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Add complex type support to div operator #17414

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23 changes: 19 additions & 4 deletions kernels/optimized/cpu/op_div.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,10 +21,11 @@ namespace native {
namespace {

ScalarType get_common_type(ScalarType a_type, ScalarType b_type) {
ET_CHECK(
!isComplexType(a_type) && !isQIntType(a_type) && !isBitsType(a_type));
ET_CHECK(
!isComplexType(b_type) && !isQIntType(b_type) && !isBitsType(b_type));
if (isComplexType(a_type) || isComplexType(b_type)) {
return promoteTypes(a_type, b_type);
}
ET_CHECK(!isQIntType(a_type) && !isBitsType(a_type));
ET_CHECK(!isQIntType(b_type) && !isBitsType(b_type));

if (isFloatingType(a_type) && isFloatingType(b_type)) {
return promoteTypes(a_type, b_type);
Expand Down Expand Up @@ -61,6 +62,20 @@ Tensor& opt_div_out(
ScalarType b_type = b.scalar_type();
ScalarType out_type = out.scalar_type();

// Handle complex types
if (isComplexType(a_type) || isComplexType(b_type)) {
ScalarType common_type = get_common_type(a_type, b_type);
ET_SWITCH_COMPLEX_TYPES(common_type, ctx, op_name, CTYPE, [&]() {
const CTYPE* a_data = a.const_data_ptr<CTYPE>();
const CTYPE* b_data = b.const_data_ptr<CTYPE>();
CTYPE* out_data = out.mutable_data_ptr<CTYPE>();
for (size_t i = 0; i < out.numel(); ++i) {
out_data[i] = a_data[i] / b_data[i];
}
});
return out;
}

if (a.numel() == 1 || b.numel() == 1) {
if (a_type == b_type && a_type == out_type && a_type != ScalarType::Half &&
a_type != ScalarType::BFloat16) {
Expand Down
47 changes: 30 additions & 17 deletions kernels/portable/cpu/op_div.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,10 @@ namespace native {
namespace {

ScalarType get_common_type(ScalarType a_type, ScalarType b_type) {
if (isFloatingType(a_type) && isFloatingType(b_type)) {
if (executorch::runtime::isComplexType(a_type) ||
executorch::runtime::isComplexType(b_type)) {
return promoteTypes(a_type, b_type);
} else if (isFloatingType(a_type) && isFloatingType(b_type)) {
return promoteTypes(a_type, b_type);
} else if (isFloatingType(a_type)) {
return a_type;
Expand Down Expand Up @@ -51,25 +54,35 @@ Tensor& div_out(
InvalidArgument,
out);

// Compute Dtype
ScalarType compute_type = utils::get_compute_type(common_type);

// @lint-ignore CLANGTIDY facebook-hte-CArray
static constexpr const char op_name[] = "div.out";

ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
utils::apply_bitensor_elementwise_fn<
CTYPE_COMPUTE,
op_name,
utils::SupportedTensorDtypes::FLOATHBF16>(
[](const auto& val_a, const auto& val_b) { return val_a / val_b; },
ctx,
a,
utils::SupportedTensorDtypes::REALHBBF16,
b,
utils::SupportedTensorDtypes::REALHBBF16,
out);
});
if (executorch::runtime::isComplexType(common_type)) {
ET_SWITCH_COMPLEX_TYPES(common_type, ctx, op_name, CTYPE, [&]() {
const CTYPE* a_data = a.const_data_ptr<CTYPE>();
const CTYPE* b_data = b.const_data_ptr<CTYPE>();
CTYPE* out_data = out.mutable_data_ptr<CTYPE>();
for (ssize_t i = 0; i < out.numel(); ++i) {
out_data[i] = a_data[i] / b_data[i];
}
});
} else {
// Compute Dtype for real types
ScalarType compute_type = utils::get_compute_type(common_type);
ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
utils::apply_bitensor_elementwise_fn<
CTYPE_COMPUTE,
op_name,
utils::SupportedTensorDtypes::FLOATHBF16>(
[](const auto& val_a, const auto& val_b) { return val_a / val_b; },
ctx,
a,
utils::SupportedTensorDtypes::REALHBBF16,
b,
utils::SupportedTensorDtypes::REALHBBF16,
out);
});
}

return out;
}
Expand Down
95 changes: 95 additions & 0 deletions kernels/test/op_div_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -601,3 +601,98 @@ TEST_F(OpDivScalarOutTest, OptimizedSanityCheck) {
// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(out, tf.make(sizes, {0.65, 1.05, 2.3, 4.1}));
}

//
// Complex Type Tests
//

TEST_F(OpDivOutTest, ComplexFloatBasic) {
TensorFactory<ScalarType::ComplexFloat> tf;

const std::vector<int32_t> sizes = {2, 2};

// (1+2i) / (1+0i) = (1+2i)
// (4+4i) / (2+0i) = (2+2i)
// (3+4i) / (1-1i) = (3+4i)(1+1i) / 2 = (-1+7i) / 2 = (-0.5+3.5i)
// (8+0i) / (2+2i) = (8)(2-2i) / 8 = (2-2i)
Tensor a = tf.make(
sizes,
{executorch::aten::complex<float>(1.0f, 2.0f),
executorch::aten::complex<float>(4.0f, 4.0f),
executorch::aten::complex<float>(3.0f, 4.0f),
executorch::aten::complex<float>(8.0f, 0.0f)});

Tensor b = tf.make(
sizes,
{executorch::aten::complex<float>(1.0f, 0.0f),
executorch::aten::complex<float>(2.0f, 0.0f),
executorch::aten::complex<float>(1.0f, -1.0f),
executorch::aten::complex<float>(2.0f, 2.0f)});

Tensor out = tf.zeros(sizes);

op_div_out(a, b, out);

Tensor expected = tf.make(
sizes,
{executorch::aten::complex<float>(1.0f, 2.0f),
executorch::aten::complex<float>(2.0f, 2.0f),
executorch::aten::complex<float>(-0.5f, 3.5f),
executorch::aten::complex<float>(2.0f, -2.0f)});

EXPECT_TENSOR_CLOSE(out, expected);
}

TEST_F(OpDivOutTest, ComplexDoubleBasic) {
TensorFactory<ScalarType::ComplexDouble> tf;

const std::vector<int32_t> sizes = {2};

Tensor a = tf.make(
sizes,
{executorch::aten::complex<double>(6.0, 8.0),
executorch::aten::complex<double>(4.0, 0.0)});

Tensor b = tf.make(
sizes,
{executorch::aten::complex<double>(2.0, 0.0),
executorch::aten::complex<double>(0.0, 2.0)});

Tensor out = tf.zeros(sizes);

op_div_out(a, b, out);

// (6+8i) / 2 = (3+4i)
// 4 / 2i = 4 * (-i) / 2 = -2i = (0-2i)
Tensor expected = tf.make(
sizes,
{executorch::aten::complex<double>(3.0, 4.0),
executorch::aten::complex<double>(0.0, -2.0)});

EXPECT_TENSOR_CLOSE(out, expected);
}

TEST_F(OpDivOutTest, ComplexFloatIdentity) {
TensorFactory<ScalarType::ComplexFloat> tf;

const std::vector<int32_t> sizes = {3};

// Dividing by 1 should return the same value
Tensor a = tf.make(
sizes,
{executorch::aten::complex<float>(1.0f, 2.0f),
executorch::aten::complex<float>(3.0f, 4.0f),
executorch::aten::complex<float>(-5.0f, 6.0f)});

Tensor one = tf.make(
sizes,
{executorch::aten::complex<float>(1.0f, 0.0f),
executorch::aten::complex<float>(1.0f, 0.0f),
executorch::aten::complex<float>(1.0f, 0.0f)});

Tensor out = tf.zeros(sizes);

op_div_out(a, one, out);

EXPECT_TENSOR_CLOSE(out, a);
}
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