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Model: sealofyou/InternVVL3_5-Lora-SFT-3750-steps-baseline Source: Original Platform
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runs/
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images/
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added_tokens.json
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{
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"</box>": 151677,
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}
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chat_template.jinja
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{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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'}}{% if message['content'] is string %}{{ message['content'] }}{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' %}{{ '<image>
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' }}{% elif content['type'] == 'video' %}{{ '<video>
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' }}{% elif content['type'] == 'text' %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}{{'<|im_end|>
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'}}{% endfor %}{% if add_generation_prompt %}{{'<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"InternVLChatModel"
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],
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"auto_map": {
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"AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
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"AutoModel": "modeling_internvl_chat.InternVLChatModel",
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"AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
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},
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"downsample_ratio": 0.5,
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"dtype": "bfloat16",
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"dynamic_image_size": true,
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"eos_token_id": 151645,
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"force_image_size": 448,
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"hidden_size": 1024,
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"keys_to_ignore_at_inference": [
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"past_key_values"
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],
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"llm_config": {
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"_name_or_path": "/root/codespace/checkpoints/Qwen3-0.6B",
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"debug": false,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"ep_size": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"micro_forward": false,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"skip_checkpoint": false,
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"sliding_window": null,
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"use_cache": false,
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"use_deepep": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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},
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"max_dynamic_patch": 12,
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"min_dynamic_patch": 1,
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"model_type": "internvl_chat",
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"output_attentions": false,
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"pad2square": false,
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"pad_token_id": 151643,
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"ps_version": "v2",
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"select_layer": -1,
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"template": "internvl2_5",
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"tie_word_embeddings": false,
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"transformers_version": null,
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"use_backbone_lora": 0,
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"use_llm_lora": 0,
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"use_thumbnail": true,
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"vision_config": {
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"architectures": [
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"InternVisionModel"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_intern_vit.InternVisionConfig",
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"AutoModel": "modeling_intern_vit.InternVisionModel"
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},
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"drop_path_rate": 0.1,
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"dropout": 0.0,
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"dtype": "bfloat16",
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"hidden_act": "gelu",
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"hidden_size": 1024,
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"image_size": 448,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-06,
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"model_type": "intern_vit_6b",
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"norm_type": "layer_norm",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"pad_token_id": 151643,
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"patch_size": 14,
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"qk_normalization": false,
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"qkv_bias": true,
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"use_fa3": false,
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"use_flash_attn": false
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}
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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configuration_intern_vit.py
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configuration_intern_vit.py
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# --------------------------------------------------------
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# InternVL
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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import os
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from typing import Union
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class InternVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
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instantiate a vision encoder according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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num_channels (`int`, *optional*, defaults to 3):
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Number of color channels in the input images (e.g., 3 for RGB).
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether to add a bias to the queries and values in the self-attention layers.
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hidden_size (`int`, *optional*, defaults to 3200):
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Dimensionality of the encoder layers and the pooler layer.
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num_attention_heads (`int`, *optional*, defaults to 25):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 12800):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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qk_normalization (`bool`, *optional*, defaults to `True`):
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Whether to normalize the queries and keys in the self-attention layers.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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use_flash_attn (`bool`, *optional*, defaults to `True`):
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Whether to use flash attention mechanism.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Dropout rate for stochastic depth.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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initializer_factor (`float`, *optional*, defaults to 0.1):
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A factor for layer scale.
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"""
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model_type = 'intern_vit_6b'
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def __init__(
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self,
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num_channels=3,
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patch_size=14,
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image_size=224,
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qkv_bias=False,
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hidden_size=3200,
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num_attention_heads=25,
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intermediate_size=12800,
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qk_normalization=True,
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num_hidden_layers=48,
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use_flash_attn=True,
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hidden_act='gelu',
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norm_type='rms_norm',
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layer_norm_eps=1e-6,
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dropout=0.0,
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drop_path_rate=0.0,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=0.1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.drop_path_rate = drop_path_rate
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.norm_type = norm_type
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self.qkv_bias = qkv_bias
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self.qk_normalization = qk_normalization
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self.use_flash_attn = use_flash_attn
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
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||||||
|
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)
|
||||||
115
configuration_internvl_chat.py
Normal file
115
configuration_internvl_chat.py
Normal file
@@ -0,0 +1,115 @@
|
|||||||
|
# --------------------------------------------------------
|
||||||
|
# InternVL
|
||||||
|
# Copyright (c) 2024 OpenGVLab
|
||||||
|
# Licensed under The MIT License [see LICENSE for details]
|
||||||
|
# --------------------------------------------------------
|
||||||
|
|
||||||
|
import copy
|
||||||
|
from typing import Dict, Any, Optional
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
from .configuration_intern_vit import InternVisionConfig
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class InternVLChatConfig(PretrainedConfig):
|
||||||
|
model_type = 'internvl_chat'
|
||||||
|
is_composition = True
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vision_config: Optional[Dict[str, Any]] = None,
|
||||||
|
llm_config: Optional[Dict[str, Any]] = 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,
|
||||||
|
**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': ['Qwen2ForCausalLM']}
|
||||||
|
logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
|
||||||
|
assert 'architectures' in llm_config, "Should specify architecture in llm_config"
|
||||||
|
|
||||||
|
if isinstance(vision_config, dict):
|
||||||
|
self.vision_config = InternVisionConfig(**vision_config)
|
||||||
|
else:
|
||||||
|
self.vision_config = vision_config
|
||||||
|
|
||||||
|
if isinstance(llm_config, dict):
|
||||||
|
architecture: str = llm_config['architectures'][0]
|
||||||
|
if architecture == 'LlamaForCausalLM':
|
||||||
|
from transformers import LlamaConfig
|
||||||
|
self.llm_config = LlamaConfig(**llm_config)
|
||||||
|
elif architecture == 'Qwen2ForCausalLM':
|
||||||
|
from transformers import Qwen2Config
|
||||||
|
self.llm_config = Qwen2Config(**llm_config)
|
||||||
|
elif architecture == 'Qwen3MoeForCausalLM':
|
||||||
|
from transformers import Qwen3MoeConfig
|
||||||
|
self.llm_config = Qwen3MoeConfig(**llm_config)
|
||||||
|
elif architecture == 'Qwen3ForCausalLM':
|
||||||
|
from transformers import Qwen3Config
|
||||||
|
self.llm_config = Qwen3Config(**llm_config)
|
||||||
|
else:
|
||||||
|
raise ValueError('Unsupported architecture: {}'.format(architecture))
|
||||||
|
else:
|
||||||
|
self.llm_config = llm_config
|
||||||
|
|
||||||
|
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.tie_word_embeddings = self.llm_config.tie_word_embeddings
|
||||||
|
|
||||||
|
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
|
||||||
391
conversation.py
Normal file
391
conversation.py
Normal file
@@ -0,0 +1,391 @@
|
|||||||
|
"""
|
||||||
|
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',
|
||||||
|
)
|
||||||
|
)
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645
|
||||||
|
],
|
||||||
|
"transformers_version": "4.56.1"
|
||||||
|
}
|
||||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1a6af56d1c56ce86652e2bcd75a1abb042ab547f997608dbc5858ae227018cbb
|
||||||
|
size 2121876280
|
||||||
433
modeling_intern_vit.py
Normal file
433
modeling_intern_vit.py
Normal file
@@ -0,0 +1,433 @@
|
|||||||
|
# --------------------------------------------------------
|
||||||
|
# InternVL
|
||||||
|
# Copyright (c) 2024 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.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_attn.bert_padding import pad_input, unpad_input
|
||||||
|
from flash_attn.flash_attn_interface import \
|
||||||
|
flash_attn_varlen_qkvpacked_func
|
||||||
|
has_flash_attn = True
|
||||||
|
except:
|
||||||
|
print('FlashAttention2 is not installed.')
|
||||||
|
has_flash_attn = False
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
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_varlen_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_varlen_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_varlen_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
|
||||||
|
|
||||||
|
|
||||||
|
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
|
||||||
|
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).to(hidden_states.dtype)) * self.ls1)
|
||||||
|
|
||||||
|
hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * 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
|
||||||
|
|
||||||
|
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'
|
||||||
|
_supports_flash_attn_2 = True
|
||||||
|
supports_gradient_checkpointing = True
|
||||||
|
config_class = InternVisionConfig
|
||||||
|
_no_split_modules = ['InternVisionEncoderLayer']
|
||||||
|
# support transformers 4.51.+
|
||||||
|
_tp_plan = ''
|
||||||
|
|
||||||
|
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,
|
||||||
|
)
|
||||||
376
modeling_internvl_chat.py
Normal file
376
modeling_internvl_chat.py
Normal file
@@ -0,0 +1,376 @@
|
|||||||
|
# --------------------------------------------------------
|
||||||
|
# InternVL
|
||||||
|
# Copyright (c) 2024 OpenGVLab
|
||||||
|
# Licensed under The MIT License [see LICENSE for details]
|
||||||
|
# --------------------------------------------------------
|
||||||
|
|
||||||
|
import warnings
|
||||||
|
from typing import List, Optional, Tuple, Union
|
||||||
|
|
||||||
|
import torch.utils.checkpoint
|
||||||
|
import transformers
|
||||||
|
from torch import nn
|
||||||
|
from torch.nn import CrossEntropyLoss
|
||||||
|
from transformers import GenerationConfig
|
||||||
|
from transformers.modeling_outputs import CausalLMOutputWithPast
|
||||||
|
from transformers.modeling_utils import PreTrainedModel
|
||||||
|
from transformers.utils import logging
|
||||||
|
from transformers import LlamaForCausalLM, Qwen2ForCausalLM, Qwen3ForCausalLM, Qwen3MoeForCausalLM
|
||||||
|
|
||||||
|
from .configuration_internvl_chat import InternVLChatConfig
|
||||||
|
from .conversation import get_conv_template
|
||||||
|
from .modeling_intern_vit import InternVisionModel, has_flash_attn
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
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 InternVLChatModel(PreTrainedModel):
|
||||||
|
config_class = InternVLChatConfig
|
||||||
|
main_input_name = 'pixel_values'
|
||||||
|
base_model_prefix = 'language_model'
|
||||||
|
_supports_flash_attn_2 = True
|
||||||
|
supports_gradient_checkpointing = True
|
||||||
|
_no_split_modules = [
|
||||||
|
"InternVisionModel",
|
||||||
|
"Qwen3DecoderLayer",
|
||||||
|
]
|
||||||
|
|
||||||
|
# support transformers 4.51.+
|
||||||
|
_tp_plan = ''
|
||||||
|
|
||||||
|
def __init__(self, config: InternVLChatConfig, 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:
|
||||||
|
architecture: str = config.llm_config.architectures[0]
|
||||||
|
if architecture == 'LlamaForCausalLM':
|
||||||
|
self.language_model = LlamaForCausalLM(config.llm_config)
|
||||||
|
elif architecture == 'Qwen2ForCausalLM':
|
||||||
|
self.language_model = Qwen2ForCausalLM(config.llm_config)
|
||||||
|
elif architecture == 'Qwen3MoeForCausalLM':
|
||||||
|
self.language_model = Qwen3MoeForCausalLM(config.llm_config)
|
||||||
|
elif architecture == 'Qwen3ForCausalLM':
|
||||||
|
self.language_model = Qwen3ForCausalLM(config.llm_config)
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(f'{architecture} is not implemented.')
|
||||||
|
|
||||||
|
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 = None
|
||||||
|
self.conv_template = get_conv_template(self.template)
|
||||||
|
self.system_message = self.conv_template.system_message
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
pixel_values: torch.FloatTensor,
|
||||||
|
input_ids: torch.LongTensor = None,
|
||||||
|
attention_mask: Optional[torch.Tensor] = None,
|
||||||
|
position_ids: Optional[torch.LongTensor] = None,
|
||||||
|
image_flags: Optional[torch.LongTensor] = None,
|
||||||
|
past_key_values: Optional[List[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]:
|
||||||
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||||
|
|
||||||
|
image_flags = image_flags.squeeze(-1)
|
||||||
|
input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
|
||||||
|
|
||||||
|
vit_embeds = self.extract_feature(pixel_values)
|
||||||
|
vit_embeds = vit_embeds[image_flags == 1]
|
||||||
|
vit_batch_size = pixel_values.shape[0]
|
||||||
|
|
||||||
|
B, N, C = input_embeds.shape
|
||||||
|
input_embeds = input_embeds.reshape(B * N, C)
|
||||||
|
|
||||||
|
# if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
|
||||||
|
# print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
|
||||||
|
|
||||||
|
input_ids = input_ids.reshape(B * N)
|
||||||
|
selected = (input_ids == self.img_context_token_id)
|
||||||
|
try:
|
||||||
|
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
|
||||||
|
except Exception as e:
|
||||||
|
vit_embeds = vit_embeds.reshape(-1, C)
|
||||||
|
print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
|
||||||
|
f'vit_embeds.shape={vit_embeds.shape}')
|
||||||
|
n_token = min(selected.sum(), vit_embeds.size(0))
|
||||||
|
input_embeds[selected][:n_token] = input_embeds[selected][:n_token] * 0.0 + vit_embeds[:n_token]
|
||||||
|
|
||||||
|
input_embeds = input_embeds.reshape(B, N, C)
|
||||||
|
|
||||||
|
outputs = self.language_model(
|
||||||
|
inputs_embeds=input_embeds,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
position_ids=position_ids,
|
||||||
|
past_key_values=past_key_values,
|
||||||
|
use_cache=use_cache,
|
||||||
|
output_attentions=output_attentions,
|
||||||
|
output_hidden_states=output_hidden_states,
|
||||||
|
return_dict=return_dict,
|
||||||
|
)
|
||||||
|
logits = outputs.logits
|
||||||
|
|
||||||
|
loss = None
|
||||||
|
if labels is not None:
|
||||||
|
# Shift so that tokens < n predict n
|
||||||
|
shift_logits = logits[..., :-1, :].contiguous()
|
||||||
|
shift_labels = labels[..., 1:].contiguous()
|
||||||
|
# Flatten the tokens
|
||||||
|
loss_fct = CrossEntropyLoss()
|
||||||
|
shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
|
||||||
|
shift_labels = shift_labels.view(-1)
|
||||||
|
# Enable model parallelism
|
||||||
|
shift_labels = shift_labels.to(shift_logits.device)
|
||||||
|
loss = loss_fct(shift_logits, shift_labels)
|
||||||
|
|
||||||
|
if not return_dict:
|
||||||
|
output = (logits,) + outputs[1:]
|
||||||
|
return (loss,) + output if loss is not None else output
|
||||||
|
|
||||||
|
return CausalLMOutputWithPast(
|
||||||
|
loss=loss,
|
||||||
|
logits=logits,
|
||||||
|
past_key_values=outputs.past_key_values,
|
||||||
|
hidden_states=outputs.hidden_states,
|
||||||
|
attentions=outputs.attentions,
|
||||||
|
)
|
||||||
|
|
||||||
|
def 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
|
||||||
|
|
||||||
|
def extract_feature(self, pixel_values):
|
||||||
|
if self.select_layer == -1:
|
||||||
|
vit_embeds = self.vision_model(
|
||||||
|
pixel_values=pixel_values,
|
||||||
|
output_hidden_states=False,
|
||||||
|
return_dict=True).last_hidden_state
|
||||||
|
else:
|
||||||
|
vit_embeds = self.vision_model(
|
||||||
|
pixel_values=pixel_values,
|
||||||
|
output_hidden_states=True,
|
||||||
|
return_dict=True).hidden_states[self.select_layer]
|
||||||
|
vit_embeds = vit_embeds[:, 1:, :]
|
||||||
|
|
||||||
|
h = w = int(vit_embeds.shape[1] ** 0.5)
|
||||||
|
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
|
||||||
|
vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
|
||||||
|
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
|
||||||
|
vit_embeds = self.mlp1(vit_embeds)
|
||||||
|
return vit_embeds
|
||||||
|
|
||||||
|
def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
|
||||||
|
history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
|
||||||
|
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
|
||||||
|
if history is not None or return_history:
|
||||||
|
print('Now multi-turn chat is not supported in batch_chat.')
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
if image_counts is not None:
|
||||||
|
num_patches_list = image_counts
|
||||||
|
print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
|
||||||
|
|
||||||
|
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
||||||
|
self.img_context_token_id = img_context_token_id
|
||||||
|
|
||||||
|
if verbose and pixel_values is not None:
|
||||||
|
image_bs = pixel_values.shape[0]
|
||||||
|
print(f'dynamic ViT batch size: {image_bs}')
|
||||||
|
|
||||||
|
queries = []
|
||||||
|
for idx, num_patches in enumerate(num_patches_list):
|
||||||
|
question = questions[idx]
|
||||||
|
if pixel_values is not None and '<image>' not in question:
|
||||||
|
question = '<image>\n' + question
|
||||||
|
template = get_conv_template(self.template)
|
||||||
|
template.system_message = self.system_message
|
||||||
|
template.append_message(template.roles[0], question)
|
||||||
|
template.append_message(template.roles[1], None)
|
||||||
|
query = template.get_prompt()
|
||||||
|
|
||||||
|
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
|
||||||
|
query = query.replace('<image>', image_tokens, 1)
|
||||||
|
queries.append(query)
|
||||||
|
|
||||||
|
tokenizer.padding_side = 'left'
|
||||||
|
model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
|
||||||
|
input_ids = model_inputs['input_ids'].to(self.device)
|
||||||
|
attention_mask = model_inputs['attention_mask'].to(self.device)
|
||||||
|
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
|
||||||
|
generation_config['eos_token_id'] = eos_token_id
|
||||||
|
generation_output = self.generate(
|
||||||
|
pixel_values=pixel_values,
|
||||||
|
input_ids=input_ids,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
**generation_config
|
||||||
|
)
|
||||||
|
responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
|
||||||
|
responses = [response.split(template.sep.strip())[0].strip() for response in responses]
|
||||||
|
return responses
|
||||||
|
|
||||||
|
def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
|
||||||
|
num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
|
||||||
|
verbose=False):
|
||||||
|
|
||||||
|
if history is None and pixel_values is not None and '<image>' not in question:
|
||||||
|
question = '<image>\n' + question
|
||||||
|
|
||||||
|
if num_patches_list is None:
|
||||||
|
num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
|
||||||
|
assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
|
||||||
|
|
||||||
|
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
||||||
|
self.img_context_token_id = img_context_token_id
|
||||||
|
|
||||||
|
template = get_conv_template(self.template)
|
||||||
|
template.system_message = self.system_message
|
||||||
|
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
|
||||||
|
|
||||||
|
history = [] if history is None else history
|
||||||
|
for (old_question, old_answer) in history:
|
||||||
|
template.append_message(template.roles[0], old_question)
|
||||||
|
template.append_message(template.roles[1], old_answer)
|
||||||
|
template.append_message(template.roles[0], question)
|
||||||
|
template.append_message(template.roles[1], None)
|
||||||
|
query = template.get_prompt()
|
||||||
|
|
||||||
|
if verbose and pixel_values is not None:
|
||||||
|
image_bs = pixel_values.shape[0]
|
||||||
|
print(f'dynamic ViT batch size: {image_bs}')
|
||||||
|
|
||||||
|
for num_patches in num_patches_list:
|
||||||
|
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
|
||||||
|
query = query.replace('<image>', image_tokens, 1)
|
||||||
|
|
||||||
|
model_inputs = tokenizer(query, return_tensors='pt')
|
||||||
|
input_ids = model_inputs['input_ids'].to(self.device)
|
||||||
|
attention_mask = model_inputs['attention_mask'].to(self.device)
|
||||||
|
generation_config['eos_token_id'] = eos_token_id
|
||||||
|
generation_output = self.generate(
|
||||||
|
pixel_values=pixel_values,
|
||||||
|
input_ids=input_ids,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
**generation_config
|
||||||
|
)
|
||||||
|
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
|
||||||
|
response = response.split(template.sep.strip())[0].strip()
|
||||||
|
history.append((question, response))
|
||||||
|
if return_history:
|
||||||
|
return response, history
|
||||||
|
else:
|
||||||
|
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
|
||||||
|
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
|
||||||
|
if verbose:
|
||||||
|
print(query_to_print, response)
|
||||||
|
return response
|
||||||
|
|
||||||
|
@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,
|
||||||
|
output_hidden_states: Optional[bool] = None,
|
||||||
|
**generate_kwargs,
|
||||||
|
) -> torch.LongTensor:
|
||||||
|
|
||||||
|
assert self.img_context_token_id is not None
|
||||||
|
if pixel_values is not None:
|
||||||
|
if visual_features is not None:
|
||||||
|
vit_embeds = visual_features
|
||||||
|
else:
|
||||||
|
vit_embeds = self.extract_feature(pixel_values)
|
||||||
|
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
||||||
|
B, N, C = input_embeds.shape
|
||||||
|
input_embeds = input_embeds.reshape(B * N, C)
|
||||||
|
|
||||||
|
input_ids = input_ids.reshape(B * N)
|
||||||
|
selected = (input_ids == self.img_context_token_id)
|
||||||
|
assert selected.sum() != 0
|
||||||
|
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
|
||||||
|
|
||||||
|
input_embeds = input_embeds.reshape(B, N, C)
|
||||||
|
else:
|
||||||
|
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
||||||
|
|
||||||
|
outputs = self.language_model.generate(
|
||||||
|
inputs_embeds=input_embeds,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
generation_config=generation_config,
|
||||||
|
output_hidden_states=output_hidden_states,
|
||||||
|
use_cache=True,
|
||||||
|
**generate_kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
@property
|
||||||
|
def lm_head(self):
|
||||||
|
return self.language_model.get_output_embeddings()
|
||||||
|
|
||||||
|
def get_output_embeddings(self):
|
||||||
|
return self.language_model.get_output_embeddings()
|
||||||
|
|
||||||
|
def get_input_embeddings(self):
|
||||||
|
return self.language_model.get_input_embeddings()
|
||||||
|
|
||||||
|
def set_input_embeddings(self, value):
|
||||||
|
return self.language_model.set_input_embeddings(value)
|
||||||
|
|
||||||
|
def set_output_embeddings(self, value):
|
||||||
|
return self.language_model.set_output_embeddings(value)
|
||||||
34
preprocessor_config.json
Normal file
34
preprocessor_config.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"crop_size": null,
|
||||||
|
"crop_to_patches": false,
|
||||||
|
"data_format": "channels_first",
|
||||||
|
"default_to_square": true,
|
||||||
|
"device": null,
|
||||||
|
"do_center_crop": null,
|
||||||
|
"do_convert_rgb": true,
|
||||||
|
"do_normalize": true,
|
||||||
|
"do_rescale": true,
|
||||||
|
"do_resize": true,
|
||||||
|
"image_mean": [
|
||||||
|
0.485,
|
||||||
|
0.456,
|
||||||
|
0.406
|
||||||
|
],
|
||||||
|
"image_processor_type": "GotOcr2ImageProcessorFast",
|
||||||
|
"image_std": [
|
||||||
|
0.229,
|
||||||
|
0.224,
|
||||||
|
0.225
|
||||||
|
],
|
||||||
|
"input_data_format": null,
|
||||||
|
"max_patches": 12,
|
||||||
|
"min_patches": 1,
|
||||||
|
"processor_class": "InternVLProcessor",
|
||||||
|
"resample": 3,
|
||||||
|
"rescale_factor": 0.00392156862745098,
|
||||||
|
"return_tensors": null,
|
||||||
|
"size": {
|
||||||
|
"height": 448,
|
||||||
|
"width": 448
|
||||||
|
}
|
||||||
|
}
|
||||||
40
special_tokens_map.json
Normal file
40
special_tokens_map.json
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>",
|
||||||
|
"<img>",
|
||||||
|
"</img>",
|
||||||
|
"<IMG_CONTEXT>",
|
||||||
|
"<quad>",
|
||||||
|
"</quad>",
|
||||||
|
"<ref>",
|
||||||
|
"</ref>",
|
||||||
|
"<box>",
|
||||||
|
"</box>"
|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
757561
tokenizer.json
Normal file
757561
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
322
tokenizer_config.json
Normal file
322
tokenizer_config.json
Normal file
@@ -0,0 +1,322 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151665": {
|
||||||
|
"content": "<tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151666": {
|
||||||
|
"content": "</tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151667": {
|
||||||
|
"content": "<think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151668": {
|
||||||
|
"content": "</think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151669": {
|
||||||
|
"content": "<img>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151670": {
|
||||||
|
"content": "</img>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151671": {
|
||||||
|
"content": "<IMG_CONTEXT>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151672": {
|
||||||
|
"content": "<quad>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151673": {
|
||||||
|
"content": "</quad>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151674": {
|
||||||
|
"content": "<ref>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151675": {
|
||||||
|
"content": "</ref>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151676": {
|
||||||
|
"content": "<box>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151677": {
|
||||||
|
"content": "</box>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>",
|
||||||
|
"<img>",
|
||||||
|
"</img>",
|
||||||
|
"<IMG_CONTEXT>",
|
||||||
|
"<quad>",
|
||||||
|
"</quad>",
|
||||||
|
"<ref>",
|
||||||
|
"</ref>",
|
||||||
|
"<box>",
|
||||||
|
"</box>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"model_max_length": 14588,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"processor_class": "InternVLProcessor",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user