perf: use async copy to accelerate memcpy

colossalai/zero/low_level/low_level_optim.py

@@ -21,7 +21,14 @@ from colossalai.amp.naive_amp.mixed_precision_mixin import (
 from colossalai.interface import OptimizerWrapper
 from colossalai.logging import get_dist_logger
 
-from ._utils import calculate_global_norm_from_list, flatten, has_inf_or_nan, release_param_grad, sync_tensor
+from ._utils import (
+    DataPrefetcher,
+    calculate_global_norm_from_list,
+    flatten,
+    has_inf_or_nan,
+    release_param_grad,
+    sync_tensor,
+)
 from .bookkeeping import BucketStore, GradientStore, ParameterStore
 
 
@@ -437,10 +444,6 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
                 if len(grads) > 0:
                     real_working_params[group_id].append(working_param)
                     grad = grads[grad_index]
-                    # no need to copy fp32 grad if master_weights is False
-                    if self._master_weights:
-                        grad = grad.to(splited_param.dtype).to(splited_param.device)
-                    splited_param.grad = grad
                     grad_partition_groups.append(grad)
                     real_master_params[group_id].append(splited_param)
 
@@ -458,27 +461,68 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
         global_norm = calculate_global_norm_from_list(norm_list=norm_groups)
         self._unscale_and_clip_grads(grad_partition_groups, global_norm)
 
-        # update the parameters
-        self.optim.step()
-
-        # release the grad
-        grad_partition_groups = []
-        for group_id in range(self.num_param_groups):
-            release_param_grad(self._master_param_groups_of_current_rank[group_id])
-
-        # update working partition updated by the current rank
         device = get_accelerator().get_current_device()
         for group_id in range(self.num_param_groups):
-            master_working_param = self.optim.param_groups[group_id]["params"]
-            for idx, splited_param in enumerate(master_working_param):
-                working_param = real_working_params[group_id][idx]
-                all_splited_param = [
-                    torch.zeros(splited_param.shape, device=device, dtype=self._dtype) for _ in range(self._world_size)
-                ]
-                dist.all_gather(all_splited_param, splited_param.to(device).to(self._dtype), group=self.dp_pg)
-                working_param.data.copy_(flatten(all_splited_param)[: working_param.numel()].reshape_as(working_param))
+
+            def load_grad(num: int):
+                """copy grads to the same device and dtype as the master weights"""
+                for i in range(num):
+                    grad = grad_partition_groups.pop(0)
+                    # no need to copy fp32 grad if master_weights is False
+                    if self._master_weights:
+                        grad = grad.to(real_master_params[group_id][i].dtype).to(
+                            real_master_params[group_id][i].device, non_blocking=True
+                        )
+                    yield grad
+
+            def load_param(num: int):
+                """copy params back to the accelerator"""
+                for i in range(num):
+                    splited_param = real_master_params[group_id][i].to(device, non_blocking=True).to(self._dtype)
+                    yield splited_param
+
+            """
+            grad (device) --> grad (host or device) --> optim.step() --> param (host or device) --> param (device)
+            """
+            grad_pre_fetcher, param_pre_fetcher = None, None
+            for idx in range(len(real_master_params[group_id]) + 1):
+                is_first_step = idx == 0
+                is_last_step = idx == len(real_master_params[group_id])
+
+                if not is_last_step:
+                    # update the parameters
+                    if grad_pre_fetcher is None:
+                        grad_pre_fetcher = DataPrefetcher(load_grad(len(real_master_params[group_id])))
+
+                    real_master_params[group_id][idx].grad = grad_pre_fetcher.next()
+                    # HACK: torch optim would skip tensor whose grad is None
+                    self.optim.step()
+                    real_master_params[group_id][idx].grad = None
+
+                if not is_first_step:
+                    # update working partition updated by the current rank
+                    if param_pre_fetcher is None:
+                        param_pre_fetcher = DataPrefetcher(load_param(len(real_master_params[group_id])))
+
+                    working_param = real_working_params[group_id][idx - 1]
+                    splited_param = param_pre_fetcher.next()
+
+                    all_splited_param = [
+                        torch.zeros(splited_param.shape, device=device, dtype=self._dtype)
+                        for _ in range(self._world_size)
+                    ]
+                    dist.all_gather(all_splited_param, splited_param, group=self.dp_pg)
+
+                    working_param.data.copy_(
+                        flatten(all_splited_param)[: working_param.numel()].reshape_as(working_param)
+                    )
+
+            # release the grad
+            release_param_grad(self._master_param_groups_of_current_rank[group_id])
             self.optim.param_groups[group_id]["params"] = self._master_param_groups_of_current_rank[group_id]
 
+        assert len(grad_partition_groups) == 0, "grad_partition_groups should be empty after step()"
+
     def _compute_grad_norm(self, gradients: List[Tensor], norm_type: int = 2) -> float:
         r"""
         Compute and return the gradient norm for gradient clipping.