Update AWQ models
This commit is contained in:
ai-modelscope
349
modeling_internvl_chat.py
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349
modeling_internvl_chat.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 warnings
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from typing import Any, List, Optional, Tuple, Union
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import torch.utils.checkpoint
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import transformers
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
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LlamaTokenizer)
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import ModelOutput, logging
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from .configuration_internvl_chat import InternVLChatConfig
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from .conversation import get_conv_template
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from .modeling_intern_vit import InternVisionModel, has_flash_attn
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from .modeling_phi3 import Phi3ForCausalLM
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logger = logging.get_logger(__name__)
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def version_cmp(v1, v2, op='eq'):
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import operator
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from packaging import version
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op_func = getattr(operator, op)
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return op_func(version.parse(v1), version.parse(v2))
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class InternVLChatModel(PreTrainedModel):
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config_class = InternVLChatConfig
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main_input_name = 'pixel_values'
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base_model_prefix = 'language_model'
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_supports_flash_attn_2 = True
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_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'Phi3DecoderLayer']
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def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
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super().__init__(config)
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assert version_cmp(transformers.__version__, '4.37.0', 'ge')
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image_size = config.force_image_size or config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.patch_size = patch_size
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self.select_layer = config.select_layer
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self.template = config.template
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self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
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self.downsample_ratio = config.downsample_ratio
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self.ps_version = config.ps_version
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use_flash_attn = use_flash_attn if has_flash_attn else False
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config.vision_config.use_flash_attn = True if use_flash_attn else False
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config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
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logger.info(f'num_image_token: {self.num_image_token}')
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logger.info(f'ps_version: {self.ps_version}')
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if vision_model is not None:
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self.vision_model = vision_model
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else:
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self.vision_model = InternVisionModel(config.vision_config)
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if language_model is not None:
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self.language_model = language_model
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else:
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if config.llm_config.architectures[0] == 'LlamaForCausalLM':
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self.language_model = LlamaForCausalLM(config.llm_config)
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elif config.llm_config.architectures[0] == 'Phi3ForCausalLM':
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self.language_model = Phi3ForCausalLM(config.llm_config)
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else:
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raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
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vit_hidden_size = config.vision_config.hidden_size
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llm_hidden_size = config.llm_config.hidden_size
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self.mlp1 = nn.Sequential(
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nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
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nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
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nn.GELU(),
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nn.Linear(llm_hidden_size, llm_hidden_size)
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)
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self.img_context_token_id = None
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self.conv_template = get_conv_template(self.template)
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self.system_message = self.conv_template.system_message
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def forward(
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self,
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pixel_values: torch.FloatTensor,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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image_flags: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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image_flags = image_flags.squeeze(-1)
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input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
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vit_embeds = self.extract_feature(pixel_values)
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vit_embeds = vit_embeds[image_flags == 1]
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vit_batch_size = pixel_values.shape[0]
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B, N, C = input_embeds.shape
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input_embeds = input_embeds.reshape(B * N, C)
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if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
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print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
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input_ids = input_ids.reshape(B * N)
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selected = (input_ids == self.img_context_token_id)
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try:
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input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
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except Exception as e:
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vit_embeds = vit_embeds.reshape(-1, C)
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print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
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f'vit_embeds.shape={vit_embeds.shape}')
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n_token = selected.sum()
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input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
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input_embeds = input_embeds.reshape(B, N, C)
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outputs = self.language_model(
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inputs_embeds=input_embeds,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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logits = outputs.logits
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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def pixel_shuffle(self, x, scale_factor=0.5):
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n, w, h, c = x.size()
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# N, W, H, C --> N, W, H * scale, C // scale
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x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
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# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
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x = x.permute(0, 2, 1, 3).contiguous()
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# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
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x = x.view(n, int(h * scale_factor), int(w * scale_factor),
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int(c / (scale_factor * scale_factor)))
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if self.ps_version == 'v1':
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warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
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'which results in a transposed image.')
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else:
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x = x.permute(0, 2, 1, 3).contiguous()
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return x
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def extract_feature(self, pixel_values):
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if self.select_layer == -1:
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vit_embeds = self.vision_model(
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pixel_values=pixel_values,
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output_hidden_states=False,
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return_dict=True).last_hidden_state
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else:
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vit_embeds = self.vision_model(
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pixel_values=pixel_values,
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output_hidden_states=True,
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return_dict=True).hidden_states[self.select_layer]
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vit_embeds = vit_embeds[:, 1:, :]
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h = w = int(vit_embeds.shape[1] ** 0.5)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
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vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
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vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
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vit_embeds = self.mlp1(vit_embeds)
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return vit_embeds
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def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
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history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
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IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
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if history is not None or return_history:
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print('Now multi-turn chat is not supported in batch_chat.')
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raise NotImplementedError
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if image_counts is not None:
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num_patches_list = image_counts
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print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
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img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
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self.img_context_token_id = img_context_token_id
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if verbose and pixel_values is not None:
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image_bs = pixel_values.shape[0]
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print(f'dynamic ViT batch size: {image_bs}')
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queries = []
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for idx, num_patches in enumerate(num_patches_list):
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question = questions[idx]
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if pixel_values is not None and '<image>' not in question:
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question = '<image>\n' + question
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template = get_conv_template(self.template)
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template.system_message = self.system_message
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template.append_message(template.roles[0], question)
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template.append_message(template.roles[1], None)
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query = template.get_prompt()
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image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
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query = query.replace('<image>', image_tokens, 1)
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queries.append(query)
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tokenizer.padding_side = 'left'
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model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
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input_ids = model_inputs['input_ids'].to(self.device)
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attention_mask = model_inputs['attention_mask'].to(self.device)
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eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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pixel_values=pixel_values,
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input_ids=input_ids,
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attention_mask=attention_mask,
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**generation_config
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)
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responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
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responses = [response.split(template.sep.strip())[0].strip() for response in responses]
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return responses
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def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
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num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
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verbose=False):
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if history is None and pixel_values is not None and '<image>' not in question:
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question = '<image>\n' + question
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if num_patches_list is None:
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num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
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assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
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img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
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self.img_context_token_id = img_context_token_id
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template = get_conv_template(self.template)
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template.system_message = self.system_message
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eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
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history = [] if history is None else history
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for (old_question, old_answer) in history:
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template.append_message(template.roles[0], old_question)
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template.append_message(template.roles[1], old_answer)
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template.append_message(template.roles[0], question)
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template.append_message(template.roles[1], None)
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query = template.get_prompt()
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if verbose and pixel_values is not None:
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image_bs = pixel_values.shape[0]
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print(f'dynamic ViT batch size: {image_bs}')
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for num_patches in num_patches_list:
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image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
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query = query.replace('<image>', image_tokens, 1)
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model_inputs = tokenizer(query, return_tensors='pt')
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input_ids = model_inputs['input_ids'].to(self.device)
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attention_mask = model_inputs['attention_mask'].to(self.device)
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generation_config['eos_token_id'] = eos_token_id
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generation_output = self.generate(
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pixel_values=pixel_values,
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input_ids=input_ids,
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attention_mask=attention_mask,
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**generation_config
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)
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response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
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response = response.split(template.sep.strip())[0].strip()
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history.append((question, response))
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if return_history:
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return response, history
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else:
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query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
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query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
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if verbose:
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print(query_to_print, response)
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return response
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@torch.no_grad()
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def generate(
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self,
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pixel_values: Optional[torch.FloatTensor] = None,
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input_ids: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.LongTensor] = None,
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visual_features: Optional[torch.FloatTensor] = None,
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generation_config: Optional[GenerationConfig] = None,
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output_hidden_states: Optional[bool] = None,
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**generate_kwargs,
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) -> torch.LongTensor:
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assert self.img_context_token_id is not None
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if pixel_values is not None:
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if visual_features is not None:
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vit_embeds = visual_features
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else:
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vit_embeds = self.extract_feature(pixel_values)
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input_embeds = self.language_model.get_input_embeddings()(input_ids)
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B, N, C = input_embeds.shape
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input_embeds = input_embeds.reshape(B * N, C)
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input_ids = input_ids.reshape(B * N)
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selected = (input_ids == self.img_context_token_id)
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assert selected.sum() != 0
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input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
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input_embeds = input_embeds.reshape(B, N, C)
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else:
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input_embeds = self.language_model.get_input_embeddings()(input_ids)
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outputs = self.language_model.generate(
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inputs_embeds=input_embeds,
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attention_mask=attention_mask,
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generation_config=generation_config,
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output_hidden_states=output_hidden_states,
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use_cache=True,
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**generate_kwargs,
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)
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return outputs
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