[Ref_Impl] Rename file paths to openvino relative (#19284)
* Move files to new directories * Use quotes for openvino includes * Provide proxy calls for transition of dependant components. * Correct includes style * Redo proxies * Fix deprecated * Move aliases to proxy files * Apply code style
This commit is contained in:
Tomasz Jankowski
GitHub
parent
20e4c629e9
commit
bcad953f5f
@ -4,8 +4,8 @@
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#include "snippets/op/broadcastload.hpp"
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#include "openvino/reference/broadcast.hpp"
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#include "snippets/itt.hpp"
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#include <ngraph/runtime/reference/broadcast.hpp>
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namespace ov {
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namespace snippets {
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@ -3,20 +3,20 @@
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//
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#include "snippets/pass/fq_decomposition.hpp"
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#include "snippets/op/convert_saturation.hpp"
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#include "snippets/itt.hpp"
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#include "openvino/opsets/opset1.hpp"
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#include "openvino/pass/pattern/op/wrap_type.hpp"
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#include "openvino/core/rt_info.hpp"
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#include "openvino/pass/constant_folding.hpp"
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#include "openvino/pass/validate.hpp"
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#include "openvino/pass/manager.hpp"
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#include <ngraph/runtime/reference/autobroadcast_binop.hpp>
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#include <ngraph/runtime/reference/broadcast.hpp>
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#include <numeric>
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#include "ngraph/runtime/reference/autobroadcast_binop.hpp"
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#include "openvino/core/rt_info.hpp"
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#include "openvino/opsets/opset1.hpp"
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#include "openvino/pass/constant_folding.hpp"
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#include "openvino/pass/manager.hpp"
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#include "openvino/pass/pattern/op/wrap_type.hpp"
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#include "openvino/pass/validate.hpp"
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#include "openvino/reference/broadcast.hpp"
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#include "snippets/itt.hpp"
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#include "snippets/op/convert_saturation.hpp"
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namespace {
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bool isValidRangesInputs(const std::shared_ptr<ov::opset1::FakeQuantize>& fq) {
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auto il = fq->input_value(1);
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@ -5,12 +5,12 @@
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#include "transformations/common_optimizations/compress_float_constants.hpp"
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#include "itt.hpp"
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#include "ngraph/runtime/reference/convert.hpp"
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#include "openvino/core/rt_info.hpp"
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#include "openvino/op/constant.hpp"
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#include "openvino/op/convert.hpp"
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#include "openvino/op/parameter.hpp"
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#include "openvino/pass/pattern/op/wrap_type.hpp"
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#include "openvino/reference/convert.hpp"
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#include "transformations/rt_info/decompression.hpp"
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#include "transformations/rt_info/disable_fp16_compression.hpp"
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#include "transformations/rt_info/old_api_map_element_type_attribute.hpp"
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@ -8,7 +8,6 @@
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#include <vector>
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#include "itt.hpp"
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#include "ngraph/runtime/reference/convert.hpp"
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#include "openvino/opsets/opset1.hpp"
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#include "openvino/opsets/opset10.hpp"
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#include "openvino/opsets/opset11.hpp"
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@ -20,6 +19,7 @@
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#include "openvino/opsets/opset9.hpp"
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#include "openvino/pass/constant_folding.hpp"
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#include "openvino/pass/manager.hpp"
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#include "openvino/reference/convert.hpp"
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#include "ov_ops/type_relaxed.hpp"
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#include "transformations/fp16_compression/align_mixed_fp32_fp16_types.hpp"
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#include "transformations/fp16_compression/mark_decompression_convert_constant_folding.hpp"
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@ -4,91 +4,12 @@
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#pragma once
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#include <cstddef>
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#include <numeric>
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#include <utility>
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#include "ngraph/coordinate_transform.hpp"
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#include "ngraph/op/util/attr_types.hpp"
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#include "ngraph/shape_util.hpp"
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#include "openvino/reference/autobroadcast_binop.hpp"
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// Proxy calls for dependant components transition to ov::reference namespace
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namespace ngraph {
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namespace runtime {
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namespace reference {
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namespace internal {
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inline void row_major_strides(const Shape& shape, size_t* strides, size_t size) noexcept {
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size_t* st = strides + size - 1;
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size_t s = 1;
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for (auto d = shape.rbegin(); d != shape.rend(); d++) {
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*st-- = s;
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s *= *d;
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}
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std::fill(strides, st + 1, s);
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}
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template <typename C, typename T>
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inline T value_with_padding_or(const C& arr, size_t padding, size_t idx, T&& default_value) {
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return idx < padding ? std::forward<T>(default_value) : static_cast<T>(arr[idx - padding]);
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}
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template <int A0, int A1, typename T, typename U, typename Functor>
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inline void numpy_autobroadcast_binop(const T* arg0,
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const T* arg1,
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U* out,
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const Shape& shape0,
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const Shape& shape1,
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const size_t* strides0,
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const size_t* strides1,
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const size_t padding0,
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const size_t padding1,
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const Shape& output_shape,
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const size_t axis,
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const size_t stride,
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Functor elementwise_functor) {
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for (CoordinateIterator it(output_shape), ite = CoordinateIterator::end();;) {
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for (size_t i = 0; i < stride; ++i)
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*out++ = elementwise_functor(arg0[i * A0], arg1[i * A1]);
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arg0 += A0 ? stride : 1;
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arg1 += A1 ? stride : 1;
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auto p = it.advance(axis);
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if (it == ite)
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break;
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if (value_with_padding_or(shape0, padding0, p, 1) == 1)
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arg0 -= strides0[p];
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if (value_with_padding_or(shape1, padding1, p, 1) == 1)
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arg1 -= strides1[p];
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}
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}
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inline size_t calculate_fixed_axis(size_t axis, const size_t* strides) {
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while (axis > 0 && strides[axis - 1] == 1)
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--axis;
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return axis;
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}
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} // namespace internal
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/// \brief Helper function to implement autobroadcasting elementwise binop references.
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///
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/// \tparam T Element type of the input tensors.
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/// \tparam U Element type of the output tensor.
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/// \tparam Functor Type of the functor for the elementwise operation. Must support
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/// operator()(T,T), and operator()(T,T) must return a value of type
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/// U.
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///
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/// \param arg0 Pointer to the buffer for left operand input tensor.
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/// \param arg1 Pointer to the buffer for right operand input tensor.
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/// \param out Pointer to the buffer for output tensor. This must be pre-allocated by
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/// the caller, and must be large enough to hold a tensor of the correct
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/// shape.
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/// \param broadcast_spec Specification of the auto-broadcasting scheme.
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/// \param elementwise_functor Functor implementing the elementwise operation to be
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/// applied across the input tensors. Must accept two
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/// arguments of type T, and return a value of type U.
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template <typename T, typename U, typename Functor>
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void autobroadcast_binop(const T* arg0,
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const T* arg1,
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@ -97,208 +18,9 @@ void autobroadcast_binop(const T* arg0,
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const Shape& arg1_shape,
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const op::AutoBroadcastSpec& broadcast_spec,
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Functor elementwise_functor) {
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switch (broadcast_spec.m_type) {
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case op::AutoBroadcastType::NONE:
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for (size_t i = 0; i < shape_size(arg0_shape); i++) {
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out[i] = static_cast<U>(elementwise_functor(arg0[i], arg1[i]));
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}
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break;
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case op::AutoBroadcastType::NUMPY:
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// We'll be using CoordinateTransform to handle the broadcasting. The general
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// procedure is as follows:
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//
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// (1) Left pad the shorter of the two shapes with ones.
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// (2) Squeeze (remove ones from) both shapes, and record the squeezed axis
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// indices.
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// (3) Using CoordinateTransform, broadcast both args to the final output
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// shape. The "broadcasted axes" will be those that were squeezed in step
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// 2.
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//
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// Example:
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//
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// Input shape->Padded shape->Squeezed Shape/Squeezed Axes
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// ----------- ------------ ----------------------------
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// a: [ 3, 2, 1] [ 3, 2, 1] [ 3, 2 ] {2}
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// b: [ 1, 6] [ 1, 1, 6] [ 6] {0,1}
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// | | |
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// v v v
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// Output shape
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// ------------
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// [ 3, 2, 6]
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{
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using namespace internal;
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size_t const shape_rank = std::max(arg0_shape.size(), arg1_shape.size()) + 1;
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// TODO: Use compiler-specific alloca() or variable-length array
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std::vector<size_t> tmp(shape_rank * 2);
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size_t* strides0 = tmp.data();
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size_t* strides1 = tmp.data() + shape_rank;
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row_major_strides(arg0_shape, strides0, shape_rank);
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row_major_strides(arg1_shape, strides1, shape_rank);
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size_t const padding0 = shape_rank - arg0_shape.size();
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size_t const padding1 = shape_rank - arg1_shape.size();
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Shape output_shape(shape_rank, 0);
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size_t axis = 0;
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for (size_t i = 0; i < shape_rank; i++) {
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auto const dim0 = value_with_padding_or(arg0_shape, padding0, i, 1);
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auto const dim1 = value_with_padding_or(arg1_shape, padding1, i, 1);
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output_shape[i] = std::max(dim0, dim1);
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if (dim0 != dim1)
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axis = std::max(axis, i);
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ov::reference::autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, elementwise_functor);
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}
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if (axis == 0) {
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for (size_t i = 0, end = strides0[0]; i < end; ++i)
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out[i] = elementwise_functor(arg0[i], arg1[i]);
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} else if (strides0[axis] == 1 && value_with_padding_or(arg0_shape, padding0, axis, 1) == 1) {
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axis = calculate_fixed_axis(axis, strides0);
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numpy_autobroadcast_binop<0, 1>(arg0,
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arg1,
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out,
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arg0_shape,
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arg1_shape,
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strides0,
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strides1,
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padding0,
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padding1,
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output_shape,
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axis,
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strides1[axis],
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elementwise_functor);
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} else if (strides1[axis] == 1 && value_with_padding_or(arg1_shape, padding1, axis, 1) == 1) {
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axis = calculate_fixed_axis(axis, strides1);
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numpy_autobroadcast_binop<1, 0>(arg0,
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arg1,
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out,
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arg0_shape,
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arg1_shape,
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strides0,
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strides1,
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padding0,
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padding1,
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output_shape,
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axis,
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strides0[axis],
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elementwise_functor);
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} else
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numpy_autobroadcast_binop<1, 1>(arg0,
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arg1,
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out,
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arg0_shape,
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arg1_shape,
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strides0,
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strides1,
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padding0,
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padding1,
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output_shape,
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axis,
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strides0[axis],
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elementwise_functor);
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}
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break;
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case op::AutoBroadcastType::PDPD:
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// We'll be using CoordinateTransform to handle the broadcasting. No need to
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// process arg0 and output shape will be the same as arg0. We need to process
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// arg1 and the general procedure is as follows:
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//
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// (1) Trim trailing ones from arg1 shape.
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// (2) Left and right pad arg1 to match arg0 shape. Axis is the index start
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// to align between arg0 and arg1.
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// (3) Squeeze (remove ones from) arg1 shape, and record the squeezed axis
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// indices.
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// (3) Using CoordinateTransform, broadcast arg1 to the final output
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// shape. The "broadcasted axes" will be those that were squeezed in step
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// 23.
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//
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// Example:
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//
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// Input shape-> Padded shape-> Squeezed Shape/Squeezed Axes
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// ----------- ------------ ----------------------------
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// a: [ 3, 4, 5, 6] [ 3, 4, 5, 6] [ 3, 4, 5, 6]
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// b: [ 4, 5, ] [ 1, 4, 5, 1] [ 4, 5 ] {0,3}
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// | | |
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// v v v
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// Output shape
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// ------------
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// [ 3, 4, 5, 6]
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{
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int64_t axis = broadcast_spec.m_axis;
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if (axis == -1) {
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axis = arg0_shape.size() - arg1_shape.size();
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}
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Shape arg1_padded_shape = arg1_shape;
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// Trim trailing ones
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while (arg1_padded_shape.size() > 0 && arg1_padded_shape.back() == 1) {
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arg1_padded_shape.pop_back();
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}
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for (int64_t i = 0; i < axis; ++i) {
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arg1_padded_shape.insert(arg1_padded_shape.begin(), 1);
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}
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while (arg1_padded_shape.size() < arg0_shape.size()) {
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arg1_padded_shape.insert(arg1_padded_shape.end(), 1);
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}
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Shape arg1_squeezed_shape;
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AxisSet arg1_squeezed_axes;
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for (size_t i = 0; i < arg0_shape.size(); i++) {
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if (arg1_padded_shape[i] == 1) {
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arg1_squeezed_axes.insert(i);
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} else {
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arg1_squeezed_shape.push_back(arg1_padded_shape[i]);
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}
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}
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NGRAPH_SUPPRESS_DEPRECATED_START
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CoordinateTransform arg0_transform(arg0_shape);
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CoordinateTransform arg1_transform(arg1_squeezed_shape);
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CoordinateTransform output_transform(arg0_shape);
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for (const Coordinate& output_coord : output_transform) {
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Coordinate arg1_coord = reduce(output_coord, arg1_squeezed_axes, false);
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out[output_transform.index(output_coord)] =
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elementwise_functor(arg0[arg0_transform.index(output_coord)],
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arg1[arg1_transform.index(arg1_coord)]);
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}
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NGRAPH_SUPPRESS_DEPRECATED_END
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}
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}
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}
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/// \brief Helper function to implement autobroadcasting elementwise ternaryop
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/// references.
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///
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/// \tparam U Element type of the selector tensor.
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/// \tparam T Element type of the input tensors.
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/// \tparam Functor Type of the functor for the elementwise operation. Must support
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/// operator()(U,T,T), and operator()(U,T,T) must return a value of type
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/// T.
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///
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/// \param arg0 Pointer to the buffer for selector tensor.
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/// \param arg1 Pointer to the buffer for left operand input tensor.
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/// \param arg2 Pointer to the buffer for right operand input tensor.
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/// \param out Pointer to the buffer for output tensor. This must be pre-allocated by
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/// the caller, and must be large enough to hold a tensor of the correct
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/// shape.
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/// \param broadcast_spec Specification of the auto-broadcasting scheme.
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/// \param elementwise_functor Functor implementing the elementwise operation to be
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/// applied across the input tensors. Must accept an argument
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/// of
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/// type U and two of type T, and return a value of type T.
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template <typename T, typename U, typename Functor>
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void autobroadcast_select(const U* arg0,
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const T* arg1,
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@ -309,172 +31,15 @@ void autobroadcast_select(const U* arg0,
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const Shape& arg2_shape,
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const op::AutoBroadcastSpec& broadcast_spec,
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Functor elementwise_functor) {
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switch (broadcast_spec.m_type) {
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case op::AutoBroadcastType::NONE:
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for (size_t i = 0; i < shape_size(arg0_shape); i++) {
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out[i] = elementwise_functor(arg0[i], arg1[i], arg2[i]);
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}
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break;
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case op::AutoBroadcastType::NUMPY:
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// Uses same approach as autobroadcast_binop.
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{
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Shape arg0_padded_shape = arg0_shape;
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Shape arg1_padded_shape = arg1_shape;
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Shape arg2_padded_shape = arg2_shape;
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size_t max_shape_size =
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std::max({arg0_padded_shape.size(), arg1_padded_shape.size(), arg2_padded_shape.size()});
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while (arg0_padded_shape.size() < max_shape_size) {
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arg0_padded_shape.insert(arg0_padded_shape.begin(), 1);
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}
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while (arg1_padded_shape.size() < max_shape_size) {
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arg1_padded_shape.insert(arg1_padded_shape.begin(), 1);
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}
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while (arg2_padded_shape.size() < max_shape_size) {
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arg2_padded_shape.insert(arg2_padded_shape.begin(), 1);
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}
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Shape arg0_squeezed_shape;
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Shape arg1_squeezed_shape;
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Shape arg2_squeezed_shape;
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AxisSet arg0_squeezed_axes;
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AxisSet arg1_squeezed_axes;
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AxisSet arg2_squeezed_axes;
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Shape output_shape;
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for (size_t i = 0; i < max_shape_size; i++) {
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if (arg1_padded_shape[i] == 1) {
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arg1_squeezed_axes.insert(i);
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} else {
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arg1_squeezed_shape.push_back(arg1_padded_shape[i]);
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}
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if (arg2_padded_shape[i] == 1) {
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arg2_squeezed_axes.insert(i);
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} else {
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arg2_squeezed_shape.push_back(arg2_padded_shape[i]);
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}
|
||||
|
||||
if (arg0_padded_shape[i] == 1) {
|
||||
arg0_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg0_squeezed_shape.push_back(arg0_padded_shape[i]);
|
||||
}
|
||||
|
||||
output_shape.push_back(std::max({arg0_padded_shape[i], arg2_padded_shape[i], arg1_padded_shape[i]}));
|
||||
}
|
||||
|
||||
CoordinateTransformBasic arg0_transform(arg0_squeezed_shape);
|
||||
CoordinateTransformBasic arg1_transform(arg1_squeezed_shape);
|
||||
CoordinateTransformBasic arg2_transform(arg2_squeezed_shape);
|
||||
CoordinateTransformBasic output_transform(output_shape);
|
||||
|
||||
const auto arg0_strides = row_major_strides(arg0_squeezed_shape);
|
||||
const auto arg1_strides = row_major_strides(arg1_squeezed_shape);
|
||||
const auto arg2_strides = row_major_strides(arg2_squeezed_shape);
|
||||
const auto output_strides = row_major_strides(output_shape);
|
||||
|
||||
for (const Coordinate& output_coord : output_transform) {
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
const Coordinate arg0_coord = reduce(output_coord, arg0_squeezed_axes, false);
|
||||
const Coordinate arg1_coord = reduce(output_coord, arg1_squeezed_axes, false);
|
||||
const Coordinate arg2_coord = reduce(output_coord, arg2_squeezed_axes, false);
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
|
||||
const size_t arg0_idx =
|
||||
std::inner_product(arg0_coord.begin(), arg0_coord.end(), arg0_strides.begin(), uint64_t(0));
|
||||
const size_t arg1_idx =
|
||||
std::inner_product(arg1_coord.begin(), arg1_coord.end(), arg1_strides.begin(), uint64_t(0));
|
||||
const size_t arg2_idx =
|
||||
std::inner_product(arg2_coord.begin(), arg2_coord.end(), arg2_strides.begin(), uint64_t(0));
|
||||
const size_t output_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
out[output_idx] = elementwise_functor(arg0[arg0_idx], arg1[arg1_idx], arg2[arg2_idx]);
|
||||
}
|
||||
}
|
||||
break;
|
||||
case op::AutoBroadcastType::PDPD: {
|
||||
// arg0 and arg2 are broadcast to arg1 shape
|
||||
int64_t axis = broadcast_spec.m_axis;
|
||||
if (axis == -1) {
|
||||
axis = arg1_shape.size() - arg2_shape.size();
|
||||
}
|
||||
|
||||
Shape arg0_padded_shape = arg0_shape;
|
||||
Shape arg2_padded_shape = arg2_shape;
|
||||
// Trim trailing ones
|
||||
while (arg0_padded_shape.size() > 0 && arg0_padded_shape.back() == 1) {
|
||||
arg0_padded_shape.pop_back();
|
||||
}
|
||||
|
||||
for (int64_t i = 0; (i < axis) && (arg0_padded_shape.size() < arg1_shape.size()); ++i) {
|
||||
arg0_padded_shape.insert(arg0_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg0_padded_shape.size() < arg1_shape.size()) {
|
||||
arg0_padded_shape.insert(arg0_padded_shape.end(), 1);
|
||||
}
|
||||
|
||||
while (arg2_padded_shape.size() > 0 && arg2_padded_shape.back() == 1) {
|
||||
arg2_padded_shape.pop_back();
|
||||
}
|
||||
for (int64_t i = 0; (i < axis) && (arg2_padded_shape.size() < arg1_shape.size()); ++i) {
|
||||
arg2_padded_shape.insert(arg2_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg2_padded_shape.size() < arg1_shape.size()) {
|
||||
arg2_padded_shape.insert(arg2_padded_shape.end(), 1);
|
||||
}
|
||||
|
||||
Shape arg0_squeezed_shape;
|
||||
AxisSet arg0_squeezed_axes;
|
||||
Shape arg2_squeezed_shape;
|
||||
AxisSet arg2_squeezed_axes;
|
||||
|
||||
for (size_t i = 0; i < arg1_shape.size(); i++) {
|
||||
if (arg0_padded_shape[i] == 1) {
|
||||
arg0_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg0_squeezed_shape.push_back(arg0_padded_shape[i]);
|
||||
}
|
||||
if (arg2_padded_shape[i] == 1) {
|
||||
arg2_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg2_squeezed_shape.push_back(arg2_padded_shape[i]);
|
||||
}
|
||||
}
|
||||
|
||||
CoordinateTransformBasic arg0_transform(arg0_squeezed_shape);
|
||||
CoordinateTransformBasic arg1_transform(arg1_shape);
|
||||
CoordinateTransformBasic arg2_transform(arg2_squeezed_shape);
|
||||
CoordinateTransformBasic output_transform(arg1_shape);
|
||||
|
||||
const auto arg0_strides = row_major_strides(arg0_squeezed_shape);
|
||||
const auto arg2_strides = row_major_strides(arg2_squeezed_shape);
|
||||
const auto output_strides = row_major_strides(arg1_shape);
|
||||
|
||||
for (const Coordinate& output_coord : output_transform) {
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
const Coordinate arg0_coord = reduce(output_coord, arg0_squeezed_axes, false);
|
||||
const Coordinate arg2_coord = reduce(output_coord, arg2_squeezed_axes, false);
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
|
||||
const size_t arg0_idx =
|
||||
std::inner_product(arg0_coord.begin(), arg0_coord.end(), arg0_strides.begin(), uint64_t(0));
|
||||
const size_t arg1_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
const size_t arg2_idx =
|
||||
std::inner_product(arg2_coord.begin(), arg2_coord.end(), arg2_strides.begin(), uint64_t(0));
|
||||
const size_t output_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
|
||||
out[output_idx] = elementwise_functor(arg0[arg0_idx], arg1[arg1_idx], arg2[arg2_idx]);
|
||||
}
|
||||
}
|
||||
}
|
||||
ov::reference::autobroadcast_select(arg0,
|
||||
arg1,
|
||||
arg2,
|
||||
out,
|
||||
arg0_shape,
|
||||
arg1_shape,
|
||||
arg2_shape,
|
||||
broadcast_spec,
|
||||
elementwise_functor);
|
||||
}
|
||||
} // namespace reference
|
||||
} // namespace runtime
|
||||
|
||||
@ -4,21 +4,16 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cmath>
|
||||
#include <cstddef>
|
||||
|
||||
#include "ngraph/op/util/attr_types.hpp"
|
||||
#include "ngraph/runtime/reference/autobroadcast_binop.hpp"
|
||||
#include "ngraph/shape.hpp"
|
||||
#include "openvino/reference/power.hpp"
|
||||
|
||||
// Proxy calls for dependant components transition to ov::reference namespace
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
namespace reference {
|
||||
|
||||
template <typename T>
|
||||
void power(const T* arg0, const T* arg1, T* out, size_t count) {
|
||||
for (size_t i = 0; i < count; i++) {
|
||||
out[i] = static_cast<T>(std::pow(arg0[i], arg1[i]));
|
||||
}
|
||||
ov::reference::power(arg0, arg1, out, count);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
@ -28,10 +23,9 @@ void power(const T* arg0,
|
||||
const Shape& arg0_shape,
|
||||
const Shape& arg1_shape,
|
||||
const op::AutoBroadcastSpec& broadcast_spec) {
|
||||
autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
|
||||
return static_cast<T>(std::pow(x, y));
|
||||
});
|
||||
ov::reference::power(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec);
|
||||
}
|
||||
|
||||
} // namespace reference
|
||||
} // namespace runtime
|
||||
} // namespace ngraph
|
||||
|
||||
@ -4,42 +4,15 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cmath>
|
||||
|
||||
#include "ngraph/coordinate_transform.hpp"
|
||||
#include "ngraph/runtime/reference/max.hpp"
|
||||
#include "ngraph/runtime/reference/sum.hpp"
|
||||
#include "ngraph/shape_util.hpp"
|
||||
#include "openvino/reference/softmax.hpp"
|
||||
|
||||
// Proxy call for dependant components transition to ov::reference namespace
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
namespace reference {
|
||||
template <typename T>
|
||||
void softmax(const T* arg, T* out, const Shape& shape, const AxisSet& axes) {
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
auto temp_shape = reduce(shape, axes, true);
|
||||
auto temp_elements = shape_size(temp_shape);
|
||||
auto temp_ptr = new T[temp_elements];
|
||||
|
||||
max(arg, temp_ptr, shape, axes);
|
||||
|
||||
CoordinateTransform transform(shape);
|
||||
CoordinateTransform temp_transform(temp_shape);
|
||||
for (const Coordinate& coord : transform) {
|
||||
Coordinate temp_coord = reduce(coord, axes, true);
|
||||
out[transform.index(coord)] =
|
||||
std::exp(arg[transform.index(coord)] - temp_ptr[temp_transform.index(temp_coord)]);
|
||||
}
|
||||
|
||||
sum(out, temp_ptr, shape, axes);
|
||||
|
||||
for (const Coordinate& coord : transform) {
|
||||
Coordinate temp_coord = reduce(coord, axes, true);
|
||||
out[transform.index(coord)] /= temp_ptr[temp_transform.index(temp_coord)];
|
||||
}
|
||||
|
||||
delete[] temp_ptr;
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
ov::reference::softmax(arg, out, shape, axes);
|
||||
}
|
||||
} // namespace reference
|
||||
} // namespace runtime
|
||||
|
||||
@ -0,0 +1,482 @@
|
||||
// Copyright (C) 2018-2023 Intel Corporation
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
//
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cstddef>
|
||||
#include <numeric>
|
||||
#include <utility>
|
||||
|
||||
#include "ngraph/coordinate_transform.hpp"
|
||||
#include "ngraph/op/util/attr_types.hpp"
|
||||
#include "ngraph/shape_util.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace reference {
|
||||
namespace internal {
|
||||
inline void row_major_strides(const Shape& shape, size_t* strides, size_t size) noexcept {
|
||||
size_t* st = strides + size - 1;
|
||||
size_t s = 1;
|
||||
for (auto d = shape.rbegin(); d != shape.rend(); d++) {
|
||||
*st-- = s;
|
||||
s *= *d;
|
||||
}
|
||||
std::fill(strides, st + 1, s);
|
||||
}
|
||||
|
||||
template <typename C, typename T>
|
||||
inline T value_with_padding_or(const C& arr, size_t padding, size_t idx, T&& default_value) {
|
||||
return idx < padding ? std::forward<T>(default_value) : static_cast<T>(arr[idx - padding]);
|
||||
}
|
||||
|
||||
template <int A0, int A1, typename T, typename U, typename Functor>
|
||||
inline void numpy_autobroadcast_binop(const T* arg0,
|
||||
const T* arg1,
|
||||
U* out,
|
||||
const Shape& shape0,
|
||||
const Shape& shape1,
|
||||
const size_t* strides0,
|
||||
const size_t* strides1,
|
||||
const size_t padding0,
|
||||
const size_t padding1,
|
||||
const Shape& output_shape,
|
||||
const size_t axis,
|
||||
const size_t stride,
|
||||
Functor elementwise_functor) {
|
||||
using ngraph::CoordinateIterator;
|
||||
for (CoordinateIterator it(output_shape), ite = CoordinateIterator::end();;) {
|
||||
for (size_t i = 0; i < stride; ++i)
|
||||
*out++ = elementwise_functor(arg0[i * A0], arg1[i * A1]);
|
||||
|
||||
arg0 += A0 ? stride : 1;
|
||||
arg1 += A1 ? stride : 1;
|
||||
|
||||
auto p = it.advance(axis);
|
||||
|
||||
if (it == ite)
|
||||
break;
|
||||
|
||||
if (value_with_padding_or(shape0, padding0, p, 1) == 1)
|
||||
arg0 -= strides0[p];
|
||||
|
||||
if (value_with_padding_or(shape1, padding1, p, 1) == 1)
|
||||
arg1 -= strides1[p];
|
||||
}
|
||||
}
|
||||
|
||||
inline size_t calculate_fixed_axis(size_t axis, const size_t* strides) {
|
||||
while (axis > 0 && strides[axis - 1] == 1)
|
||||
--axis;
|
||||
return axis;
|
||||
}
|
||||
} // namespace internal
|
||||
|
||||
/// \brief Helper function to implement autobroadcasting elementwise binop references.
|
||||
///
|
||||
/// \tparam T Element type of the input tensors.
|
||||
/// \tparam U Element type of the output tensor.
|
||||
/// \tparam Functor Type of the functor for the elementwise operation. Must support
|
||||
/// operator()(T,T), and operator()(T,T) must return a value of type
|
||||
/// U.
|
||||
///
|
||||
/// \param arg0 Pointer to the buffer for left operand input tensor.
|
||||
/// \param arg1 Pointer to the buffer for right operand input tensor.
|
||||
/// \param out Pointer to the buffer for output tensor. This must be pre-allocated by
|
||||
/// the caller, and must be large enough to hold a tensor of the correct
|
||||
/// shape.
|
||||
/// \param broadcast_spec Specification of the auto-broadcasting scheme.
|
||||
/// \param elementwise_functor Functor implementing the elementwise operation to be
|
||||
/// applied across the input tensors. Must accept two
|
||||
/// arguments of type T, and return a value of type U.
|
||||
template <typename T, typename U, typename Functor>
|
||||
void autobroadcast_binop(const T* arg0,
|
||||
const T* arg1,
|
||||
U* out,
|
||||
const Shape& arg0_shape,
|
||||
const Shape& arg1_shape,
|
||||
const op::AutoBroadcastSpec& broadcast_spec,
|
||||
Functor elementwise_functor) {
|
||||
switch (broadcast_spec.m_type) {
|
||||
case op::AutoBroadcastType::NONE:
|
||||
for (size_t i = 0; i < shape_size(arg0_shape); i++) {
|
||||
out[i] = static_cast<U>(elementwise_functor(arg0[i], arg1[i]));
|
||||
}
|
||||
break;
|
||||
case op::AutoBroadcastType::NUMPY:
|
||||
// We'll be using CoordinateTransform to handle the broadcasting. The general
|
||||
// procedure is as follows:
|
||||
//
|
||||
// (1) Left pad the shorter of the two shapes with ones.
|
||||
// (2) Squeeze (remove ones from) both shapes, and record the squeezed axis
|
||||
// indices.
|
||||
// (3) Using CoordinateTransform, broadcast both args to the final output
|
||||
// shape. The "broadcasted axes" will be those that were squeezed in step
|
||||
// 2.
|
||||
//
|
||||
// Example:
|
||||
//
|
||||
// Input shape->Padded shape->Squeezed Shape/Squeezed Axes
|
||||
// ----------- ------------ ----------------------------
|
||||
// a: [ 3, 2, 1] [ 3, 2, 1] [ 3, 2 ] {2}
|
||||
// b: [ 1, 6] [ 1, 1, 6] [ 6] {0,1}
|
||||
// | | |
|
||||
// v v v
|
||||
// Output shape
|
||||
// ------------
|
||||
// [ 3, 2, 6]
|
||||
{
|
||||
using namespace internal;
|
||||
|
||||
size_t const shape_rank = std::max(arg0_shape.size(), arg1_shape.size()) + 1;
|
||||
|
||||
// TODO: Use compiler-specific alloca() or variable-length array
|
||||
std::vector<size_t> tmp(shape_rank * 2);
|
||||
|
||||
size_t* strides0 = tmp.data();
|
||||
size_t* strides1 = tmp.data() + shape_rank;
|
||||
|
||||
row_major_strides(arg0_shape, strides0, shape_rank);
|
||||
row_major_strides(arg1_shape, strides1, shape_rank);
|
||||
|
||||
size_t const padding0 = shape_rank - arg0_shape.size();
|
||||
size_t const padding1 = shape_rank - arg1_shape.size();
|
||||
|
||||
Shape output_shape(shape_rank, 0);
|
||||
|
||||
size_t axis = 0;
|
||||
|
||||
for (size_t i = 0; i < shape_rank; i++) {
|
||||
auto const dim0 = value_with_padding_or(arg0_shape, padding0, i, 1);
|
||||
auto const dim1 = value_with_padding_or(arg1_shape, padding1, i, 1);
|
||||
|
||||
output_shape[i] = std::max(dim0, dim1);
|
||||
|
||||
if (dim0 != dim1)
|
||||
axis = std::max(axis, i);
|
||||
}
|
||||
|
||||
if (axis == 0) {
|
||||
for (size_t i = 0, end = strides0[0]; i < end; ++i)
|
||||
out[i] = elementwise_functor(arg0[i], arg1[i]);
|
||||
} else if (strides0[axis] == 1 && value_with_padding_or(arg0_shape, padding0, axis, 1) == 1) {
|
||||
axis = calculate_fixed_axis(axis, strides0);
|
||||
|
||||
numpy_autobroadcast_binop<0, 1>(arg0,
|
||||
arg1,
|
||||
out,
|
||||
arg0_shape,
|
||||
arg1_shape,
|
||||
strides0,
|
||||
strides1,
|
||||
padding0,
|
||||
padding1,
|
||||
output_shape,
|
||||
axis,
|
||||
strides1[axis],
|
||||
elementwise_functor);
|
||||
} else if (strides1[axis] == 1 && value_with_padding_or(arg1_shape, padding1, axis, 1) == 1) {
|
||||
axis = calculate_fixed_axis(axis, strides1);
|
||||
|
||||
numpy_autobroadcast_binop<1, 0>(arg0,
|
||||
arg1,
|
||||
out,
|
||||
arg0_shape,
|
||||
arg1_shape,
|
||||
strides0,
|
||||
strides1,
|
||||
padding0,
|
||||
padding1,
|
||||
output_shape,
|
||||
axis,
|
||||
strides0[axis],
|
||||
elementwise_functor);
|
||||
} else
|
||||
numpy_autobroadcast_binop<1, 1>(arg0,
|
||||
arg1,
|
||||
out,
|
||||
arg0_shape,
|
||||
arg1_shape,
|
||||
strides0,
|
||||
strides1,
|
||||
padding0,
|
||||
padding1,
|
||||
output_shape,
|
||||
axis,
|
||||
strides0[axis],
|
||||
elementwise_functor);
|
||||
}
|
||||
break;
|
||||
case op::AutoBroadcastType::PDPD:
|
||||
// We'll be using CoordinateTransform to handle the broadcasting. No need to
|
||||
// process arg0 and output shape will be the same as arg0. We need to process
|
||||
// arg1 and the general procedure is as follows:
|
||||
//
|
||||
// (1) Trim trailing ones from arg1 shape.
|
||||
// (2) Left and right pad arg1 to match arg0 shape. Axis is the index start
|
||||
// to align between arg0 and arg1.
|
||||
// (3) Squeeze (remove ones from) arg1 shape, and record the squeezed axis
|
||||
// indices.
|
||||
// (3) Using CoordinateTransform, broadcast arg1 to the final output
|
||||
// shape. The "broadcasted axes" will be those that were squeezed in step
|
||||
// 23.
|
||||
//
|
||||
// Example:
|
||||
//
|
||||
// Input shape-> Padded shape-> Squeezed Shape/Squeezed Axes
|
||||
// ----------- ------------ ----------------------------
|
||||
// a: [ 3, 4, 5, 6] [ 3, 4, 5, 6] [ 3, 4, 5, 6]
|
||||
// b: [ 4, 5, ] [ 1, 4, 5, 1] [ 4, 5 ] {0,3}
|
||||
// | | |
|
||||
// v v v
|
||||
// Output shape
|
||||
// ------------
|
||||
// [ 3, 4, 5, 6]
|
||||
{
|
||||
int64_t axis = broadcast_spec.m_axis;
|
||||
if (axis == -1) {
|
||||
axis = arg0_shape.size() - arg1_shape.size();
|
||||
}
|
||||
|
||||
Shape arg1_padded_shape = arg1_shape;
|
||||
// Trim trailing ones
|
||||
while (arg1_padded_shape.size() > 0 && arg1_padded_shape.back() == 1) {
|
||||
arg1_padded_shape.pop_back();
|
||||
}
|
||||
|
||||
for (int64_t i = 0; i < axis; ++i) {
|
||||
arg1_padded_shape.insert(arg1_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg1_padded_shape.size() < arg0_shape.size()) {
|
||||
arg1_padded_shape.insert(arg1_padded_shape.end(), 1);
|
||||
}
|
||||
|
||||
Shape arg1_squeezed_shape;
|
||||
AxisSet arg1_squeezed_axes;
|
||||
|
||||
for (size_t i = 0; i < arg0_shape.size(); i++) {
|
||||
if (arg1_padded_shape[i] == 1) {
|
||||
arg1_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg1_squeezed_shape.push_back(arg1_padded_shape[i]);
|
||||
}
|
||||
}
|
||||
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
ngraph::CoordinateTransform arg0_transform(arg0_shape);
|
||||
ngraph::CoordinateTransform arg1_transform(arg1_squeezed_shape);
|
||||
ngraph::CoordinateTransform output_transform(arg0_shape);
|
||||
|
||||
for (const Coordinate& output_coord : output_transform) {
|
||||
Coordinate arg1_coord = ngraph::reduce(output_coord, arg1_squeezed_axes, false);
|
||||
out[output_transform.index(output_coord)] =
|
||||
elementwise_functor(arg0[arg0_transform.index(output_coord)],
|
||||
arg1[arg1_transform.index(arg1_coord)]);
|
||||
}
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// \brief Helper function to implement autobroadcasting elementwise ternaryop
|
||||
/// references.
|
||||
///
|
||||
/// \tparam U Element type of the selector tensor.
|
||||
/// \tparam T Element type of the input tensors.
|
||||
/// \tparam Functor Type of the functor for the elementwise operation. Must support
|
||||
/// operator()(U,T,T), and operator()(U,T,T) must return a value of type
|
||||
/// T.
|
||||
///
|
||||
/// \param arg0 Pointer to the buffer for selector tensor.
|
||||
/// \param arg1 Pointer to the buffer for left operand input tensor.
|
||||
/// \param arg2 Pointer to the buffer for right operand input tensor.
|
||||
/// \param out Pointer to the buffer for output tensor. This must be pre-allocated by
|
||||
/// the caller, and must be large enough to hold a tensor of the correct
|
||||
/// shape.
|
||||
/// \param broadcast_spec Specification of the auto-broadcasting scheme.
|
||||
/// \param elementwise_functor Functor implementing the elementwise operation to be
|
||||
/// applied across the input tensors. Must accept an argument
|
||||
/// of
|
||||
/// type U and two of type T, and return a value of type T.
|
||||
template <typename T, typename U, typename Functor>
|
||||
void autobroadcast_select(const U* arg0,
|
||||
const T* arg1,
|
||||
const T* arg2,
|
||||
T* out,
|
||||
const Shape& arg0_shape,
|
||||
const Shape& arg1_shape,
|
||||
const Shape& arg2_shape,
|
||||
const op::AutoBroadcastSpec& broadcast_spec,
|
||||
Functor elementwise_functor) {
|
||||
using ngraph::CoordinateTransformBasic;
|
||||
|
||||
switch (broadcast_spec.m_type) {
|
||||
case op::AutoBroadcastType::NONE:
|
||||
for (size_t i = 0; i < shape_size(arg0_shape); i++) {
|
||||
out[i] = elementwise_functor(arg0[i], arg1[i], arg2[i]);
|
||||
}
|
||||
break;
|
||||
case op::AutoBroadcastType::NUMPY:
|
||||
// Uses same approach as autobroadcast_binop.
|
||||
{
|
||||
Shape arg0_padded_shape = arg0_shape;
|
||||
Shape arg1_padded_shape = arg1_shape;
|
||||
Shape arg2_padded_shape = arg2_shape;
|
||||
|
||||
size_t max_shape_size =
|
||||
std::max({arg0_padded_shape.size(), arg1_padded_shape.size(), arg2_padded_shape.size()});
|
||||
|
||||
while (arg0_padded_shape.size() < max_shape_size) {
|
||||
arg0_padded_shape.insert(arg0_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg1_padded_shape.size() < max_shape_size) {
|
||||
arg1_padded_shape.insert(arg1_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg2_padded_shape.size() < max_shape_size) {
|
||||
arg2_padded_shape.insert(arg2_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
Shape arg0_squeezed_shape;
|
||||
Shape arg1_squeezed_shape;
|
||||
Shape arg2_squeezed_shape;
|
||||
AxisSet arg0_squeezed_axes;
|
||||
AxisSet arg1_squeezed_axes;
|
||||
AxisSet arg2_squeezed_axes;
|
||||
Shape output_shape;
|
||||
|
||||
for (size_t i = 0; i < max_shape_size; i++) {
|
||||
if (arg1_padded_shape[i] == 1) {
|
||||
arg1_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg1_squeezed_shape.push_back(arg1_padded_shape[i]);
|
||||
}
|
||||
|
||||
if (arg2_padded_shape[i] == 1) {
|
||||
arg2_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg2_squeezed_shape.push_back(arg2_padded_shape[i]);
|
||||
}
|
||||
|
||||
if (arg0_padded_shape[i] == 1) {
|
||||
arg0_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg0_squeezed_shape.push_back(arg0_padded_shape[i]);
|
||||
}
|
||||
|
||||
output_shape.push_back(std::max({arg0_padded_shape[i], arg2_padded_shape[i], arg1_padded_shape[i]}));
|
||||
}
|
||||
|
||||
CoordinateTransformBasic arg0_transform(arg0_squeezed_shape);
|
||||
CoordinateTransformBasic arg1_transform(arg1_squeezed_shape);
|
||||
CoordinateTransformBasic arg2_transform(arg2_squeezed_shape);
|
||||
CoordinateTransformBasic output_transform(output_shape);
|
||||
|
||||
const auto arg0_strides = row_major_strides(arg0_squeezed_shape);
|
||||
const auto arg1_strides = row_major_strides(arg1_squeezed_shape);
|
||||
const auto arg2_strides = row_major_strides(arg2_squeezed_shape);
|
||||
const auto output_strides = row_major_strides(output_shape);
|
||||
|
||||
for (const Coordinate& output_coord : output_transform) {
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
const Coordinate arg0_coord = ngraph::reduce(output_coord, arg0_squeezed_axes, false);
|
||||
const Coordinate arg1_coord = ngraph::reduce(output_coord, arg1_squeezed_axes, false);
|
||||
const Coordinate arg2_coord = ngraph::reduce(output_coord, arg2_squeezed_axes, false);
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
|
||||
const size_t arg0_idx =
|
||||
std::inner_product(arg0_coord.begin(), arg0_coord.end(), arg0_strides.begin(), uint64_t(0));
|
||||
const size_t arg1_idx =
|
||||
std::inner_product(arg1_coord.begin(), arg1_coord.end(), arg1_strides.begin(), uint64_t(0));
|
||||
const size_t arg2_idx =
|
||||
std::inner_product(arg2_coord.begin(), arg2_coord.end(), arg2_strides.begin(), uint64_t(0));
|
||||
const size_t output_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
out[output_idx] = elementwise_functor(arg0[arg0_idx], arg1[arg1_idx], arg2[arg2_idx]);
|
||||
}
|
||||
}
|
||||
break;
|
||||
case op::AutoBroadcastType::PDPD: {
|
||||
// arg0 and arg2 are broadcast to arg1 shape
|
||||
int64_t axis = broadcast_spec.m_axis;
|
||||
if (axis == -1) {
|
||||
axis = arg1_shape.size() - arg2_shape.size();
|
||||
}
|
||||
|
||||
Shape arg0_padded_shape = arg0_shape;
|
||||
Shape arg2_padded_shape = arg2_shape;
|
||||
// Trim trailing ones
|
||||
while (arg0_padded_shape.size() > 0 && arg0_padded_shape.back() == 1) {
|
||||
arg0_padded_shape.pop_back();
|
||||
}
|
||||
|
||||
for (int64_t i = 0; (i < axis) && (arg0_padded_shape.size() < arg1_shape.size()); ++i) {
|
||||
arg0_padded_shape.insert(arg0_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg0_padded_shape.size() < arg1_shape.size()) {
|
||||
arg0_padded_shape.insert(arg0_padded_shape.end(), 1);
|
||||
}
|
||||
|
||||
while (arg2_padded_shape.size() > 0 && arg2_padded_shape.back() == 1) {
|
||||
arg2_padded_shape.pop_back();
|
||||
}
|
||||
for (int64_t i = 0; (i < axis) && (arg2_padded_shape.size() < arg1_shape.size()); ++i) {
|
||||
arg2_padded_shape.insert(arg2_padded_shape.begin(), 1);
|
||||
}
|
||||
|
||||
while (arg2_padded_shape.size() < arg1_shape.size()) {
|
||||
arg2_padded_shape.insert(arg2_padded_shape.end(), 1);
|
||||
}
|
||||
|
||||
Shape arg0_squeezed_shape;
|
||||
AxisSet arg0_squeezed_axes;
|
||||
Shape arg2_squeezed_shape;
|
||||
AxisSet arg2_squeezed_axes;
|
||||
|
||||
for (size_t i = 0; i < arg1_shape.size(); i++) {
|
||||
if (arg0_padded_shape[i] == 1) {
|
||||
arg0_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg0_squeezed_shape.push_back(arg0_padded_shape[i]);
|
||||
}
|
||||
if (arg2_padded_shape[i] == 1) {
|
||||
arg2_squeezed_axes.insert(i);
|
||||
} else {
|
||||
arg2_squeezed_shape.push_back(arg2_padded_shape[i]);
|
||||
}
|
||||
}
|
||||
|
||||
CoordinateTransformBasic arg0_transform(arg0_squeezed_shape);
|
||||
CoordinateTransformBasic arg1_transform(arg1_shape);
|
||||
CoordinateTransformBasic arg2_transform(arg2_squeezed_shape);
|
||||
CoordinateTransformBasic output_transform(arg1_shape);
|
||||
|
||||
const auto arg0_strides = row_major_strides(arg0_squeezed_shape);
|
||||
const auto arg2_strides = row_major_strides(arg2_squeezed_shape);
|
||||
const auto output_strides = row_major_strides(arg1_shape);
|
||||
|
||||
for (const Coordinate& output_coord : output_transform) {
|
||||
NGRAPH_SUPPRESS_DEPRECATED_START
|
||||
const Coordinate arg0_coord = ngraph::reduce(output_coord, arg0_squeezed_axes, false);
|
||||
const Coordinate arg2_coord = ngraph::reduce(output_coord, arg2_squeezed_axes, false);
|
||||
NGRAPH_SUPPRESS_DEPRECATED_END
|
||||
|
||||
const size_t arg0_idx =
|
||||
std::inner_product(arg0_coord.begin(), arg0_coord.end(), arg0_strides.begin(), uint64_t(0));
|
||||
const size_t arg1_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
const size_t arg2_idx =
|
||||
std::inner_product(arg2_coord.begin(), arg2_coord.end(), arg2_strides.begin(), uint64_t(0));
|
||||
const size_t output_idx =
|
||||
std::inner_product(output_coord.begin(), output_coord.end(), output_strides.begin(), uint64_t(0));
|
||||
|
||||
out[output_idx] = elementwise_functor(arg0[arg0_idx], arg1[arg1_idx], arg2[arg2_idx]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace reference
|
||||
} // namespace ov
|
||||
@ -4,8 +4,8 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/runtime/reference/convolution.hpp"
|
||||
#include "ngraph/shape.hpp"
|
||||
#include "openvino/reference/convolution.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -411,4 +411,4 @@ void convolution(const T* in,
|
||||
} // namespace ngraph
|
||||
|
||||
// can't be removed currently due to arm-plugin dependency
|
||||
#include "ngraph/runtime/reference/convolution_backprop_data.hpp"
|
||||
#include "openvino/reference/convolution_backprop_data.hpp"
|
||||
@ -10,9 +10,9 @@
|
||||
#include <numeric>
|
||||
|
||||
#include "ngraph/axis_vector.hpp"
|
||||
#include "ngraph/runtime/reference/convolution.hpp"
|
||||
#include "ngraph/runtime/reference/reverse.hpp"
|
||||
#include "ngraph/util.hpp"
|
||||
#include "openvino/reference/convolution.hpp"
|
||||
#include "openvino/reference/reverse.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -4,7 +4,7 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/runtime/reference/convolution.hpp"
|
||||
#include "openvino/reference/convolution.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -4,7 +4,7 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/runtime/reference/normalize_l2.hpp"
|
||||
#include "openvino/reference/normalize_l2.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -4,8 +4,8 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/runtime/reference/convolution.hpp"
|
||||
#include "ngraph/runtime/reference/helpers.hpp"
|
||||
#include "openvino/reference/convolution.hpp"
|
||||
#include "openvino/reference/helpers.hpp"
|
||||
|
||||
namespace {
|
||||
constexpr size_t filter_group_axis = 0;
|
||||
@ -4,8 +4,8 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/runtime/reference/group_convolution.hpp"
|
||||
#include "ngraph/util.hpp"
|
||||
#include "openvino/reference/group_convolution.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -7,9 +7,9 @@
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
|
||||
#include "ngraph/runtime/reference/mean.hpp"
|
||||
#include "ngraph/runtime/reference/sum.hpp"
|
||||
#include "openvino/core/shape.hpp"
|
||||
#include "openvino/reference/mean.hpp"
|
||||
#include "openvino/reference/sum.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace reference {
|
||||
@ -5,15 +5,16 @@
|
||||
#pragma once
|
||||
|
||||
#include <cmath>
|
||||
#include <ngraph/runtime/reference/add.hpp>
|
||||
#include <ngraph/runtime/reference/clamp.hpp>
|
||||
#include <ngraph/runtime/reference/matmul.hpp>
|
||||
#include <ngraph/runtime/reference/multiply.hpp>
|
||||
#include <ngraph/runtime/reference/relu.hpp>
|
||||
#include <ngraph/runtime/reference/sigmoid.hpp>
|
||||
#include <ngraph/runtime/reference/split.hpp>
|
||||
#include <ngraph/runtime/reference/subtract.hpp>
|
||||
#include <ngraph/runtime/reference/tanh.hpp>
|
||||
|
||||
#include "openvino/reference/add.hpp"
|
||||
#include "openvino/reference/clamp.hpp"
|
||||
#include "openvino/reference/matmul.hpp"
|
||||
#include "openvino/reference/multiply.hpp"
|
||||
#include "openvino/reference/relu.hpp"
|
||||
#include "openvino/reference/sigmoid.hpp"
|
||||
#include "openvino/reference/split.hpp"
|
||||
#include "openvino/reference/subtract.hpp"
|
||||
#include "openvino/reference/tanh.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -7,9 +7,9 @@
|
||||
#include <cmath>
|
||||
|
||||
#include "ngraph/coordinate_transform.hpp"
|
||||
#include "ngraph/runtime/reference/max.hpp"
|
||||
#include "ngraph/runtime/reference/sum.hpp"
|
||||
#include "ngraph/shape_util.hpp"
|
||||
#include "openvino/reference/max.hpp"
|
||||
#include "openvino/reference/sum.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -5,15 +5,16 @@
|
||||
#pragma once
|
||||
|
||||
#include <cmath>
|
||||
#include <ngraph/runtime/reference/add.hpp>
|
||||
#include <ngraph/runtime/reference/clamp.hpp>
|
||||
#include <ngraph/runtime/reference/matmul.hpp>
|
||||
#include <ngraph/runtime/reference/multiply.hpp>
|
||||
#include <ngraph/runtime/reference/relu.hpp>
|
||||
#include <ngraph/runtime/reference/sigmoid.hpp>
|
||||
#include <ngraph/runtime/reference/split.hpp>
|
||||
#include <ngraph/runtime/reference/subtract.hpp>
|
||||
#include <ngraph/runtime/reference/tanh.hpp>
|
||||
|
||||
#include "openvino/reference/add.hpp"
|
||||
#include "openvino/reference/clamp.hpp"
|
||||
#include "openvino/reference/matmul.hpp"
|
||||
#include "openvino/reference/multiply.hpp"
|
||||
#include "openvino/reference/relu.hpp"
|
||||
#include "openvino/reference/sigmoid.hpp"
|
||||
#include "openvino/reference/split.hpp"
|
||||
#include "openvino/reference/subtract.hpp"
|
||||
#include "openvino/reference/tanh.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
@ -10,8 +10,8 @@
|
||||
#include <vector>
|
||||
|
||||
#include "ngraph/runtime/opt_kernel/reshape.hpp"
|
||||
#include "ngraph/runtime/reference/broadcast.hpp"
|
||||
#include "ngraph/shape_util.hpp"
|
||||
#include "openvino/reference/broadcast.hpp"
|
||||
|
||||
namespace ngraph {
|
||||
namespace runtime {
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user