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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
def neighbour_exchange(from_rank, to_rank, tensor, group=None):
tensor_recv = torch.zeros_like(tensor)
send_op = torch.distributed.P2POp(
torch.distributed.isend,
tensor,
to_rank,
group=group,
)
recv_op = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_recv,
from_rank,
group=group,
)
reqs = torch.distributed.batch_isend_irecv([send_op, recv_op])
for req in reqs:
req.wait()
return tensor_recv
def neighbour_exchange_bidir(
left_rank, right_rank, tensor_to_left, tensor_to_right, group=None
):
tensor_from_left = torch.zeros_like(tensor_to_right)
tensor_from_right = torch.zeros_like(tensor_to_left)
send_op_left = torch.distributed.P2POp(
torch.distributed.isend,
tensor_to_left,
left_rank,
group=group,
)
send_op_right = torch.distributed.P2POp(
torch.distributed.isend,
tensor_to_right,
right_rank,
group=group,
)
recv_op_left = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_from_left,
left_rank,
group=group,
)
recv_op_right = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_from_right,
right_rank,
group=group,
)
reqs = torch.distributed.batch_isend_irecv(
[send_op_right, send_op_left, recv_op_right, recv_op_left]
)
for req in reqs:
req.wait()
return tensor_from_right, tensor_from_left
class NeighbourExchange(torch.autograd.Function):
@staticmethod
def forward(ctx, from_rank, to_rank, group, tensor):
ctx.group = group
ctx.from_rank = from_rank
ctx.to_rank = to_rank
return neighbour_exchange(from_rank, to_rank, tensor, group=group)
@staticmethod
def backward(ctx, grad_output):
return (None, None, None) + (
NeighbourExchange.apply(ctx.to_rank, ctx.from_rank, ctx.group, grad_output),
)
def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None):
return NeighbourExchange.apply(from_rank, to_rank, group, tensor)
class NeighbourExchangeBidir(torch.autograd.Function):
@staticmethod
def forward(ctx, left_rank, right_rank, group, tensor_to_left, tensor_to_right):
ctx.group = group
ctx.left_rank = left_rank
ctx.right_rank = right_rank
return neighbour_exchange_bidir(
left_rank, right_rank, tensor_to_left, tensor_to_right, group=group
)
@staticmethod
def backward(ctx, *grad_outputs):
return (None, None, None) + NeighbourExchangeBidir.apply(
ctx.right_rank, ctx.left_rank, ctx.group, *grad_outputs
)
def neighbour_exchange_bidir_with_grad(
left_rank, right_rank, tensor_to_left, tensor_to_right, group=None
):
return NeighbourExchangeBidir.apply(
left_rank, right_rank, group, tensor_to_left, tensor_to_right
)
class SigLipLoss(nn.Module):
"""Sigmoid Loss for Language Image Pre-Training (SigLIP) - https://arxiv.org/abs/2303.15343
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
"""
def __init__(
self,
cache_labels=False,
rank=0,
world_size=1,
bidir=True,
use_horovod=False,
):
super().__init__()
self.cache_labels = cache_labels
self.rank = rank
self.world_size = world_size
assert not use_horovod # FIXME need to look at hvd ops for ring transfers
self.use_horovod = use_horovod
self.bidir = bidir
# cache state FIXME cache not currently used, worthwhile?
self.prev_num_logits = 0
self.labels = {}
def get_ground_truth(
self, device, dtype, num_logits, negative_only=False
) -> torch.Tensor:
labels = -torch.ones((num_logits, num_logits), device=device, dtype=dtype)
if not negative_only:
labels = 2 * torch.eye(num_logits, device=device, dtype=dtype) + labels
return labels
def get_logits(self, image_features, text_features, logit_scale, logit_bias=None):
logits = logit_scale * image_features @ text_features.T
if logit_bias is not None:
logits += logit_bias
return logits
def _loss(
self,
image_features,
text_features,
logit_scale,
logit_bias=None,
negative_only=False,
):
logits = self.get_logits(image_features, text_features, logit_scale, logit_bias)
labels = self.get_ground_truth(
image_features.device,
image_features.dtype,
image_features.shape[0],
negative_only=negative_only,
)
loss = -F.logsigmoid(labels * logits).sum() / image_features.shape[0]
return loss
def forward(
self, image_features, text_features, logit_scale, logit_bias, output_dict=False
):
loss = self._loss(image_features, text_features, logit_scale, logit_bias)
if self.world_size > 1:
# exchange text features w/ neighbour world_size - 1 times
right_rank = (self.rank + 1) % self.world_size
left_rank = (self.rank - 1 + self.world_size) % self.world_size
if self.bidir:
text_features_to_right = text_features_to_left = text_features
num_bidir, remainder = divmod(self.world_size - 1, 2)
for i in range(num_bidir):
text_features_recv = neighbour_exchange_bidir_with_grad(
left_rank,
right_rank,
text_features_to_left,
text_features_to_right,
)
for f in text_features_recv:
loss += self._loss(
image_features,
f,
logit_scale,
logit_bias,
negative_only=True,
)
text_features_to_left, text_features_to_right = text_features_recv
if remainder:
text_features_recv = neighbour_exchange_with_grad(
left_rank, right_rank, text_features_to_right
)
loss += self._loss(
image_features,
text_features_recv,
logit_scale,
logit_bias,
negative_only=True,
)
else:
text_features_to_right = text_features
for i in range(self.world_size - 1):
text_features_from_left = neighbour_exchange_with_grad(
left_rank, right_rank, text_features_to_right
)
loss += self._loss(
image_features,
text_features_from_left,
logit_scale,
logit_bias,
negative_only=True,
)
text_features_to_right = text_features_from_left
return {"contrastive_loss": loss} if output_dict else loss
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