[zero] get memory usage of sharded optim v2. (#542)

colossalai/utils/memory_tracer/model_data_memtracer.py

@@ -1,18 +1,11 @@
 from colossalai.context.singleton_meta import SingletonMeta
 from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
+from colossalai.utils.memory_utils.utils import colo_tensor_mem_usage
 import torch
 from typing import Union, Tuple, Optional
 from colossalai.logging import DistributedLogger
 
 
-def _col_tensor_mem_usage(t: Union[torch.Tensor, ShardedTensor]) -> int:
-    if isinstance(t, ShardedTensor):
-        target = t.payload
-    else:
-        target = t
-    return target.numel() * target.element_size()
-
-
 def col_model_data_mem_usage(model: torch.nn.Module) -> Tuple[int, int]:
     """ 
     Trace the model memory usage.

colossalai/utils/memory_utils/utils.py

@@ -1,12 +1,32 @@
+from psutil import cpu_count
 import torch
 from colossalai.utils import get_current_device
 from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
 
-from typing import Union
+from typing import Tuple, Union
 
 _GLOBAL_CUDA_MEM_FRACTION = 1.0
 
 
+def colo_tensor_mem_usage(tensor: Union[torch.Tensor, ShardedTensor]) -> Tuple[int, int]:
+    if isinstance(tensor, ShardedTensor):
+        t = tensor.payload
+    elif isinstance(tensor, torch.Tensor):
+        t = tensor
+    else:
+        return 0, 0
+
+    cuda_use, cpu_use = 0, 0
+
+    mem_use = t.numel() * t.element_size()
+    if t.device.type == 'cuda':
+        cuda_use += mem_use
+    elif t.device.type == 'cpu':
+        cpu_use += mem_use
+
+    return cuda_use, cpu_use
+
+
 def colo_set_process_memory_fraction(ratio: float) -> None:
     """colo_set_process_memory_fraction 
 

colossalai/zero/sharded_optim/sharded_optim_v2.py

@@ -1,5 +1,6 @@
 from enum import Enum
-from typing import Dict, Optional
+from os import stat
+from typing import Dict, Optional, Tuple
 
 import torch
 import torch.distributed as dist
@@ -16,7 +17,7 @@ from torch.distributed import ProcessGroup
 from torch.nn.parameter import Parameter
 from torch.optim import Optimizer
 from colossalai.zero.sharded_optim._utils import has_inf_or_nan
-from colossalai.utils.memory_utils.utils import colo_model_data_tensor_move
+from colossalai.utils.memory_utils.utils import colo_model_data_tensor_move, colo_tensor_mem_usage
 
 
 class OptimState(Enum):
@@ -26,14 +27,20 @@ class OptimState(Enum):
 
 class ShardedOptimizerV2(ColossalaiOptimizer):
     """A wrapper for optimizer. `ShardedOptimizerV2` and `ShardedModelV2` implement Zero Redundancy Optimizer (ZeRO).
+
     By default the ZeRO optimizer stage 3 offload Optimizer States on CPU.
+    
     We apply the Device-aware Operator Placement technique for OS placement from the following paper.
+
     PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
     https://arxiv.org/abs/2108.05818
+    
     GPU margin space is the remaining space after removing peak non-model data from the overall GPU memory,
     which is detected by a runtime memory tracer. 
+    
     We place as many OS chunks in the margin space as possible. 
-    The size of margin space can be controlled by `gpu_margin_mem_ratio`
+    
+    The size of margin space can be controlled by `gpu_margin_mem_ratio`。
     If it is set as 0.0, it is the same as classical ZeRO optimizer.
 
     NOTE() You must use `ShardedOptimizerV2` with `ShardedModelV2`.
@@ -99,7 +106,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
                                              hysteresis=hysteresis,
                                              max_scale=max_scale)
         self._found_overflow: Tensor = torch.FloatTensor([0]).to(torch.cuda.current_device())
-        self._logger = get_dist_logger()
+        self._logger = get_dist_logger("ShardedOptimizerV2")
 
         # Store fp32 param shards
         self.master_params: Dict[Parameter, Tensor] = {}
@@ -119,6 +126,37 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
                     # So we gather here
                     self.shard_strategy.gather([p.col_attr.sharded_data_tensor], self.dp_process_group)
 
+        self._logger.debug(f"After init ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory!",
+                           ranks=[0])
+
+    def get_memory_usage(self) -> Tuple[int, int]:
+        """
+        Get the memory usage of the optimizer. Including master_params (param fp32),
+        momentum (self.state[p]['exp_avg']) variance (self.state[p]['exp_avg_sq'])
+
+        Returns:
+            Tuple[int, int]: cuda/cpu memory usage in Byte.
+        """
+        cuda_use = 0
+        cpu_use = 0
+
+        def update_mem_use(t):
+            nonlocal cuda_use
+            nonlocal cpu_use
+            t_cuda_use, t_cpu_use = colo_tensor_mem_usage(t)
+            cuda_use += t_cuda_use
+            cpu_use += t_cpu_use
+
+        for _, p_fp32 in self.master_params.items():
+            update_mem_use(p_fp32)
+        for group in self.optim.param_groups:
+            for p in group['params']:
+                state = self.optim.state[p]
+                for k, v in state.items():
+                    update_mem_use(v)
+
+        return cuda_use, cpu_use
+
     def step(self, *args, **kwargs):
         self._maybe_move_fp32_shards()
 
@@ -130,7 +168,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         self.grad_scaler.update(found_inf)
 
         if found_inf:
-            self._logger.info('found inf during ShardedOptimV2 step')
+            self._logger.warning('found inf during ShardedOptimV2 step')
             self.zero_grad()
             return
 
@@ -142,8 +180,13 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
                 # Now p.data is sharded
                 # So optimizer states are sharded naturally
 
+        self._logger.debug(f"Before step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory!",
+                           ranks=[0])
+
         ret = self.optim.step(*args, **kwargs)
 
+        self._logger.debug(f"After step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory!",
+                           ranks=[0])
         # Copy master param data (fp32) to payload of col_attr (fp16)
         # TODO() improve efficiency by gathering tensors into a chunk and transfering
         # a chunk.

colossalai/zero/sharded_param/sharded_param.py

@@ -2,6 +2,7 @@ import torch
 import torch.distributed as dist
 from colossalai.zero.sharded_param import ShardedTensor
 from typing import Optional, Tuple
+from colossalai.utils.memory_utils.utils import colo_tensor_mem_usage
 
 
 class ShardedParamV2(object):
@@ -55,10 +56,9 @@ class ShardedParamV2(object):
             assert isinstance(t, torch.Tensor)
             nonlocal cuda_mem_use
             nonlocal cpu_mem_use
-            if t.device.type == 'cpu':
+            t_cuda, t_cpu = colo_tensor_mem_usage(t)
-                cpu_mem_use += t.numel() * t.element_size()
+            cuda_mem_use += t_cuda
-            elif t.device.type == 'cuda':
+            cpu_mem_use += t_cpu
-                cuda_mem_use += t.numel() * t.element_size()
 
         address_set = set()
         _update_mem_use(self.sharded_data_tensor.payload)

tests/test_zero_data_parallel/test_sharded_optim_v2.py

@@ -1,6 +1,7 @@
 from functools import partial
 
 import colossalai
+from colossalai.utils.cuda import get_current_device
 import pytest
 import torch
 import torch.distributed as dist
@@ -57,11 +58,12 @@ def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, g
         get_components_func = non_distributed_component_funcs.get_callable(model_name)
         model_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
 
-        with ZeroInitContext(convert_fp16=True,
+        with ZeroInitContext(
-                             target_device=torch.device(f'cpu:0'),
+                convert_fp16=True,
-                             shard_strategy=shard_strategy,
+                target_device=torch.device(f'cpu:0') if cpu_offload else torch.device(f'cuda:{get_current_device()}'),
-                             shard_param=True,
+                shard_strategy=shard_strategy,
-                             rm_torch_payload_on_the_fly=False):
+                shard_param=True,
+                rm_torch_payload_on_the_fly=False):
             zero_model = model_builder(checkpoint=True)
         zero_model = ShardedModelV2(zero_model,
                                     shard_strategy,