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Merge pull request #7 from dijopaul/main
Adding add sub mul and div optimizations
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| Original file line number | Diff line number | Diff line change |
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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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| int a_dim = a.dim(), b_dim = b.dim(), out_dim = out.dim(); | ||
| int fall_back = 0; | ||
| /*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); | ||
| int max_dim = a.dim() > b.dim() ? a.dim() : b.dim(); | ||
| max_dim = out.dim() > max_dim ? out.dim() : max_dim; | ||
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| if( (out_type != ScalarType::Float) || (alpha_val != 1.0)) | ||
| fall_back = 1; | ||
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| if( (a_dim == 0) || (b_dim == 0) ) | ||
| fall_back = 1; | ||
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| if((broadcast == 1) && (max_dim > NNLIB_MAX_DIM)) | ||
| fall_back = 1; | ||
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| if (!fall_back) | ||
| { | ||
| 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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