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configuration_intern_vit.py

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+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            norm_type='rms_norm',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.norm_type = norm_type
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)

configuration_yuan.py

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+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class YuanConfig(PretrainedConfig):
+    model_type = "yuan2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=135040,
+        hidden_size=2048,
+        ffn_hidden_size=8192,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        hidden_act="silu",
+        model_max_length=8192,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=77185,
+        bos_token_id=77185,
+        eos_token_id=77185,
+        tie_word_embeddings=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.model_max_length = model_max_length
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        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_yuanvl.py

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+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import copy
+
+from transformers import AutoConfig, LlamaConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+from .configuration_yuan import YuanConfig
+
+logger = logging.get_logger(__name__)
+
+
+class YuanVLChatConfig(PretrainedConfig):
+    model_type = 'yuanvl'
+    is_composition = True
+
+    def __init__(
+            self,
+            vision_config=None,
+            llm_config=None,
+            use_backbone_lora=0,
+            use_llm_lora=0,
+            select_layer=-1,
+            force_image_size=None,
+            downsample_ratio=0.5,
+            template=None,
+            dynamic_image_size=False,
+            use_thumbnail=False,
+            ps_version='v1',
+            min_dynamic_patch=1,
+            max_dynamic_patch=6,
+            img_context_token_id=77188,
+            **kwargs):
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {'architectures': ['InternVisionModel']}
+            logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
+
+        if llm_config is None:
+            llm_config = {'architectures': ['YuanForCausalLM']}
+            logger.info('llm_config is None. Initializing the YuanForCausalLM config with default values (`YuanForCausalLM`).')
+
+        self.vision_config = InternVisionConfig(**vision_config)
+        if llm_config.get('architectures')[0] == 'YuanForCausalLM':
+            self.llm_config = YuanConfig(**llm_config)
+        else:
+            raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.img_context_token_id = img_context_token_id
+
+        logger.info(f'vision_select_layer: {self.select_layer}')
+        logger.info(f'ps_version: {self.ps_version}')
+        logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
+        logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output['vision_config'] = self.vision_config.to_dict()
+        output['llm_config'] = self.llm_config.to_dict()
+        output['model_type'] = self.__class__.model_type
+        output['use_backbone_lora'] = self.use_backbone_lora
+        output['use_llm_lora'] = self.use_llm_lora
+        output['select_layer'] = self.select_layer
+        output['force_image_size'] = self.force_image_size
+        output['downsample_ratio'] = self.downsample_ratio
+        output['template'] = self.template
+        output['dynamic_image_size'] = self.dynamic_image_size
+        output['use_thumbnail'] = self.use_thumbnail
+        output['ps_version'] = self.ps_version
+        output['min_dynamic_patch'] = self.min_dynamic_patch
+        output['max_dynamic_patch'] = self.max_dynamic_patch
+
+        return output

conversation.py

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+"""
+Conversation prompt templates.
+We kindly request that you import fastchat instead of copying this file if you wish to use it.
+If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
+Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+"""
+
+import dataclasses
+from enum import IntEnum, auto
+from typing import Dict, List, Tuple, Union
+
+
+class SeparatorStyle(IntEnum):
+    """Separator styles."""
+
+    ADD_COLON_SINGLE = auto()
+    ADD_COLON_TWO = auto()
+    ADD_COLON_SPACE_SINGLE = auto()
+    NO_COLON_SINGLE = auto()
+    NO_COLON_TWO = auto()
+    ADD_NEW_LINE_SINGLE = auto()
+    LLAMA2 = auto()
+    CHATGLM = auto()
+    CHATML = auto()
+    CHATINTERN = auto()
+    DOLLY = auto()
+    RWKV = auto()
+    PHOENIX = auto()
+    ROBIN = auto()
+    FALCON_CHAT = auto()
+    CHATGLM3 = auto()
+    INTERNVL_ZH = auto()
+    MPT = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that manages prompt templates and keeps all conversation history."""
+
+    # The name of this template
+    name: str
+    # The template of the system prompt
+    system_template: str = '{system_message}'
+    # The system message
+    system_message: str = ''
+    # The names of two roles
+    roles: Tuple[str] = ('USER', 'ASSISTANT')
+    # All messages. Each item is (role, message).
+    messages: List[List[str]] = ()
+    # The number of few shot examples
+    offset: int = 0
+    # The separator style and configurations
+    sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
+    sep: str = '\n'
+    sep2: str = None
+    # Stop criteria (the default one is EOS token)
+    stop_str: Union[str, List[str]] = None
+    # Stops generation if meeting any token in this list
+    stop_token_ids: List[int] = None
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+        if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ': ' + message + seps[i % 2]
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ': '  # must be end with a space
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+            ret = '' if system_prompt == '' else system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + message + self.sep
+                else:
+                    ret += role + '\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+            ret = system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + message + seps[i % 2]
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.RWKV:
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += (
+                        role
+                        + ': '
+                        + message.replace('\r\n', '\n').replace('\n\n', '\n')
+                    )
+                    ret += '\n\n'
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.LLAMA2:
+            seps = [self.sep, self.sep2]
+            if self.system_message:
+                ret = system_prompt
+            else:
+                ret = '[INST] '
+            for i, (role, message) in enumerate(self.messages):
+                tag = self.roles[i % 2]
+                if message:
+                    if i == 0:
+                        ret += message + ' '
+                    else:
+                        ret += tag + ' ' + message + seps[i % 2]
+                else:
+                    ret += tag
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATGLM:
+            # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+            # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+            round_add_n = 1 if self.name == 'chatglm2' else 0
+            if system_prompt:
+                ret = system_prompt + self.sep
+            else:
+                ret = ''
+
+            for i, (role, message) in enumerate(self.messages):
+                if i % 2 == 0:
+                    ret += f'[Round {i//2 + round_add_n}]{self.sep}'
+
+                if message:
+                    ret += f'{role}：{message}{self.sep}'
+                else:
+                    ret += f'{role}：'
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATML:
+            ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + message + self.sep + '\n'
+                else:
+                    ret += role + '\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATGLM3:
+            ret = ''
+            if self.system_message:
+                ret += system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + ' ' + message
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATINTERN:
+            # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                # if i % 2 == 0:
+                #     ret += "<s>"
+                if message:
+                    ret += role + ':' + message + seps[i % 2] + '\n'
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.DOLLY:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ':\n' + message + seps[i % 2]
+                    if i % 2 == 1:
+                        ret += '\n\n'
+                else:
+                    ret += role + ':\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.PHOENIX:
+            ret = system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + '<s>' + message + '</s>'
+                else:
+                    ret += role + ': ' + '<s>'
+            return ret
+        elif self.sep_style == SeparatorStyle.ROBIN:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ':\n' + message + self.sep
+                else:
+                    ret += role + ':\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.FALCON_CHAT:
+            ret = ''
+            if self.system_message:
+                ret += system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ':'
+
+            return ret
+        elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
+            seps = [self.sep, self.sep2]
+            ret = self.system_message + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ': ' + message + seps[i % 2]
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.MPT:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
+        else:
+            raise ValueError(f'Invalid style: {self.sep_style}')
+
+    def set_system_message(self, system_message: str):
+        """Set the system message."""
+        self.system_message = system_message
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+    def update_last_message(self, message: str):
+        """Update the last output.
+        The last message is typically set to be None when constructing the prompt,
+        so we need to update it in-place after getting the response from a model.
+        """
+        self.messages[-1][1] = message
+
+    def to_gradio_chatbot(self):
+        """Convert the conversation to gradio chatbot format."""
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def to_openai_api_messages(self):
+        """Convert the conversation to OpenAI chat completion format."""
+        ret = [{'role': 'system', 'content': self.system_message}]
+
+        for i, (_, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append({'role': 'user', 'content': msg})
+            else:
+                if msg is not None:
+                    ret.append({'role': 'assistant', 'content': msg})
+        return ret
+
+    def copy(self):
+        return Conversation(
+            name=self.name,
+            system_template=self.system_template,
+            system_message=self.system_message,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            stop_str=self.stop_str,
+            stop_token_ids=self.stop_token_ids,
+        )
+
+    def dict(self):
+        return {
+            'template_name': self.name,
+            'system_message': self.system_message,
+            'roles': self.roles,
+            'messages': self.messages,
+            'offset': self.offset,
+        }
+
+
+# A global registry for all conversation templates
+conv_templates: Dict[str, Conversation] = {}
+
+
+def register_conv_template(template: Conversation, override: bool = False):
+    """Register a new conversation template."""
+    if not override:
+        assert (
+            template.name not in conv_templates
+        ), f'{template.name} has been registered.'
+
+    conv_templates[template.name] = template
+
+
+def get_conv_template(name: str) -> Conversation:
+    """Get a conversation template."""
+    return conv_templates[name].copy()
+
+
+# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
+# is that during training, the preprocessing function for the Hermes-2 template doesn't add
+# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
+# Therefore, they are completely equivalent during inference.
+register_conv_template(
+    Conversation(
+        name='Hermes-2',
+        system_template='<|im_start|>system\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>',
+        stop_str='<|endoftext|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='internlm2-chat',
+        system_template='<|im_start|>system\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='phi3-chat',
+        system_template='<|system|>\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|user|>\n', '<|assistant|>\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|end|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='internvl2_5',
+        system_template='<|im_start|>system\n{system_message}',
+        system_message='你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>\n',
+    )
+)
+
+register_conv_template(
+    Conversation(
+        name='yuan-chat',
+        system_template='<|im_start|>system\n{system_message}',
+        system_message='你是IEI-源多模态模型，英文名是YuanVL，是由浪潮信息开发的多模态大语言模型。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>\n',
+    )
+)

flash_attention.py

@@ -0,0 +1,76 @@
+# https://github.com/Dao-AILab/flash-attention/blob/v0.2.8/flash_attn/flash_attention.py
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+try:  # v1
+    from flash_attn.flash_attn_interface import \
+        flash_attn_unpadded_qkvpacked_func
+except:  # v2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+from flash_attn.bert_padding import pad_input, unpad_input
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_unpadded_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_unpadded_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_unpadded_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None

modeling_intern_vit.py

@@ -0,0 +1,367 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+    from flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        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)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+NORM2FN = {
+    'rms_norm': InternRMSNorm,
+    'layer_norm': nn.LayerNorm,
+}
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        target_dtype = pos_embed.dtype
+        pos_embed = pos_embed.float().reshape(
+            1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
+        pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False).\
+            reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
+        return pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, channel, width, height]
+        batch_size, _, height, width = patch_embeds.shape
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        position_embedding = torch.cat([
+            self.position_embedding[:, :1, :],
+            self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
+        ], dim=1)
+        embeddings = embeddings + position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        self.use_flash_attn = True # modify
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.norm_type = config.norm_type
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            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.
+        """
+        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
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+            #import pdb
+            #pdb.set_trace()
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    config_class = InternVisionConfig
+    _no_split_modules = ['InternVisionEncoderLayer']
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        self.embeddings.image_size = new_size
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        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 pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

modeling_yuanlm2.py

@@ -0,0 +1,1639 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Yuan model."""
+import math
+from typing import List, Optional, Tuple, Union
+import torch.nn.functional as F
+import torch
+import torch.utils.checkpoint
+from torch import einsum, nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from configuration_yuan import YuanConfig
+from einops import rearrange
+# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
+#from apex.normalization import MixedFusedRMSNorm as RMSNorm
+#from flash_attn import flash_attn_func
+from transformer_engine.pytorch import RMSNorm
+import pdb
+import copy
+try:
+    import grouped_gemm as gg
+except ImportError:
+    gg = None
+try:
+    from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
+    from flash_attn import flash_attn_func
+except ImportError:
+    flash_attn_unpadded_func = None
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "YuanConfig"
+
+"""
+class YuanRotaryEmbedding(nn.Module):
+    def __init__(self, dim, base=10000, dtype=torch.float32, device=None, scaling_factor=1.0, rope_type='default'):
+        super().__init__()
+        inv_freq = (1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))).to(dtype)#.to('cuda:1')
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, max_seq_len, offset=0):
+        self.inv_freq = self.inv_freq.to(torch.float32)
+        seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
+        freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        emb = torch.cat((freqs, freqs), dim=-1)
+        # emb [seq_length, .., dim]
+        return emb[:, None, None, :]"""
+
+class YuanRotaryEmbedding(nn.Module):
+    def __init__(self, dim, base=10000, dtype=torch.float32, rotary_interleaved=False, seq_len_interpolation_factor=None):
+        super().__init__()
+        self.base = base
+        self.dim = dim
+        self.rotary_interleaved = rotary_interleaved
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
+
+    def get_rotary_seq_len(
+            self,
+            inference_param=None,
+            transformer_input: torch.Tensor=None,
+            transformer_config=None,
+    ):
+        if inference_param is not None:
+            rotary_seq_len = inference_param.max_sequence_length
+        else:
+            rotary_seq_len = transformer_input.size[0]
+        if transformer_config.sequence_parallel:
+            rotary_seq_len *= transformer_config.tensor_model_parallel_size
+        
+        return rotary_seq_len
+
+    def forward(self, max_seq_len, offset=0):
+
+        """Forward pass of RoPE embedding.
+
+        Args:
+            max_seq_len (int): Maximum size of sequence
+            offset (int, optional): _description_. Defaults to 0.
+
+        Returns:
+            Tensor: Embeddings after applying RoPE.
+        """
+        inv_freq = (1.0 / ( self.base**(torch.arange(0, self.dim, 2, dtype=torch.float32, device=torch.cuda.current_device()) / self.dim))).to(torch.float32)
+        
+        #max_seq_len_int = max_seq_len.item() if max_seq_len.numel() == 1 else max_seq_len.max().item()
+        seq = (
+            torch.arange(max_seq_len, device=inv_freq.device, dtype=inv_freq.dtype)
+            + offset
+        )
+
+        if self.seq_len_interpolation_factor is not None:
+            seq *= 1 / self.seq_len_interpolation_factor
+
+        freqs = torch.outer(seq, inv_freq)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        if not self.rotary_interleaved:
+            emb = torch.cat((freqs, freqs), dim=-1)
+        else:
+            emb = torch.stack((freqs.view(-1, 1), freqs.view(-1, 1)), dim=-1).view(
+                freqs.shape[0], -1
+            )
+        # emb [seq_length, .., dim]
+        emb = emb[:, None, None, :]
+        #emb = emb[:, None, :] 
+        return emb
+
+
+def _rotate_half(x, rotary_interleaved):
+    """huggingface version
+    change sign so the last dimension becomes [-odd, +even]
+    
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+    """
+    if not rotary_interleaved:
+        x1, x2 = torch.chunk(x, 2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1 = x[:, :, :, ::2]
+        x2 = x[:, :, :, 1::2]
+        x_new = torch.stack((-x2, x1), dim=-1)
+        return x_new.view(x_new.shape[0], x_new.shape[1], x_new.shape[2], -1)
+
+def apply_rotary_pos_emb(t, freqs, position_ids, rotary_interleaved=False):
+
+    rot_dim = freqs.shape[-1]
+    #if position_ids.shape[1] > 1:
+    freqs = freqs[position_ids]
+    freqs = freqs.view(t.shape[1],freqs.shape[1],freqs.shape[2],freqs.shape[4]).transpose(0,1)
+    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
+    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]
+
+    # first part is cosine component
+    # second part is sine component, need to change signs with _rotate_half method
+    t_type = t.dtype
+    cos_ = torch.cos(freqs).to(t.dtype)
+    sin_ = torch.sin(freqs).to(t.dtype)
+
+    t = (t * cos_) + (_rotate_half(t, rotary_interleaved) * sin_)
+    return torch.cat((t, t_pass), dim=-1)
+    """huggingface version
+    input tensor t is of shape [seq_length, ..., dim]
+    rotary positional embeding tensor freqs is of shape [seq_length, ..., dim]
+    check https://kexue.fm/archives/8265 for detailed formulas
+    
+    dtype = t.dtype
+    rot_dim = freqs.shape[-1]
+    t_pass = t[..., rot_dim:]
+    if position_ids.shape[1] > 1:
+       freqs = freqs[position_ids]
+       freqs = freqs.view(t.shape[1],freqs.shape[1],freqs.shape[2],freqs.shape[4]).transpose(0,1)
+    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
+    t = t[..., :rot_dim]
+    # first part is cosine component
+    # second part is sine component, need to change signs with _rotate_half method
+    t = (t * freqs.cos()) + (_rotate_half(t) * freqs.sin())
+    t = t.to(dtype)
+    """
+
+    return torch.cat((t, t_pass), dim=-1)
+
+class LocalizedFiltering(torch.nn.Module):
+    """
+    Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
+    variable names and moving away from the stateful representation of incremental decoding state. See
+    "https://arxiv.org/abs/2209.10655" for more details.
+    """
+
+    def __init__(self, hidden_size, lf_conv2d_group, lf_conv2d_num_pad):
+        super().__init__()
+
+        self.embed_dim = hidden_size
+        self.lf_conv2d_group = lf_conv2d_group
+        self.lf_conv2d_num_pad = lf_conv2d_num_pad
+        if self.lf_conv2d_num_pad == 1:
+            self.training = True
+        self.conv1 = torch.nn.Conv2d(self.embed_dim, self.embed_dim // 2, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
+        self.conv2 = torch.nn.Conv2d(self.embed_dim // 2, self.embed_dim, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
+        self.output_layernorm = RMSNorm(self.embed_dim, eps=1e-6)
+
+    def _train_forward(self, inputs):
+        inputs = inputs.transpose(0,1)
+        seq_len, bsz, embed_dim = inputs.size()
+        if embed_dim != self.embed_dim:
+            raise ValueError(
+                f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
+            )
+        residual = inputs
+
+        inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+        output1 = self.conv1(inputs)
+        output1 = output1[:, :, :seq_len, :]
+
+        output2 = self.conv2(output1)
+        output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
+        output2 = output2.view(seq_len, bsz, embed_dim)
+        assert output2.shape == residual.shape
+
+        torch.cuda.set_device(output2.device)
+        lf_output = self.output_layernorm(output2 + residual)
+        lf_output = lf_output.transpose(0,1)
+        return lf_output
+
+    def _inference_forward(self, inputs, before_hidden_states):
+
+        if before_hidden_states is None:
+            residual = inputs
+            seq_len, bsz, embed_dim = inputs.size()
+
+            inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+
+            pad_zero1 = torch.zeros(bsz, embed_dim, 1, 1).to(inputs)
+            inputs = torch.cat((pad_zero1, inputs), dim=2).contiguous()
+            output1 = self.conv1(inputs)
+
+            pad_zero2 = torch.zeros(bsz, embed_dim // 2, 1, 1).to(output1)
+            output1 = torch.cat((pad_zero2, output1), dim=2).contiguous()
+            output2 = self.conv2(output1)
+
+            output2 = output2.permute(2, 3, 0, 1).contiguous()
+
+            output2 = output2.view(seq_len, bsz, embed_dim)
+
+            assert output2.shape == residual.shape
+
+            lf_output = self.output_layernorm(output2 + residual)
+
+        else:
+            residual = inputs
+
+            seq_len, bsz, embed_dim = inputs.size()
+            seq_len_before, _, _ = before_hidden_states.size()
+
+            assert seq_len == 1 and seq_len_before == 2
+
+            inputs = torch.cat((before_hidden_states, inputs), dim=0)
+            inputs = inputs.view(3, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+
+            output1 = self.conv1(inputs)
+            output2 = self.conv2(output1)
+            output2 = output2.view(1, bsz, embed_dim)
+
+            assert output2.shape == residual.shape
+
+            lf_output = self.output_layernorm(output2 + residual)
+
+        return lf_output
+        '''#IEIyuan huggingface version
+        if before_hidden_states == None:
+            inputs = inputs.transpose(0,1)
+            seq_len, bsz, embed_dim = inputs.size()
+            if embed_dim != self.embed_dim:
+                raise ValueError(
+                    f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
+                )
+            residual = inputs
+            inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+            inputs = torch.cat((torch.zeros(bsz, embed_dim, 1, 1, dtype=inputs.dtype, device=inputs.device), inputs), dim=2).contiguous()
+            output1 = self.conv1(inputs)
+
+            output1 = torch.cat((torch.zeros(bsz, embed_dim // 2, 1, 1, dtype=inputs.dtype, device=inputs.device), output1), dim=2).contiguous()
+            output2 = self.conv2(output1).permute(2, 3, 0, 1).contiguous()
+            output2 = output2.view(seq_len, bsz, embed_dim)
+            assert output2.shape == residual.shape
+            norm_input = (output2 + residual)#.to('cuda:0')
+            torch.cuda.set_device(norm_input.device)
+            lf_output = self.output_layernorm(norm_input)
+            lf_output = lf_output#.to('cuda:1')
+            lf_output = lf_output.transpose(0,1)
+            return lf_output
+        else:
+            inputs = inputs.transpose(0,1)
+            before_hidden_states = before_hidden_states.transpose(0,1)
+            seq_len, bsz, embed_dim = inputs.size()
+            if embed_dim != self.embed_dim:
+                raise ValueError(
+                    f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
+                )
+            residual = inputs
+            inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+            before_hidden_states = before_hidden_states.view(2, 1, bsz, embed_dim).permute(2, 3, 0, 1)
+            inputs = torch.cat((before_hidden_states, inputs), dim=2).contiguous()
+            output1 = self.conv1(inputs)
+            output2 = self.conv2(output1).permute(2, 3, 0, 1).contiguous()
+            output2 = output2.view(seq_len, bsz, embed_dim)
+            assert output2.shape == residual.shape
+
+            norm_input = (output2 + residual)#.to('cuda:0')
+            torch.cuda.set_device(norm_input.device)
+            lf_output = self.output_layernorm(norm_input)
+            lf_output = lf_output#.to('cuda:1')
+            lf_output = lf_output.transpose(0,1)
+            return lf_output
+        '''
+
+
+    def forward(
+        self,
+        inputs,
+        before_hidden_states = None,
+    ) -> torch.Tensor:
+        # assert self.lf_conv2d_num_pad == 1
+        if self.training:
+            lf_output = self._train_forward(inputs)
+        else:
+            lf_output = self._inference_forward(inputs, before_hidden_states)
+
+        return lf_output
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class YuanRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        YuanRMSNorm is equivalent to LlamaRMSNorm
+        """
+        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)
+
+# flash attn
+class FlashSelfAttention(torch.nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+    def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
+                 device=None, dtype=None):
+        super().__init__()
+        assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, '
+                                                      'e.g., with pip install flash-attn')
+        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, q, k, v):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
+        """
+
+        assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
+        assert all((i.is_cuda for i in (q,k,v)))
+
+        batch_size, seqlen_q = q.shape[1], q.shape[0]
+        seqlen_k = k.shape[0]
+        q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
+        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
+        if self.training:
+            # during training q,k,v always have same seqlen
+            assert seqlen_k == seqlen_q
+            is_causal = self.causal
+            cu_seqlens_k = cu_seqlens_q
+            dropout_p = self.dropout_p
+        else:
+            # turn off FA causal mask after first inference autoregressive iteration
+            # only on first autoregressive step q,k,v have same seqlen
+            is_causal = seqlen_q == seqlen_k
+            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=q.device) 
+            #cu_seqlens_q = [cu_seqlens_q[0], cu_seqlens_q[-1]]
+            #cu_seqlens_k = [cu_seqlens_k[0], cu_seqlens_k[-1]]
+            dropout_p = 0
+
+        output = flash_attn_unpadded_func(q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, dropout_p, softmax_scale=self.softmax_scale, causal=is_causal)
+
+        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+        return output
+
+class ParallelAttention_router(nn.Module):
+    def __init__(self, config):
+        super(ParallelAttention_router, self).__init__()
+        layer_number=0
+        self.layer_number = max(1, layer_number)
+        
+        self.hidden_size = config.hidden_size
+        self.projection_size = config.moe_config['moe_num_experts']
+        
+        self.num_attention_router_heads = config.moe_config['num_attention_router_heads']
+        self.hidden_size_per_attention_head = config.max_position_embeddings // self.num_attention_router_heads
+        self.query_key_value = nn.Linear(self.hidden_size, self.projection_size*3, bias=False)
+
+    def forward(self, hidden_states, attention_mask=None, enc_position_ids=None,
+                encoder_output=None, inference_params=None,
+                rotary_pos_emb=None):
+        is_first_step = False
+        before_hidden_states = None
+        
+        #mixed_x_layer = torch.matmul(hidden_states, self.query_key_value)
+        mixed_x_layer = self.query_key_value(hidden_states)
+        (query_layer, key_layer, value_layer) = torch.split(mixed_x_layer, self.projection_size, -1)
+        b, s, z = query_layer.shape
+
+        # use fp32 router
+        query_layer = query_layer.float().view(b,s,z,1)
+        key_layer = key_layer.float().view(b,s,z,1)
+        value_layer = value_layer.float().view(b,s,z,1)
+
+        attn_weights = torch.matmul(query_layer, key_layer.transpose(2, 3))
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+        attn_output = torch.matmul(attn_weights, value_layer)
+        router_output = attn_output.view(-1, z)
+        return router_output 
+
+class YuanExpertMLP(nn.Module):
+    def __init__(self, config):
+        super(YuanExpertMLP, self).__init__()
+        self.gated_linear_unit = config.moe_config['gated_linear_unit']
+        #self.ffn_hidden_size = config.moe_config['ffn_hidden_size']
+        self.ffn_hidden_size = config.ffn_hidden_size
+
+
+        if self.gated_linear_unit:
+            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size*2, bias=False)
+              
+        else:
+            self.w1 = nn.Linear(config.hidden_size, self.ffn_hidden_size, bias=False)
+            
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.w2 = nn.Linear(self.ffn_hidden_size, config.hidden_size, bias=False)
+        
+
+    def forward(self, x):
+        x = self.w1(x)
+        if self.gated_linear_unit:
+            x = torch.chunk(x, 2, dim=-1)
+            x = self.act_fn(x[0]) * x[1]
+        else:
+            x = self.act_fn(x)  
+        x = self.w2(x)  
+        return x
+    
+
+
+class YuanMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str
+    ):
+        super().__init__()
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.gate_proj(x) * self.act_fn(self.up_proj(x)))
+        
+
+class YuanAttention(nn.Module):
+    """Localized Filtering-based Attention 'YUAN 2.0: A Large Language Model with Localized Filtering-based Attention' paper"""
+
+    def __init__(self, config: YuanConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.lf_conv2d_group = config.lf_conv2d_group
+        self.lf_conv2d_num_pad = config.lf_conv2d_num_pad
+
+        try:
+            self.attention_projection_size = config.attention_projection_size
+        except:
+            self.attention_projection_size = None
+        
+        if self.attention_projection_size is None:
+            self.head_dim = self.hidden_size // self.num_heads
+        else:
+            self.head_dim = self.attention_projection_size // self.num_heads
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.causal_mask = config.causal_mask
+        self.attn_mask_type = config.attn_mask_type
+        self.softmax_scale = 1.0 / math.sqrt(self.head_dim)
+        self.use_flash_attention = config.use_flash_attention
+        try:
+            self.use_shareqk = config.use_shareqk
+        except Exception as e:
+            self.use_shareqk=False
+        self.dropout = 0.0
+        self.attention_projection_size = config.attention_projection_size
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        
+        if self.use_shareqk:
+            self.qk_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+            self.qk_weight = nn.Parameter(torch.Tensor(2, self.hidden_size))
+            self.qk_bias = nn.Parameter(torch.Tensor(2, self.hidden_size))
+        else:
+            self.lf_gate = LocalizedFiltering(self.hidden_size, self.lf_conv2d_group, self.lf_conv2d_num_pad)
+            self.get_query_key = nn.Linear(self.hidden_size, 2 * self.attention_projection_size, bias=False)
+        self.core_attention = FlashSelfAttention(causal=True, attention_dropout=config.attn_dropout, softmax_scale=self.softmax_scale)
+        #self.core_attention_flash = DotProductAttention(num_attention_heads=self.num_heads, 
+        #                                          kv_channels=self.head_dim)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        position_ids_k: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        rotary_pos_emb: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        
+        q_len, bsz, _ = hidden_states.size()
+        hidden_states = hidden_states#.to('cuda:1')
+        is_first_step = False
+        import pdb
+        if use_cache:
+            if past_key_value is None:
+                before_hidden_states = None
+                is_first_step = True
+                if q_len > 1:
+                    inference_hidden_states_memory = hidden_states[-2:, :, :]
+                else:
+                    inference_hidden_states_memory = torch.cat((torch.zeros_like(hidden_states), hidden_states), dim=0)
+            else:
+                before_hidden_states = past_key_value[2]
+                inference_hidden_states_memory = torch.cat((before_hidden_states[-1:, :, :], hidden_states), dim=0)
+        value_states = self.v_proj(hidden_states).view(q_len, bsz, self.num_heads, self.head_dim)
+        if self.use_shareqk:
+            qk_states = self.qk_proj(hidden_states).view(q_len, bsz, self.num_heads*self.head_dim)
+            query_key = qk_states.unsqueeze(2) * self.qk_weight + self.qk_bias
+            query_states, key_states = torch.unbind(query_key, dim=2)
+
+            query_states = query_states.view(q_len, bsz, self.num_heads, self.head_dim).transpose(1, 2)
+            key_states = key_states.view(q_len, bsz, self.num_heads, self.head_dim).transpose(1, 2)
+        else:
+            hidden_states = self.lf_gate(hidden_states, before_hidden_states)
+            mixed_qk_layer = self.get_query_key(hidden_states)
+            #mixed_qk_layer = torch.matmul(hidden_states, qk_tensor)
+            new_tensor_shape = mixed_qk_layer.size()[:-1] + (self.num_heads, 2 * self.head_dim)
+            mixed_qk_layer = mixed_qk_layer.view(*new_tensor_shape)
+            (query_states, key_states) = torch.split(mixed_qk_layer, self.head_dim, dim=-1)
+
+        
+        kv_seq_len = key_states.shape[1]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[1]
+        
+        # duplicate the pos_emb for self attention
+        if rotary_pos_emb is not None:
+            if position_ids.shape[1] == 1:
+                q_seq_start = position_ids[0,-1]
+                #seq_start = past_key_value[0].shape[0]
+                q_seq_end = q_seq_start + 1
+                k_seq_end = q_seq_end
+            else:
+                q_seq_start = 0
+                q_seq_end = q_seq_start+key_states.shape[0]
+                k_seq_end = q_seq_end
+            
+            rotary_pos_shape = rotary_pos_emb.shape
+            if isinstance(rotary_pos_emb, tuple):
+                rotary_pos_emb = rotary_pos_emb
+            else:
+                rotary_pos_emb = ((rotary_pos_emb,) * 2)
+            q_pos_emb, k_pos_emb = rotary_pos_emb
+            #q_pos_emb = q_pos_emb[q_seq_start:q_seq_end]
+            #k_pos_emb = k_pos_emb[:k_seq_end]
+        #import pdb
+        #pdb.set_trace()
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=0)
+            value_states = torch.cat([past_key_value[1], value_states], dim=0)
+        past_key_value = (key_states, value_states, inference_hidden_states_memory) if use_cache else None
+        #query_states = apply_rotary_pos_emb(query_states.permute(1, 0, 2, 3), q_pos_emb, position_ids)
+        #key_states = apply_rotary_pos_emb(key_states.permute(1, 0, 2, 3), k_pos_emb, position_ids)
+        query_states = apply_rotary_pos_emb(query_states, q_pos_emb, position_ids)
+        key_states = apply_rotary_pos_emb(key_states, k_pos_emb, position_ids_k)
+
+        attn_weights = None
+        #query_states = query_states.transpose(0,1)
+        #key_states = key_states.transpose(0,1)
+        #value_states = value_states
+        attn_output = self.core_attention(query_states, key_states, value_states)
+        #attn_output = self.core_attention(query_states, key_states, value_states, attention_mask)
+        q_len, bsz, _, _ = attn_output.shape
+        attn_output = attn_output.reshape(q_len, bsz, -1)
+        
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights, past_key_value
+
+class MoEDroplessTokenDispatcher:
+    def __init__(self, num_experts: int, config: YuanConfig) -> None:
+        self.num_experts = num_experts
+        assert self.num_experts > 0, "Expected at least one expert"
+        self.router_topk = config.moe_config['moe_top_k']
+
+    def token_permutation(
+        self, hidden_states: torch.Tensor, max_prob: torch.Tensor, max_ind: torch.Tensor
+    ):
+        self.hidden_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_shape[-1])
+
+        if self.router_topk > 1:
+            global_local_map = torch.ones_like(max_ind).bool()
+            local_indices = max_ind.masked_select(global_local_map)
+            local_probs = max_prob.masked_select(global_local_map)
+            global_local_map = global_local_map.nonzero()[:, 0]
+            global_local_map = global_local_map.view(-1, 1).expand(-1, hidden_states.shape[-1])
+            local_hidden_states = torch.gather(hidden_states, 0, global_local_map)
+
+        indices = torch.argsort(local_indices, dim=0)
+        tokens_per_expert = torch.histc(
+            local_indices,
+            bins=self.num_experts,
+            min=0,
+            max=self.num_experts - 1,
+        )
+        tokens_per_expert = tokens_per_expert.cpu().to(torch.long)
+
+        indices = indices.view(-1, 1).expand(-1, hidden_states.shape[-1])
+        permuted_local_hidden_states = torch.gather(local_hidden_states, 0, indices)
+        return (permuted_local_hidden_states, tokens_per_expert, local_probs, indices, global_local_map)
+
+    def token_unpermutation(
+        self,
+        hidden_states: torch.Tensor,
+        scores: torch.Tensor,
+        indices: torch.Tensor,
+        global_local_map: torch.Tensor = None,
+    ):
+        scores = scores.to(dtype=hidden_states.dtype)
+        unpermuted_local_hidden = torch.zeros_like(hidden_states)
+        assert indices.shape == hidden_states.shape, f'{indices.shape}, {hidden_states.shape}'
+        unpermuted_local_hidden = unpermuted_local_hidden.scatter(0, indices, hidden_states)
+
+        if self.router_topk > 1:
+            unpermuted_local_hidden = unpermuted_local_hidden * scores.view(-1, 1)
+        unpermuted_local_bias = None
+        output_total = unpermuted_local_hidden
+        output_bias_total = unpermuted_local_bias
+
+        if self.router_topk > 1:
+            global_num_tokens = self.hidden_shape[0] * self.hidden_shape[1]
+            global_hidden_shape = [global_num_tokens, hidden_states.shape[-1]]
+            unpermuted_global_hidden = torch.zeros(
+                global_hidden_shape,
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+            )
+            output_total = unpermuted_global_hidden.scatter_add(
+                0, global_local_map, unpermuted_local_hidden
+            )
+
+        output_total = output_total.view(self.hidden_shape)
+
+        return output_total
+
+class GroupedMLP(nn.Module):
+    """An efficient implementation of the Experts layer using CUTLASS GroupedGEMM.
+
+    This class is designed to execute multiple experts in parallel, thereby maximizing computational efficiency.
+    """
+
+    def __init__(self, num_experts: int, config: YuanConfig):
+        super().__init__()
+        self.num_experts = num_experts
+        self.config = config
+
+        def glu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return torch.nn.functional.silu(x[0]) * x[1]
+
+        self.activation_func = glu
+        #self.ffn_hidden_size = config.moe_config['ffn_hidden_size']
+        self.ffn_hidden_size = config.ffn_hidden_size
+        fc1_output_size_per_partition = self.ffn_hidden_size * 2
+        fc2_input_size = self.ffn_hidden_size
+        
+        self.w1 = nn.ModuleList([nn.Linear(self.config.hidden_size, self.ffn_hidden_size * 2, bias=False) for _ in range(num_experts)])
+        self.w2 = nn.ModuleList([nn.Linear(self.ffn_hidden_size, self.config.hidden_size, bias=False) for _ in range(num_experts)])
+    def forward(self, permuted_hidden_states, tokens_per_expert):
+        torch.cuda.set_device(permuted_hidden_states.device)
+        permuted_hidden_states = permuted_hidden_states#.to('cuda:0')
+        #fc1_output = gg.ops.gmm(permuted_hidden_states, self.weight1, tokens_per_expert.cpu(), trans_b=False)
+
+        #intermediate_parallel = self.activation_func(fc1_output)
+        #fc2_output = gg.ops.gmm(intermediate_parallel, self.weight2, tokens_per_expert.cpu(), trans_b=False)
+
+        fc2_outputs = []
+        start_idx = 0
+        for i in range(self.num_experts):
+            if tokens_per_expert[i] == 0:
+                continue
+            end_idx = start_idx + tokens_per_expert[i]
+            #fc1_output = torch.matmul(permuted_hidden_states[start_idx:end_idx], self.w1[i])
+            # Use custom attributes for each expert's Linear layers
+            
+            fc1_output = self.w1[i](permuted_hidden_states[start_idx:end_idx])
+            #print("shape1:", self.w1[i].shape, "shape2:", permuted_hidden_states[start_idx:end_idx].shape)
+            intermediate_parallel = self.activation_func(fc1_output)
+            #fc2_output = torch.matmul(intermediate_parallel, self.w2[i])
+            fc2_output = self.w2[i](intermediate_parallel)
+            fc2_outputs.append(fc2_output)
+            start_idx = end_idx
+        fc2_output = torch.cat(fc2_outputs, dim=0)
+        return fc2_output#.to('cuda:1')
+
+class YuanMoeLayer(nn.Module):
+    def __init__(self, config:YuanConfig):
+        super().__init__()
+        self.config = config
+        self.num_experts = config.moe_config['moe_num_experts']
+        self.top_k = config.moe_config['moe_top_k']
+        self.norm_topk_prob = config.moe_config['norm_topk_prob']
+        self.hidden_size = config.hidden_size
+        
+        expert_indices_offset = (0)
+
+        #self.gate = ParallelAttention_router(config)
+        self.router = ParallelAttention_router(config)
+        self.token_dispatcher = MoEDroplessTokenDispatcher(self.num_experts, config=self.config)
+        self.experts = GroupedMLP(self.num_experts, self.config)
+
+    def routing(self, logits: torch.Tensor) -> torch.Tensor:
+        top_logits, indices = torch.topk(logits, k=self.top_k, dim=1)
+        scores = torch.softmax(top_logits, dim=-1, dtype=torch.float32).type_as(logits)
+        return scores, indices
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        #logits = self.gate(hidden_states)
+        logits = self.router(hidden_states)
+        scores, indices = self.routing(logits)
+        scores = scores.to(hidden_states.dtype)
+        (dispatched_input, tokens_per_expert, scores, indices, global_local_map, ) = self.token_dispatcher.token_permutation(hidden_states, scores, indices)
+        expert_output = self.experts(dispatched_input, tokens_per_expert)
+        output = self.token_dispatcher.token_unpermutation(expert_output, scores, indices, global_local_map)
+        return output
+
+class YuanDecoderLayer(nn.Module):
+    def __init__(self, config: YuanConfig, num_layer):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = YuanAttention(config=config)
+        self.num_layer = num_layer
+        
+        if config.moe_config['moe_num_experts'] > 0:
+            self.mlp = YuanMoeLayer(config)
+        else:
+            self.mlp = YuanMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+            )
+        
+        
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(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,
+        position_ids_k: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        rotary_pos_emb: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> 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, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
+            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
+        """
+        import pdb
+        residual = hidden_states#.to('cuda:1')
+        torch.cuda.set_device(hidden_states.device)
+        hidden_states = self.input_layernorm(hidden_states) #.to('cuda:0')).to('cuda:1')
+        
+        # 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,
+            position_ids_k=position_ids_k,
+            past_key_value=past_key_value,
+            rotary_pos_emb=rotary_pos_emb,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+
+        import pdb
+        #print(hidden_states)
+        #pdb.set_trace()
+        hidden_states = residual + hidden_states.permute(1, 0, 2)
+
+        # Fully Connected
+        residual = hidden_states#.to('cuda:1')
+        torch.cuda.set_device(hidden_states.device)
+        hidden_states = self.post_attention_layernorm(hidden_states) #.to('cuda:0')).to('cuda:1')
+        hidden_states = self.mlp(hidden_states)# .to('cuda:1')
+        hidden_states = residual + hidden_states
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+    
+        return outputs
+
+
+YUAN_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 ([`YuanConfig`]):
+            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 Yuan Model outputting raw hidden-states without any specific head on top.",
+   YUAN_START_DOCSTRING,
+)
+class YuanPreTrainedModel(PreTrainedModel):
+    config_class = YuanConfig 
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["YuanDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    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_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, YuanModel):
+            module.gradient_checkpointing = value
+
+
+YUAN_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 `decoder_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 (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_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 Yuan Model outputting raw hidden-states without any specific head on top.",
+    YUAN_START_DOCSTRING,
+)
+class YuanModel(YuanPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`YuanDecoderLayer`]
+
+    Args:
+        config: YuanConfig
+    """
+
+    def __init__(self, config: YuanConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        
+        #TODO: control it by config
+        self.eod_token = config.eod_token
+        self.reset_attention_mask = config.reset_attention_mask
+        self.reset_position_ids = config.reset_position_ids
+        self.max_position_embeddings = config.max_position_embeddings
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([YuanDecoderLayer(config, i) for i in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.seq_length = config.max_position_embeddings
+        rotary_dim = config.hidden_size // config.num_attention_heads
+        if config.rotary_percent < 1.0:
+            rotary_dim = int(rotary_dim * config.rotary_percent)
+        self.rotary_pos_emb = YuanRotaryEmbedding(rotary_dim, base=config.rotary_base, dtype=config.torch_dtype)
+
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def _prepare_decoder_attention_mask_training(self, input_id, inputs_embeds, eod_token, reset_mask_flag ,reset_attention_mask=True, reset_position_ids=True):
+    
+        micro_batch_size, seq_length = input_id.size()
+        
+        attention_mask = torch.tril(torch.ones(
+            (micro_batch_size, seq_length, seq_length), device=inputs_embeds.device)).view(
+                micro_batch_size, 1, seq_length, seq_length)    
+                
+        position_ids = torch.arange(seq_length, dtype=torch.long,
+                                    device=inputs_embeds.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_id)
+               
+        if reset_position_ids:
+            position_ids = position_ids.clone()
+        
+        if reset_position_ids or reset_attention_mask:
+            # Loop through the batches:
+            for b in range(micro_batch_size):
+
+                # Find indecies where EOD token is.
+                eod_index = position_ids[b, input_id[b] == eod_token]
+
+                # Detach indecies from positions if going to modify positions.
+                if reset_position_ids:
+                    eod_index = eod_index.clone()
+                # Loop through EOD indecies:
+                prev_index = 0
+                for j in range(eod_index.size()[0]):
+                    i = eod_index[j]
+                    # Mask attention loss.
+                    if reset_attention_mask:
+                        attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
+                    # Reset positions.
+                    if reset_position_ids:
+                        position_ids[b, (i + 1):] -= (i + 1 - prev_index)
+                        prev_index = i + 1
+                        
+        inverted_mask = 1 - attention_mask   
+        output_attn_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min)
+        if reset_mask_flag:
+            output_attn_mask = output_attn_mask[:,:,-1:,:]
+        return output_attn_mask, position_ids
+
+    @add_start_docstrings_to_model_forward(YUAN_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,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast, torch.Tensor]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        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
+        input_ids1 = copy.deepcopy(input_ids)
+        reset_mask_flag = False
+        if past_key_values:
+            input_ids = input_ids
+            input_ids = input_ids[:,-1:]
+            if use_cache:
+                reset_mask_flag = True
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_ids = input_ids
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            inputs_embeds = inputs_embeds.transpose(0,1)
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            #past_key_values_length = past_key_values[0][0].shape[2]
+            #modify
+            past_key_values_length = past_key_values[0][0].shape[0]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        # modify to reset position ids
+        if past_key_values is not None:
+            pos_start = position_ids[:,-1]+1
+            pos_end = pos_start+past_key_values[0][0].shape[0]-position_ids.shape[1]+1
+            position_ids_k = torch.arange(pos_start.item(), pos_end.item()).to(position_ids.device)
+            position_ids_k = position_ids_k.unsqueeze(0)
+            position_ids_k = torch.cat((position_ids, position_ids_k), dim=1)
+            position_ids = position_ids[:,-1]+past_key_values[0][0].shape[0]-position_ids.shape[1]+1
+            position_ids = position_ids.unsqueeze(0)
+            #print(position_ids_k,position_ids)
+            #print(position_ids_k.shape,position_ids.shape)
+        else:
+            position_ids_k = position_ids
+            #print(position_ids)
+            #import pdb
+            #pdb.set_trace()
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            #position_ids = position_ids.view(-1, seq_length).long()
+            pass
+        
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids).transpose(0,1)
+        
+        if self.training or self.reset_position_ids:
+            attention_mask, _ = self._prepare_decoder_attention_mask_training(input_ids1, inputs_embeds, self.eod_token, reset_mask_flag, self.reset_attention_mask, self.reset_position_ids)
+        else: 
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+                attention_mask = self._prepare_decoder_attention_mask(
+                    attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+                )
+
+        #rotary_pos_emb = self.rotary_pos_emb(self.max_position_embeddings)
+        # Rotary positional embeddings (embedding is None for PP intermediate devices)
+        rotary_pos_emb = None
+        '''
+        rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len(
+                transformer_input=inputs_embeds
+            )
+        '''
+        rotary_pos_emb = self.rotary_pos_emb(self.max_position_embeddings)
+
+        hidden_states = inputs_embeds
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+        position_ids = position_ids.cpu()
+        position_ids_k = position_ids_k.cpu()
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    position_ids_k=position_ids_k,
+                    past_key_value=past_key_value,
+                    rotary_pos_emb=rotary_pos_emb,
+                    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 = hidden_states#.to('cuda:0')
+        torch.cuda.set_device(hidden_states.device)
+        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 = next_decoder_cache if use_cache else None
+        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 YuanForCausalLM(YuanPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        '''
+        self.eod_token = config.eod_token
+        self.sep_token = config.sep_token
+        self.use_loss_mask = config.use_loss_mask
+        self.model = YuanModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+        '''
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.model = YuanModel(config)
+        #self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        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
+
+    def get_loss_mask(self, input_ids, labels, eod_token, sep_token):
+        micro_batch_size, seq_length = input_ids.size()
+        loss_mask = torch.ones(input_ids.size(), dtype=torch.float, device=input_ids.device)
+        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids) 
+
+
+        """modify loss_mask to only calculate the loss of the answer (separated with [SEP])"""
+
+        for b in range(micro_batch_size):
+            eod_indexs = position_ids[b, input_ids[b] == eod_token]
+            sep_indexs = position_ids[b, input_ids[b] == sep_token]
+
+            if len(eod_indexs) == 0 or len(sep_indexs) == 0:
+                loss_mask[b] = 1.0
+            else:
+                if eod_indexs[0] > sep_indexs[0]:
+                    loss_mask[b, 0:sep_indexs[0]] = 0
+
+                    if len(eod_indexs) == len(sep_indexs):
+                        for ii, eod_index in enumerate(eod_indexs):
+                            start_index = eod_index
+                            if ii == (len(sep_indexs) - 1):
+                                stop_index = seq_length
+                            else:
+                                stop_index = sep_indexs[ii + 1]
+                            loss_mask[b, start_index:stop_index] = 0.0
+                    else:
+                        if len(eod_indexs) > len(sep_indexs):
+                            loss_mask[b,:] = 1.0
+                        else:
+                            for ii, eod_index in enumerate(eod_indexs):
+                                start_index = eod_index
+                                stop_index = sep_indexs[ii + 1]
+
+                                loss_mask[b, start_index:stop_index] = 0.0
+
+                elif eod_indexs[0] < sep_indexs[0]:
+
+                    if len(eod_indexs) == len(sep_indexs):
+                        for ii, eod_index in enumerate(eod_indexs):
+                            start_index = eod_index
+                            stop_index = sep_indexs[ii]
+                            loss_mask[b, start_index:stop_index] = 0.0
+
+                    else:
+                        if len(eod_indexs) < len(sep_indexs):
+                            loss_mask[b,:] = 1.0
+                        else:
+                            for ii, eod_index in enumerate(eod_indexs):
+                                start_index = eod_index
+                                if ii >= len(sep_indexs):
+                                    stop_index = seq_length
+                                else:
+                                    stop_index = sep_indexs[ii]
+                                loss_mask[b, start_index:stop_index] = 0.0
+
+        loss_mask[input_ids == eod_token] = 1.0
+        return loss_mask
+    @add_start_docstrings_to_model_forward(YUAN_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]:
+        """
+        ## modify delete routers
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, YuanForCausalLM
+
+        >>> model = YuanForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? 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 consciours? Can you talk to me?\nI'm not consciours, 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
+        
+        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].transpose(0,1)
+        
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            if self.use_loss_mask:
+                loss_mask = self.get_loss_mask(input_ids, labels, self.eod_token, self.sep_token)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            if self.use_loss_mask:
+                loss_fct = CrossEntropyLoss(reduction='none')
+                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)
+                loss = torch.sum(loss * loss_mask) / loss_mask.sum()
+            else:
+                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=hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+
+        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[:, -1].unsqueeze(-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) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Yuan Model transformer with a sequence classification head on top (linear layer).
+
+    [`YuanForSequenceClassification`] 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).
+    """,
+    YUAN_START_DOCSTRING,
+)
+class YuanForSequenceClassification(YuanPreTrainedModel):
+    #_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = YuanModel(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(YUAN_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        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.ne(input_ids, self.config.pad_token_id).sum(-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_yuanvl_chat.py

@@ -0,0 +1,410 @@
+# --------------------------------------------------------
+# YuanVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import warnings
+from typing import (Any, Callable, Iterable, List, Literal, Mapping, Optional,
+                    Set, Tuple, Type, TypedDict, Union)
+
+import torch.utils.checkpoint
+import transformers
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
+                          LlamaTokenizer)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging
+
+from transformer_engine.pytorch import RMSNorm
+from transformers.activations import ACT2FN
+
+from .configuration_yuanvl import YuanVLChatConfig
+from .conversation import get_conv_template
+from .modeling_intern_vit import InternVisionModel, has_flash_attn
+from .modeling_yuanlm2 import YuanForCausalLM
+from .utils import flatten_bn, merge_multimodal_embeddings
+
+logger = logging.get_logger(__name__)
+
+class InternVLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    Shape: `(batch_size, 1 + num_patches, num_channels, height, width)`
+
+    Note that `num_patches` may be different for each batch, in which case
+    the data is passed as a list instead of a batched tensor.
+    """
+    patches_per_image: List[int]
+    """
+    List of number of total patches for each image in the batch.
+    """
+
+
+class InternVLImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Any # in vllm vision this is a NestedTensors
+    """
+    A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
+    or a list of tensors of shape `(total_image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+InternVLImageInputs = Union[InternVLImagePixelInputs,
+                            InternVLImageEmbeddingInputs]
+
+
+def version_cmp(v1, v2, op='eq'):
+    import operator
+
+    from packaging import version
+    op_func = getattr(operator, op)
+    return op_func(version.parse(v1), version.parse(v2))
+
+class YuanImageMLP(nn.Module):
+    
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        output_size: int,
+        hidden_act: str,
+    ) -> None:
+        super().__init__()
+        #self.up_proj = ColumnParallelLinear(hidden_size, intermediate_size, bias=False,)
+        #self.gate_proj = ColumnParallelLinear(hidden_size, intermediate_size, bias=False,)
+        #self.down_proj = RowParallelLinear(intermediate_size, output_size, bias=False,)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, output_size, bias=False)
+
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. Only silu is supported for now.")
+        
+        self.act_fn = ACT2FN[hidden_act]
+    
+    @torch.compile
+    def swiglu(self, y_1, y_2):
+        return self.act_fn(y_1) * y_2
+    
+    def forward(self, x):
+        #import pdb
+        x1 = self.up_proj(x)
+        x2 = self.gate_proj(x)
+        x3 = self.swiglu(x1, x2)
+        #x3 = self.act_fn(x1)
+        #x2 = self.gate_proj(x)
+        x = self.down_proj(x3)
+        return x
+
+class YuanVLChatModel(PreTrainedModel):
+    config_class = YuanVLChatConfig
+    main_input_name = 'pixel_values'
+    base_model_prefix = 'language_model'
+    _supports_flash_attn_2 = True
+    _no_split_modules = ['InternVisionModel', 'YuanDeocderLayer']
+
+    def __init__(self, config: YuanVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
+        super().__init__(config)
+
+        assert version_cmp(transformers.__version__, '4.37.0', 'ge')
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.select_layer = config.select_layer
+        self.template = config.template
+        self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+        use_flash_attn = use_flash_attn if has_flash_attn else False
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
+
+        logger.info(f'num_image_token: {self.num_image_token}')
+        logger.info(f'ps_version: {self.ps_version}')
+        if vision_model is not None:
+            self.vision_model = vision_model
+        else:
+            self.vision_model = InternVisionModel(config.vision_config)
+        if language_model is not None:
+            self.language_model = language_model
+        else:
+            if config.llm_config.architectures[0] == 'YuanForCausalLM':
+                self.language_model = YuanForCausalLM(config.llm_config)
+            else:
+                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
+
+        self.pixel_unshuffle = torch.nn.PixelUnshuffle(downscale_factor=2)
+        #vit_hidden_size = config.vision_config.hidden_size
+        #llm_hidden_size = config.llm_config.hidden_size
+        #vit_mlp_ffn_hidden_size = config.vit_mlp_ffn_hidden_size
+        #layernorm_epsilon = config.llm_config.layernorm_epsilon
+        layernorm_epsilon = config.llm_config.rms_norm_eps
+
+        self.imagemlp_input_hiddensize = int(config.vision_config.hidden_size / self.downsample_ratio ** 2)
+        self.imagemlp_ffn_hidden_size = config.llm_config.ffn_hidden_size
+
+        self.imagemlp = YuanImageMLP(self.imagemlp_input_hiddensize, self.imagemlp_ffn_hidden_size,
+                     output_size=config.llm_config.hidden_size, hidden_act="silu")
+        self.imagemlp_layernorm = RMSNorm(config.llm_config.hidden_size, eps=layernorm_epsilon)
+
+        '''
+        # modify internvl vision
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.llm_config.hidden_size
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1/self.downsample_ratio) ** 2),
+            nn.Linear(vit_hidden_size * int(1/self.downsample_ratio) ** 2, llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size)
+        )
+        '''
+
+        self.img_context_token_id = config.img_context_token_id
+        self.conv_template = get_conv_template(self.template)
+        self.system_message = self.conv_template.system_message
+
+    def _validate_pixel_values(self,
+                                data: Union[torch.Tensor, List[torch.Tensor]]
+                                ) -> Union[torch.Tensor, List[torch.Tensor]]:
+        
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape)
+            if actual_dims != expected_dims:
+                # expected_expr = ("num_patches", *map(str, expected_dims))
+                expected_expr = (expected_dims)
+                raise ValueError("The expected shape of pixel values in each batch element "
+                                 f" is {expected_expr}. You supplied {tuple(d.shape)}.")
+            # data的数据类型可以是tensor，也可以是List[tensor]
+            # 从这一段上来看，image tensor的个数为 imbs*num_images
+        for d in data:
+            _validate_shape(d)
+        return data
+
+
+
+    def _parse_and_validate_image_input(self, 
+                                        pixel_values: List[torch.Tensor] = None, 
+                                        image_token_id: torch.Tensor = None, 
+                                        image_embeds: torch.Tensor = None,
+                                        ) -> Optional[InternVLImagePixelInputs]:
+        # 没有图像数据
+        if pixel_values is None and image_embeds is None:
+            return None
+        
+        # 传入数据有image_embeds
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return InternVLImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+        
+        #self.img_context_token_id = image_token_id[0]
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+            patches_per_image = []
+            # bsz/request循环
+            for request_pixel_values in pixel_values:
+                # 每个request的images循环
+                patches_per_image.append(request_pixel_values.shape[0])
+
+            # We need to flatten (B, N, P) to (B*N*P)
+            # so we call flatten_bn twice.
+            # (total_patches, 3, h, w)
+            return InternVLImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(flatten_bn(pixel_values)),
+                patches_per_image=patches_per_image)
+        raise AssertionError("This line should be unreachable")
+    
+    def _process_image_input(
+            self,
+            image_input: InternVLImageInputs,
+    ) -> Tuple[torch.Tensor] :
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+        assert self.vision_model is not None
+        # (total_patches, tokens_per_image, llm_config.hidden_size)
+        image_embeds = self.extract_feature(image_input["data"])
+
+        patches_per_image = image_input["patches_per_image"]
+
+        # Only one image in the current batch
+        # bsz=1的情况，直接返回image_embeds
+        if len(patches_per_image) == 1:
+            # 返回一个tensor，[1, num_patches*256, text_config.hidden_size]
+            image_embeds = image_embeds.view(-1, self.config.llm_config.hidden_size).unsqueeze(1)
+            return image_embeds
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        # feature_size 每个patch 256个token位置
+        feature_size = image_embeds.shape[1]
+        # (total_image_tokens, llm_config.hidden_size)
+        image_embeds = image_embeds.view(-1, self.config.llm_config.hidden_size)
+        image_feature_sizes = [num_patches * feature_size for num_patches in patches_per_image]
+        # 切分后得到一个Tuple，元组每个元胞表示一个image的image_embed, [num_patches * 256, llm_config.hidden_size]
+        image_embeds = image_embeds.split(image_feature_sizes)
+
+        return image_embeds
+        
+
+    
+    def get_multimodal_embeddings(self,
+                                  pixel_values: Optional[List[torch.Tensor]] = None,
+                                  image_token_id: Optional[List[torch.Tensor]] = None,
+                                  image_embeds: Optional[List[torch.Tensor]] = None,
+                                  image_input: InternVLImageInputs = None,
+                                  ):
+        image_input = self._parse_and_validate_image_input(pixel_values, image_token_id, image_embeds)
+        if image_input is None:
+            return None
+        
+        # image_input: (total_patches, 3, h, w)
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+    
+    def get_input_embeddings(
+            self,
+            input_ids: torch.Tensor,
+            multimodal_embeddings: Optional[torch.Tensor]
+            ) -> torch.Tensor:
+        # 生成 token_embeddings
+        inputs_embeds = self.language_model.model.get_input_embeddings(input_ids)
+        # 将image embed放到img_context_token_id的位置
+        if multimodal_embeddings is not None:
+            assert self.img_context_token_id is not None
+            # input_ids: torch.Tensor
+            # inputs_embeds: torch.Tensor 
+            # multimodal_embeddings: torch.Tensor
+            # placeholder_token_id: img_context_token_id
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.img_context_token_id)
+        return inputs_embeds
+    
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: torch.Tensor = None,
+            position_ids: torch.LongTensor = None,
+            past_key_values: 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,
+            pixel_values: Optional[List[torch.Tensor]] = None,
+            image_token_id: Optional[List[torch.Tensor]] = None,
+            image_embeds: Optional[List[torch.Tensor]] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        import pdb
+        pdb.set_trace()
+        if inputs_embeds is None:
+            # (images, patches * token_per_image)
+            vision_embeddings = self.get_multimodal_embeddings(pixel_values, image_token_id, image_embeds)
+            # (tokens, hidden_size)
+            inputs_embeds = self.get_input_embeddings(input_ids, vision_embeddings).permute(1, 0, 2)
+            input_ids = None
+        
+        hidden_states = self.language_model.model(input_ids, attention_mask, position_ids, past_key_values, 
+                                                  inputs_embeds, labels, use_cache, output_attentions, 
+                                                  output_hidden_states, return_dict)
+
+        return hidden_states
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
+                          'which results in a transposed image.')
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    # Internvl vision
+    def extract_feature(self, pixel_values):
+        # pixel_values: (imbs * num_image, ic, ih, iw)
+        pixel_values = pixel_values.to(torch.bfloat16)
+        output = self.vision_model(pixel_values=pixel_values)
+        vit_embeds=output[0]
+        # vit_embeds: (imbs * num_images, h*w, vit_dim)
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        '''h = w = int(vit_embeds.shape[1]**0.5)
+        # vit_embeds: (imbs * num_images, vit_dim, h, w)
+        vit_embeds = vit_embeds.view(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        '''
+        pn, phw, pc = vit_embeds.shape
+        ph = pw = int(phw**0.5)
+        vit_embeds = vit_embeds.view(pn, ph, pw, pc).permute(0, 3, 1, 2)
+        vit_embeds = self.pixel_unshuffle(vit_embeds)
+        pn, pc, ph, pw = vit_embeds.shape
+        vit_embeds = vit_embeds.view(pn, pc, ph * pw).permute(0, 2, 1)
+        num_images, cvs, chs = vit_embeds.shape
+        #_, cvs, chs = vit_embeds.shape
+        #assert self.imagemlp_ffn_hidden_size == chs
+        #vit_embeds = vit_embeds.contiguous().view(imbs, num_image * cvs, chs).permute(1, 0, 2).contiguous()
+        vit_embeds = vit_embeds.reshape(1, -1, vit_embeds.shape[-1]).permute(1, 0, 2)
+        vit_embeds = self.imagemlp(vit_embeds)
+        vit_embeds = self.imagemlp_layernorm(vit_embeds)
+        vit_embeds = vit_embeds.view(num_images, cvs, -1)
+        return vit_embeds
+    
+    @torch.no_grad()
+    def generate(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        input_ids: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        visual_features: Optional[torch.FloatTensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> torch.LongTensor:
+
+        
+        if pixel_values is not None:
+            if visual_features is not None:
+                vit_embeds = visual_features
+            else:
+                vit_embeds = self.get_multimodal_embeddings(pixel_values)
+                inputs_embeds = self.get_input_embeddings(input_ids, vit_embeds)
+                input_ids = None
+        
+        
+        outputs = self.language_model.generate(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            position_ids=position_ids,
+            max_length=8192,
+            use_cache=True,
+        )
+
+
+        return outputs

mq_test_demo.py

@@ -0,0 +1,576 @@
+import torch
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
+    CenterCrop, ColorJitter, Grayscale
+import math
+
+FILE_EXTENSIONS = ('.jpeg', '.txt', '.idx')
+'''
+args = {
+    "patch_size": 16, 
+    "patch_num_width": 16,
+    "patch_num_height": 16,
+    "position_embedding_length": 4096,
+    "clip_model_name": 'InternViT-448',
+    "image_segment_method": 'dynamic',
+    "max_split_tile_num_multi_image": 1,
+    "clip_visual_size": 1024,
+    "clip_hidden_size": 1024,
+    "downsample_ratio": 0.5
+}
+'''
+class args:
+    patch_size = 16
+    patch_num_width = 16
+    patch_num_height = 16
+    position_embedding_length = 4096
+    clip_model_name = 'InternViT-448'
+    image_segment_method =  'dynamic'    ##'adaptive'
+    max_split_tile_num_multi_image = 1
+    max_split_tile_num_single_image = 9
+    clip_visual_size = 1024
+    clip_hidden_size = 1024
+    downsample_ratio = 0.5
+    shape_change_threshold = 0.5
+    bf16 = True
+    fp16 = False
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size, threshold):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        size_diff_length = abs(((ratio[0]*image_size + ratio[1]*image_size)-(width+height)) / (width+height))
+        if ratio_diff < best_ratio_diff and size_diff_length <= threshold:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def build_transform(input_size):
+    #MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = Compose([
+        Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        _convert_to_rgb,
+        ToTensor(),
+        Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
+    ])
+    return transform
+
+def torch_extract_patches(image_tensor, patch_height, patch_width):
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+    image_tensor = image_tensor.unsqueeze(0)
+    patches = torch.nn.functional.unfold(image_tensor, (patch_height, patch_width), stride=(patch_height, patch_width))
+    patches = patches.reshape(image_tensor.size(0), image_tensor.size(1), patch_height, patch_width, -1)
+    patches = patches.permute(0, 4, 2, 3, 1).reshape(
+        image_tensor.size(2) // patch_height,
+        image_tensor.size(3) // patch_width,
+        image_tensor.size(1) * patch_height * patch_width,
+    )
+    return patches.unsqueeze(0)
+
+# 用于计算adapt需要输入图片的大小
+def adapt_size(originHeight:int,originWeight:int):
+    ### 用于计算adapt的图片大小
+    # 参数说明
+    # originHeight:              原图高度
+    # originWidth:               原图宽度
+    # patchHeight:               patch高度
+    # patchWidth:                patch宽度
+    # maxPatches:                patch数目上限
+    # 返回值说明:
+    # resized_height:            插值后图片高度
+    # resized_width:             插值后图片宽度
+    # resized_patch_height_num:  插值后图片垂直patch数目
+    # resized_patch_width_num:   插值后图片水平patch数目
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+    patchHeight = PATCH_SIZE
+    patchWidth = PATCH_SIZE
+    maxPatches = MAX_PATCHES
+    scale = math.sqrt(maxPatches * (patchHeight / originHeight) * (patchWidth / originWeight))
+    resized_patch_height_num = max(min(math.floor(scale * originHeight / patchHeight), maxPatches), 1)
+    resized_patch_width_num = max(min(math.floor(scale * originWeight / patchWidth), maxPatches), 1)
+    resized_height = max(resized_patch_height_num * PATCH_SIZE, 1)
+    resized_width = max(resized_patch_width_num * PATCH_SIZE, 1)
+    return resized_height, resized_width, resized_patch_height_num, resized_patch_width_num
+
+def cal_num_of_slices(origin_image_width, origin_image_height, max_num):
+    #import pdb
+    #pdb.set_trace()
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+    scale = origin_image_width*origin_image_height/(IMAGE_WIDTH*IMAGE_HEIGHT)
+
+    scale = math.ceil(scale)
+    max_num_img=max_num
+    if scale > max_num_img:
+        scale = max_num_img
+    def factorize(n):
+        factors = []
+        for i in range(1, n + 1):
+            if n % i == 0:
+                factors.append((i/(n/i), i, n // i))
+        return factors
+    numbers = [1, 2, 3, 4, 5, 6, 7,8,9,10,11,12,13,14,15]
+    factor_dict = {}
+    for num in numbers:
+        factor_dict[num] = factorize(num)
+    log_origin_ratio = math.log(origin_image_width/origin_image_height)
+    available_ratios = []
+    if scale<=2:
+        available_ratios = factor_dict[scale] + factor_dict[scale + 1]
+    else :
+        available_ratios = factor_dict[scale-1] + factor_dict[scale]+factor_dict[scale+1]
+    
+    min_dif = 1000
+    best_w = 0
+    best_h = 0
+    for (r,w_slice,h_slice) in available_ratios:
+        log_r = math.log(r)
+        if min_dif > abs(log_r - log_origin_ratio):
+            min_dif = abs(log_r - log_origin_ratio)
+            best_w = w_slice
+            best_h = h_slice
+    return best_w,best_h
+# 做图片切片
+def get_patch_nums(origin_image_width, origin_image_height, max_num):
+    # 输入原图的尺寸
+    # 返回：
+    # slice_w_num 切片的w方向有多少个patch
+    # slice_h_num 切片的h方向有多少个patch
+    # abstract_w_num 原图的w方向有多少个patch
+    # abstract_h_num 原图的h方向有多少个patch
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+
+    best_w, best_h = cal_num_of_slices(origin_image_width,origin_image_height, max_num)
+    slice_width = origin_image_width//best_w
+    slice_height = origin_image_height//best_h
+    _,_,slice_h_num,slice_w_num = adapt_size(slice_height,slice_width)
+    _,_,abstract_h_num,abstract_w_num = adapt_size(origin_image_height,origin_image_width)
+    #print(slice_w_num,slice_h_num,abstract_w_num,abstract_h_num)
+    return slice_w_num,slice_h_num,abstract_w_num,abstract_h_num
+
+def slice_image(image, max_num):
+
+    # slice the image according to our princeple
+    # return an array of slices
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+
+    origin_image_width  = image.size[0]
+    origin_image_height = image.size[1]
+
+    best_w, best_h = cal_num_of_slices(origin_image_width=origin_image_width, origin_image_height=origin_image_height, max_num=max_num )
+    slices = []
+    # print(best_w,best_h)
+
+    for j in range(best_h):
+        for i in range(best_w):
+
+            box = (i * origin_image_width//best_w, j * origin_image_height//best_h, (i + 1) * origin_image_width//best_w, (j + 1) * origin_image_height//best_h)
+            # 切割图片
+            region = image.crop(box).convert("RGB")
+            # 添加到列表
+            slices.append(region)
+
+    return slices
+def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, threshold=1):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size, threshold)
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        print(box)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def process_image(image, image_size, max_num):
+    PATCH_SIZE       = args.patch_size
+    PATCH_NUM_WIDTH  = args.patch_num_width
+    PATCH_NUM_HEIGHT = args.patch_num_height
+    POSITION_EMBEDDING_LENGTH = args.position_embedding_length
+    print(PATCH_SIZE,PATCH_NUM_WIDTH,PATCH_NUM_HEIGHT,POSITION_EMBEDDING_LENGTH)
+    # 576
+    MAX_PATCHES      = PATCH_NUM_WIDTH * PATCH_NUM_HEIGHT
+    #
+    TOKEN_LENGTH     = 3 * PATCH_SIZE * PATCH_SIZE
+    # 336 336
+    IMAGE_WIDTH      = PATCH_SIZE * PATCH_NUM_WIDTH
+    IMAGE_HEIGHT     = PATCH_SIZE * PATCH_NUM_HEIGHT
+
+    origin_image_width  = image.size[0]
+    origin_image_height = image.size[1]
+    image = image.convert("RGB")
+    slices = slice_image(image, max_num)
+    if len(slices) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        slices.append(thumbnail_img)
+    # 计算resize之后的图片大小
+    resized_height, resized_width, resized_patch_height, resized_patch_width = \
+    adapt_size(origin_image_height,origin_image_width)
+    image = slices[0]
+    image_w = image.size[0]
+    image_h = image.size[1]
+    resized_height, resized_width, resized_patch_height, resized_patch_width = \
+    adapt_size(image_h,image_w)
+    image = ToTensor()(image)
+
+    image = torch.nn.functional.interpolate(
+            image.unsqueeze(0),
+            size=(resized_height, resized_width),
+            mode="bilinear",
+            align_corners=False,
+            antialias=True,
+        ).squeeze(0)
+    # 需要mask的patch数
+    num_patches_to_pad = MAX_PATCHES - resized_patch_height*resized_patch_width
+    # raprint("mask: ",num_patches_to_pad)
+    # 切割resize好的图片
+    image = torch_extract_patches(image,PATCH_SIZE, PATCH_SIZE)
+    image = image.reshape([resized_patch_width*resized_patch_height,TOKEN_LENGTH])
+    # 用0补全需要mask的图片部分
+    image = torch.nn.functional.pad(image, [0, 0, 0, num_patches_to_pad]).float()  #torch.Size([196, 768])
+    image = image.reshape(PATCH_NUM_WIDTH, PATCH_NUM_HEIGHT, PATCH_SIZE, PATCH_SIZE, 3).permute(0, 2, 1, 3, 4).reshape(IMAGE_WIDTH, IMAGE_HEIGHT, 3).permute(2, 0 ,1)
+    #print(image.shape)
+    #image = torch.stack(image)
+    return slices
+
+def _convert_to_rgb(image):
+    return image.convert('RGB')
+
+def load_image(image_file, input_size=448, max_num=9):
+    image = Image.open(image_file).convert('RGB')
+    # image.save('seg_imge/'+image_file.split('/')[-1])
+    # print(max_num)
+    if args.clip_model_name == 'InternViT-448':
+        transform = build_transform(input_size=input_size)
+        #image_processor = CLIPImageProcessor.from_pretrained(args.clip_download_path)
+        #'/mnt/beegfs1/shenqiang/internvit-448/models--InternViT-300M-448px/'args.clip_download_path
+        if args.image_segment_method == 'adaptive':
+            images_processed = process_image(image, input_size, max_num)
+        elif args.image_segment_method == 'dynamic':
+            images_processed = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num, threshold=args.shape_change_threshold)
+        # pixel_values = [image_processor(images=image, return_tensors='pt').pixel_values.squeeze(0) for image in images_processed]
+        pixel_values = [transform(image) for image in images_processed]
+    else:
+        transform = build_transform(input_size=input_size)
+        if args.image_segment_method == 'adaptive':
+            images_processed = process_image(image, input_size, max_num)
+        elif args.image_segment_method == 'dynamic':
+            images_processed = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        pixel_values = [transform(image) for image in images_processed]
+    
+    pixel_values = torch.stack(pixel_values)
+
+    return pixel_values
+
+def preocess_imput(args, num_token_per_tile, image_path, question):
+    image_prompts = ''
+    if len(image_path) >= 2:
+        image_list = []
+        num_tile_per_image_list = []
+        for ipath in image_path:
+            images = load_image(ipath, max_num=args.max_split_tile_num_multi_image)
+            #images = load_image(ipath, max_num=args.max_split_tile_num_multi_image).view(1, -1, 3, 448, 448).cuda()
+            num_tile_this_image = len(images)
+            num_tile_per_image_list.append(num_tile_this_image)
+            image_list.append(images)
+            image_prompts = image_prompts + '<IMAGE>' + '<pad>' * num_tile_this_image * num_token_per_tile + '</IMAGE>'
+        num_tile_per_image_tensor = torch.Tensor(num_tile_per_image_list).long().cuda()
+        image_tensor = torch.cat(image_list, dim=0).view(1, -1, 3, 448, 448).cuda()
+    
+    else:
+        #images_tensor = load_image(image_path, max_num=args.max_split_tile_num_single_image).view(1, -1, 3, 448, 448).cuda()
+        images = load_image(image_path[0], max_num=args.max_split_tile_num_single_image)
+        num_tile_this_image = len(images)
+        num_tile_per_image_tensor = torch.Tensor([num_tile_this_image]).long().cuda()
+        image_tensor = images.view(1, -1, 3, 448, 448).cuda()
+        image_prompts = image_prompts + '<IMAGE>' + '<pad>' * num_tile_this_image * num_token_per_tile + '</IMAGE>'
+
+    if args.fp16:
+        image_tensor = image_tensor.half()
+    elif args.bf16:
+        image_tensor = image_tensor.bfloat16()
+    else:
+        image_tensor = image_tensor.float()
+
+    images_input = {'num_tile_per_image_tensor': num_tile_per_image_tensor,
+                    'image_tensor': image_tensor}
+    
+    prompts = ['<BOS>' + image_prompts + question[0] + '<sep>']
+
+    return prompts, images_input
+
+
+def _build_yuanvl_attention_mask_and_position_ids(tokenizer, tokens, images_input=None):
+    """Build the attention mask and postition ids for the input tokens."""
+
+    # Since we are not interested in loss-mask and reset attention/position
+    # is also False, eod_token is not used so it is safe to set it to None.
+    
+    bos_token, image_start_token, image_end_token, pad_token, sep_tpken, eod_token = (tokenizer(tok)['input_ids'][0] for tok in ['<BOS>','<IMAGE>', '</IMAGE>', '<pad>', '<sep>', '<eod>'])
+    #eod_token = tokenizer("<eod>")['input_ids'][0]
+    
+    attention_mask, position_ids, image_info = get_ltor_masks_and_position_ids_yuanvl_inference(
+            tokens,
+            bos_token,
+            image_start_token,
+            image_end_token,
+            eod_token,
+            pad_token,
+            images_input)
+    
+    
+    '''attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
+        data=tokens,
+        eod_token=None,
+        reset_position_ids=False,
+        reset_attention_mask=False,
+        eod_mask_loss=False)'''
+
+    return attention_mask, position_ids, image_info
+
+def get_ltor_masks_and_position_ids_yuanvl_inference(data,
+                                    bos_token,
+                                    image_start_token,
+                                    image_end_token,
+                                    eod_token,
+                                    pad_token,
+                                    images_input,
+                                    reset_attention_mask=False):
+    """Build masks and position id for left to right model."""
+    # Extract batch size and sequence length.
+    micro_batch_size, seq_length = data.size()
+    assert micro_batch_size == 1, 'yuanvl support mbs = 1 only'
+
+    # Attention mask (lower triangular).
+    if reset_attention_mask:
+        att_mask_batch = micro_batch_size
+    else:
+        att_mask_batch = 1
+    attention_mask = torch.tril(torch.ones(
+        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
+            att_mask_batch, 1, seq_length, seq_length)
+
+
+    # Position ids.
+    position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=data.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(data)
+    #input_pad = []
+    #image_info = {}
+
+    #import pdb
+    #pdb.set_trace()
+    #if torch.distributed.get_rank() == 0:
+
+        #pdb.set_trace()
+    if images_input is not None:
+        num_tile_per_image_tensor = images_input['num_tile_per_image_tensor']
+        images_tensor = images_input['image_tensor']
+        input_pad = []
+        image_info = {}
+        position_ids_use = torch.zeros(data.shape).to(position_ids)
+        for b in range(micro_batch_size):
+            bos_index = position_ids[b, data[b] == bos_token]
+            pad_index = position_ids[b, data[b] == pad_token]
+            image_start_index = position_ids[b, data[b] == image_start_token]
+            image_end_index = position_ids[b, data[b] == image_end_token]
+            #eod_index = position_ids[b, data[b] == eod_token]
+            #assert len(bos_index) == len(eod_index)
+            num_image = len(num_tile_per_image_tensor)
+
+            #num_tile = pad_index.shape[0] // clip_visual_size
+            #image_info['num_image'] = num_image
+            image_info['num_tile'] = num_tile_per_image_tensor
+            #image_info['bos_pos'] = bos_index.tolist()
+            image_info['image_start_pos'] = image_start_index.tolist()
+            #image_info['image_end_pos'] = image_end_index.tolist()
+
+            #for j in range(image_index.size()[0]):
+            #    start_idx = image_index[j]
+            #    diff = seq_length - start_idx
+            #    position_ids_use[b][start_idx : ] = torch.arange(diff, dtype=torch.long,
+            #                                                     device=data.device)
+            start_idx = image_end_index[-1]
+            diff = seq_length - start_idx
+            position_ids_use[b][start_idx : ] = torch.arange(diff, dtype=torch.long,
+                                                                 device=data.device)
+    else:
+        position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=data.device)
+        position_ids = position_ids.unsqueeze(0)#.expand_as(data)
+        position_ids_use = position_ids
+        image_info = None
+        #image_info['eod_pos'] = eod_index.tolist()
+        #for j in range(bos_index.size()[0]):
+        #    start_idx = bos_index[j]
+        #    end_idx = eod_index[j]
+        #    input_pad = input_pad + [bos_token] + [pad_token] * clip_visual_size + data[b][start_idx + 1 : end_idx + 1].tolist()
+        #data_nopad = data[b][:eod_index[j]+1].view(1, -1)
+    #input_pad = input_pad + [pad_token]
+
+
+    # Position ids.
+    #position_ids = torch.arange(seq_length + clip_visual_size * num_image, dtype=torch.long,
+    #position_ids = torch.arange(seq_length, dtype=torch.long,
+    #                            device=data.device)
+    #position_ids = position_ids.unsqueeze(0)#.expand_as(data)
+
+
+
+    # Convert attention mask to binary:
+    attention_mask = (attention_mask < 0.5)
+
+    '''xattn_position_ids = torch.arange(seq_length, dtype=torch.long,
+                                device=data.device)
+    xattn_position_ids = xattn_position_ids.unsqueeze(0).expand_as(data)
+
+    for b in range(micro_batch_size):
+
+        bos_index = xattn_position_ids[b, data[b] == bos_token]
+
+        num_image = len(bos_index)
+
+        xattn_mask = torch.zeros((micro_batch_size, seq_length, num_image * clip_visual_size), device = data.device).view(micro_batch_size, 1, seq_length, num_image * clip_visual_size)
+
+        for j in range(bos_index.size()[0]):
+            sidx = bos_index[j]
+
+            image_sidx = j * clip_visual_size
+            image_eidx = (j + 1) * clip_visual_size
+
+            #xattn_mask[b, 0, (sidx + 1) : , image_sidx : image_eidx] = 1
+            xattn_mask[b, 0, sidx : , image_sidx : image_eidx] = 1
+            #xattn_mask[b, 0, sidx : (eidx + 1), image_sidx : image_eidx] = 1
+
+    xattn_mask = (xattn_mask < 0.5)'''
+
+    return attention_mask, position_ids_use, image_info
+
+tokenizer_loadpath = "/mnt/beegfs3/zhaoxudong/code/yuanvl_hf_40B_stage2_pcase4_12pp/"
+model_loadpath = "/mnt/beegfs3/zhaoxudong/code/yuanvl_hf_40B_stage2_pcase4_12pp/"
+
+
+# 加载本地模型
+model = AutoModel.from_pretrained(
+    model_loadpath,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=False,
+    device_map="auto",
+    trust_remote_code=True).eval()
+
+
+print("Creat model finish")
+
+# 加载本地 Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_loadpath)
+
+
+num_token_per_tile = int(args.clip_visual_size * args.downsample_ratio**2)
+
+# demo 1
+image_path = ['/mnt/beegfs3/zhaoxudong/code/image.jpeg']
+question = ['Please describe the picture']
+question = ['请描述这张图片的内容']
+
+prompts, images_input = preocess_imput(args, num_token_per_tile, image_path, question)
+
+input=tokenizer(prompts, return_tensors="pt")
+input_ids = input['input_ids'].to("cuda")
+pixel_values=images_input['image_tensor']
+
+attention_mask, position_ids, image_info = _build_yuanvl_attention_mask_and_position_ids(
+                tokenizer, input_ids, images_input)
+
+attention_mask = input['attention_mask'].to("cuda")
+
+output = model.generate(pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids)
+print(tokenizer.decode(output[0]))

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+  "crop_size": 448,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "image_processor_type": "CLIPImageProcessor",
+  "processor_class": "CLIPProcessor",
+  "tokenizer_class": "LlamaTokenizer",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "size": 448
+}

special_tokens_map.json

@@ -0,0 +1,1086 @@
+{
+  "additional_special_tokens": [
+    "<s>",
+    "<eod>",
+    "<unk>",
+    "<sep>",
+    "<pad>",
+    "<mask>",
+    "<predict>",
+    "<FIM_SUFFIX>",
+    "<FIM_PREFIX>",
+    "<FIM_MIDDLE>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<repo_name>",
+    "<file_sep>",
+    "<BOS>",
+    "<IMAGE>",
+    "</IMAGE>",
+    "<grounding>",
+    "<obj>",
+    "</obj>",
+    "<box>",
+    "</box>",
+    "<point>",
+    "</point>",
+    "<3dbox>",
+    "</3dbox>",
+    "<depth>",
+    "</depth>",
+    "s000",
+    "s001",
+    "s002",
+    "s003",
+    "s004",
+    "s005",
+    "s006",
+    "s007",
+    "s008",
+    "s009",
+    "s010",
+    "s011",
+    "s012",
+    "s013",
+    "s014",
+    "s015",
+    "s016",
+    "s017",
+    "s018",
+    "s019",
+    "s020",
+    "s021",
+    "s022",
+    "s023",
+    "s024",
+    "s025",
+    "s026",
+    "s027",
+    "s028",
+    "s029",
+    "s030",
+    "s031",
+    "s032",
+    "s033",
+    "s034",
+    "s035",
+    "s036",
+    "s037",
+    "s038",
+    "s039",
+    "s040",
+    "s041",
+    "s042",
+    "s043",
+    "s044",
+    "s045",
+    "s046",
+    "s047",
+    "s048",
+    "s049",
+    "s050",
+    "s051",
+    "s052",
+    "s053",
+    "s054",
+    "s055",
+    "s056",
+    "s057",
+    "s058",
+    "s059",
+    "s060",
+    "s061",
+    "s062",
+    "s063",
+    "s064",
+    "s065",
+    "s066",
+    "s067",
+    "s068",
+    "s069",
+    "s070",
+    "s071",
+    "s072",
+    "s073",
+    "s074",
+    "s075",
+    "s076",
+    "s077",
+    "s078",
+    "s079",
+    "s080",
+    "s081",
+    "s082",
+    "s083",
+    "s084",
+    "s085",
+    "s086",
+    "s087",
+    "s088",
+    "s089",
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+    "s091",
+    "s092",
+    "s093",
+    "s094",
+    "s095",
+    "s096",
+    "s097",
+    "s098",
+    "s099",
+    "s100",
+    "s101",
+    "s102",
+    "s103",
+    "s104",
+    "s105",
+    "s106",
+    "s107",
+    "s108",
+    "s109",
+    "s110",
+    "s111",
+    "s112",
+    "s113",
+    "s114",
+    "s115",
+    "s116",
+    "s117",
+    "s118",
+    "s119",
+    "s120",
+    "s121",
+    "s122",
+    "s123",
+    "s124",
+    "s125",
+    "s126",
+    "s127",
+    "s128",
+    "s129",
+    "s130",
+    "s131",
+    "s132",
+    "s133",
+    "s134",
+    "s135",
+    "s136",
+    "s137",
+    "s138",
+    "s139",
+    "s140",
+    "s141",
+    "s142",
+    "s143",
+    "s144",
+    "s145",
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+    "s148",
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+    "s168",
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+    "s175",
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+    "s180",
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+    "s182",
+    "s183",
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+    "s186",
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+    "s188",
+    "s189",
+    "s190",
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+    "s828",
+    "s829",
+    "s830",
+    "s831",
+    "s832",
+    "s833",
+    "s834",
+    "s835",
+    "s836",
+    "s837",
+    "s838",
+    "s839",
+    "s840",
+    "s841",
+    "s842",
+    "s843",
+    "s844",
+    "s845",
+    "s846",
+    "s847",
+    "s848",
+    "s849",
+    "s850",
+    "s851",
+    "s852",
+    "s853",
+    "s854",
+    "s855",
+    "s856",
+    "s857",
+    "s858",
+    "s859",
+    "s860",
+    "s861",
+    "s862",
+    "s863",
+    "s864",
+    "s865",
+    "s866",
+    "s867",
+    "s868",
+    "s869",
+    "s870",
+    "s871",
+    "s872",
+    "s873",
+    "s874",
+    "s875",
+    "s876",
+    "s877",
+    "s878",
+    "s879",
+    "s880",
+    "s881",
+    "s882",
+    "s883",
+    "s884",
+    "s885",
+    "s886",
+    "s887",
+    "s888",
+    "s889",
+    "s890",
+    "s891",
+    "s892",
+    "s893",
+    "s894",
+    "s895",
+    "s896",
+    "s897",
+    "s898",
+    "s899",
+    "s900",
+    "s901",
+    "s902",
+    "s903",
+    "s904",
+    "s905",
+    "s906",
+    "s907",
+    "s908",
+    "s909",
+    "s910",
+    "s911",
+    "s912",
+    "s913",
+    "s914",
+    "s915",
+    "s916",
+    "s917",
+    "s918",
+    "s919",
+    "s920",
+    "s921",
+    "s922",
+    "s923",
+    "s924",
+    "s925",
+    "s926",
+    "s927",
+    "s928",
+    "s929",
+    "s930",
+    "s931",
+    "s932",
+    "s933",
+    "s934",
+    "s935",
+    "s936",
+    "s937",
+    "s938",
+    "s939",
+    "s940",
+    "s941",
+    "s942",
+    "s943",
+    "s944",
+    "s945",
+    "s946",
+    "s947",
+    "s948",
+    "s949",
+    "s950",
+    "s951",
+    "s952",
+    "s953",
+    "s954",
+    "s955",
+    "s956",
+    "s957",
+    "s958",
+    "s959",
+    "s960",
+    "s961",
+    "s962",
+    "s963",
+    "s964",
+    "s965",
+    "s966",
+    "s967",
+    "s968",
+    "s969",
+    "s970",
+    "s971",
+    "s972",
+    "s973",
+    "s974",
+    "s975",
+    "s976",
+    "s977",
+    "s978",
+    "s979",
+    "s980",
+    "s981",
+    "s982",
+    "s983",
+    "s984",
+    "s985",
+    "s986",
+    "s987",
+    "s988",
+    "s989",
+    "s990",
+    "s991",
+    "s992",
+    "s993",
+    "s994",
+    "s995",
+    "s996",
+    "s997",
+    "s998",
+    "s999",
+    "<eop>",
+    "<eog>",
+    "<|begin_of_sentence|>",
+    "<|end_of_sentence|>",
+    "<|User|>",
+    "<|Assistant|>",
+    "<think>",
+    "</think>",
+    "<search_result>",
+    "</search_result>",
+    "<search_query>",
+    "</search_query>",
+    "<code_query>",
+    "</code_query>",
+    "<code_result>",
+    "</code_result>",
+    "<infer>",
+    "</infer>",
+    "<inferresult>",
+    "</inferresult>",
+    "<tool_calls>",
+    "</tool_calls>",
+    "<tool_response>",
+    "</tool_response>",
+    "<final_answer>",
+    "</final_answer>"
+  ],
+  "bos_token": "<s>",
+  "pad_token": null,
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<eod>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "max_token_id": 136000,
+  "chat_template": "{% for message in messages if message.role == 'user' and message.content is iterable and message.content is not string %}\n    {% for item in message.content if item.type == 'image' %}\n        {{- '<image>' -}}\n    {% endfor %}\n{% endfor %}\n\n{{- '<|begin_of_sentence|>' -}}\n\n{%- set system_message = namespace(value=none) -%}\n{%- for message in messages if message.role == 'system' -%}\n    {%- set system_message.value = message.content -%}\n{%- endfor -%}\n{%- if system_message.value -%}\n    {{- system_message.value -}}\n{%- endif -%}\n\n{%- for message in messages -%}\n    {%- if message.role == \"user\" -%}\n        {{- '<|User|>' -}}\n        {%- if message.content is string -%}\n            {{- message.content -}}\n        {%- elif message.content is iterable and message.content is not string -%}\n            {%- for item in message.content if item.type == \"text\" -%}\n                {{- item.text -}}\n            {%- endfor -%}\n        {%- endif -%}\n\n    {%- elif message.role == \"assistant\" -%}\n        {%- set thinking_tag = \"\" -%}\n        {%- if enable_thinking is defined -%}\n            {%- set thinking_tag = \"</think>\" if not enable_thinking else \"<think>\" -%}\n        {%- endif -%}\n        {{- '<|Assistant|>' + thinking_tag -}}\n\n        {%- if message.content is string -%}\n            {{- message.content -}}\n        {%- elif message.content is iterable and message.content is not string -%}\n            {%- for item in message.content if item.type == \"text\" -%}\n                {{- item.text -}}\n            {%- endfor -%}\n        {%- endif -%}\n\n        {{- '<|end_of_sentence|>' -}}\n    {%- endif -%}\n{%- endfor -%}\n\n{%- if add_generation_prompt -%}\n    {{- '<|Assistant|>' -}}\n    {%- if enable_thinking is defined -%}\n        {{- \"</think>\" if not enable_thinking else \"<think>\" -}}\n    {%- endif -%}\n{%- endif -%}"
+}

utils.py

@@ -0,0 +1,144 @@
+from typing import (Callable, Dict, Iterable, List, Literal, Mapping, Optional, Protocol, Set, Tuple, Union, overload)
+import torch
+from torch.func import functional_call
+
+@overload
+def flatten_bn(x: torch.Tensor) -> torch.Tensor:
+    ...
+
+
+@overload
+def flatten_bn(x: List[torch.Tensor]) -> List[torch.Tensor]:
+    ...
+
+
+@overload
+def flatten_bn(
+    x: Union[List[torch.Tensor], torch.Tensor],
+    *,
+    concat: Literal[True],
+) -> torch.Tensor:
+    ...
+
+
+@overload
+def flatten_bn(
+    x: Union[List[torch.Tensor], torch.Tensor],
+    *,
+    concat: bool = False,
+) -> Union[List[torch.Tensor], torch.Tensor]:
+    ...
+
+
+def flatten_bn(
+    x: Union[List[torch.Tensor], torch.Tensor],
+    *,
+    concat: bool = False,
+) -> Union[List[torch.Tensor], torch.Tensor]:
+    """
+    Flatten the ``B`` and ``N`` dimensions of batched multimodal inputs.
+
+    The input tensor should have shape ``(B, N, ...)```.
+    """
+    if isinstance(x, torch.Tensor):
+        return x.flatten(0, 1)
+
+    if concat:
+        return torch.cat(x)
+
+    return [x_n for x_b in x for x_n in x_b]
+
+def _flatten_embeddings(embeddings: torch.Tensor) -> torch.Tensor:
+    """
+    Recursively flattens and concatenates NestedTensors on all but the last
+    dimension.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        # Flatten all but the last dimension.
+        return embeddings.flatten(0, -2)
+
+    return torch.cat(tuple(_flatten_embeddings(t) for t in embeddings))
+
+def _embedding_count_expression(embeddings: torch.Tensor) -> str:
+    """
+    Constructs a debugging representation of the number of embeddings in the
+    Tensors.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        return " x ".join([str(dim) for dim in embeddings.shape[:-1]])
+
+    return " + ".join(
+        _embedding_count_expression(inner) for inner in embeddings)
+
+def _merge_multimodal_embeddings(
+    inputs_embeds: torch.Tensor,
+    is_multimodal: torch.Tensor,
+    multimodal_embeddings: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
+    positions in ``inputs_embeds`` corresponding to placeholder tokens in
+    ``input_ids``.
+
+    Note:
+        This updates ``inputs_embeds`` in place.
+    """
+    num_expected_tokens = is_multimodal.sum().item()
+    assert isinstance(num_expected_tokens, int)
+    # [total_patches, text_config.hidden_size]
+    flattened = _flatten_embeddings(multimodal_embeddings)
+    if flattened.shape[0] != num_expected_tokens:
+        expr = _embedding_count_expression(multimodal_embeddings)
+        raise ValueError(
+            f"Attempted to assign {expr} = {flattened.shape[0]} "
+            f"multimodal tokens to {num_expected_tokens} placeholders")
+
+    inputs_embeds[is_multimodal] = flattened
+    return inputs_embeds
+
+def merge_multimodal_embeddings(
+    input_ids: torch.Tensor,
+    inputs_embeds: torch.Tensor,
+    multimodal_embeddings: torch.Tensor,
+    placeholder_token_id: Union[int, List[int]],
+) -> torch.Tensor:
+    """
+    Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
+    positions in ``inputs_embeds`` corresponding to placeholder tokens in
+    ``input_ids``.
+
+    ``placeholder_token_id`` can be a list of token ids (e.g, token ids
+    of img_start, img_break, and img_end tokens) when needed: This means
+    the order of these tokens in the ``input_ids`` MUST MATCH the order of
+    their embeddings in ``multimodal_embeddings`` since we need to
+    slice-merge instead of individually scattering.
+
+    For example, if input_ids is "TTTTTSIIIBIIIBIIIETTT", where
+    - T is text token
+    - S is image start token
+    - I is image embedding token
+    - B is image break token
+    - E is image end token.
+
+    Then the image embeddings (that correspond to I's) from vision encoder
+    must be padded with embeddings of S, B, and E in the same order of
+    input_ids for a correct embedding merge.
+
+    Note:
+        This updates ``inputs_embeds`` in place.
+    """
+    if isinstance(placeholder_token_id, list):
+        placeholder_token_id = torch.tensor(placeholder_token_id,
+                                            device=input_ids.device)
+        return _merge_multimodal_embeddings(
+            inputs_embeds,
+            torch.isin(input_ids, placeholder_token_id),
+            multimodal_embeddings,
+        )
+    return _merge_multimodal_embeddings(
+        inputs_embeds,
+        (input_ids == placeholder_token_id),
+        multimodal_embeddings,
+    )