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[SME][Test] Add additional conv2d tests for asymmetric parameters #17055

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Jun 8, 2024
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[SME][Test] Add additional conv2d tests for asymmetric parameters #17055

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70 changes: 27 additions & 43 deletions tests/python/relay/strategy/arm_cpu/test_conv2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,48 +120,10 @@ class TestConv2d_NCHW_Spatial_Pack(Conv2dTests):
schedule_name = parameter("conv2d_nchw_spatial_pack.arm_cpu")


in_dtype = tvm.testing.parameter("float16", "float32")
out_dtype = tvm.testing.parameter("float32")

batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation = tvm.testing.parameters(
# Pad M, N, K
(1, 1, 1, 1, 1, 1, "SAME", 1),
(1, 1, 3, 15, 1, 1, "SAME", 1),
# Pad M, K
(1, 3, 9, 16, 3, 1, "SAME", 1),
# Pad M, N
(1, 2, 9, 15, 4, 1, "SAME", 1),
# Pad K, N
(1, 7, 4, 15, 3, 1, "SAME", 1),
# Pad M
(1, 2, 9, 16, 4, 1, "SAME", 1),
# Pad K
(1, 7, 4, 16, 3, 1, "SAME", 1),
# Pad N
(1, 2, 4, 15, 4, 1, "SAME", 1),
(1, 2, 4, 20, 1, 1, "SAME", 1),
# Large workloads
(1, 128, 32, 128, 3, 1, "SAME", 1),
(4, 64, 16, 64, 5, 2, "SAME", 1),
(1, 128, 32, 128, 3, 1, "VALID", 1),
(4, 64, 16, 64, 5, 2, "VALID", 1),
(1, 64, 16, 64, 3, 2, (0, 0, 1, 1), 1),
(1, 64, 16, 64, 3, 2, (1, 1, 2, 2), 1),
(1, 64, 16, 64, 5, 2, (3, 3, 2, 2), 1),
(1, 64, 16, 64, 3, 2, (0, 1, 2, 3), 1),
(1, 64, 32, 64, 3, 1, "SAME", 2),
(1, 64, 32, 64, 3, 1, (1, 1, 2, 2), 2),
)


@tvm.testing.fixture()
def ref_data(
in_dtype, out_dtype, batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation
):
def ref_data(in_dtype, out_dtype, data_shape, num_filter, kernel_size, stride, padding, dilation):
np.random.seed(0)
in_height = in_width = in_size
a_shape = (batch, in_height, in_width, in_channel)
w_shape = (kernel, kernel, in_channel, num_filter)
a_shape = data_shape
w_shape = (kernel_size[0], kernel_size[1], data_shape[3], num_filter)

a_np = np.random.uniform(size=a_shape).astype(in_dtype)
w_np = np.random.uniform(size=w_shape).astype(in_dtype)
Expand All @@ -175,9 +137,31 @@ def ref_data(
@pytest.mark.skipif(
llvm_version_major() < 16, reason="SME is not supported in earlier versions of LLVM"
)
@pytest.mark.parametrize(
"data_shape,kernel_size,num_filter,stride,padding,dilation",
[
((1, 1, 1, 1), (3, 3), 1, 1, "SAME", 1),
((1, 9, 9, 1), (3, 3), 16, 1, "SAME", 1),
((1, 32, 32, 1), (3, 3), 12, 1, "SAME", 1),
((1, 32, 10, 3), (3, 3), 16, 1, 0, 1),
((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1),
((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1),
((1, 32, 32, 16), (3, 4), 16, 1, 0, 1),
((1, 9, 31, 7), (3, 3), 7, 1, "VALID", 1),
((1, 32, 32, 16), (5, 5), 16, 1, (0, 2, 2, 0), 2),
((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2),
((1, 134, 153, 32), (3, 3), 2, (2, 2), "VALID", 1),
((1, 16, 16, 64), (1, 1), 8, (1, 1), "SAME", 1),
],
)
@pytest.mark.parametrize("in_dtype,out_dtype", [("float32", "float32"), ("float16", "float32")])
@tvm.testing.requires_aprofile_aem_fvp
def test_conv2d_sme(target, ref_data, in_dtype, out_dtype, stride, padding, dilation):
a_np, w_np, dw_np, b_np = ref_data
def test_conv2d_sme(
target, data_shape, kernel_size, num_filter, stride, padding, dilation, in_dtype, out_dtype
):
a_np, w_np, dw_np, b_np = ref_data(
in_dtype, out_dtype, data_shape, num_filter, kernel_size, stride, padding, dilation
)

kernel_size = get_const_tuple(w_np.shape[:2])
out_channels = w_np.shape[3]
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