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93 lines
3.8 KiB
93 lines
3.8 KiB
from typing import Callable, List, Tuple
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import torch
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from colossalai._analyzer._subclasses.flop_tensor import ewise_flop_counter as elementwise_flop_counter
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from colossalai._analyzer.fx.node_util import compute_size_in_bytes as activation_size
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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__ = ["elementwise_meta_info"]
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def elementwise_meta_info(temp_mem_scale: float = 0, buffer_mem_scale: float = 0) -> Callable:
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"""This is a function to create the meta information generator for elementwise operations
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Args:
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temp_mem_scale (float, optional): temp memory scaling factor for backward. Defaults to 0.
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buffer_mem_scale (float, optional): buffer memory scaling factor for forward. Defaults to 0.
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Returns:
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Callable: meta information generator
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"""
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def meta_func(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
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input_tensor = next(
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filter(
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lambda x: (x.type == OperationDataType.ARG or x.type == OperationDataType.PARAM)
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and x.name != "softmax_dim",
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args,
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)
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).data
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output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
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is_inplace = 1 if kwargs.get("inplace", False) else 0
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flop_counter = elementwise_flop_counter(1, 0)
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# calculate compute cost
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fwd_compute_cost = flop_counter([input_tensor], [output_tensor])
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bwd_compute_cost = flop_counter([output_tensor], [input_tensor])
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compute_cost = TrainCycleItem(
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fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost
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)
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# calculate memory cost
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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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# NOTE: if in_place is True, we will not create a new tensor in forward
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fwd_memory_cost = MemoryCost(
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activation=activation_size(input_tensor) * (2 - is_inplace),
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parameter=0,
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temp=0,
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buffer=activation_size(input_tensor) * buffer_mem_scale,
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)
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# temp_mem_scale is for situation like softmax backward
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# the buffer will be removed during backward phase
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bwd_memory_cost = MemoryCost(
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activation=activation_size(input_tensor) - activation_size(input_tensor) * buffer_mem_scale,
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parameter=0,
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temp=activation_size(input_tensor) * temp_mem_scale + activation_size(input_tensor) * buffer_mem_scale,
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buffer=0,
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)
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# total cost is the sum of forward and backward cost
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total_cost = MemoryCost(
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activation=fwd_memory_cost.activation + bwd_memory_cost.activation,
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parameter=fwd_memory_cost.parameter + bwd_memory_cost.parameter,
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temp=fwd_memory_cost.temp + bwd_memory_cost.temp,
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buffer=fwd_memory_cost.buffer + bwd_memory_cost.buffer,
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)
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memory_cost = TrainCycleItem(fwd=fwd_memory_cost, bwd=bwd_memory_cost, total=total_cost)
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# store fwd_in, fwd_buffer, fwd_out
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fwd_in = []
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fwd_buffer = [torch.zeros_like(output_tensor, device="meta")]
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fwd_out = [torch.zeros_like(output_tensor, device="meta")]
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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 meta information
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# (0, 0)
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meta_register.register([torch.nn.ReLU, torch.nn.functional.relu, torch.tanh])(elementwise_meta_info(0, 0))
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# (1, 0)
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meta_register.register([torch.nn.Softmax, torch.nn.functional.softmax])(elementwise_meta_info(1, 0))
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# (0, 0.25) for dropout, the buffer is in bool type so that the buffer memory cost is 0.25 times of input tensor
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meta_register.register([torch.nn.Dropout, torch.nn.functional.dropout])(elementwise_meta_info(0, 0.25))
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