[zero] use GeminiMemoryManager when sampling model data (#850)

colossalai/gemini/memory_tracer/memstats_collector.py

@@ -1,6 +1,6 @@
-from colossalai.gemini.memory_tracer import GLOBAL_MODEL_DATA_TRACER
 from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor
 from colossalai.utils.memory import colo_device_memory_used
+from colossalai.gemini.stateful_tensor import StatefulTensor
 
 import torch
 import time
@@ -92,7 +92,8 @@ class MemStatsCollector:
         """Sampling model data statistics.
         """
         if self._start_flag:
-            cuda_mem, cpu_mem = GLOBAL_MODEL_DATA_TRACER.both_mem_usage
+            cuda_mem = StatefulTensor.GST_MGR.total_mem['cuda']
+            cpu_mem = StatefulTensor.GST_MGR.total_mem['cpu']
             self._model_data_cuda_list.append(cuda_mem)
             self._model_data_cpu_list.append(cpu_mem)
 
@@ -114,24 +115,6 @@ class MemStatsCollector:
             self._sampling_time.append(time.time())
             self._mem_monitor.start()
 
-    def sample_memstats(self) -> None:
-        """
-        Sampling memory statistics.
-        Record the current model data CUDA memory usage as well as system CUDA memory usage.
-        Advance the sampling cnter.
-        """
-        if self._start_flag:
-            self._model_data_cuda_list.append(GLOBAL_MODEL_DATA_TRACER.cuda_usage)
-            self._overall_cuda_list.append(self._mem_monitor.finish())
-            self._non_model_data_cuda_list.append(self._overall_cuda_list[-1] - self._model_data_cuda_list[-1])
-
-            self._model_data_cpu_list.append(GLOBAL_MODEL_DATA_TRACER.cpu_usage)
-            # FIXME(jiaruifang) cpu sys used should also return from self._mem_monitor()
-            self._overall_cpu_list.append(colo_device_memory_used(torch.device(f'cpu')))
-            self._non_model_data_cpu_list.append(self._overall_cpu_list[-1] - self._model_data_cpu_list[-1])
-            self._sampling_time.append(time.time())
-            self._mem_monitor.start()
-
     def clear(self) -> None:
         self._model_data_cuda_list = []
         self._overall_cuda_list = []

colossalai/gemini/tensor_placement_policy.py

@@ -7,7 +7,6 @@ from colossalai.utils.memory import colo_device_memory_capacity
 from colossalai.gemini.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage
 from colossalai.gemini.stateful_tensor import StatefulTensor
 from colossalai.gemini.memory_tracer import MemStatsCollector
-from colossalai.gemini.memory_tracer import GLOBAL_MODEL_DATA_TRACER
 from typing import Type
 
 
@@ -79,7 +78,7 @@ class AutoTensorPlacementPolicy(TensorPlacementPolicy):
         """
         volume = 0
         cuda_capacity = colo_device_memory_capacity(get_current_device())
-        used_cuda_model_data = GLOBAL_MODEL_DATA_TRACER.cuda_usage
+        used_cuda_model_data = StatefulTensor.GST_MGR.total_mem['cuda']
         if warmup:
             # We designate a part of CUDA memory for model data in warmup iterations.
             max_cuda_non_model_data_per_period = cuda_capacity * self._warmup_non_model_data_ratio

colossalai/zero/sharded_model/sharded_model_v2.py

@@ -13,8 +13,6 @@ from colossalai.engine.paramhooks import BaseParamHookMgr
 from colossalai.logging import get_dist_logger
 from colossalai.utils import get_current_device, disposable
 from colossalai.gemini.memory_tracer.memstats_collector import MemStatsCollector
-from colossalai.gemini.memory_tracer.model_data_memtracer import \
-    GLOBAL_MODEL_DATA_TRACER
 from colossalai.utils.memory import colo_device_memory_capacity
 from colossalai.zero.shard_utils import BaseShardStrategy
 from colossalai.zero.sharded_model.reduce_scatter import ReduceScatterBucketer
@@ -106,7 +104,6 @@ class ShardedModelV2(nn.Module):
 
         self._use_memory_tracer = tensor_placement_policy == 'auto'
         if self._use_memory_tracer:
-            GLOBAL_MODEL_DATA_TRACER.register_model(self)
             self._memstats_collector = MemStatsCollector()
             self._start_collect_memstats = disposable(self._memstats_collector.start_collection)
             self._finish_collect_memstats = disposable(self._memstats_collector.finish_collection)

colossalai/zero/sharded_optim/sharded_optim_v2.py

@@ -10,10 +10,7 @@ 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.gemini.memory_tracer.model_data_memtracer import \
+from colossalai.gemini.tensor_utils import (colo_model_data_tensor_move_inline, colo_tensor_mem_usage)
-    GLOBAL_MODEL_DATA_TRACER
-from colossalai.gemini.tensor_utils import (colo_model_data_tensor_move_inline, colo_model_tensor_clone,
-                                            colo_tensor_mem_usage)
 from colossalai.zero.sharded_model import ShardedModelV2
 from colossalai.zero.sharded_model._utils import cast_tensor_to_fp32
 from torch import Tensor
@@ -130,8 +127,6 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
                 f"After init ShardedOptimizerV2 consumes {self.get_memory_usage()[0] / 1e6} MB CUDA Memory!", ranks=[0])
 
         self._use_memory_tracer = self.model.use_memory_tracer
-        if self._use_memory_tracer:
-            GLOBAL_MODEL_DATA_TRACER.register_optimizer(self)
 
     @property
     def loss_scale(self):