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import torch
from torch import nn
from uniperceiver.config import configurable
from .build import PREDICTOR_REGISTRY
import math
import pickle
import torch.nn.functional as F
import numpy as np
from uniperceiver.utils import comm
import torch.distributed as dist
from uniperceiver.modeling.layers import FP16LayerNorm
from torch.cuda.amp import autocast
__all__ = ["EmbedClsAsRetrievalPredictor"]
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
)
def gelu(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x.float()).type_as(x)
@PREDICTOR_REGISTRY.register()
class EmbedClsAsRetrievalPredictor(nn.Module):
@configurable
def __init__(
self,
temperature,
use_norm,
temp_learn,
mb_list,
queue_len,
feat_dim,
task2tempname,
fc_prompt_feature_index,
output_proj,
cfg,
):
super(EmbedClsAsRetrievalPredictor, self).__init__()
self.cfg = cfg
self.use_norm = use_norm
self.temp_learn = temp_learn
if temp_learn:
self.logit_scale_img_cls = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_video_cls = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_text_mlm = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_text_caption = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_caption = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_mlm = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_retrieve = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_tqa_mlm = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_tqa_caption = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_tqa_retrieve = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
self.logit_scale_downstream = nn.Parameter(torch.ones([]) * np.log(1 / temperature))
else:
self.logit_scale_img_cls = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_video_cls = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_text_mlm = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_text_caption = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_caption = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_mlm = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_retrieve = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_tqa_mlm = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_tqa_caption = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_tqa_retrieve = torch.ones([]).cuda() * np.log(1 / temperature)
self.logit_scale_downstream = torch.ones([]).cuda() * np.log(1 / temperature)
self.task2tempname = task2tempname
self.memory_save = []
self.queue_len = queue_len
self.feat_dim = feat_dim
self.fc_prompt_feature_index = fc_prompt_feature_index
for task_name in mb_list:
self.memory_save.append(task_name)
self.register_buffer('queue_h1_{}'.format(task_name), torch.randn(queue_len, feat_dim ))
self.register_buffer('queue_h2_{}'.format(task_name), torch.randn(queue_len, feat_dim ))
setattr(self, 'queue_h1_{}'.format(task_name), nn.functional.normalize(getattr(self, 'queue_h1_{}'.format(task_name)), dim=1))
setattr(self, 'queue_h2_{}'.format(task_name), nn.functional.normalize(getattr(self, 'queue_h2_{}'.format(task_name)), dim=1))
self.register_buffer("queue_ptr1_{}".format(task_name), torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_ptr2_{}".format(task_name), torch.zeros(1, dtype=torch.long))
pass
self.output_proj = output_proj
if self.output_proj:
# if cfg.MODEL.LN_FP32:
# self.ln_post = CustomLayernorm(feat_dim)
# else:
# self.ln_post = nn.LayerNorm(feat_dim)
if self.cfg.SOLVER.FORCE_LN_FP16:
self.ln_post = FP16LayerNorm(feat_dim)
else:
self.ln_post = nn.LayerNorm(feat_dim)
self.proj = nn.Linear(feat_dim, feat_dim)
if cfg.MODEL.FEATURE_GATHER_FORCE:
assert cfg.DATALOADER.STRATEGY == 'turn'
self.gather_feature = True
else:
self.gather_feature = len(cfg.TASKS) == 1 and getattr(cfg.MODEL, "FEATURE_GATHER", False)
@torch.no_grad()
def _dequeue_and_enqueue(self, q1, q2, task_name):
"""Update queue."""
# gather keys before updating queue
batch_size1 = q1.shape[0]
batch_size2 = q2.shape[0]
ptr1 = int(getattr(self, "queue_ptr1_{}".format(task_name)))
ptr2 = int(getattr(self, "queue_ptr2_{}".format(task_name)))
assert self.queue_len % batch_size1 == 0 # for simplicity
# replace the keys at ptr (dequeue and enqueue)
getattr(self, 'queue_h1_{}'.format(task_name))[ptr1:ptr1+batch_size1, :] = q1 # save text features
getattr(self, 'queue_h2_{}'.format(task_name))[ptr2:ptr2+batch_size2, :] = q2 # save img features
ptr1 = (ptr1 + batch_size1) % self.queue_len # move pointer
ptr2 = (ptr2 + batch_size2) % self.queue_len # move pointer
getattr(self, "queue_ptr1_{}".format(task_name))[0] = ptr1
getattr(self, "queue_ptr2_{}".format(task_name))[0] = ptr2
pass
def replace_weight(self, weight):
pass
def replace_module_hidden(self,dense, layer_norm):
pass
@classmethod
def from_config(cls, cfg):
mb_list = []
task2tempname = {}
if len(cfg.TASKS) > 0:
for task_config in cfg.TASKS:
if 'MODEL' in task_config and task_config['MODEL'].get('MEMORY_BANK', False):
mb_list.append(task_config['NAME'])
task2tempname[task_config['NAME']] = task_config['MODEL']['TEMP_NAME']
ret = { "temperature": cfg.MODEL.PRED_TEMPERATURE,
"use_norm": cfg.MODEL.PRED_USE_NORM,
'temp_learn': getattr(cfg.MODEL, "LEARN_TEMP", False),
'mb_list': mb_list,
'queue_len': cfg.MODEL.QUEUE_LEN,
'feat_dim': cfg.MODEL.ENCODER_DIM,
'task2tempname': task2tempname,
"fc_prompt_feature_index": cfg.MODEL.FC_PROMPT_INDEX,
"output_proj": cfg.MODEL.OUTPUT_PROJ,
"cfg": cfg,
}
print(f'********* using temperature {cfg.MODEL.PRED_TEMPERATURE} **********')
return ret
@classmethod
def add_config(cls, cfg):
pass
def test_forward(self, logits):
return { "output": logits }
def postproj(self, hidden_states):
x = self.ln_post(hidden_states)
if self.proj is not None:
x = self.proj(x)
return x
def forward(self,
input_sample_list,
target_sample_list,
shared_target_sets,
target_set_list,
target_idx_list,
task_info,
**kwargs):
if len(target_sample_list) > 0:
q2_hidden_states = target_sample_list[0]['data']
else:
if len(target_set_list) > 1:
raise NotImplementedError('only one target supported now')
target_set_name = target_set_list[0]
q2_hidden_states = shared_target_sets[target_set_name][0]['data']
q1_hidden_states = input_sample_list[0]['data']
q2_hidden_states = q2_hidden_states[:, 0]
task_type = task_info.get('task_type')
if task_type in ['image_classification', 'video_classification']:
q1_hidden_states = q1_hidden_states[:, 0]
elif task_type in ['image_retrieval', 'video_retrieval']:
q1_hidden_states = q1_hidden_states[:, 0]
elif task_type == 'text_mlm':
mask_tokens = target_idx_list[0].ne(-1) # -1 is unmasked position
q1_hidden_states = q1_hidden_states[:, -mask_tokens.size(1):][mask_tokens]
target_idx_list[0] = target_idx_list[0][mask_tokens]
elif task_type in ['image_caption', 'video_caption']:
if self.training:
sample_info = input_sample_list[0]['sample_info']
if isinstance(sample_info, list):
sample_info = sample_info[0]
text_length = sample_info['data_length'][-1] // 2
q1_hidden_states = q1_hidden_states[:, -text_length:, :]
mask_tokens = target_idx_list[0].ne(-1) # -1 is padding position
q1_hidden_states = q1_hidden_states[mask_tokens] # .flatten(0, 1)
target_idx_list[0] = target_idx_list[0][mask_tokens] # .flatten(0, 1)
else:
q1_hidden_states = q1_hidden_states[:, -1]
elif task_type in ['text_classification', 'vqa']:
sample_info = input_sample_list[0]['sample_info']
if isinstance(sample_info, list):
sample_info = sample_info[0]
sample_infos = sample_info if isinstance(sample_info, list) else sample_info['sample_info_per_sample'][-1]
if 'spe_index' in sample_infos[0]:
text_length = sample_info['data_length'][-1]
q1_hidden_states = q1_hidden_states[:, -text_length:, :] # get text part; remove the first spe or the prompt embedding part
# gather spe representation from the 'spe_index' from text part via index of spe token
spe_index = torch.tensor([si['spe_index'] for si in sample_infos], device=q1_hidden_states.device).view(-1, 1, 1).expand(-1, -1, q1_hidden_states.size(2))
q1_hidden_states = torch.gather(q1_hidden_states, 1, spe_index)[:, 0]
else:
q1_hidden_states = q1_hidden_states[:, 0]
else:
raise NotImplementedError
if self.output_proj:
q1_hidden_states = self.postproj(q1_hidden_states)
q2_hidden_states = self.postproj(q2_hidden_states)
feat = q1_hidden_states
if len(target_sample_list) == 0:
# in1k
logits = self._forward(q1_hidden_states, q2_hidden_states, task_name=task_info.get("task_name", None))
ret = { "logits": [logits], "feats": [feat], "loss_names": [''] }
if len(target_idx_list) > 0:
ret.update({"targets": [target_idx_list[0]]})
if not self.training:
ret_test = self.test_forward(logits)
ret.update(ret_test)
# ret = self.test_forward(logits)
else:
# image and text retrieval in one forwarding:
if not self.training:
return {
"input_feats": q1_hidden_states / q1_hidden_states.norm(dim=-1, keepdim=True),
"tgt_feats": q2_hidden_states / q2_hidden_states.norm(dim=-1, keepdim=True),
}
if self.gather_feature:
local_q1 = q1_hidden_states
local_q2 = q2_hidden_states
packed_feature = torch.cat([local_q1, local_q2], dim=1).float()
gathered_features = [ torch.zeros_like(packed_feature) for _ in range(comm.get_world_size())]
dist.all_gather(gathered_features, packed_feature)
all_features = torch.cat([packed_feature] +
gathered_features[:comm.get_rank()] +
gathered_features[comm.get_rank() + 1:]).to(local_q1)
q1_hidden_states, q2_hidden_states = torch.split(all_features, [local_q1.size(1), local_q2.size(1)], dim=1)
if task_info.get("task_name", None) in self.memory_save:
# retrieval task with memory buffer
logits, logits_per_cls = self._forward_with_mb(
q1_hidden_states,
q2_hidden_states,
task_name=task_info.get("task_name", None))
else:
logits, logits_per_cls = self._forward(q1_hidden_states,
q2_hidden_states,
task_name=task_info.get(
"task_name", None),
mutual_retrieval=True)
target = torch.arange(logits.shape[0], dtype=torch.long, device=logits.device)
target_per_cls = target
ret = {
"logits": [logits, logits_per_cls],
"targets": [target, target_per_cls],
"loss_names": ['i2t', 't2i'],
}
return ret
def _forward(self, g, cls_name, task_name, mutual_retrieval=False,):
temperature = self.temperature_task(task_name)
if self.cfg.SOLVER.FORCE_TEMP_FP16:
temperature = temperature.half()
if self.temp_learn and temperature > 100.0:
temperature = 100.0
# getattr(self, self.task2tempname[task_name]).data.clamp_(max=math.log(100.0))
if self.use_norm:
if not self.cfg.SOLVER.FORCE_NORM_FP16:
g = g / g.norm(dim=-1, keepdim=True)
cls_name = cls_name / cls_name.norm(dim=-1, keepdim=True)
else:
with autocast(enabled=False):
g = g / g.norm(dim=-1, keepdim=True)
cls_name = cls_name / cls_name.norm(dim=-1, keepdim=True)
logits = (g @ cls_name.t()) * temperature
if mutual_retrieval:
logits_per_cls = logits.transpose(0, 1)
return logits, logits_per_cls
return logits
def _forward_with_mb(self, g, cls_name, task_name):
temperature = self.temperature_task(task_name)
if self.temp_learn and temperature > 100.0:
temperature = 100.0
# if self.temp_learn:
# getattr(self, self.task2tempname[task_name]).data.clamp_(max=math.log(100.0))
if self.use_norm:
g = g / g.norm(dim=-1, keepdim=True)
cls_name = cls_name / cls_name.norm(dim=-1, keepdim=True)
logits_per_image = (g @ cls_name.t()) * temperature
logits_per_cls = logits_per_image.transpose(0, 1)
logits_per_image_neg = (g @ getattr(self, 'queue_h1_{}'.format(task_name)).clone().detach().t()) * temperature
logits_per_cls_neg = (cls_name @ getattr(self, 'queue_h2_{}'.format(task_name)).clone().detach().t()) * temperature
self._dequeue_and_enqueue(cls_name, g, task_name) # reverse sequnce to save
return torch.cat([logits_per_image, logits_per_image_neg], dim=-1) , torch.cat([logits_per_cls, logits_per_cls_neg], dim=-1)
@property
def temperature_dict(self):
return {
'temperature/img_cls': 1/self.logit_scale_img_cls.exp(),
'temperature/video_cls': 1/self.logit_scale_video_cls.exp(),
'temperature/text_mlm': 1/self.logit_scale_text_mlm.exp(),
'temperature/text_caption': 1/self.logit_scale_text_caption.exp(),
'temperature/caption': 1/self.logit_scale_caption.exp(),
'temperature/mlm': 1/self.logit_scale_mlm.exp(),
'temperature/retrieve': 1/self.logit_scale_retrieve.exp(),
'temperature/tqa_mlm': 1/self.logit_scale_tqa_mlm.exp(),
'temperature/tqa_caption': 1/self.logit_scale_tqa_caption.exp(),
'temperature/tqa_retrieve': 1/self.logit_scale_tqa_retrieve.exp(),
'temperature/downstream': 1/self.logit_scale_downstream.exp(),
}
def temperature_task(self, taskname):
return getattr(self, self.task2tempname[taskname]).exp()
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