Support BFloat16 in convolution_backward (pytorch#7807)

swolchok · Zonglin Peng · commit 8e7b91e4c62c · 2025-01-30T15:17:40.000-08:00
Partial fix for pytorch#7748.
diff --git a/kernels/portable/cpu/op_convolution_backward.cpp b/kernels/portable/cpu/op_convolution_backward.cpp
@@ -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,
diff --git a/kernels/test/CMakeLists.txt b/kernels/test/CMakeLists.txt
@@ -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"
diff --git a/kernels/test/op_convolution_backward_test.cpp b/kernels/test/op_convolution_backward_test.cpp
@@ -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);
+      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
 }
