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from typing import Optional
import torch
import torch.nn as nn
import re
from transformers import PretrainedConfig, Blip2PreTrainedModel, Blip2Config, Blip2QFormerModel
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": 'identity'}
class SimpleResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.pre_norm = nn.LayerNorm(channels)
self.proj = nn.Sequential(
nn.Linear(channels, channels),
nn.GELU(),
nn.Linear(channels, channels)
)
def forward(self, x):
x = self.pre_norm(x)
return x + self.proj(x)
# def build_vision_projector(config, delay_load=False, **kwargs):
# projector_type = getattr(config, 'mm_projector_type', 'linear')
#
# if projector_type == 'linear':
# return nn.Linear(config.mm_hidden_size, config.hidden_size)
#
# mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
# if mlp_gelu_match:
# mlp_depth = int(mlp_gelu_match.group(1))
# modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
# for _ in range(1, mlp_depth):
# modules.append(nn.GELU())
# modules.append(nn.Linear(config.hidden_size, config.hidden_size))
# return nn.Sequential(*modules)
#
# if projector_type == 'identity':
# return IdentityMap()
#
# raise ValueError(f'Unknown projector type: {projector_type}')
class Blip2Model(Blip2PreTrainedModel):
def __init__(self, config: Blip2Config):
super().__init__(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.proj = nn.Linear(config.mm_hidden_size, config.hidden_size)
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size), nn.GELU(), nn.Linear(config.hidden_size, config.hidden_size)]
self.proj = nn.Sequential(*modules)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Returns:
vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`):
The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that
contains the image features, the pooled image features and the hidden states if
`output_hidden_states=True`.
Examples:
```python
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from transformers import Blip2Processor, Blip2Model
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
>>> qformer_outputs = model.get_qformer_features(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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
# vision_outputs = self.vision_model(
# pixel_values=pixel_values,
# output_attentions=output_attentions,
# output_hidden_states=output_hidden_states,
# return_dict=return_dict,
# )
#
# image_embeds = vision_outputs[0]
# image_embeds = self.proj(pixel_values)
image_embeds = pixel_values
# print('pixel_values to proj', pixel_values.shape, image_embeds.shape)
# 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(
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,
).last_hidden_state
# print('qformer out', query_outputs.shape)
query_outputs = self.proj(query_outputs)
return query_outputs
def qformer_config_template(config, projector_type):
pattern = r"qformer(\d+)_(\d+)"
match = re.search(pattern, projector_type)
num_hidden_layers = int(match.group(1))
num_query_tokens = int(match.group(2))
qformer_config = type('Blip2Config', (PretrainedConfig,), {
"initializer_factor": 1.0,
"initializer_range": 0.02,
"model_type": "blip-2",
"num_query_tokens": num_query_tokens,
"hidden_size": config.hidden_size,
"mm_hidden_size": config.mm_hidden_size,
"qformer_config": type('qformer_config', (PretrainedConfig,), {
"_name_or_path": "",
"add_cross_attention": False,
"architectures": None,
"attention_probs_dropout_prob": 0.0,
"bad_words_ids": None,
"begin_suppress_tokens": None,
"bos_token_id": None,
"chunk_size_feed_forward": 0,
"classifier_dropout": None,
"cross_attention_frequency": 1,
"cross_attention_hidden_size": None,
"decoder_start_token_id": None,
"diversity_penalty": 0.0,
"do_sample": False,
"early_stopping": False,
"encoder_hidden_size": config.mm_hidden_size,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": None,
"exponential_decay_length_penalty": None,
"finetuning_task": None,
"forced_bos_token_id": None,
"forced_eos_token_id": None,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": config.mm_hidden_size,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_range": 0.02,
"intermediate_size": config.mm_hidden_size * 4,
"is_decoder": False,
"is_encoder_decoder": False,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-12,
"length_penalty": 1.0,
"max_length": 20,
"max_position_embeddings": 512,
"min_length": 0,
"model_type": "blip_2_qformer",
"no_repeat_ngram_size": 0,
"num_attention_heads": 32,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": num_hidden_layers,
"num_return_sequences": 1,
"output_attentions": False,
"output_hidden_states": False,
"output_scores": False,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"prefix": None,
"problem_type": None,
"pruned_heads": {},
"remove_invalid_values": False,
"repetition_penalty": 1.0,
"return_dict": True,
"return_dict_in_generate": False,
"sep_token_id": None,
"suppress_tokens": None,
"task_specific_params": None,
"temperature": 1.0,
"tf_legacy_loss": False,
"tie_encoder_decoder": False,
"tie_word_embeddings": True,
"tokenizer_class": None,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": None,
"torchscript": False,
"transformers_version": "4.27.0.dev0",
"typical_p": 1.0,
"use_bfloat16": False,
"vocab_size": 30522
})()
})()
return qformer_config
def build_vision_projector(config, delay_load=False, **kwargs):
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == 'linear':
return nn.Linear(config.mm_hidden_size, config.hidden_size)
elif projector_type == 'identity':
return IdentityMap()
elif projector_type.startswith('qformer'): # qformer2_64
qformer_config = qformer_config_template(config, projector_type)
return Blip2Model(qformer_config)
else:
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
if mlp_gelu_match:
mlp_depth = int(mlp_gelu_match.group(1))
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
for _ in range(1, mlp_depth):
modules.append(nn.GELU())
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
return nn.Sequential(*modules)
raise ValueError(f'Unknown projector type: {projector_type}') |