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[hotfix] fix a bug in model data stats tracing (#655)
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@ -1,7 +1,7 @@
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from colossalai.utils.memory_tracer.model_data_memtracer import GLOBAL_MODEL_DATA_TRACER
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from colossalai.utils.memory_tracer.model_data_memtracer import GLOBAL_MODEL_DATA_TRACER
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from colossalai.utils.memory_utils.utils import colo_device_memory_used
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from colossalai.utils.memory_utils.utils import colo_device_memory_used
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from colossalai.utils import get_current_device
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from colossalai.utils import get_current_device
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from colossalai.utils.memory_tracer.async_memtracer import AsyncMemoryMonitor
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import torch
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import torch
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import time
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import time
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from typing import List
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from typing import List
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@ -37,6 +37,7 @@ class MemStatsCollector:
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def __init__(self) -> None:
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def __init__(self) -> None:
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self._sampling_cnter = SamplingCounter()
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self._sampling_cnter = SamplingCounter()
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self._mem_monitor = AsyncMemoryMonitor()
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self._model_data_cuda_list = []
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self._model_data_cuda_list = []
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self._overall_cuda_list = []
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self._overall_cuda_list = []
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@ -101,6 +102,7 @@ class MemStatsCollector:
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def start_collection(self):
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def start_collection(self):
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self._start_flag = True
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self._start_flag = True
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self._mem_monitor.start()
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def finish_collection(self):
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def finish_collection(self):
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self._start_flag = False
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self._start_flag = False
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@ -115,17 +117,20 @@ class MemStatsCollector:
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sampling_cnt = self._sampling_cnter.sampling_cnt
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sampling_cnt = self._sampling_cnter.sampling_cnt
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assert sampling_cnt == len(self._overall_cuda_list)
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assert sampling_cnt == len(self._overall_cuda_list)
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self._model_data_cuda_list.append(GLOBAL_MODEL_DATA_TRACER.cuda_usage)
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self._model_data_cuda_list.append(GLOBAL_MODEL_DATA_TRACER.cuda_usage)
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self._overall_cuda_list.append(colo_device_memory_used(get_current_device()))
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self._overall_cuda_list.append(self._mem_monitor.finish())
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self._model_data_cpu_list.append(GLOBAL_MODEL_DATA_TRACER.cpu_usage)
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self._model_data_cpu_list.append(GLOBAL_MODEL_DATA_TRACER.cpu_usage)
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# FIXME() cpu sys used should also return from self._mem_monitor()
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self._overall_cpu_list.append(colo_device_memory_used(torch.device(f'cpu')))
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self._overall_cpu_list.append(colo_device_memory_used(torch.device(f'cpu')))
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self._sampling_time.append(time.time())
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self._sampling_time.append(time.time())
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self._mem_monitor.start()
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self._sampling_cnter.advance()
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self._sampling_cnter.advance()
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def reset_sampling_cnter(self) -> None:
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def reset_sampling_cnter(self) -> None:
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self._sampling_cnter.reset()
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self._sampling_cnter.reset()
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self._mem_monitor.finish()
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def clear(self) -> None:
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def clear(self) -> None:
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self._model_data_cuda_list = []
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self._model_data_cuda_list = []
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@ -136,3 +141,4 @@ class MemStatsCollector:
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self._start_flag = False
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self._start_flag = False
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self._sampling_cnter.reset()
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self._sampling_cnter.reset()
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self._mem_monitor.finish()
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@ -33,7 +33,7 @@ def colo_model_mem_usage(model: torch.nn.Module) -> Tuple[int, int]:
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def _get_tensor_mem_use(t: Optional[torch.Tensor]):
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def _get_tensor_mem_use(t: Optional[torch.Tensor]):
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if t is None:
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if t is None:
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return
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return 0, 0
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assert isinstance(t, torch.Tensor)
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assert isinstance(t, torch.Tensor)
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_cpu_mem_usage, _cuda_mem_usage = 0, 0
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_cpu_mem_usage, _cuda_mem_usage = 0, 0
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if t.device.type == 'cpu':
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if t.device.type == 'cpu':
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@ -139,10 +139,6 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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if self._use_memory_tracer:
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if self._use_memory_tracer:
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GLOBAL_MODEL_DATA_TRACER.register_optimizer(self)
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GLOBAL_MODEL_DATA_TRACER.register_optimizer(self)
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self._use_memory_tracer = self.model.use_memory_tracer
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if self._use_memory_tracer:
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GLOBAL_MODEL_DATA_TRACER.register_optimizer(self)
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def get_memory_usage(self) -> Tuple[int, int]:
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def get_memory_usage(self) -> Tuple[int, int]:
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""" Get the memory usage of the optimizer. Including master_params (param fp32),
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""" Get the memory usage of the optimizer. Including master_params (param fp32),
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momentum (``self.state[p]['exp_avg']``) variance (``self.state[p]['exp_avg_sq']``)
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momentum (``self.state[p]['exp_avg']``) variance (``self.state[p]['exp_avg_sq']``)
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@ -186,7 +182,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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self._zero_grad(recover_data=True)
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self._zero_grad(recover_data=True)
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return
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return
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self._prepare_data()
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self._point_param_fp16_to_master_param()
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self._logger.debug(
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self._logger.debug(
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f"Before step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory, {self.get_memory_usage()[1]/1e6} MB CUDA Memory!",
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f"Before step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory, {self.get_memory_usage()[1]/1e6} MB CUDA Memory!",
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@ -197,7 +193,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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self._logger.debug(
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self._logger.debug(
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f"After step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory, {self.get_memory_usage()[1]/1e6} MB CUDA Memory!",
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f"After step ShardedOptimizerV2 consumes {self.get_memory_usage()[0]/1e6} MB CUDA Memory, {self.get_memory_usage()[1]/1e6} MB CUDA Memory!",
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ranks=[0])
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ranks=[0])
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self._write_back_data()
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self._copy_master_param_to_param_fp16()
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return ret
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return ret
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def backward(self, loss: Tensor) -> None:
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def backward(self, loss: Tensor) -> None:
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@ -319,7 +315,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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# Set p.data to empty tensor, in case of memory leaking
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# Set p.data to empty tensor, in case of memory leaking
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p.colo_attr.remove_torch_payload()
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p.colo_attr.remove_torch_payload()
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def _prepare_data(self):
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def _point_param_fp16_to_master_param(self):
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# assign master param pointers to p.data.
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# assign master param pointers to p.data.
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# We will not trigger data copy here.
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# We will not trigger data copy here.
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for group in self.optim.param_groups:
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for group in self.optim.param_groups:
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@ -329,7 +325,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
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# Now p.data is sharded
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# Now p.data is sharded
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# So optimizer states are sharded naturally
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# So optimizer states are sharded naturally
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def _write_back_data(self):
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def _copy_master_param_to_param_fp16(self):
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# Copy master param data (fp32) to payload of colo_attr (fp16)
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# Copy master param data (fp32) to payload of colo_attr (fp16)
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# TODO() improve efficiency by gathering tensors into a chunk and transfering
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# TODO() improve efficiency by gathering tensors into a chunk and transfering
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# a chunk.
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# a chunk.
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@ -91,6 +91,7 @@ def _run_dist(rank, world_size, port):
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@pytest.mark.dist
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2, 4])
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@pytest.mark.parametrize("world_size", [2, 4])
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@pytest.mark.skip("Under development")
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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def test_moe_zero_init(world_size):
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def test_moe_zero_init(world_size):
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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