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[shardformer] added embedding gradient check (#4124)

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This commit is contained in:
Frank Lee
2023-06-30 16:16:44 +08:00
parent 44a190e6ac
commit ae035d305d
14 changed files with 255 additions and 74 deletions
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29
tests/test_shardformer/test_model/test_shard_bert.py
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@@ -18,20 +18,35 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
org_loss.backward()
shard_loss.backward()
# check grad equality
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# check grad
if org_model.__class__.__name__ == 'BertModel':
org_grad = org_model.encoder.layer[0].attention.self.query.weight.grad
shard_grad = sharded_model.encoder.layer[0].attention.self.query.weight.grad
bert = org_model
sharded_bert = sharded_model
else:
org_grad = org_model.bert.encoder.layer[0].attention.self.query.weight.grad
shard_grad = sharded_model.bert.encoder.layer[0].attention.self.query.weight.grad
bert = org_model.bert
sharded_bert = sharded_model.bert
# compare self attention grad
org_grad = bert.encoder.layer[0].attention.self.query.weight.grad
shard_grad = sharded_bert.encoder.layer[0].attention.self.query.weight.grad
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
# compare embedding grad
org_grad = bert.embeddings.word_embeddings.weight.grad
shard_grad = sharded_bert.embeddings.word_embeddings.weight.grad
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"