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# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch.utils.checkpoint
import transformers
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import AutoModel, GenerationConfig, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput, logging
from .configuration_internvl_chat import InternVLChatConfig
from .conversation import get_conv_template
from .modeling_intern_vit import InternVisionModel
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'
_no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer']
def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None):
super().__init__(config)
assert version_cmp(transformers.__version__, '4.36.2', '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
logger.info(f'num_image_token: {self.num_image_token}')
logger.info(f'ps_version: {self.ps_version}')
if vision_model is not None:
self.vision_model = vision_model
else:
self.vision_model = InternVisionModel(config.vision_config)
if language_model is not None:
self.language_model = language_model
else:
if config.llm_config.architectures[0] == 'LlamaForCausalLM':
self.language_model = LlamaForCausalLM(config.llm_config)
else:
raise NotImplementedError(f'{config.llm_config.architectures[0]} 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
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)
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.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 = selected.sum()
input_embeds[selected] = input_embeds[selected] * 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.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'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
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)[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)
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
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'].cuda()
attention_mask = model_inputs['attention_mask'].cuda()
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)[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,
return_dict: 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,
return_dict=return_dict,
use_cache=True,
**generate_kwargs,
)
return outputs
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