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Handle broadcast semantics for last dim #6240

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8 changes: 8 additions & 0 deletions kernels/optimized/cpu/binary_ops.h
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@ enum class ElementwiseOptimizedPath {
kBroadcast2dBy1dReverseArguments,
kBroadcastNdByNd,
kBroadcastNdByNdReverseArguments,
kBroadcastLastDim,
kBroadcastLastDimReverseArguments,
};

namespace internal {
Expand Down Expand Up @@ -117,6 +119,12 @@ inline ElementwiseOptimizedPath select_broadcast_optimized_path(
} else {
return ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments;
}
} else if (broadcast_dim == -1) {
if (std::count_if(lhs_begin, lhs_end, [](Tensor::SizesType x) { return x == 1; }) == 1) {
return ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments;
} else {
return ElementwiseOptimizedPath::kBroadcastLastDim;
}
}
return ElementwiseOptimizedPath::kNone;
}
Expand Down
163 changes: 113 additions & 50 deletions kernels/optimized/cpu/op_mul.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
#include <executorch/kernels/optimized/vec/vec.h>
#include <executorch/kernels/portable/cpu/scalar_utils.h>
#include <executorch/kernels/portable/cpu/util/broadcast_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 Down Expand Up @@ -66,6 +67,117 @@ template <
typename CTYPE_OUT>
struct MulInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT>
: public ReportCanCastBug {};

Tensor& handle_last_dim_broadcast(
KernelRuntimeContext& ctx,
const Tensor& a,
const Tensor& b,
Tensor& out,
const ElementwiseOptimizedPath selected_optimized_path) {
ScalarType out_type = out.scalar_type();
const Tensor* lhs;
const Tensor* rhs;
if (selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments) {
lhs = &b;
rhs = &a;
} else {
lhs = &a;
rhs = &b;
}
auto error = resize_tensor(out, lhs->sizes());
ET_KERNEL_CHECK_MSG(
ctx,
error == Error::Ok,
InvalidArgument,
out,
"Failed to resize output tensor.");
const size_t outer_size = getLeadingDims(out, out.dim() - 1);
const auto broadcast_size = out.size(out.dim() - 1);
ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::broadcasting_map_broadcast_last_dim<CTYPE>(
[](Vec x, Vec y) { return x * y; },
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
outer_size,
broadcast_size);
});
return out;
}

Tensor& handle_broadcast_mul(
KernelRuntimeContext& ctx,
const Tensor& a,
const Tensor& b,
Tensor& out,
const ElementwiseOptimizedPath selected_optimized_path) {

if ((selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcastLastDim) ||
(selected_optimized_path == ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments)) {
return handle_last_dim_broadcast(
ctx, a, b, out, selected_optimized_path);
}

ScalarType out_type = out.scalar_type();
const Tensor* lhs;
const Tensor* rhs;
if ((selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1dReverseArguments) ||
(selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
lhs = &b;
rhs = &a;
} else {
// Catch failure to update logic when adding new broadcasting possibility.
ET_DCHECK(
(selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1d) ||
(selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcastNdByNd));
lhs = &a;
rhs = &b;
}
auto error = resize_tensor(out, lhs->sizes());
ET_KERNEL_CHECK_MSG(
ctx,
error == Error::Ok,
InvalidArgument,
out,
"Failed to resize output tensor.");
int64_t outer_size = 1;
int64_t broadcast_size;
int64_t inner_size;
if ((selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd) ||
(selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
int32_t broadcast_dim = internal::get_broadcast_dim(*lhs, *rhs);
int32_t broadcast_dim_lhs = lhs->dim() + broadcast_dim;
int32_t broadcast_dim_rhs = rhs->dim() + broadcast_dim;
auto normalized_tensor_size_lhs =
get_normalized_tensor_size(*lhs, broadcast_dim_lhs);
outer_size = normalized_tensor_size_lhs[0];
broadcast_size = normalized_tensor_size_lhs[1];
inner_size = normalized_tensor_size_lhs[2];
} else {
broadcast_size = lhs->sizes()[lhs->dim() - 2];
inner_size = lhs->sizes()[lhs->dim() - 1];
}
ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::broadcasting_map_3d_and_unsqueezed_3d<CTYPE>(
[](Vec x, Vec y) { return x * y; },
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
outer_size,
broadcast_size,
inner_size);
});
return out;
}
} // namespace

Tensor& opt_mul_out(
Expand Down Expand Up @@ -128,56 +240,7 @@ Tensor& opt_mul_out(
out.numel());
});
} else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
const Tensor* lhs;
const Tensor* rhs;
if ((selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1dReverseArguments) ||
(selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
lhs = &b;
rhs = &a;
} else {
// Catch failure to update logic when adding new broadcasting possibility.
ET_DCHECK(
(selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1d) ||
(selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd));
lhs = &a;
rhs = &b;
}
auto error = resize_tensor(out, lhs->sizes());
ET_KERNEL_CHECK_MSG(
ctx,
error == Error::Ok,
InvalidArgument,
out,
"Failed to resize output tensor.");
int64_t outer_size = 1;
int64_t broadcast_size;
int64_t inner_size;
if ((selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd) ||
(selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
int32_t broadcast_dim = internal::get_broadcast_dim(*lhs, *rhs);
int32_t broadcast_dim_lhs = lhs->dim() + broadcast_dim;
int32_t broadcast_dim_rhs = rhs->dim() + broadcast_dim;
auto normalized_tensor_size_lhs = get_normalized_tensor_size(*lhs, broadcast_dim_lhs);
outer_size = normalized_tensor_size_lhs[0];
broadcast_size = normalized_tensor_size_lhs[1];
inner_size = normalized_tensor_size_lhs[2];
} else {
broadcast_size = lhs->sizes()[lhs->dim() - 2];
inner_size = lhs->sizes()[lhs->dim() - 1];
}
ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::broadcasting_map_3d_and_unsqueezed_3d<CTYPE>(
[](Vec x, Vec y) { return x * y; },
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
outer_size,
broadcast_size,
inner_size);
});
return handle_broadcast_mul(ctx, a, b, out, selected_optimized_path);
} else {
ScalarType common_type =
promoteTypes(a_type, b_type, /*half_to_float*/ true);
Expand Down
1 change: 1 addition & 0 deletions kernels/optimized/cpu/targets.bzl
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@ _OPTIMIZED_ATEN_OPS = (
":binary_ops",
"//executorch/kernels/portable/cpu:scalar_utils",
"//executorch/kernels/portable/cpu/util:broadcast_util",
"//executorch/runtime/core/exec_aten/util:tensor_util",
],
),
op_target(
Expand Down
29 changes: 29 additions & 0 deletions kernels/optimized/vec/functional_base.h
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Original file line number Diff line number Diff line change
Expand Up @@ -378,5 +378,34 @@ inline void broadcasting_map_2d_by_1d(
broadcasting_map_3d_and_unsqueezed_3d(vec_fun, output_data, input_data, input_data2, 1, size, size2);
}

template <typename scalar_t, typename Op>
inline void broadcasting_map_broadcast_last_dim(
const Op& vec_fun,
scalar_t* output_data,
const scalar_t* lhs,
const scalar_t* rhs,
int64_t outer_size,
int64_t broadcast_size) {
using Vec = vec::Vectorized<scalar_t>;
int64_t outer_stride_lhs = broadcast_size;
int64_t outer_stride_rhs = 1;
for (int64_t outer_idx = 0; outer_idx < outer_size; ++outer_idx) {
const scalar_t* lhs_outer = lhs + outer_idx * outer_stride_lhs;
scalar_t* output_data_row = output_data + outer_idx * outer_stride_lhs;
int64_t inner_idx = 0;
Vec data_vec2 = Vec(rhs[outer_idx]);
for (; inner_idx < broadcast_size - (broadcast_size % Vec::size()); inner_idx += Vec::size()) {
Vec data_vec = Vec::loadu(lhs_outer + inner_idx);
Vec output_vec = vec_fun(data_vec, data_vec2);
output_vec.store(output_data_row + inner_idx);
}
if (broadcast_size - inner_idx > 0) {
Vec data_vec = Vec::loadu(lhs_outer + inner_idx, broadcast_size - inner_idx);
Vec output_vec = vec_fun(data_vec, data_vec2);
output_vec.store(output_data_row + inner_idx, broadcast_size - inner_idx);
}
}
}

} // namespace vec
} // namespace executorch
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