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import torch
import torch.nn as nn
from torch.nn import functional as F
try:
import torch.distributed.nn
from torch import distributed as dist
has_distributed = True
except ImportError:
has_distributed = False
try:
import horovod.torch as hvd
except ImportError:
hvd = None
def gather_features(
image_features,
text_features,
local_loss=False,
gather_with_grad=False,
rank=0,
world_size=1,
use_horovod=False
):
assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
if use_horovod:
assert hvd is not None, 'Please install horovod'
if gather_with_grad:
all_image_features = hvd.allgather(image_features)
all_text_features = hvd.allgather(text_features)
else:
with torch.no_grad():
all_image_features = hvd.allgather(image_features)
all_text_features = hvd.allgather(text_features)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
gathered_image_features[rank] = image_features
gathered_text_features[rank] = text_features
all_image_features = torch.cat(gathered_image_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
else:
# We gather tensors from all gpus
if gather_with_grad:
all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
else:
gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
dist.all_gather(gathered_image_features, image_features)
dist.all_gather(gathered_text_features, text_features)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_image_features[rank] = image_features
gathered_text_features[rank] = text_features
all_image_features = torch.cat(gathered_image_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
return all_image_features, all_text_features
class ClipLoss(nn.Module):
def __init__(
self,
local_loss=False,
gather_with_grad=False,
cache_labels=False,
rank=0,
world_size=1,
use_horovod=False,
):
super().__init__()
self.local_loss = local_loss
self.gather_with_grad = gather_with_grad
self.cache_labels = cache_labels
self.rank = rank
self.world_size = world_size
self.use_horovod = use_horovod
# cache state
self.prev_num_logits = 0
self.labels = {}
def get_ground_truth(self, device, num_logits) -> torch.Tensor:
# calculated ground-truth and cache if enabled
if self.prev_num_logits != num_logits or device not in self.labels:
labels = torch.arange(num_logits, device=device, dtype=torch.long)
if self.world_size > 1 and self.local_loss:
labels = labels + num_logits * self.rank
if self.cache_labels:
self.labels[device] = labels
self.prev_num_logits = num_logits
else:
labels = self.labels[device]
return labels
def get_logits(self, image_features, text_features, logit_scale):
if self.world_size > 1:
all_image_features, all_text_features = gather_features(
image_features, text_features,
self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod)
if self.local_loss:
logits_per_image = logit_scale * image_features @ all_text_features.T
logits_per_text = logit_scale * text_features @ all_image_features.T
else:
logits_per_image = logit_scale * all_image_features @ all_text_features.T
logits_per_text = logits_per_image.T
else:
logits_per_image = logit_scale * image_features @ text_features.T
logits_per_text = logit_scale * text_features @ image_features.T
return logits_per_image, logits_per_text
def forward(self, image_features, text_features, logit_scale, output_dict=False):
device = image_features.device
logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale)
labels = self.get_ground_truth(device, logits_per_image.shape[0])
total_loss = (
F.cross_entropy(logits_per_image, labels) +
F.cross_entropy(logits_per_text, labels)
) / 2
return {"contrastive_loss": total_loss} if output_dict else total_loss
class CoCaLoss(ClipLoss):
def __init__(
self,
caption_loss_weight,
clip_loss_weight,
pad_id=0, # pad_token for open_clip custom tokenizer
local_loss=False,
gather_with_grad=False,
cache_labels=False,
rank=0,
world_size=1,
use_horovod=False,
):
super().__init__(
local_loss=local_loss,
gather_with_grad=gather_with_grad,
cache_labels=cache_labels,
rank=rank,
world_size=world_size,
use_horovod=use_horovod
)
self.clip_loss_weight = clip_loss_weight
self.caption_loss_weight = caption_loss_weight
self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id)
def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False):
clip_loss = torch.tensor(0)
if self.clip_loss_weight:
clip_loss = super().forward(image_features, text_features, logit_scale)
clip_loss = self.clip_loss_weight * clip_loss
caption_loss = self.caption_loss(
logits.permute(0, 2, 1),
labels,
)
caption_loss = caption_loss * self.caption_loss_weight
if output_dict:
return {"contrastive_loss": clip_loss, "caption_loss": caption_loss}
return clip_loss, caption_loss
class DistillClipLoss(ClipLoss):
def dist_loss(self, teacher_logits, student_logits):
return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0)
def forward(
self,
image_features,
text_features,
logit_scale,
dist_image_features,
dist_text_features,
dist_logit_scale,
output_dict=False,
):
logits_per_image, logits_per_text = \
self.get_logits(image_features, text_features, logit_scale)
dist_logits_per_image, dist_logits_per_text = \
self.get_logits(dist_image_features, dist_text_features, dist_logit_scale)
labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0])
contrastive_loss = (
F.cross_entropy(logits_per_image, labels) +
F.cross_entropy(logits_per_text, labels)
) / 2
distill_loss = (
self.dist_loss(dist_logits_per_image, logits_per_image) +
self.dist_loss(dist_logits_per_text, logits_per_text)
) / 2
if output_dict:
return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss}
return contrastive_loss, distill_loss
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