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import torch
import torch.distributed as dist
import torch.distributed.rpc as rpc
from torch import nn, Tensor
from torch.nn import functional as F
class InfoNCELoss(nn.Module):
def __init__(self, temp=0.01):
super().__init__()
self.temp = temp
@staticmethod
def _norm_and_stack(feat: Tensor) -> Tensor:
local_feat_norm = F.normalize(feat)
local_feat_norm_stack = torch.stack(local_feat_norm.chunk(2))
return local_feat_norm_stack
def forward(self, feature: Tensor) -> tuple[Tensor, Tensor]:
feat_norm = torch.cat([
rpc.rpc_sync(f"worker{i}", self._norm_and_stack, (feature,))
for i in range(dist.get_world_size())
], dim=1)
bz = feat_norm.size(1)
feat1_norm, feat2_norm = feat_norm[0], feat_norm[1]
logits = feat1_norm @ feat2_norm.T
pos_logits_mask = torch.eye(bz, dtype=torch.bool)
pos_logits = logits[pos_logits_mask].unsqueeze(-1)
neg_logits = logits[~pos_logits_mask].view(bz, -1)
# Put the positive at first (0-th) and maximize its likelihood
logits = torch.cat([pos_logits, neg_logits], dim=1)
labels = torch.zeros(bz, dtype=torch.long, device=feature.device)
loss_contra = F.cross_entropy(logits / self.temp, labels)
acc_contra = (logits.argmax(dim=1) == labels).float().mean()
return loss_contra, acc_contra
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