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[ExecuTorch] Add broadcast support for optimized add op #8205

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[ExecuTorch] Add broadcast support for optimized add op #8205

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56 changes: 43 additions & 13 deletions kernels/optimized/cpu/binary_ops.h
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
#pragma once

#include <executorch/kernels/optimized/vec/functional.h>
#include <executorch/kernels/portable/cpu/scalar_utils.h>
#include <executorch/runtime/kernel/kernel_includes.h>

namespace torch {
Expand Down Expand Up @@ -198,7 +199,8 @@ Tensor& handle_last_dim_broadcast_elementwise(
const Tensor& a,
const Tensor& b,
Tensor& out,
const ElementwiseOptimizedPath selected_optimized_path) {
const ElementwiseOptimizedPath selected_optimized_path,
executorch::aten::optional<Scalar>& alpha = {}) {
ScalarType out_type = out.scalar_type();
const Tensor* lhs;
const Tensor* rhs;
Expand All @@ -220,8 +222,21 @@ Tensor& handle_last_dim_broadcast_elementwise(
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,
using Vec = executorch::vec::Vectorized<CTYPE>;
CTYPE alpha_val;
Vec alpha_val_vec(alpha_val);
if (alpha.has_value()) {
ET_KERNEL_CHECK(
ctx,
native::utils::extract_scalar(alpha.value(), &alpha_val),
InvalidArgument, );
alpha_val_vec = Vec(alpha_val);
}
auto vec_fun_alpha = [vec_fun, alpha_val_vec](const Vec& a, const Vec& b) {
return vec_fun(a, b, alpha_val_vec);
};
executorch::vec::broadcasting_map_broadcast_last_dim<CTYPE>(
vec_fun_alpha,
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
Expand All @@ -238,13 +253,14 @@ Tensor& handle_broadcast_elementwise(
const Tensor& a,
const Tensor& b,
Tensor& out,
const ElementwiseOptimizedPath selected_optimized_path) {
const ElementwiseOptimizedPath selected_optimized_path,
executorch::aten::optional<Scalar> alpha = {}) {
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);
ctx, vec_fun, a, b, out, selected_optimized_path, alpha);
}

ScalarType out_type = out.scalar_type();
Expand Down Expand Up @@ -291,14 +307,28 @@ Tensor& handle_broadcast_elementwise(
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);
using Vec = executorch::vec::Vectorized<CTYPE>;
CTYPE alpha_val;
Vec alpha_val_vec;
if (alpha.has_value()) {
ET_KERNEL_CHECK(
ctx,
native::utils::extract_scalar(alpha.value(), &alpha_val),
InvalidArgument, );
alpha_val_vec = Vec(alpha_val);
}
auto vec_fun_alpha = [vec_fun, alpha_val_vec](const Vec& a, const Vec& b) {
return vec_fun(a, b, alpha_val_vec);
};
executorch::vec::
broadcasting_map_3d_and_unsqueezed_3d<CTYPE, decltype(vec_fun_alpha)>(
vec_fun_alpha,
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
outer_size,
broadcast_size,
inner_size);
});
return out;
}
Expand Down
40 changes: 5 additions & 35 deletions kernels/optimized/cpu/op_add.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -140,41 +140,11 @@ Tensor& opt_add_out(
out.numel());
});
} else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
const Tensor* lhs;
const Tensor* rhs;
if (selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1dReverseArguments) {
lhs = &b;
rhs = &a;
} else {
// Catch failure to update logic when adding new broadcasting possibility.
ET_DCHECK(
selected_optimized_path ==
ElementwiseOptimizedPath::kBroadcast2dBy1d);
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.");
ET_SWITCH_REALB_TYPES(out_type, ctx, "add.out", CTYPE, [&]() {
CTYPE alpha_val;
ET_KERNEL_CHECK(
ctx, utils::extract_scalar(alpha, &alpha_val), InvalidArgument, );

using Vec = executorch::vec::Vectorized<CTYPE>;
executorch::vec::broadcasting_map_2d_by_1d<CTYPE>(
[alpha_val](Vec x, Vec y) { return x + Vec(alpha_val) * y; },
out.mutable_data_ptr<CTYPE>(),
lhs->const_data_ptr<CTYPE>(),
rhs->const_data_ptr<CTYPE>(),
lhs->sizes()[lhs->dim() - 2],
lhs->sizes()[lhs->dim() - 1]);
});
auto add_lambda = [](auto x, auto y, auto alpha_val) {
return x + alpha_val * y;
};
return torch::executor::handle_broadcast_elementwise(
ctx, add_lambda, a, b, out, selected_optimized_path, alpha);
} else {
ScalarType common_type =
promoteTypes(a_type, b_type, /*half_to_float*/ true);
Expand Down
6 changes: 5 additions & 1 deletion kernels/optimized/cpu/op_mul.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,11 @@ Tensor& opt_mul_out(
out.numel());
});
} else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
auto mul_lambda = [](auto x, auto y) { return x * y; };
// Reason for using alpha:
auto mul_lambda = [](auto x, auto y, auto alpha) {
(void)alpha;
return x * y;
};
return torch::executor::handle_broadcast_elementwise(
ctx, mul_lambda, a, b, out, selected_optimized_path);
} else {
Expand Down
133 changes: 133 additions & 0 deletions kernels/test/op_add_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,122 @@ class OpAddOutKernelTest : public OperatorTest {
// tests.
EXPECT_TENSOR_CLOSE(out, tf.make(sizes, /*data=*/{2.5, 3.5, 5.75, 10.125}));
}

template <ScalarType DTYPE>
void test_broadcast_3D() {
TensorFactory<DTYPE> tf_a;

Tensor a =
tf_a.make({2, 2, 3}, /*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
Tensor b = tf_a.make({2, 1, 3}, /*data=*/{2, 3, 4, 5, 6, 7});

// Destination for output of mul.
Tensor out =
tf_a.make({2, 2, 3}, /*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
Tensor expected = tf_a.make(
{2, 2, 3}, /*data=*/{3, 5, 7, 6, 8, 10, 12, 14, 16, 15, 17, 19});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);
}

template <ScalarType DTYPE>
void test_broadcast_4D() {
TensorFactory<DTYPE> tf_a;

Tensor a = tf_a.make(
{2, 2, 3, 5},
/*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60});
Tensor b = tf_a.make(
{2, 1, 3, 5},
/*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30});

// Destination for output of mul.
Tensor out = tf_a.zeros({2, 2, 3, 5});
Tensor expected = tf_a.make(
{2, 2, 3, 5},
/*data=*/{2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30,
17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45,
47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75,
62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);

b = tf_a.make(
{2, 2, 1, 5}, /*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20});
out = tf_a.zeros({2, 2, 3, 5});
expected = tf_a.make(
{2, 2, 3, 5},
/*data=*/{2, 4, 6, 8, 10, 7, 9, 11, 13, 15, 12, 14, 16, 18, 20,
22, 24, 26, 28, 30, 27, 29, 31, 33, 35, 32, 34, 36, 38, 40,
42, 44, 46, 48, 50, 47, 49, 51, 53, 55, 52, 54, 56, 58, 60,
62, 64, 66, 68, 70, 67, 69, 71, 73, 75, 72, 74, 76, 78, 80});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);
}

template <ScalarType DTYPE>
void test_broadcast_last_dim() {
TensorFactory<DTYPE> tf_a;

Tensor a =
tf_a.make({4, 3}, /*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
Tensor b = tf_a.make({4, 1}, /*data=*/{2, 3, 4, 5});

// Destination for output of mul.
Tensor out = tf_a.zeros({4, 3});
Tensor expected =
tf_a.make({4, 3}, /*data=*/{3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 16, 17});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);

a = tf_a.make({2, 2, 3}, /*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
b = tf_a.make({2, 2, 1}, /*data=*/{2, 3, 4, 5});

// Destination for output of mul.
out = tf_a.zeros({2, 2, 3});
expected = tf_a.make(
{2, 2, 3}, /*data=*/{3, 4, 5, 7, 8, 9, 11, 12, 13, 15, 16, 17});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);

a = tf_a.make(
{2, 2, 3, 5},
/*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60});
b = tf_a.make(
{2, 2, 3, 1},
/*data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});

// Destination for output of mul.
out = tf_a.zeros({2, 2, 3, 5});
expected = tf_a.make(
{2, 2, 3, 5},
/*data=*/{2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18,
20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36,
38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54,
56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72});

// Check that it matches the expected output.
EXPECT_TENSOR_CLOSE(op_add_out(a, b, 1.0, out), expected);
EXPECT_TENSOR_CLOSE(op_add_out(b, a, 1.0, out), expected);
}
};

class OpAddScalarOutKernelTest : public OperatorTest {
Expand Down Expand Up @@ -371,6 +487,23 @@ TEST_F(OpAddOutKernelTest, BroadcastOneElementRank0Tensor) {
EXPECT_TENSOR_EQ(out, ret);
}

TEST_F(OpAddOutKernelTest, BroadcastNDTest) {
// Test 3D tensors
test_broadcast_3D<ScalarType::Float>();
test_broadcast_3D<ScalarType::Half>();
test_broadcast_3D<ScalarType::BFloat16>();

// Test 4D tensors
test_broadcast_4D<ScalarType::Float>();
test_broadcast_4D<ScalarType::Half>();
test_broadcast_4D<ScalarType::BFloat16>();

// Test broadcasting on the last dimension
test_broadcast_last_dim<ScalarType::Float>();
test_broadcast_last_dim<ScalarType::Half>();
test_broadcast_last_dim<ScalarType::BFloat16>();
}

//
// Death Tests
//
Expand Down
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