mirror of https://github.com/hpcaitech/ColossalAI
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
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from typing import Optional
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import torch
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import torch.nn as nn
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from torch.fx import symbolic_trace
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from colossalai.fx.passes.meta_info_prop import MetaInfoProp
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from colossalai.fx.profiler import calculate_fwd_out, calculate_fwd_tmp, is_compatible_with_meta
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from colossalai.gemini.chunk import ChunkManager
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if is_compatible_with_meta():
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from colossalai.fx.profiler import MetaTensor
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from .chunk_memstats_collector import ChunkMemStatsCollector
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class ModuleInfos:
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def __init__(self, module: torch.nn.Module, module_name: str, module_full_name: str,
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parent_module: torch.nn.Module):
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self.module = module
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self.module_name = module_name
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self.module_full_name = module_full_name
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self.parent_module = parent_module
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class StaticMemStatsCollector(ChunkMemStatsCollector):
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"""
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A Static Memory statistic collector.
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"""
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def __init__(self, module: nn.Module, chunk_manager: ChunkManager) -> None:
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super().__init__(chunk_manager)
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self.module = module
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self.module_info_list = []
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def init_mem_stats(self, *inputs):
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self.register_opnodes_recursively(self.module)
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self.refactor_module()
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self.module = self.module.cpu()
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self.module.train()
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data = [MetaTensor(torch.rand(inp.shape, device='meta'), fake_device='cpu') for inp in inputs]
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gm = symbolic_trace(self.module)
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interp = MetaInfoProp(gm)
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interp.propagate(*data)
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total_mem = 0
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for inp in inputs:
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total_mem += inp.numel() * inp.element_size()
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last_node = None
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module_name_list = [mInfo.module_full_name for mInfo in self.module_info_list]
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for node in gm.graph.nodes:
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total_mem = total_mem + calculate_fwd_tmp(node) + calculate_fwd_out(node)
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if node.op == "call_module":
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if node.name.endswith("_0") and node.name[:-2] in module_name_list:
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self._non_model_data_cuda_list.append(total_mem)
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last_node = node
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self._non_model_data_cuda_list.append(total_mem)
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self._non_model_data_cuda_list = self._non_model_data_cuda_list[1:]
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cur_module_mem_fwd = 0
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cur_module_mem_bwd = 0
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grad_module_out = last_node.meta["fwd_mem_out"]
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for node in gm.graph.nodes.__reversed__():
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cur_module_mem_fwd = cur_module_mem_fwd + calculate_fwd_tmp(node) + calculate_fwd_out(node)
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cur_module_mem_bwd = cur_module_mem_bwd + node.meta["bwd_mem_tmp"] + node.meta["bwd_mem_out"]
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if node.op == "call_module":
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if node.name.endswith("_0") and node.name[:-2] in module_name_list:
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self._non_model_data_cuda_list.append(total_mem + grad_module_out + cur_module_mem_bwd)
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total_mem = total_mem - cur_module_mem_fwd
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cur_module_mem_fwd = 0
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cur_module_mem_bwd = 0
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grad_module_out = node.meta["bwd_mem_out"]
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self._step_total = len(self._non_model_data_cuda_list)
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self.recover_module()
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def refactor_module(self):
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for modInfo in self.module_info_list:
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temp_node = nn.Sequential(nn.ReLU(), modInfo.module)
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modInfo.parent_module.__setattr__(modInfo.module_name, temp_node)
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def recover_module(self):
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for modInfo in self.module_info_list:
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modInfo.parent_module.__setattr__(modInfo.module_name, modInfo.module)
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def register_opnodes_recursively(self,
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module: torch.nn.Module,
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name: str = "",
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full_name: str = "",
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parent_module: Optional[torch.nn.Module] = None):
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assert isinstance(module, torch.nn.Module)
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for child_name, child in module.named_children():
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self.register_opnodes_recursively(child, child_name, full_name + "_" + child_name, module)
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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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self.module_info_list.append(ModuleInfos(module, name, full_name[1:], parent_module))
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