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80 lines
3.3 KiB
80 lines
3.3 KiB
from typing import Callable, List, Tuple
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import torch
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from colossalai._analyzer._subclasses.flop_tensor import flop_mapping
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from colossalai._analyzer.fx.node_util import compute_size_in_bytes
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from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
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from ..registry import meta_register
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__all__ = ["tensor_related_metainfo"]
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def tensor_related_metainfo(bwd_mem_out_factor: float = 1, bwd_mem_tmp_factor: float = 0) -> Callable:
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"""torch.Tensor related metainfo generator template
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Args:
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bwd_mem_out_factor (float, optional): backward activation memory cost factor. Defaults to 1.
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bwd_mem_tmp_factor (float, optional): backward temp memory cost factor. Defaults to 0.
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Returns:
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Callable: torch.Tensor related metainfo generator
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"""
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def meta_func(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
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"""torch.Tensor related metainfo generator
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Returns:
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Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
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"""
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outputs = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
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# compute costs are all zero
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compute_cost = TrainCycleItem(fwd=0, bwd=0, total=0)
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# memory costs
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# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
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fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(outputs) * 2, parameter=0, temp=0, buffer=0)
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bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(outputs) * bwd_mem_out_factor,
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parameter=0,
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temp=compute_size_in_bytes(outputs) * bwd_mem_tmp_factor,
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buffer=0)
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total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation,
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parameter=fwd_mem_cost.parameter + bwd_mem_cost.parameter,
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temp=fwd_mem_cost.temp + bwd_mem_cost.temp,
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buffer=fwd_mem_cost.buffer + bwd_mem_cost.buffer)
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memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
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# store fwd_in, fwd_buffer, fwd_out
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fwd_in = []
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fwd_buffer = []
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if isinstance(outputs, tuple) or isinstance(outputs, list) or isinstance(outputs, dict):
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# tuple of tensors
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fwd_out = [torch.zeros_like(tensor) for tensor in outputs]
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else:
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# enaged_tensors is a single tensor
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fwd_out = [torch.zeros_like(outputs)]
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return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out
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return meta_func
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# register torch.Tensor related metainfo
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# (0, 0)
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meta_register.register([torch.tensor, torch.Tensor.to, torch.Tensor.unsqueeze, torch.unsqueeze,
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torch.arange])(tensor_related_metainfo(0, 0))
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# (1, 0)
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meta_register.register([
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torch.Tensor.flatten, torch.flatten, torch.Tensor.transpose, torch.transpose, torch.Tensor.permute, torch.permute,
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torch.Tensor.split, torch.split, torch.Tensor.view
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])(tensor_related_metainfo(1, 0))
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# (1, 1)
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meta_register.register([torch.Tensor.type, torch.Tensor.contiguous])(tensor_related_metainfo(1, 1))
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