mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
4705d182d5
- add wip functionality for negative masks - ci: add code linter that removes unused imports - add instructions to install rust on osx
412 lines
15 KiB
Python
412 lines
15 KiB
Python
"""
|
|
* Copyright (c) 2022, salesforce.com, inc.
|
|
* All rights reserved.
|
|
* SPDX-License-Identifier: BSD-3-Clause
|
|
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
|
|
* By Junnan Li
|
|
"""
|
|
import transformers
|
|
from models.med import BertConfig, BertLMHeadModel, BertModel
|
|
|
|
transformers.logging.set_verbosity_error()
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from models.blip import create_vit, init_tokenizer
|
|
from torch import nn
|
|
|
|
|
|
class BLIP_Pretrain(nn.Module):
|
|
def __init__(
|
|
self,
|
|
med_config="configs/bert_config.json",
|
|
image_size=224,
|
|
vit="base",
|
|
vit_grad_ckpt=False,
|
|
vit_ckpt_layer=0,
|
|
embed_dim=256,
|
|
queue_size=57600,
|
|
momentum=0.995,
|
|
):
|
|
"""
|
|
Args:
|
|
med_config (str): path for the mixture of encoder-decoder model's configuration file
|
|
image_size (int): input image size
|
|
vit (str): model size of vision transformer
|
|
"""
|
|
super().__init__()
|
|
|
|
self.visual_encoder, vision_width = create_vit(
|
|
vit, image_size, vit_grad_ckpt, vit_ckpt_layer, 0
|
|
)
|
|
|
|
if vit == "base":
|
|
checkpoint = torch.hub.load_state_dict_from_url(
|
|
url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
|
|
map_location="cpu",
|
|
check_hash=True,
|
|
)
|
|
state_dict = checkpoint["model"]
|
|
msg = self.visual_encoder.load_state_dict(state_dict, strict=False)
|
|
elif vit == "large":
|
|
from timm.models.helpers import load_custom_pretrained
|
|
from timm.models.vision_transformer import default_cfgs
|
|
|
|
load_custom_pretrained(
|
|
self.visual_encoder, default_cfgs["vit_large_patch16_224_in21k"]
|
|
)
|
|
|
|
self.tokenizer = init_tokenizer()
|
|
encoder_config = BertConfig.from_json_file(med_config)
|
|
encoder_config.encoder_width = vision_width
|
|
self.text_encoder = BertModel.from_pretrained(
|
|
"bert-base-uncased", config=encoder_config, add_pooling_layer=False
|
|
)
|
|
self.text_encoder.resize_token_embeddings(len(self.tokenizer))
|
|
|
|
text_width = self.text_encoder.config.hidden_size
|
|
|
|
self.vision_proj = nn.Linear(vision_width, embed_dim)
|
|
self.text_proj = nn.Linear(text_width, embed_dim)
|
|
|
|
self.itm_head = nn.Linear(text_width, 2)
|
|
|
|
# create momentum encoders
|
|
self.visual_encoder_m, vision_width = create_vit(vit, image_size)
|
|
self.vision_proj_m = nn.Linear(vision_width, embed_dim)
|
|
self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)
|
|
self.text_proj_m = nn.Linear(text_width, embed_dim)
|
|
|
|
self.model_pairs = [
|
|
[self.visual_encoder, self.visual_encoder_m],
|
|
[self.vision_proj, self.vision_proj_m],
|
|
[self.text_encoder, self.text_encoder_m],
|
|
[self.text_proj, self.text_proj_m],
|
|
]
|
|
self.copy_params()
|
|
|
|
# create the queue
|
|
self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
|
|
self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
|
|
self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))
|
|
|
|
self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
|
|
self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
|
|
|
|
self.queue_size = queue_size
|
|
self.momentum = momentum
|
|
self.temp = nn.Parameter(0.07 * torch.ones([]))
|
|
|
|
# create the decoder
|
|
decoder_config = BertConfig.from_json_file(med_config)
|
|
decoder_config.encoder_width = vision_width
|
|
self.text_decoder = BertLMHeadModel.from_pretrained(
|
|
"bert-base-uncased", config=decoder_config
|
|
)
|
|
self.text_decoder.resize_token_embeddings(len(self.tokenizer))
|
|
tie_encoder_decoder_weights(
|
|
self.text_encoder, self.text_decoder.bert, "", "/attention"
|
|
)
|
|
|
|
def forward(self, image, caption, alpha):
|
|
with torch.no_grad():
|
|
self.temp.clamp_(0.001, 0.5)
|
|
|
|
image_embeds = self.visual_encoder(image)
|
|
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
|
|
image.device
|
|
)
|
|
image_feat = F.normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1)
|
|
|
|
text = self.tokenizer(
|
|
caption,
|
|
padding="max_length",
|
|
truncation=True,
|
|
max_length=30,
|
|
return_tensors="pt",
|
|
).to(image.device)
|
|
text_output = self.text_encoder(
|
|
text.input_ids,
|
|
attention_mask=text.attention_mask,
|
|
return_dict=True,
|
|
mode="text",
|
|
)
|
|
text_feat = F.normalize(
|
|
self.text_proj(text_output.last_hidden_state[:, 0, :]), dim=-1
|
|
)
|
|
|
|
# get momentum features
|
|
with torch.no_grad():
|
|
self._momentum_update()
|
|
image_embeds_m = self.visual_encoder_m(image)
|
|
image_feat_m = F.normalize(
|
|
self.vision_proj_m(image_embeds_m[:, 0, :]), dim=-1
|
|
)
|
|
image_feat_all = torch.cat(
|
|
[image_feat_m.t(), self.image_queue.clone().detach()], dim=1
|
|
)
|
|
|
|
text_output_m = self.text_encoder_m(
|
|
text.input_ids,
|
|
attention_mask=text.attention_mask,
|
|
return_dict=True,
|
|
mode="text",
|
|
)
|
|
text_feat_m = F.normalize(
|
|
self.text_proj_m(text_output_m.last_hidden_state[:, 0, :]), dim=-1
|
|
)
|
|
text_feat_all = torch.cat(
|
|
[text_feat_m.t(), self.text_queue.clone().detach()], dim=1
|
|
)
|
|
|
|
sim_i2t_m = image_feat_m @ text_feat_all / self.temp
|
|
sim_t2i_m = text_feat_m @ image_feat_all / self.temp
|
|
|
|
sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
|
|
sim_targets.fill_diagonal_(1)
|
|
|
|
sim_i2t_targets = (
|
|
alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
|
|
)
|
|
sim_t2i_targets = (
|
|
alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
|
|
)
|
|
|
|
sim_i2t = image_feat @ text_feat_all / self.temp
|
|
sim_t2i = text_feat @ image_feat_all / self.temp
|
|
|
|
loss_i2t = -torch.sum(
|
|
F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets, dim=1
|
|
).mean()
|
|
loss_t2i = -torch.sum(
|
|
F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets, dim=1
|
|
).mean()
|
|
|
|
loss_ita = (loss_i2t + loss_t2i) / 2
|
|
|
|
self._dequeue_and_enqueue(image_feat_m, text_feat_m)
|
|
|
|
###============== Image-text Matching ===================###
|
|
encoder_input_ids = text.input_ids.clone()
|
|
encoder_input_ids[:, 0] = self.tokenizer.enc_token_id
|
|
|
|
# forward the positve image-text pair
|
|
bs = image.size(0)
|
|
output_pos = self.text_encoder(
|
|
encoder_input_ids,
|
|
attention_mask=text.attention_mask,
|
|
encoder_hidden_states=image_embeds,
|
|
encoder_attention_mask=image_atts,
|
|
return_dict=True,
|
|
)
|
|
with torch.no_grad():
|
|
weights_t2i = F.softmax(sim_t2i[:, :bs], dim=1) + 1e-4
|
|
weights_t2i.fill_diagonal_(0)
|
|
weights_i2t = F.softmax(sim_i2t[:, :bs], dim=1) + 1e-4
|
|
weights_i2t.fill_diagonal_(0)
|
|
|
|
# select a negative image for each text
|
|
image_embeds_neg = []
|
|
for b in range(bs):
|
|
neg_idx = torch.multinomial(weights_t2i[b], 1).item()
|
|
image_embeds_neg.append(image_embeds[neg_idx])
|
|
image_embeds_neg = torch.stack(image_embeds_neg, dim=0)
|
|
|
|
# select a negative text for each image
|
|
text_ids_neg = []
|
|
text_atts_neg = []
|
|
for b in range(bs):
|
|
neg_idx = torch.multinomial(weights_i2t[b], 1).item()
|
|
text_ids_neg.append(encoder_input_ids[neg_idx])
|
|
text_atts_neg.append(text.attention_mask[neg_idx])
|
|
|
|
text_ids_neg = torch.stack(text_ids_neg, dim=0)
|
|
text_atts_neg = torch.stack(text_atts_neg, dim=0)
|
|
|
|
text_ids_all = torch.cat([encoder_input_ids, text_ids_neg], dim=0)
|
|
text_atts_all = torch.cat([text.attention_mask, text_atts_neg], dim=0)
|
|
|
|
image_embeds_all = torch.cat([image_embeds_neg, image_embeds], dim=0)
|
|
image_atts_all = torch.cat([image_atts, image_atts], dim=0)
|
|
|
|
output_neg = self.text_encoder(
|
|
text_ids_all,
|
|
attention_mask=text_atts_all,
|
|
encoder_hidden_states=image_embeds_all,
|
|
encoder_attention_mask=image_atts_all,
|
|
return_dict=True,
|
|
)
|
|
|
|
vl_embeddings = torch.cat(
|
|
[
|
|
output_pos.last_hidden_state[:, 0, :],
|
|
output_neg.last_hidden_state[:, 0, :],
|
|
],
|
|
dim=0,
|
|
)
|
|
vl_output = self.itm_head(vl_embeddings)
|
|
|
|
itm_labels = torch.cat(
|
|
[torch.ones(bs, dtype=torch.long), torch.zeros(2 * bs, dtype=torch.long)],
|
|
dim=0,
|
|
).to(image.device)
|
|
loss_itm = F.cross_entropy(vl_output, itm_labels)
|
|
|
|
##================= LM ========================##
|
|
decoder_input_ids = text.input_ids.clone()
|
|
decoder_input_ids[:, 0] = self.tokenizer.bos_token_id
|
|
decoder_targets = decoder_input_ids.masked_fill(
|
|
decoder_input_ids == self.tokenizer.pad_token_id, -100
|
|
)
|
|
|
|
decoder_output = self.text_decoder(
|
|
decoder_input_ids,
|
|
attention_mask=text.attention_mask,
|
|
encoder_hidden_states=image_embeds,
|
|
encoder_attention_mask=image_atts,
|
|
labels=decoder_targets,
|
|
return_dict=True,
|
|
)
|
|
|
|
loss_lm = decoder_output.loss
|
|
return loss_ita, loss_itm, loss_lm
|
|
|
|
@torch.no_grad()
|
|
def copy_params(self):
|
|
for model_pair in self.model_pairs:
|
|
for param, param_m in zip(
|
|
model_pair[0].parameters(), model_pair[1].parameters()
|
|
):
|
|
param_m.data.copy_(param.data) # initialize
|
|
param_m.requires_grad = False # not update by gradient
|
|
|
|
@torch.no_grad()
|
|
def _momentum_update(self):
|
|
for model_pair in self.model_pairs:
|
|
for param, param_m in zip(
|
|
model_pair[0].parameters(), model_pair[1].parameters()
|
|
):
|
|
param_m.data = param_m.data * self.momentum + param.data * (
|
|
1.0 - self.momentum
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def _dequeue_and_enqueue(self, image_feat, text_feat):
|
|
# gather keys before updating queue
|
|
image_feats = concat_all_gather(image_feat)
|
|
text_feats = concat_all_gather(text_feat)
|
|
|
|
batch_size = image_feats.shape[0]
|
|
|
|
ptr = int(self.queue_ptr)
|
|
assert self.queue_size % batch_size == 0 # for simplicity
|
|
|
|
# replace the keys at ptr (dequeue and enqueue)
|
|
self.image_queue[:, ptr : ptr + batch_size] = image_feats.T
|
|
self.text_queue[:, ptr : ptr + batch_size] = text_feats.T
|
|
ptr = (ptr + batch_size) % self.queue_size # move pointer
|
|
|
|
self.queue_ptr[0] = ptr
|
|
|
|
|
|
def blip_pretrain(**kwargs):
|
|
model = BLIP_Pretrain(**kwargs)
|
|
return model
|
|
|
|
|
|
@torch.no_grad()
|
|
def concat_all_gather(tensor):
|
|
"""
|
|
Performs all_gather operation on the provided tensors.
|
|
*** Warning ***: torch.distributed.all_gather has no gradient.
|
|
"""
|
|
tensors_gather = [
|
|
torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
|
|
]
|
|
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
|
|
|
|
output = torch.cat(tensors_gather, dim=0)
|
|
return output
|
|
|
|
|
|
from typing import List
|
|
|
|
|
|
def tie_encoder_decoder_weights(
|
|
encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key: str
|
|
):
|
|
uninitialized_encoder_weights: List[str] = []
|
|
if decoder.__class__ != encoder.__class__:
|
|
logger.info(
|
|
f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
|
|
)
|
|
|
|
def tie_encoder_to_decoder_recursively(
|
|
decoder_pointer: nn.Module,
|
|
encoder_pointer: nn.Module,
|
|
module_name: str,
|
|
uninitialized_encoder_weights: List[str],
|
|
skip_key: str,
|
|
depth=0,
|
|
):
|
|
assert isinstance(decoder_pointer, nn.Module) and isinstance(
|
|
encoder_pointer, nn.Module
|
|
), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
|
|
if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
|
|
assert hasattr(encoder_pointer, "weight")
|
|
encoder_pointer.weight = decoder_pointer.weight
|
|
if hasattr(decoder_pointer, "bias"):
|
|
assert hasattr(encoder_pointer, "bias")
|
|
encoder_pointer.bias = decoder_pointer.bias
|
|
print(module_name + " is tied")
|
|
return
|
|
|
|
encoder_modules = encoder_pointer._modules
|
|
decoder_modules = decoder_pointer._modules
|
|
if len(decoder_modules) > 0:
|
|
assert (
|
|
len(encoder_modules) > 0
|
|
), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
|
|
|
|
all_encoder_weights = set(
|
|
[module_name + "/" + sub_name for sub_name in encoder_modules.keys()]
|
|
)
|
|
encoder_layer_pos = 0
|
|
for name, module in decoder_modules.items():
|
|
if name.isdigit():
|
|
encoder_name = str(int(name) + encoder_layer_pos)
|
|
decoder_name = name
|
|
if not isinstance(
|
|
decoder_modules[decoder_name],
|
|
type(encoder_modules[encoder_name]),
|
|
) and len(encoder_modules) != len(decoder_modules):
|
|
# this can happen if the name corresponds to the position in a list module list of layers
|
|
# in this case the decoder has added a cross-attention that the encoder does not have
|
|
# thus skip this step and subtract one layer pos from encoder
|
|
encoder_layer_pos -= 1
|
|
continue
|
|
elif name not in encoder_modules:
|
|
continue
|
|
elif depth > 500:
|
|
raise ValueError(
|
|
"Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
|
|
)
|
|
else:
|
|
decoder_name = encoder_name = name
|
|
tie_encoder_to_decoder_recursively(
|
|
decoder_modules[decoder_name],
|
|
encoder_modules[encoder_name],
|
|
module_name + "/" + name,
|
|
uninitialized_encoder_weights,
|
|
skip_key,
|
|
depth=depth + 1,
|
|
)
|
|
all_encoder_weights.remove(module_name + "/" + encoder_name)
|
|
|
|
uninitialized_encoder_weights += list(all_encoder_weights)
|
|
|
|
# tie weights recursively
|
|
tie_encoder_to_decoder_recursively(
|
|
decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key
|
|
)
|