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Fix the displayed loss in the sft trainer for gradient accumulation > 1 #102

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Jan 14, 2025
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Fix the displayed loss in the sft trainer for gradient accumulation > 1 #102

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7 changes: 5 additions & 2 deletions verl/trainer/fsdp_sft_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -256,9 +256,11 @@ def training_step(self, batch: TensorDict):

micro_batches = batch.split(self.config.data.micro_batch_size)
n_micro_batches = len(micro_batches)
step_loss = 0
for micro_batch in micro_batches:
loss = self._compute_loss(batch=micro_batch) / n_micro_batches
loss.backward()
step_loss += loss.item()

self.fsdp_model.clip_grad_norm_(max_norm=self.config.optim.clip_grad)

Expand All @@ -275,8 +277,9 @@ def training_step(self, batch: TensorDict):

log_gpu_memory_usage('After offload weights', logger=logger)

# TODO: all reduce to get accurate loss
return {'train/loss': loss.detach().item(), 'train/lr(1e-3)': lr * 1e3}
step_loss = torch.tensor(step_loss).cuda()
torch.distributed.all_reduce(step_loss, op=torch.distributed.ReduceOp.AVG)
return {'train/loss': step_loss.detach().item(), 'train/lr(1e-3)': lr * 1e3}

def validation_step(self, batch: TensorDict):
self.fsdp_model.eval()
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