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[TOPI][Testing] Enable conv2d NHWC fp16 topi testing for arm_cpu #17007

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May 22, 2024
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[TOPI][Testing] Enable conv2d NHWC fp16 topi testing for arm_cpu #17007

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e2eefdf
[TOPI][Testing] Enable conv2d NHWC fp16 topi testing for `arm_cpu`
Anndrey24 May 17, 2024
5566d46
Guard execution of fp16 SIMD instructions
Anndrey24 May 21, 2024
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7 changes: 7 additions & 0 deletions python/tvm/testing/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1057,6 +1057,13 @@ def _has_cpu_feat(features):
)


requires_arm_fp16 = Feature(
"arm_fp16",
"Arm(R) Neon(TM) instructions for FP16",
run_time_check=lambda: _has_cpu_feat("fullfp16"),
)


requires_aarch64_sve = Feature(
"arm_sve",
"AArch64 SVE",
Expand Down
49 changes: 40 additions & 9 deletions tests/python/topi/test_topi_conv2d_nhwc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,7 @@
topi.arm_cpu.schedule_conv2d_nhwc_spatial_pack,
),
(
"llvm --device arm_cpu --mtriple aarch64-linux-gnu -mattr=+v8.2a",
"llvm --device arm_cpu --mtriple aarch64-linux-gnu -mattr=+v8.2a,+fullfp16",
topi.arm_cpu.compute_conv2d_NHWC_hybrid,
topi.arm_cpu.schedule_conv2d_NHWC_hybrid,
),
Expand All @@ -64,7 +64,7 @@
),
)

dtype = tvm.testing.parameter("float32")
dtype = tvm.testing.parameter("float16", "float32")

batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation = tvm.testing.parameters(
# Pad M, N, K
Expand Down Expand Up @@ -104,14 +104,36 @@ def ref_data(dtype, batch, in_channel, in_size, num_filter, kernel, stride, padd
a_shape = (batch, in_height, in_width, in_channel)
w_shape = (kernel, kernel, in_channel, num_filter)

np.random.seed(0)
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)

# scipy.signal.convolve2d does not support float16 data types,
# and the python fallback would be too slow for general use.
conv_dtype = "float32" if dtype == "float16" else dtype
b_np = tvm.topi.testing.conv2d_nhwc_python(
a_np.astype(conv_dtype), dw_np.astype(conv_dtype), stride, padding
).astype(dtype)
return a_np, w_np, b_np


def test_conv2d_nhwc_gemm_fp32(device, ref_data, dtype, stride, padding, dilation):
def get_tolerance(dtype, w_np, b_np):
if dtype == "float16":
# A summation in float16 with a single accumulator very
# quickly runs into large rounding errors.
# This tolerance is necessary to ensure no false negatives,
# but it may introduce false positives, depending on schedule behaviour.
num_values_summed = w_np.shape[0] * w_np.shape[1] * w_np.shape[2]
next_float_gap_size = np.nextafter(b_np.max(), np.inf, dtype=b_np.dtype) - b_np.max()
tol = {"rtol": 1e-5, "atol": num_values_summed * next_float_gap_size / 2}
else:
tol = {"rtol": 1e-5, "atol": 1e-7}

return tol


def test_conv2d_nhwc_gemm(device, ref_data, dtype, stride, padding, dilation):
a_np, w_np, b_np = ref_data

A = te.placeholder(a_np.shape, name="A", dtype=dtype)
Expand All @@ -130,14 +152,21 @@ def test_conv2d_nhwc_gemm_fp32(device, ref_data, dtype, stride, padding, dilatio

# Run only on AArch64 devices
# Do not run SVE schedules on non-SVE devices
build_only = platform.machine() != "aarch64" or (
target.features.has_sve and not tvm.testing.requires_aarch64_sve.run_time_check()
build_only = (
platform.machine() != "aarch64"
or (target.features.has_sve and not tvm.testing.requires_aarch64_sve.run_time_check())
or (
dtype == "float16"
and target.features.has_fp16_simd
and not tvm.testing.requires_arm_fp16.run_time_check()
)
)
if build_only:
return

func(a, w, b)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
tol = get_tolerance(dtype, w_np, b_np)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])


def test_conv2d_nhwc_hwio(target, dev, ref_data, dtype, stride, padding, dilation):
Expand All @@ -155,7 +184,8 @@ def test_conv2d_nhwc_hwio(target, dev, ref_data, dtype, stride, padding, dilatio
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)
tol = get_tolerance(dtype, w_np, b_np)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])


def test_conv2d_nhwc_ohwi(ref_data, dtype, stride, padding, dilation):
Expand Down Expand Up @@ -184,7 +214,8 @@ def test_conv2d_nhwc_ohwi(ref_data, dtype, stride, padding, dilation):
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)
tol = get_tolerance(dtype, w_np_hwio, b_np)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])


if __name__ == "__main__":
Expand Down