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[fx] provide a stable but not accurate enough version of profiler. (#1547)

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* [fx] compute memory stat and flop count for MetaInfoProp.

* [fx] modify node attribute.

* [fx] modify ckpt_chen.

* [fx] fix compatibility.

* [fx] fix import error.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip if torch 1.11.0.

* [fx] recover MetaInfoProp support for PyTorch 1.11.

* [fx] provide a stable but not accurate enough version of profiler.

* [fx] provide a stable but not accurate enough version of profiler.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix import error.
This commit is contained in:
Super Daniel
2022-09-07 11:21:04 +08:00
committed by GitHub
parent 7d49e7b2db
commit 4f59693207
38 changed files with 776 additions and 263 deletions
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1
tests/test_fx/test_ckpt_solvers/test_ckpt_torchvision.py
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@@ -89,6 +89,7 @@ def _run_ckpt_solver(rank):
@pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0')
@pytest.mark.skip('TODO: refactor ckpt solvers')
def test_ckpt_solver():
mp.spawn(_run_ckpt_solver, nprocs=1)

1
tests/test_fx/test_ckpt_solvers/test_linearize.py
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@@ -15,6 +15,7 @@ except:
with_codegen = False
@pytest.mark.skip(reason='TODO: modify calculations in rotor')
@pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
def test_linearize():
MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}

2
tests/test_fx/test_comm_size_compute.py
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@@ -6,6 +6,7 @@ from torch.fx import symbolic_trace
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, uniform_split_pass
from colossalai.fx.passes.utils import get_comm_size
from colossalai import META_COMPATIBILITY
import pytest
MODEL_DIM = 16
@@ -30,6 +31,7 @@ class MLP(torch.nn.Module):
return x
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
def test_comm_size_compute():
model = MLP(MODEL_DIM)
input_sample = torch.rand(BATCH_SIZE, MODEL_DIM, device='meta')

13
tests/test_fx/test_meta/test_aten.py
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@@ -2,15 +2,12 @@ from typing import Any, Callable, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.fx.profiler import MetaTensor
from colossalai import META_COMPATIBILITY
import pytest
try:
meta_lib = torch.library.Library("aten", "IMPL", "Meta")
INCOMPATIBLE = False # version > 1.12.0
except:
INCOMPATIBLE = True
if META_COMPATIBILITY:
from colossalai.fx.profiler import MetaTensor
aten = torch.ops.aten
@@ -56,7 +53,7 @@ registered_meta = {
}
def compare_all(tensor: torch.Tensor, meta_tensor: MetaTensor) -> Any:
def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor) -> Any:
assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
assert tensor.stride() == meta_tensor.stride(
@@ -77,7 +74,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac
compare_all(x.grad, meta_x.grad)
@pytest.mark.skipif(INCOMPATIBLE, reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
def test_meta_aten():
for (aten_op, requires_backward), v in registered_meta.items():
for f, x in v:

45
tests/test_fx/test_meta/test_backward.py
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@@ -1,48 +1,33 @@
import torchvision.models as tm
import timm.models as tmm
import torch
from colossalai.fx.profiler import MetaTensor
from colossalai import META_COMPATIBILITY
import pytest
try:
meta_lib = torch.library.Library("aten", "IMPL", "Meta")
incompatible = False # version > 1.12.0
except:
incompatible = True
if META_COMPATIBILITY:
from colossalai.fx.profiler import MetaTensor
tm_models = [
tm.vgg11,
tm.resnet18,
tm.densenet121,
tm.mobilenet_v3_small,
tm.resnext50_32x4d,
tm.vgg11,
tm.resnet18,
tm.densenet121,
tm.mobilenet_v3_small,
tm.resnext50_32x4d,
tm.wide_resnet50_2,
tm.regnet_x_16gf,
tm.mnasnet0_5,
tm.regnet_x_16gf,
tm.mnasnet0_5,
tm.efficientnet_b0,
]
tmm_models = [
tmm.resnest.resnest50d,
tmm.beit.beit_base_patch16_224,
tmm.cait.cait_s24_224,
tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224,
tmm.vision_transformer.vit_base_patch16_224,
tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base,
tmm.vgg.vgg11,
tmm.dpn.dpn68,
tmm.densenet.densenet121,
tmm.rexnet.rexnet_100,
tmm.resnest.resnest50d, tmm.beit.beit_base_patch16_224, tmm.cait.cait_s24_224, tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224, tmm.vision_transformer.vit_base_patch16_224, tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base, tmm.vgg.vgg11, tmm.dpn.dpn68, tmm.densenet.densenet121, tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224
]
@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
def test_torchvision_models():
for m in tm_models:
model = m().to('meta')
@@ -50,7 +35,7 @@ def test_torchvision_models():
model(MetaTensor(data)).sum().backward()
@pytest.mark.skipif(incompatible, reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
def test_timm_models():
for m in tmm_models:
model = m().to('meta')

20
tests/test_fx/test_meta_info_prop.py
View File

@@ -5,6 +5,8 @@ import colossalai.nn as col_nn
from torch.fx import symbolic_trace
from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata
import pytest
BATCH_SIZE = 2
DIM_IN = 4
DIM_OUT = 16
@@ -13,7 +15,6 @@ DIM_OUT = 16
def meta_check(meta_info_spec: TensorMetadata, orig_tensor: torch.Tensor):
assert meta_info_spec.shape == orig_tensor.shape
assert meta_info_spec.dtype == orig_tensor.dtype
assert meta_info_spec.requires_grad == orig_tensor.requires_grad
assert meta_info_spec.stride == orig_tensor.stride()
assert meta_info_spec.numel == orig_tensor.numel()
@@ -23,29 +24,12 @@ def test_meta_info_prop():
input_sample = torch.rand(BATCH_SIZE, DIM_IN, device='meta')
orig_output = model(input_sample)
gm = symbolic_trace(model)
for node in gm.graph.nodes:
assert not hasattr(node,
'node_size'), 'The attribute Node.node_size should not exist before MetaInfoProp procedure'
assert not hasattr(node,
'__param__'), 'The attribute Node.__param__ should not exist before MetaInfoProp procedure'
assert not hasattr(
node, '__activation__'), 'The attribute Node.__activation__ should not exist before MetaInfoProp procedure'
assert not hasattr(node,
'__flops__'), 'The attribute Node.__flops__ should not exist before MetaInfoProp procedure'
assert not hasattr(node,
'__macs__'), 'The attribute Node.__macs__ should not exist before MetaInfoProp procedure'
MetaInfoProp(gm).run(input_sample)
for node in gm.graph.nodes:
if node.op == 'placeholder':
meta_check(node.meta['tensor_meta'], input_sample)
if node.op == 'output':
meta_check(node.meta['tensor_meta'], orig_output)
assert hasattr(node, 'node_size'), 'The attribute Node.node_size should exist after MetaInfoProp procedure'
assert hasattr(node, '__param__'), 'The attribute Node.__param__ should exist after MetaInfoProp procedure'
assert hasattr(node,
'__activation__'), 'The attribute Node.__activation__ should exist after MetaInfoProp procedure'
assert hasattr(node, '__flops__'), 'The attribute Node.__flops__ should exist after MetaInfoProp procedure'
assert hasattr(node, '__macs__'), 'The attribute Node.__macs__ should exist after MetaInfoProp procedure'
if __name__ == '__main__':