[Executorch] Refactor op_mul's broadcasting utils

kernels/optimized/cpu/binary_ops.h

@@ -8,6 +8,7 @@
 
 #pragma once
 
+#include <executorch/kernels/optimized/vec/functional.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
 
 namespace torch {
@@ -190,5 +191,116 @@ std::array<int32_t, 3> inline get_normalized_tensor_size(
   return normalized_tensor_size;
 }
 
+template <typename Op>
+Tensor& handle_last_dim_broadcast_elementwise(
+    KernelRuntimeContext& ctx,
+    const Op& vec_fun,
+    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, [&]() {
+    executorch::vec::broadcasting_map_broadcast_last_dim<CTYPE, Op>(
+        vec_fun,
+        out.mutable_data_ptr<CTYPE>(),
+        lhs->const_data_ptr<CTYPE>(),
+        rhs->const_data_ptr<CTYPE>(),
+        outer_size,
+        broadcast_size);
+  });
+  return out;
+}
+
+template <typename Op>
+Tensor& handle_broadcast_elementwise(
+    KernelRuntimeContext& ctx,
+    const Op& vec_fun,
+    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_elementwise(
+        ctx, vec_fun, 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;
+    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, [&]() {
+    executorch::vec::broadcasting_map_3d_and_unsqueezed_3d<CTYPE, Op>(
+        vec_fun,
+        out.mutable_data_ptr<CTYPE>(),
+        lhs->const_data_ptr<CTYPE>(),
+        rhs->const_data_ptr<CTYPE>(),
+        outer_size,
+        broadcast_size,
+        inner_size);
+  });
+  return out;
+}
 } // namespace executor
 } // namespace torch

kernels/optimized/cpu/op_mul.cpp

@@ -68,114 +68,6 @@ template <
 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;
-    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(
@@ -238,7 +130,9 @@ Tensor& opt_mul_out(
           out.numel());
     });
   } else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
-    return handle_broadcast_mul(ctx, a, b, out, selected_optimized_path);
+    auto mul_lambda = [](auto x, auto y) { return x * y; };
+    return torch::executor::handle_broadcast_elementwise(
+        ctx, mul_lambda, a, b, out, selected_optimized_path);
   } else {
     ScalarType common_type =
         promoteTypes(a_type, b_type, /*half_to_float*/ true);

kernels/test/op_mul_test.cpp

@@ -453,13 +453,6 @@ TEST_F(OpMulOutTest, BroadcastNDTest) {
   test_broadcast_last_dim<ScalarType::BFloat16>();
 }
 
-TEST_F(OpMulOutTest, BroadcastLastDimTest) {
-  // Test broadcasting on the last dimension
-  test_broadcast_last_dim<ScalarType::Float>();
-  test_broadcast_last_dim<ScalarType::Half>();
-  test_broadcast_last_dim<ScalarType::BFloat16>();
-}
-
 // Broadcast tensor a and b's size to a new size c.
 TEST_F(OpMulOutTest, BroadcastAB2CTest) {
   TensorFactory<ScalarType::Int> tf_a;