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Merge pull request #5 from dijopaul/nishak_where_broadcast
adding broadcast where operator
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,150 @@ | ||
| /* | ||
| * 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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|
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| #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 "kernels.h" | ||
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| namespace torch { | ||
| namespace executor { | ||
| namespace native { | ||
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| #define NNLIB_MAX_DIM 4 | ||
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| Tensor& where_out( | ||
| RuntimeContext& ctx, | ||
| const Tensor& cond, | ||
| const Tensor& a, | ||
| const Tensor& b, | ||
| Tensor& out) { | ||
| ScalarType cond_type = cond.scalar_type(); | ||
| 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_KERNEL_CHECK(ctx, common_type == out_type, InvalidArgument, out); | ||
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| // Determine output size and resize for dynamic shapes | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_to_broadcast_target_size(a, b, cond, out) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
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| constexpr auto name = "where.self_out"; | ||
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| ET_CHECK_MSG( | ||
| cond_type == ScalarType::Bool || cond_type == ScalarType::Byte, | ||
| "Unhandled dtype %s for where.self_out", | ||
| torch::executor::toString(cond_type)); | ||
|
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| /*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 cond_is_broadcasted = !out.sizes().equals(cond.sizes()); | ||
| const int broadcast = (a_is_broadcasted || b_is_broadcasted || cond_is_broadcasted); | ||
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| int max_dim = a.dim() > b.dim() ? a.dim() : b.dim(); | ||
| max_dim = out.dim() > max_dim ? out.dim() : max_dim; | ||
| bool fall_back = 0; | ||
| if((a_type != ScalarType::Float) || (b_type != ScalarType::Float)) | ||
| 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* a_data = a.const_data_ptr<float>(); | ||
| const float* b_data = b.const_data_ptr<float>(); | ||
| float* out_data = out.mutable_data_ptr<float>(); | ||
| const unsigned char* con = cond.const_data_ptr<uint8_t>(); | ||
|
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| if(broadcast == 1) | ||
| { | ||
| int out_shape[NNLIB_MAX_DIM]; | ||
| int inp1_shape[NNLIB_MAX_DIM]; | ||
| int inp2_shape[NNLIB_MAX_DIM]; | ||
| int con_shape[NNLIB_MAX_DIM]; | ||
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| for(int i = 0; i < NNLIB_MAX_DIM; i++) | ||
| { | ||
| con_shape[i] = 1; | ||
| 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(); | ||
| int off_c = NNLIB_MAX_DIM - cond.dim(); | ||
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| for(int i = 0; i < out.dim(); i++){ | ||
| out_shape[i+off_o] = out.size(i);} | ||
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| 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); | ||
| for(int i = 0; i < cond.dim(); i++) | ||
| con_shape[i+off_c] = cond.size(i); | ||
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| /* Add fallback if broadcast and condition dimension are larger than inputs dimension */ | ||
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| if(con_shape[0] != out_shape[0] || con_shape[1] != out_shape[1] || con_shape[2] != out_shape[2] || con_shape[3] != out_shape[3]) | ||
| { | ||
| void* p_scratch = malloc(out_shape[0]*out_shape[1]*out_shape[2]*out_shape[3]); | ||
| const unsigned char *p_brd_cond = (const unsigned char*)p_scratch; | ||
| xa_nn_broadcast_8_8((WORD8* __restrict__) p_brd_cond, out_shape, (const WORD8* __restrict__) con, con_shape, 4); | ||
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| for(int i = 0; i < 4; i++) | ||
| { | ||
| con_shape[i] = out_shape[i]; | ||
| } | ||
| xa_nn_elm_where_broadcast_4D_f32xf32_f32(out_data, out_shape, a_data, inp1_shape, | ||
| b_data, inp2_shape, p_brd_cond, con_shape); | ||
| free(p_scratch); | ||
| } | ||
| else | ||
| { | ||
| xa_nn_elm_where_broadcast_4D_f32xf32_f32(out_data, out_shape, a_data, inp1_shape, b_data, inp2_shape, con, con_shape); | ||
| } | ||
| } | ||
| else | ||
| { | ||
| xa_nn_elm_where_f32xf32_f32(out_data, a_data, b_data, con, out.numel()); | ||
| } | ||
| } | ||
| else | ||
| { | ||
| ET_SWITCH_REALHB_TYPES(a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_REALHB_TYPES(b_type, ctx, name, CTYPE_B, [&]() { | ||
| using CTYPE_OUT = | ||
| typename torch::executor::promote_types<CTYPE_A, CTYPE_B>::type; | ||
| apply_ternary_elementwise_fn<CTYPE_A, CTYPE_B, uint8_t, CTYPE_OUT>( | ||
| [](const CTYPE_A val_a, const CTYPE_B val_b, const uint8_t val_c) { | ||
| CTYPE_OUT a_casted = static_cast<CTYPE_OUT>(val_a); | ||
| CTYPE_OUT b_casted = static_cast<CTYPE_OUT>(val_b); | ||
| return val_c ? a_casted : b_casted; | ||
| }, | ||
| a, | ||
| b, | ||
| cond, | ||
| out); | ||
| }); | ||
| }); | ||
| } | ||
| return out; | ||
| } | ||
|
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||
| } // namespace native | ||
| } // namespace executor | ||
| } // namespace torch |
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