[Gemini] Use async stream to prefetch and h2d data moving

colossalai/zero/gemini/chunk/manager.py

@@ -25,6 +25,7 @@ def __init__(
         chunk_configuration,
         init_device: Optional[torch.device] = None,
         reuse_fp16_chunk: bool = True,
+        max_prefetch: int = 0,
     ) -> None:
         self.device = init_device or get_accelerator().get_current_device()
         self.dp_degree_chunk_size_dict: Dict[int, int] = dict()
@@ -42,6 +43,7 @@ def __init__(
         # Whether model is accumulating gradients,
         self.accumulating_grads = False
         self.overflow_counter = torch.tensor([0], dtype=torch.int, device=get_accelerator().get_current_device())
+        self._prefetch_stream = get_accelerator().Stream() if max_prefetch else None
 
     def register_tensor(
         self,

colossalai/zero/gemini/chunk/utils.py

@@ -21,6 +21,7 @@ def init_chunk_manager(
     hidden_dim: Optional[int] = None,
     reuse_fp16_chunk: bool = True,
     verbose: bool = False,
+    max_prefetch: int = 0,
     **kwargs,
 ) -> ChunkManager:
     if hidden_dim:
@@ -51,9 +52,5 @@ def init_chunk_manager(
         )
     dist.barrier()
 
-    chunk_manager = ChunkManager(
-        config_dict,
-        init_device,
-        reuse_fp16_chunk=reuse_fp16_chunk,
-    )
+    chunk_manager = ChunkManager(config_dict, init_device, reuse_fp16_chunk=reuse_fp16_chunk, max_prefetch=max_prefetch)
     return chunk_manager

colossalai/zero/gemini/gemini_ddp.py

@@ -104,9 +104,7 @@ def __init__(
         self.enable_gradient_accumulation = enable_gradient_accumulation
         if chunk_config_dict is not None:
             self.chunk_manager = ChunkManager(
-                chunk_config_dict,
-                chunk_init_device,
-                reuse_fp16_chunk=reuse_fp16_chunk,
+                chunk_config_dict, chunk_init_device, reuse_fp16_chunk=reuse_fp16_chunk, max_prefetch=max_prefetch
             )
         else:
             # some ugly hotfix for the compatibility with Lightning
@@ -122,6 +120,7 @@ def __init__(
                 process_group=zero_group,
                 reuse_fp16_chunk=reuse_fp16_chunk,
                 verbose=verbose,
+                max_prefetch=max_prefetch,
             )
         self.gemini_manager = GeminiManager(
             placement_policy,

colossalai/zero/gemini/gemini_hook.py

@@ -5,6 +5,7 @@
 
 import torch
 
+from colossalai.accelerator import get_accelerator
 from colossalai.tensor.param_op_hook import ColoParamOpHook
 from colossalai.utils import is_ddp_ignored
 from colossalai.zero.gemini import TensorState
@@ -54,10 +55,11 @@ def pre_op(self, params):
         )
 
         # prefetch
-        for chunk in chunks_fetch_async:
-            maybe_work = self._chunk_manager.access_chunk(chunk, async_access=True)
-            if maybe_work is not None:
-                self._gemini_manager.add_work(chunk, maybe_work)
+        with get_accelerator().stream(self._gemini_manager.chunk_manager._prefetch_stream):
+            for chunk in chunks_fetch_async:
+                maybe_work = self._chunk_manager.access_chunk(chunk, async_access=True)
+                if maybe_work is not None:
+                    self._gemini_manager.add_work(chunk, maybe_work)
 
         # record cuda model data of the current OP, including memory for prefetched chunks
         self._gemini_manager.record_model_data_volume()