image number should be compatible"
+
+        return tokenized_data, masked_tokenized_data, images_list, images_seq_mask, images_spatial_crop, num_image_tokens
+
+    def format_prompts(
+            self,
+            prompts: str,
+            sft_format: str = "deepseek",
+            system_prompt: str = "",
+    ):
+        """
+        Applies the SFT template to prompts.
+
+        Args:
+            prompts (str): the non-sft formatted prompt;
+            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)
+        conv.append_message(conv.roles[0], prompts.strip())
+        conv.append_message(conv.roles[1], "")
+
+        sft_prompt = conv.get_prompt().strip()
+
+        return sft_prompt
+
+    @property
+    def bos_id(self):
+        return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return self.tokenizer.eos_token_id
+
+    @property
+    def pad_id(self):
+        return self.tokenizer.pad_token_id
+
+    def encode(self, text: str, bos: bool = True, eos: bool = False):
+        t = self.tokenizer.encode(text, add_special_tokens=False)
+
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+
+        return t
+
+    def decode(self, t: List[int], **kwargs) -> str:
+        return self.tokenizer.decode(t, **kwargs)
+
+    def process_one(
+            self,
+            prompt: str = None,
+            conversations: List[Dict[str, str]] = None,
+            images: List[Image.Image] = None,
+            apply_sft_format: bool = False,
+            inference_mode: bool = True,
+            system_prompt: str = "",
+            **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt;
+                if conversations is not None, then it will always apply the SFT format to conversations;
+            inference_mode (bool): if True, then remove the last eos token;
+            system_prompt (str): the system prompt;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        assert (
+                prompt is None or conversations is None
+        ), "prompt and conversations cannot be used at the same time."
+
+        if prompt is None:
+            # apply sft format
+            sft_format = self.format_messages(
+                conversations=conversations,
+                sft_format=self.sft_format,
+                system_prompt=system_prompt,
+            )
+            tokenized_str, masked_tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens = self.format_messages_v2(
+                conversations, images)
+        else:
+            if apply_sft_format:
+                sft_format = self.format_prompts(
+                    prompts=prompt,
+                    sft_format=self.sft_format,
+                    system_prompt=system_prompt
+                )
+            else:
+                sft_format = prompt
+            tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens = self.tokenize_with_images(
+                sft_format, images, bos=True, eos=True, cropping=len(images) <= 2)
+            masked_tokenized_str = []
+            for token_index in tokenized_str:
+                if token_index != self.image_token_id:
+                    masked_tokenized_str.append(token_index)
+                else:
+                    masked_tokenized_str.append(self.ignore_id)
+
+        assert len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \
+            (f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
+             f"imags_seq_mask's length {len(images_seq_mask)}, are not equal")
+
+        input_ids = torch.LongTensor(tokenized_str)
+        target_ids = torch.LongTensor(masked_tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
+        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = self.ignore_id
+        input_ids[input_ids < 0] = self.pad_id
+
+        if inference_mode:
+            # 去掉结尾的eos token
+            assert input_ids[-1] == self.eos_id
+            input_ids = input_ids[:-1]
+            target_ids = target_ids[:-1]
+            images_seq_mask = images_seq_mask[:-1]
+
+        if len(images_list) == 0:
+            images = torch.zeros((1, 3, self.image_size, self.image_size))
+            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
+        else:
+            images = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+
+        prepare = VLChatProcessorOutput(
+            sft_format=sft_format,
+            input_ids=input_ids,
+            target_ids=target_ids,
+            images=images,
+            images_seq_mask=images_seq_mask,
+            images_spatial_crop=images_spatial_crop,
+            num_image_tokens=num_image_tokens
+        )
+
+        return prepare
+
+    def __call__(
+            self,
+            *,
+            prompt: str = None,
+            conversations: List[Dict[str, str]] = None,
+            images: List[Image.Image] = None,
+            apply_sft_format: bool = False,
+            force_batchify: bool = True,
+            inference_mode: bool = True,
+            system_prompt: str = "",
+            **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt;
+                if conversations is not None, then it will always apply the SFT format to conversations;
+            force_batchify (bool): force batchify the inputs;
+            inference_mode (bool): if True, then remove the last eos token;
+            system_prompt (str): the system prompt;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        prepare = self.process_one(
+            prompt=prompt,
+            conversations=conversations,
+            images=images,
+            apply_sft_format=apply_sft_format,
+            inference_mode=inference_mode,
+            system_prompt=system_prompt
+        )
+
+        if force_batchify:
+            prepare = self.batchify([prepare])
+
+        return prepare
+
+    def tokenize_with_images(
+            self,
+            conversation: str,
+            images: List[Image.Image],
+            bos: bool = True,
+            eos: bool = True,
+            cropping: bool = True,
+    ):
+        """Tokenize text with <image> tags."""
+        assert conversation.count(self.image_token) == len(images)
+        text_splits = conversation.split(self.image_token)
+        images_list, images_seq_mask, images_spatial_crop = [], [], []
+        num_image_tokens = []
+        tokenized_str = []
+        for text_sep, image in zip(text_splits, images):
+            """encode text_sep"""
+            tokenized_sep = self.encode(text_sep, bos=False, eos=False)
+            tokenized_str += tokenized_sep
+            images_seq_mask += [False] * len(tokenized_sep)
+
+            """select best resolution for anyres"""
+            if cropping:
+                best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
+            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"""
+            global_view = ImageOps.pad(image, (self.image_size, self.image_size),
+                                       color=tuple(int(x * 255) for x in self.image_transform.mean))
+            images_list.append(self.image_transform(global_view))
+
+            """process the local views"""
+            local_view = ImageOps.pad(image, (best_width, best_height),
+                                      color=tuple(int(x * 255) for x in self.image_transform.mean))
+            for i in range(0, best_height, self.image_size):
+                for j in range(0, best_width, self.image_size):
+                    images_list.append(
+                        self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
+
+            """record height / width crop num"""
+            num_width_tiles, num_height_tiles = best_width // self.image_size, best_height // self.image_size
+            images_spatial_crop.append([num_width_tiles, num_height_tiles])
+
+            """add image tokens"""
+            h = w = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)
+            # global views tokens h * (w + 1), 1 is for line seperator
+            tokenized_image = [self.image_token_id] * h * (w + 1)
+            # add a seperator between global and local views
+            tokenized_image += [self.image_token_id]
+            # local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
+            tokenized_image += [self.image_token_id] * (num_height_tiles * h) * (num_width_tiles * w + 1)
+
+            tokenized_str += tokenized_image
+            images_seq_mask += [True] * len(tokenized_image)
+            num_image_tokens.append(len(tokenized_image))
+            # print(width_crop_num, height_crop_num, len(tokenized_image)) # test the correctness of the number of image-related tokens
+
+        """process the last text split"""
+        tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos and eos tokens"""
+        if bos:
+            tokenized_str = [self.bos_id] + tokenized_str
+            images_seq_mask = [False] + images_seq_mask
+        if eos:
+            tokenized_str = tokenized_str + [self.eos_id]
+            images_seq_mask = images_seq_mask + [False]
+
+        assert len(tokenized_str) == len(
+            images_seq_mask), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
+
+        return tokenized_str, images_list, images_seq_mask, images_spatial_crop, num_image_tokens
+
+    def batchify(
+            self,
+            sample_list: List[VLChatProcessorOutput],
+            padding: Literal["left", "right"] = "left"
+    ) -> BatchCollateOutput:
+        """
+        Preprocesses the inputs for multimodal inference.
+
+        Args:
+            sample_list (List[VLChatProcessorOutput]): A list of VLChatProcessorOutput.
+            padding (str): The padding method. Defaults to "left".
+
+        Returns:
+            BatchCollateOutput: A dictionary of the inputs to use for multimodal inference.
+        """
+
+        batched_sft_format = [sample.sft_format for sample in sample_list]
+        batched_input_ids = [sample.input_ids for sample in sample_list]
+        batched_labels = [sample.target_ids for sample in sample_list]
+        batched_images_seq_mask = [sample["images_seq_mask"] for sample in sample_list]
+        seq_lens = [len(sample) for sample in sample_list]
+
+        """padding input_ids and images_seq_mask"""
+        if padding == "left":
+            # the tokenizer is default to pad at left
+            ## TODO, You're using a LlamaTokenizerFast tokenizer.
+            #   Please note that with a fast tokenizer, using the `__call__` method is faster than
+            #   using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
+            padded_input_ids = self.tokenizer.pad({"input_ids": batched_input_ids})
+            batched_input_ids, batched_attention_mask = padded_input_ids["input_ids"], padded_input_ids[
+                "attention_mask"].bool()
+            batched_labels = self.tokenizer.pad({"input_ids": batched_labels})["input_ids"]
+            batched_labels[batched_labels == self.pad_id] = self.ignore_id  # labels正常不会出现pad_id，无需额外保护
+            batched_images_seq_mask = self.tokenizer.pad({"input_ids": batched_images_seq_mask})["input_ids"]
+            batched_images_seq_mask[batched_images_seq_mask == self.pad_id] = False
+        else:
+            batched_input_ids = pad_sequence(batched_input_ids, batch_first=True, padding_value=self.pad_id)
+            batched_labels = pad_sequence(batched_labels, batch_first=True, padding_value=self.ignore_id)
+            batched_images_seq_mask = pad_sequence(batched_images_seq_mask, batch_first=True, padding_value=0)
+            batched_attention_mask = batched_input_ids != self.pad_id
+
+        """padding images to max_patch_num"""
+        max_n_patches = max(sample["images"].shape[0] for sample in sample_list)
+        batched_images = []
+        for sample in sample_list:
+            images = sample["images"]
+            n_pads = max_n_patches - images.shape[0]
+            if n_pads > 0:
+                pad_images = torch.zeros((n_pads, *images.shape[1:]), dtype=images.dtype)
+                images = torch.cat([images, pad_images], dim=0)
+            batched_images.append(images)
+        batched_images = torch.stack(batched_images, dim=0)
+
+        """padding images_spatial_crop to max_n_images"""
+        max_n_images = max(sample["images_spatial_crop"].shape[0] for sample in sample_list)
+        batched_images_spatial_crop = []
+        for sample in sample_list:
+            images_spatial_crop = sample["images_spatial_crop"]
+            n_pads = max_n_images - sample["images_spatial_crop"].shape[0]
+            if n_pads > 0:
+                pad_images_spatial_crop = torch.full((n_pads, 2), 0, dtype=images_spatial_crop.dtype)
+                images_spatial_crop = torch.cat([images_spatial_crop, pad_images_spatial_crop], dim=0)
+            batched_images_spatial_crop.append(images_spatial_crop)
+        batched_images_spatial_crop = torch.stack(batched_images_spatial_crop, dim=0)
+
+        batched_samples = BatchCollateOutput(
+            input_ids=batched_input_ids,
+            attention_mask=batched_attention_mask,
+            labels=batched_labels,
+            images=batched_images,
+            images_seq_mask=batched_images_seq_mask,
+            images_spatial_crop=batched_images_spatial_crop,
+            sft_format=batched_sft_format,
+            seq_lens=seq_lens
+        )
+
+        return batched_samples

siglip_vit.py

@@ -0,0 +1,660 @@
+# https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py
+from dataclasses import dataclass
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Final, Optional, Callable, Union, Tuple, List, Set, Dict, Type, Literal, Sequence
+import math
+import warnings
+from timm.layers import (
+    PatchEmbed, Mlp, DropPath,
+    AttentionPoolLatent, PatchDropout, resample_abs_pos_embed, LayerType
+)
+from timm.models._manipulate import named_apply, checkpoint_seq, adapt_input_conv
+from transformers.modeling_utils import is_flash_attn_2_available
+from xformers.ops import memory_efficient_attention
+from functools import partial
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_qkvpacked_func
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)  # noqa: E741
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
+    r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its orignal dtype.
+    Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
+    from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+
+    with torch.no_grad():
+        dtype = tensor.dtype
+        tensor_fp32 = tensor.float()
+        tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
+        tensor_dtype = tensor_fp32.to(dtype=dtype)
+        tensor.copy_(tensor_dtype)
+
+
+def init_weights(self):
+    if self.pos_embed is not None:
+        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+    trunc_normal_(self.latent, std=self.latent_dim ** -0.5)
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = '') -> None:
+    """ ViT weight initialization, original timm impl (for reproducibility) """
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif hasattr(module, 'init_weights'):
+        module.init_weights()
+
+
+class Attention(nn.Module):
+    fused_attn: Final[bool]
+
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int = 8,
+            qkv_bias: bool = False,
+            qk_norm: bool = False,
+            attn_drop: float = 0.,
+            proj_drop: float = 0.,
+            norm_layer: nn.Module = nn.LayerNorm,
+            deterministic: bool = False,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+        self.qk_norm = qk_norm
+        self.fused_attn = True
+        self.deterministic = deterministic
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0. else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
+
+        if not self.qk_norm:
+            if self.head_dim % 32 == 0 and is_flash_attn_2_available():
+                # flashattn must have head_dim as a multiple of 32
+                x = flash_attn_qkvpacked_func(qkv, dropout_p=self.attn_drop.p if self.training else 0.,
+                                              deterministic=self.deterministic)
+            else:
+                q, k, v = qkv.unbind(2)
+                x = memory_efficient_attention(q, k, v, p=self.attn_drop.p if self.training else 0.)
+            x = x.reshape(B, N, C)
+            x = self.proj(x)
+            x = self.proj_drop(x)
+            return x
+
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.fused_attn:
+            with torch.backends.cuda.sdp_kernel(enable_math=False, enable_mem_efficient=False):
+                # 用上下文的方式强行使用fa
+                x = F.scaled_dot_product_attention(
+                    q, k, v,
+                    dropout_p=self.attn_drop.p if self.training else 0.,
+                )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class LayerScale(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            init_values: float = 1e-5,
+            inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class Block(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            num_heads: int,
+            mlp_ratio: float = 4.,
+            qkv_bias: bool = False,
+            qk_norm: bool = False,
+            proj_drop: float = 0.,
+            attn_drop: float = 0.,
+            init_values: Optional[float] = None,
+            drop_path: float = 0.,
+            act_layer: nn.Module = nn.GELU,
+            norm_layer: nn.Module = nn.LayerNorm,
+            mlp_layer: nn.Module = Mlp,
+            deterministic: bool = False,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+            deterministic=deterministic,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = mlp_layer(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """ Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+    """
+    dynamic_img_size: Final[bool]
+
+    def __init__(
+            self,
+            img_size: Union[int, Tuple[int, int]] = 224,
+            patch_size: Union[int, Tuple[int, int]] = 16,
+            in_chans: int = 3,
+            num_classes: int = 1000,
+            global_pool: Literal['', 'avg', 'token', 'map'] = 'token',
+            embed_dim: int = 768,
+            depth: int = 12,
+            num_heads: int = 12,
+            mlp_ratio: float = 4.,
+            qkv_bias: bool = True,
+            qk_norm: bool = False,
+            init_values: Optional[float] = None,
+            class_token: bool = True,
+            no_embed_class: bool = False,
+            reg_tokens: int = 0,
+            pre_norm: bool = False,
+            fc_norm: Optional[bool] = None,
+            dynamic_img_size: bool = False,
+            dynamic_img_pad: bool = False,
+            drop_rate: float = 0.,
+            pos_drop_rate: float = 0.,
+            patch_drop_rate: float = 0.,
+            proj_drop_rate: float = 0.,
+            attn_drop_rate: float = 0.,
+            drop_path_rate: float = 0.,
+            weight_init: Literal['skip', 'jax', 'jax_nlhb', 'moco', ''] = '',
+            embed_layer: Callable = PatchEmbed,
+            norm_layer: Optional[LayerType] = None,
+            act_layer: Optional[LayerType] = None,
+            block_fn: Type[nn.Module] = Block,
+            mlp_layer: Type[nn.Module] = Mlp,
+            ignore_head: bool = False,
+            deterministic: bool = False,
+            num_recomputing_layers: int = 0
+    ) -> None:
+        """
+        Args:
+            img_size: Input image size.
+            patch_size: Patch size.
+            in_chans: Number of image input channels.
+            num_classes: Mumber of classes for classification head.
+            global_pool: Type of global pooling for final sequence (default: 'token').
+            embed_dim: Transformer embedding dimension.
+            depth: Depth of transformer.
+            num_heads: Number of attention heads.
+            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
+            qkv_bias: Enable bias for qkv projections if True.
+            init_values: Layer-scale init values (layer-scale enabled if not None).
+            class_token: Use class token.
+            no_embed_class: Don't include position embeddings for class (or reg) tokens.
+            reg_tokens: Number of register tokens.
+            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
+            drop_rate: Head dropout rate.
+            pos_drop_rate: Position embedding dropout rate.
+            attn_drop_rate: Attention dropout rate.
+            drop_path_rate: Stochastic depth rate.
+            weight_init: Weight initialization scheme.
+            embed_layer: Patch embedding layer.
+            norm_layer: Normalization layer.
+            act_layer: MLP activation layer.
+            block_fn: Transformer block layer.
+        """
+        super().__init__()
+        assert global_pool in ('', 'avg', 'token', 'map')
+        assert class_token or global_pool != 'token'
+        use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
+        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
+        # act_layer = get_act_layer(act_layer) or nn.GELU
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        # siglip use PytorchGELUTanh() rather than the vanilla nn.GELU()
+        # https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/siglip/configuration_siglip.py#L191
+        act_layer = partial(nn.GELU, approximate='tanh')
+
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.num_prefix_tokens += reg_tokens
+        self.num_reg_tokens = reg_tokens
+        self.has_class_token = class_token
+        self.no_embed_class = no_embed_class  # don't embed prefix positions (includes reg)
+        self.dynamic_img_size = dynamic_img_size
+        self.grad_checkpointing = False
+        self.ignore_head = ignore_head
+        self.num_recomputing_layers = num_recomputing_layers
+
+        embed_args = {}
+        if dynamic_img_size:
+            # flatten deferred until after pos embed
+            embed_args.update(dict(strict_img_size=False, output_fmt='NHWC'))
+        self.patch_embed = embed_layer(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
+            dynamic_img_pad=dynamic_img_pad,
+            **embed_args,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        self.reg_token = nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
+        embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
+        self.pos_drop = nn.Dropout(p=pos_drop_rate)
+        if patch_drop_rate > 0:
+            self.patch_drop = PatchDropout(
+                patch_drop_rate,
+                num_prefix_tokens=self.num_prefix_tokens,
+            )
+        else:
+            self.patch_drop = nn.Identity()
+        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(*[
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_norm=qk_norm,
+                init_values=init_values,
+                proj_drop=proj_drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                mlp_layer=mlp_layer,
+                deterministic=deterministic,
+            )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
+
+        # Classifier Head
+        if global_pool == 'map':
+            AttentionPoolLatent.init_weights = init_weights
+            self.attn_pool = AttentionPoolLatent(
+                self.embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.attn_pool = None
+        self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
+        self.head_drop = nn.Dropout(drop_rate)
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        if weight_init != 'skip':
+            self.init_weights(weight_init)
+
+    def init_weights(self, mode: Literal['jax', 'jax_nlhb', 'moco', ''] = '') -> None:
+        assert mode in ('jax', 'jax_nlhb', 'moco', '')
+        head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
+        trunc_normal_(self.pos_embed, std=.02)
+        if self.cls_token is not None:
+            nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return {'pos_embed', 'cls_token', 'dist_token'}
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r'^cls_token|pos_embed|patch_embed',  # stem and embed
+            blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head
+
+    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
+        self.num_classes = num_classes
+        if global_pool is not None:
+            assert global_pool in ('', 'avg', 'token', 'map')
+            if global_pool == 'map' and self.attn_pool is None:
+                assert False, "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != 'map ' and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
+            self.global_pool = global_pool
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dynamic_img_size:
+            B, H, W, C = x.shape
+            pos_embed = resample_abs_pos_embed(
+                self.pos_embed,
+                (H, W),
+                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
+            )
+            x = x.view(B, -1, C)
+        else:
+            pos_embed = self.pos_embed
+
+        to_cat = []
+        if self.cls_token is not None:
+            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
+        if self.reg_token is not None:
+            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
+
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + pos_embed
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+            x = x + pos_embed
+
+        return self.pos_drop(x)
+
+    def _intermediate_layers(
+            self,
+            x: torch.Tensor,
+            n: Union[int, Sequence] = 1,
+    ) -> List[torch.Tensor]:
+        outputs, num_blocks = [], len(self.blocks)
+        take_indices = set(range(num_blocks - n, num_blocks) if isinstance(n, int) else n)
+
+        # forward pass
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in take_indices:
+                outputs.append(x)
+
+        return outputs
+
+    def get_intermediate_layers(
+            self,
+            x: torch.Tensor,
+            n: Union[int, Sequence] = 1,
+            reshape: bool = False,
+            return_prefix_tokens: bool = False,
+            norm: bool = False,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        """ Intermediate layer accessor (NOTE: This is a WIP experiment).
+        Inspired by DINO / DINOv2 interface
+        """
+        # take last n blocks if n is an int, if in is a sequence, select by matching indices
+        outputs = self._intermediate_layers(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        prefix_tokens = [out[:, 0:self.num_prefix_tokens] for out in outputs]
+        outputs = [out[:, self.num_prefix_tokens:] for out in outputs]
+
+        if reshape:
+            grid_size = self.patch_embed.grid_size
+            outputs = [
+                out.reshape(x.shape[0], grid_size[0], grid_size[1], -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+
+        if return_prefix_tokens:
+            return tuple(zip(outputs, prefix_tokens))
+        return tuple(outputs)
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        if getattr(self, "is_first_stage", True):
+            x = self.patch_embed(x)
+            x = self._pos_embed(x)
+            x = self.patch_drop(x)
+            x = self.norm_pre(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            skip_last = max(1, len(self.blocks) - self.num_recomputing_layers)
+            x = checkpoint_seq(self.blocks, x, skip_last=skip_last)
+        else:
+            x = self.blocks(x)
+        if getattr(self, "is_last_stage", True):
+            x = self.norm(x)
+        return x
+
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
+        if not getattr(self, "is_last_stage", True):
+            return x
+        if self.attn_pool is not None:
+            x = self.attn_pool(x)
+        elif self.global_pool == 'avg':
+            x = x[:, self.num_prefix_tokens:].mean(dim=1)
+        elif self.global_pool:
+            x = x[:, 0]  # class token
+        x = self.fc_norm(x)
+        x = self.head_drop(x)
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.forward_features(x)
+        if not self.ignore_head:
+            x = self.forward_head(x)
+        return x
+
+    def to_pipeline(self, pp_size, pp_rank, pp_splits: Optional[List[int]] = None):
+        self.is_first_stage = pp_rank == 0
+        self.is_last_stage = pp_rank == pp_size - 1
+        if not self.is_first_stage and hasattr(self, "patch_embed"):
+            del self.patch_embed, self.cls_token, self.reg_token, self.pos_embed, self.pos_drop, self.patch_drop, self.norm_pre
+        if not self.is_last_stage and hasattr(self, "norm"):
+            del self.norm, self.attn_pool, self.fc_norm, self.head_drop, self.head
+        if pp_splits is not None:
+            assert len(self.blocks) == sum(pp_splits)
+            splits = np.cumsum([0] + pp_splits)
+            self.blocks = self.blocks[splits[pp_rank]:splits[pp_rank + 1]]
+        return self
+
+
+@dataclass
+class SigLIPVisionCfg:
+    width: int = 1152
+    layers: Union[Tuple[int, int, int, int], int] = 27
+    heads: int = 16
+    patch_size: int = 14
+    image_size: Union[Tuple[int, int], int] = 336
+    global_pool: str = "map"
+    mlp_ratio: float = 3.7362
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+
+
+SigLIP_MODEL_CONFIG = {
+    "siglip_so400m_patch14_384": {
+        "image_size": 384,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False
+    },
+
+    "siglip_so400m_patch14_224": {
+        "image_size": 224,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False
+    },
+
+    "siglip_large_patch16_384": {
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1024,
+        "layers": 24,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False
+    }
+}
+
+
+def create_siglip_vit(
+        model_name: str = "siglip_so400m_patch14_384",
+        image_size: int = 384,
+        select_layer: int = -1,
+        ckpt_path: str = "",
+        **kwargs
+):
+    assert model_name in SigLIP_MODEL_CONFIG.keys(), f"model name should be in {SigLIP_MODEL_CONFIG.keys()}"
+
+    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
+
+    if select_layer <= 0:
+        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
+    else:
+        layers = min(vision_cfg.layers, select_layer)
+
+    model = VisionTransformer(
+        img_size=image_size,
+        patch_size=vision_cfg.patch_size,
+        embed_dim=vision_cfg.width,
+        depth=layers,
+        num_heads=vision_cfg.heads,
+        mlp_ratio=vision_cfg.mlp_ratio,
+        class_token=vision_cfg.class_token,
+        global_pool=vision_cfg.global_pool,
+        ignore_head=kwargs.get("ignore_head", True),
+        weight_init=kwargs.get("weight_init", "skip"),
+        num_classes=0,
+        deterministic=kwargs.get("deterministic", False),
+        num_recomputing_layers=kwargs.get("num_recomputing_layers", 0)
+    )
+
+    if ckpt_path:
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        incompatible_keys = model.load_state_dict(state_dict, strict=False)
+        print(f"SigLIP-ViT restores from {ckpt_path},\n"
+              f"\tincompatible_keys:', {incompatible_keys}.")
+
+    return model