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README.md

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+---
+language: en
+pipeline_tag: text-generation
+tags:
+- mlx
+library_name: mlx
+---
+
+# mlx-community/Kimi-K2.7-Code-4bit
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("mlx-community/Kimi-K2.7-Code-4bit")
+
+prompt = "hello"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, return_dict=False,
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```

chat_template.jinja

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+{%- macro render_content(msg) -%}
+    {%- set c = msg.get('content') -%}
+    {%- if c is string -%}
+      {{ c }}
+    {%- elif c is not none -%}
+      {% for content in c -%}
+        {% if content['type'] == 'image' or content['type'] == 'image_url' -%}
+          <|media_begin|>image<|media_content|><|media_pad|><|media_end|>
+        {% elif content['type'] == 'video' or content['type']== 'video_url'-%}
+          <|kimi_k25_video_placeholder|>
+        {% else -%}
+          {{ content['text'] }}
+        {%- endif -%}
+      {%- endfor -%}
+    {%- endif -%}
+{%- endmacro -%}
+
+{% macro set_roles(message) -%}
+  {%- set role_name =  message.get('name') or  message['role'] -%}
+  {%- if message['role'] == 'user' -%}
+    <|im_user|>{{role_name}}<|im_middle|>
+  {%- elif message['role'] == 'assistant' -%}
+    <|im_assistant|>{{role_name}}<|im_middle|>
+  {%- else -%}
+    <|im_system|>{{role_name}}<|im_middle|>
+  {%- endif -%}
+{%- endmacro -%}
+
+
+{%- macro render_toolcalls(message) -%}
+  <|tool_calls_section_begin|>
+  {%- for tool_call in message['tool_calls'] -%}
+    {%- set formatted_id = tool_call['id'] -%}
+    <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
+  {%- endfor -%}
+  <|tool_calls_section_end|>
+{%- endmacro -%}
+
+
+{%- set preserve_thinking = preserve_thinking | default(true) -%}
+{# Find last non-tool-call assistant message. If preserve_thinking, keep -1 so hist is empty and all msgs use suffix (retain reasoning). #}
+{%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%}
+{%- if not preserve_thinking -%}
+{%- for idx in range(messages|length-1, -1, -1) -%}
+    {%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%}
+        {%- set ns.last_non_tool_call_assistant_msg = idx -%}
+        {%- break -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- endif -%}
+
+{# split all messages into history & suffix, reasoning_content in suffix should be reserved.#}
+{%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%}
+{%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%}
+
+{%- if tools -%}
+  {%- if tools_ts_str -%}
+    <|im_system|>tool_declare<|im_middle|>{{ tools_ts_str }}<|im_end|>
+  {%- else -%}
+    <|im_system|>tool_declare<|im_middle|>{{ tools | tojson(separators=(',', ':')) }}<|im_end|>
+  {%- endif -%}
+{%- endif -%}
+
+  
+{%- for message in hist_msgs -%}
+  {{set_roles(message)}}
+  {%- if message['role'] == 'assistant' -%}
+    <think></think>{{render_content(message)}}
+    {%- if message.get('tool_calls') -%}
+      {{render_toolcalls(message)}}
+    {%- endif -%}
+  {%- elif message['role'] == 'tool' -%}
+    {%- set tool_call_id = message.tool_call_id -%}
+    ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+  {%- elif message['content'] is not none -%}
+    {{render_content(message)}}
+  {%- endif -%}
+  <|im_end|>
+{%- endfor -%}
+
+{%- for message in suffix_msgs -%}
+  {{set_roles(message)}}
+  {%- if message['role'] == 'assistant' -%}
+    {%- if thinking is defined and thinking is false and preserve_thinking is false -%}
+    <think></think>{{render_content(message)}}
+    {%- else -%}
+    {%- set rc = message.get('reasoning', message.get('reasoning_content', '')) -%}
+    <think>{{rc}}</think>{{render_content(message)}}
+    {%- endif -%}
+    {%- if message.get('tool_calls') -%}
+     {{render_toolcalls(message)}}
+    {%- endif -%}
+  {%- elif message['role'] == 'tool' -%}
+    {%- set tool_call_id = message.tool_call_id -%}
+    ## Return of {{ tool_call_id }}
+{{render_content(message)}}
+  {%- elif message['content'] is not none -%}
+    {{render_content(message)}}
+  {%- endif -%}
+  <|im_end|>
+{%- endfor -%}
+
+
+{%- if add_generation_prompt -%}
+  <|im_assistant|>assistant<|im_middle|>
+  {%- if thinking is defined and thinking is false -%}
+  <think></think>
+  {%- else -%}
+  <think>
+  {%- endif -%}
+{%- endif -%}

configuration_deepseek.py

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+# Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class DeepseekV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. 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-V3.
+
+    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 129280):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        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_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the DeepSeekV3 Model.
+        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.
+
+    ```python
+    >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
+    >>> # Initializing a Deepseek-V3 style configuration
+    >>> configuration = DeepseekV3Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size=2048,
+        num_hidden_layers=61,
+        num_nextn_predict_layers=1,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts=1,
+        n_routed_experts=256,
+        ep_size=1,
+        routed_scaling_factor=2.5,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        topk_method='noaux_tc',
+        n_group=8,
+        topk_group=4,
+        num_experts_per_tok=8,
+        moe_layer_freq=1,
+        first_k_dense_replace=3,
+        norm_topk_prob=True,
+        scoring_func='sigmoid',
+        aux_loss_alpha=0.001,
+        seq_aux=True,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **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_nextn_predict_layers = num_nextn_predict_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 = 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
+
+        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,
+        )

configuration_kimi_k25.py

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+from transformers.configuration_utils import PretrainedConfig
+
+try:
+    from configuration_deepseek import DeepseekV3Config
+except ImportError:
+    from .configuration_deepseek import DeepseekV3Config
+
+
+class KimiK25VisionConfig(PretrainedConfig):
+
+    def __init__(
+            self,
+            patch_size: int = 14,
+            init_pos_emb_height: int = 64,
+            init_pos_emb_width: int = 64,
+            init_pos_emb_time: int = 4,
+            pos_emb_type: str = 'divided_fixed',
+            vt_num_attention_heads: int = 16,
+            vt_num_hidden_layers: int = 27,
+            vt_hidden_size: int = 1152,
+            vt_intermediate_size: int = 4304,
+            merge_kernel_size: tuple = (2, 2),
+            video_attn_type: str = 'spatial_temporal',
+            merge_type: str = 'sd2_tpool',
+            _attn_implementation: str = 'flash_attention_2',
+            # MM Projector parameters
+            mm_projector_type: str = 'patchmerger',
+            mm_hidden_size: int | None = None,
+            projector_hidden_act: str = "gelu",
+            projector_ln_eps: float = 1e-5,
+            # Other parameters
+            ignore_index: int = -100,
+            media_placeholder_token_id: int = 163605,
+            pad_token_id: int = 0,
+            use_unified_vision_chunk: bool = True,
+            video_placeholder="<|kimi_k25_video_placeholder|>",
+            text_hidden_size=7168,
+            **vision_config_kwargs):
+
+        self.patch_size = patch_size
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        self.init_pos_emb_time = init_pos_emb_time
+        self.pos_emb_type = pos_emb_type
+        self.vt_num_attention_heads = vt_num_attention_heads
+        self.vt_num_hidden_layers = vt_num_hidden_layers
+        self.vt_hidden_size = vt_hidden_size
+        self.vt_intermediate_size = vt_intermediate_size
+        self.merge_kernel_size = merge_kernel_size
+        self.video_attn_type = video_attn_type
+        self.merge_type = merge_type
+        self._attn_implementation = _attn_implementation
+
+        # MM Projector config
+        self.mm_projector_type = mm_projector_type
+        self.mm_hidden_size = mm_hidden_size if mm_hidden_size is not None else vt_hidden_size
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_ln_eps = projector_ln_eps
+        self.text_hidden_size = text_hidden_size
+
+
+class KimiK25Config(PretrainedConfig):
+    """Kimi-K2.5 model configuration.
+
+    Args:
+        text_config (dict | DeepseekV3Config): Configuration for the text model.
+        
+        Vision Tower Parameters (from MoonViT3dConfig):
+            patch_size (int): Patch size for vision tower.
+            init_pos_emb_height (int): Initial position embedding height.
+            init_pos_emb_width (int): Initial position embedding width.
+            init_pos_emb_time (int): Initial position embedding time dimension.
+            pos_emb_type (str): Type of position embedding.
+            vt_num_attention_heads (int): Number of attention heads in vision tower.
+            vt_num_hidden_layers (int): Number of hidden layers in vision tower.
+            vt_hidden_size (int): Hidden size of vision tower.
+            vt_intermediate_size (int): Intermediate size in vision tower FFN.
+            merge_kernel_size (tuple): Kernel size for patch merging.
+            video_attn_type (str): Type of video attention.
+            merge_type (str): Type of merge operation.
+            _attn_implementation (str): Attention implementation type.
+        
+        MM Projector Parameters (from MultiModalProjectorConfig):
+            mm_projector_type (str): Type of multimodal projector.
+            mm_hidden_size (int): Hidden size from vision tower (should match vt_hidden_size).
+            projector_hidden_act (str): Activation function for projector.
+            projector_ln_eps (float): Layer norm epsilon for projector.
+        
+        Other Parameters:
+            ignore_index (int): The ignore index for the loss function.
+            media_placeholder_token_id (int): The token ID to use for media placeholders.
+            pad_token_id (int): The token ID to use for padding.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        text_config: dict | DeepseekV3Config = None,
+        vision_config: dict | KimiK25VisionConfig = None,
+        # Other parameters
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        use_unified_vision_chunk: bool = True,
+        video_placeholder="<|kimi_k25_video_placeholder|>",
+        **kwargs,
+    ):
+        if isinstance(text_config, dict):
+            text_config = DeepseekV3Config(**text_config)
+        if isinstance(vision_config, dict):
+            vision_config = KimiK25VisionConfig(**vision_config)
+        self.text_config = text_config
+        self.vision_config = vision_config
+        # Other config
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+        self.use_unified_vision_chunk = use_unified_vision_chunk
+        self.video_placeholder = video_placeholder
+        if getattr(self.text_config, "quantization_config", None) is not None:
+            self.quantization_config = self.text_config.quantization_config
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)

generation_config.json

@@ -0,0 +1,4 @@
+{
+  "max_length": 262144,
+  "eos_token_id": 163586
+}

kimi_k25_processor.py

@@ -0,0 +1,165 @@
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessorMixin
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class KimiK25Processor(ProcessorMixin):
+    r"""
+    Constructs a KimiK25 processor which wraps a KimiK25 image processor and a tokenizer into a single processor.
+
+    [`KimiK25Processor`] offers all the functionalities of [`KimiK25ImageProcessor`] and [`TikTokenTokenizer`]. See the
+    [`~KimiK25Processor.__call__`] and [`~KimiK25Processor.decode`] for more information.
+
+    Args:
+        image_processor ([`KimiK25ImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`TikTokenTokenizer`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        super().__init__(image_processor,
+                         tokenizer,
+                         chat_template=chat_template)
+        self.media_processor = image_processor
+        # A special temporal placeholder to be replaced by actual video placeholders
+        self.video_placeholder = "<|kimi_k25_video_placeholder|>"
+
+    def update_raw_text(self, text: str, video_prompts: list[str]) -> str:
+        # replace video prompt in text with video chunk prompts
+        video_count = text.count(self.video_placeholder)
+        if video_count == 0:
+            return text
+        assert video_count == len(video_prompts)
+        text_parts = text.split(self.video_placeholder)
+        assert len(text_parts) == len(video_prompts) + 1
+        text = "".join([
+            text_parts[i] + video_prompts[i] for i in range(len(video_prompts))
+        ])
+        text += text_parts[-1]
+        return text
+
+    def preprocess_medias(self, medias: list[dict]) -> list[dict]:
+        updated_medias = []
+        video_prompts = []
+        for media in medias:
+            if media['type'] == 'image':
+                updated_medias.append(media)
+            elif media['type'] == 'video':
+                video_chunks = self.media_processor.split_video_chunks(
+                    media['video'])
+                updated_medias.extend(video_chunks)
+                video_prompts.append("".join(
+                    [vc['prompt'] for vc in video_chunks]))
+            else:
+                raise ValueError(f"unsupported media type: {media['type']}")
+        return updated_medias, video_prompts
+
+    def __call__(self,
+                 messages: list[dict] = None,
+                 medias: list[dict] = None,
+                 text: str = None,
+                 return_tensors: str = "pt",
+                 **kwargs) -> BatchFeature:
+        """
+        Process multimodal inputs for Kimi-K2.5 model.
+
+        This processor accepts ordered messages and extracts both media and text in a single pass.
+        text will be automatically updated if video input detected in messages
+
+        Args:
+            messages: List of message dicts with 'role' and 'content' fields.
+                     If provided, medias and text will be extracted automatically.
+            medias: Pre-extracted list of media dicts. If None, extracted from messages.
+            text: Pre-formatted text string. If None, generated via apply_chat_template.
+            return_tensors: Format of returned tensors ('pt', 'np', 'tf'). Default: 'pt'.
+            **kwargs: Additional arguments passed to tokenizer.apply_chat_template.
+
+        Returns:
+            BatchFeature with fields: input_ids, attention_mask, pixel_values, grid_thws.
+        """
+        if messages is None and (medias is None or text is None):
+            raise ValueError(
+                "Provide either 'messages' or both 'medias' and 'text'")
+
+        if medias is not None and text is not None:
+            updated_medias, video_prompts = self.preprocess_medias(medias)
+            preprocessed = self.media_processor.preprocess(
+                updated_medias, return_tensors=return_tensors)
+            text = self.update_raw_text(text, video_prompts)
+            text_inputs = self.tokenizer(text, return_tensors=return_tensors)
+            return BatchFeature(data={**text_inputs, **preprocessed.data})
+
+        if medias is None:
+            medias = self._extract_medias_from_messages(messages)
+        updated_medias, video_prompts = self.preprocess_medias(medias)
+        preprocessed = self.media_processor.preprocess(
+            updated_medias, return_tensors=return_tensors)
+
+        # Generate text if not provided
+        if text is None:
+            text = self.tokenizer.apply_chat_template(messages, **kwargs)
+
+        text = self.update_raw_text(text, video_prompts)
+
+        text_inputs = self.tokenizer(text, return_tensors=return_tensors)
+        return BatchFeature(data={**text_inputs, **preprocessed.data})
+
+    @staticmethod
+    def _extract_medias_from_messages(messages: list[dict]) -> list[dict]:
+        """
+        Extract media items from messages in a single pass.
+        
+        This is an optimized version that processes messages only once.
+        Kept as internal method since external callers should use __call__.
+        """
+        medias = []
+        for msg in messages:
+            if msg['role'] != 'user' or not msg.get('content'):
+                continue
+
+            for content_part in msg['content']:
+                if not isinstance(content_part, dict):
+                    continue
+
+                content_type = content_part.get('type')
+                if content_type in ['video_url', 'video']:
+                    medias.append({
+                        'type': 'video',
+                        'video': content_part['video_url']['url'],
+                        'first_frame_timestamp': 0.0
+                    })
+                elif content_type in ['image_url', 'image']:
+                    medias.append({
+                        'type': 'image',
+                        'image': content_part['image_url'],
+                    })
+        return medias
+
+    def apply_chat_template(self, messages, **kwargs):
+        return self.tokenizer.apply_chat_template(messages, **kwargs)
+
+    def batch_decode(self, *args, **kwargs):
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        return ['input_ids', 'attention_mask', 'pixel_values', 'grid_thws']

kimi_k25_vision_processing.py

@@ -0,0 +1,251 @@
+"""Image processor class for Kimi-K2.5.
+"""
+
+import json
+from typing import Any, Dict, Optional, Union
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers.image_processing_utils import (BaseImageProcessor,
+                                                 BatchFeature)
+from transformers.utils import TensorType
+
+from .media_utils import (MediaInput, VideoChunkInput, _to_tensor,
+                          ensure_media_type, get_video_meta, image_to_np,
+                          navit_patchify, navit_resize_image,
+                          navit_resize_video, normalize,
+                          real_sample_fps_and_max_num_frames, timestamp_as_str)
+
+try:
+    from mecord import VideoReader
+except ImportError:
+    VideoReader = None
+
+
+def resampling(video_bytes: bytes,
+               sample_indices: list[int],
+               key_indices=None,
+               frame_time_info=None,
+               num_threads=4) -> str:
+    video = VideoReader(video_bytes,
+                        num_threads=num_threads,
+                        frame_time_info=frame_time_info,
+                        key_indices=key_indices)
+    # extract target frames
+    frames = video[sample_indices]
+    frames = [Image.fromarray(frame) for frame in frames]
+    return frames
+
+
+class KimiK25VisionProcessor(BaseImageProcessor):
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        media_proc_cfg: dict,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.media_proc_cfg = media_proc_cfg
+        self.num_frames_per_chunk = media_proc_cfg[
+            'temporal_merge_kernel_size']
+
+    def media_tokens_calculator(self, media: MediaInput):
+        media = ensure_media_type(media)
+        ret = self.get_resize_config(media)
+        return ret['num_tokens']
+
+    @classmethod
+    def make_chunk_prompt(cls, timestamp_text: str) -> str:
+        return f"{timestamp_text}<|media_begin|>video<|media_content|><|media_pad|><|media_end|>"
+
+    def split_video_chunks(self,
+                           video_url: str | bytes) -> list[list[Image.Image]]:
+        # video_url should be base64 str or bytes
+        video_spec = get_video_meta(video_url)
+        sample_fps = min(self.media_proc_cfg['sample_fps'], video_spec.fps)
+        sampled_nframes = max(
+            round(video_spec.num_frames * sample_fps / video_spec.fps), 1)
+        frame_inds = np.linspace(0, video_spec.num_frames - 1,
+                                 sampled_nframes).round().astype(int)
+        frame_inds = frame_inds.tolist()
+        sampled_frame_ids = []
+        temporal_merge_kernel_size = self.media_proc_cfg[
+            "temporal_merge_kernel_size"]
+        num_chunks = 0
+        chunk_timestamp = []
+        for i in range(0, len(frame_inds), temporal_merge_kernel_size):
+            sampled_frame_ids.extend(frame_inds[i:i +
+                                                temporal_merge_kernel_size])
+            start_time = frame_inds[i] / float(video_spec.fps)
+            timestamp_text = timestamp_as_str(
+                start_time, self.media_proc_cfg["timestamp_mode"])
+            chunk_timestamp.append(timestamp_text)
+            num_chunks += 1
+
+        sampled_frames = resampling(video_url, sampled_frame_ids)
+        chunks = []
+        for chunk_id in range(num_chunks):
+            chunk = sampled_frames[chunk_id *
+                                   temporal_merge_kernel_size:(chunk_id + 1) *
+                                   temporal_merge_kernel_size]
+            chunks.append(
+                VideoChunkInput(type="video_chunk",
+                                video_chunk=chunk,
+                                prompt=self.make_chunk_prompt(
+                                    chunk_timestamp[chunk_id])))
+        return chunks
+
+    def get_resize_config(self, media_input: MediaInput) -> dict:
+        if media_input['type'] == 'image':
+            w, h = media_input['image'].size
+            ret = navit_resize_image(
+                w, h, self.media_proc_cfg['patch_size'],
+                self.media_proc_cfg['merge_kernel_size'],
+                self.media_proc_cfg['in_patch_limit'],
+                self.media_proc_cfg['patch_limit_on_one_side'],
+                self.media_proc_cfg['fixed_output_tokens'])
+            return ret
+        elif media_input['type'] == 'video_chunk':
+            frame = media_input['video_chunk'][0]
+            width, height = frame.size
+            num_frames = len(media_input["video_chunk"])
+            fps = 1.0
+
+            sample_fps, max_num_frames_each_video = real_sample_fps_and_max_num_frames(
+                media_input["type"],
+                self.media_proc_cfg['sample_fps'],
+                self.media_proc_cfg['max_num_frames_each_video'],
+            )
+
+            in_patch_limit_each_frame = self.media_proc_cfg[
+                'in_patch_limit_each_frame']
+            if in_patch_limit_each_frame is None:
+                in_patch_limit_each_frame = self.media_proc_cfg[
+                    'in_patch_limit']
+
+            ret = navit_resize_video(
+                width,
+                height,
+                num_frames,
+                fps,
+                sample_fps,
+                self.media_proc_cfg['patch_size'],
+                self.media_proc_cfg['merge_kernel_size'],
+                in_patch_limit_each_frame,
+                self.media_proc_cfg['patch_limit_on_one_side'],
+                self.media_proc_cfg['in_patch_limit_video'],
+                max_num_frames_each_video,
+                self.media_proc_cfg['fixed_output_tokens'],
+            )
+            return ret
+        else:
+            raise ValueError("Unsupported type: {}".format(
+                media_input['type']))
+
+    def resize_image(self, image: Image.Image, new_width: int, new_height: int,
+                     pad_width: int, pad_height: int) -> np.ndarray:
+        image_np = image_to_np(image, (new_width, new_height), "resize")
+        image_np = np.pad(
+            image_np,
+            ((0, pad_height), (0, pad_width), (0, 0)),
+            mode="constant",
+            constant_values=0,
+        )
+        return image_np
+
+    def preprocess(
+        self,
+        medias: list[MediaInput],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> BatchFeature:
+        """
+        Preprocess a atom vision input (images/video_chunk) into model-ready tensors.
+        
+        Args:
+            medias: List of MediaInput.
+            return_tensors: Desired output format ('pt', 'np', 'tf', or None).
+        
+        Returns:
+            BatchFeature containing 'pixel_values' and 'grid_thws' tensors.
+        """
+        if not isinstance(medias, list):
+            medias = [medias]
+        if medias:
+            pixel_values = []
+            for item in medias:
+                item = ensure_media_type(item)
+                resize_config = self.get_resize_config(item)
+                new_width, new_height, pad_width, pad_height = resize_config[
+                    'new_width'], resize_config['new_height'], resize_config[
+                        'pad_width'], resize_config['pad_height']
+                if item['type'] == 'image':
+                    image = item['image']
+                    image_np = self.resize_image(image, new_width, new_height,
+                                                 pad_width, pad_height)
+                    pixel_values.append(np.expand_dims(image_np, axis=0))
+                elif item['type'] == 'video_chunk':
+                    pixels = []
+                    for frame in item['video_chunk']:
+                        frame_np = self.resize_image(frame, new_width,
+                                                     new_height, pad_width,
+                                                     pad_height)
+                        pixels.append(frame_np)
+                    pixel_values.append(np.stack(pixels, axis=0))
+                else:
+                    raise ValueError("Unsupported type: {}".format(
+                        item['type']))
+            normalized_pixel_values = []
+            image_std_inv = 1.0 / np.array(self.media_proc_cfg['image_std'])
+            image_mean = np.array(self.media_proc_cfg['image_mean'])
+            for pixels in pixel_values:
+                pixels = normalize(pixels, image_mean, image_std_inv)
+                pixels_and_thw = navit_patchify(
+                    pixels,
+                    self.media_proc_cfg['patch_size'],
+                )
+                normalized_pixel_values.append(pixels_and_thw)
+
+            pixel_values = torch.cat([
+                _to_tensor(pixel_value['pixel_values'])
+                for pixel_value in normalized_pixel_values
+            ])
+            grid_thws = torch.cat([
+                _to_tensor(pixel_value['grid_thw'],
+                           dtype=torch.int64).unsqueeze(0)
+                for pixel_value in normalized_pixel_values
+            ])
+
+            data = {
+                'pixel_values': pixel_values,
+                'grid_thws': grid_thws,
+            }
+
+        else:
+            data = {}
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def __repr__(self):
+        return f"KimiK25VisionProcessor(media_proc_cfg={self.media_proc_cfg})"
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = super().to_dict()
+        output["media_proc_cfg"] = self.media_proc_cfg
+        if "media_processor" in output:
+            del output["media_processor"]
+        return output
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
+        config = config_dict.copy()
+        media_proc_cfg = config.pop("media_proc_cfg", {})
+        return cls(media_proc_cfg=media_proc_cfg, **config, **kwargs)
+
+    def to_json_string(self):
+        dictionary = self.to_dict()
+        for key, value in dictionary.items():
+            if hasattr(value, 'tolist'):
+                dictionary[key] = value.tolist()
+        return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"

media_utils.py

@@ -0,0 +1,368 @@
+import base64
+import io
+import math
+import os
+from datetime import datetime, timezone
+from typing import List, Literal, Optional, TypedDict
+
+import numpy as np
+from PIL import Image
+from pydantic import BaseModel, Field
+
+try:
+    from mecord import VideoReader
+except ImportError:
+    VideoReader = None
+
+
+class VideoSpec(BaseModel):
+    media_type: str = Literal['video']
+    height: int = Field(..., gt=0, description="video frame height")
+    width: int = Field(..., gt=0, description="video frame width")
+    num_frames: int = Field(..., gt=0, description="num frames")
+    fps: float = Field(..., gt=0, description="average fps")
+
+    # optional, help to accelerate video reading
+    key_indices: list[int] = Field(None, description="key indices")
+    frame_time_info: dict = Field(None, description="frame time info")
+
+
+class ImageInput(TypedDict):
+    type: Literal['image']
+    image: Image.Image
+
+
+class VideoChunkInput(TypedDict):
+    type: Literal['video_chunk']
+    video_chunk: List[Image.Image]
+    prompt: Optional[str] = None
+
+
+MediaInput = ImageInput | VideoChunkInput
+
+
+def get_video_meta(video_src: bytes | str | os.PathLike,
+                   accurate: bool = True) -> dict:
+    """Get the dimensions of a video."""
+    if isinstance(video_src, os.PathLike):
+        video_src = str(video_src)
+    # if b64 string, decode to bytes
+    if isinstance(video_src,
+                  str) and video_src.startswith('data:video/mp4;base64,'):
+        video_src = base64.b64decode(video_src.split(',')[1])
+    video = VideoReader(video_src, auto_init=accurate, num_threads=1)
+    assert video.num_frames > 0, "Invalid video format."
+    assert video.original_width > 0 and video.original_height > 0, (
+        "Invalid video format.")
+    assert video.avg_fps > 0, "Invalid video format."
+    return VideoSpec(media_type='video',
+                     height=video.original_height,
+                     width=video.original_width,
+                     num_frames=video.num_frames,
+                     fps=video.avg_fps,
+                     key_indices=video.key_indices,
+                     frame_time_info=video.frame_time_info)
+
+
+def timestamp_as_str(timestamp: float,
+                     timestamp_mode: str = "hh:mm:ss.fff") -> str:
+    """Convert a timestamp to a string in the format of HH:MM:SS.mmm."""
+    if timestamp_mode == "hh:mm:ss.fff":
+        return (datetime.fromtimestamp(timestamp,
+                                       tz=timezone.utc).strftime("%H:%M:%S") +
+                f".{int((timestamp % 1) * 1000):03d}")
+    elif timestamp_mode == "mm:ss.fff":
+        return (datetime.fromtimestamp(timestamp,
+                                       tz=timezone.utc).strftime("%M:%S") +
+                f".{int((timestamp % 1) * 1000):03d}")
+    elif timestamp_mode == "mm:ss":
+        return datetime.fromtimestamp(timestamp,
+                                      tz=timezone.utc).strftime("%M:%S")
+    else:
+        raise ValueError(f"Invalid timestamp mode: {timestamp_mode}")
+
+
+def navit_resize_image(
+    width: int,
+    height: int,
+    patch_size: int,
+    merge_kernel_size: int,
+    in_patch_limit: int,
+    patch_limit_on_one_side: int,
+    fixed_output_tokens: int | None,
+):
+    # Apply the patch limits.
+    s1 = math.sqrt(
+        in_patch_limit /
+        (max(1.0, width // patch_size) * max(1.0, height // patch_size)))
+    s2 = patch_limit_on_one_side * patch_size / width
+    s3 = patch_limit_on_one_side * patch_size / height
+    scale = min(1.0, s1, s2, s3)
+    new_w, new_h = max(1, int(width * scale)), max(1, int(height * scale))
+    new_w = min(new_w, patch_limit_on_one_side * patch_size)
+    new_h = min(new_h, patch_limit_on_one_side * patch_size)
+
+    # Calculate the padding to make the height and width divisible by the merge kernel size and patch size.
+    factor = merge_kernel_size * patch_size
+
+    pad_height = (factor - new_h % factor) % factor
+    pad_width = (factor - new_w % factor) % factor
+
+    if fixed_output_tokens is not None:
+        num_tokens = fixed_output_tokens
+    else:
+        # Calculate new dimensions after padding and patching
+        token_height = (new_h + pad_height) // factor
+        token_width = (new_w + pad_width) // factor
+
+        assert token_height * merge_kernel_size <= patch_limit_on_one_side, (
+            f"token_height {token_height} * merge_kernel_size {merge_kernel_size} > patch_limit_on_one_side {patch_limit_on_one_side}"
+        )
+        assert token_width * merge_kernel_size <= patch_limit_on_one_side, (
+            f"token_width {token_width} * merge_kernel_size {merge_kernel_size} > patch_limit_on_one_side {patch_limit_on_one_side}"
+        )
+
+        num_tokens = token_height * token_width
+    return {
+        "num_tokens": num_tokens,
+        "new_width": new_w,
+        "new_height": new_h,
+        "pad_width": pad_width,
+        "pad_height": pad_height,
+        "sampled_nframes": 1,
+    }
+
+
+def navit_resize_video(
+    width: int,
+    height: int,
+    nframes: int,
+    avg_fps: float,
+    sample_fps: float,
+    patch_size: int,
+    merge_kernel_size: int,
+    in_patch_limit_each_frame: int,
+    patch_limit_on_one_side: int,
+    in_patch_limit_total: int | None,
+    max_num_frames_each_video: int | None,
+    fixed_output_tokens_each_frame: int | None,
+):
+    sample_fps = min(sample_fps, avg_fps)
+    # Calculate the number of frames to sample based on target FPS
+    sampled_nframes = max(round(nframes * sample_fps / avg_fps), 1)
+    if max_num_frames_each_video is not None:
+        sampled_nframes = min(sampled_nframes, max_num_frames_each_video)
+
+    if in_patch_limit_total is not None:
+        in_patch_limit_each_frame = min(
+            round(in_patch_limit_total / sampled_nframes),
+            in_patch_limit_each_frame)
+
+    ret = navit_resize_image(
+        width,
+        height,
+        patch_size,
+        merge_kernel_size,
+        in_patch_limit_each_frame,
+        patch_limit_on_one_side,
+        fixed_output_tokens_each_frame,
+    )
+    ret["sampled_nframes"] = sampled_nframes
+    return ret
+
+
+def real_sample_fps_and_max_num_frames(
+    type_name: Literal["video", "video_chunk"],
+    sample_fps: float,
+    max_num_frames_each_video: int | None,
+) -> tuple[int, int | None]:
+    if type_name == "video":
+        return sample_fps, max_num_frames_each_video
+    elif type_name == "video_chunk":
+        max_num_frames_each_video = None
+        sample_fps = math.inf
+        return sample_fps, max_num_frames_each_video
+    else:
+        return math.inf, None
+
+
+def _to_pil(data: str | bytes):
+    if isinstance(data, Image.Image):
+
+        return data.convert("RGB")
+    elif isinstance(data, str):
+        if data.startswith("data:"):
+            raw_base64 = data.split(",")[1]
+            return Image.open(io.BytesIO(
+                base64.b64decode(raw_base64))).convert("RGB")
+        else:
+            return Image.open(data).convert("RGB")
+    elif isinstance(data, bytes):
+        return Image.open(io.BytesIO(data)).convert("RGB")
+    else:
+        raise ValueError(f"Unsupported data type: {type(data)}")
+
+
+def ensure_media_type(media: MediaInput) -> MediaInput:
+    if media['type'] == 'image':
+        media['image'] = _to_pil(media['image'])
+        return media
+    elif media['type'] == 'video_chunk':
+        media['video_chunk'] = [
+            _to_pil(frame) for frame in media['video_chunk']
+        ]
+        return media
+    else:
+        raise ValueError(f"Unsupported media type: {media['type']}")
+
+
+def image_to_np(
+    image: Image.Image,
+    resize_to: tuple[int, int] | None = None,
+    mode: str = "resize",
+    raise_error_for_ill_resize: bool = True,
+) -> np.ndarray:
+    """Convert an image to a numpy array.
+
+    Args:
+        content: The image to convert.
+        resize_to: The size to resize the image to.
+        mode: The mode to resize the image to.
+        raise_error_for_ill_resize: Whether to raise an error for ill-sized resize.
+
+    Returns:
+        A numpy array.
+    """
+    assert isinstance(image, Image.Image), "image must be a PIL Image"
+    if resize_to is not None:
+        if mode == "resize":
+            image = image.resize(resize_to, resample=Image.Resampling.BICUBIC)
+
+        elif mode == "rescale_and_pad_to_center":
+            scale = min(resize_to[0] / image.width,
+                        resize_to[1] / image.height, 1.0)
+            new_width = round(image.width * scale)
+            new_height = round(image.height * scale)
+            if new_width == 0 or new_height == 0:
+                if raise_error_for_ill_resize:
+                    raise ValueError(
+                        f"Invalid resize to: {resize_to}, from image size: {image.size}"
+                    )
+                else:
+                    return np.zeros((resize_to[1], resize_to[0], 3),
+                                    dtype=np.uint8)
+
+            image = image.resize((new_width, new_height),
+                                 resample=Image.Resampling.BICUBIC)
+            padding_left = (resize_to[0] - new_width) // 2
+            padding_right = resize_to[0] - new_width - padding_left
+            padding_top = (resize_to[1] - new_height) // 2
+            padding_bottom = resize_to[1] - new_height - padding_top
+            image = np.asarray(image)
+            image = np.pad(
+                image,
+                ((padding_top, padding_bottom), (padding_left, padding_right),
+                 (0, 0)),
+                mode="constant",
+                constant_values=0,
+            )
+            assert image.shape == (resize_to[1], resize_to[0], 3)
+
+        elif mode == "rescale_and_pad_to_rightbottom":
+            scale = min(resize_to[0] / image.width,
+                        resize_to[1] / image.height, 1.0)
+            new_width = round(image.width * scale)
+            new_height = round(image.height * scale)
+            if new_width == 0 or new_height == 0:
+                if raise_error_for_ill_resize:
+                    raise ValueError(
+                        f"Invalid resize to: {resize_to}, from image size: {image.size}"
+                    )
+                else:
+                    return np.zeros((resize_to[1], resize_to[0], 3),
+                                    dtype=np.uint8)
+
+            image = image.resize((new_width, new_height),
+                                 resample=Image.Resampling.BICUBIC)
+            padding_right = resize_to[0] - new_width
+            padding_bottom = resize_to[1] - new_height
+            image = np.asarray(image)
+            image = np.pad(
+                image,
+                ((0, padding_bottom), (0, padding_right), (0, 0)),
+                mode="constant",
+                constant_values=0,
+            )
+            assert image.shape == (resize_to[1], resize_to[0], 3)
+
+        else:
+            raise ValueError(f"Invalid mode: {mode}")
+
+    if isinstance(image, Image.Image):
+        return np.asarray(image)
+    else:
+        return image
+
+
+def navit_patchify(pixel_values: np.ndarray,
+                   patch_size: int) -> dict[str, np.ndarray]:
+    """Reshape the pixel values to a navit shape.
+
+    Args:
+        pixel_values: np.ndarray, shape (t, h, w, c)
+        patch_size: int
+
+    Returns:
+        dict[str, np.ndarray]
+        - patches: np.ndarray, shape (t * h//patch_size * w//patch_size, c, patch_size, patch_size)
+        - grid_thw: np.ndarray, (t, h//patch_size, w//patch_size)
+    """
+    T, H, W, C = pixel_values.shape
+    assert C == 3, "pixel_values must have 3 channels"
+
+    patches = pixel_values.reshape(T, H // patch_size, patch_size,
+                                   W // patch_size, patch_size, C)
+    # (T, H//patch_size, W//patch_size, C, patch_size, patch_size)
+    patches = patches.transpose(0, 1, 3, 5, 2, 4)
+    patches = patches.reshape(-1, C, patch_size, patch_size)
+    grid_thw = np.array([T, H // patch_size, W // patch_size])
+    return {"pixel_values": patches, "grid_thw": grid_thw}
+
+
+def normalize(x: np.ndarray,
+              mean,
+              std_inv,
+              pixels_dtype: np.dtype = np.float32) -> np.ndarray:
+    """Normalize the image.
+
+    Args:
+        x: The image to normalize. The shape is (..., 3). The dtype is uint8. The range is [0, 255].
+        mean: The mean of the image.
+        std_inv: The inverse of the std of the image.
+        pixels_dtype: The dtype of the image.
+    Returns:
+        The normalized image. The shape is (..., 3). The dtype is determined by the pixels_dtype.
+    """
+    x = (x / 255.0).astype(pixels_dtype)
+    x -= mean
+    x *= std_inv
+    return x
+
+
+def _to_tensor(data, **kwargs):
+    import torch
+
+    if isinstance(data, np.ndarray):
+        return torch.from_numpy(data).to(**kwargs)
+    elif isinstance(data, torch.Tensor):
+        return data.to(**kwargs)
+    elif isinstance(data, list):
+        return [_to_tensor(item, **kwargs) for item in data]
+    elif isinstance(data, tuple):
+        return tuple(_to_tensor(item, **kwargs) for item in data)
+    elif isinstance(data, dict):
+        return {k: _to_tensor(v, **kwargs) for k, v in data.items()}
+    elif data is None:
+        return None
+    else:
+        raise ValueError(f"Unsupported data type: {type(data)}")

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modeling_deepseek.py

@@ -0,0 +1,1815 @@
+# 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."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import \
+    _prepare_4d_causal_attention_mask
+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)
+
+try:
+    from transformers.utils.import_utils import is_torch_fx_available
+except ImportError:
+
+    def is_torch_fx_available() -> bool:
+        return hasattr(torch, "fx")
+
+
+from .configuration_deepseek import DeepseekV3Config
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import pad_input  # noqa
+    from flash_attn.bert_padding import index_first_axis, unpad_input
+
+# 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 = "DeepseekV3Config"
+
+
+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,
+    )
+
+
+# code modified from transformers 4.48.3 to amend breaks in newer transformers versions
+def get_usable_length(past_key_value,
+                      new_seq_length: int,
+                      layer_idx: Optional[int] = 0) -> int:
+    max_length = past_key_value.get_max_cache_shape()
+    previous_seq_length = past_key_value.get_seq_length(layer_idx)
+    if max_length is not None and max_length > 0 and previous_seq_length + new_seq_length > max_length:
+        return max_length - new_seq_length
+    return previous_seq_length
+
+
+class DeepseekV3RMSNorm(nn.Module):
+
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DeepseekV3RMSNorm 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(DeepseekV3RMSNorm)
+
+
+class DeepseekV3RotaryEmbedding(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->DeepseekV3
+class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+    """DeepseekV3RotaryEmbedding 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->DeepseekV3
+class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding):
+    """DeepseekV3RotaryEmbedding 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 DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding):
+
+    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 DeepseekV3MLP(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.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 == "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 == "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)
+        else:
+            raise NotImplementedError(
+                f"insupportable TopK function for MoE gating: {self.topk_method}"
+            )
+
+        ### 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
+        topk_weight = topk_weight * self.routed_scaling_factor  # must multiply the scaling factor
+
+        return topk_idx, topk_weight
+
+
+class DeepseekV3MoE(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([
+                (DeepseekV3MLP(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([
+                DeepseekV3MLP(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 = DeepseekV3MLP(
+                config=config, intermediate_size=intermediate_size)
+
+    def forward(self, hidden_states):
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        # flat_topk_idx = topk_idx.view(-1)
+        if not self.training:
+            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->DeepseekV3
+class DeepseekV3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self,
+                 config: DeepseekV3Config,
+                 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 = DeepseekV3RMSNorm(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 = DeepseekV3RMSNorm(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 = DeepseekV3RotaryEmbedding(
+                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 = DeepseekV3LinearScalingRotaryEmbedding(
+                    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 = DeepseekV3DynamicNTKScalingRotaryEmbedding(
+                    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 = DeepseekV3YarnRotaryEmbedding(
+                    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)
+        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]
+        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 += get_usable_length(past_key_value, 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 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)
+
+        attn_weights = (
+            torch.matmul(query_states, key_states.transpose(2, 3)) *
+            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(
+                                                 query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights,
+                                             p=self.attention_dropout,
+                                             training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.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->DeepseekV3
+class DeepseekV3FlashAttention2(DeepseekV3Attention):
+    """
+    DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` 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 alignment, 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]]]:
+        # DeepseekV3FlashAttention2 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 += get_usable_length(past_key_value, 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])
+
+        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. (DeepseekV3RMSNorm 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 DeepseekV3FlashAttention2 __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": DeepseekV3Attention,
+    "flash_attention_2": DeepseekV3FlashAttention2,
+}
+
+
+class DeepseekV3DecoderLayer(nn.Module):
+
+    def __init__(self, config: DeepseekV3Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
+            config=config, layer_idx=layer_idx)
+
+        self.mlp = (DeepseekV3MoE(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
+                    DeepseekV3MLP(config))
+        self.input_layernorm = DeepseekV3RMSNorm(config.hidden_size,
+                                                 eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekV3RMSNorm(
+            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
+
+
+DeepseekV3_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 ([`DeepseekV3Config`]):
+            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 DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3PreTrainedModel(PreTrainedModel):
+    config_class = DeepseekV3Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekV3DecoderLayer"]
+    _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_()
+
+
+DeepseekV3_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 DeepseekV3 Model outputting raw hidden-states without any specific head on top.",
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3Model(DeepseekV3PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`]
+
+    Args:
+        config: DeepseekV3Config
+    """
+
+    def __init__(self, config: DeepseekV3Config):
+        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([
+            DeepseekV3DecoderLayer(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 = DeepseekV3RMSNorm(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(DeepseekV3_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,
+    ) -> 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")
+
+        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 = get_usable_length(past_key_values,
+                                                       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, )
+
+            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 DeepseekV3ForCausalLM(DeepseekV3PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = DeepseekV3Model(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(DeepseekV3_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast,
+                               config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, 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, DeepseekV3ForCausalLM
+
+        >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = (output_attentions if output_attentions is not None
+                             else self.config.output_attentions)
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else
+                                self.config.output_hidden_states)
+        return_dict = (return_dict if return_dict is not None else
+                       self.config.use_return_dict)
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        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,
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                # seen_tokens 可能在某些 transformers 版本中不存在，使用 getattr 安全访问
+                past_length = getattr(past_key_values, 'seen_tokens',
+                                      cache_length)
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if (attention_mask is not None
+                    and attention_mask.shape[1] > input_ids.shape[1]):
+                input_ids = input_ids[:, -(attention_mask.shape[1] -
+                                           past_length):]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (max_cache_length is not None and attention_mask is not None
+                    and cache_length + input_ids.shape[1] > max_cache_length):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update({
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "attention_mask": attention_mask,
+        })
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past), )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The DeepseekV3 Model transformer with a sequence classification head on top (linear layer).
+
+    [`DeepseekV3ForSequenceClassification`] 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).
+    """,
+    DeepseekV3_START_DOCSTRING,
+)
+class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = DeepseekV3Model(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(DeepseekV3_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_kimi_k25.py

@@ -0,0 +1,1294 @@
+# coding=utf-8
+# Copyright 2025-2026 The Moonshot AI Team, DeepSeek-AI, and HuggingFace Inc. team. All rights reserved.
+#
+# The code is based on llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py), but modified for Kimi-K2.5.
+#
+# Licensing Information:
+# - Code derived from llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py) is licensed under the Apache License, Version 2.0.
+# - Other parts of the code are licensed under the MIT License.
+#
+# Apache License, Version 2.0:
+# 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.
+#
+# MIT License:
+# 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.
+import math
+from collections.abc import Sequence
+from copy import deepcopy
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import activations
+
+try:
+    from transformers.activations import PytorchGELUTanh
+except ImportError:
+    from transformers.activations import GELUTanh
+    activations.PytorchGELUTanh = GELUTanh
+    PytorchGELUTanh = GELUTanh
+from transformers.activations import PytorchGELUTanh
+from transformers.cache_utils import Cache
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.llava.modeling_llava import \
+    LlavaCausalLMOutputWithPast
+from transformers.utils import is_flash_attn_2_available
+
+from .configuration_kimi_k25 import KimiK25Config
+from .modeling_deepseek import DeepseekV3ForCausalLM
+
+# Flash attention imports
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+else:
+    flash_attn_varlen_func = None
+
+
+def multihead_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    q_cu_seqlens: torch.Tensor | None = None,
+    k_cu_seqlens: torch.Tensor | None = None,
+    max_seqlen_q: int | None = None,
+    max_seqlen_k: int | None = None,
+    deterministic: bool = False,
+):
+    """Multi-head attention using flash attention 2.
+
+    Args:
+        q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        q_cu_seqlens (torch.Tensor): cumulative sequence lengths of q.
+            The first element should be 0 and the last element should be q.shape[0].
+        k_cu_seqlens (torch.Tensor): cumulative sequence lengths of k.
+            The first element should be 0 and the last element should be k.shape[0].
+
+    Returns:
+        output: shape (batch_size, seqlen, dim) or (tot_seqlens, dim) if packing,
+            where dim = num_heads * head_dim
+    """
+    attn_out = flash_attn_varlen_func(
+        q,
+        k,
+        v,
+        q_cu_seqlens,
+        k_cu_seqlens,
+        max_seqlen_q,
+        max_seqlen_k,
+        causal=False,
+        deterministic=deterministic,
+    )
+    if isinstance(attn_out, tuple):
+        attn_out = attn_out[0]
+
+    attn_out = attn_out.flatten(start_dim=-2)
+
+    return attn_out
+
+
+def eager_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    q_cu_seqlens: Optional[torch.Tensor] = None,
+    k_cu_seqlens: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> torch.Tensor:
+    seq_length = q.shape[0]
+    attention_mask = torch.zeros([1, seq_length, seq_length],
+                                 device=q.device,
+                                 dtype=torch.bool)
+    for i in range(1, len(q_cu_seqlens)):
+        attention_mask[
+            ...,
+            q_cu_seqlens[i - 1]:q_cu_seqlens[i],
+            q_cu_seqlens[i - 1]:q_cu_seqlens[i],
+        ] = True
+    q = q.transpose(0, 1)
+    k = k.transpose(0, 1)
+    v = v.transpose(0, 1)
+
+    attn_weight = q @ k.transpose(-2, -1) / math.sqrt(q.shape[-1])
+    attn_weight += attention_mask
+    attn_weight = torch.softmax(attn_weight, dim=-1,
+                                dtype=torch.float32).to(q.dtype)
+
+    attn_output = attn_weight @ v
+    attn_output = attn_output.transpose(0, 1)
+    attn_output = attn_output.reshape(seq_length, -1)
+    return attn_output
+
+
+VL_VISION_ATTENTION_FUNCTIONS = {
+    "flash_attention_2": multihead_attention,
+    "eager": eager_attention,
+}
+
+
+def _apply_rope_input_validation(x, freqs_cis):
+    assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape)
+    assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape)
+    assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape)
+    assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype
+
+
+def get_rope_shape_decorate(func):
+    _get_rope_shape_first_call_flag = set()
+
+    def wrapper(org, interpolation_mode, shape):
+        key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
+        if key not in _get_rope_shape_first_call_flag:
+            _get_rope_shape_first_call_flag.add(key)
+            _ = func(org, interpolation_mode, shape=(64, 64))
+        return func(org, interpolation_mode, shape)
+
+    return wrapper
+
+
+@get_rope_shape_decorate
+@torch.compile(dynamic=True)
+def get_rope_shape(org, interpolation_mode, shape):
+    return (F.interpolate(
+        org.permute((2, 0, 1)).unsqueeze(0),
+        size=shape,
+        mode=interpolation_mode,
+    ).squeeze(0).permute((1, 2, 0)).flatten(end_dim=1))
+
+
+def apply_rope(xq: torch.Tensor, xk: torch.Tensor,
+               freqs_cis: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args: (The leading dimensions of all inputs should be the same)
+        xq: query, tensor of shape (..., num_heads, head_dim)
+        xk: key, tensor of shape (..., num_heads, head_dim)
+        freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid.
+    Returns:
+        xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
+    """
+    _apply_rope_input_validation(xq, freqs_cis)
+    _apply_rope_input_validation(xk, freqs_cis)
+
+    freqs_cis = freqs_cis.unsqueeze(-2)  # ..., 1, head_dim/2
+    # ..., num_heads, head_dim/2
+    xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(
+        -2)  # ..., num_heads, head_dim
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(
+        -2)  # ..., num_heads, head_dim
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    From:
+    https://github.com/OpenGVLab/InternVideo/blob/421f6d2361fc8f61a3394244571f2601a4e99e29/InternVideo2/multi_modality/models/backbones/internvideo2/pos_embed.py#L86
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
+    """
+    t_size: int of the temporal size
+    return:
+    pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_t = np.arange(t_size, dtype=np.float32)
+    pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed],
+                                   axis=0)
+    return pos_embed
+
+
+def _first_layer_key_first_token_vector(past_key_values):
+    """``past_key_values[0][0][..., 0]`` for LLaVA-style cache masking (shape ``[batch, heads, seq]``).
+    Legacy caches are ``list`` of ``(key, value)`` per layer. Transformers v4.36+ / v5 use ``Cache`` (e.g.
+    ``DynamicCache``) with per-layer ``.keys`` tensors instead of subscripting ``[0][0]``.
+    """
+    if isinstance(past_key_values, Cache):
+        layers = getattr(past_key_values, "layers", None) or []
+        if not layers:
+            return None
+        layer0 = layers[0]
+        keys = getattr(layer0, "keys", None)
+        if keys is None or keys.numel() == 0 or keys.ndim < 4:
+            return None
+        return keys[:, :, :, 0]
+    return past_key_values[0][0][:, :, :, 0]
+
+
+def _first_layer_past_seq_length(past_key_values):
+    """Layer-0 KV cache sequence length (BHSD keys: ``shape[2] == seq_len``).
+    """
+    if isinstance(past_key_values, Cache):
+        try:
+            return int(past_key_values.get_seq_length(0))
+        except Exception:
+            return None
+    try:
+        k0 = past_key_values[0][0]
+        if k0 is None or k0.ndim < 3:
+            return None
+        return int(k0.shape[2])
+    except Exception:
+        return None
+
+
+class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
+
+    def __init__(self,
+                 height: int,
+                 width: int,
+                 num_frames: int,
+                 dim: int,
+                 interpolation_mode: str = 'bicubic') -> None:
+        super().__init__()
+        self.height = height
+        self.width = width
+        self.num_frames = num_frames
+        self.dim = dim
+        self.interpolation_mode = interpolation_mode
+        self.weight = nn.Parameter(torch.empty(height, width, dim))
+        self.register_buffer('time_weight',
+                             torch.from_numpy(
+                                 get_1d_sincos_pos_embed(
+                                     self.dim,
+                                     self.num_frames)).float().unsqueeze(1),
+                             persistent=False)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight)
+
+    def forward(self, x: torch.Tensor,
+                grid_thws: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thws.tolist():
+            assert t <= self.num_frames, f't:{t} > self.num_frames:{self.num_frames}'
+            if (h, w) == self.weight.shape[:-1]:
+                pos_emb_2d = self.weight.flatten(end_dim=1)
+            else:
+                pos_emb_2d = get_rope_shape(
+                    self.weight,
+                    interpolation_mode=self.interpolation_mode,
+                    shape=(h, w),
+                )
+
+            if t == 1:
+                pos_emb_3d = pos_emb_2d
+            else:
+                pos_emb_3d = pos_emb_2d.unsqueeze(0).repeat(
+                    t, 1, 1) + self.time_weight[0:t]
+
+            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
+
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+class MoonVision3dPatchEmbed(nn.Module):
+
+    def __init__(self,
+                 out_dim: int,
+                 in_dim: int = 3,
+                 patch_size: int | tuple[int, int] = (14, 14),
+                 pos_emb_height: int = 14,
+                 pos_emb_width: int = 14,
+                 pos_emb_time: int = 4,
+                 pos_emb_type: str = 'divided_fixed'):
+        super().__init__()
+        assert isinstance(
+            patch_size,
+            int | Sequence), f'Invalid patch_size type: {type(patch_size)}'
+        if isinstance(patch_size, int):
+            patch_size = (patch_size, patch_size)
+        assert (len(patch_size) == 2
+                ), f'Expected patch_size to be a tuple of 2, got {patch_size}'
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(in_dim,
+                              out_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+
+        if pos_emb_type == 'divided_fixed':
+            self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
+                height=pos_emb_height,
+                width=pos_emb_width,
+                num_frames=pos_emb_time,
+                dim=out_dim)
+        else:
+            raise NotImplementedError(
+                f'Not support pos_emb_type: {pos_emb_type}')
+
+    def forward(self, x: torch.Tensor,
+                grid_thws: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (L, Channels): input tensor
+            grid_hws (N, 3): temporal, height and width
+
+        Returns:
+            (L, Cout) tensor
+        """
+        x = self.proj(x).view(x.size(0), -1)
+        # apply positional embedding
+        x = self.pos_emb(x, grid_thws)
+        return x
+
+
+class Rope2DPosEmbRepeated(nn.Module):
+    """2D rotary position embedding with multi-resolution support.
+
+    This class is intended to be used in the following way:
+    1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis.
+    2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration.
+    3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation.
+        The rope is shared across all attention layers and all heads.
+
+    Refs:
+    - RoFormer: https://arxiv.org/abs/2104.09864
+    - VisionLLaMA: https://arxiv.org/abs/2403.00522
+    - https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py
+
+    Args:
+        dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed)
+        max_height (int): the maximum height of the 2D grid
+        max_width (int): the maximum width of the 2D grid
+        theta_base (float): the base of the theta
+        device (str): the device to store the precomputed cis
+    """
+
+    def __init__(self,
+                 dim: int,
+                 max_height: int,
+                 max_width: int,
+                 theta_base=10000):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 4 == 0, 'dim must be divisible by 4'
+        self.max_height = max_height
+        self.max_width = max_width
+        self.theta_base = theta_base
+
+    def extra_repr(self):
+        return f'dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}'
+
+    def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
+        """Calculate the cis(freqs) for each position in the 2D grid.
+
+        Return: complex tensor of shape (max_height, max_width, dim//2) and value:
+            height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim))
+            weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim))   with (i in [0, dim//4))
+            note: `cis` is a mathematical notation defined by cis x = cos x + i sin x,
+        """
+        N = self.max_height * self.max_width
+        flat_pos = torch.arange(0, N).float().to(device)
+        x_pos = flat_pos % self.max_width
+        y_pos = flat_pos // self.max_width
+        dim_range = (torch.arange(0, self.dim,
+                                  4)[:(self.dim // 4)].float().to(device)
+                     )  # C/4
+        freqs = 1.0 / (self.theta_base**(dim_range / self.dim))
+        x_freqs = torch.outer(x_pos, freqs).float()  # N, C/4
+        y_freqs = torch.outer(y_pos, freqs).float()  # N, C/4
+        x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)  # N, C/4
+        y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)  # N, C/4
+        # N, C/4, 2
+        freqs_cis = torch.cat(
+            [x_cis.unsqueeze(dim=-1),
+             y_cis.unsqueeze(dim=-1)], dim=-1)
+        # max_height, max_width, C/2
+        freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
+        return freqs_cis
+
+    def get_freqs_cis(self, grid_thws: torch.Tensor,
+                      device: torch.device) -> torch.Tensor:
+        """
+        Args:
+            grid_thws (torch.Tensor): grid time, height and width
+
+        Returns:
+            freqs_cis: tensor of shape (sum(t * height * width), dim//2)
+        """
+        if not hasattr(self, 'freqs_cis'):
+            self.register_buffer('freqs_cis',
+                                 self._precompute_freqs_cis(device),
+                                 persistent=False)
+
+        shapes = grid_thws.tolist()
+        assert all(1 <= h <= self.max_height and 1 <= w <= self.max_width
+                   for t, h, w in shapes), (
+                       shapes,
+                       self.max_height,
+                       self.max_width,
+                   )
+        freqs_cis = torch.cat(
+            [
+                self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
+                for t, h, w in shapes
+            ],
+            dim=0,
+        )
+        return freqs_cis
+
+
+class MLP2(nn.Module):
+    """
+    Args:
+        dims: [in_dim, hidden_dim, out_dim]
+        bias: whether to use bias in linear layer.
+    """
+
+    def __init__(self, dims: list[int], activation, bias=True):
+        super().__init__()
+        assert len(dims) == 3
+        self.fc0 = nn.Linear(dims[0], dims[1], bias=bias)
+        self.fc1 = nn.Linear(dims[1], dims[2], bias=bias)
+        self.activation = activation
+        for m in [self.fc0, self.fc1]:
+            nn.init.trunc_normal_(m.weight, std=math.sqrt(2 / m.in_features))
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc0(x)
+        x = self.activation(x)
+        return self.fc1(x)
+
+
+class MoonViTEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        num_heads: int,
+        hidden_dim: int,
+        mlp_dim: int,
+        *,
+        attn_implementation: str = 'flash_attention_2',
+        activation=F.gelu,
+        attn_bias: bool = False,
+        use_deterministic_attn: bool = False,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
+        self.attn_implementation = attn_implementation
+        self.use_deterministic_attn = use_deterministic_attn
+
+        self.norm0 = nn.LayerNorm(hidden_dim)
+        self.norm1 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation)
+        self.wqkv = nn.Linear(hidden_dim, hidden_dim * 3, bias=attn_bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=attn_bias)
+
+    def attention_qkvpacked(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: torch.Tensor,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        """
+        Args:
+            x (torch.Tensor): (batch_size, seqlen, hidden_dim)
+            cu_seqlens (torch.Tensor):
+        """
+        xqkv = self.wqkv(x)
+
+        qkv_shape = xqkv.size()[:-1] + (
+            3,
+            self.num_heads,
+            self.hidden_size_per_attention_head,
+        )
+        # xqkv: (batch_size, seqlen, 3, nheads, headdim)
+        xqkv = xqkv.view(*qkv_shape)
+        xq, xk, xv = torch.unbind(xqkv, dim=-3)
+
+        xq, xk = apply_rope(xq, xk, rope_freqs_cis)
+
+        attn_func = VL_VISION_ATTENTION_FUNCTIONS[self.attn_implementation]
+        attn_out = attn_func(xq,
+                             xk,
+                             xv,
+                             q_cu_seqlens=cu_seqlens,
+                             k_cu_seqlens=cu_seqlens,
+                             max_seqlen_k=max_seqlen,
+                             max_seqlen_q=max_seqlen,
+                             deterministic=self.use_deterministic_attn)
+
+        attn_out = self.wo(attn_out)
+        return attn_out
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: int,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        residual = hidden_states
+        hidden_states = self.norm0(hidden_states)
+
+        hidden_states = self.attention_qkvpacked(hidden_states, cu_seqlens,
+                                                 max_seqlen, rope_freqs_cis)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class MoonViT3dEncoder(nn.Module):
+
+    def __init__(self,
+                 hidden_dim: int,
+                 num_layers: int,
+                 block_cfg: dict,
+                 video_attn_type: str = 'spatial_temporal',
+                 use_deterministic_attn: bool = False) -> None:
+        super().__init__()
+
+        assert video_attn_type == 'spatial_temporal', f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
+        self.video_attn_type = video_attn_type
+        self.rope_2d = Rope2DPosEmbRepeated(
+            block_cfg['hidden_dim'] // block_cfg['num_heads'], 512, 512)
+        self.blocks = nn.ModuleList([
+            MoonViTEncoderLayer(**block_cfg,
+                                use_deterministic_attn=use_deterministic_attn)
+            for _ in range(num_layers)
+        ])
+        self.final_layernorm = nn.LayerNorm(hidden_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thws: torch.Tensor,
+    ) -> torch.Tensor:
+        rope_freqs_cis = self.rope_2d.get_freqs_cis(
+            grid_thws=grid_thws, device=hidden_states.device)
+
+        lengths = torch.cat((
+            torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
+            grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
+        ))
+
+        max_seqlen = lengths.max()
+        cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0,
+                                                             dtype=torch.int32)
+        for block in self.blocks:
+            hidden_states = block(hidden_states,
+                                  cu_seqlens,
+                                  max_seqlen,
+                                  rope_freqs_cis=rope_freqs_cis)
+
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+def tpool_patch_merger(
+        x: torch.Tensor,
+        grid_thws: torch.Tensor,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+) -> list[torch.Tensor]:
+    d_model = x.size(-1)
+
+    outputs = []
+    pre_sum = 0
+    for t, h, w in grid_thws.tolist():
+        # Get the current sequence
+        seq = x[pre_sum:pre_sum + t * h * w]
+        # Reshape along self.merge_kernel_size and concat to the last dimension
+        kernel_height, kernel_width = merge_kernel_size
+        new_height, new_width = h // kernel_height, w // kernel_width
+        reshaped_seq = seq.view(t, new_height, kernel_height, new_width,
+                                kernel_width, d_model)
+        reshaped_seq = reshaped_seq.permute(0, 1,
+                                            3, 2, 4, 5).contiguous().mean(
+                                                dim=0)  # temporal pooling
+        padded_seq = reshaped_seq.view(new_height * new_width,
+                                       kernel_height * kernel_width, -1)
+        outputs.append(padded_seq)
+        pre_sum += t * h * w
+
+    return outputs
+
+
+class MoonViT3dPretrainedModel(PreTrainedModel):
+    config_class = None
+    model_type = 'moonvit3d'
+    _no_split_modules = ['PackingTransformer']
+    _supports_flash_attn_2 = True
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        config = deepcopy(config)
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_size = config.patch_size
+        self.merge_type = config.merge_type
+
+        self.patch_embed = MoonVision3dPatchEmbed(
+            out_dim=config.hidden_size,
+            patch_size=config.patch_size,
+            pos_emb_height=config.init_pos_emb_height,
+            pos_emb_width=config.init_pos_emb_width,
+            pos_emb_time=config.init_pos_emb_time,
+            pos_emb_type=config.pos_emb_type,
+        )
+
+        self.encoder = MoonViT3dEncoder(hidden_dim=config.hidden_size,
+                                        num_layers=config.num_hidden_layers,
+                                        block_cfg={
+                                            'num_heads':
+                                            config.num_attention_heads,
+                                            'hidden_dim':
+                                            config.hidden_size,
+                                            'mlp_dim':
+                                            config.intermediate_size,
+                                            'activation':
+                                            PytorchGELUTanh(),
+                                            'attn_bias':
+                                            True,
+                                            'attn_implementation':
+                                            config._attn_implementation,
+                                        },
+                                        video_attn_type=config.video_attn_type)
+
+    def forward(self, pixel_values: torch.Tensor,
+                grid_thws: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (torch.Tensor): The input pixel values.
+            grid_thws (torch.Tensor): Temporal, height and width.
+
+        Returns:
+            torch.Tensor: The output tokens.
+        """
+        # grid_thws = grid_thws.to('cpu')
+        assert grid_thws.ndim == 2, f'grid_thws should be 2D, got {grid_thws.ndim}'
+        assert grid_thws.size(1) == 3, f'No support for thw: {grid_thws}'
+        hidden_states = self.patch_embed(pixel_values, grid_thws)
+        hidden_states = self.encoder(hidden_states, grid_thws)
+        if self.merge_type == 'sd2_tpool':  # spatial downsampling 2x with temporal pooling all
+            hidden_states = tpool_patch_merger(
+                hidden_states,
+                grid_thws,
+                merge_kernel_size=self.merge_kernel_size)
+        else:
+            raise NotImplementedError(f'Not support {self.merge_type}')
+
+        return hidden_states
+
+
+# ============================================================================
+# MM Projector Helper Classes (from mm_projector/modeling_mm_projectors.py)
+# ============================================================================
+
+
+class IdentityMap(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        # TODO, use faster LayerNorm
+        self.pre_norm = nn.LayerNorm(config.mm_hidden_size)
+        self.proj = nn.Sequential(
+            nn.Linear(config.mm_hidden_size, config.hidden_size), nn.GELU(),
+            nn.Linear(config.hidden_size, config.hidden_size))
+
+    def forward(self, x, *args, **kwargs):
+        assert isinstance(x,
+                          list | tuple), f'x is not a list or tuple: {type(x)}'
+        lengths = [item.shape[0] for item in x]
+        x = torch.cat(x, dim=0)
+        x = self.pre_norm(x)
+        x = self.proj(x)
+        x = torch.split(x, lengths, dim=0)
+
+        return x
+
+
+class PatchMergerMLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        eps = config.projector_ln_eps
+        self.hidden_size = config.mm_hidden_size * (
+            config.merge_kernel_size[0] * config.merge_kernel_size[1])
+        self.pre_norm = nn.LayerNorm(config.mm_hidden_size, eps=eps)
+        self.proj = nn.Sequential(
+            nn.Linear(self.hidden_size, self.hidden_size),
+            nn.GELU(),
+            nn.Linear(self.hidden_size, config.hidden_size),
+        )
+
+    def forward(self, x, *args, **kwargs):
+        if isinstance(x, list) or isinstance(x, tuple):
+            x = [
+                self.proj(self.pre_norm(item).view(item.shape[0], -1))
+                for item in x
+            ]
+        else:
+            # B, N, N_k, C = x.shape
+            B = x.shape[0]
+            x = self.proj(self.pre_norm(x).view(B, -1, self.hidden_size))
+        return x
+
+
+class KimiK25PreTrainedModel(PreTrainedModel):
+    config_class = KimiK25Config
+    base_model_prefix = "model"
+    _no_split_modules = [
+        "MoonViT3dPretrainedModel",
+        "MoonViTEncoderLayer",
+        "DeepseekDecoderLayer",
+        "PatchMergerMLP",
+    ]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_flash_attn = True
+    _supports_sdpa = False
+
+    def _init_weights(self, module):
+        # important: this ported version of Llava isn't meant for training from scratch - only
+        # inference and fine-tuning - so the proper init weights code has been removed - the original codebase
+        # https://github.com/haotian-liu/LLaVA/tree/main/llava should serve for that purpose
+        std = (self.config.initializer_range if hasattr(
+            self.config, "initializer_range") else
+               self.config.text_config.initializer_range)
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            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_()
+
+
+class VisionTowerConfig(PretrainedConfig):
+    model_type = 'moonvit3d'
+
+    def __init__(self, config: KimiK25Config, **kwargs):
+        super().__init__(**kwargs)
+        self.patch_size = config.patch_size
+        self.init_pos_emb_height = config.init_pos_emb_height
+        self.init_pos_emb_width = config.init_pos_emb_width
+        self.init_pos_emb_time = config.init_pos_emb_time
+        self.pos_emb_type = config.pos_emb_type
+        self.num_attention_heads = config.vt_num_attention_heads
+        self.num_hidden_layers = config.vt_num_hidden_layers
+        self.hidden_size = config.vt_hidden_size
+        self.intermediate_size = config.vt_intermediate_size
+        self.merge_kernel_size = config.merge_kernel_size
+        self.video_attn_type = config.video_attn_type
+        self.merge_type = config.merge_type
+        self._attn_implementation = config._attn_implementation
+
+
+class ProjectorConfig:
+
+    def __init__(self, config: KimiK25Config):
+        self.mm_projector_type = config.mm_projector_type
+        self.mm_hidden_size = config.mm_hidden_size
+        self.hidden_size = config.text_hidden_size
+        self.merge_kernel_size = config.merge_kernel_size
+        self.projector_hidden_act = config.projector_hidden_act
+        self.projector_ln_eps = config.projector_ln_eps
+
+
+# ref https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/llava/modeling_llava.py#L240
+class KimiK25ForConditionalGeneration(KimiK25PreTrainedModel):
+
+    def __init__(self, config: KimiK25Config):
+        super().__init__(config)
+
+        vt_config = VisionTowerConfig(config.vision_config)
+        self.vision_tower = MoonViT3dPretrainedModel(vt_config)
+
+        proj_config = ProjectorConfig(config.vision_config)
+        if proj_config.mm_projector_type == 'identity':
+            self.mm_projector = IdentityMap()
+        elif proj_config.mm_projector_type == 'mlp':
+            self.mm_projector = MLP(proj_config)
+        elif proj_config.mm_projector_type == 'patchmerger':
+            self.mm_projector = PatchMergerMLP(proj_config)
+        else:
+            raise ValueError(
+                f"Unsupported mm_projector_type: {proj_config.mm_projector_type}"
+            )
+
+        self.language_model = DeepseekV3ForCausalLM(config.text_config)
+        self.post_init()
+
+        if hasattr(self.language_model, 'dtype'):
+            target_dtype = self.language_model.dtype
+            self.vision_tower = self.vision_tower.to(dtype=target_dtype)
+            self.mm_projector = self.mm_projector.to(dtype=target_dtype)
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def tie_weights(self, *args, **kwargs):
+        # Transformers >=5 passes ``missing_keys`` / ``recompute_mapping``; forward for the text backbone only.
+        return self.language_model.tie_weights(*args, **kwargs)
+
+    def resize_token_embeddings(self,
+                                new_num_tokens: int | None = None,
+                                pad_to_multiple_of=None) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(
+            new_num_tokens, pad_to_multiple_of)
+        # update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+        return model_embeds
+
+    def _merge_input_ids_with_image_features(
+        self,
+        image_features: list[torch.Tensor],
+        inputs_embeds: torch.Tensor,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        labels: torch.Tensor | None = None,
+    ):
+        """
+        Args:
+            image_features (:obj:`torch.Tensor` of shape :obj:`(num_image_tokens, embed_dim)`):
+                The image features to merge with the input embeddings.
+            inputs_embeds (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length, embed_dim)`):
+                The input embeddings.
+            input_ids (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`):
+                The input ids.
+            attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`):
+                The attention mask.
+            labels (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, *optional*):
+                The labels.
+        """
+        _, embed_dim = image_features[0].shape
+        feature_lengths = [x.shape[0] for x in image_features]
+        image_features = torch.cat(image_features, dim=0)
+
+        image_token_index: int = self.config.media_placeholder_token_id
+        pad_token_id: int = self.config.pad_token_id
+        ignore_index: int = self.config.ignore_index
+
+        batch_size, sequence_length = input_ids.shape
+        left_padding = not torch.sum(
+            input_ids[:, -1] == torch.tensor(pad_token_id))
+
+        # 1. Create a mask to know where special image tokens are
+        _token_occupation_table = torch.ones_like(input_ids.flatten())
+        _token_occupation_table[input_ids.flatten() ==
+                                image_token_index] = torch.tensor(
+                                    feature_lengths,
+                                    dtype=torch.long,
+                                    device=input_ids.device)
+        _token_occupation_table = _token_occupation_table.reshape(
+            input_ids.shape)
+
+        max_embed_dim = _token_occupation_table.sum(-1).max().item()
+        assert (
+            max_embed_dim >= sequence_length
+        ), f"The maximum embedding dimension ({max_embed_dim}) is less than the sequence length ({sequence_length})"
+        batch_indices, non_image_indices = torch.where(
+            input_ids != image_token_index)
+
+        # 2. Compute the positions where text should be written
+        # Calculate new positions for text tokens in merged image-text sequence.
+        new_token_positions = torch.cumsum(_token_occupation_table, -1) - 1
+        nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
+        if left_padding:
+            new_token_positions += nb_image_pad[:,
+                                                None]  # offset for left padding
+        text_to_overwrite = new_token_positions[batch_indices,
+                                                non_image_indices]
+
+        # 3. Create the full embedding, already padded to the maximum position
+        final_embedding = torch.zeros(
+            batch_size,
+            max_embed_dim,
+            embed_dim,
+            dtype=inputs_embeds.dtype,
+            device=inputs_embeds.device,
+        )
+        final_attention_mask = torch.zeros(batch_size,
+                                           max_embed_dim,
+                                           dtype=attention_mask.dtype,
+                                           device=inputs_embeds.device)
+        if labels is not None:
+            final_labels = torch.full(
+                (batch_size, max_embed_dim),
+                ignore_index,
+                dtype=input_ids.dtype,
+                device=input_ids.device,
+            )
+        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
+        # set the corresponding tensors into their correct target device.
+        target_device = inputs_embeds.device
+        batch_indices, non_image_indices, text_to_overwrite = (
+            batch_indices.to(target_device),
+            non_image_indices.to(target_device),
+            text_to_overwrite.to(target_device),
+        )
+        attention_mask = attention_mask.to(target_device)
+
+        # 4. Fill the embeddings based on the mask.
+        final_embedding[batch_indices,
+                        text_to_overwrite] = inputs_embeds[batch_indices,
+                                                           non_image_indices]
+        final_attention_mask[batch_indices,
+                             text_to_overwrite] = attention_mask[
+                                 batch_indices, non_image_indices]
+        if labels is not None:
+            final_labels[batch_indices,
+                         text_to_overwrite] = labels[batch_indices,
+                                                     non_image_indices]
+
+        # 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
+        image_to_overwrite = torch.full((batch_size, max_embed_dim),
+                                        True,
+                                        dtype=torch.bool,
+                                        device=inputs_embeds.device)
+        image_to_overwrite[batch_indices, text_to_overwrite] = False
+        image_to_overwrite &= image_to_overwrite.cumsum(
+            -1) - 1 >= nb_image_pad[:, None].to(target_device)
+
+        if image_to_overwrite.sum() != image_features.shape[:-1].numel():
+            raise ValueError(
+                f"The input provided to the model are wrong. The number of image tokens is {image_to_overwrite.sum()} while"
+                f" the number of image features given to the model is {image_features.shape[:-1].numel()}. "
+                "This prevents correct indexing and breaks batch generation.")
+
+        final_embedding[image_to_overwrite] = (
+            image_features.contiguous().reshape(-1,
+                                                embed_dim).to(target_device))
+        final_attention_mask |= image_to_overwrite
+        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_(
+            (final_attention_mask == 0), 1)
+
+        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
+        batch_indices, pad_indices = torch.where(input_ids == pad_token_id)
+        indices_to_mask = new_token_positions[batch_indices, pad_indices]
+
+        final_embedding[batch_indices, indices_to_mask] = 0
+
+        if labels is None:
+            final_labels = None
+
+        return final_embedding, final_attention_mask, final_labels, position_ids
+
+    def _extract_image_features(self, pixel_values: torch.Tensor,
+                                grid_thws: torch.Tensor) -> list[torch.Tensor]:
+        """
+        Args:
+            pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_channels, height, width)`):
+                The pixel values of the images processed by image processor.
+            grid_thws (:obj:`torch.Tensor` of shape :obj:`(batch_size, 3)`):
+                The grid, height, width of the images.
+
+        Returns:
+            selected_image_feature (:obj:`torch.FloatTensor` of shape :obj:`(num_image_tokens, embed_dim)`):
+                The selected image features to use as input to the projector head.
+
+        """
+
+        target_dtype = self.vision_tower.patch_embed.proj.weight.dtype
+        pixel_values = pixel_values.to(target_dtype)
+
+        image_features = self.vision_tower(pixel_values, grid_thws)
+        return image_features
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | list[torch.FloatTensor]
+        | None = None,
+        grid_thws: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | LlavaCausalLMOutputWithPast:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        ```"""
+        assert self.vision_tower is not None, "vision_tower is not loaded"
+        output_attentions = (output_attentions if output_attentions is not None
+                             else self.config.output_attentions)
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else
+                                self.config.output_hidden_states)
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if inputs_embeds is None:
+            # 1. Extra the input embeddings
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+            # 2. Merge text and images
+            if pixel_values is not None and len(
+                    pixel_values) > 0 and input_ids.shape[1] != 1:
+                image_features = self._extract_image_features(
+                    pixel_values, grid_thws)
+                if self.mm_projector:
+                    image_features = self.mm_projector(image_features)
+
+                inputs_embeds = inputs_embeds.to(
+                    image_features[0].dtype)  # num_tokens, embed_dim
+                inputs_embeds, attention_mask, labels, position_ids = (
+                    self._merge_input_ids_with_image_features(
+                        image_features,
+                        inputs_embeds,
+                        input_ids,
+                        attention_mask,
+                        labels,
+                    ))
+
+            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
+            # generation with cache
+            elif (past_key_values is not None and pixel_values is not None
+                  and input_ids.shape[1] == 1):
+                first_layer_past_key_value = _first_layer_key_first_token_vector(
+                    past_key_values)
+                if first_layer_past_key_value is not None:
+                    # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
+                    batch_index, non_attended_tokens = torch.where(
+                        first_layer_past_key_value.float().sum(-2) == 0)
+
+                    # Get the target length
+                    target_length = input_ids.shape[1]
+                    past_length = _first_layer_past_seq_length(past_key_values)
+                    if past_length is None:
+                        past_length = int(first_layer_past_key_value.shape[-1])
+
+                    extended_attention_mask = torch.ones(
+                        (attention_mask.shape[0], past_length),
+                        dtype=attention_mask.dtype,
+                        device=attention_mask.device,
+                    )
+
+                    # Filter out only the tokens that can be un-attended, this can happen
+                    # if one uses Llava + Fused modules where the cache on the
+                    # first iteration is already big enough, or if one passes custom cache
+                    valid_indices = non_attended_tokens < extended_attention_mask.size(
+                        -1)
+                    new_batch_index = batch_index[valid_indices]
+                    new_non_attended_tokens = non_attended_tokens[
+                        valid_indices]
+
+                    # Zero-out the places where we don't need to attend
+                    extended_attention_mask[new_batch_index,
+                                            new_non_attended_tokens] = 0
+
+                    attention_mask = torch.cat(
+                        (extended_attention_mask,
+                         attention_mask[:, -target_length:]),
+                        dim=1)
+                    position_ids = torch.sum(attention_mask,
+                                             dim=1).unsqueeze(-1) - 1
+
+        outputs = self.language_model(
+            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,
+        )
+
+        logits = outputs[0]
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                shift_attention_mask = attention_mask[..., 1:]
+                shift_logits = logits[..., :-1, :][shift_attention_mask.to(
+                    logits.device) != 0].contiguous()
+                shift_labels = labels[..., 1:][shift_attention_mask.to(
+                    labels.device) != 0].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)),
+                shift_labels.view(-1).to(shift_logits.device),
+            )
+
+        if not return_dict:
+            output = (logits, ) + outputs[1:]
+            return (loss, ) + output if loss is not None else output
+
+        return LlavaCausalLMOutputWithPast(
+            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,
+        inputs_embeds=None,
+        pixel_values=None,
+        grid_thws=None,
+        attention_mask=None,
+        **kwargs,
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = getattr(past_key_values, 'seen_tokens',
+                                      cache_length)
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+
+            # 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.
+            elif self.config.media_placeholder_token_id in input_ids:
+                input_ids = input_ids[:, input_ids.shape[1] - 1:]
+            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
+            # older attention values, as their corresponding values are not part of the input.
+            if cache_length < past_length and attention_mask is not None:
+                attention_mask = attention_mask[:, -(cache_length +
+                                                     input_ids.shape[1]):]
+
+        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]:]
+
+        # Generation (especially transformers v5) may supply ``position_ids`` for the full sequence while
+        # ``input_ids`` here is only the new suffix (e.g. length 1). RoPE must index with the current step length.
+        if past_key_values is not None and position_ids is not None:
+            cur_len = input_ids.shape[1]
+            if position_ids.shape[-1] > cur_len:
+                position_ids = position_ids[..., -cur_len:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update({
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "grid_thws": grid_thws,
+        })
+        return model_inputs
+
+    def _reorder_cache(self, *args, **kwargs):
+        return self.language_model._reorder_cache(*args, **kwargs)

tokenization_kimi.py

@@ -0,0 +1,371 @@
+import os
+from collections import OrderedDict
+from logging import getLogger
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, cast
+
+import tiktoken
+from tiktoken.load import load_tiktoken_bpe
+from tokenizers import AddedToken
+from transformers.convert_slow_tokenizer import bytes_to_unicode
+from transformers.tokenization_utils import PreTrainedTokenizer
+
+from .tool_declaration_ts import encode_tools_to_typescript_style
+
+logger = getLogger(__name__)
+VOCAB_FILES_NAMES = {"vocab_file": "tiktoken.model"}
+
+
+class TikTokenTokenizer(PreTrainedTokenizer):
+    """
+    Tokenizing and encoding/decoding text using the Tiktoken tokenizer. See megatron/tokenizer/tiktoken_tokenizer.py.
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            The path to the Tiktoken model file.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|begin_of_text|>",`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|end_of_text|>"`):
+            The end of sequence token.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_249|>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead. The second to last item in special_tokens.
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_250|>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        additional_special_tokens (list of `str`, *optional*):
+            A tuple or a list of additional tokens, which will be marked as `special`, meaning that they will be
+            skipped when decoding if `skip_special_tokens` is set to `True`.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+
+    model_input_names = ["input_ids", "attention_mask"]
+
+    special_tokens: Dict[str, int]
+
+    num_reserved_special_tokens = 256
+
+    pat_str = "|".join([
+        r"""[\p{Han}]+""",
+        r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
+        r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
+        r"""\p{N}{1,3}""",
+        r""" ?[^\s\p{L}\p{N}]+[\r\n]*""",
+        r"""\s*[\r\n]+""",
+        r"""\s+(?!\S)""",
+        r"""\s+""",
+    ])
+
+    def __init__(
+        self,
+        vocab_file,
+        bos_token: Union[str, AddedToken] = "[BOS]",
+        eos_token: Union[str, AddedToken] = "[EOS]",
+        unk_token: Union[str, AddedToken, None] = None,
+        pad_token: Union[str, AddedToken, None] = None,
+        additional_special_tokens: List[str] = None,
+        added_tokens_decoder: Optional[dict] = None,
+        **kwargs,
+    ):
+        assert os.path.isfile(vocab_file), vocab_file
+
+        # Transformers ≥5 may supply ``extra_special_tokens`` instead of
+        # ``additional_special_tokens``; treat empty dict as absent.
+        if additional_special_tokens is None:
+            extra = kwargs.pop("extra_special_tokens", None)
+            if isinstance(extra, dict) and not extra:
+                extra = None
+            if isinstance(extra, (list, tuple)):
+                additional_special_tokens = list(extra)
+
+        if additional_special_tokens is None:
+            additional_special_tokens = [
+                "<|im_end|>",
+                "<|im_user|>",
+                "<|im_assistant|>",
+                "<|start_header_id|>",
+                "<|end_header_id|>",
+                "[EOT]",
+                "<|im_system|>",
+                "<|im_middle|>",
+            ]
+
+        if added_tokens_decoder:
+            special_tokens_mapping = {
+                i: added_tokens_decoder[i].content
+                for i in added_tokens_decoder
+            }
+        else:
+            special_tokens_mapping = {}
+
+        self.vocab_file = vocab_file
+        mergeable_ranks = load_tiktoken_bpe(vocab_file)
+        num_base_tokens = len(mergeable_ranks)
+        self.special_tokens = {
+            special_tokens_mapping.get(i, f"<|reserved_token_{i}|>"): i
+            for i in range(num_base_tokens, num_base_tokens +
+                           self.num_reserved_special_tokens)
+        }
+
+        self.model = tiktoken.Encoding(
+            name=Path(vocab_file).name,
+            pat_str=self.pat_str,
+            mergeable_ranks=mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        logger.info(f"Reloaded tiktoken model from {vocab_file}")
+
+        self.n_words: int = self.model.n_vocab
+        # BOS / EOS token IDs
+        self.bos_id: int = self.special_tokens[str(bos_token)]
+        self.eos_id: int = self.special_tokens[str(eos_token)]
+        logger.info(
+            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+        )
+
+        self.pad_id: int = self.special_tokens[str(pad_token)]
+        self.unk_id: int = self.special_tokens[str(unk_token)]
+
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+
+        self.decoder = {}
+        for i in range(self.n_words):
+            # Taken from https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
+            decoding = ''.join([
+                self.byte_encoder[ord(char)] for char in
+                self.model.decode_single_token_bytes(i).decode('latin-1')
+            ])
+            self.decoder[i] = decoding
+
+        self.encoder = {}
+        for i in range(self.n_words):
+            if i in self.decoder:
+                self.encoder[self.decoder[i]] = i
+
+        self._token_config_cache = OrderedDict()
+        self._cache_max_size = 128
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            additional_special_tokens=additional_special_tokens,
+            added_tokens_decoder=added_tokens_decoder,
+            **kwargs,
+        )
+        self.all_special_ids_set = set(self.all_special_ids)
+
+    def encode(self,
+               text: str,
+               allow_special_tokens: bool = True,
+               **kwargs) -> List[int]:
+        """
+        Encodes a string into a list of token IDs.
+
+        Args:
+            text (str): The input string to be encoded.
+
+        Returns:
+            list[int]: A list of token IDs.
+        """
+        # If there are other args, we should call super().encode because there are a lot of code
+        # to handle those args. supper().encode finally will call _tokenize and _convert_token_to_id.
+        # NOTE: our encode method is not compatible with the super().encode method,
+        #   e.g. split_special_tokens' default is True in our encode method.
+        if len(kwargs) > 0:
+            logger.warning(f"Calling super().encode with {kwargs}")
+            return super().encode(text, **kwargs)
+
+        assert type(text) is str
+
+        # The tiktoken tokenizer can handle <=400k chars without
+        # pyo3_runtime.PanicException.
+        TIKTOKEN_MAX_ENCODE_CHARS = 400_000
+
+        # https://github.com/openai/tiktoken/issues/195
+        # Here we iterate over subsequences and split if we exceed the limit
+        # of max consecutive non-whitespace or whitespace characters.
+        MAX_NO_WHITESPACES_CHARS = 25_000
+
+        texts = self.pre_tokenizer_process(text)
+
+        all_substrs = []
+        for text in texts:
+            substrs = (
+                substr for i in range(0, len(text), TIKTOKEN_MAX_ENCODE_CHARS)
+                for substr in self._split_whitespaces_or_nonwhitespaces(
+                    text[i:i +
+                         TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS))
+            all_substrs.extend(substrs)
+
+        t: List[int] = []
+        for substr in all_substrs:
+            if allow_special_tokens:
+                t.extend(
+                    # we should consider special token as a common token
+                    self.model.encode(
+                        substr,
+                        allowed_special="all",
+                    ))
+            else:
+                t.extend(
+                    # we should consider special token as a common token
+                    self.model.encode(
+                        substr,
+                        disallowed_special=(),
+                    ))
+
+        return t
+
+    def decode(self, token_ids: Union[int, List[int]], **kwargs) -> str:
+        """
+        Decodes a list of token IDs into a string.
+
+        Args:
+            token_ids (List[int]): The list of token IDs to be decoded.
+
+        Returns:
+            str: The decoded string.
+        """
+        # If there are other args, we should call super().decode because there are a lot of code
+        # to handle those args. supper().encode finally will call convert_tokens_to_string and _convert_id_to_token.
+        if len(kwargs) > 0:
+            return super().decode(token_ids, **kwargs)
+
+        if type(token_ids) is int:
+            token_ids = [token_ids]
+
+        return self.model.decode(cast(List[int], token_ids))
+
+    @staticmethod
+    def _split_whitespaces_or_nonwhitespaces(
+            s: str, max_consecutive_slice_len: int) -> Iterator[str]:
+        """
+        Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len`
+        consecutive whitespaces or consecutive non-whitespaces.
+        """
+        current_slice_len = 0
+        current_slice_is_space = s[0].isspace() if len(s) > 0 else False
+        slice_start = 0
+
+        for i in range(len(s)):
+            is_now_space = s[i].isspace()
+
+            if current_slice_is_space ^ is_now_space:
+                current_slice_len = 1
+                current_slice_is_space = is_now_space
+            else:
+                current_slice_len += 1
+                if current_slice_len > max_consecutive_slice_len:
+                    yield s[slice_start:i]
+                    slice_start = i
+                    current_slice_len = 1
+        yield s[slice_start:]
+
+    def pre_tokenizer_process(self, text: str) -> List[str]:
+        """
+        pre-tokenizes the input text into a list of tokens.
+        This method is used to split the input text into smaller chunks for internal processing.
+        """
+        return [text]
+
+    """ ----- Below are the abstract methods required by PreTrainedTokenizer ----- """
+
+    @property
+    def vocab_size(self) -> int:
+        return self.n_words
+
+    def get_vocab(self) -> Dict[str, int]:
+        return self.encoder
+
+    def _tokenize(self, text: str, **kwargs) -> List[str]:
+        return [self.decoder[t] for t in self.encode(text)]
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self.encoder.get(token, self.unk_id)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.decoder.get(index)
+
+    @staticmethod
+    def clean_up_tokenization(out_string: str) -> str:
+        return out_string
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        text = ''.join(tokens)
+        text = bytearray([self.byte_decoder[c]
+                          for c in text]).decode('utf-8', 'replace')
+        return text
+
+    def save_vocabulary(self,
+                        save_directory: str,
+                        filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            raise ValueError(
+                f"vocabulary path ({save_directory}) should be a directory")
+        out_vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") +
+            VOCAB_FILES_NAMES["vocab_file"])
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(
+                out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file, )
+
+    def apply_chat_template(self,
+                            conversation,
+                            tools: Optional[list[dict]] = None,
+                            tokenize: bool = False,
+                            add_generation_prompt: bool = True,
+                            thinking: bool = True,
+                            preserve_thinking: bool = True,
+                            **kwargs):
+
+        tools = deep_sort_dict(tools)
+
+        # Convert tools to TypeScript style string if tools are provided
+        tools_ts_str = None
+        if tools:
+            try:
+                tools_ts_str = encode_tools_to_typescript_style(tools)
+
+            except Exception as e:
+                print(f"Failed to convert tools to TypeScript style: {e}")
+                tools_ts_str = None
+
+        # Store the TypeScript string in kwargs so it can be accessed by the template
+        if tools_ts_str is not None:
+            kwargs['tools_ts_str'] = tools_ts_str
+
+        if not thinking:
+            logger.warning("thinking=False is not supported, overriding to True")
+            thinking = True
+
+        if not preserve_thinking:
+            logger.warning("preserve_thinking=False is not supported, overriding to True")
+            preserve_thinking = True
+
+        return super().apply_chat_template(
+            conversation,
+            tools=tools,
+            tokenize=tokenize,
+            add_generation_prompt=add_generation_prompt,
+            thinking=thinking,
+            preserve_thinking=preserve_thinking,
+            **kwargs)
+
+
+def deep_sort_dict(obj: Any) -> Any:
+    if isinstance(obj, dict):
+        return {k: deep_sort_dict(v) for k, v in sorted(obj.items())}
+    if isinstance(obj, list):
+        return [deep_sort_dict(item) for item in obj]
+    return obj

tokenizer_config.json

@@ -0,0 +1,219 @@
+{
+  "added_tokens_decoder": {
+    "163584": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163585": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163586": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163587": {
+      "content": "<|im_user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163588": {
+      "content": "<|im_assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163590": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163591": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163593": {
+      "content": "[EOT]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163594": {
+      "content": "<|im_system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163595": {
+      "content": "<|tool_calls_section_begin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163596": {
+      "content": "<|tool_calls_section_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163597": {
+      "content": "<|tool_call_begin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163598": {
+      "content": "<|tool_call_argument_begin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163599": {
+      "content": "<|tool_call_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163601": {
+      "content": "<|im_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163602": {
+      "content": "<|media_begin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163603": {
+      "content": "<|media_content|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163604": {
+      "content": "<|media_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163605": {
+      "content": "<|media_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163606": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163607": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "163838": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163839": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_kimi.TikTokenTokenizer",
+      null
+    ]
+  },
+  "backend": "custom",
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": [
+    "<|im_end|>",
+    "<|im_user|>",
+    "<|im_assistant|>",
+    "<|start_header_id|>",
+    "<|end_header_id|>",
+    "[EOT]",
+    "<|im_system|>",
+    "<|im_middle|>",
+    "<|media_begin|>",
+    "<|media_content|>",
+    "<|media_end|>",
+    "<|media_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "TikTokenTokenizer",
+  "tool_parser_type": "kimi_k2",
+  "unk_token": "[UNK]"
+}

tool_declaration_ts.py

@@ -0,0 +1,479 @@
+"""
+Encode structured tool declaration to typescript style string.
+"""
+import dataclasses
+import json
+import logging
+from collections.abc import Sequence
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_TS_INDENT = "  "
+_TS_FIELD_DELIMITER = ",\n"
+
+
+class _SchemaRegistry:
+    """Registry for schema definitions to handle $ref resolution"""
+
+    def __init__(self):
+        self.definitions = {}
+        self.has_self_ref = False
+
+    def register_definitions(self, defs: dict[str, Any]):
+        """Register schema definitions from $defs section"""
+        if not defs:
+            return
+        for def_name, def_schema in defs.items():
+            self.definitions[def_name] = def_schema
+
+    def resolve_ref(self, ref: str) -> dict[str, Any]:
+        """Resolve a reference to its schema definition"""
+        if ref == "#":
+            self.has_self_ref = True
+            return {"$self_ref": True}
+        elif ref.startswith("#/$defs/"):
+            def_name = ref.split("/")[-1]
+            if def_name not in self.definitions:
+                raise ValueError(f"Reference not found: {ref}")
+            return self.definitions[def_name]
+        else:
+            raise ValueError(f"Unsupported reference format: {ref}")
+
+
+def _format_description(description: str, indent: str = "") -> str:
+    return "\n".join([
+        f"{indent}// {line}" if line else ""
+        for line in description.split("\n")
+    ])
+
+
+class _BaseType:
+    description: str
+    constraints: dict[str, Any]
+
+    def __init__(
+            self,
+            extra_props: dict[str, Any],
+            *,
+            allowed_constraint_keys: Sequence[str] = (),
+    ):
+        self.description = extra_props.get("description", "")
+        self.constraints = {
+            k: v
+            for k, v in extra_props.items() if k in allowed_constraint_keys
+        }
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        raise NotImplementedError
+
+    def format_docstring(self, indent: str) -> str:
+        lines = []
+        if self.description:
+            lines.append(_format_description(self.description, indent))
+        if self.constraints:
+            constraints_str = ", ".join(f"{k}: {v}" for k, v in sorted(
+                self.constraints.items(), key=lambda kv: kv[0]))
+            lines.append(f"{indent}// {constraints_str}")
+
+        return "".join(x + "\n" for x in lines)
+
+
+class _ParameterTypeScalar(_BaseType):
+    type: str
+
+    def __init__(self, type: str, extra_props: dict[str, Any] | None = None):
+        self.type = type
+
+        allowed_constraint_keys: list[str] = []
+        if self.type == "string":
+            allowed_constraint_keys = ["maxLength", "minLength", "pattern"]
+        elif self.type in ("number", "integer"):
+            allowed_constraint_keys = ["maximum", "minimum"]
+
+        super().__init__(extra_props or {},
+                         allowed_constraint_keys=allowed_constraint_keys)
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        # Map integer to number in TypeScript
+        if self.type == "integer":
+            return "number"
+        return self.type
+
+
+class _ParameterTypeObject(_BaseType):
+    properties: list["_Parameter"]
+    additional_properties: Any | None = None
+
+    def __init__(self,
+                 json_schema_object: dict[str, Any],
+                 registry: _SchemaRegistry | None = None):
+        super().__init__(json_schema_object)
+
+        self.properties = []
+        self.additional_properties = None
+
+        if not json_schema_object:
+            return
+
+        if "$defs" in json_schema_object and registry:
+            registry.register_definitions(json_schema_object["$defs"])
+
+        self.additional_properties = json_schema_object.get(
+            "additionalProperties")
+        if isinstance(self.additional_properties, dict):
+            self.additional_properties = _parse_parameter_type(
+                self.additional_properties, registry)
+
+        if "properties" not in json_schema_object:
+            return
+
+        required_parameters = json_schema_object.get("required", [])
+        optional_parameters = set(
+            json_schema_object["properties"].keys()) - set(required_parameters)
+
+        self.properties = [
+            _Parameter(
+                name=name,
+                type=_parse_parameter_type(prop, registry),
+                optional=name in optional_parameters,
+                default=prop.get("default")
+                if isinstance(prop, dict) else None,
+            ) for name, prop in json_schema_object["properties"].items()
+        ]
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        # sort by optional, make the required parameters first
+        parameters = [p for p in self.properties if not p.optional]
+        opt_params = [p for p in self.properties if p.optional]
+
+        parameters = sorted(parameters, key=lambda p: p.name)
+        parameters.extend(sorted(opt_params, key=lambda p: p.name))
+
+        param_strs = []
+        for p in parameters:
+            one = p.to_typescript_style(indent=indent + _TS_INDENT)
+            param_strs.append(one)
+
+        if self.additional_properties is not None:
+            ap_type_str = "any"
+            if self.additional_properties is True:
+                ap_type_str = "any"
+            elif self.additional_properties is False:
+                ap_type_str = "never"
+            elif isinstance(self.additional_properties, _ParameterType):
+                ap_type_str = self.additional_properties.to_typescript_style(
+                    indent=indent + _TS_INDENT)
+            else:
+                raise ValueError(
+                    f"Unknown additionalProperties: {self.additional_properties}"
+                )
+            param_strs.append(
+                f"{indent + _TS_INDENT}[k: string]: {ap_type_str}")
+
+        if not param_strs:
+            return "{}"
+
+        params_str = _TS_FIELD_DELIMITER.join(param_strs)
+        if params_str:
+            # add new line before and after
+            params_str = f"\n{params_str}\n"
+        # always wrap with object
+        return f"{{{params_str}{indent}}}"
+
+
+class _ParameterTypeArray(_BaseType):
+    item: "_ParameterType"
+
+    def __init__(self,
+                 json_schema_object: dict[str, Any],
+                 registry: _SchemaRegistry | None = None):
+        super().__init__(json_schema_object,
+                         allowed_constraint_keys=("minItems", "maxItems"))
+        if json_schema_object.get("items"):
+            self.item = _parse_parameter_type(json_schema_object["items"],
+                                              registry)
+        else:
+            self.item = _ParameterTypeScalar(type="any")
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        item_docstring = self.item.format_docstring(indent + _TS_INDENT)
+        if item_docstring:
+            return ("Array<\n" + item_docstring + indent + _TS_INDENT +
+                    self.item.to_typescript_style(indent=indent + _TS_INDENT) +
+                    "\n" + indent + ">")
+        else:
+            return f"Array<{self.item.to_typescript_style(indent=indent)}>"
+
+
+class _ParameterTypeEnum(_BaseType):
+    # support scalar types only
+    enum: list[str | int | float | bool | None]
+
+    def __init__(self, json_schema_object: dict[str, Any]):
+        super().__init__(json_schema_object)
+        self.enum = json_schema_object["enum"]
+
+        # Validate enum values against declared type if present
+        if "type" in json_schema_object:
+            typ = json_schema_object["type"]
+            if isinstance(typ, list):
+                if len(typ) == 1:
+                    typ = typ[0]
+                elif len(typ) == 2:
+                    if "null" not in typ:
+                        raise ValueError(f"Enum type {typ} is not supported")
+                    else:
+                        typ = typ[0] if typ[0] != "null" else typ[1]
+                else:
+                    raise ValueError(f"Enum type {typ} is not supported")
+            for val in self.enum:
+                if val is None:
+                    continue
+                if typ == "string" and not isinstance(val, str):
+                    raise ValueError(f"Enum value {val} is not a string")
+                elif typ == "number" and not isinstance(val, (int, float)):
+                    raise ValueError(f"Enum value {val} is not a number")
+                elif typ == "integer" and not isinstance(val, int):
+                    raise ValueError(f"Enum value {val} is not an integer")
+                elif typ == "boolean" and not isinstance(val, bool):
+                    raise ValueError(f"Enum value {val} is not a boolean")
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        return " | ".join(
+            [f'"{e}"' if isinstance(e, str) else str(e) for e in self.enum])
+
+
+class _ParameterTypeAnyOf(_BaseType):
+    types: list["_ParameterType"]
+
+    def __init__(
+        self,
+        json_schema_object: dict[str, Any],
+        registry: _SchemaRegistry | None = None,
+    ):
+        super().__init__(json_schema_object)
+        self.types = [
+            _parse_parameter_type(t, registry)
+            for t in json_schema_object["anyOf"]
+        ]
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        return " | ".join(
+            [t.to_typescript_style(indent=indent) for t in self.types])
+
+
+class _ParameterTypeUnion(_BaseType):
+    types: list[str]
+
+    def __init__(self, json_schema_object: dict[str, Any]):
+        super().__init__(json_schema_object)
+
+        mapping = {
+            "string": "string",
+            "number": "number",
+            "integer": "number",
+            "boolean": "boolean",
+            "null": "null",
+            "object": "{}",
+            "array": "Array<any>",
+        }
+        self.types = [mapping[t] for t in json_schema_object["type"]]
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        return " | ".join(self.types)
+
+
+class _ParameterTypeRef(_BaseType):
+    ref_name: str
+    is_self_ref: bool = False
+
+    def __init__(self, json_schema_object: dict[str, Any],
+                 registry: _SchemaRegistry):
+        super().__init__(json_schema_object)
+
+        ref = json_schema_object["$ref"]
+        resolved_schema = registry.resolve_ref(ref)
+
+        if resolved_schema.get("$self_ref", False):
+            self.ref_name = "parameters"
+            self.is_self_ref = True
+        else:
+            self.ref_name = ref.split("/")[-1]
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        return self.ref_name
+
+
+_ParameterType = (_ParameterTypeScalar
+                  | _ParameterTypeObject
+                  | _ParameterTypeArray
+                  | _ParameterTypeEnum
+                  | _ParameterTypeAnyOf
+                  | _ParameterTypeUnion
+                  | _ParameterTypeRef)
+
+
+@dataclasses.dataclass
+class _Parameter:
+    """
+    A parameter in a function, or a field in a object.
+    It consists of the type as well as the name.
+    """
+
+    type: _ParameterType
+    name: str = "_"
+    optional: bool = True
+    default: Any | None = None
+
+    @classmethod
+    def parse_extended(cls, attributes: dict[str, Any]) -> "_Parameter":
+        if not attributes:
+            raise ValueError("attributes is empty")
+
+        return cls(
+            name=attributes.get("name", "_"),
+            type=_parse_parameter_type(attributes),
+            optional=attributes.get("optional", False),
+            default=attributes.get("default"),
+        )
+
+    def to_typescript_style(self, indent: str = "") -> str:
+        comments = self.type.format_docstring(indent)
+
+        if self.default is not None:
+            default_repr = (json.dumps(self.default, ensure_ascii=False)
+                            if not isinstance(self.default, (int, float, bool))
+                            else repr(self.default))
+            comments += f"{indent}// Default: {default_repr}\n"
+
+        return (
+            comments +
+            f"{indent}{self.name}{'?' if self.optional else ''}: {self.type.to_typescript_style(indent=indent)}"
+        )
+
+
+def _parse_parameter_type(
+        json_schema_object: dict[str, Any] | bool,
+        registry: _SchemaRegistry | None = None) -> _ParameterType:
+    if isinstance(json_schema_object, bool):
+        if json_schema_object:
+            return _ParameterTypeScalar(type="any")
+        else:
+            logger.warning(
+                f"Warning: Boolean value {json_schema_object} is not supported, use null instead."
+            )
+            return _ParameterTypeScalar(type="null")
+
+    if "$ref" in json_schema_object and registry:
+        return _ParameterTypeRef(json_schema_object, registry)
+
+    if "anyOf" in json_schema_object:
+        return _ParameterTypeAnyOf(json_schema_object, registry)
+    elif "enum" in json_schema_object:
+        return _ParameterTypeEnum(json_schema_object)
+    elif "type" in json_schema_object:
+        typ = json_schema_object["type"]
+        if isinstance(typ, list):
+            return _ParameterTypeUnion(json_schema_object)
+        elif typ == "object":
+            return _ParameterTypeObject(json_schema_object, registry)
+        elif typ == "array":
+            return _ParameterTypeArray(json_schema_object, registry)
+        else:
+            return _ParameterTypeScalar(typ, json_schema_object)
+    elif json_schema_object == {}:
+        return _ParameterTypeScalar(type="any")
+    else:
+        raise ValueError(f"Invalid JSON Schema object: {json_schema_object}")
+
+
+def _openai_function_to_typescript_style(function: dict[str, Any], ) -> str:
+    """Convert OpenAI function definition (dict) to TypeScript style string."""
+    registry = _SchemaRegistry()
+    parameters = function.get("parameters") or {}
+    parsed = _ParameterTypeObject(parameters, registry)
+
+    interfaces = []
+    root_interface_name = None
+    if registry.has_self_ref:
+        root_interface_name = "parameters"
+        params_str = _TS_FIELD_DELIMITER.join([
+            p.to_typescript_style(indent=_TS_INDENT) for p in parsed.properties
+        ])
+        params_str = f"\n{params_str}\n" if params_str else ""
+        interface_def = f"interface {root_interface_name} {{{params_str}}}"
+        interfaces.append(interface_def)
+
+    definitions_copy = dict(registry.definitions)
+    for def_name, def_schema in definitions_copy.items():
+        obj_type = _parse_parameter_type(def_schema, registry)
+        params_str = obj_type.to_typescript_style()
+
+        description_part = ""
+        if obj_description := def_schema.get("description", ""):
+            description_part = _format_description(obj_description) + "\n"
+
+        interface_def = f"{description_part}interface {def_name} {params_str}"
+        interfaces.append(interface_def)
+
+    interface_str = "\n".join(interfaces)
+    function_name = function.get("name", "function")
+    if root_interface_name:
+        type_def = f"type {function_name} = (_: {root_interface_name}) => any;"
+    else:
+        params_str = parsed.to_typescript_style()
+        type_def = f"type {function_name} = (_: {params_str}) => any;"
+
+    description = function.get("description")
+    return "\n".join(
+        filter(
+            bool,
+            [
+                interface_str,
+                ((description and _format_description(description)) or ""),
+                type_def,
+            ],
+        ))
+
+
+def encode_tools_to_typescript_style(tools: list[dict[str, Any]], ) -> str:
+    """
+    Convert tools (list of dict) to TypeScript style string.
+    
+    Supports OpenAI format: {"type": "function", "function": {...}}
+    
+    Args:
+        tools: List of tool definitions in dict format
+        
+    Returns:
+        TypeScript style string representation of the tools
+    """
+    if not tools:
+        return ""
+
+    functions = []
+
+    for tool in tools:
+        tool_type = tool.get("type")
+        if tool_type == "function":
+            func_def = tool.get("function", {})
+            if func_def:
+                functions.append(
+                    _openai_function_to_typescript_style(func_def))
+        else:
+            # Skip unsupported tool types (like "_plugin")
+            continue
+
+    if not functions:
+        return ""
+
+    functions_str = "\n".join(functions)
+    result = "# Tools\n\n"
+
+    if functions_str:
+        result += "## functions\nnamespace functions {\n"
+        result += functions_str + "\n"
+        result += "}\n"
+
+    return result