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Support BFloat16 in convolution_backward #7807

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Merged
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Jan 23, 2025
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Support BFloat16 in convolution_backward #7807

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2 changes: 1 addition & 1 deletion kernels/portable/cpu/op_convolution_backward.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -289,7 +289,7 @@ std::tuple<Tensor&, Tensor&, Tensor&> convolution_backward_out(

constexpr auto name = "convolution_backward.out";

ET_SWITCH_FLOATH_TYPES(input.scalar_type(), ctx, name, CTYPE, [&]() {
ET_SWITCH_FLOATHBF16_TYPES(input.scalar_type(), ctx, name, CTYPE, [&]() {
conv2d_backward_impl<CTYPE>(
grad_output,
input,
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1 change: 1 addition & 0 deletions kernels/test/CMakeLists.txt
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Expand Up @@ -117,6 +117,7 @@ set(all_test_sources
"op_clamp_test.cpp"
"op_clone_test.cpp"
"op_constant_pad_nd_test.cpp"
"op_convolution_backward_test.cpp"
"op_convolution_test.cpp"
"op_copy_test.cpp"
"op_cos_test.cpp"
Expand Down
274 changes: 145 additions & 129 deletions kernels/test/op_convolution_backward_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -62,139 +62,155 @@ class OpConvolutionBackwardOutTest : public OperatorTest {
grad_weight,
grad_bias);
}
};

TEST_F(OpConvolutionBackwardOutTest, SmokeTest) {
TensorFactory<ScalarType::Float> tf;
template <ScalarType DTYPE>
void test_dtype() {
TensorFactory<DTYPE> tf;

std::vector<float> grad_output_data = {
10, 12, 87, 13, 34, 87, 55, 22, 48, 33, 29, 38, 60, 49, 88, 30,
99, 19, 42, 37, 61, 31, 33, 58, 38, 23, 2, 33, 3, 21, 32, 2,
30, 72, 10, 67, 92, 19, 11, 16, 65, 37, 60, 74, 4, 19, 45, 37};
std::vector<float> input_data = {
9, 89, 45, 39, 25, 2, 97, 55, 80, 24, 18, 33, 28, 89, 19, 16, 19, 33,
69, 61, 34, 84, 58, 30, 33, 18, 75, 30, 6, 33, 42, 10, 80, 41, 66, 64,
47, 51, 67, 62, 58, 10, 97, 71, 24, 44, 84, 34, 33, 54, 8, 73, 90, 15,
21, 92, 55, 22, 56, 12, 10, 63, 32, 76, 65, 38, 95, 92, 22, 15, 37, 12,
67, 14, 60, 44, 73, 74, 23, 4, 56, 64, 88, 90, 82, 32, 91, 3, 6, 87,
55, 95, 7, 14, 24, 69, 52, 44, 14, 37, 75, 52, 37, 40, 25, 54, 4, 15,
97, 51, 46, 28, 65, 95, 50, 82, 23, 39, 50, 55, 97, 52, 91, 16, 19, 49,
61, 50, 42, 47, 87, 99, 9, 60, 22, 71, 47, 17, 0, 80, 28, 88, 93, 43,
65, 25, 88, 67, 21, 89, 24, 81, 3, 71, 20, 34, 17, 17, 94, 10, 82, 25,
10, 11, 7, 28, 77, 39, 74, 79, 17, 40, 67, 54, 49, 54, 21, 89, 17, 7,
52, 64, 68, 80, 7, 72, 44, 35, 92, 47, 4, 13, 10, 43, 64, 66, 83, 49,
81, 78, 58, 22, 86, 48, 35, 64, 98, 79, 8, 52, 56, 23, 38, 74, 16, 63,
51, 70, 44, 28, 43, 13, 51, 85, 42, 29, 64, 26, 54, 91, 9, 96, 41, 56,
7, 52, 27, 22, 69, 13, 8, 20, 22, 49, 66, 98, 77, 42, 54, 38, 70, 83,
13, 8, 21, 56, 78, 37, 28, 69, 42, 30, 91, 5, 28, 15, 20, 14, 16, 39,
95, 66, 4, 72, 52, 35, 54, 93, 87, 77, 3, 49, 82, 70, 84, 3, 73, 99,
32, 95, 58, 65, 32, 75, 34, 22, 12, 84, 63, 72, 85, 66, 63, 27, 3, 73,
45, 37, 61, 52, 41, 16, 37, 14, 80, 17, 48, 8, 87, 98, 69, 63, 92, 68,
42, 63, 5, 22, 66, 91, 74, 11, 17, 45, 45, 33, 40, 85, 26, 75, 73, 81,
54, 27, 80, 1, 44, 66, 10, 21, 15, 10, 76, 96, 0, 43, 39, 3, 57, 79,
45, 64, 58, 92, 44, 42, 7, 28, 94, 4, 8, 22, 22, 31, 75, 44, 3, 70,
83, 72, 87, 12, 20, 55, 84, 31, 50, 34, 25, 49, 29, 71, 57, 97, 25, 82,
84, 42, 86, 41, 54, 92, 34, 30, 52, 34, 84, 25, 54, 37, 38, 26, 76, 82,
34, 14, 85, 28, 93, 9};
std::vector<float> weight_data = {
2, 54, 9, 37, 0, 47, 70, 9, 84, 69, 56, 79, 25, 35, 54, 13,
65, 46, 38, 28, 74, 27, 66, 61, 20, 60, 62, 58, 15, 44, 75, 55,
7, 52, 13, 36, 39, 64, 62, 45, 100, 6, 79, 63, 63, 52, 37, 60,
78, 12, 69, 2, 74, 56, 93, 39, 62, 22, 55, 67, 68, 74, 12, 69,
15, 73, 28, 70, 86, 20, 90, 49, 52, 26, 58, 2, 82, 17, 70, 55,
54, 83, 70, 11, 27, 9, 5, 42, 34, 62, 29, 94, 69, 81, 54, 4};
std::vector<float> expected_grad_input_data = {
1134, 7578, 686, 2682, 0, 4148, 7136, 2406, 8698, 0,
3759, 6003, 2163, 2395, 0, 2929, 5830, 3469, 6955, 0,
720, 6201, 495, 2063, 0, 5260, 5989, 3060, 7079, 0,
9690, 3423, 3385, 1932, 0, 7644, 8499, 1323, 2613, 0,
4334, 6624, 8532, 9719, 0, 5496, 8601, 1157, 2215, 0,
4676, 7600, 6524, 10069, 0, 4047, 6117, 1612, 2567, 0,
5931, 5651, 5669, 6623, 0, 7674, 3291, 2748, 1654, 0,
10455, 4290, 4145, 796, 0, 9835, 5483, 11649, 5952, 0,
7098, 5460, 3101, 2443, 0, 7788, 5909, 8582, 6298, 0,
9462, 4845, 3041, 2067, 0, 7038, 6336, 10438, 6377, 0,
7518, 8187, 2079, 2773, 0, 10036, 2642, 3952, 1166, 0,
16014, 2250, 10025, 1908, 0, 9610, 298, 3868, 122, 0,
16629, 4338, 11335, 3527, 0, 11514, 5965, 4762, 2207, 0,
18552, 10755, 13309, 5996, 0, 12454, 6787, 4960, 2875, 0,
8750, 6999, 3534, 3233, 0, 14160, 9399, 9595, 8922, 0,
9110, 6567, 3820, 2351, 0, 12969, 11814, 9436, 5870, 0,
7631, 7061, 2877, 2499, 0, 8553, 13527, 3631, 6863, 0,
1361, 8634, 515, 3372, 0, 3394, 10206, 1504, 4112, 0,
5505, 17421, 4702, 11891, 0, 4233, 11894, 1739, 5014, 0,
11787, 14634, 8981, 10759, 0, 11777, 6701, 4719, 3111, 0,
18459, 7761, 12044, 7627, 0, 11214, 4556, 4374, 1594, 0,
604, 1908, 1506, 6102, 0, 2532, 4024, 1713, 6121, 0,
1878, 1814, 4761, 5397, 0, 1127, 3885, 4373, 5832, 0,
450, 1414, 1080, 4719, 0, 5210, 2683, 2765, 4252, 0,
2390, 1668, 7710, 4257, 0, 378, 1698, 3276, 6021, 0,
2866, 4881, 3547, 6822, 0, 502, 1238, 2784, 5199, 0,
2496, 3975, 2700, 5004, 0, 1220, 1990, 3633, 5763, 0,
4501, 2679, 4504, 5412, 0, 1968, 1376, 6246, 3669, 0,
3130, 272, 9345, 1950, 0, 5167, 3278, 9097, 2138, 0,
2446, 1946, 6942, 5460, 0, 5732, 3404, 7919, 5534, 0,
2038, 1614, 6978, 4635, 0, 4544, 4839, 7367, 5574, 0,
1242, 1922, 4842, 6333, 0, 1066, 236, 2236, 686, 0,
17238, 2254, 10413, 1592, 0, 991, 30, 2206, 70, 0,
18823, 6392, 12173, 2470, 0, 1142, 684, 2742, 1219, 0,
21256, 11293, 12719, 7512, 0, 1303, 649, 2818, 1669, 0,
898, 574, 2018, 1929, 0, 15720, 11989, 10517, 5972, 0,
885, 781, 2210, 1281, 0, 14601, 12198, 7915, 4958, 0,
856, 850, 1601, 1355, 0, 7039, 14083, 4113, 7490, 0,
152, 927, 287, 1902, 0, 301, 1051, 886, 2346, 0,
6821, 19615, 4491, 13281, 0, 424, 1146, 999, 2906, 0,
15177, 15480, 8849, 12442, 0, 1222, 544, 2687, 1859, 0,
20215, 9693, 11441, 4964, 0, 1206, 555, 2466, 860, 0};
std::vector<float> expected_grad_weight_data = {
9246, 22073, 12431, 19714, 11179, 19032, 8458, 6495, 18707, 13830,
20445, 17089, 17124, 18710, 11827, 17236, 16824, 9008, 14086, 18834,
17419, 16759, 13152, 9339, 13801, 20888, 13976, 27277, 13010, 23949,
9838, 11220, 17658, 15019, 25337, 17583, 13270, 21754, 16908, 20563,
20732, 13413, 20868, 27521, 19537, 21170, 15888, 10034, 19195, 16370,
40243, 25890, 40472, 30460, 21228, 21625, 13289, 24435, 19876, 29816,
24188, 23619, 13752, 16251, 18741, 19368, 24517, 34261, 27054, 31257,
21238, 18909, 15776, 16881, 34604, 22534, 28101, 23834, 18479, 16469,
12852, 16551, 14204, 29983, 20167, 24150, 14281, 17501, 15897, 16019,
21661, 32765, 23874, 26527, 20463, 18661};
std::vector<float> expected_grad_bias_data = {363, 438, 585, 501};
using CTYPE = typename decltype(tf)::ctype;
std::vector<CTYPE> grad_output_data = {
10, 12, 87, 13, 34, 87, 55, 22, 48, 33, 29, 38, 60, 49, 88, 30,
99, 19, 42, 37, 61, 31, 33, 58, 38, 23, 2, 33, 3, 21, 32, 2,
30, 72, 10, 67, 92, 19, 11, 16, 65, 37, 60, 74, 4, 19, 45, 37};
std::vector<CTYPE> input_data = {
9, 89, 45, 39, 25, 2, 97, 55, 80, 24, 18, 33, 28, 89, 19, 16, 19, 33,
69, 61, 34, 84, 58, 30, 33, 18, 75, 30, 6, 33, 42, 10, 80, 41, 66, 64,
47, 51, 67, 62, 58, 10, 97, 71, 24, 44, 84, 34, 33, 54, 8, 73, 90, 15,
21, 92, 55, 22, 56, 12, 10, 63, 32, 76, 65, 38, 95, 92, 22, 15, 37, 12,
67, 14, 60, 44, 73, 74, 23, 4, 56, 64, 88, 90, 82, 32, 91, 3, 6, 87,
55, 95, 7, 14, 24, 69, 52, 44, 14, 37, 75, 52, 37, 40, 25, 54, 4, 15,
97, 51, 46, 28, 65, 95, 50, 82, 23, 39, 50, 55, 97, 52, 91, 16, 19, 49,
61, 50, 42, 47, 87, 99, 9, 60, 22, 71, 47, 17, 0, 80, 28, 88, 93, 43,
65, 25, 88, 67, 21, 89, 24, 81, 3, 71, 20, 34, 17, 17, 94, 10, 82, 25,
10, 11, 7, 28, 77, 39, 74, 79, 17, 40, 67, 54, 49, 54, 21, 89, 17, 7,
52, 64, 68, 80, 7, 72, 44, 35, 92, 47, 4, 13, 10, 43, 64, 66, 83, 49,
81, 78, 58, 22, 86, 48, 35, 64, 98, 79, 8, 52, 56, 23, 38, 74, 16, 63,
51, 70, 44, 28, 43, 13, 51, 85, 42, 29, 64, 26, 54, 91, 9, 96, 41, 56,
7, 52, 27, 22, 69, 13, 8, 20, 22, 49, 66, 98, 77, 42, 54, 38, 70, 83,
13, 8, 21, 56, 78, 37, 28, 69, 42, 30, 91, 5, 28, 15, 20, 14, 16, 39,
95, 66, 4, 72, 52, 35, 54, 93, 87, 77, 3, 49, 82, 70, 84, 3, 73, 99,
32, 95, 58, 65, 32, 75, 34, 22, 12, 84, 63, 72, 85, 66, 63, 27, 3, 73,
45, 37, 61, 52, 41, 16, 37, 14, 80, 17, 48, 8, 87, 98, 69, 63, 92, 68,
42, 63, 5, 22, 66, 91, 74, 11, 17, 45, 45, 33, 40, 85, 26, 75, 73, 81,
54, 27, 80, 1, 44, 66, 10, 21, 15, 10, 76, 96, 0, 43, 39, 3, 57, 79,
45, 64, 58, 92, 44, 42, 7, 28, 94, 4, 8, 22, 22, 31, 75, 44, 3, 70,
83, 72, 87, 12, 20, 55, 84, 31, 50, 34, 25, 49, 29, 71, 57, 97, 25, 82,
84, 42, 86, 41, 54, 92, 34, 30, 52, 34, 84, 25, 54, 37, 38, 26, 76, 82,
34, 14, 85, 28, 93, 9};
std::vector<CTYPE> weight_data = {
2, 54, 9, 37, 0, 47, 70, 9, 84, 69, 56, 79, 25, 35, 54, 13,
65, 46, 38, 28, 74, 27, 66, 61, 20, 60, 62, 58, 15, 44, 75, 55,
7, 52, 13, 36, 39, 64, 62, 45, 100, 6, 79, 63, 63, 52, 37, 60,
78, 12, 69, 2, 74, 56, 93, 39, 62, 22, 55, 67, 68, 74, 12, 69,
15, 73, 28, 70, 86, 20, 90, 49, 52, 26, 58, 2, 82, 17, 70, 55,
54, 83, 70, 11, 27, 9, 5, 42, 34, 62, 29, 94, 69, 81, 54, 4};
std::vector<CTYPE> expected_grad_input_data = {
1134, 7578, 686, 2682, 0, 4148, 7136, 2406, 8698, 0,
3759, 6003, 2163, 2395, 0, 2929, 5830, 3469, 6955, 0,
720, 6201, 495, 2063, 0, 5260, 5989, 3060, 7079, 0,
9690, 3423, 3385, 1932, 0, 7644, 8499, 1323, 2613, 0,
4334, 6624, 8532, 9719, 0, 5496, 8601, 1157, 2215, 0,
4676, 7600, 6524, 10069, 0, 4047, 6117, 1612, 2567, 0,
5931, 5651, 5669, 6623, 0, 7674, 3291, 2748, 1654, 0,
10455, 4290, 4145, 796, 0, 9835, 5483, 11649, 5952, 0,
7098, 5460, 3101, 2443, 0, 7788, 5909, 8582, 6298, 0,
9462, 4845, 3041, 2067, 0, 7038, 6336, 10438, 6377, 0,
7518, 8187, 2079, 2773, 0, 10036, 2642, 3952, 1166, 0,
16014, 2250, 10025, 1908, 0, 9610, 298, 3868, 122, 0,
16629, 4338, 11335, 3527, 0, 11514, 5965, 4762, 2207, 0,
18552, 10755, 13309, 5996, 0, 12454, 6787, 4960, 2875, 0,
8750, 6999, 3534, 3233, 0, 14160, 9399, 9595, 8922, 0,
9110, 6567, 3820, 2351, 0, 12969, 11814, 9436, 5870, 0,
7631, 7061, 2877, 2499, 0, 8553, 13527, 3631, 6863, 0,
1361, 8634, 515, 3372, 0, 3394, 10206, 1504, 4112, 0,
5505, 17421, 4702, 11891, 0, 4233, 11894, 1739, 5014, 0,
11787, 14634, 8981, 10759, 0, 11777, 6701, 4719, 3111, 0,
18459, 7761, 12044, 7627, 0, 11214, 4556, 4374, 1594, 0,
604, 1908, 1506, 6102, 0, 2532, 4024, 1713, 6121, 0,
1878, 1814, 4761, 5397, 0, 1127, 3885, 4373, 5832, 0,
450, 1414, 1080, 4719, 0, 5210, 2683, 2765, 4252, 0,
2390, 1668, 7710, 4257, 0, 378, 1698, 3276, 6021, 0,
2866, 4881, 3547, 6822, 0, 502, 1238, 2784, 5199, 0,
2496, 3975, 2700, 5004, 0, 1220, 1990, 3633, 5763, 0,
4501, 2679, 4504, 5412, 0, 1968, 1376, 6246, 3669, 0,
3130, 272, 9345, 1950, 0, 5167, 3278, 9097, 2138, 0,
2446, 1946, 6942, 5460, 0, 5732, 3404, 7919, 5534, 0,
2038, 1614, 6978, 4635, 0, 4544, 4839, 7367, 5574, 0,
1242, 1922, 4842, 6333, 0, 1066, 236, 2236, 686, 0,
17238, 2254, 10413, 1592, 0, 991, 30, 2206, 70, 0,
18823, 6392, 12173, 2470, 0, 1142, 684, 2742, 1219, 0,
21256, 11293, 12719, 7512, 0, 1303, 649, 2818, 1669, 0,
898, 574, 2018, 1929, 0, 15720, 11989, 10517, 5972, 0,
885, 781, 2210, 1281, 0, 14601, 12198, 7915, 4958, 0,
856, 850, 1601, 1355, 0, 7039, 14083, 4113, 7490, 0,
152, 927, 287, 1902, 0, 301, 1051, 886, 2346, 0,
6821, 19615, 4491, 13281, 0, 424, 1146, 999, 2906, 0,
15177, 15480, 8849, 12442, 0, 1222, 544, 2687, 1859, 0,
20215, 9693, 11441, 4964, 0, 1206, 555, 2466, 860, 0};
std::vector<CTYPE> expected_grad_weight_data = {
9246, 22073, 12431, 19714, 11179, 19032, 8458, 6495, 18707, 13830,
20445, 17089, 17124, 18710, 11827, 17236, 16824, 9008, 14086, 18834,
17419, 16759, 13152, 9339, 13801, 20888, 13976, 27277, 13010, 23949,
9838, 11220, 17658, 15019, 25337, 17583, 13270, 21754, 16908, 20563,
20732, 13413, 20868, 27521, 19537, 21170, 15888, 10034, 19195, 16370,
40243, 25890, 40472, 30460, 21228, 21625, 13289, 24435, 19876, 29816,
24188, 23619, 13752, 16251, 18741, 19368, 24517, 34261, 27054, 31257,
21238, 18909, 15776, 16881, 34604, 22534, 28101, 23834, 18479, 16469,
12852, 16551, 14204, 29983, 20167, 24150, 14281, 17501, 15897, 16019,
21661, 32765, 23874, 26527, 20463, 18661};
std::vector<CTYPE> expected_grad_bias_data = {363, 438, 585, 501};

auto grad_output = tf.make({2, 4, 3, 2}, grad_output_data);
auto input = tf.make({2, 6, 7, 5}, input_data);
auto weight = tf.make({4, 3, 4, 2}, weight_data);
int64_t bias_sizes[1] = {4};
int64_t stride[2] = {1, 2};
int64_t padding[2] = {1, 0};
int64_t dilation[2] = {2, 1};
bool transposed = false;
int64_t output_padding[2] = {0, 0};
int64_t groups = 2;
std::array<bool, 3> output_mask_a = {true, true, true};
auto grad_input = tf.zeros({2, 6, 7, 5});
auto grad_weight = tf.zeros({4, 3, 4, 2});
auto grad_bias = tf.zeros({4});
auto grad_output = tf.make({2, 4, 3, 2}, grad_output_data);
auto input = tf.make({2, 6, 7, 5}, input_data);
auto weight = tf.make({4, 3, 4, 2}, weight_data);
int64_t bias_sizes[1] = {4};
int64_t stride[2] = {1, 2};
int64_t padding[2] = {1, 0};
int64_t dilation[2] = {2, 1};
bool transposed = false;
int64_t output_padding[2] = {0, 0};
int64_t groups = 2;
std::array<bool, 3> output_mask_a = {true, true, true};
auto grad_input = tf.zeros({2, 6, 7, 5});
auto grad_weight = tf.zeros({4, 3, 4, 2});
auto grad_bias = tf.zeros({4});

op_convolution_backward_out(
grad_output,
input,
weight,
IntArrayRef{bias_sizes, 1},
IntArrayRef{stride, 2},
IntArrayRef{padding, 2},
IntArrayRef{dilation, 2},
transposed,
IntArrayRef{output_padding, 2},
groups,
output_mask_a,
grad_input,
grad_weight,
grad_bias);

op_convolution_backward_out(
grad_output,
input,
weight,
IntArrayRef{bias_sizes, 1},
IntArrayRef{stride, 2},
IntArrayRef{padding, 2},
IntArrayRef{dilation, 2},
transposed,
IntArrayRef{output_padding, 2},
groups,
output_mask_a,
grad_input,
grad_weight,
grad_bias);
auto expected_grad_input = tf.make({2, 6, 7, 5}, expected_grad_input_data);
auto expected_grad_weight =
tf.make({4, 3, 4, 2}, expected_grad_weight_data);
auto expected_grad_bias = tf.make({4}, expected_grad_bias_data);

auto expected_grad_input = tf.make({2, 6, 7, 5}, expected_grad_input_data);
auto expected_grad_weight = tf.make({4, 3, 4, 2}, expected_grad_weight_data);
auto expected_grad_bias = tf.make({4}, expected_grad_bias_data);
if (DTYPE == ScalarType::Half || DTYPE == ScalarType::BFloat16) {
EXPECT_TENSOR_CLOSE_WITH_TOL(grad_input, expected_grad_input, 1e-2, 1e-8);
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@manuelcandales manuelcandales Jan 22, 2025

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Why not use defaults here? EXPECT_TENSOR_CLOSE_WITH_TOL should apply the right tolerance given the type

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@swolchok swolchok Jan 22, 2025

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because the default rtol is 1e-5; rtol and atol are different

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@manuelcandales manuelcandales Jan 23, 2025

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right, but in the same way that we have kDefaultHalfAtol and kDefaultBFloat16Atol I think we should have kDefaultHalfRtol and kDefaultBFloat16Rtol and set it to a proper value.
You seem to be using 1e-2 for most of these tests. Why not introduced kDefaultHalfRtol and kDefaultBFloat16Rtol with value 1e-2?

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@swolchok swolchok Jan 23, 2025

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Why not introduced kDefaultHalfRtol and kDefaultBFloat16Rtol with value 1e-2?

Because not all operators require the higher rtol.

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@swolchok swolchok Jan 23, 2025

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It is not particularly uncommon to need to set rtol in pytorch core: https://github.com/search?q=repo%3Apytorch%2Fpytorch+%2Frtol%3D%5B1-9%5D%2F&type=code

EXPECT_TENSOR_CLOSE_WITH_TOL(
grad_weight, expected_grad_weight, 2e-2, 1e-8);
EXPECT_TENSOR_CLOSE_WITH_TOL(grad_bias, expected_grad_bias, 1e-2, 1e-8);
} else {
EXPECT_TENSOR_CLOSE(grad_input, expected_grad_input);
EXPECT_TENSOR_CLOSE(grad_weight, expected_grad_weight);
EXPECT_TENSOR_CLOSE(grad_bias, expected_grad_bias);
}
}
};

EXPECT_TENSOR_CLOSE(grad_input, expected_grad_input);
EXPECT_TENSOR_CLOSE(grad_weight, expected_grad_weight);
EXPECT_TENSOR_CLOSE(grad_bias, expected_grad_bias);
TEST_F(OpConvolutionBackwardOutTest, SmokeTest) {
#define TEST_ENTRY(ctype, dtype) test_dtype<ScalarType::dtype>();
ET_FORALL_FLOATHBF16_TYPES(TEST_ENTRY);
#undef TEST_ENTRY
}
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