Merge branch 'prefetch' of github.com:botbw/ColossalAI into botbw-prefetch

colossalai/booster/plugin/gemini_plugin.py

@@ -329,6 +329,7 @@ class GeminiPlugin(DPPluginBase):
         chunk_init_device: Optional[torch.device] = None,
         placement_policy: str = "static",
         enable_gradient_accumulation: bool = False,
+        max_prefetch: int = 0,
         shard_param_frac: float = 1.0,  # only for static placement
         offload_optim_frac: float = 0.0,  # only for static placement
         offload_param_frac: float = 0.0,  # only for static placement
@@ -386,6 +387,7 @@ class GeminiPlugin(DPPluginBase):
             memstats=memstats,
             mixed_precision=PRECISION_STR_TO_DTYPE[precision],
             master_weights=master_weights,
+            max_prefetch=max_prefetch,
         )
         self.zero_optim_config = dict(
             gpu_margin_mem_ratio=gpu_margin_mem_ratio,

colossalai/zero/gemini/chunk/chunk.py

@@ -357,14 +357,14 @@ class Chunk:
         else:
             raise NotImplementedError
 
-    def access_chunk(self):
+    def access_chunk(self, async_access: bool = False) -> Optional[dist.Work]:
         """Make the chunk usable for the parameters inside it. It's an operation done in CUDA."""
         # sanity check
         assert self.chunk_temp is None
-
         if not self.is_gathered:
-            self.__gather()
+            return self.__gather(async_op=async_access)
         self.__update_tensors_ptr()
+        return None
 
     def release_chunk(self):
         """Release the usable chunk. It's an operation done in CUDA."""
@@ -498,17 +498,19 @@ class Chunk:
     def get_tensors(self) -> List[torch.Tensor]:
         return list(self.tensors_info.keys())
 
-    def __gather(self):
+    def __gather(self, async_op: bool = False) -> Optional[dist.Work]:
         if not self.is_gathered:
             # sanity check
             assert self.cuda_shard is not None
 
             alloc_storage(self.cuda_global_chunk)
             gather_list = list(torch.chunk(input=self.cuda_global_chunk, chunks=self.pg_size, dim=0))
-            dist.all_gather(gather_list, self.cuda_shard, self.torch_pg)
+            work = dist.all_gather(gather_list, self.cuda_shard, self.torch_pg, async_op=async_op)
 
             self.cuda_shard = None
             self.is_gathered = True
+            return work
+        return None
 
     def __scatter(self):
         if self.keep_gathered:

colossalai/zero/gemini/chunk/manager.py

@@ -111,15 +111,16 @@ class ChunkManager:
         for group_name in self.chunk_groups:
             self.__close_one_chunk(self.chunk_groups[group_name][-1])
 
-    def access_chunk(self, chunk: Chunk) -> None:
+    def access_chunk(self, chunk: Chunk, async_access: bool = False) -> Optional[dist.Work]:
         """Make the chunk can be used for calculation."""
         if chunk in self.accessed_chunks:
-            return
+            return None
         self.__sub_memory_usage(chunk.memory_usage)
         if chunk.device_type == "cpu":
             chunk.shard_move(get_accelerator().get_current_device())
-        self.__add_accessed_chunk(chunk)
+        maybe_work = self.__add_accessed_chunk(chunk, async_access=async_access)
         self.__add_memory_usage(chunk.memory_usage)
+        return maybe_work
 
     def release_chunk(self, chunk: Chunk) -> None:
         """Scatter the chunk in CUDA."""
@@ -251,10 +252,11 @@ class ChunkManager:
         for k, v in usage.items():
             self.total_mem[k] += v
 
-    def __add_accessed_chunk(self, chunk: Chunk):
+    def __add_accessed_chunk(self, chunk: Chunk, async_access: bool = False) -> Optional[dist.Work]:
-        chunk.access_chunk()
+        maybe_work = chunk.access_chunk(async_access=async_access)
         self.accessed_chunks.add(chunk)
         self.accessed_mem += chunk.chunk_mem
+        return maybe_work
 
     def __sub_accessed_chunk(self, chunk: Chunk):
         chunk.release_chunk()

colossalai/zero/gemini/gemini_ddp.py

@@ -78,6 +78,7 @@ class GeminiDDP(ModelWrapper):
         chunk_init_device: torch.device = torch.device("cpu"),
         placement_policy: str = "static",
         enable_gradient_accumulation: bool = False,
+        max_prefetch: int = 0,
         shard_param_frac: float = 1.0,  # only for static placement
         offload_optim_frac: float = 0.0,  # only for static placement
         offload_param_frac: float = 0.0,  # only for static placement
@@ -132,7 +133,7 @@ class GeminiDDP(ModelWrapper):
             steady_cuda_cap_ratio=steady_cuda_cap_ratio,
         )
         self.force_outputs_fp32 = force_outputs_fp32
-        self.param_op_hook = GeminiZeROHook(self.gemini_manager)
+        self.param_op_hook = GeminiZeROHook(self.gemini_manager, max_prefetch=max_prefetch)
         self.fp32_params: List[torch.Tensor] = list()
         self.fp16_params: List[ColoParameter] = list()
         self.grads_device: Dict[torch.Tensor, torch.device] = dict()

colossalai/zero/gemini/gemini_hook.py

@@ -1,39 +1,67 @@
 from contextlib import contextmanager
 from enum import Enum
 from functools import partial
-from typing import List
+from typing import Dict, List
 
 import torch
+import torch.distributed as dist
 
+from colossalai.logging import DistributedLogger
 from colossalai.tensor.param_op_hook import ColoParamOpHook
 from colossalai.utils import is_ddp_ignored
 from colossalai.zero.gemini import TensorState
 from colossalai.zero.gemini.gemini_mgr import GeminiManager
 
+from .chunk import Chunk
+
 
 class TrainingPhase(Enum):
     FORWARD = 0
     BACKWARD = 1
 
 
+logger = DistributedLogger("gemini_hook")
+
+
 class GeminiZeROHook(ColoParamOpHook):
-    def __init__(self, gemini_manager: GeminiManager) -> None:
+    def __init__(self, gemini_manager: GeminiManager, max_prefetch: int = 0) -> None:
         super().__init__()
         self._gemini_manager = gemini_manager
         self._chunk_manager = gemini_manager.chunk_manager
         self._training_phase = TrainingPhase.FORWARD
+        self._max_prefetch = max_prefetch
+        self._async_works: Dict[Chunk, dist.work] = {}
+
+    def wait_chunks(self, chunks: List[Chunk]) -> List[Chunk]:
+        non_prefetched_chunks = []
+        for chunk in chunks:
+            if chunk in self._async_works:
+                print(f"prefetched {chunk.count_id}")
+                self._async_works[chunk].wait()
+                del self._async_works[chunk]
+            else:
+                non_prefetched_chunks.append(chunk)
+        return non_prefetched_chunks
 
     def pre_op(self, params):
         params = [p for p in params if not is_ddp_ignored(p)]
-        chunks = self._chunk_manager.get_chunks(params)
+        all_chunks = self._chunk_manager.get_chunks(params)
+        # wait for prefetched chunks, filter those are not prefetched
+        chunks_fetch_sync = tuple(self.wait_chunks(all_chunks))
         for p in params:
             self._chunk_manager.trans_tensor_state(p, TensorState.COMPUTE)
         self._gemini_manager.sample_overall_data()
-        self._gemini_manager.adjust_layout(chunks)
+        self._gemini_manager.adjust_layout(all_chunks, record_anyway=self._max_prefetch > 0)
-        for chunk in chunks:
+        # fetch the rest chunks synchronously
+        for chunk in chunks_fetch_sync:
             self._chunk_manager.access_chunk(chunk)
+        chunks_fetch_async = self._gemini_manager.placement_policy.get_prefetch_chunks(max_prefetch=self._max_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._async_works[chunk] = maybe_work
 
-        # record cuda model data of the current OP
+        # record cuda model data of the current OP, including memory for prefetched chunks
         self._gemini_manager.record_model_data_volume()
 
     def post_op(self, params):
@@ -60,6 +88,11 @@ class GeminiZeROHook(ColoParamOpHook):
 
     @contextmanager
     def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
+        if training_phase == TrainingPhase.FORWARD:
+            self._cur_param_idx = 0
+        else:
+            self._cur_param_idx = len(self._param_visited_order) - 1
+
         old_training_phase = self._training_phase
         try:
             self._training_phase = training_phase

colossalai/zero/gemini/gemini_mgr.py

@@ -6,7 +6,7 @@ import torch
 
 from .chunk import Chunk, ChunkManager
 from .memory_tracer import ChunkMemStatsCollector, MemStats
-from .placement_policy import PlacementPolicyFactory
+from .placement_policy import PlacementPolicy, PlacementPolicyFactory
 
 
 class GeminiManager:
@@ -91,13 +91,13 @@ class GeminiManager:
         self._warmup = False
         self.reset_attributes()
 
-    def adjust_layout(self, chunks: Tuple[Chunk, ...]) -> None:
+    def adjust_layout(self, chunks: Tuple[Chunk, ...], record_anyway: bool = False) -> None:
         """Adjust the layout of stateful tensors according to the information provided
         by mem_stats_collector, which should belongs to a Sharded Model.
         """
         # find stateful tensor in state COMPUTE
         start = time()
-        self._record_chunks_order(chunks)
+        self._record_warmup_chunks_order(chunks, record_anyway=record_anyway)
         cuda_demand, hold_cuda_tensor_list = self._get_layout_info(self._compute_idx, self._warmup, chunks)
         self._layout_time += time() - start
 
@@ -133,9 +133,9 @@ class GeminiManager:
         can_evict_chunks = self._chunk_manager.get_cuda_movable_chunks()
         return cuda_demand, can_evict_chunks
 
-    def _record_chunks_order(self, chunks: Tuple[Chunk, ...]) -> None:
+    def _record_warmup_chunks_order(self, chunks: Tuple[Chunk, ...], record_anyway: bool = False) -> None:
         self._compute_idx += 1
-        if self._warmup and self._placement_policy.need_mem_stats:
+        if self._warmup and (self._placement_policy.need_mem_stats or record_anyway):
             self._compute_list.append(chunks)
 
     def sample_overall_data(self):
@@ -156,6 +156,18 @@ class GeminiManager:
             return self._mem_stats_collector.cuda_margin_mem
         return None
 
+    @property
+    def compute_list(self) -> List[Tuple[Chunk, ...]]:
+        return self._compute_list
+
+    @property
+    def compute_idx(self) -> int:
+        return self._compute_idx
+
+    @property
+    def placement_policy(self) -> PlacementPolicy:
+        return self._placement_policy
+
     @property
     def is_cuda_margin_mem_avail(self) -> bool:
         return self._placement_policy.need_mem_stats

colossalai/zero/gemini/placement_policy.py

@@ -33,6 +33,10 @@ class PlacementPolicy(ABC):
     ) -> None:
         raise NotImplementedError
 
+    @abstractmethod
+    def get_prefetch_chunks(self, max_prefetch: int) -> List[Chunk]:
+        raise NotImplementedError
+
 
 class StaticPlacementPolicy(PlacementPolicy):
     def __init__(
@@ -95,6 +99,18 @@ class StaticPlacementPolicy(PlacementPolicy):
         self.keep_gathered_chunk_mem = total_chunk_mem * (1 - self.shard_param_frac)
         self.keep_cuda_chunk_mem = total_chunk_mem * (1 - self.offload_param_frac)
 
+    def get_prefetch_chunks(self, max_prefetch: int) -> List[Chunk]:
+        prefetch = []
+        for i in range(self.chunk_manager.compute_idx + 1, len(self.chunk_manager.compute_list)):
+            for chunk in self.chunk_manager.compute_list[i]:
+                if len(prefetch) >= max_prefetch:
+                    break
+                if chunk not in prefetch:
+                    prefetch.append(chunk)
+            if len(prefetch) >= max_prefetch:
+                break
+        return prefetch
+
 
 class AutoPlacementPolicy(PlacementPolicy):
     need_mem_stats: bool = True
@@ -198,6 +214,9 @@ class AutoPlacementPolicy(PlacementPolicy):
             else:
                 grads_device_map[p] = torch.device("cpu")
 
+    def get_prefetch_chunks(self, max_prefetch: int) -> List[Chunk]:
+        return []  # TODO @botbw: implement prefetching for auto
+
 
 class PlacementPolicyFactory:
     policies: Dict[str, Type[PlacementPolicy]] = {