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from typing import List, Optional, Tuple
import numpy as np
import torch
from PIL import Image
from transformers import GenerationMixin, PreTrainedModel, PreTrainedTokenizer
try:
from transformers.models.qwen2_vl.image_processing_qwen2_vl import ( # noqa
Qwen2VLImageProcessor,
)
from transformers.models.qwen2_vl.modeling_qwen2_vl import PatchMerger
except ImportError:
print('Please upgrade transformers to version 4.46.3 or higher')
from .configuration_pointsv15_chat import POINTSV15ChatConfig
from .modeling_llama import CustomLlamaForCausalLM
try:
from wepoints.models import Qwen2VisionTransformerForNavitPOINTS
except ImportError:
print('Please install WePOINTS, and refer to https://github.com/WePOINTS/WePOINTS')
class POINTSV15ChatModel(PreTrainedModel, GenerationMixin):
config_class = POINTSV15ChatConfig
_no_split_modules = ["CustomLlamaLayer",
"Qwen2VisionTransformerPretrainedModel"]
"""Chat model for POINTSv1.5.
Args:
config (POINTSChatConfigV15): The model config.
"""
def __init__(self, config: POINTSV15ChatConfig) -> None:
super().__init__(config)
self.llm = CustomLlamaForCausalLM(config.llm_config)
self.vision_encoder = Qwen2VisionTransformerForNavitPOINTS._from_config( # noqa
config.vision_config, attn_implementation="flash_attention_2"
)
self.vision_projector = PatchMerger(config.llm_config.hidden_size,
context_dim=1280)
def process_images(self, images: torch.Tensor,
image_grid_thws: List[list]) -> torch.Tensor:
"""Obtain image features from the vision encoder.
Args:
images (torch.Tensor): The input images.
image_grid_thws (List[list]): The grid thresholds for the images.
Returns:
torch.Tensor: The image features.
"""
image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
image_features = self.vision_projector(image_features)
return image_features
def construct_prompt(self, messages: List[dict],
image_processor: Qwen2VLImageProcessor) -> Tuple[str, List[Image.Image], List[list]]: # noqa
"""Construct the prompt for the chat model.
Args:
messages (List[dict]): The input messages.
Returns:
Tuple[str, List[Image.Image], List[list]]:
The prompt, images, and image grid shape.
"""
images = []
image_grid_thws = []
reconstructed_messages = []
for message in messages:
role = message['role']
content_from_role = ''
for item in message['content']:
if item['type'] == 'text':
content_from_role += item['text']
elif item['type'] == 'image':
image_path = item['image']
image = Image.open(image_path).convert('RGB')
image_data = image_processor(images=image)
pixel_values = image_data['pixel_values']
image_grid_thw = image_data['image_grid_thw']
images.extend(pixel_values)
image_grid_thws.append(image_grid_thw)
seq_len = int(image_grid_thw[0][1] * image_grid_thw[0][2] / 4) # noqa
content_from_role += '<|vision_start|>' + '<|image_pad|>' * seq_len + '<|vision_end|>' + '\n' # noqa
reconstructed_messages.append({
'role': role,
'content': content_from_role
})
prompt = self.apply_chat_template(reconstructed_messages)
return prompt, images, image_grid_thws
def apply_chat_template(self, messages: List[dict]) -> str:
"""Apply the chat template to the input messages.
Args:
messages (List[dict]): The input messages.
Returns:
str: The prompt.
"""
role_prefix_mapping = {
'user': '<|im_start|>user\n',
'assistant': '<|im_start|>assistant\n'
}
role = 'user'
prompt = ''
for message in messages:
role = message['role']
content = message['content']
prompt += role_prefix_mapping[role] + content + '<|im_end|>\n'
if role == 'user':
prompt += '<|im_start|>assistant\n'
return prompt
@torch.no_grad()
def chat(self,
messages: List[dict],
tokenizer: PreTrainedTokenizer,
image_processor: object,
generation_config: dict = None) -> str:
"""Generate a response to the input prompt.
Args:
messages (List[dict]): The input messages.
tokenizer (PreTrainedTokenizer): The tokenizer to use.
image_processor (object): The image processor to use.
generation_config (dict, optional): The generation config.
Defaults to None.
Returns:
str: The generated response.
"""
prompt, images, image_grid_thws = self.construct_prompt(
messages, image_processor
)
images = np.array(images)
images = torch.from_numpy(images).to(self.vision_encoder.device).to(self.vision_encoder.dtype) # noqa
image_grid_thws = np.concatenate(image_grid_thws, axis=0)
image_grid_thws = (
torch.from_numpy(image_grid_thws)
.cuda()
.long()
)
image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
image_features = self.vision_projector(image_features)
model_inputs = tokenizer(prompt, return_tensors='pt')
input_ids = model_inputs['input_ids'].to(self.device)
attention_mask = model_inputs['attention_mask'].to(self.device)
# stop token
eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
# image token
image_token_id = tokenizer.convert_tokens_to_ids("<|image_pad|>")
generation_config.update(
{
'eos_token_id': eos_token_id,
}
)
outputs = self.generate(
input_ids=input_ids,
image_grid_thws=image_grid_thws,
attention_mask=attention_mask,
image_features=[image_features],
image_token_id=image_token_id,
**generation_config
)
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
return response
def _split_input_ids(self, input_ids, special_token):
special_pos = input_ids == special_token
pos = (special_pos[:-1] != special_pos[1:]).nonzero() + 1
if pos.shape[0] % 2 != 0:
pos = torch.cat([torch.tensor([[0]]).to(pos.device), pos])
pos = pos.reshape(-1, 2).tolist()
return pos
def generate(self,
input_ids: torch.LongTensor,
image_grid_thws: torch.LongTensor,
attention_mask: torch.LongTensor,
image_features: List[torch.Tensor],
image_token_id: int,
generation_config: Optional[dict] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**generate_kwargs) -> torch.LongTensor:
input_embeddings = self.llm.lm.embed_in(input_ids)
batch_size = input_ids.shape[0]
assert len(image_features) == batch_size
for i in range(batch_size):
pos = self._split_input_ids(input_ids[i], image_token_id)
assert len(pos) == len(image_grid_thws)
image_pos = [
int(image_grid_thw[1] * image_grid_thw[2] / 4)
for image_grid_thw in image_grid_thws
]
image_pos.insert(0, 0)
image_pos = np.cumsum(image_pos)
for j, (start, end) in enumerate(pos):
input_embeddings[i, start:end] = \
image_features[i][image_pos[j]:image_pos[j+1]]
outputs = self.llm.generate(
inputs_embeds=input_embeddings,
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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