mirror of https://github.com/hpcaitech/ColossalAI
138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
# this code is inspired by the DeepSpeed library and implemented with our own design from scratch
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from abc import ABC, abstractmethod
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from typing import Callable, List, Optional
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import torch
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class BaseOpHook(ABC):
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"""This class allows users to add customized operations
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before and after the execution of a PyTorch submodule"""
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def __init__(self):
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pass
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@abstractmethod
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def pre_fwd_exec(self, module: torch.nn.Module, *args):
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pass
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@abstractmethod
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def post_fwd_exec(self, module: torch.nn.Module, *args):
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pass
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@abstractmethod
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def pre_bwd_exec(self, module: torch.nn.Module, input, output):
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pass
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@abstractmethod
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def post_bwd_exec(self, module: torch.nn.Module, input):
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pass
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@abstractmethod
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def post_iter(self):
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pass
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# apply torch.autograd.Function that calls a backward_function to tensors in output
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def _apply_to_tensors_only(module, functional, backward_function, outputs):
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if type(outputs) is tuple:
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touched_outputs = []
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for output in outputs:
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touched_output = _apply_to_tensors_only(module, functional, backward_function, output)
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touched_outputs.append(touched_output)
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return tuple(touched_outputs)
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elif type(outputs) is torch.Tensor:
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return functional.apply(module, backward_function, outputs)
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else:
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return outputs
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class PreBackwardFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, module, pre_backward_function, outputs):
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ctx.module = module
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ctx.pre_backward_function = pre_backward_function
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module.applied_pre_backward = False
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outputs = outputs.detach()
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return outputs
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@staticmethod
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def backward(ctx, *args):
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ctx.pre_backward_function(ctx.module)
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return (None, None) + args
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class PostBackwardFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, module, pre_backward_function, output):
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ctx.module = module
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output = output.detach()
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ctx.pre_backward_function = pre_backward_function
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return output
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@staticmethod
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def backward(ctx, *args):
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"""
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Args:
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activation_grad of the next layer.
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Returns:
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grad of the input activation.
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"""
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ctx.pre_backward_function(ctx.module)
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return (None, None) + args
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def register_ophooks_recursively(
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module: torch.nn.Module, ophook_list: List[BaseOpHook], name: str = "", filter_fn: Optional[Callable] = None
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):
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r"""Recursively register pre/post hooks for all submodules in the module in FWD and BWD."""
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assert isinstance(module, torch.nn.Module)
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assert isinstance(ophook_list, (list, tuple))
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assert len(ophook_list) > 0, "expected at least 1 hook in the argument ophook_list but found 0"
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for hook in ophook_list:
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assert isinstance(hook, BaseOpHook)
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# Add hooks for submodules
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for child_name, child in module.named_children():
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register_ophooks_recursively(child, ophook_list, name + child_name, filter_fn)
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# Early return on modules with no parameters.
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if len(list(module.parameters(recurse=False))) == 0:
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return
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# return from filtered module
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if filter_fn is not None and filter_fn(module):
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return
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def _pre_forward_module_hook(submodule, *args):
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for hook in ophook_list:
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assert isinstance(submodule, torch.nn.Module)
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hook.pre_fwd_exec(submodule, *args)
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def _post_forward_module_hook(submodule, *args):
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for hook in ophook_list:
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assert isinstance(submodule, torch.nn.Module)
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hook.post_fwd_exec(submodule, *args)
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def _pre_backward_module_hook(submodule, inputs, output):
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def _run_before_backward_function(submodule):
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for hook in ophook_list:
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assert isinstance(submodule, torch.nn.Module)
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hook.pre_bwd_exec(submodule, inputs, output)
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return _apply_to_tensors_only(submodule, PreBackwardFunction, _run_before_backward_function, output)
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def _post_backward_module_hook(submodule, inputs):
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def _run_after_backward_function(submodule):
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for hook in ophook_list:
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assert isinstance(submodule, torch.nn.Module)
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hook.post_bwd_exec(submodule, inputs)
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return _apply_to_tensors_only(submodule, PostBackwardFunction, _run_after_backward_function, inputs)
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module.register_forward_pre_hook(_pre_forward_module_hook)
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module.register_forward_hook(_post_forward_module_hook)
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module.register_forward_hook(_pre_backward_module_hook)
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module.register_forward_pre_hook(_post_backward_module_hook)
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