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[hotfix] fix bugs for unsharded parameters when restore data (#664)
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@@ -264,8 +264,14 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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reuse_fp16_shard = p.colo_attr.saved_grad.data_ptr() == p.colo_attr.sharded_data_tensor.data_ptr()
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p.colo_attr.saved_grad.set_null()
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if recover_data and reuse_fp16_shard:
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# We should write like this to trigger ForceFP32Paramter's half method
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p.data = self.master_params[p].payload
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p.colo_attr.sharded_data_tensor.reset_payload(
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colo_model_tensor_clone(self.master_params[p].payload.half(), torch.cuda.current_device()))
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colo_model_tensor_clone(p.half(), torch.cuda.current_device()))
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if not p.colo_attr.param_is_sharded:
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# FIXME(hhc): add hook for unsharded parameters
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p.data = p.colo_attr.sharded_data_tensor.payload
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def sync_grad(self):
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pass
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@@ -281,7 +287,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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# As we only store param shard, we shard it here
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self.shard_strategy.shard([p.colo_attr.sharded_data_tensor], self.dp_process_group)
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self.master_params[p] = StatefulTensor(
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cast_tensor_to_fp32(p.colo_attr.sharded_data_tensor.payload).to(self.device))
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cast_tensor_to_fp32(p.colo_attr.sharded_data_tensor.payload.to(self.device)))
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if not is_param_sharded and not self.keep_unshard:
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# In this branch, there's no need to shard param
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# So we gather here
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