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Aug 29, 2025
Merged

Clean up optimized op_mul #13763

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188 changes: 60 additions & 128 deletions kernels/optimized/cpu/op_mul.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
#include <ATen/cpu/vec/vec.h>
#include <executorch/kernels/optimized/cpu/binary_ops.h>
#include <executorch/kernels/portable/cpu/scalar_utils.h>
#include <executorch/kernels/portable/cpu/util/broadcast_util.h>
#include <executorch/kernels/portable/cpu/util/elementwise_util.h>
#include <executorch/runtime/core/exec_aten/util/tensor_util.h> // IWYU pragma: export
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/platform/assert.h>
Expand All @@ -22,76 +22,35 @@ namespace native {
using Tensor = executorch::aten::Tensor;
using ScalarType = executorch::aten::ScalarType;

namespace {

template <
bool can_cast,
typename CTYPE_A,
typename CTYPE_B,
typename CTYPE_IN,
typename CTYPE_OUT>
struct MulInner;

template <
typename CTYPE_A,
typename CTYPE_B,
typename CTYPE_IN,
typename CTYPE_OUT>
struct MulInner<true, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> {
static void run(const Tensor& a, const Tensor& b, Tensor& out) {
apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>(
// NOLINTNEXTLINE(facebook-hte-ConstantArgumentPassByValue)
[](const CTYPE_A val_a, const CTYPE_B val_b) {
CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
CTYPE_IN value = a_casted * b_casted;

return static_cast<CTYPE_OUT>(value);
},
a,
b,
out);
}
};

struct ReportCanCastBug {
static void run(const Tensor&, const Tensor&, Tensor&) {
ET_DCHECK_MSG(false, "BUG: canCast should have been checked above");
}
};

template <
typename CTYPE_A,
typename CTYPE_B,
typename CTYPE_IN,
typename CTYPE_OUT>
struct MulInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT>
: public ReportCanCastBug {};

} // namespace

Tensor& opt_mul_out(
KernelRuntimeContext& ctx,
const Tensor& a,
const Tensor& b,
Tensor& out) {
(void)ctx;

ScalarType a_type = a.scalar_type();
ScalarType b_type = b.scalar_type();
ScalarType out_type = out.scalar_type();
ScalarType common_type = promoteTypes(a_type, b_type);

ET_KERNEL_CHECK(ctx, canCast(common_type, out_type), InvalidArgument, out);

ET_KERNEL_CHECK(
ctx, tensors_have_same_dim_order(a, b, out), InvalidArgument, out);

ET_KERNEL_CHECK(
ctx,
resize_to_broadcast_target_size(a, b, out) == Error::Ok,
InvalidArgument,
out);

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

if (b.numel() == 1) {
if (a_type == b_type && a_type == out_type && a_type != ScalarType::Half &&
a_type != ScalarType::BFloat16) {
ET_KERNEL_CHECK(
ctx,
resize_to_broadcast_target_size(a, b, out) == Error::Ok,
InvalidArgument,
out);

ET_SWITCH_REALB_TYPES(a_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_REALB_TYPES(b_type, ctx, "mul.out", CTYPE_B, [&]() {
ET_SWITCH_REALB_TYPES(a_type, ctx, op_name, CTYPE, [&]() {
ET_SWITCH_REALB_TYPES(b_type, ctx, op_name, CTYPE_B, [&]() {
CTYPE_B b_val = *b.const_data_ptr<CTYPE_B>();
CTYPE b_casted = static_cast<CTYPE>(b_val);

Expand All @@ -111,17 +70,11 @@ Tensor& opt_mul_out(

auto selected_optimized_path = select_optimized_path(a, b, out);
if (selected_optimized_path == ElementwiseOptimizedPath::kTreatAs1d) {
ET_KERNEL_CHECK(
ctx,
resize_to_broadcast_target_size(a, b, out) == Error::Ok,
InvalidArgument,
out);

if (executorch::runtime::isComplexType(out_type)) {
ET_KERNEL_CHECK(
ctx, a_type == b_type && a_type == out_type, InvalidArgument, out);

ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, op_name, CTYPE, [&]() {
using Vec = at::vec::Vectorized<CTYPE>;
at::vec::map2<CTYPE>(
[](Vec x, Vec y) { return x * y; },
Expand All @@ -131,7 +84,7 @@ Tensor& opt_mul_out(
out.numel());
});
} else {
ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_REALB_TYPES(out_type, ctx, op_name, CTYPE, [&]() {
using Vec = at::vec::Vectorized<CTYPE>;
at::vec::map2<CTYPE>(
[](Vec x, Vec y) { return x * y; },
Expand All @@ -146,63 +99,47 @@ Tensor& opt_mul_out(
ET_KERNEL_CHECK(
ctx, a_type == b_type && a_type == out_type, InvalidArgument, out);

ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, op_name, CTYPE, [&]() {
auto mul_lambda = [](auto x, auto y) { return x * y; };
torch::executor::handle_broadcast_elementwise<CTYPE>(
ctx, mul_lambda, a, b, out, selected_optimized_path);
});
} else {
ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_REALB_TYPES(out_type, ctx, op_name, CTYPE, [&]() {
auto mul_lambda = [](auto x, auto y) { return x * y; };
torch::executor::handle_broadcast_elementwise<CTYPE>(
ctx, mul_lambda, a, b, out, selected_optimized_path);
});
}
} else {
ScalarType common_type =
promoteTypes(a_type, b_type, /*half_to_float*/ true);
ET_KERNEL_CHECK(ctx, canCast(common_type, out_type), InvalidArgument, out);

ET_KERNEL_CHECK(
ctx,
resize_to_broadcast_target_size(a, b, out) == Error::Ok,
InvalidArgument,
out);

if (executorch::runtime::isComplexType(a_type) ||
executorch::runtime::isComplexType(b_type) ||
executorch::runtime::isComplexType(out_type)) {
ET_KERNEL_CHECK(
ctx, a_type == b_type && a_type == out_type, InvalidArgument, out);

ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
ET_SWITCH_COMPLEXH_TYPES(out_type, ctx, op_name, CTYPE, [&]() {
apply_binary_elementwise_fn<CTYPE, CTYPE, CTYPE>(
[](const CTYPE val_a, const CTYPE val_b) { return val_a * val_b; },
a,
b,
out);
});
} else {
ET_SWITCH_REALHBBF16_TYPES(a_type, ctx, "mul.out", CTYPE_A, [&]() {
ET_SWITCH_REALHBBF16_TYPES(b_type, ctx, "mul.out", CTYPE_B, [&]() {
using CTYPE_IN = typename torch::executor::
promote_types<CTYPE_A, CTYPE_B, /*half_to_float*/ true>::type;
ET_DCHECK(CppTypeToScalarType<CTYPE_IN>::value == common_type);
ET_SWITCH_REALHBBF16_TYPES(
out_type, ctx, "mul.out", CTYPE_OUT, [&]() {
apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>(
[](const CTYPE_A val_a, const CTYPE_B val_b) {
CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
CTYPE_IN value = a_casted * b_casted;

return static_cast<CTYPE_OUT>(value);
},
a,
b,
out);
});
});
ScalarType compute_type = utils::internal::get_compute_type(common_type);

ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
utils::apply_bitensor_elementwise_fn<
CTYPE_COMPUTE,
op_name,
utils::SupportedTensorDtypes::REALHBBF16>(
[](const auto val_a, const auto val_b) { return val_a * val_b; },
ctx,
a,
utils::SupportedTensorDtypes::REALHBBF16,
b,
utils::SupportedTensorDtypes::REALHBBF16,
out);
});
}
}
Expand All @@ -215,26 +152,24 @@ Tensor& opt_mul_scalar_out(
const Tensor& a,
const Scalar& b,
Tensor& out) {
(void)ctx;

ScalarType a_type = a.scalar_type();
ScalarType common_type =
utils::promote_type_with_scalar(a_type, b, /*half_to_float*/ false);
ScalarType common_type = utils::promote_type_with_scalar(a_type, b);
ScalarType out_type = out.scalar_type();

ET_CHECK(common_type == out_type);
ET_KERNEL_CHECK(ctx, common_type == out_type, InvalidArgument, out);

if (common_type == ScalarType::Half || common_type == ScalarType::BFloat16) {
common_type = ScalarType::Float;
}
ET_KERNEL_CHECK(
ctx, tensors_have_same_dim_order(a, out), InvalidArgument, out);

// Resize for dynamic shape
auto error = resize_tensor(out, a.sizes());
ET_CHECK_MSG(error == Error::Ok, "Failed to resize output tensor.");
ET_KERNEL_CHECK(
ctx, resize_tensor(out, a.sizes()) == Error::Ok, InvalidArgument, out);

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

if (a_type == common_type && a_type == out_type &&
a_type != ScalarType::Half && a_type != ScalarType::BFloat16) {
ET_SWITCH_REALB_TYPES(a_type, ctx, "mul.Scalar_out", CTYPE, [&]() {
ET_SWITCH_REALB_TYPES(a_type, ctx, op_name, CTYPE, [&]() {
CTYPE b_casted = utils::scalar_to<CTYPE>(b);

using Vec = at::vec::Vectorized<CTYPE>;
Expand All @@ -245,22 +180,19 @@ Tensor& opt_mul_scalar_out(
out.numel());
});
} else {
ET_SWITCH_REALHBBF16_TYPES(a_type, ctx, "mul.Scalar_out", CTYPE_A, [&]() {
ET_SWITCH_REALB_TYPES(
common_type, ctx, "mul.Scalar_out", CTYPE_IN, [&]() {
ET_SWITCH_REALHBBF16_TYPES(
out_type, ctx, "mul.Scalar_out", CTYPE_OUT, [&]() {
CTYPE_IN b_casted = utils::scalar_to<CTYPE_IN>(b);

const size_t n = a.numel();
const CTYPE_A* a_data = a.const_data_ptr<CTYPE_A>();
CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>();
for (auto i = 0; i < n; ++i) {
out_data[i] = static_cast<CTYPE_OUT>(
static_cast<CTYPE_IN>(a_data[i]) * b_casted);
}
});
});
ScalarType compute_type = utils::internal::get_compute_type(common_type);

ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
utils::apply_unitensor_elementwise_fn<
CTYPE_COMPUTE,
op_name,
utils::SupportedTensorDtypes::SAME_AS_COMMON>(
[val_b](const auto val_a) { return val_a * val_b; },
ctx,
a,
utils::SupportedTensorDtypes::REALHBBF16,
out);
});
}

Expand Down
2 changes: 2 additions & 0 deletions shim_et/xplat/executorch/kernels/optimized/op_registration_util.bzl
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -261,6 +261,8 @@ OPTIMIZED_ATEN_OPS = (
":binary_ops",
"//executorch/kernels/portable/cpu:scalar_utils",
"//executorch/kernels/portable/cpu/util:broadcast_util",
"//executorch/kernels/portable/cpu/util:dtype_util",
"//executorch/kernels/portable/cpu/util:elementwise_util",
"//executorch/runtime/core/exec_aten/util:tensor_util",
"//executorch/runtime/core/portable_type/c10/c10:aten_headers_for_executorch",
],
Expand Down
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