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

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* [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
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26
colossalai/auto_parallel/meta_profiler/meta_registry/conv.py
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@@ -2,6 +2,8 @@ from typing import Callable, Dict, List, Tuple, Union
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,
OperationData,
@@ -10,8 +12,6 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
StrategiesVector,
TrainCycleItem,
)
from colossalai.fx.profiler.memory_utils import activation_size
from colossalai.fx.profiler.opcount import flop_mapping
from colossalai.tensor.sharding_spec import ShardingSpec
from ..registry import meta_register
@@ -110,18 +110,18 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
# calculate memory cost
# TODO: use profiler to check conv temp memory
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(
activation=activation_size([input_tensor, output_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
fwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, output_tensor]),
parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
if has_bias else compute_size_in_bytes(weight_tensor),
temp=0,
buffer=0)
bwd_memory_cost = MemoryCost(
activation=activation_size([input_tensor, weight_tensor, bias_tensor])
if has_bias else activation_size([input_tensor, weight_tensor]),
parameter=activation_size([weight_tensor, bias_tensor]) if has_bias else activation_size(weight_tensor),
temp=0,
buffer=0)
bwd_memory_cost = MemoryCost(activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
if has_bias else compute_size_in_bytes([input_tensor, weight_tensor]),
parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
if has_bias else compute_size_in_bytes(weight_tensor),
temp=0,
buffer=0)
# total cost is the sum of forward and backward cost
total_cost = MemoryCost(activation=fwd_memory_cost.activation + bwd_memory_cost.activation,