mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
53 lines
2.6 KiB
53 lines
2.6 KiB
from typing import List, Tuple
|
|
|
|
import torch
|
|
|
|
from colossalai._analyzer._subclasses.flop_tensor import flop_mapping
|
|
from colossalai._analyzer.fx.node_util import compute_size_in_bytes
|
|
from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, OperationDataType, TrainCycleItem
|
|
|
|
from ..registry import meta_register
|
|
|
|
__all__ = ["embedding_meta_info"]
|
|
|
|
|
|
@meta_register.register(torch.nn.Embedding)
|
|
def embedding_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]:
|
|
"""torch.nn.Embedding metainfo generator
|
|
|
|
Returns:
|
|
Tuple[TrainCycleItem, TrainCycleItem, List[torch.Tensor]]: compute cost, memory cost and forward inputs
|
|
"""
|
|
input_tensor = next(filter(lambda x: x.type == OperationDataType.ARG, args)).data
|
|
weight_tensor = next(filter(lambda x: x.type == OperationDataType.PARAM, args)).data
|
|
output_tensor = next(filter(lambda x: x.type == OperationDataType.OUTPUT, args)).data
|
|
|
|
# compute cost
|
|
fwd_compute_cost = flop_mapping[torch.ops.aten.embedding.default]([weight_tensor, input_tensor], [output_tensor])
|
|
bwd_compute_cost = flop_mapping[torch.ops.aten.embedding_dense_backward.default]([output_tensor, weight_tensor],
|
|
[weight_tensor])
|
|
|
|
compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
|
|
|
|
# memory cost
|
|
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
|
|
# NOTE: during the backward phase of torch.nn.Embedding, it seems when the input is large enough, it will
|
|
# have a temp memory which is kind of weird and we don't know the reason yet, so currently we just assume
|
|
# that there will be no temp memory, as the temp memory is significantly smaller than the gradient memory
|
|
fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]),
|
|
parameter=0,
|
|
temp=0,
|
|
buffer=0)
|
|
bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([weight_tensor]), parameter=0, temp=0, buffer=0)
|
|
|
|
total_memory_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation)
|
|
|
|
memory_cost = TrainCycleItem(fwd=fwd_memory_cost, bwd=bwd_memory_cost, total=total_memory_cost)
|
|
|
|
# store fwd_in, fwd_buffer, fwd_out
|
|
fwd_in = [torch.zeros_like(input_tensor)]
|
|
fwd_buffer = []
|
|
fwd_out = [torch.zeros_like(output_tensor)]
|
|
|
|
return compute_cost, memory_cost, fwd_in, fwd_buffer, fwd_out
|