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[Vulkan][Topi] Parametrizing additional topi tests, marking vulkan failures #8904

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Sep 2, 2021
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[Vulkan][Topi] Parametrizing additional topi tests, marking vulkan failures #8904

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1f8f754
[Pytest] Fixed TestTargetAutoParametrization in cases where LLVM is d…
Lunderberg Aug 30, 2021
1cd8bcb
[UnitTests][Vulkan] Improved robustness of test_tir_intrin::test_clz
Lunderberg Aug 30, 2021
d1860e1
[UnitTest][Topi] Parametrized several unit tests, identify vulkan fai…
Lunderberg Aug 30, 2021
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3 changes: 3 additions & 0 deletions src/target/spirv/spirv_support.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,9 @@ SPIRVSupport::SPIRVSupport(tvm::Target target) {
if (target->GetAttr<Bool>("supports_float16")) {
supports_float16 = target->GetAttr<Bool>("supports_float16").value();
}
if (target->GetAttr<Bool>("supports_float64")) {
supports_float64 = target->GetAttr<Bool>("supports_float64").value();
}
if (target->GetAttr<Bool>("supports_int8")) {
supports_int8 = target->GetAttr<Bool>("supports_int8").value();
}
Expand Down
161 changes: 89 additions & 72 deletions tests/python/topi/python/test_topi_conv1d_transpose_ncw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,90 +15,107 @@
# specific language governing permissions and limitations
# under the License.
"""Test code for transposed convolution."""
import numpy as np

import itertools
import os

import numpy as np

import tvm
from tvm import te
from tvm import topi
import tvm.testing
import tvm.topi.testing
from tvm.contrib.pickle_memoize import memoize

from tvm import te, topi
from tvm.topi.utils import get_const_tuple
import tvm.testing

_conv1d_transpose_ncw_implement = {
"generic": (topi.nn.conv1d_transpose_ncw, topi.generic.schedule_conv1d_transpose_ncw),
"gpu": (topi.cuda.conv1d_transpose_ncw, topi.cuda.schedule_conv1d_transpose_ncw),
}


def verify_conv1d_transpose_ncw(
batch, in_channel, in_size, num_filter, kernel, stride, padding, output_padding
(
batch,
in_channel,
in_size,
num_filter,
kernel,
stride,
padding,
output_padding,
) = tvm.testing.parameters(
(1, 3, 224, 32, 5, 1, 0, (0,)),
(1, 3, 224, 32, 7, 1, 2, (0,)),
(1, 3, 224, 32, 5, 2, 1, (0,)),
(1, 3, 224, 32, 5, 2, 1, (1,)),
(1, 3, 224, 32, 5, 2, 0, (0,)),
(1, 32, 32, 128, 5, 1, 0, (0,)),
(1, 32, 32, 128, 5, 2, 1, (0,)),
(1, 1, 1024, 1, 512, 1, 256, (0,)),
(1, 1, 1024, 1, 512, 2, 256, (0,)),
(1, 1, 1024, 1, 512, 5, 256, (0,)),
(1, 1, 1024, 1, 512, 5, 256, (3,)),
(1, 2, 1024, 1, 128, 128, 0, (0,)),
(1, 1, 1024, 2, 128, 128, 0, (0,)),
(1, 1, 1024, 2, 2, 2, 0, (0,)),
(1, 1, 10, 1, 5, 1, (0, 3), (0,)),
(1, 1, 10, 1, 5, 1, (1, 3), (0,)),
(1, 1, 10, 1, 5, 1, (2, 3), (0,)),
(1, 257, 128, 1, 512, 128, 256, (0,)),
)

dtype = tvm.testing.parameter("float32")


@tvm.testing.fixture(cache_return_value=True)
def ref_data(
dtype, batch, in_channel, in_size, num_filter, kernel, stride, padding, output_padding
):
dtype = "float32"
a_shape = (batch, in_channel, in_size)
w_shape = (in_channel, num_filter, kernel)

a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
b_np = tvm.topi.testing.conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding)
c_np = np.maximum(b_np, 0)
return a_np, w_np, b_np, c_np


@tvm.testing.known_failing_targets("vulkan")
def test_conv1d_transpose_ncw(
target,
dev,
ref_data,
dtype,
stride,
padding,
output_padding,
):
in_width = in_size
A = te.placeholder((batch, in_channel, in_width), name="A")
W = te.placeholder((in_channel, num_filter, kernel), name="W")

a_shape = get_const_tuple(A.shape)
w_shape = get_const_tuple(W.shape)
dtype = A.dtype

@memoize("topi.tests.test_topi_conv1d_transpose.verify_conv1d_transpose_ncw")
def get_ref_data():
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
b_np = tvm.topi.testing.conv1d_transpose_ncw_python(
a_np, w_np, stride, padding, output_padding
)
c_np = np.maximum(b_np, 0)
return a_np, w_np, b_np, c_np

a_np, w_np, b_np, c_np = get_ref_data()

def check_target(target, dev):
dev = tvm.device(target, 0)
with tvm.target.Target(target):
fcompute, fschedule = tvm.topi.testing.dispatch(target, _conv1d_transpose_ncw_implement)
B = fcompute(A, W, stride, padding, A.dtype, output_padding)
C = topi.nn.relu(B)
s1 = fschedule([B])
s2 = fschedule([C])
a = tvm.nd.array(a_np, dev)
w = tvm.nd.array(w_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)

func1 = tvm.build(s1, [A, W, B], target)
func2 = tvm.build(s2, [A, W, C], target)
func1(a, w, b)
func2(a, w, c)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)

for target, dev in tvm.testing.enabled_targets():
check_target(target, dev)


@tvm.testing.uses_gpu
def test_conv1d_transpose_ncw():
verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 1, 0, (0,))
verify_conv1d_transpose_ncw(1, 3, 224, 32, 7, 1, 2, (0,))
verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 1, (0,))
verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 1, (1,))
verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 0, (0,))
verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 1, 0, (0,))
verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 2, 1, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 1, 256, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 2, 256, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 5, 256, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 5, 256, (3,))
verify_conv1d_transpose_ncw(1, 2, 1024, 1, 128, 128, 0, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 2, 128, 128, 0, (0,))
verify_conv1d_transpose_ncw(1, 1, 1024, 2, 2, 2, 0, (0,))
verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (0, 3), (0,))
verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (1, 3), (0,))
verify_conv1d_transpose_ncw(1, 1, 10, 1, 5, 1, (2, 3), (0,))
verify_conv1d_transpose_ncw(1, 257, 128, 1, 512, 128, 256, (0,))

a_np, w_np, b_np, c_np = ref_data

A = te.placeholder(a_np.shape, name="A", dtype=dtype)
W = te.placeholder(w_np.shape, name="W", dtype=dtype)

with tvm.target.Target(target):
fcompute, fschedule = tvm.topi.testing.dispatch(target, _conv1d_transpose_ncw_implement)
B = fcompute(A, W, stride, padding, A.dtype, output_padding)
C = topi.nn.relu(B)
s1 = fschedule([B])
s2 = fschedule([C])
a = tvm.nd.array(a_np, dev)
w = tvm.nd.array(w_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)

func1 = tvm.build(s1, [A, W, B], target)
func2 = tvm.build(s2, [A, W, C], target)
func1(a, w, b)
func2(a, w, c)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)


if __name__ == "__main__":
test_conv1d_transpose_ncw()
sys.exit(pytest.main(sys.argv))
92 changes: 43 additions & 49 deletions tests/python/topi/python/test_topi_conv2d_nhwc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,8 @@


_conv2d_nhwc_implement = {
"llvm": (topi.nn.conv2d_nhwc, topi.generic.schedule_conv2d_nhwc),
"cuda": (topi.cuda.conv2d_nhwc, topi.cuda.schedule_conv2d_nhwc),
"generic": (topi.nn.conv2d_nhwc, topi.generic.schedule_conv2d_nhwc),
"gpu": (topi.cuda.conv2d_nhwc, topi.cuda.schedule_conv2d_nhwc),
"cpu": (topi.nn.conv2d_nhwc, topi.x86.schedule_conv2d_nhwc),
"arm_cpu": (
topi.arm_cpu.conv2d_nhwc_spatial_pack,
Expand All @@ -45,61 +45,55 @@
"hls": (topi.nn.conv2d_nhwc, topi.hls.schedule_conv2d_nhwc),
}

dtype = tvm.testing.parameter("float32")

def verify_conv2d_nhwc(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1):
in_height = in_width = in_size

A = te.placeholder((batch, in_height, in_width, in_channel), name="A")
W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W")
batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation = tvm.testing.parameters(
(1, 256, 32, 256, 3, 1, "SAME", 1),
(4, 128, 16, 128, 5, 2, "SAME", 1),
(4, 128, 16, 256, 5, 2, "SAME", 1),
(1, 256, 32, 256, 3, 1, "VALID", 1),
(1, 256, 32, 256, 3, 1, "VALID", 1),
(4, 128, 16, 128, 5, 2, "VALID", 1),
(4, 128, 16, 256, 5, 2, "VALID", 1),
(1, 128, 16, 256, 3, 2, (0, 0, 1, 1), 1),
(1, 128, 16, 256, 3, 2, (1, 1, 2, 2), 1),
(1, 128, 16, 128, 5, 2, (3, 3, 2, 2), 1),
(1, 128, 16, 256, 3, 2, (0, 1, 2, 3), 1),
(1, 256, 32, 256, 3, 1, "SAME", 2),
(1, 256, 32, 256, 3, 1, (1, 1, 2, 2), 2),
)

a_shape = get_const_tuple(A.shape)
w_shape = get_const_tuple(W.shape)
dtype = A.dtype

@memoize("topi.tests.test_topi_conv2d_nhwc.verify_nhwc.v2")
def get_ref_data():
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
dw_np = tvm.topi.testing.dilate_python(w_np, (dilation, dilation, 1, 1))
b_np = tvm.topi.testing.conv2d_nhwc_python(a_np, dw_np, stride, padding)
return a_np, w_np, b_np
@tvm.testing.fixture(cache_return_value=True)
def ref_data(dtype, batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation):
in_height = in_width = in_size
a_shape = (batch, in_height, in_width, in_channel)
w_shape = (kernel, kernel, in_channel, num_filter)

a_np, w_np, b_np = get_ref_data()
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
dw_np = tvm.topi.testing.dilate_python(w_np, (dilation, dilation, 1, 1))
b_np = tvm.topi.testing.conv2d_nhwc_python(a_np, dw_np, stride, padding)
return a_np, w_np, b_np

def check_device(target, dev):
print("Running on target: %s" % target)
with tvm.target.Target(target):
fcompute, fschedule = tvm.topi.testing.dispatch(target, _conv2d_nhwc_implement)
B = fcompute(A, W, stride, padding, dilation, dtype)
s = fschedule([B])
a = tvm.nd.array(a_np, dev)
w = tvm.nd.array(w_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
func = tvm.build(s, [A, W, B], target)
func(a, w, b)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)

for target, dev in tvm.testing.enabled_targets():
check_device(target, dev)
def test_conv2d_nhwc(target, dev, ref_data, dtype, stride, padding, dilation):
a_np, w_np, b_np = ref_data

A = te.placeholder(a_np.shape, name="A", dtype=dtype)
W = te.placeholder(w_np.shape, name="W", dtype=dtype)

@tvm.testing.uses_gpu
def test_conv2d_nhwc():
verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "SAME")
verify_conv2d_nhwc(4, 128, 16, 128, 5, 2, "SAME")
verify_conv2d_nhwc(4, 128, 16, 256, 5, 2, "SAME")
verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "VALID")
verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "VALID")
verify_conv2d_nhwc(4, 128, 16, 128, 5, 2, "VALID")
verify_conv2d_nhwc(4, 128, 16, 256, 5, 2, "VALID")
verify_conv2d_nhwc(1, 128, 16, 256, 3, 2, (0, 0, 1, 1))
verify_conv2d_nhwc(1, 128, 16, 256, 3, 2, (1, 1, 2, 2))
verify_conv2d_nhwc(1, 128, 16, 128, 5, 2, (3, 3, 2, 2))
verify_conv2d_nhwc(1, 128, 16, 256, 3, 2, (0, 1, 2, 3))
# dilation = 2
verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "SAME", dilation=2)
verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, (1, 1, 2, 2), dilation=2)
with tvm.target.Target(target):
fcompute, fschedule = tvm.topi.testing.dispatch(target, _conv2d_nhwc_implement)
B = fcompute(A, W, stride, padding, dilation, dtype)
s = fschedule([B])
a = tvm.nd.array(a_np, dev)
w = tvm.nd.array(w_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
func = tvm.build(s, [A, W, B], target)
func(a, w, b)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)


if __name__ == "__main__":
test_conv2d_nhwc()
sys.exit(pytest.main(sys.argv))
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