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Adding atan2 operator kernel optimization (#26)
* Adding atan2 operator kernel optimization * Adding atan2 operator kernel optimization --------- Co-authored-by: dijopaul <[email protected]>
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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/backends/cadence/hifi/kernels/kernels.h> | ||
| #include <executorch/kernels/portable/cpu/util/broadcast_util.h> | ||
| #include <executorch/runtime/kernel/kernel_includes.h> | ||
| #include <cmath> | ||
|
|
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| using exec_aten::ScalarType; | ||
| using exec_aten::Tensor; | ||
| using executorch::runtime::KernelRuntimeContext; | ||
| using torch::executor::Error; | ||
|
|
||
| namespace impl { | ||
| namespace HiFi { | ||
| namespace native { | ||
|
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| Tensor& atan2_out( | ||
| KernelRuntimeContext& ctx, | ||
| const Tensor& a, | ||
| const Tensor& b, | ||
| Tensor& out) { | ||
| // Determine output size and resize for dynamic shapes | ||
| ET_KERNEL_CHECK( | ||
| ctx, | ||
| resize_to_broadcast_target_size(a, b, out) == Error::Ok, | ||
| InvalidArgument, | ||
| out); | ||
|
|
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| ET_KERNEL_CHECK( | ||
| ctx, tensors_have_same_dim_order(a, b, out), InvalidArgument, out); | ||
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| ScalarType a_type = a.scalar_type(); | ||
| ScalarType b_type = b.scalar_type(); | ||
| ScalarType out_type = out.scalar_type(); | ||
|
|
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| constexpr auto name = "atan2.out"; | ||
| constexpr int kNnlibMaxDim = 16; | ||
| int a_dim = a.dim(), b_dim = b.dim(), out_dim = out.dim(); | ||
| bool optimized = 1; | ||
|
|
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| const bool a_is_broadcasted = !out.sizes().equals(a.sizes()); | ||
| const bool b_is_broadcasted = !out.sizes().equals(b.sizes()); | ||
| const bool 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) | ||
| optimized = 0; | ||
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| if ((broadcast == 1) && (max_dim > kNnlibMaxDim)) | ||
| optimized = 0; | ||
|
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| WORD32 num_elm = out.numel(); | ||
|
|
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| if (optimized) { | ||
| if (broadcast) { | ||
| WORD32* __restrict__ ptr1 = | ||
| (WORD32* __restrict__)malloc(num_elm * sizeof(WORD32)); | ||
| WORD32* __restrict__ ptr2 = | ||
| (WORD32* __restrict__)malloc(num_elm * sizeof(WORD32)); | ||
|
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| WORD32* __restrict__ pin1 = | ||
| (WORD32* __restrict__)a.const_data_ptr<float>(); | ||
| WORD32* __restrict__ pin2 = | ||
| (WORD32* __restrict__)b.const_data_ptr<float>(); | ||
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| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_inp1_shape[kNnlibMaxDim]; | ||
| WORD32 p_inp2_shape[kNnlibMaxDim]; | ||
|
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| for (int i = 0; i < out_dim; i++) | ||
| p_out_shape[i] = out.size(i); | ||
| for (int i = 0; i < a_dim; i++) | ||
| p_inp1_shape[i] = a.size(i); | ||
| for (int i = 0; i < b_dim; i++) | ||
| p_inp2_shape[i] = b.size(i); | ||
|
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| xa_nn_broadcast_32_32(ptr1, p_out_shape, pin1, p_inp1_shape, out_dim); | ||
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| xa_nn_broadcast_32_32(ptr2, p_out_shape, pin2, p_inp2_shape, out_dim); | ||
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| FLOAT32* __restrict__ p_out = | ||
| (FLOAT32* __restrict__)out.mutable_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp1 = (const FLOAT32* __restrict__)ptr1; | ||
| const FLOAT32* __restrict__ p_inp2 = (const FLOAT32* __restrict__)ptr2; | ||
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| xa_nn_elm_atan2_f32(p_out, p_inp1, p_inp2, num_elm); | ||
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| free(ptr1); | ||
| free(ptr2); | ||
| } else if (a_is_broadcasted && (!b_is_broadcasted)) { | ||
| FLOAT32* __restrict__ ptr1 = | ||
| (FLOAT32* __restrict__)malloc(num_elm * sizeof(WORD32)); | ||
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| FLOAT32* __restrict__ pin1 = | ||
| (FLOAT32* __restrict__)a.const_data_ptr<float>(); | ||
|
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| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_inp1_shape[kNnlibMaxDim]; | ||
|
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| for (int i = 0; i < out_dim; i++) | ||
| p_out_shape[i] = out.size(i); | ||
| for (int i = 0; i < a_dim; i++) | ||
| p_inp1_shape[i] = a.size(i); | ||
|
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| xa_nn_broadcast_32_32( | ||
| (WORD32*)ptr1, p_out_shape, (WORD32*)pin1, p_inp1_shape, out_dim); | ||
|
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| FLOAT32* __restrict__ p_out = | ||
| (FLOAT32* __restrict__)out.mutable_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp1 = (const FLOAT32* __restrict__)ptr1; | ||
| const FLOAT32* __restrict__ p_inp2 = | ||
| (const FLOAT32* __restrict__)b.const_data_ptr<float>(); | ||
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| xa_nn_elm_atan2_f32(p_out, p_inp1, p_inp2, num_elm); | ||
|
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| free(ptr1); | ||
| } else if (b_is_broadcasted && (!a_is_broadcasted)) { | ||
| WORD32* __restrict__ ptr1 = | ||
| (WORD32* __restrict__)malloc(num_elm * sizeof(WORD32)); | ||
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| WORD32* __restrict__ pin1 = | ||
| (WORD32* __restrict__)b.const_data_ptr<float>(); | ||
|
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| WORD32 p_out_shape[kNnlibMaxDim]; | ||
| WORD32 p_inp1_shape[kNnlibMaxDim]; | ||
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| for (int i = 0; i < out_dim; i++) | ||
| p_out_shape[i] = out.size(i); | ||
| for (int i = 0; i < b_dim; i++) | ||
| p_inp1_shape[i] = b.size(i); | ||
|
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| xa_nn_broadcast_32_32(ptr1, p_out_shape, pin1, p_inp1_shape, out_dim); | ||
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| FLOAT32* __restrict__ p_out = | ||
| (FLOAT32* __restrict__)out.mutable_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp1 = | ||
| (const FLOAT32* __restrict__)a.const_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp2 = (const FLOAT32* __restrict__)ptr1; | ||
|
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| xa_nn_elm_atan2_f32(p_out, p_inp1, p_inp2, num_elm); | ||
|
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| free(ptr1); | ||
| } else { | ||
| FLOAT32* __restrict__ p_out = | ||
| (FLOAT32* __restrict__)out.mutable_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp1 = | ||
| (const FLOAT32* __restrict__)a.const_data_ptr<float>(); | ||
| const FLOAT32* __restrict__ p_inp2 = | ||
| (const FLOAT32* __restrict__)b.const_data_ptr<float>(); | ||
|
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| xa_nn_elm_atan2_f32(p_out, p_inp1, p_inp2, num_elm); | ||
| } | ||
| return out; | ||
| } | ||
|
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| ET_SWITCH_REALHB_TYPES(a_type, ctx, name, CTYPE_A, [&]() { | ||
| ET_SWITCH_REALHB_TYPES(b_type, ctx, name, CTYPE_B, [&]() { | ||
| ET_SWITCH_FLOATH_TYPES(out_type, ctx, name, CTYPE_OUT, [&]() { | ||
| torch::executor:: | ||
| apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>( | ||
| [](const CTYPE_A val_a, const CTYPE_B val_b) { | ||
| CTYPE_OUT casted_a = static_cast<CTYPE_OUT>(val_a); | ||
| CTYPE_OUT casted_b = static_cast<CTYPE_OUT>(val_b); | ||
| return static_cast<CTYPE_OUT>(std::atan2(casted_a, casted_b)); | ||
| }, | ||
| a, | ||
| b, | ||
| out); | ||
| }); | ||
| }); | ||
| }); | ||
|
|
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| return out; | ||
| } | ||
|
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| } // namespace native | ||
| } // namespace HiFi | ||
| } // namespace impl |
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