def test_constant_chunk_negative_indices():
"""Test of prim::ConstantChunk node on glow"""
def test_f(x):
return torch.chunk(x + x, 3, -2) # shapes: [(4,11), (4,11), (2,11)]
x = torch.rand((10, 11))
jitVsGlow(test_f, x,
expected_fused_ops={"prim::ConstantChunk"})
def test_elementwise_max(self):
"""Test of the PyTorch max Node on Glow."""
def test_f(a, b):
c = torch.max(a, b)
return torch.max(c, c)
x = torch.randn(4)
y = torch.randn(4)
jitVsGlow(test_f, x, y, expected_fused_ops={"aten::max"})
def test_constant_chunk_basic():
"""Test of prim::ConstantChunk node on glow"""
def test_f(x):
return torch.chunk(x + x, 3, 1) # shapes: [(10,4), (10,4), (10,3)]
x = torch.rand((10, 11))
jitVsGlow(test_f, x,
expected_fused_ops={"prim::ConstantChunk"})
def test_mm_basic():
"""Test of the PyTorch MatMul Node on Glow."""
def test_mm(a, b, t):
r = torch.mm(a, b)
return r.mm(t)
lhs = torch.randn(2, 3)
rhs = torch.randn(3, 4)
t = torch.randn(4, 2)
jitVsGlow(test_mm, lhs, rhs, t)
def test_cat_basic(self):
"""Basic test of the PyTorch cat Node on Glow."""
def test_f(a, b):
c = torch.cat((a, b), 0)
d = torch.cat((c, c), 1)
return torch.cat((d, d), 2)
x = torch.randn(2, 3, 4)
y = torch.randn(2, 3, 4)
jitVsGlow(test_f, x, y, expected_fused_ops={"prim::FusedConcat"})
def test_masked_fill_inplace(self):
"""Test of the PyTorch aten::masked_fill_ op on Glow"""
def masked_fill(a, mask):
b = a + a
b.masked_fill_(mask, 42.0)
return b
x = torch.randn([3])
m = torch.tensor([True, False, True], dtype=torch.bool)
jitVsGlow(masked_fill, x, m, expected_fused_ops={"aten::masked_fill_"})
def test_cat_neg_dim(self):
"""Test negative dimension index for the PyTorch cat Node on Glow."""
def test_f(a, b):
c = torch.cat((a, b), -3)
d = torch.cat((c, c), -2)
return torch.cat((d, d), -1)
x = torch.randn(2, 3, 4)
y = torch.randn(2, 3, 4)
jitVsGlow(test_f, x, y, expected_fused_ops={"prim::FusedConcat"})
def test_masked_fill_broadcasted_multi_dim(self):
"""Test of the PyTorch aten::masked_fill op on Glow with a
broadcasted mask where the mask's size has a non 1 lead dim"""
def masked_fill(a, mask):
return torch.masked_fill(a + a, mask, 42.0)
x = torch.randn([2, 4, 3, 3])
m = torch.tensor([[[[True, False, True]]], [[[True, False, True]]]],
dtype=torch.bool)
jitVsGlow(masked_fill, x, m, expected_fused_ops={"aten::masked_fill"})
def test_stack_basic():
"""Basic test of the PyTorch aten::stack Node on Glow."""
def test_f(a, b):
c = torch.stack((a, b), 0)
d = torch.stack((c, c), 1)
return torch.stack((d, d), 2)
x = torch.randn(2, 3, 4)
y = torch.randn(2, 3, 4)
jitVsGlow(test_f, x, y, expected_fused_ops={"glow::fused_stack"})
def test_relu_inplace(self):
"""Test of the PyTorch relu_ Node on Glow."""
def test_f(a):
b = F.relu(a, inplace=True)
return F.relu(b, inplace=True)
x = torch.randn(4)
# make sure we have at least one negative
x[0] = -2.0
jitVsGlow(test_f, x, expected_fused_ops={"aten::relu_"})
def test_layernorm_basic(self):
"""Basic test of the PyTorch layernorm Node on Glow."""
def test_f(inputs, weight, bias):
return F.layer_norm(inputs, [5], weight, bias)
inputs = torch.randn(1, 4, 5, 5)
weight = torch.randn(5)
bias = torch.randn(5)
jitVsGlow(test_f, inputs, weight, bias, expected_fused_ops={"aten::layer_norm"})
def test_conv2d_with_bias():
def conv2d_with_bias(inputs, filters, bias):
conv = F.conv2d(inputs, filters, bias)
return F.relu(conv)
inputs = torch.randn(1, 4, 5, 5)
filters = torch.randn(8, 4, 3, 3)
bias = torch.randn(8)
jitVsGlow(conv2d_with_bias, inputs, filters, bias)
def test_sqrt_basic():
"""Test of the PyTorch sqrt Node on Glow."""
def test_f(a):
b = torch.sqrt(a)
return torch.sqrt(b)
# Make sure the input is positive and not super close to zero.
x = torch.rand(4) + 5
jitVsGlow(test_f, x, expected_fused_ops={"aten::sqrt"})
def test_sqrt_inplace():
"""Test of the PyTorch inplace sqrt Node on Glow."""
def test_f(a):
b = a.sqrt_()
return b.sqrt_()
# Make sure the input is positive and not super close to zero.
x = torch.rand(4) + 5
jitVsGlow(test_f, x, expected_fused_ops={"aten::sqrt_"})
def test_mm_basic(self):
"""Test of the PyTorch mm Node on Glow."""
def test_f(a, b, t):
r = torch.mm(a, b)
return r.mm(t)
x = torch.randn(2, 3)
y = torch.randn(4, 3).t()
t = torch.randn(4, 2)
jitVsGlow(test_f, x, y, t, expected_fused_ops={"aten::mm"})
def test_sub_broadcast_3():
"""Test of the PyTorch sub Node on Glow with broadcasting."""
def test_f(a, b):
c = a.sub(b)
return c.sub(c)
x = torch.randn(4, 2)
y = torch.randn(8, 3, 4, 2)
jitVsGlow(test_f, x, y, expected_fused_ops={"aten::sub"})
def test_relu_basic(self):
"""Basic test of the PyTorch relu Node on Glow."""
def test_f(a):
b = F.relu(a)
return F.relu(b)
x = torch.randn(4)
# make sure we have at least one negative
x[0] = -2.0
jitVsGlow(test_f, x, expected_fused_ops={"aten::relu"})
def test_sub_basic():
"""Basic test of the PyTorch sub Node on Glow."""
def test_f(a, b):
c = a.sub(b)
return c.sub(c)
x = torch.randn(4)
y = torch.randn(4)
jitVsGlow(test_f, x, y, expected_fused_ops={"aten::sub"})
def test_typeas_self(self):
"""Test of the PyTorch mul Node on Glow doing empty convert (float to float)."""
def test_f(a, b):
a = a + a
c = a.type_as(b)
return c + c
x = torch.randn(4)
y = x
jitVsGlow(test_f, x, y, expected_fused_ops={})
def test_typeas_self_f2f2(self):
"""Test of the PyTorch type_as Node on Glow float to float."""
def test_f(a, b):
a = a + a
c = a.type_as(b)
return c + c
x = torch.randn(4, 2)
y = torch.randn(8, 3, 4, 2)
jitVsGlow(test_f, x, y, expected_fused_ops={})
def test_typeas_self_f2i2(self):
"""Test of the PyTorch type_as Node on Glow with float to int32"""
def test_f(a, b):
a = a + a
c = a.type_as(b)
return c + c
x = torch.randn(4, 2)
y = torch.randn(8, 3, 4, 2).to(dtype=torch.int32)
jitVsGlow(test_f, x, y, expected_fused_ops={"aten::type_as"})
def test_cat_oob_neg_dim(self):
"""Test out of bounds negative dimension index for the PyTorch cat Node on Glow."""
def test_f(a, b):
c = torch.cat((a, b), -4)
d = torch.cat((c, c), -2)
return torch.cat((d, d), -1)
x = torch.randn(2, 3, 4)
y = torch.randn(2, 3, 4)
with self.assertRaises(IndexError):
jitVsGlow(test_f, x, y, expected_fused_ops={"prim::FusedConcat"})
def test_batchnorm_basic():
"""Basic test of the PyTorch batchnorm Node on Glow."""
def test_f(inputs, running_mean, running_var):
return F.batch_norm(inputs, running_mean, running_var)
inputs = torch.randn(1, 4, 5, 5)
running_mean = torch.rand(4)
running_var = torch.rand(4)
jitVsGlow(test_f, inputs, running_mean, running_var,
expected_fused_ops={"aten::batch_norm"})
def test_gelu_basic():
"""Basic test of the PyTorch gelu Node on Glow."""
def test_f(a):
return F.gelu(a + a)
for i in range(100):
x = torch.randn(10)
jitVsGlow(test_f,
x,
check_trace=False,
atol=1e-3,
expected_fused_ops={"aten::gelu"})
def test_batch_permutation_basic(self):
"""Basic test of the _caffe2::BatchPermutation Node on Glow."""
def test_f(a, indices):
return torch.ops._caffe2.BatchPermutation(a + a, indices)
x = torch.randn(4, 2, 3)
indices = torch.tensor([1, 3, 0, 2], dtype=torch.int32)
jitVsGlow(test_f,
x,
indices,
expected_fused_ops={"_caffe2::BatchPermutation"})
def test_prelu_basic(self):
"""Basic test of the PyTorch prelu Node on Glow."""
def prelu_basic(inputs, weight):
return F.prelu(inputs + inputs, weight)
inputs = torch.randn(1, 4, 5, 5)
weight = torch.tensor([0.25])
jitVsGlow(prelu_basic,
inputs,
weight,
expected_fused_ops={"aten::prelu"})
def test_batchnorm_relu_basic(self):
"""
Basic test of the PyTorch 3D batchnorm RELU Node on Glow.
"""
class SimpleQuantizedBatchNormRelu(nn.Module):
def __init__(self, w, b, m, v):
super(SimpleQuantizedBatchNormRelu, self).__init__()
self.bn = torch.nn.BatchNorm3d(4)
self.relu = torch.nn.ReLU()
self.bn.weight = torch.nn.Parameter(w)
self.bn.bias = torch.nn.Parameter(b)
self.bn.running_mean = m
self.bn.running_var = v
self.q = QuantStub()
self.dq = DeQuantStub()
def forward(self, x):
qx = self.q(x)
qy = self.bn(qx)
qy_relu = self.relu(qy)
y = self.dq(qy_relu)
return y
C = 4
weight = torch.ones(C) + torch.rand(C) * 0.001
bias = torch.rand(C) * 0.0001
running_mean = torch.zeros(C)
running_var = torch.ones(C)
inputs = torch.randn((10, C, 2, 3, 4), requires_grad=False)
model = SimpleQuantizedBatchNormRelu(weight, bias, running_mean,
running_var)
model.eval()
model.qconfig = my_qconfig
modules_to_fuse = [["bn", "relu"]]
fuse_modules(model, modules_to_fuse, inplace=True)
prepare(model, inplace=True)
model.forward(inputs)
convert(model, inplace=True)
# Because of the difference of quantization between PyTorch & Glow
# We set eps big enough.
# Batchnorm introduced great accuracy issues, which could create up to
# ~1e-2 difference in some rare cases. In order to prevent this test
# to be flaky, atol is set to be 0.1.
jitVsGlow(
model,
inputs,
expected_fused_ops={"quantized::batch_norm3d_relu"},
atol=1e-1,
use_fp16=True,
)
def test_jit_vs_glow_inplace(self):
"""Test JIT vs. Glow logging with in-place op"""
torch_glow.enable_jit_vs_glow_compare()
def test_f(a, b):
a += b
return a
a = torch.randn(5, 6)
b = torch.randn(5, 6)
jitVsGlow(test_f, a, b, expected_fused_ops={"aten::add_"})
def test_conv2d_basic(self):
"""Basic test of the PyTorch conv2d Node on Glow."""
def test_f(inputs, filters):
conv = F.conv2d(inputs, filters, padding=1)
return F.relu(conv)
inputs = torch.randn(1, 4, 5, 5)
filters = torch.randn(8, 4, 3, 3)
jitVsGlow(test_f,
inputs,
filters,
expected_fused_ops={"aten::_convolution"})
def test_conv2d_non_square_dilation(self):
"""Test of the PyTorch conv2d Node on Glow with non-square dilation."""
def test_f(inputs, filters):
conv = F.conv2d(inputs, filters, dilation=[1, 2])
return F.relu(conv)
inputs = torch.randn(1, 4, 5, 5)
filters = torch.randn(8, 4, 3, 3)
jitVsGlow(test_f,
inputs,
filters,
expected_fused_ops={"aten::_convolution"})
