github
/
ColossalAI
mirror of https://github.com/hpcaitech/ColossalAI
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
ColossalAI/tests/test_fx/test_tracer/test_patched_module.py

534 lines
17 KiB
Python
Raw Blame History

import torch
from colossalai.fx.tracer.meta_patch import patched_module
from colossalai.testing import clear_cache_before_run
def _run(data, module, patch_fn):
try:
if isinstance(data, dict):
output = patch_fn(module, **data)
if isinstance(data, tuple) or isinstance(data, list):
output = patch_fn(module, *data)
else:
output = patch_fn(module, data)
return output
except Exception as e:
return e
def _assert_output_shape(data, module, patch_fn, expect_exception, output_shape):
output = _run(data, module, patch_fn)
if expect_exception:
assert isinstance(output, AssertionError)
else:
assert not isinstance(output, Exception)
if isinstance(output, tuple):
for item, shape in zip(output, output_shape):
assert item.is_meta
assert item.shape == shape
else:
assert output.is_meta
assert output.shape == output_shape
@clear_cache_before_run()
def test_linear():
# test linear patch can produce the meta output with correct shape
data = torch.rand(2, 4, device="meta")
module = torch.nn.Linear(4, 2)
_assert_output_shape(data, module, patched_module.torch_nn_linear, False, torch.Size([2, 2]))
# test if the linear patch can catch exception when dimension does not match
data = torch.rand(2, 2, device="meta")
_assert_output_shape(data, module, patched_module.torch_nn_linear, True, None)
@clear_cache_before_run()
def test_rnn():
# test rnn patch can produce the meta output with correct shape
data = (torch.randn(5, 3, 10), torch.randn(2, 3, 20))
module = torch.nn.RNN(10, 20, 2)
output, hn = module(*data)
meta_data = (torch.randn(5, 3, 10).to("meta"), torch.randn(2, 3, 20).to("meta"))
_assert_output_shape(meta_data, module, patched_module.torch_nn_rnn, False, (output.shape, hn.shape))
# test if the rnn patch can catch exception when dimension does not match
data = (torch.randn(5, 3, 10), torch.randn(2, 3, 20))
module = torch.nn.RNN(10, 20, 2)
output, hn = module(*data)
meta_data = (torch.randn(5, 3, 1).to("meta"), torch.randn(2, 3, 20).to("meta"))
_assert_output_shape(meta_data, module, patched_module.torch_nn_rnn, True, None)
@clear_cache_before_run()
def test_embedding():
data = torch.rand(2, 4, device="meta")
# test layernorm
ln = torch.nn.LayerNorm(4)
_assert_output_shape(data, ln, patched_module.torch_nn_normalize, False, data.shape)
# test group norm
gn = torch.nn.GroupNorm(4, num_channels=8)
_assert_output_shape(data, gn, patched_module.torch_nn_normalize, False, data.shape)
# test batch norm 1d
bn1d = torch.nn.BatchNorm1d(4)
data = torch.rand(2, 4, device="meta")
_assert_output_shape(
data=data,
module=bn1d,
patch_fn=patched_module.torch_nn_normalize,
expect_exception=False,
output_shape=data.shape,
)
data = torch.rand(2, 4, device="meta")
_assert_output_shape(
data=data,
module=bn1d,
patch_fn=patched_module.torch_nn_normalize,
expect_exception=False,
output_shape=data.shape,
)
data = torch.rand(2, 3, 4, device="meta")
_assert_output_shape(
data=data,
module=bn1d,
patch_fn=patched_module.torch_nn_normalize,
expect_exception=False,
output_shape=data.shape,
)
data = torch.rand(1, 2, 3, 4, device="meta")
_assert_output_shape(
data=data, module=bn1d, patch_fn=patched_module.torch_nn_normalize, expect_exception=True, output_shape=None
)
# test batch norm 2d
bn2d = torch.nn.BatchNorm2d(4)
data = torch.rand(1, 2, 3, 4, device="meta")
_assert_output_shape(
data=data,
module=bn2d,
patch_fn=patched_module.torch_nn_normalize,
expect_exception=False,
output_shape=data.shape,
)
data = torch.rand(2, 3, 4, device="meta")
_assert_output_shape(
data=data, module=bn2d, patch_fn=patched_module.torch_nn_normalize, expect_exception=True, output_shape=None
)
# # test batch size 3d
bn3d = torch.nn.BatchNorm3d(4)
data = torch.rand(1, 1, 2, 3, 4, device="meta")
_assert_output_shape(
data=data,
module=bn3d,
patch_fn=patched_module.torch_nn_normalize,
expect_exception=False,
output_shape=data.shape,
)
data = torch.rand(1, 2, 3, 4, device="meta")
_assert_output_shape(
data=data, module=bn3d, patch_fn=patched_module.torch_nn_normalize, expect_exception=True, output_shape=None
)
@clear_cache_before_run()
def test_conv1d():
# test conv 1d
data = torch.rand(2, 3, 4)
conv1d = torch.nn.Conv1d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = conv1d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=conv1d,
patch_fn=patched_module.torch_nn_conv1d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv1d = torch.nn.Conv1d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = conv1d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=conv1d,
patch_fn=patched_module.torch_nn_conv1d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv1d = torch.nn.Conv1d(
in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2, padding_mode="reflect"
)
materialized_output = conv1d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=conv1d,
patch_fn=patched_module.torch_nn_conv1d,
expect_exception=False,
output_shape=materialized_output.shape,
)
def test_conv2d():
# test conv 2d
data = torch.rand(2, 3, 4, 4)
conv2d = torch.nn.Conv2d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = conv2d(data)
_assert_output_shape(
data=data,
module=conv2d,
patch_fn=patched_module.torch_nn_conv2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv2d = torch.nn.Conv2d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = conv2d(data)
_assert_output_shape(
data=data,
module=conv2d,
patch_fn=patched_module.torch_nn_conv2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv2d = torch.nn.Conv2d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2)
materialized_output = conv2d(data)
_assert_output_shape(
data=data,
module=conv2d,
patch_fn=patched_module.torch_nn_conv2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv2d = torch.nn.Conv2d(
in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2, padding_mode="reflect"
)
materialized_output = conv2d(data)
_assert_output_shape(
data=data,
module=conv2d,
patch_fn=patched_module.torch_nn_conv2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
@clear_cache_before_run()
def test_conv3d():
# test conv 3d
data = torch.rand(2, 3, 4, 4, 4)
conv3d = torch.nn.Conv3d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = conv3d(data)
_assert_output_shape(
data=data,
module=conv3d,
patch_fn=patched_module.torch_nn_conv3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv3d = torch.nn.Conv3d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = conv3d(data)
_assert_output_shape(
data=data,
module=conv3d,
patch_fn=patched_module.torch_nn_conv3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv3d = torch.nn.Conv3d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2)
materialized_output = conv3d(data)
_assert_output_shape(
data=data,
module=conv3d,
patch_fn=patched_module.torch_nn_conv3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
conv3d = torch.nn.Conv3d(
in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2, padding_mode="reflect"
)
materialized_output = conv3d(data)
_assert_output_shape(
data=data,
module=conv3d,
patch_fn=patched_module.torch_nn_conv3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
@clear_cache_before_run()
def test_conv_transpose1d():
# test conv transpose1d
data = torch.rand(2, 3, 4)
convtrans1d = torch.nn.ConvTranspose1d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = convtrans1d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans1d,
patch_fn=patched_module.torch_nn_convtranspose1d,
expect_exception=False,
output_shape=materialized_output.shape,
)
convtrans1d = torch.nn.ConvTranspose1d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = convtrans1d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans1d,
patch_fn=patched_module.torch_nn_convtranspose1d,
expect_exception=False,
output_shape=materialized_output.shape,
)
@clear_cache_before_run()
def test_conv_transpose2d():
# test conv transpose2d
data = torch.rand(2, 3, 4, 4)
convtrans2d = torch.nn.ConvTranspose2d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = convtrans2d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans2d,
patch_fn=patched_module.torch_nn_convtranspose2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
convtrans2d = torch.nn.ConvTranspose2d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = convtrans2d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans2d,
patch_fn=patched_module.torch_nn_convtranspose2d,
expect_exception=False,
output_shape=materialized_output.shape,
)
@clear_cache_before_run()
def test_conv_transpose3d():
# test conv transpose2d
data = torch.rand(2, 3, 4, 4, 4)
convtrans3d = torch.nn.ConvTranspose3d(in_channels=3, out_channels=4, kernel_size=2)
materialized_output = convtrans3d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans3d,
patch_fn=patched_module.torch_nn_convtranspose3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
convtrans3d = torch.nn.ConvTranspose3d(in_channels=3, out_channels=4, kernel_size=2, padding=1)
materialized_output = convtrans3d(data)
meta_data = data.to("meta")
_assert_output_shape(
data=meta_data,
module=convtrans3d,
patch_fn=patched_module.torch_nn_convtranspose3d,
expect_exception=False,
output_shape=materialized_output.shape,
)
@clear_cache_before_run()
def test_pool1d():
combinations = [
[torch.nn.MaxPool1d, patched_module.torch_nn_maxpool1d],
[torch.nn.AvgPool1d, patched_module.torch_nn_avgpool1d],
]
for layer_cls, patch_func in combinations:
pooler = layer_cls(kernel_size=3)
data = torch.rand(2, 3, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
data = torch.rand(2, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
data = torch.rand(2, 3, 4, 4)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
@clear_cache_before_run()
def test_pool2d():
combinations = [
[torch.nn.MaxPool2d, patched_module.torch_nn_maxpool2d],
[torch.nn.AvgPool2d, patched_module.torch_nn_avgpool2d],
]
for layer_cls, patch_func in combinations:
pooler = layer_cls(kernel_size=3)
# test max pool 3d
data = torch.rand(2, 3, 4, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
# test max pool 3d
data = torch.rand(2, 4, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
# test max pool 3d
data = torch.rand(2, 3, 4, 4, 4)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
@clear_cache_before_run()
def test_pool3d():
combinations = [
[torch.nn.MaxPool3d, patched_module.torch_nn_maxpool3d],
[torch.nn.AvgPool3d, patched_module.torch_nn_avgpool3d],
]
for layer_cls, patch_func in combinations:
pooler = layer_cls(kernel_size=3)
# test max pool 3d
data = torch.rand(2, 3, 4, 4, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
# test max pool 3d
data = torch.rand(2, 4, 4, 4)
materialized_output = pooler(data)
_assert_output_shape(
data=data,
module=pooler,
patch_fn=patch_func,
expect_exception=False,
output_shape=materialized_output.shape,
)
# test max pool 3d
data = torch.rand(2, 3, 4)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
# adapative pooling is different from other pooling, so test it individually
@clear_cache_before_run()
def test_adaptive_pooling_1d():
pooler = torch.nn.AdaptiveAvgPool1d(output_size=3)
patch_func = patched_module.torch_nn_adapative_pooling_1d
data = torch.rand(3, 4)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
data = torch.rand(2, 3, 4)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
data = torch.rand(2, 3, 4, 5)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
@clear_cache_before_run()
def test_adaptive_pooling_2d():
pooler = torch.nn.AdaptiveAvgPool2d(output_size=3)
patch_func = patched_module.torch_nn_adapative_pooling_2d
data = torch.rand(3, 4)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
data = torch.rand(2, 3, 4)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
data = torch.rand(2, 3, 4, 5)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
@clear_cache_before_run()
def test_adaptive_pooling_3d():
pooler = torch.nn.AdaptiveAvgPool3d(output_size=3)
patch_func = patched_module.torch_nn_adapative_pooling_3d
data = torch.rand(3, 4, 5)
_assert_output_shape(data=data, module=pooler, patch_fn=patch_func, expect_exception=True, output_shape=None)
data = torch.rand(2, 3, 4, 5)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
data = torch.rand(2, 3, 4, 5, 6)
output = pooler(data)
_assert_output_shape(
data=data, module=pooler, patch_fn=patch_func, expect_exception=False, output_shape=output.shape
)
Reference in New Issue View Git Blame Copy Permalink