mirror of https://github.com/hpcaitech/ColossalAI
115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
from contextlib import contextmanager
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from enum import Enum
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from functools import partial
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from typing import List
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import torch
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from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor
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from colossalai.gemini.memory_tracer.model_data_memtracer import GLOBAL_CUDA_MEM_INFO
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from colossalai.gemini.tensor_utils import alloc_storage, free_storage
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from colossalai.tensor.param_op_hook import ColoParamOpHook
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class TrainingPhase(Enum):
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FORWARD = 0
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BACKWARD = 1
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class GradMemTracerHook():
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def __init__(self, module: torch.nn.Module):
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self.module = module
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self.grad_hook_list = []
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def grad_handle(self, p, grad):
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assert GLOBAL_CUDA_MEM_INFO.unreleased_grad_flag[p]
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free_storage(grad)
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GLOBAL_CUDA_MEM_INFO.unreleased_grad_volume -= grad.numel() * grad.element_size()
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GLOBAL_CUDA_MEM_INFO.unreleased_grad_flag[p] = False
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def register_grad_hook(self):
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for p in self.module.parameters():
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if p.requires_grad:
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self.grad_hook_list.append(p.register_hook(partial(self.grad_handle, p)))
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GLOBAL_CUDA_MEM_INFO.unreleased_grad_flag[p] = False
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def remove_grad_hook(self):
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for hook in self.grad_hook_list:
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hook.remove()
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class ParamMemTracerHook(ColoParamOpHook):
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def __init__(self) -> None:
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super().__init__()
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self._training_phase = TrainingPhase.FORWARD
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self.mem_monitor = SyncCudaMemoryMonitor()
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def _free_cuda_params(self, params):
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for p in params:
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if p.data.device.type == "cpu":
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raise NotImplementedError("Only free cuda memory")
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free_storage(p.data)
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def _allocate_params_on_cuda(self, params):
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for p in params:
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cur_dev = p.data.device.type
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if cur_dev == "cpu":
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if p.grad is not None and p.grad.device.type == "cpu":
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raise NotImplementedError("Only run in forward propagation")
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p.data = torch.empty(p.data.shape,
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device="cuda",
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dtype=p.data.dtype,
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requires_grad=p.data.requires_grad)
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elif cur_dev == "cuda":
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alloc_storage(p.data)
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def sample_model_data(self, params):
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data_volume = GLOBAL_CUDA_MEM_INFO.unreleased_grad_volume
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for p in params:
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cur_model_data_volume = p.data.numel() * p.data.element_size()
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data_volume += cur_model_data_volume
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if self._training_phase == TrainingPhase.BACKWARD and p.requires_grad:
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# add param.grad, actually param.grad is None in this time
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data_volume += cur_model_data_volume
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if not GLOBAL_CUDA_MEM_INFO.unreleased_grad_flag[p]:
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GLOBAL_CUDA_MEM_INFO.unreleased_grad_volume += cur_model_data_volume
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GLOBAL_CUDA_MEM_INFO.unreleased_grad_flag[p] = True
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GLOBAL_CUDA_MEM_INFO.model_data_list.append(data_volume)
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def pre_op(self, params):
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cuda_volume = self.mem_monitor.finish()
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if len(GLOBAL_CUDA_MEM_INFO.model_data_list):
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GLOBAL_CUDA_MEM_INFO.non_model_data_list.append(cuda_volume - GLOBAL_CUDA_MEM_INFO.model_data_list[-1])
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self._allocate_params_on_cuda(params)
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self.sample_model_data(params)
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self.mem_monitor.start()
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def post_op(self, params):
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self._free_cuda_params(params)
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def pre_forward(self, params: List[torch.Tensor]) -> None:
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self.pre_op(params)
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def post_forward(self, params: List[torch.Tensor]) -> None:
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self.post_op(params)
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def pre_backward(self, params: List[torch.Tensor]) -> None:
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self.pre_op(params)
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def post_backward(self, params: List[torch.Tensor]) -> None:
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self.post_op(params)
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@contextmanager
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def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
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old_training_phase = self._training_phase
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try:
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self._training_phase = training_phase
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yield
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finally:
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self._training_phase = old_training_phase
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switch_to_backward = switch_training_phase
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switch_to_forward = partial(switch_to_backward, training_phase=TrainingPhase.FORWARD)
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