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# Owner(s): ["module: mkldnn"]
import torch
import unittest
import itertools
import torch.nn as nn
import torch.nn.functional as F
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing._internal.common_utils import run_tests, TEST_SCIPY, IS_WINDOWS, IS_MACOS
LLGA_FUSION_GROUP = 'prim::oneDNNFusionGroup'
LLGA_NOT_ENABLED = not torch._C.has_mkldnn or IS_WINDOWS or IS_MACOS
def warmup_forward(f, *args, profiling_count=2):
for i in range(profiling_count):
results = f(*args)
return results
class JitLlgaTestCase(JitTestCase):
def setUp(self):
torch.jit.enable_onednn_fusion(True)
def tearDown(self):
torch.jit.enable_onednn_fusion(False)
def checkTrace(self, m, x, *args, **kwargs):
if isinstance(m, torch.nn.Module):
m.eval()
with torch.no_grad(), \
torch._jit_internal._disable_emit_hooks():
traced = torch.jit.trace(m, x)
if isinstance(m, torch.nn.Module):
traced = torch.jit.freeze(traced)
warmup_forward(traced, *x)
fwd_graph = traced.graph_for(*x)
ref_o = m(*x)
jit_o = traced(*x)
self.assertEqual(jit_o, ref_o)
return traced, fwd_graph
def assertFused(self, graph, fused_patterns):
for pat in fused_patterns:
self.assertGraphContainsExactly(graph, pat, 0)
try:
import torchvision
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
except RuntimeError:
HAS_TORCHVISION = False
skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, 'no torchvision')
def get_eltwise_fn(name):
if hasattr(torch, name):
return getattr(torch, name)
elif hasattr(F, name):
return getattr(F, name)
else:
raise NameError('Eltwise function %s not found' % name)
@unittest.skipIf(LLGA_NOT_ENABLED, "MKL-DNN build is disabled")
class TestOp(JitLlgaTestCase):
def test_conv2d(self):
for [spatial, in_channels, out_channels, kernel, padding, stride, dilation, g, bias] in itertools.product(
[7, 8],
[8, 15],
[7, 16],
[3, 4],
[0, 2],
[1, 2],
[1, 2],
[1, 2],
[True, False]):
m = nn.Conv2d(in_channels=in_channels * g,
out_channels=out_channels * g,
kernel_size=kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=g,
bias=bias)
x = torch.rand(1, in_channels * g, spatial, spatial)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_bn2d(self):
m = nn.BatchNorm2d(32).eval()
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
# single-op partition shouldn't be created for softmax
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 0)
def test_eltwise(self):
class M(nn.Module):
def __init__(self, eltwise_fn):
super(M, self).__init__()
self.eltwise = eltwise_fn
def forward(self, x):
return self.eltwise(x)
for eltwise in ['relu', 'gelu']:
eltwise_fn = get_eltwise_fn(eltwise)
m = M(eltwise_fn)
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
# single-op partition shouldn't be created.
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 0)
def test_max_pool2d(self):
for [spatial, kernel, padding, stride, dilation, ceil_mode] in itertools.product(
[15, 16, 17, 18, 19],
[4, 5],
[0, 1, 2],
[1, 2], # [1, 2, 4], TODO: fix issue in pad calculation
[1], # [1, 2], TODO: backend support for dilation
[True, False]):
m = nn.MaxPool2d(kernel_size=kernel,
stride=stride,
padding=padding,
dilation=dilation,
ceil_mode=ceil_mode)
x = torch.rand(1, 4, spatial, spatial)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_avg_pool2d(self):
for [spatial, kernel, padding, stride, ceil_mode, count_include_pad] in itertools.product(
[15, 16, 17, 18, 19],
[4, 5],
[0, 1, 2],
[1, 2, 4],
[False], # TODO: oneDNN Graph does not fully support ceil_mode=True
[True, False]):
m = nn.AvgPool2d(kernel_size=kernel,
stride=stride,
padding=padding,
ceil_mode=ceil_mode,
count_include_pad=count_include_pad)
x = torch.rand(1, 4, spatial, spatial)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_variable_kernel_avg_pool2d(self):
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
def forward(self, x):
x = F.avg_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=0, count_include_pad=False)
return x
x = torch.randn(1, 1000, 1, 1)
m = M()
_, graph = self.checkTrace(m, [x])
# kernel_size is not Constant, shouldn't have any LLGA_FUSION_GROUP
# TODO: with shape specialization, should have 1 LLGA_FUSION_GROUP
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 0)
def test_softmax(self):
for dim in [-4, -3, -2, -1, 0, 1, 2, 3]:
m = nn.Softmax(dim=dim)
x = torch.rand(8, 12, 12, 12)
_, graph = self.checkTrace(m, [x])
# single-op partition shouldn't be created for softmax
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 0)
def test_linear(self):
for bias in [True, False]:
x = torch.rand(32, 28)
m = torch.nn.Linear(in_features=28, out_features=64, bias=bias)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::linear'])
def _gen_binary_inputs(self, gen_permute=True):
for xshape, yshape in [
[[1, 32, 28, 28], [1, 32, 28, 28]],
[[1, 32, 28, 28], [1, 1, 28, 28]],
[[1, 32, 28, 28], [28]],
[[1, 32, 28, 28], [1]],
]:
yield torch.rand(xshape), torch.rand(yshape)
if gen_permute and xshape != yshape:
yield torch.rand(yshape), torch.rand(xshape)
def test_add(self):
def forward_add(x, y):
return torch.add(x, y, alpha=2)
for x, y in self._gen_binary_inputs():
_, graph = self.checkTrace(forward_add, [x, y])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_add_scalar(self):
def add_scalar(x):
return 42 + x + 3.14
x = torch.rand(32, 32)
_, graph = self.checkTrace(add_scalar, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_addmm(self):
def addmm(x, y, z):
# alpha and beta are 1, by default
return torch.addmm(z, x, y)
x = torch.rand(64, 32)
y = torch.rand(32, 32)
z = torch.rand(64, 32)
_, graph = self.checkTrace(addmm, [x, y, z])
# single-op partition should be created for matmul with bias.
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_mul(self):
def forward_mul(x, y):
return torch.mul(x, y) * 3
for x, y in self._gen_binary_inputs():
_, graph = self.checkTrace(forward_mul, [x, y])
# single-op partitions shouldn't be created
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_identity_binary(self):
def forward(x):
return x * 1 + 0.0
x = torch.rand(32)
_, graph = self.checkTrace(forward, [x])
self.assertFused(graph, ['aten::add', 'aten::mul'])
def test_layer_norm(self):
# TODO: support more normalized_shape
m = torch.nn.LayerNorm(10)
x = torch.randn(2, 5, 10, 10)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_cat(self):
def cat_along_dim(d):
def forward_cat(*inputs):
return torch.cat(inputs, d)
return forward_cat
for xshape in [
[8, 8, 8, 8],
[64, 8, 32],
[2048, 64],
]:
for d in range(len(xshape)):
x = torch.rand(xshape)
_, graph = self.checkTrace(cat_along_dim(d), [x, x, x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
def test_typecheck(self):
x = torch.rand(32, 28)
m = torch.nn.Linear(in_features=28, out_features=64, bias=True)
traced, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::linear'])
# change the shape of the input, we should enter fallback graph
x = torch.rand(5, 28)
self.assertEqual(m(x), traced(x))
@unittest.skipIf(LLGA_NOT_ENABLED, "MKL-DNN build is disabled")
class TestFusionPattern(JitLlgaTestCase):
def test_conv2d_eltwise(self):
class M(nn.Module):
def __init__(self, eltwise_fn):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1, bias=False)
self.eltwise = eltwise_fn
def forward(self, x):
x = self.conv1(x)
x = self.eltwise(x)
x = self.conv2(x)
x = self.eltwise(x)
return x
# for eltwise in ['relu', 'sigmoid', 'sqrt', 'abs', 'square', 'hardtanh']:
for eltwise in ['relu']:
for inplace in [True, False]:
eltwise_fn_name = eltwise + '_' if inplace else eltwise
eltwise_fn = get_eltwise_fn(eltwise_fn_name)
m = M(eltwise_fn)
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 2)
# test if relu_ is replace with relu by mutation removal pass
self.assertFused(graph, ['aten::' + eltwise_fn_name])
# test if relu is fused into the fusion group
self.assertFused(graph, ['aten::' + eltwise])
def test_conv2d_bn(self):
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
self.bn1 = nn.BatchNorm2d(32)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
return x
m = M().eval()
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::_convolution', 'aten::batch_norm'])
def test_conv2d_bn_relu(self):
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
self.bn1 = nn.BatchNorm2d(32)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
return x
m = M().eval()
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::_convolution', 'aten::batch_norm',
'aten::relu'])
def test_bn2d_eltwise(self):
class M(nn.Module):
def __init__(self, eltwise_fn):
super(M, self).__init__()
self.eltwise = eltwise_fn
self.bn = nn.BatchNorm2d(32)
def forward(self, x):
x = self.bn(x)
x = self.eltwise(x)
return x
for eltwise in ['relu']:
eltwise_fn = get_eltwise_fn(eltwise)
m = M(eltwise_fn).eval()
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::' + eltwise])
def test_linear_eltwise(self):
class M(nn.Module):
def __init__(self, eltwise_fn, bias):
super(M, self).__init__()
self.linear = nn.Linear(28, 64, bias)
self.eltwise = eltwise_fn
def forward(self, x):
x = self.linear(x)
x = self.eltwise(x)
return x
for [has_bias, eltwise] in itertools.product(
[True, False],
['relu', 'gelu', 'sigmoid', 'hardtanh', 'relu6', 'elu']):
eltwise_fn = get_eltwise_fn(eltwise)
m = M(eltwise_fn, has_bias)
x = torch.rand(32, 28, requires_grad=False)
_, graph = self.checkTrace(m, [x])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::' + eltwise])
def test_conv2d_sum(self):
class M(nn.Module):
def __init__(self, bias=False):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=bias)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1, bias=bias)
self.bn2 = nn.BatchNorm2d(32)
self.relu = nn.ReLU()
self.conv3 = nn.Conv2d(32, 32, 3, padding=1, bias=bias)
self.bn3 = nn.BatchNorm2d(32)
def forward(self, x, y):
x = self.conv1(x)
x = self.bn1(x)
y = self.conv2(y)
y = self.bn2(y)
z = self.relu(x + y)
z = self.conv3(z)
z = self.bn3(z)
return z
for bias in [True, False]:
m = M(bias).eval()
x = torch.rand(1, 32, 16, 16, requires_grad=False)
y = torch.rand(1, 32, 16, 16, requires_grad=False)
_, graph = self.checkTrace(m, [x, y])
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 3)
def test_wildcard(self):
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
self.eltwise = nn.ReLU()
def forward(self, x):
x = self.conv1(x)
y = self.eltwise(x)
return [x, y]
# The pattern is as the following:
# conv
# | \
# eltwise \
# | \
# ListConstruct
#
# The output of conv is used by a wildcard op: ListConstruct.
# Thus conv-eltwise cannot be selected into the same Partition.
m = M()
x = torch.rand(1, 32, 28, 28)
_, graph = self.checkTrace(m, [x])
# conv can exist in a single-op oneDNN Graph partition but not relu
self.assertGraphContainsExactly(graph, LLGA_FUSION_GROUP, 1)
self.assertFused(graph, ['aten::_convolution'])
def test_rewrap_tensor_input_to_pytorch(self):
class M(nn.Module):
def __init__(self, eltwise_fn, data_type):
super(M, self).__init__()
self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True, dtype=data_type)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1, bias=True, dtype=data_type)
self.eltwise = eltwise_fn
self.adaptive_avg_pool_2d = nn.AdaptiveAvgPool2d((5, 7))
def forward(self, x, y):
x = self.conv1(x)
x = self.eltwise(x)
x = self.conv2(x)
x = self.eltwise(x)
x = torch.add(x, y)
x = self.adaptive_avg_pool_2d(x)
return x
eltwise_fn_name = 'relu'
eltwise_fn = get_eltwise_fn(eltwise_fn_name)
# Add bfloat16 later
for data_type in [torch.float]:
m = M(eltwise_fn, data_type)
m = m.to(memory_format=torch.channels_last)
x = torch.rand(1, 32, 28, 28, dtype=data_type).to(memory_format=torch.channels_last)
y = torch.rand(1, 32, 28, 28, dtype=data_type).to(memory_format=torch.channels_last)
# Simply test if the output is accurate
# The output of the second partition is input to adaptive_avg_pool2d, which is
# unsupported by LLGA, so it must be handled by PyTorch, which should receive
# correct strides info of the channels-last tensor.
graph, _ = self.checkTrace(m, [x, y])
@unittest.skipIf(LLGA_NOT_ENABLED, "MKL-DNN build is disabled")
class TestEnableDisableLlgaFuser(JitTestCase):
def setUp(self):
super().setUp()
self.is_enabled = torch._C._jit_set_llga_enabled(False)
def tearDown(self):
torch._C._jit_set_llga_enabled(self.is_enabled)
super().tearDown()
def test_context_manager(self):
x = torch.randn(4, 8)
y = torch.randn(4, 8)
with torch.jit.fuser('fuser3'):
with torch.jit.fuser('fuser3'):
def t1(x, y):
o = x + y
o = o + 2.0
return o
t_jit = torch.jit.script(t1)
t_jit(x, y)
t_jit(x, y)
self.assertGraphContains(t_jit.graph_for(x, y), LLGA_FUSION_GROUP)
def t2(x, y):
o = x + y
o = o + 3.0
return o
t_jit_2 = torch.jit.script(t2)
t_jit_2(x, y)
t_jit_2(x, y)
self.assertGraphContains(t_jit_2.graph_for(x, y), LLGA_FUSION_GROUP)
def t3(x, y):
o = x + y
o = o + 4.0
return o
t_jit_3 = torch.jit.script(t3)
t_jit_3(x, y)
t_jit_3(x, y)
self.assertGraphContainsExactly(t_jit_3.graph_for(x, y), LLGA_FUSION_GROUP, 0)
@unittest.skipIf(LLGA_NOT_ENABLED, "MKL-DNN build is disabled")
class TestModel(JitLlgaTestCase):
@skipIfNoTorchVision
def _test_vision(self, model_name):
m = getattr(torchvision.models, model_name)().eval()
x = torch.rand(1, 3, 224, 224) / 10
_, graph = self.checkTrace(m, [x])
self.assertFused(graph, ['aten::_convolution', 'aten::batch_norm',
'aten::relu', 'aten::linear',
'aten::avg_pool2d', 'aten::max_pool2d'])
for model_name, enabled in [
['resnet50', True],
['resnext50_32x4d', True],
['resnext101_32x8d', True],
['densenet121', True],
['googlenet', TEST_SCIPY],
['mobilenet_v2', True],
['mnasnet1_0', True],
['squeezenet1_0', True],
['vgg16', True],
['alexnet', True],
['shufflenet_v2_x1_0', True],
['wide_resnet50_2', True],
]:
def wrapper(mname):
@unittest.skipIf(not enabled, 'Disabled')
def test(self):
return self._test_vision(mname)
return test
setattr(TestModel, 'test_vision_%s' % model_name, wrapper(model_name))
if __name__ == '__main__':
run_tests()
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