mirror of
https://github.com/saymrwulf/onnxruntime.git
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Implement OneHot op. (#213)
* Implementation of OneHot op * Implementation of OneHot op * Implementation of OneHot op. * one hot op * onehot * OneHot op. * Disable some Eigen related warnings * fix build * Add comment for potential optimization opportunity using Eigen threads * Remove this op from the broken tests
This commit is contained in:
Pranav Sharma
GitHub
parent
bddeb3d001
commit
e7e90c0322
6 changed files with 360 additions and 4 deletions
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@ -196,6 +196,12 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Eye
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, IsNaN);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MLFloat16, IsNaN);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Erf);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_int64_t_int64_t, OneHot);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_int64_t_int64_t, OneHot);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_string_int64_t, OneHot);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_string_int64_t, OneHot);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_float_float, OneHot);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_int32_t_float, OneHot);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MaxUnpool);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh);
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@ -387,6 +393,12 @@ void RegisterOnnxOperatorKernels(std::function<void(KernelCreateInfo&&)> fn) {
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, IsNaN)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MLFloat16, IsNaN)>());
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Erf)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_int64_t_int64_t, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_int64_t_int64_t, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_string_int64_t, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_string_int64_t, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float_float_float, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t_int32_t_float, OneHot)>());
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MaxUnpool)>());
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh)>());
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fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh)>());
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169
onnxruntime/core/providers/cpu/tensor/onehot.cc
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169
onnxruntime/core/providers/cpu/tensor/onehot.cc
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@ -0,0 +1,169 @@
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/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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/* Modifications Copyright (c) Microsoft. */
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#include "core/providers/cpu/tensor/onehot.h"
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// build\windows\debug\external\eigen3\unsupported\eigen\cxx11\src/Tensor/Tensor.h(76):
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// warning C4554: '&': check operator precedence for possible error; use parentheses to clarify precedence
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#ifdef _MSC_VER
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#pragma warning(push)
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#pragma warning(disable : 4554)
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#endif
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#include "core/util/eigen_common_wrapper.h"
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#ifdef _MSC_VER
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#pragma warning(pop)
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#endif
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#include "core/platform/env.h"
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#define EIGEN_USE_THREADS
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using namespace ::onnxruntime::common;
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using namespace std;
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namespace onnxruntime {
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// spec: https://github.com/onnx/onnx/blob/master/docs/Operators.md#OneHot
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// T1: indices, T2: depth, T3: values
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#define REG_TYPED_ONE_HOT_OP(types_str, in_type, out_type, depth_type) \
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ONNX_CPU_OPERATOR_TYPED_KERNEL( \
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OneHot, \
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9, \
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types_str, \
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KernelDefBuilder() \
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.TypeConstraint("T1", DataTypeImpl::GetTensorType<in_type>()) \
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.TypeConstraint("T2", DataTypeImpl::GetTensorType<depth_type>()) \
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.TypeConstraint("T3", DataTypeImpl::GetTensorType<out_type>()), \
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OneHotOp<in_type, out_type, depth_type>);
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#define REG_ONE_HOT_OP(in_type, out_type, depth_type) \
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REG_TYPED_ONE_HOT_OP(in_type##_##out_type##_##depth_type, in_type, out_type, depth_type)
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REG_ONE_HOT_OP(int64_t, int64_t, int64_t);
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REG_ONE_HOT_OP(float, int64_t, int64_t);
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REG_ONE_HOT_OP(int64_t, string, int64_t);
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REG_ONE_HOT_OP(float, string, int64_t);
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REG_ONE_HOT_OP(float, float, float); // added this to satisfy onnx model tests
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REG_ONE_HOT_OP(int64_t, int32_t, float); // added this to satisfy onnx model tests
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Status ValidateInputs(const Tensor* depth,
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const Tensor* values) {
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// validation scenarios
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// depth should be scalar and > 0
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if (!depth->Shape().IsScalar()) {
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return Status(ONNXRUNTIME, INVALID_ARGUMENT, "Invalid argument for depth; it's not a scalar.");
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}
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// values Rank 1 tensor containing exactly two elements
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if (!(values->Shape().NumDimensions() == 1 && values->Shape().Size() == 2)) {
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return Status(ONNXRUNTIME, INVALID_ARGUMENT,
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"Invalid argument for values; either it's rank is more than 1"
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" or it has more than 2 elements");
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}
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return Status::OK();
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}
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// Helper to define Tensor types given that the scalar is of type T.
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template <typename T, int NDIMS = 1, typename IndexType = Eigen::DenseIndex>
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struct EigenTensorTypes {
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using EigenTensorMap = Eigen::TensorMap<Eigen::Tensor<T, NDIMS, Eigen::RowMajor, IndexType>, Eigen::Aligned>;
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using ConstEigenTensorMap = Eigen::TensorMap<Eigen::Tensor<const T, NDIMS, Eigen::RowMajor, IndexType>, Eigen::Aligned>;
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using Scalar = Eigen::TensorMap<Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, IndexType>, Eigen::Aligned>;
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using ConstScalar = Eigen::TensorMap<Eigen::TensorFixedSize<const T, Eigen::Sizes<>, Eigen::RowMajor, IndexType>, Eigen::Aligned>;
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using ConstMatrix = Eigen::TensorMap<Eigen::Tensor<const T, 2, Eigen::RowMajor, IndexType>, Eigen::Aligned>;
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};
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namespace generator {
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template <typename in_type, typename out_type>
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class OneGenerator {
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public:
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
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OneGenerator(const typename EigenTensorTypes<in_type>::ConstMatrix& indices,
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const typename EigenTensorTypes<out_type>::ConstScalar& on_value,
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const typename EigenTensorTypes<out_type>::ConstScalar& off_value)
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: indices_(indices), on_value_(on_value), off_value_(off_value) {}
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE out_type
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operator()(const Eigen::array<Eigen::DenseIndex, 3>& pre_depth_suff) const {
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return (indices_(pre_depth_suff[0], pre_depth_suff[2]) == pre_depth_suff[1])
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? on_value_()
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: off_value_();
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}
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private:
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const typename EigenTensorTypes<in_type>::ConstMatrix indices_;
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const typename EigenTensorTypes<out_type>::ConstScalar on_value_;
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const typename EigenTensorTypes<out_type>::ConstScalar off_value_;
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};
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} // namespace generator
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template <typename in_type, typename out_type, typename depth_type>
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Status OneHotOp<in_type, out_type, depth_type>::Compute(OpKernelContext* p_op_kernel_context) const {
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const auto* indices = p_op_kernel_context->Input<Tensor>(0);
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const auto* depth = p_op_kernel_context->Input<Tensor>(1);
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const auto* values = p_op_kernel_context->Input<Tensor>(2);
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ORT_RETURN_IF_ERROR(ValidateInputs(depth, values));
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// prepare output shape
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const auto* depth_data = depth->Data<depth_type>();
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const int64_t depth_val = static_cast<int64_t>(*depth_data); // As per spec in case 'depth' is of non-integer type, it will be casted to int64 before use.
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if (depth_val <= 0) {
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return Status(ONNXRUNTIME, INVALID_ARGUMENT, "Depth is negative.");
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}
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const auto& indices_shape = indices->Shape();
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const auto& indices_dims = indices_shape.GetDims();
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const auto indices_num_dims = indices_shape.NumDimensions();
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std::vector<int64_t> output_shape(indices_shape.GetDims());
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output_shape.insert(axis_ == -1 ? output_shape.end() : output_shape.begin() + axis_,
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depth_val);
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// allocate output
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const auto* values_data = values->Data<out_type>();
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Tensor* output = p_op_kernel_context->Output(0, TensorShape(output_shape));
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const int64_t axis = (axis_ == -1) ? indices_num_dims : axis_;
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int64_t prefix_dim_size = 1;
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for (int64_t i = 0; i < axis; ++i) {
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prefix_dim_size *= indices_dims[i];
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}
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const int64_t suffix_dim_size = indices_shape.Size() / prefix_dim_size;
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// Split indices into matrix of size prefix_dim_size x suffix_dim_size
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Eigen::array<Eigen::DenseIndex, 2> indices_dims_e = {{prefix_dim_size, suffix_dim_size}};
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const auto* indices_data = indices->Data<in_type>();
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typename EigenTensorTypes<in_type, 2>::ConstEigenTensorMap indices_tensor_e(indices_data, indices_dims_e);
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// Split output into 3-Tensor of size:
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// prefix_dim_size x depth x suffix_dim_size.
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Eigen::array<Eigen::DenseIndex, 3> output_dims_e = {{prefix_dim_size, depth_val, suffix_dim_size}};
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auto* output_data = output->MutableData<out_type>();
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typename EigenTensorTypes<out_type, 3>::EigenTensorMap output_tensor_e(output_data, output_dims_e);
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typename EigenTensorTypes<out_type>::ConstScalar on_value_e(values_data + 1);
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typename EigenTensorTypes<out_type>::ConstScalar off_value_e(values_data);
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generator::OneGenerator<in_type, out_type> generator(indices_tensor_e, on_value_e, off_value_e);
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// TODO potential optimization opportunity
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// TODO figure out the eigen threadpool stuff for use here
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output_tensor_e = output_tensor_e.generate(generator);
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return Status::OK();
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}
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} // namespace onnxruntime
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31
onnxruntime/core/providers/cpu/tensor/onehot.h
Normal file
31
onnxruntime/core/providers/cpu/tensor/onehot.h
Normal file
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@ -0,0 +1,31 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#include "core/common/common.h"
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#include "core/framework/op_kernel.h"
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namespace onnxruntime {
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template <typename in_type, typename out_type, typename depth_type>
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class OneHotOp final : public OpKernel {
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public:
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explicit OneHotOp(const OpKernelInfo& op_kernel_info) : OpKernel(op_kernel_info) {
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int64_t tmp_axis;
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if (op_kernel_info.GetAttr<int64_t>("axis", &tmp_axis).IsOK()) {
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if (tmp_axis < -1) { // as per spec it can be -1 or more
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ORT_THROW("Value of axis is < -1");
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}
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axis_ = tmp_axis;
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}
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}
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Status Compute(OpKernelContext* p_op_kernel_context) const override;
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private:
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ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(OneHotOp);
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int64_t axis_ = -1;
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};
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} // namespace onnxruntime
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@ -310,8 +310,6 @@ int real_main(int argc, char* argv[]) {
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{"prelu_broadcast", "disable reason"},
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{"prelu_example", "disable reason"},
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{"upsample_nearest", "opset 9 not supported yet"},
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{"onehot_with_axis", "opset 9 not supported yet"},
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{"onehot_without_axis", "opset 9 not supported yet"}, // also has bug in current test re: output type. Spandan to fix.
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{"asinh", "opset 9 not supported yet"},
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{"acosh", "opset 9 not supported yet"},
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{"atanh", "opset 9 not supported yet"},
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148
onnxruntime/test/providers/cpu/tensor/onehot_op_test.cc
Normal file
148
onnxruntime/test/providers/cpu/tensor/onehot_op_test.cc
Normal file
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@ -0,0 +1,148 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "gtest/gtest.h"
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#include "test/providers/provider_test_utils.h"
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using namespace std;
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namespace onnxruntime {
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namespace test {
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TEST(OneHotOpTest, DefaultAxis) {
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OpTester test("OneHot", 9);
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test.AddInput<int64_t>("indices", {2, 3}, {1, 9, 8, 2, 4, 6});
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test.AddInput<int64_t>("depth", {1}, {10});
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test.AddInput<int64_t>("values", {2}, {0, 1});
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test.AddOutput<int64_t>("output", {2, 3, 10}, {0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
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0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
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0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 0, 0, 0,});
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test.Run();
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}
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TEST(OneHotOpTest, DefaultAxis_float_float_float /*indices, output, depth*/) {
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OpTester test("OneHot", 9);
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test.AddInput<float>("indices", {2, 3}, {1., 9., 8., 2., 4., 6.});
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test.AddInput<float>("depth", {1}, {10.});
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test.AddInput<float>("values", {2}, {0., 1.});
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test.AddOutput<float>("output", {2, 3, 10}, {0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
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0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
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0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,});
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test.Run();
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}
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TEST(OneHotOpTest, DefaultAxis_int64_int32_float /*indices, output, depth*/) {
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OpTester test("OneHot", 9);
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test.AddInput<int64_t>("indices", {2, 3}, {1, 9, 8, 2, 4, 6});
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test.AddInput<float>("depth", {1}, {10.});
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test.AddInput<int32_t>("values", {2}, {0, 1});
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test.AddOutput<int32_t>("output", {2, 3, 10}, {0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
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0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
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0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 0, 0, 0,});
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test.Run();
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}
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TEST(OneHotOpTest, Axis_0) {
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OpTester test("OneHot", 9);
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int64_t axis = 0;
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test.AddAttribute("axis", axis);
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test.AddInput<int64_t>("indices", {2, 3}, {1, 9, 8, 2, 4, 6});
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test.AddInput<int64_t>("depth", {1}, {10});
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test.AddInput<int64_t>("values", {2}, {0, 1});
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test.AddOutput<int64_t>("output", {10, 2, 3}, { 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0,
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0, 0, 0, 1, 0, 0,
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0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 1, 0,
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0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 1,
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0, 0, 0, 0, 0, 0,
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0, 0, 1, 0, 0, 0,
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0, 1, 0, 0, 0, 0,});
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test.Run();
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}
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TEST(OneHotOpTest, Axis_1) {
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OpTester test("OneHot", 9);
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int64_t axis = 1;
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test.AddAttribute("axis", axis);
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test.AddInput<int64_t>("indices", {2, 3}, {1, 9, 8, 2, 4, 6});
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test.AddInput<int64_t>("depth", {1}, {10});
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test.AddInput<int64_t>("values", {2}, {0, 1});
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test.AddOutput<int64_t>("output", {2, 10, 3}, { 0, 0, 0,
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1, 0, 0,
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0, 0, 0,
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0, 0, 0,
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0, 0, 0,
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0, 0, 0,
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0, 0, 0,
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0, 0, 0,
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0, 0, 1,
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0, 1, 0,
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0, 0, 0,
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0, 0, 0,
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1, 0, 0,
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0, 0, 0,
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0, 1, 0,
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0, 0, 0,
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0, 0, 1,
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0, 0, 0,
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0, 0, 0,
|
||||
0, 0, 0,});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(OneHotOpTest, Axis_2) {
|
||||
OpTester test("OneHot", 9);
|
||||
int64_t axis = 2;
|
||||
test.AddAttribute("axis", axis);
|
||||
test.AddInput<int64_t>("indices", {2, 3}, {1, 9, 8, 2, 4, 6});
|
||||
test.AddInput<int64_t>("depth", {1}, {10});
|
||||
test.AddInput<int64_t>("values", {2}, {0, 1});
|
||||
test.AddOutput<int64_t>("output", {2, 3, 10}, { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
|
||||
0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 0, 0, 0,});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(OneHotOpTest, FloatInt64) {
|
||||
OpTester test("OneHot", 9);
|
||||
test.AddInput<float>("indices", {2, 3}, {1.f, 9.f, 8.f, 2.f, 4.f, 6.f});
|
||||
test.AddInput<int64_t>("depth", {1}, {10});
|
||||
test.AddInput<int64_t>("values", {2}, {0, 1});
|
||||
test.AddOutput<int64_t>("output", {2, 3, 10}, {0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
|
||||
0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 0, 0, 0,});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(OneHotOpTest, FloatString) {
|
||||
OpTester test("OneHot", 9);
|
||||
test.AddInput<float>("indices", {2, 3}, {1.f, 9.f, 8.f, 2.f, 4.f, 6.f});
|
||||
test.AddInput<int64_t>("depth", {1}, {10});
|
||||
test.AddInput<string>("values", {2}, {"off", "on"});
|
||||
test.AddOutput<string>("output", {2, 3, 10}, {"off", "on", "off", "off", "off", "off", "off", "off", "off", "off",
|
||||
"off", "off", "off", "off", "off", "off", "off", "off", "off", "on",
|
||||
"off", "off", "off", "off", "off", "off", "off", "off", "on", "off",
|
||||
"off", "off", "on", "off", "off", "off", "off", "off", "off", "off",
|
||||
"off", "off", "off", "off", "on", "off", "off", "off", "off", "off",
|
||||
"off", "off", "off", "off", "off", "off", "on", "off", "off", "off",});
|
||||
test.Run();
|
||||
}
|
||||
}
|
||||
} // namespace onnxruntime
|
||||
|
|
@ -34,8 +34,6 @@ backend_test.exclude(r'(test_acosh_cpu.*'
|
|||
'|test_eyelike_with_dtype_cpu.*'
|
||||
'|test_eyelike_without_dtype_cpu.*'
|
||||
'|test_gru_seq_length_cpu.*'
|
||||
'|test_onehot_with_axis_cpu.*'
|
||||
'|test_onehot_without_axis_cpu.*'
|
||||
'|test_scan_sum_cpu.*'
|
||||
'|test_scatter_with_axis_cpu.*'
|
||||
'|test_scatter_without_axis_cpu.*'
|
||||
|
|
|
|||
Loading…
Reference in a new issue