[zero] fix grad offload (#528)

* [zero] fix grad offload

* polish code

colossalai/utils/memory_utils/utils.py

@@ -114,3 +114,20 @@ def colo_model_data_move_to_cpu(t: Union[ShardedTensor, torch.Tensor]) -> None:
     GLOBAL_MODEL_DATA_TRACER.delete_tensor(t_payload)
     t_payload.data = t_payload.data.cpu()
     GLOBAL_MODEL_DATA_TRACER.add_tensor(t_payload)
+
+
+def colo_model_tensor_clone(t: Union[ShardedTensor, torch.Tensor], target_device: torch.device) -> torch.Tensor:
+    """
+    Clone a model data tensor
+
+    Args:
+        t (Union[ShardedTensor, torch.Tensor]): a model data tensor
+        target_device (torch.device): the target device
+    Returns:
+        torch.Tensor: a cloned torch tensor
+    """
+    t_payload = t.payload if isinstance(t, ShardedTensor) else t
+
+    ret = t_payload.to(target_device)
+    GLOBAL_MODEL_DATA_TRACER.add_tensor(ret)
+    return ret

colossalai/zero/sharded_model/sharded_model_v2.py

@@ -11,7 +11,7 @@ from colossalai.engine.ophooks import register_ophooks_recursively
 from colossalai.engine.ophooks.zero_hook import ZeroHook
 from colossalai.engine.paramhooks import BaseParamHookMgr
 from colossalai.logging import get_dist_logger
-from colossalai.utils.memory_utils.utils import colo_model_data_move_to_cpu, colo_cuda_memory_capacity
+from colossalai.utils.memory_utils.utils import colo_model_data_move_to_cpu, colo_cuda_memory_capacity, colo_model_tensor_clone
 from colossalai.utils.memory_tracer.memstats_collector import MemStatsCollector
 from colossalai.zero.shard_utils import BaseShardStrategy
 from colossalai.zero.sharded_model.reduce_scatter import ReduceScatterBucketer
@@ -198,16 +198,16 @@ class ShardedModelV2(nn.Module):
             # the shape `grad` is the same as unsharded param
             # So we can just use `view(-1)` to ensure grad is a flat tensor shard
             if self.reuse_fp16_shard:
-                grad = p.col_attr.sharded_data_tensor.payload
+                grad_payload = p.col_attr.sharded_data_tensor.payload
             else:
-                grad = cast_tensor_to_fp32(p.col_attr.fp16_grad)
+                grad_payload = cast_tensor_to_fp32(p.col_attr.fp16_grad)
             if p.col_attr.offload_grad:
-                colo_model_data_move_to_cpu(grad)
+                grad_payload = colo_model_tensor_clone(grad_payload, torch.device('cpu'))
             if p.col_attr.fp32_grad is not None:
                 assert not self.reuse_fp16_shard, 'Gradien accumulation is not supported when reuse_fp16_shard=True'
-                p.col_attr.fp32_grad.add_(grad.view_as(p.col_attr.fp32_grad))
+                p.col_attr.fp32_grad.add_(grad_payload.view_as(p.col_attr.fp32_grad))
-                grad = p.col_attr.fp32_grad
+                grad_payload = p.col_attr.fp32_grad
-            p.grad.data = grad
+            p.grad.data = grad_payload
             p.col_attr.fp16_grad = None
             p.col_attr.fp32_grad = None
 

colossalai/zero/sharded_optim/sharded_optim_v2.py

@@ -9,6 +9,7 @@ from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer import ColossalaiOptimizer
+from colossalai.utils.memory_tracer.model_data_memtracer import GLOBAL_MODEL_DATA_TRACER
 from colossalai.zero.sharded_model import ShardedModelV2
 from colossalai.zero.sharded_model._utils import cast_tensor_to_fp32
 from torch import Tensor
@@ -217,6 +218,9 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         # We must set grad to None
         # Because we will judge whether local grad accumulation
         # is enabled by wheter grad is None
+        for group in self.param_groups:
+            for p in group['params']:
+                GLOBAL_MODEL_DATA_TRACER.delete_tensor(p.grad)
         self.optim.zero_grad(set_to_none=True)
 
     def sync_grad(self):