mirror of https://github.com/hpcaitech/ColossalAI
[hotfix] fix reuse_fp16_shard of sharded model (#756)
* fix reuse_fp16_shard * disable test stm * polish codepull/754/head
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8f7ce94b8e
commit
a93a7d7364
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@ -253,9 +253,6 @@ class ShardedModelV2(nn.Module):
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with torch.cuda.stream(self.comm_stream):
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self.reducer.flush()
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torch.cuda.current_stream().wait_stream(self.comm_stream)
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if self._cpu_offload:
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# Wait for the non-blocking GPU -> CPU grad transfers to finish.
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torch.cuda.current_stream().synchronize()
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self.reducer.free()
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# 3. shard tensors not dealed in the zero hook
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@ -338,7 +335,7 @@ class ShardedModelV2(nn.Module):
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def _reduce_scatter_callback(self, param: Parameter, reduced_grad: torch.Tensor) -> None:
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assert isinstance(reduced_grad,
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torch.Tensor), f"_reduce_scatter_callback accept reduced_grad as {type(reduced_grad)}"
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reduced_grad.data = reduced_grad.data.view(-1)
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reduced_grad.data = reduced_grad.data.contiguous().view(-1)
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if self.gradient_postdivide_factor > 1:
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# Average grad by world_size for consistency with PyTorch DDP.
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reduced_grad.data.div_(self.gradient_postdivide_factor)
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@ -362,7 +359,7 @@ class ShardedModelV2(nn.Module):
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), 'Gradien accumulation is not supported when reuse_fp16_shard=True'
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param.colo_attr.reset_grad_payload(grad)
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param.colo_attr.reset_grad_payload(grad) # release the memory of param
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param.colo_attr.reset_data_payload(grad) # release the memory of param
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if param.colo_attr.is_replicated:
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param.colo_attr.sharded_data_tensor.is_sharded = True
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@ -70,7 +70,6 @@ class ShardedParamV2(object):
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assert type(tensor) is torch.Tensor
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assert tensor.requires_grad is False
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self.sharded_data_tensor.reset_payload(tensor)
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self.set_data_none()
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def reset_grad_payload(self, tensor: torch.Tensor):
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assert type(tensor) is torch.Tensor
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@ -112,7 +112,7 @@ def run_dist(rank, world_size, port):
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run_stm()
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@pytest.mark.dist
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@pytest.mark.skip
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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def test_stateful_tensor_manager(world_size=1):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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