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import math
from typing import Callable
import torch
from torch import distributed
from torch.nn.functional import linear
from torch.nn.functional import normalize
class PartialFC_V2(torch.nn.Module):
"""
https://arxiv.org/abs/2203.15565
A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).
When sample rate less than 1, in each iteration, positive class centers and a random subset of
negative class centers are selected to compute the margin-based softmax loss, all class
centers are still maintained throughout the whole training process, but only a subset is
selected and updated in each iteration.
.. note::
When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1).
Example:
--------
>>> module_pfc = PartialFC(embedding_size=512, num_classes=8000000, sample_rate=0.2)
>>> for img, labels in data_loader:
>>> embeddings = net(img)
>>> loss = module_pfc(embeddings, labels)
>>> loss.backward()
>>> optimizer.step()
"""
_version = 2
def __init__(
self,
margin_loss: Callable,
embedding_size: int,
num_classes: int,
sample_rate: float = 1.0,
fp16: bool = False,
):
"""
Paramenters:
-----------
embedding_size: int
The dimension of embedding, required
num_classes: int
Total number of classes, required
sample_rate: float
The rate of negative centers participating in the calculation, default is 1.0.
"""
super(PartialFC_V2, self).__init__()
assert distributed.is_initialized(), "must initialize distributed before create this"
self.rank = distributed.get_rank()
self.world_size = distributed.get_world_size()
self.dist_cross_entropy = DistCrossEntropy()
self.embedding_size = embedding_size
self.sample_rate: float = sample_rate
self.fp16 = fp16
self.num_local: int = num_classes // self.world_size + int(self.rank < num_classes % self.world_size)
self.class_start: int = num_classes // self.world_size * self.rank + min(
self.rank, num_classes % self.world_size
)
self.num_sample: int = int(self.sample_rate * self.num_local)
self.last_batch_size: int = 0
self.is_updated: bool = True
self.init_weight_update: bool = True
self.weight = torch.nn.Parameter(torch.normal(0, 0.01, (self.num_local, embedding_size)))
# margin_loss
if isinstance(margin_loss, Callable):
self.margin_softmax = margin_loss
else:
raise
def sample(self, labels, index_positive):
"""
This functions will change the value of labels
Parameters:
-----------
labels: torch.Tensor
pass
index_positive: torch.Tensor
pass
optimizer: torch.optim.Optimizer
pass
"""
with torch.no_grad():
positive = torch.unique(labels[index_positive], sorted=True).cuda()
if self.num_sample - positive.size(0) >= 0:
perm = torch.rand(size=[self.num_local]).cuda()
perm[positive] = 2.0
index = torch.topk(perm, k=self.num_sample)[1].cuda()
index = index.sort()[0].cuda()
else:
index = positive
self.weight_index = index
labels[index_positive] = torch.searchsorted(index, labels[index_positive])
return self.weight[self.weight_index]
def forward(
self,
local_embeddings: torch.Tensor,
local_labels: torch.Tensor,
):
"""
Parameters:
----------
local_embeddings: torch.Tensor
feature embeddings on each GPU(Rank).
local_labels: torch.Tensor
labels on each GPU(Rank).
Returns:
-------
loss: torch.Tensor
pass
"""
local_labels.squeeze_()
local_labels = local_labels.long()
batch_size = local_embeddings.size(0)
if self.last_batch_size == 0:
self.last_batch_size = batch_size
assert (
self.last_batch_size == batch_size
), f"last batch size do not equal current batch size: {self.last_batch_size} vs {batch_size}"
_gather_embeddings = [torch.zeros((batch_size, self.embedding_size)).cuda() for _ in range(self.world_size)]
_gather_labels = [torch.zeros(batch_size).long().cuda() for _ in range(self.world_size)]
_list_embeddings = AllGather(local_embeddings, *_gather_embeddings)
distributed.all_gather(_gather_labels, local_labels)
embeddings = torch.cat(_list_embeddings)
labels = torch.cat(_gather_labels)
labels = labels.view(-1, 1)
index_positive = (self.class_start <= labels) & (labels < self.class_start + self.num_local)
labels[~index_positive] = -1
labels[index_positive] -= self.class_start
if self.sample_rate < 1:
weight = self.sample(labels, index_positive)
else:
weight = self.weight
with torch.cuda.amp.autocast(self.fp16):
norm_embeddings = normalize(embeddings)
norm_weight_activated = normalize(weight)
logits = linear(norm_embeddings, norm_weight_activated)
if self.fp16:
logits = logits.float()
logits = logits.clamp(-1, 1)
logits = self.margin_softmax(logits, labels)
loss = self.dist_cross_entropy(logits, labels)
return loss
class DistCrossEntropyFunc(torch.autograd.Function):
"""
CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax.
Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):
"""
@staticmethod
def forward(ctx, logits: torch.Tensor, label: torch.Tensor):
""" """
batch_size = logits.size(0)
# for numerical stability
max_logits, _ = torch.max(logits, dim=1, keepdim=True)
# local to global
distributed.all_reduce(max_logits, distributed.ReduceOp.MAX)
logits.sub_(max_logits)
logits.exp_()
sum_logits_exp = torch.sum(logits, dim=1, keepdim=True)
# local to global
distributed.all_reduce(sum_logits_exp, distributed.ReduceOp.SUM)
logits.div_(sum_logits_exp)
index = torch.where(label != -1)[0]
# loss
loss = torch.zeros(batch_size, 1, device=logits.device)
loss[index] = logits[index].gather(1, label[index])
distributed.all_reduce(loss, distributed.ReduceOp.SUM)
ctx.save_for_backward(index, logits, label)
return loss.clamp_min_(1e-30).log_().mean() * (-1)
@staticmethod
def backward(ctx, loss_gradient):
"""
Args:
loss_grad (torch.Tensor): gradient backward by last layer
Returns:
gradients for each input in forward function
`None` gradients for one-hot label
"""
(
index,
logits,
label,
) = ctx.saved_tensors
batch_size = logits.size(0)
one_hot = torch.zeros(size=[index.size(0), logits.size(1)], device=logits.device)
one_hot.scatter_(1, label[index], 1)
logits[index] -= one_hot
logits.div_(batch_size)
return logits * loss_gradient.item(), None
class DistCrossEntropy(torch.nn.Module):
def __init__(self):
super(DistCrossEntropy, self).__init__()
def forward(self, logit_part, label_part):
return DistCrossEntropyFunc.apply(logit_part, label_part)
class AllGatherFunc(torch.autograd.Function):
"""AllGather op with gradient backward"""
@staticmethod
def forward(ctx, tensor, *gather_list):
gather_list = list(gather_list)
distributed.all_gather(gather_list, tensor)
return tuple(gather_list)
@staticmethod
def backward(ctx, *grads):
grad_list = list(grads)
rank = distributed.get_rank()
grad_out = grad_list[rank]
dist_ops = [
distributed.reduce(grad_out, rank, distributed.ReduceOp.SUM, async_op=True)
if i == rank
else distributed.reduce(grad_list[i], i, distributed.ReduceOp.SUM, async_op=True)
for i in range(distributed.get_world_size())
]
for _op in dist_ops:
_op.wait()
grad_out *= len(grad_list) # cooperate with distributed loss function
return (grad_out, *[None for _ in range(len(grad_list))])
AllGather = AllGatherFunc.apply
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