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Support Half/BFloat16 in native_batch_norm #7842

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merged 2 commits into from
Jan 23, 2025
Merged

Support Half/BFloat16 in native_batch_norm #7842

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61c54c2
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swolchok Jan 22, 2025
05700c8
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swolchok Jan 22, 2025
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10 changes: 6 additions & 4 deletions kernels/portable/cpu/op_native_batch_norm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ std::tuple<Tensor&, Tensor&, Tensor&> _native_batch_norm_legit_no_training_out(

constexpr auto name = "native_batch_norm_legit_no_training.out";

ET_SWITCH_FLOAT_TYPES(in.scalar_type(), ctx, name, CTYPE, [&] {
ET_SWITCH_FLOATHBF16_TYPES(in.scalar_type(), ctx, name, CTYPE, [&] {
const CTYPE* in_data = in.const_data_ptr<CTYPE>();
CTYPE* out_data = out.mutable_data_ptr<CTYPE>();

Expand Down Expand Up @@ -261,7 +261,7 @@ std::tuple<Tensor&, Tensor&, Tensor&> _native_batch_norm_legit_no_stats_out(

constexpr auto name = "_native_batch_norm_legit.no_stats_out";

ET_SWITCH_FLOAT_TYPES(in.scalar_type(), ctx, name, CTYPE, [&] {
ET_SWITCH_FLOATHBF16_TYPES(in.scalar_type(), ctx, name, CTYPE, [&] {
const CTYPE* in_data = in.const_data_ptr<CTYPE>();
CTYPE* out_data = out.mutable_data_ptr<CTYPE>();
CTYPE* mean_data = mean_out.mutable_data_ptr<CTYPE>();
Expand All @@ -282,10 +282,12 @@ std::tuple<Tensor&, Tensor&, Tensor&> _native_batch_norm_legit_no_stats_out(
}

// Compute mean and invstd for each channel
const CTYPE elements_per_channel_ct =
static_cast<CTYPE>(elements_per_channel);
for (size_t c = 0; c < C; ++c) {
CTYPE mean = mean_data[c] / elements_per_channel;
CTYPE mean = mean_data[c] / elements_per_channel_ct;
// Var[x] = E[x^2] - E[x]^2
CTYPE var = invstd_data[c] / elements_per_channel - mean * mean;
CTYPE var = invstd_data[c] / elements_per_channel_ct - mean * mean;
CTYPE invstd = 1.0 / std::sqrt(var + eps);
mean_data[c] = mean;
invstd_data[c] = invstd;
Expand Down
296 changes: 174 additions & 122 deletions kernels/test/op_native_batch_norm_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,112 @@ class OpNativeBatchNormLegitNoTrainingOutTest : public OperatorTest {
out1,
out2);
}

template <exec_aten::ScalarType DTYPE>
void test_2d_dtype() {
torch::executor::testing::TensorFactory<DTYPE> tf;

exec_aten::Tensor input = tf.make(
{4, 7}, {2.876736640930176, 7.67944860458374, 5.701690196990967,
9.299789428710938, 3.023690700531006, 5.315116882324219,
7.185585021972656, 6.911304473876953, 7.61051082611084,
1.4963287115097046, 0.7381612062454224, 8.588483810424805,
6.583977699279785, 8.831110000610352, 0.8165055513381958,
7.087201118469238, 5.572513580322266, 4.446897983551025,
4.444573402404785, 6.254056930541992, 5.906398296356201,
9.971039772033691, 3.5423521995544434, 7.452159881591797,
9.93700122833252, 1.8560808897018433, 1.524025797843933,
7.3222975730896});
exec_aten::optional<exec_aten::Tensor> weight =
exec_aten::optional<exec_aten::Tensor>(tf.make(
{7},
{8.287437438964844,
8.227645874023438,
6.65926456451416,
9.436124801635742,
4.119281768798828,
8.593960762023926,
2.3760855197906494}));
exec_aten::optional<exec_aten::Tensor> bias =
exec_aten::optional<exec_aten::Tensor>(tf.make(
{7},
{7.824275970458984,
6.84327507019043,
8.354326248168945,
8.773970603942871,
3.89609694480896,
3.0753469467163086,
3.1105971336364746}));
exec_aten::Tensor running_mean = tf.make(
{7},
{9.700226783752441,
0.1234668493270874,
7.527220249176025,
8.993252754211426,
0.4736626148223877,
7.7135701179504395,
5.12320613861084});
exec_aten::Tensor running_var = tf.make(
{7},
{3.585531234741211,
6.615292549133301,
0.24084866046905518,
5.175800323486328,
0.5886000394821167,
6.23909854888916,
1.5029621124267578});
double momentum = 0.1;
double eps = 0;
exec_aten::Tensor out0 = tf.zeros({4, 7});
exec_aten::Tensor out1 = tf.zeros({0});
exec_aten::Tensor out2 = tf.zeros({0});
exec_aten::Tensor out0_expected = tf.make(
{4, 7}, {-22.039867401123047, 31.014127731323242, -16.416650772094727,
10.04538631439209, 17.5877628326416, -5.17673921585083,
7.1078033447265625, -4.381907939910889, 30.793603897094727,
-73.48003387451172, -25.46548080444336, 47.46636962890625,
-0.8111140131950378, 10.29708194732666, -31.056814193725586,
29.119586944580078, -18.16947364807129, -10.082839965820312,
25.216796875, -1.9462348222732544, 4.628543376922607,
9.00953483581543, 17.779958724975586, 7.335818767547607,
12.688335418701172, 11.318607330322266, -18.22031593322754,
7.372773170471191});
exec_aten::Tensor out1_expected = tf.make({0}, {});
exec_aten::Tensor out2_expected = tf.make({0}, {});
op_native_batch_norm_legit_no_training_out(
input,
weight,
bias,
running_mean,
running_var,
momentum,
eps,
out0,
out1,
out2);
if (DTYPE == exec_aten::ScalarType::Half ||
DTYPE == exec_aten::ScalarType::BFloat16) {
EXPECT_TENSOR_CLOSE_WITH_TOL(
out0,
out0_expected,
4e-2,
executorch::runtime::testing::internal::kDefaultAtol);
EXPECT_TENSOR_CLOSE_WITH_TOL(
out1,
out1_expected,
2e-2,
executorch::runtime::testing::internal::kDefaultAtol);
EXPECT_TENSOR_CLOSE_WITH_TOL(
out2,
out2_expected,
2e-2,
executorch::runtime::testing::internal::kDefaultAtol);
} else {
EXPECT_TENSOR_CLOSE(out0, out0_expected);
EXPECT_TENSOR_CLOSE(out1, out1_expected);
EXPECT_TENSOR_CLOSE(out2, out2_expected);
}
}
};

class OpNativeBatchNormLegitOutTest : public OperatorTest {
Expand Down Expand Up @@ -103,92 +209,72 @@ class OpNativeBatchNormLegitNoStatsOutTest : public OperatorTest {
out1,
out2);
}
};

TEST_F(OpNativeBatchNormLegitNoTrainingOutTest, SampleAtomicTest2D) {
torch::executor::testing::TensorFactory<exec_aten::ScalarType::Float> tfFloat;
template <exec_aten::ScalarType DTYPE>
void test_2d_dtype() {
torch::executor::testing::TensorFactory<DTYPE> tf;

exec_aten::Tensor input = tfFloat.make(
{4, 7}, {2.876736640930176, 7.67944860458374, 5.701690196990967,
9.299789428710938, 3.023690700531006, 5.315116882324219,
7.185585021972656, 6.911304473876953, 7.61051082611084,
1.4963287115097046, 0.7381612062454224, 8.588483810424805,
6.583977699279785, 8.831110000610352, 0.8165055513381958,
7.087201118469238, 5.572513580322266, 4.446897983551025,
4.444573402404785, 6.254056930541992, 5.906398296356201,
9.971039772033691, 3.5423521995544434, 7.452159881591797,
9.93700122833252, 1.8560808897018433, 1.524025797843933,
7.3222975730896});
exec_aten::optional<exec_aten::Tensor> weight =
exec_aten::optional<exec_aten::Tensor>(tfFloat.make(
{7},
{8.287437438964844,
8.227645874023438,
6.65926456451416,
9.436124801635742,
4.119281768798828,
8.593960762023926,
2.3760855197906494}));
exec_aten::optional<exec_aten::Tensor> bias =
exec_aten::optional<exec_aten::Tensor>(tfFloat.make(
{7},
{7.824275970458984,
6.84327507019043,
8.354326248168945,
8.773970603942871,
3.89609694480896,
3.0753469467163086,
3.1105971336364746}));
exec_aten::Tensor running_mean = tfFloat.make(
{7},
{9.700226783752441,
0.1234668493270874,
7.527220249176025,
8.993252754211426,
0.4736626148223877,
7.7135701179504395,
5.12320613861084});
exec_aten::Tensor running_var = tfFloat.make(
{7},
{3.585531234741211,
6.615292549133301,
0.24084866046905518,
5.175800323486328,
0.5886000394821167,
6.23909854888916,
1.5029621124267578});
double momentum = 0.1;
double eps = 0;
exec_aten::Tensor out0 = tfFloat.zeros({4, 7});
exec_aten::Tensor out1 = tfFloat.zeros({0});
exec_aten::Tensor out2 = tfFloat.zeros({0});
exec_aten::Tensor out0_expected = tfFloat.make(
{4, 7}, {-22.039867401123047, 31.014127731323242, -16.416650772094727,
10.04538631439209, 17.5877628326416, -5.17673921585083,
7.1078033447265625, -4.381907939910889, 30.793603897094727,
-73.48003387451172, -25.46548080444336, 47.46636962890625,
-0.8111140131950378, 10.29708194732666, -31.056814193725586,
29.119586944580078, -18.16947364807129, -10.082839965820312,
25.216796875, -1.9462348222732544, 4.628543376922607,
9.00953483581543, 17.779958724975586, 7.335818767547607,
12.688335418701172, 11.318607330322266, -18.22031593322754,
7.372773170471191});
exec_aten::Tensor out1_expected = tfFloat.make({0}, {});
exec_aten::Tensor out2_expected = tfFloat.make({0}, {});
op_native_batch_norm_legit_no_training_out(
input,
weight,
bias,
running_mean,
running_var,
momentum,
eps,
out0,
out1,
out2);
EXPECT_TENSOR_CLOSE(out0, out0_expected);
EXPECT_TENSOR_CLOSE(out1, out1_expected);
EXPECT_TENSOR_CLOSE(out2, out2_expected);
exec_aten::Tensor input =
tf.make({3, 4}, {0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121});
exec_aten::optional<exec_aten::Tensor> weight =
exec_aten::optional<exec_aten::Tensor>();
exec_aten::optional<exec_aten::Tensor> bias =
exec_aten::optional<exec_aten::Tensor>();
bool training = true;
double momentum = 1e-3;
double eps = 1e-5;
exec_aten::Tensor out0 = tf.zeros({3, 4});
exec_aten::Tensor out1 = tf.zeros({4});
exec_aten::Tensor out2 = tf.zeros({4});
exec_aten::Tensor out0_expected = tf.make(
{3, 4},
{-0.98058063,
-1.03422451,
-1.06904495,
-1.09332705,
-0.39223224,
-0.31822300,
-0.26726127,
-0.23017406,
1.37281299,
1.35244739,
1.33630610,
1.32350123});
exec_aten::Tensor out1_expected =
tf.make({4}, {26.66666603, 35.66666794, 46.66666794, 59.66666794});
exec_aten::Tensor out2_expected =
tf.make({4}, {0.03677177, 0.02983340, 0.02505574, 0.02157882});
op_native_batch_norm_legit_no_stats_out(
input, weight, bias, training, momentum, eps, out0, out1, out2);
if (DTYPE == exec_aten::ScalarType::Half ||
DTYPE == exec_aten::ScalarType::BFloat16) {
EXPECT_TENSOR_CLOSE_WITH_TOL(
out0,
out0_expected,
2e-2,
executorch::runtime::testing::internal::kDefaultAtol);
EXPECT_TENSOR_CLOSE_WITH_TOL(
out1,
out1_expected,
1e-2,
executorch::runtime::testing::internal::kDefaultAtol);
EXPECT_TENSOR_CLOSE_WITH_TOL(
out2,
out2_expected,
2e-2,
executorch::runtime::testing::internal::kDefaultAtol);
} else {
EXPECT_TENSOR_CLOSE(out0, out0_expected);
EXPECT_TENSOR_CLOSE(out1, out1_expected);
EXPECT_TENSOR_CLOSE(out2, out2_expected);
}
}
};

TEST_F(OpNativeBatchNormLegitNoTrainingOutTest, SampleAtomicTest2D){
#define TEST_ENTRY(ctype, dtype) test_2d_dtype<exec_aten::ScalarType::dtype>();
ET_FORALL_FLOATHBF16_TYPES(TEST_ENTRY)
#undef TEST_ENTRY
}

TEST_F(OpNativeBatchNormLegitNoTrainingOutTest, SampleAtomicTest3D) {
Expand Down Expand Up @@ -977,44 +1063,10 @@ TEST_F(OpNativeBatchNormLegitOutTest, SampleAtomicTest2D) {
EXPECT_TENSOR_CLOSE(out2, out2_expected);
}

TEST_F(OpNativeBatchNormLegitNoStatsOutTest, SampleAtomicTest2D) {
torch::executor::testing::TensorFactory<exec_aten::ScalarType::Float> tfFloat;

exec_aten::Tensor input =
tfFloat.make({3, 4}, {0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121});
exec_aten::optional<exec_aten::Tensor> weight =
exec_aten::optional<exec_aten::Tensor>();
exec_aten::optional<exec_aten::Tensor> bias =
exec_aten::optional<exec_aten::Tensor>();
bool training = true;
double momentum = 1e-3;
double eps = 1e-5;
exec_aten::Tensor out0 = tfFloat.zeros({3, 4});
exec_aten::Tensor out1 = tfFloat.zeros({4});
exec_aten::Tensor out2 = tfFloat.zeros({4});
exec_aten::Tensor out0_expected = tfFloat.make(
{3, 4},
{-0.98058063,
-1.03422451,
-1.06904495,
-1.09332705,
-0.39223224,
-0.31822300,
-0.26726127,
-0.23017406,
1.37281299,
1.35244739,
1.33630610,
1.32350123});
exec_aten::Tensor out1_expected =
tfFloat.make({4}, {26.66666603, 35.66666794, 46.66666794, 59.66666794});
exec_aten::Tensor out2_expected =
tfFloat.make({4}, {0.03677177, 0.02983340, 0.02505574, 0.02157882});
op_native_batch_norm_legit_no_stats_out(
input, weight, bias, training, momentum, eps, out0, out1, out2);
EXPECT_TENSOR_CLOSE(out0, out0_expected);
EXPECT_TENSOR_CLOSE(out1, out1_expected);
EXPECT_TENSOR_CLOSE(out2, out2_expected);
TEST_F(OpNativeBatchNormLegitNoStatsOutTest, SampleAtomicTest2D){
#define TEST_ENTRY(ctype, dtype) test_2d_dtype<exec_aten::ScalarType::dtype>();
ET_FORALL_FLOATHBF16_TYPES(TEST_ENTRY)
#undef TEST_ENTRY
}

TEST_F(OpNativeBatchNormLegitNoStatsOutTest, SampleAtomicTest3D) {
Expand Down
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