forked from pytorch/executorch
-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #5 from dijopaul/main
Adding sub optimized code
- Loading branch information
Showing
2 changed files
with
263 additions
and
10 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,252 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
|
||
| #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/kernels/portable/cpu/util/kernel_ops_util.h> | ||
| #include <executorch/runtime/kernel/kernel_includes.h> | ||
| #include <executorch/runtime/platform/assert.h> | ||
| #include "kernels.h" | ||
|
|
||
| #define NNLIB_MAX_DIM 4 /* Add fallback if broadcast and dim > 4 */ | ||
|
|
||
| namespace torch { | ||
| namespace executor { | ||
| namespace native { | ||
| namespace { | ||
|
|
||
| template < | ||
| bool can_cast, | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct SubInner; | ||
|
|
||
| template < | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct SubInner<true, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> { | ||
| static void | ||
| run(const Tensor& a, const Tensor& b, CTYPE_IN alpha_val, Tensor& out) { | ||
| apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>( | ||
| // NOLINTNEXTLINE(facebook-hte-ConstantArgumentPassByValue) | ||
| [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; | ||
|
|
||
| return static_cast<CTYPE_OUT>(value); | ||
| }, | ||
| a, | ||
| b, | ||
| out); | ||
| } | ||
| }; | ||
|
|
||
| template <typename CTYPE_IN> | ||
| struct ReportCanCastBug { | ||
| static void run(const Tensor&, const Tensor&, CTYPE_IN, Tensor&) { | ||
| ET_DCHECK_MSG(false, "BUG: canCast should have been checked above"); | ||
| } | ||
| }; | ||
|
|
||
| template < | ||
| typename CTYPE_A, | ||
| typename CTYPE_B, | ||
| typename CTYPE_IN, | ||
| typename CTYPE_OUT> | ||
| struct SubInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> | ||
| : public ReportCanCastBug<CTYPE_IN> {}; | ||
|
|
||
| } // namespace | ||
|
|
||
| Tensor& sub_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Tensor& b, | ||
| const Scalar& alpha, | ||
| Tensor& out) { | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_to_broadcast_target_size(a, b, out) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
|
|
||
| ET_KERNEL_CHECK(ctx, tensor_is_realh_type(out), InvalidArgument, out); | ||
|
|
||
| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = b.scalar_type(); | ||
| ScalarType alpha_type = utils::get_scalar_dtype(alpha); | ||
| ScalarType common_type = promoteTypes(a_type, b_type, /*half_to_float*/ true); | ||
| ScalarType out_type = out.scalar_type(); | ||
|
|
||
| ET_KERNEL_CHECK(ctx, canCast(common_type, out_type), InvalidArgument, out); | ||
| ET_KERNEL_CHECK( | ||
| ctx, check_alpha_type(alpha_type, common_type), InvalidArgument, out); | ||
|
|
||
| float alpha_val; | ||
| utils::extract_scalar(alpha, &alpha_val); | ||
|
|
||
| constexpr auto name = "sub.out"; | ||
|
|
||
|
|
||
| int a_dim = a.dim(), b_dim = b.dim(), out_dim = out.dim(); | ||
| int fall_back = 0; | ||
|
|
||
| if( (a_dim == 0) || (b_dim == 0) ) | ||
| { | ||
| fall_back = 1; | ||
| } | ||
| if( (out_type != ScalarType::Float) || (alpha_val != 1.0)) | ||
| { | ||
| fall_back = 1; | ||
| } | ||
|
|
||
|
|
||
| if(!fall_back) | ||
| { | ||
| /*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); | ||
|
|
||
| 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]; | ||
|
|
||
| for(int i = 0; i < NNLIB_MAX_DIM; i++) | ||
| { | ||
| out_shape[i] = 1; | ||
| inp1_shape[i] = 1; | ||
| inp2_shape[i] = 1; | ||
| } | ||
|
|
||
| int off_o = NNLIB_MAX_DIM - out_dim; | ||
| int off_a = NNLIB_MAX_DIM - a_dim; | ||
| int off_b = NNLIB_MAX_DIM - b_dim; | ||
| 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); | ||
|
|
||
| xa_nn_elm_sub_broadcast_4D_f32xf32_f32(out_data, out_shape, a_data, inp1_shape,b_data, inp2_shape); | ||
| } | ||
| else | ||
| { | ||
| xa_nn_elm_sub_f32xf32_f32(out_data, a_data, b_data, out.numel()); | ||
| } | ||
|
|
||
| } | ||
| else | ||
| { | ||
|
|
||
| ET_SWITCH_REALH_TYPES(a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_REALH_TYPES(b_type, ctx, name, CTYPE_B, [&]() { | ||
| using CTYPE_IN = typename torch::executor:: | ||
| promote_types<CTYPE_A, CTYPE_B, /*half_to_float*/ true>::type; | ||
| ET_DCHECK(CppTypeToScalarType<CTYPE_IN>::value == common_type); | ||
| CTYPE_IN alpha_val; | ||
| utils::extract_scalar(alpha, &alpha_val); | ||
| ET_SWITCH_REALH_TYPES(out_type, ctx, name, CTYPE_OUT, [&]() { | ||
| SubInner< | ||
| can_cast<CTYPE_IN, CTYPE_OUT>::value, | ||
| CTYPE_A, | ||
| CTYPE_B, | ||
| CTYPE_IN, | ||
| CTYPE_OUT>::run(a, b, alpha_val, out); | ||
| }); | ||
| }); | ||
| }); | ||
| } | ||
|
|
||
| return out; | ||
| } | ||
|
|
||
| Tensor& sub_scalar_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Scalar& b, | ||
| const Scalar& alpha, | ||
| Tensor& out) { | ||
| (void)ctx; | ||
|
|
||
| // Resize for dynamic shape | ||
| ET_KERNEL_CHECK_MSG( | ||
| ctx, | ||
| resize_tensor(out, a.sizes()) == Error::Ok, | ||
| InvalidArgument, | ||
| out, | ||
| "Failed to resize output tensor."); | ||
|
|
||
| ET_KERNEL_CHECK(ctx, tensor_is_realh_type(out), InvalidArgument, out); | ||
|
|
||
| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = utils::get_scalar_dtype(b); | ||
| ScalarType alpha_type = utils::get_scalar_dtype(alpha); | ||
| ScalarType common_type = | ||
| utils::promote_type_with_scalar(a_type, b, /*half_to_float*/ false); | ||
| ScalarType out_type = out.scalar_type(); | ||
|
|
||
| ET_KERNEL_CHECK(ctx, common_type == out_type, InvalidArgument, out); | ||
| ET_KERNEL_CHECK(ctx, canCast(alpha_type, common_type), InvalidArgument, out); | ||
|
|
||
| if (common_type == ScalarType::Half) { | ||
| common_type = ScalarType::Float; | ||
| } | ||
|
|
||
| constexpr auto name = "sub.Scalar_out"; | ||
|
|
||
| ET_SWITCH_REALH_TYPES(a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_SCALAR_OBJ_REAL_TYPES(b_type, ctx, name, CTYPE_B, [&]() { | ||
| using CTYPE_IN = typename utils::promote_type_with_scalar_type< | ||
| CTYPE_A, | ||
| CTYPE_B, | ||
| /*half_to_float*/ true>::type; | ||
| ET_DCHECK(CppTypeToScalarType<CTYPE_IN>::value == common_type); | ||
|
|
||
| CTYPE_B b_val; | ||
| utils::extract_scalar(b, &b_val); | ||
| CTYPE_IN b_casted = static_cast<CTYPE_IN>(b_val); | ||
|
|
||
| CTYPE_IN alpha_val; | ||
| utils::extract_scalar(alpha, &alpha_val); | ||
|
|
||
| using CTYPE_OUT = typename std::conditional< | ||
| std::is_same<CTYPE_A, internal::F2>::value, | ||
| internal::F2, | ||
| CTYPE_IN>::type; | ||
|
|
||
| apply_unary_map_fn( | ||
| [b_casted, alpha_val](const CTYPE_A val_a) { | ||
| CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a); | ||
| CTYPE_IN value = a_casted - alpha_val * b_casted; | ||
| return static_cast<CTYPE_OUT>(value); | ||
| }, | ||
| a.const_data_ptr<CTYPE_A>(), | ||
| out.mutable_data_ptr<CTYPE_OUT>(), | ||
| out.numel()); | ||
| }); | ||
| }); | ||
|
|
||
| return out; | ||
| } | ||
|
|
||
| } // namespace native | ||
| } // namespace executor | ||
| } // namespace torch |