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braodcast in op_le #11568

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81 changes: 36 additions & 45 deletions kernels/optimized/cpu/op_le.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -6,12 +6,14 @@
* LICENSE file in the root directory of this source tree.
*/

#include <executorch/kernels/optimized/cpu/binary_ops.h>
#include <executorch/kernels/optimized/vec/functional.h>
#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/kernel/kernel_includes.h>
#include <executorch/runtime/platform/assert.h>
#include <executorch/kernels/portable/cpu/pattern/comparison_op.h>

namespace torch {
namespace executor {
Expand Down Expand Up @@ -79,52 +81,41 @@ Tensor& opt_le_tensor_out(
return out;
}

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

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

if (a_type == b_type && a_type == out_type) {
ET_SWITCH_REAL_TYPES_AND(
Bool, out_type, ctx, "le.Tensor_out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::map2<CTYPE>(
[](Vec x, Vec y) { return x.le(y); },
out.mutable_data_ptr<CTYPE>(),
a.const_data_ptr<CTYPE>(),
b.const_data_ptr<CTYPE>(),
a.numel());
});
// Check for optimized broadcast paths
auto selected_optimized_path = select_optimized_path(a, b, out);
printf("selected_optimized_path: %d\n", static_cast<int>(selected_optimized_path));
if (selected_optimized_path == ElementwiseOptimizedPath::kTreatAs1d) {
// Resize for dynamic shape
auto error = resize_tensor(out, a.sizes());
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this is introducing the same bug you are fixing for op_mul in #11590

ET_KERNEL_CHECK_MSG(
ctx,
error == Error::Ok,
InvalidArgument,
out,
"Failed to resize output tensor.");

ET_SWITCH_REALB_TYPES(a_type, ctx, "le.Tensor_out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::map2<CTYPE>(
[](Vec x, Vec y) { return x.le(y); },
out.mutable_data_ptr<CTYPE>(),
a.const_data_ptr<CTYPE>(),
b.const_data_ptr<CTYPE>(),
out.numel());
});
} else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
// Handle optimized broadcast cases
ET_SWITCH_REALB_TYPES(out_type, ctx, "le.Tensor_out", CTYPE, [&]() {
using Vec = executorch::vec::Vectorized<CTYPE>;
auto le_lambda = [](auto x, auto y) { return x.le(y); };
return torch::executor::handle_broadcast_elementwise<CTYPE>(
ctx, le_lambda, a, b, out, selected_optimized_path);
});
} else {
ET_SWITCH_REAL_TYPES_AND(
Bool, a_type, ctx, "le.Tensor_out", CTYPE_A, [&]() {
ET_SWITCH_REAL_TYPES_AND(
Bool, b_type, ctx, "le.Tensor_out", CTYPE_B, [&]() {
using CTYPE_IN = typename torch::executor::
promote_types<CTYPE_A, CTYPE_B>::type;
ET_DCHECK(
CppTypeToScalarType<CTYPE_IN>::value ==
promoteTypes(a_type, b_type));
ET_SWITCH_REAL_TYPES_AND(
Bool, out_type, ctx, "le.Tensor_out", CTYPE_OUT, [&]() {
const size_t n = a.numel();
const CTYPE_A* a_data = a.const_data_ptr<CTYPE_A>();
const CTYPE_B* b_data = b.const_data_ptr<CTYPE_B>();
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]) <=
static_cast<CTYPE_IN>(b_data[i]));
}
});
});
});
// @lint-ignore CLANGTIDY facebook-hte-CArray
static constexpr const char op_name[] = "le.Tensor_out";
return internal::comparison_tensor_out<std::less_equal, op_name>(
ctx, a, b, out);
}

return out;
Expand Down
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