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import copy
from typing import Callable, List, Optional, Tuple, Union
import torch
import warnings
from torch import Tensor, nn
from transformers import (
PreTrainedModel,
Blip2VisionModel,
Blip2QFormerModel,
GenerationConfig,
)
from transformers.utils import logging
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
from .modeling_chatglm import (
ChatGLMForConditionalGeneration,
InvalidScoreLogitsProcessor,
)
from .configuration_blip2chatglm import Blip2ChatGLMConfig
logger = logging.get_logger(__name__)
class Blip2ForChatGLM(PreTrainedModel):
def __init__(self, config: Blip2ChatGLMConfig):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(
torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size)
)
self.qformer = Blip2QFormerModel(config.qformer_config)
self.language_projection = nn.Linear(
config.qformer_config.hidden_size, config.text_config.hidden_size
)
def forward(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
vision_outputs = self.vision_model.forward(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[0]
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
image_attention_mask = torch.ones(
image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device
)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_outputs = self.qformer.forward(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
query_output = query_outputs[0]
# step 3: use the language model, conditioned on the query outputs and the prompt
language_model_inputs = self.language_projection.forward(query_output)
return vision_outputs, query_outputs, language_model_inputs
class Blip2ChatGLM(PreTrainedModel):
config_class = Blip2ChatGLMConfig
def __init__(
self,
config: Blip2ChatGLMConfig,
blip2: Blip2ForChatGLM,
lm: ChatGLMForConditionalGeneration,
) -> None:
super().__init__(config)
self.blip2 = blip2
self.language = lm
@torch.no_grad()
def stream_chat(
self,
tokenizer,
query: Union[str, Tuple[str, torch.Tensor]],
history: List[Tuple[Union[str, Tuple[str, torch.Tensor]], str]] = [],
num_beams=5,
max_length=128,
top_p=0.9,
do_sample=True,
temperature=1,
):
device = self.blip2.device
# 1. Prepare token ids
images = []
image_slots = []
nvtokens = self.blip2.query_tokens.size(1)
if history:
input_ids = tokenizer(
f"[Round {len(history)}]\n问:", add_special_tokens=False
).input_ids
slot_offset = len(input_ids)
if isinstance(query, tuple):
qtext, qimg = query
# image slot, embedding will be replaced by image embeddings
input_ids.extend([tokenizer.unk_token_id] * nvtokens)
else:
qtext = query
qimg = None
input_ids += tokenizer(qtext + f"\n答:").input_ids
if qimg is not None:
images.append(qimg)
image_slots.append(len(input_ids) - slot_offset) # count from backward
for ri, (q, r) in enumerate(reversed(history)):
if len(input_ids) >= max_length:
break
i = len(history) - ri - 1
cur_input_ids: List[int] = tokenizer(
f"[Round {i}]\n问:", add_special_tokens=False
).input_ids
slot_offset = len(cur_input_ids)
if isinstance(q, tuple):
qtext, qimg = q
# image slot, embedding will be replaced by image embeddings
cur_input_ids.extend([tokenizer.unk_token_id] * nvtokens)
else:
qtext = q
qimg = None
cur_input_ids += tokenizer(
qtext + f"\n答:{r}\n", add_special_tokens=False
).input_ids
input_ids = cur_input_ids + input_ids
if qimg is not None:
images.append(qimg)
image_slots.append(
len(input_ids) - slot_offset
) # count from backward
else:
input_ids = []
if isinstance(query, tuple):
qtext, qimg = query
# image slot, embedding will be replaced by image embeddings
input_ids.extend([tokenizer.unk_token_id] * nvtokens)
else:
qtext = query
qimg = None
input_ids += tokenizer(qtext).input_ids
if qimg is not None:
images.append(qimg)
image_slots.append(len(input_ids)) # count from backward
if len(input_ids) >= max_length:
# truncate
if image_slots[-1] > max_length and image_slots[-1] - nvtokens < max_length:
# A non-intact image slot is not allowed
input_ids = input_ids[-(image_slots[-1] - nvtokens) :]
else:
input_ids = input_ids[-max_length:]
if image_slots[-1] > max_length:
image_slots.pop()
images.pop()
# 2. Prepare image embeddings
if len(images) != 0:
image = torch.cat(list(images), dim=0)
vision_outputs = self.blip2.vision_model.forward(image)
image_embeds = vision_outputs[0]
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
device
)
query_tokens = self.blip2.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_outputs = self.blip2.qformer.forward(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
)
query_output = query_outputs[0]
vtokens = self.blip2.language_projection(query_output)
else:
vtokens = []
# 3. Place image embeddings into slots
input_ids = torch.as_tensor(input_ids, dtype=torch.long).to(device).unsqueeze(0)
inputs_embeds = self.language.transformer.word_embeddings(input_ids)
for slot, vimg in zip(image_slots, vtokens):
inputs_embeds[0][-slot : -slot + nvtokens, :] = vimg
logits_processor = LogitsProcessorList()
logits_processor.append(InvalidScoreLogitsProcessor())
gen_kwargs = {
"max_length": max_length,
"num_beams": num_beams,
"do_sample": do_sample,
"top_p": top_p,
"temperature": temperature,
"logits_processor": logits_processor,
}
for outputs in self.mm_stream_generate(
input_ids=input_ids, inputs_embeds=inputs_embeds, **gen_kwargs
):
outputs = outputs.tolist()[0][len(input_ids[0]) :]
response = tokenizer.decode(outputs)
response = self.language.process_response(response)
new_history = history + [(query, response)]
yield response, new_history
@torch.no_grad()
def mm_stream_generate(
self,
input_ids,
inputs_embeds,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[
Callable[[int, torch.Tensor], List[int]]
] = None,
**kwargs,
):
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None:
generation_config = self.language.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs)
bos_token_id, eos_token_id = (
generation_config.bos_token_id,
generation_config.eos_token_id,
)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
has_default_max_length = (
kwargs.get("max_length") is None
and generation_config.max_length is not None
)
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
generation_config.max_length = (
generation_config.max_new_tokens + input_ids_seq_length
)
if not has_default_max_length:
logger.warn(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
UserWarning,
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = (
"decoder_input_ids"
if self.language.config.is_encoder_decoder
else "input_ids"
)
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 2. Set generation parameters if not already defined
logits_processor = (
logits_processor if logits_processor is not None else LogitsProcessorList()
)
stopping_criteria = (
stopping_criteria
if stopping_criteria is not None
else StoppingCriteriaList()
)
logits_processor = self.language._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=input_ids,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
stopping_criteria = self.language._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
logits_warper = self.language._get_logits_warper(generation_config)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
scores = None
while True:
model_inputs = self.language.prepare_inputs_for_generation(
input_ids, inputs_embeds=inputs_embeds, **model_kwargs
)
# forward pass to get next token
outputs = self.language(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
if generation_config.do_sample:
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
next_tokens = torch.argmax(probs, dim=-1)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
inputs_embeds = torch.cat(
[
inputs_embeds,
self.language.get_input_embeddings()(next_tokens)[:, None, :],
],
dim=1,
)
model_kwargs = self.language._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder=self.language.config.is_encoder_decoder,
)
unfinished_sequences = unfinished_sequences.mul(
(sum(next_tokens != i for i in eos_token_id)).long()
)
# stop when each sentence is finished, or if we exceed the maximum length
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
break
yield input_ids
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