mirror of https://github.com/hpcaitech/ColossalAI
499 lines
19 KiB
Python
499 lines
19 KiB
Python
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)
|