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/* | ||
* Copyright (c) Meta Platforms, Inc. and affiliates. | ||
* All rights reserved. | ||
* | ||
* This source code is licensed under the BSD-style license found in the | ||
* LICENSE file in the root directory of this source tree. | ||
*/ | ||
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#include <executorch/kernels/portable/cpu/scalar_utils.h> | ||
#include <executorch/kernels/portable/cpu/util/broadcast_util.h> | ||
#include <executorch/kernels/portable/cpu/util/functional_util.h> | ||
#include <executorch/runtime/kernel/kernel_includes.h> | ||
#include <executorch/runtime/platform/assert.h> | ||
#include "kernels.h" | ||
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namespace torch { | ||
namespace executor { | ||
namespace native { | ||
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#define NNLIB_MAX_DIM 4 /* Add fallback if broadcast and dim > 4 */ | ||
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Tensor& add_out( | ||
RuntimeContext& ctx, | ||
const Tensor& a, | ||
const Tensor& b, | ||
const Scalar& alpha, | ||
Tensor& out) { | ||
(void)ctx; | ||
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ScalarType a_type = a.scalar_type(); | ||
ScalarType b_type = b.scalar_type(); | ||
ScalarType common_type = promoteTypes(a_type, b_type); | ||
ScalarType out_type = out.scalar_type(); | ||
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ET_CHECK_MSG(a_type == ScalarType::Float, "Input tensor not a float.\n"); | ||
ET_CHECK_MSG(b_type == ScalarType::Float, "Input tensor not a float.\n"); | ||
ET_CHECK_MSG(out_type == ScalarType::Float, "Output tensor not a float.\n"); | ||
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ET_CHECK(canCast(common_type, out_type)); | ||
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using CTYPE_A = float; | ||
using CTYPE_B = float; | ||
using CTYPE_IN = float; | ||
using CTYPE_OUT = float; | ||
CTYPE_IN alpha_val; | ||
ET_EXTRACT_SCALAR(alpha, alpha_val); | ||
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if((alpha_val == 1.0) && (out_type == ScalarType::Float)) | ||
{ | ||
/*logic to find broadcast*/ | ||
const int a_is_broadcasted = !out.sizes().equals(a.sizes()); | ||
const int b_is_broadcasted = !out.sizes().equals(b.sizes()); | ||
const int broadcast = (a_is_broadcasted || b_is_broadcasted); | ||
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const float* const a_data = a.const_data_ptr<float>(); | ||
const float* const b_data = b.const_data_ptr<float>(); | ||
float* const out_data = out.mutable_data_ptr<float>(); | ||
if(broadcast == 1) | ||
{ | ||
int out_shape[NNLIB_MAX_DIM]; | ||
int inp1_shape[NNLIB_MAX_DIM]; | ||
int inp2_shape[NNLIB_MAX_DIM]; | ||
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for(int i = 0; i < NNLIB_MAX_DIM; i++) | ||
{ | ||
out_shape[i] = 1; | ||
inp1_shape[i] = 1; | ||
inp2_shape[i] = 1; | ||
} | ||
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int off_o = NNLIB_MAX_DIM - out.dim(); | ||
int off_a = NNLIB_MAX_DIM - a.dim(); | ||
int off_b = NNLIB_MAX_DIM - b.dim(); | ||
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for(int i = 0; i < out.dim(); i++) | ||
out_shape[i+off_o] = out.size(i); | ||
for(int i = 0; i < a.dim(); i++) | ||
inp1_shape[i+off_a] = a.size(i); | ||
for(int i = 0; i < b.dim(); i++) | ||
inp2_shape[i+off_b] = b.size(i); | ||
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xa_nn_elm_add_broadcast_4D_f32xf32_f32(out_data, out_shape, a_data, inp1_shape, | ||
b_data, inp2_shape); | ||
} | ||
else | ||
xa_nn_elm_add_f32xf32_f32(out_data, a_data, b_data, out.numel()); | ||
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} | ||
else | ||
{ | ||
apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>( | ||
[alpha_val](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 + alpha_val * b_casted; | ||
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return static_cast<CTYPE_OUT>(value); | ||
}, | ||
a, | ||
b, | ||
out); | ||
} | ||
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return out; | ||
} | ||
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} // namespace native | ||
} // namespace executor | ||
} // namespace torch |
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