github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-26 04:03:58 +00:00
Code Issues Packages Projects Releases Wiki Activity

[autoparallel]integrate auto parallel feature with new tracer (#3408)

Browse Source 
* [autoparallel] integrate new analyzer in module level

* unify the profiling method

* polish

* fix no codegen bug

* fix pass bug

* fix liveness test

* polish
This commit is contained in:
YuliangLiu0306
2023-04-04 17:40:45 +08:00
committed by GitHub
parent 573af84184
commit ffcdbf0f65
46 changed files with 396 additions and 470 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
colossalai/auto_parallel/meta_profiler/meta_registry/pooling.py
View File

@@ -2,9 +2,9 @@ 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 colossalai.fx.profiler.memory_utils import activation_size
from colossalai.fx.profiler.opcount import flop_mapping
from ..registry import meta_register
@@ -52,8 +52,8 @@ def avgpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem,
compute_cost = TrainCycleItem(fwd=fwd_compute_cost, bwd=bwd_compute_cost, total=fwd_compute_cost + bwd_compute_cost)
# calculate memory cost
fwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(output_tensor))
bwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=activation_size(input_tensor))
fwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=compute_size_in_bytes(output_tensor))
bwd_mem_cost = MemoryCost() if is_inplace else MemoryCost(activation=compute_size_in_bytes(input_tensor))
# total cost
total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation)
@@ -114,11 +114,11 @@ def maxpool_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem,
# calculate memory cost
# NOTE: the index matrix will be discarded in backward phase
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_mem_cost = MemoryCost(activation=activation_size([input_tensor, output_tensor, index_matrix]))
fwd_mem_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor, index_matrix]))
# temp memory for backward is the index matrix to be discarded
bwd_mem_cost = MemoryCost(activation=activation_size(input_tensor) - activation_size(index_matrix),
temp=activation_size(index_matrix))
bwd_mem_cost = MemoryCost(activation=compute_size_in_bytes(input_tensor) - compute_size_in_bytes(index_matrix),
temp=compute_size_in_bytes(index_matrix))
# total cost
total_mem_cost = MemoryCost(activation=fwd_mem_cost.activation + bwd_mem_cost.activation, temp=bwd_mem_cost.temp)