wehub-resource-sync
1177 lines
51 KiB
C++
1177 lines
51 KiB
C++
/* Copyright 2017 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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#include <stddef.h>
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#include <stdint.h>
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#include <algorithm>
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#include <array>
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#include <numeric>
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#include <random>
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#include <utility>
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#include <vector>
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#include <gmock/gmock.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class BaseAddOpModel : public SingleOpModel {
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public:
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BaseAddOpModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output,
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ActivationFunctionType activation_type) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_ADD, BuiltinOptions_AddOptions,
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CreateAddOptions(builder_, activation_type).Union());
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SetBypassDefaultDelegates();
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BuildInterpreter({GetShape(input1_), GetShape(input2_)});
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}
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BaseAddOpModel(TensorType type, const std::vector<int>& input1_shape,
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const std::vector<int>& input2_shape,
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ActivationFunctionType activation_type) {
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input1_ = AddInput(type);
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input2_ = AddInput(type);
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output_ = AddOutput(type);
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SetBuiltinOp(BuiltinOperator_ADD, BuiltinOptions_AddOptions,
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CreateAddOptions(builder_, activation_type).Union());
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SetBypassDefaultDelegates();
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BuildInterpreter({input1_shape, input2_shape});
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}
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int input1() { return input1_; }
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int input2() { return input2_; }
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void Resize(const std::vector<int>& input1_shape,
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const std::vector<int>& input2_shape) {
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interpreter_->ResizeInputTensor(input1_, input1_shape);
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interpreter_->ResizeInputTensor(input2_, input2_shape);
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AllocateTensors();
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}
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protected:
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int input1_;
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int input2_;
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int output_;
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};
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template <typename T>
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class AddOpModel : public BaseAddOpModel {
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public:
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using BaseAddOpModel::BaseAddOpModel;
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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};
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template <typename T>
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class FloatAddTest : public ::testing::Test {};
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using FloatAddTestTypes = ::testing::Types<float, half, Eigen::bfloat16>;
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TYPED_TEST_SUITE(FloatAddTest, FloatAddTestTypes);
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class IntegerAddOpModel : public BaseAddOpModel {
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public:
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using BaseAddOpModel::BaseAddOpModel;
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template <typename T>
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std::vector<T> GetOutput() {
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return ExtractVector<T>(output_);
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}
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};
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class QuantizedAddOpModel : public BaseAddOpModel {
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public:
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QuantizedAddOpModel(TensorData input1, TensorData input2, TensorData output,
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ActivationFunctionType activation_type)
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: BaseAddOpModel(SymmetricInt16Scaling(std::move(input1)),
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SymmetricInt16Scaling(std::move(input2)),
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SymmetricInt16Scaling(std::move(output)),
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activation_type) {}
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template <typename integer_dtype>
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std::vector<float> GetDequantizedOutput() {
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return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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std::vector<float> GetDequantizedOutputInt16() {
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return Dequantize<int16_t>(ExtractVector<int16_t>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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private:
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TensorData SymmetricInt16Scaling(TensorData tensor) {
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// Symmetric range and null zero-point is required for INT16 tensors. As
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// SingleOpModel::QuantizationParams calculates the scale on an asymmetric
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// base [int_type::min, int_type::max], manually calculate the scale on a
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// symmetric range [int_type::min+1, int_type::max] to ensure a null
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// zero-point.
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if (tensor.type == TensorType_INT16) {
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CHECK_EQ(std::abs(tensor.min), tensor.max);
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tensor.scale = tensor.max / std::numeric_limits<int16_t>::max();
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tensor.zero_point = 0;
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tensor.min = 0;
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tensor.max = 0;
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}
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return tensor;
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}
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};
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// for quantized Add, the error shouldn't exceed step
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template <typename T>
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float GetTolerance(float min, float max) {
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float kQuantizedStep =
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2.0 * (max - min) /
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(std::numeric_limits<T>::max() - std::numeric_limits<T>::min());
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return kQuantizedStep;
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}
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TYPED_TEST(FloatAddTest, NoActivationInplaceInput0) {
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using T = TypeParam;
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AddOpModel<T> m({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}}, {GetTensorType<T>(), {}},
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ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.5});
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const int kInplaceInputTensorIdx = 0;
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const int kInplaceOutputTensorIdx = 0;
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const TfLiteTensor* input_tensor = m.GetInputTensor(kInplaceInputTensorIdx);
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TfLiteTensor* output_tensor = m.GetOutputTensor(kInplaceOutputTensorIdx);
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output_tensor->data.data = input_tensor->data.data;
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.9, 0.4, 1.0, 1.3},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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EXPECT_EQ(output_tensor->data.data, input_tensor->data.data);
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}
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TYPED_TEST(FloatAddTest, NoActivationInplaceInput1) {
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using T = TypeParam;
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AddOpModel<T> m({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}}, {GetTensorType<T>(), {}},
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ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.5});
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const int kInplaceInputTensorIdx = 1;
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const int kInplaceOutputTensorIdx = 0;
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const TfLiteTensor* input_tensor = m.GetInputTensor(kInplaceInputTensorIdx);
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TfLiteTensor* output_tensor = m.GetOutputTensor(kInplaceOutputTensorIdx);
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output_tensor->data.data = input_tensor->data.data;
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.9, 0.4, 1.0, 1.3},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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EXPECT_EQ(output_tensor->data.data, input_tensor->data.data);
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}
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TYPED_TEST(FloatAddTest, NoActivation) {
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using T = TypeParam;
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AddOpModel<T> m({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}}, {GetTensorType<T>(), {}},
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ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.5});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.9, 0.4, 1.0, 1.3},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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}
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TYPED_TEST(FloatAddTest, ActivationRELU_N1_TO_1) {
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using T = TypeParam;
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AddOpModel<T> m({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}}, {GetTensorType<T>(), {}},
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ActivationFunctionType_RELU_N1_TO_1);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.5});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.0, 0.4, 1.0, 1.0},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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}
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TYPED_TEST(FloatAddTest, VariousInputShapes) {
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using T = TypeParam;
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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AddOpModel<T> m({GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), {}}, ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.9, 0.4, 1.0, 1.3, 2.2, 2.1},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)))
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<< "With shape number " << i;
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}
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}
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TYPED_TEST(FloatAddTest, WithBroadcast) {
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using T = TypeParam;
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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AddOpModel<T> m({GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), {}}, // always a scalar
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{GetTensorType<T>(), {}}, ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.template PopulateTensor<T>(m.input2(), {0.1});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-1.9, 0.3, 0.8, 0.9, 1.2, 2.1},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)))
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<< "With shape number " << i;
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}
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}
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TYPED_TEST(FloatAddTest, WithBroadcastGeneric) {
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using T = TypeParam;
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std::vector<int> test_shape1 = {1, 3, 1};
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std::vector<int> test_shape2 = {2, 1, 2};
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AddOpModel<T> m({GetTensorType<T>(), test_shape1},
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{GetTensorType<T>(), test_shape2}, {GetTensorType<T>(), {}},
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ActivationFunctionType_NONE);
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m.template PopulateTensor<T>(m.input1(), {0.1, 0.2, 0.3});
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m.template PopulateTensor<T>(m.input2(), {0.1, 0.2, 0.3, 0.4});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{0.2, 0.3, 0.3, 0.4, 0.4, 0.5, 0.4, 0.5, 0.5, 0.6, 0.6, 0.7},
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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}
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TYPED_TEST(FloatAddTest, WithBroadcastRankSeven) {
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using T = TypeParam;
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const std::vector<int> input1_shape = {2, 1, 2, 1, 2, 1, 2};
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const std::vector<int> input2_shape = {1, 2, 1, 2, 1, 2, 1};
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const std::vector<int> output_shape = {2, 2, 2, 2, 2, 2, 2};
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AddOpModel<T> m({GetTensorType<T>(), input1_shape},
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{GetTensorType<T>(), input2_shape}, {GetTensorType<T>(), {}},
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ActivationFunctionType_NONE);
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std::vector<float> input1(16);
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std::vector<float> input2(8);
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std::iota(input1.begin(), input1.end(), 1.0f);
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std::iota(input2.begin(), input2.end(), 0.25f);
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m.template PopulateTensor<T>(m.input1(), ToVector<T>(input1));
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m.template PopulateTensor<T>(m.input2(), ToVector<T>(input2));
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TFLITE_INVOKE_AND_CHECK(T, &m);
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auto strides_for = [](const std::vector<int>& shape) {
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std::vector<int> strides(shape.size());
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int stride = 1;
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for (int i = shape.size() - 1; i >= 0; --i) {
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strides[i] = stride;
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stride *= shape[i];
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}
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return strides;
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};
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const std::vector<int> input1_strides = strides_for(input1_shape);
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const std::vector<int> input2_strides = strides_for(input2_shape);
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const std::vector<int> output_strides = strides_for(output_shape);
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std::vector<float> expected(128);
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for (int output_index = 0; output_index < expected.size(); ++output_index) {
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int remaining_index = output_index;
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int input1_index = 0;
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int input2_index = 0;
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for (int dim = 0; dim < output_shape.size(); ++dim) {
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const int coordinate = remaining_index / output_strides[dim];
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remaining_index %= output_strides[dim];
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if (input1_shape[dim] != 1) {
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input1_index += coordinate * input1_strides[dim];
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}
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if (input2_shape[dim] != 1) {
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input2_index += coordinate * input2_strides[dim];
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}
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}
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expected[output_index] = input1[input1_index] + input2[input2_index];
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}
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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expected, static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
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}
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TYPED_TEST(FloatAddTest, MixedBroadcast) {
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using T = TypeParam;
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const std::vector<int> base_shape = {2, 3, 1, 2};
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std::vector<std::vector<int>> test_shapes = {
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{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
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std::vector<std::vector<float>> test_outputs = {
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{-0.1f, 2.6f, -0.7f, 2.8f, 0.7f, 3.2f, 1.1f, 0.8f, 0.5f,
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1.0f, 1.9f, 1.4f, 1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
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1.4f, 3.1f, 0.8f, 3.3f, 2.2f, 3.7f, -1.4f, 0.3f, -2.0f,
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0.5f, -0.6f, 0.9f, 0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
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{-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.1f, -2.0f, 0.5f, 1.7f,
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-1.3f},
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{-0.1f, 2.5f, 0.0f, 2.6f, -0.7f, 1.9f, 1.1f, 0.7f, 1.2f,
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0.8f, 0.5f, 0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
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1.7f, 3.3f, 2.2f, 3.8f, 2.1f, 3.7f, -1.1f, 0.5f, -0.6f,
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1.0f, -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
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{-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.3f, -0.6f, 1.0f, 1.6f,
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-1.3f}};
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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AddOpModel<T> model_fixture(
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{GetTensorType<T>(), base_shape}, {GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), {}}, ActivationFunctionType_NONE);
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model_fixture.template PopulateTensor<T>(
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model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
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2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
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model_fixture.template PopulateTensor<T>(
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model_fixture.input2(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
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TFLITE_INVOKE_AND_CHECK(T, &model_fixture);
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EXPECT_THAT(model_fixture.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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test_outputs[i],
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)))
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<< "With shape number " << i;
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}
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// Re-run with exchanged inputs.
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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AddOpModel<T> model_fixture(
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{GetTensorType<T>(), test_shapes[i]}, {GetTensorType<T>(), base_shape},
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{GetTensorType<T>(), {}}, ActivationFunctionType_NONE);
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model_fixture.template PopulateTensor<T>(
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model_fixture.input1(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
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model_fixture.template PopulateTensor<T>(
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model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
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2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
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TFLITE_INVOKE_AND_CHECK(T, &model_fixture);
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EXPECT_THAT(model_fixture.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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test_outputs[i],
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static_cast<float>(NumericLimits<T>::epsilon()) * 10)))
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<< "With shape number " << i;
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}
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}
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constexpr int kDim1 = 2;
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constexpr int kDim2 = 3;
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constexpr int kDim3 = 4;
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constexpr int kDim4 = 5;
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constexpr int kDim5 = 6;
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constexpr int kDim6 = 7;
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template <typename T>
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void TestFloatBroadcast(AddOpModel<T>& m, const std::vector<int>& input1_shape,
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const std::vector<int>& input2_shape) {
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std::array<int, 6> input1_dims;
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std::array<int, 6> input2_dims;
|
|
std::array<int, 6> output_dims;
|
|
std::array<int, 6> input1_strides;
|
|
std::array<int, 6> input2_strides;
|
|
std::array<int, 6> output_strides;
|
|
std::fill(input1_dims.begin(), input1_dims.end(), 1);
|
|
std::fill(input2_dims.begin(), input2_dims.end(), 1);
|
|
std::fill(output_dims.begin(), output_dims.end(), 1);
|
|
std::copy(input1_shape.cbegin(), input1_shape.cend(),
|
|
input1_dims.end() - input1_shape.size());
|
|
std::copy(input2_shape.cbegin(), input2_shape.cend(),
|
|
input2_dims.end() - input2_shape.size());
|
|
|
|
for (size_t i = 0; i < 6; i++) {
|
|
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
|
|
ASSERT_EQ(input1_dims[i], input2_dims[i]);
|
|
}
|
|
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
|
|
}
|
|
// Compute generalized strides.
|
|
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
|
|
for (size_t i = 6; i != 0; i--) {
|
|
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
|
|
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
|
|
output_strides[i - 1] = output_stride;
|
|
input1_stride *= input1_dims[i - 1];
|
|
input2_stride *= input2_dims[i - 1];
|
|
output_stride *= output_dims[i - 1];
|
|
}
|
|
const int num_input1_elements = std::accumulate(
|
|
input1_dims.begin(), input1_dims.end(), 1, std::multiplies<int>());
|
|
const int num_input2_elements = std::accumulate(
|
|
input2_dims.begin(), input2_dims.end(), 1, std::multiplies<int>());
|
|
const int num_output_elements = std::accumulate(
|
|
output_dims.begin(), output_dims.end(), 1, std::multiplies<int>());
|
|
std::vector<T> input1(num_input1_elements);
|
|
std::vector<T> input2(num_input2_elements);
|
|
std::vector<T> output_ref(num_output_elements);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
std::uniform_real_distribution<float> f32dist(0.01f, 1.0f);
|
|
|
|
std::generate(input1.begin(), input1.end(),
|
|
[&]() { return static_cast<T>(f32dist(rng)); });
|
|
std::generate(input2.begin(), input2.end(),
|
|
[&]() { return static_cast<T>(f32dist(rng)); });
|
|
|
|
// Compute reference results.
|
|
for (size_t i = 0; i < output_dims[0]; i++) {
|
|
for (size_t j = 0; j < output_dims[1]; j++) {
|
|
for (size_t k = 0; k < output_dims[2]; k++) {
|
|
for (size_t l = 0; l < output_dims[3]; l++) {
|
|
for (size_t m = 0; m < output_dims[4]; m++) {
|
|
for (size_t n = 0; n < output_dims[5]; n++) {
|
|
output_ref[i * output_strides[0] + j * output_strides[1] +
|
|
k * output_strides[2] + l * output_strides[3] +
|
|
m * output_strides[4] + n * output_strides[5]] =
|
|
static_cast<T>(
|
|
static_cast<float>(
|
|
input1[i * input1_strides[0] + j * input1_strides[1] +
|
|
k * input1_strides[2] + l * input1_strides[3] +
|
|
m * input1_strides[4] +
|
|
n * input1_strides[5]]) +
|
|
static_cast<float>(
|
|
input2[i * input2_strides[0] + j * input2_strides[1] +
|
|
k * input2_strides[2] + l * input2_strides[3] +
|
|
m * input2_strides[4] +
|
|
n * input2_strides[5]]));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
m.Resize(input1_shape, input2_shape);
|
|
m.template PopulateTensor<T>(m.input1(), input1);
|
|
m.template PopulateTensor<T>(m.input2(), input2);
|
|
TFLITE_INVOKE_AND_CHECK(T, &m);
|
|
EXPECT_THAT(
|
|
m.GetOutput(),
|
|
ElementsAreArray(ArrayFloatNear(
|
|
output_ref, static_cast<float>(NumericLimits<T>::epsilon()) * 10)));
|
|
}
|
|
|
|
template <typename IntegerType>
|
|
void TestIntegerBroadcast(IntegerAddOpModel& m,
|
|
const std::vector<int>& input1_shape,
|
|
const std::vector<int>& input2_shape) {
|
|
std::array<int, 6> input1_dims;
|
|
std::array<int, 6> input2_dims;
|
|
std::array<int, 6> output_dims;
|
|
std::array<int, 6> input1_strides;
|
|
std::array<int, 6> input2_strides;
|
|
std::array<int, 6> output_strides;
|
|
std::fill(input1_dims.begin(), input1_dims.end(), 1);
|
|
std::fill(input2_dims.begin(), input2_dims.end(), 1);
|
|
std::fill(output_dims.begin(), output_dims.end(), 1);
|
|
std::copy(input1_shape.cbegin(), input1_shape.cend(),
|
|
input1_dims.end() - input1_shape.size());
|
|
std::copy(input2_shape.cbegin(), input2_shape.cend(),
|
|
input2_dims.end() - input2_shape.size());
|
|
|
|
for (size_t i = 0; i < 6; i++) {
|
|
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
|
|
ASSERT_EQ(input1_dims[i], input2_dims[i]);
|
|
}
|
|
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
|
|
}
|
|
// Compute generalized strides.
|
|
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
|
|
for (size_t i = 6; i != 0; i--) {
|
|
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
|
|
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
|
|
output_strides[i - 1] = output_stride;
|
|
input1_stride *= input1_dims[i - 1];
|
|
input2_stride *= input2_dims[i - 1];
|
|
output_stride *= output_dims[i - 1];
|
|
}
|
|
const int num_input1_elements = std::accumulate(
|
|
input1_dims.begin(), input1_dims.end(), 1, std::multiplies<int>());
|
|
const int num_input2_elements = std::accumulate(
|
|
input2_dims.begin(), input2_dims.end(), 1, std::multiplies<int>());
|
|
const int num_output_elements = std::accumulate(
|
|
output_dims.begin(), output_dims.end(), 1, std::multiplies<int>());
|
|
std::vector<IntegerType> input1(num_input1_elements);
|
|
std::vector<IntegerType> input2(num_input2_elements);
|
|
std::vector<IntegerType> output_ref(num_output_elements);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
std::uniform_int_distribution<IntegerType> dist(0, 256);
|
|
|
|
std::generate(input1.begin(), input1.end(), [&]() { return dist(rng); });
|
|
std::generate(input2.begin(), input2.end(), [&]() { return dist(rng); });
|
|
|
|
// Compute reference results.
|
|
for (size_t i = 0; i < output_dims[0]; i++) {
|
|
for (size_t j = 0; j < output_dims[1]; j++) {
|
|
for (size_t k = 0; k < output_dims[2]; k++) {
|
|
for (size_t l = 0; l < output_dims[3]; l++) {
|
|
for (size_t m = 0; m < output_dims[4]; m++) {
|
|
for (size_t n = 0; n < output_dims[5]; n++) {
|
|
output_ref[i * output_strides[0] + j * output_strides[1] +
|
|
k * output_strides[2] + l * output_strides[3] +
|
|
m * output_strides[4] + n * output_strides[5]] =
|
|
input1[i * input1_strides[0] + j * input1_strides[1] +
|
|
k * input1_strides[2] + l * input1_strides[3] +
|
|
m * input1_strides[4] + n * input1_strides[5]] +
|
|
input2[i * input2_strides[0] + j * input2_strides[1] +
|
|
k * input2_strides[2] + l * input2_strides[3] +
|
|
m * input2_strides[4] + n * input2_strides[5]];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
m.Resize(input1_shape, input2_shape);
|
|
m.PopulateTensor<IntegerType>(m.input1(), input1);
|
|
m.PopulateTensor<IntegerType>(m.input2(), input2);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<IntegerType>(), testing::ContainerEq(output_ref));
|
|
}
|
|
|
|
// To improve automatic test sharding (via shard_count in the BUILD file),
|
|
// we need to ensure that each individual test case runs in a reasonable time,
|
|
// otherwise we end up being limited by the performance of the longest shard.
|
|
// Since TestFloat32MultiDimBroadcast has 2^12 iterations, it takes a
|
|
// long time (over 30 seconds) to execute all iterations -- too long for a
|
|
// single shard. So we split it into a few "subshards" and have a separate
|
|
// TYPED_TEST macro invocation for each subshard.
|
|
|
|
template <typename T>
|
|
void RunFloatMultiDimBroadcastTest(int d1, int d2) {
|
|
const int dims_constants[] = {kDim1, kDim2, kDim3, kDim4, kDim5, kDim6};
|
|
std::vector<int> initial_shape1(d1, 1);
|
|
std::vector<int> initial_shape2(d2, 1);
|
|
AddOpModel<T> m({GetTensorType<T>(), initial_shape1},
|
|
{GetTensorType<T>(), initial_shape2},
|
|
{GetTensorType<T>(), {}}, ActivationFunctionType_NONE);
|
|
|
|
for (uint32_t bm1 = 0; bm1 < (static_cast<uint32_t>(1) << d1); bm1++) {
|
|
for (uint32_t bm2 = 0; bm2 < (static_cast<uint32_t>(1) << d2); bm2++) {
|
|
std::vector<int> input1_shape(d1);
|
|
std::vector<int> input2_shape(d2);
|
|
for (int i = 0; i < d1; ++i) {
|
|
bool broadcast = bm1 & (1 << i);
|
|
input1_shape[i] = broadcast ? 1 : dims_constants[6 - d1 + i];
|
|
}
|
|
for (int i = 0; i < d2; ++i) {
|
|
bool broadcast = bm2 & (1 << i);
|
|
input2_shape[i] = broadcast ? 1 : dims_constants[6 - d2 + i];
|
|
}
|
|
TestFloatBroadcast<T>(m, input1_shape, input2_shape);
|
|
if (testing::Test::IsSkipped()) {
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#define INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, d2) \
|
|
TYPED_TEST(FloatAddTest, MultiDimBroadcast_##d1##_##d2) { \
|
|
RunFloatMultiDimBroadcastTest<TypeParam>(d1, d2); \
|
|
}
|
|
|
|
#define INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(d1) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 1) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 2) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 3) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 4) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 5) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST(d1, 6)
|
|
|
|
#define INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TESTS() \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(1) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(2) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(3) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(4) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(5) \
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TEST_D2(6)
|
|
|
|
INSTANTIATE_FLOAT_ADD_MULTI_DIM_BROADCAST_TESTS()
|
|
|
|
template <typename T>
|
|
class IntegerAddOpTest : public ::testing::Test {};
|
|
|
|
using Int16OrInt32Or64Types = ::testing::Types<int16_t, int32_t, int64_t>;
|
|
TYPED_TEST_SUITE(IntegerAddOpTest, Int16OrInt32Or64Types);
|
|
|
|
// To improve automatic test sharding (via shard_count in the BUILD file),
|
|
// we need to ensure that each individual test case runs in a reasonable time,
|
|
// otherwise we end up being limited by the performance of the longest shard.
|
|
// Since TestIntegerMultiDimBroadcast has 2^12 iterations, it takes a
|
|
// long time (over 30 seconds) to execute all iterations -- too long for a
|
|
// single shard. So we split it into a few "subshards" and have a separate
|
|
// TYPED_TEST macro invocation for each subshard.
|
|
|
|
template <typename TypeParam>
|
|
void RunIntegerMultiDimBroadcastTest(int d1, int d2) {
|
|
const int dims_constants[] = {kDim1, kDim2, kDim3, kDim4, kDim5, kDim6};
|
|
std::vector<int> initial_shape1(d1, 1);
|
|
std::vector<int> initial_shape2(d2, 1);
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), initial_shape1,
|
|
initial_shape2, ActivationFunctionType_NONE);
|
|
|
|
for (uint32_t bm1 = 0; bm1 < (static_cast<uint32_t>(1) << d1); bm1++) {
|
|
for (uint32_t bm2 = 0; bm2 < (static_cast<uint32_t>(1) << d2); bm2++) {
|
|
std::vector<int> input1_shape(d1);
|
|
std::vector<int> input2_shape(d2);
|
|
for (int i = 0; i < d1; ++i) {
|
|
bool broadcast = bm1 & (1 << i);
|
|
input1_shape[i] = broadcast ? 1 : dims_constants[6 - d1 + i];
|
|
}
|
|
for (int i = 0; i < d2; ++i) {
|
|
bool broadcast = bm2 & (1 << i);
|
|
input2_shape[i] = broadcast ? 1 : dims_constants[6 - d2 + i];
|
|
}
|
|
TestIntegerBroadcast<TypeParam>(m, input1_shape, input2_shape);
|
|
if (testing::Test::IsSkipped()) {
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#define INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, d2) \
|
|
TYPED_TEST(IntegerAddOpTest, MultiDimBroadcast_##d1##_##d2) { \
|
|
RunIntegerMultiDimBroadcastTest<TypeParam>(d1, d2); \
|
|
}
|
|
|
|
#define INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(d1) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 1) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 2) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 3) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 4) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 5) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST(d1, 6)
|
|
|
|
#define INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TESTS() \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(1) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(2) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(3) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(4) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(5) \
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TEST_D2(6)
|
|
|
|
INSTANTIATE_INTEGER_ADD_MULTI_DIM_BROADCAST_TESTS()
|
|
|
|
TYPED_TEST(IntegerAddOpTest, NoActivation) {
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), {1, 2, 2, 1}, {1, 2, 2, 1},
|
|
ActivationFunctionType_NONE);
|
|
m.PopulateTensor<TypeParam>(m.input1(), {-20, 2, 7, 8});
|
|
m.PopulateTensor<TypeParam>(m.input2(), {1, 2, 3, 5});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<TypeParam>(), ElementsAreArray({-19, 4, 10, 13}));
|
|
}
|
|
|
|
TYPED_TEST(IntegerAddOpTest, ActivationRELU_N1_TO_1) {
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), {1, 2, 2, 1}, {1, 2, 2, 1},
|
|
ActivationFunctionType_RELU_N1_TO_1);
|
|
m.PopulateTensor<TypeParam>(m.input1(), {-20, 2, 7, 8});
|
|
m.PopulateTensor<TypeParam>(m.input2(), {1, 2, 3, 5});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<TypeParam>(), ElementsAreArray({-1, 1, 1, 1}));
|
|
}
|
|
|
|
TYPED_TEST(IntegerAddOpTest, VariousInputShapes) {
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), test_shapes[i],
|
|
test_shapes[i], ActivationFunctionType_NONE);
|
|
m.PopulateTensor<TypeParam>(m.input1(), {-20, 2, 7, 8, 11, 20});
|
|
m.PopulateTensor<TypeParam>(m.input2(), {1, 2, 3, 5, 11, 1});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<TypeParam>(),
|
|
ElementsAreArray({-19, 04, 10, 13, 22, 21}))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TYPED_TEST(IntegerAddOpTest, WithBroadcast) {
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), test_shapes[i],
|
|
{}, // always a scalar
|
|
ActivationFunctionType_NONE);
|
|
m.PopulateTensor<TypeParam>(m.input1(), {-20, 2, 7, 8, 11, 20});
|
|
m.PopulateTensor<TypeParam>(m.input2(), {1});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<TypeParam>(),
|
|
ElementsAreArray({-19, 3, 8, 9, 12, 21}))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TYPED_TEST(IntegerAddOpTest, Int32MultiDimBroadcast) {
|
|
IntegerAddOpModel m(GetTensorType<TypeParam>(), {1, 2}, {2, 1},
|
|
ActivationFunctionType_NONE);
|
|
m.PopulateTensor<TypeParam>(m.input1(), {3, 5});
|
|
m.PopulateTensor<TypeParam>(m.input2(), {1, 4});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<TypeParam>(), ElementsAreArray({4, 6, 7, 9}));
|
|
}
|
|
|
|
template <typename QuantizedType>
|
|
void TestQuantizedBroadcast(QuantizedAddOpModel& m,
|
|
const std::vector<int>& input1_shape,
|
|
const std::vector<int>& input2_shape) {
|
|
std::array<int, 6> input1_dims;
|
|
std::array<int, 6> input2_dims;
|
|
std::array<int, 6> output_dims;
|
|
std::array<int, 6> input1_strides;
|
|
std::array<int, 6> input2_strides;
|
|
std::array<int, 6> output_strides;
|
|
std::fill(input1_dims.begin(), input1_dims.end(), 1);
|
|
std::fill(input2_dims.begin(), input2_dims.end(), 1);
|
|
std::fill(output_dims.begin(), output_dims.end(), 1);
|
|
std::copy(input1_shape.cbegin(), input1_shape.cend(),
|
|
input1_dims.end() - input1_shape.size());
|
|
std::copy(input2_shape.cbegin(), input2_shape.cend(),
|
|
input2_dims.end() - input2_shape.size());
|
|
|
|
for (size_t i = 0; i < 6; i++) {
|
|
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
|
|
ASSERT_EQ(input1_dims[i], input2_dims[i]);
|
|
}
|
|
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
|
|
}
|
|
// Compute generalized strides.
|
|
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
|
|
for (size_t i = 6; i != 0; i--) {
|
|
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
|
|
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
|
|
output_strides[i - 1] = output_stride;
|
|
input1_stride *= input1_dims[i - 1];
|
|
input2_stride *= input2_dims[i - 1];
|
|
output_stride *= output_dims[i - 1];
|
|
}
|
|
const int num_input1_elements = std::accumulate(
|
|
input1_dims.begin(), input1_dims.end(), 1, std::multiplies<int>());
|
|
const int num_input2_elements = std::accumulate(
|
|
input2_dims.begin(), input2_dims.end(), 1, std::multiplies<int>());
|
|
const int num_output_elements = std::accumulate(
|
|
output_dims.begin(), output_dims.end(), 1, std::multiplies<int>());
|
|
std::vector<float> input1(num_input1_elements);
|
|
std::vector<float> input2(num_input2_elements);
|
|
std::vector<float> output_ref(num_output_elements);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
|
|
std::uniform_real_distribution<float> dist(-0.5f, 0.5f);
|
|
|
|
std::generate(input1.begin(), input1.end(), [&]() { return dist(rng); });
|
|
std::generate(input2.begin(), input2.end(), [&]() { return dist(rng); });
|
|
|
|
m.Resize(input1_shape, input2_shape);
|
|
m.QuantizeAndPopulate<QuantizedType>(m.input1(), input1);
|
|
m.QuantizeAndPopulate<QuantizedType>(m.input2(), input2);
|
|
// Compute reference results.
|
|
for (size_t i = 0; i < output_dims[0]; i++) {
|
|
for (size_t j = 0; j < output_dims[1]; j++) {
|
|
for (size_t k = 0; k < output_dims[2]; k++) {
|
|
for (size_t l = 0; l < output_dims[3]; l++) {
|
|
for (size_t m = 0; m < output_dims[4]; m++) {
|
|
for (size_t n = 0; n < output_dims[5]; n++) {
|
|
float x = input1[i * input1_strides[0] + j * input1_strides[1] +
|
|
k * input1_strides[2] + l * input1_strides[3] +
|
|
m * input1_strides[4] + n * input1_strides[5]];
|
|
float y = input2[i * input2_strides[0] + j * input2_strides[1] +
|
|
k * input2_strides[2] + l * input2_strides[3] +
|
|
m * input2_strides[4] + n * input2_strides[5]];
|
|
output_ref[i * output_strides[0] + j * output_strides[1] +
|
|
k * output_strides[2] + l * output_strides[3] +
|
|
m * output_strides[4] + n * output_strides[5]] = x + y;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (float& output_value : output_ref) {
|
|
output_value = std::max<float>(output_value, -1.0f);
|
|
output_value = std::min<float>(output_value, 1.0f);
|
|
}
|
|
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
std::vector<float> output = m.GetDequantizedOutput<QuantizedType>();
|
|
for (size_t i = 0; i < output_dims[0]; i++) {
|
|
for (size_t j = 0; j < output_dims[1]; j++) {
|
|
for (size_t k = 0; k < output_dims[2]; k++) {
|
|
for (size_t l = 0; l < output_dims[3]; l++) {
|
|
for (size_t m = 0; m < output_dims[4]; m++) {
|
|
for (size_t n = 0; n < output_dims[5]; n++) {
|
|
const size_t index =
|
|
i * output_strides[0] + j * output_strides[1] +
|
|
k * output_strides[2] + l * output_strides[3] +
|
|
m * output_strides[4] + n * output_strides[5];
|
|
EXPECT_NEAR(output[index], output_ref[index], 0.6f)
|
|
<< "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k
|
|
<< ", " << l << ", " << m << ", " << n << ")";
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// To improve automatic test sharding (via shard_count in the BUILD file),
|
|
// we need to ensure that each individual test case runs in a reasonable time,
|
|
// otherwise we end up being limited by the performance of the longest shard.
|
|
// Since TestQuantizedMultiDimBroadcast has 2^12 iterations, it takes a
|
|
// long time (over 30 seconds) to execute all iterations -- too long for a
|
|
// single shard. So we split it into a few "subshards" and have a separate
|
|
// TEST macro invocation for each subshard.
|
|
|
|
template <typename T>
|
|
void RunQuantizedMultiDimBroadcastTest(int d1, int d2) {
|
|
const int dims_constants[] = {kDim1, kDim2, kDim3, kDim4, kDim5, kDim6};
|
|
std::vector<int> initial_shape1(d1, 1);
|
|
std::vector<int> initial_shape2(d2, 1);
|
|
QuantizedAddOpModel m({GetTensorType<T>(), initial_shape1, -0.5f, 0.5f},
|
|
{GetTensorType<T>(), initial_shape2, -0.5f, 0.5f},
|
|
{GetTensorType<T>(), {}, -1.f, 1.f},
|
|
ActivationFunctionType_NONE);
|
|
|
|
for (uint32_t bm1 = 0; bm1 < (static_cast<uint32_t>(1) << d1); bm1++) {
|
|
for (uint32_t bm2 = 0; bm2 < (static_cast<uint32_t>(1) << d2); bm2++) {
|
|
std::vector<int> input1_shape(d1);
|
|
std::vector<int> input2_shape(d2);
|
|
for (int i = 0; i < d1; ++i) {
|
|
bool broadcast = bm1 & (1 << i);
|
|
input1_shape[i] = broadcast ? 1 : dims_constants[6 - d1 + i];
|
|
}
|
|
for (int i = 0; i < d2; ++i) {
|
|
bool broadcast = bm2 & (1 << i);
|
|
input2_shape[i] = broadcast ? 1 : dims_constants[6 - d2 + i];
|
|
}
|
|
TestQuantizedBroadcast<T>(m, input1_shape, input2_shape);
|
|
if (testing::Test::IsSkipped()) {
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#define INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, \
|
|
d2) \
|
|
TEST(QuantizedAddOpModel, \
|
|
TypeName##QuantizedMultiDimBroadcast_##d1##_##d2) { \
|
|
RunQuantizedMultiDimBroadcastTest<T>(d1, d2); \
|
|
}
|
|
|
|
#define INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, d1) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 1) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 2) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 3) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 4) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 5) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST(T, TypeName, d1, 6)
|
|
|
|
#define INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TESTS(T, TypeName) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 1) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 2) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 3) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 4) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 5) \
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TEST_D2(T, TypeName, 6)
|
|
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TESTS(int8_t, Int8)
|
|
INSTANTIATE_QUANTIZED_ADD_MULTI_DIM_BROADCAST_TESTS(uint8_t, Uint8)
|
|
|
|
template <TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedTestsNoActivation() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-1.0, 1.0);
|
|
std::vector<std::vector<float>> inputs1 = {
|
|
{0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, 0.7, 0.3}};
|
|
std::vector<std::vector<float>> inputs2 = {
|
|
{0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, -0.8, 0.5}};
|
|
std::vector<std::vector<float>> results = {
|
|
{0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}};
|
|
for (size_t i = 0; i < inputs1.size(); ++i) {
|
|
QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
|
|
{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
|
|
{tensor_type, {}, -1.0, 1.0},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
m.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
|
|
<< "With test number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationUInt8) {
|
|
QuantizedTestsNoActivation<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt8) {
|
|
QuantizedTestsNoActivation<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt16) {
|
|
float kQuantizedTolerance = GetTolerance<int16_t>(-1.0, 1.0);
|
|
std::vector<std::vector<float>> inputs1 = {{0.1, 0.2, 0.3, 0.4, 0.9, 0.7},
|
|
{-0.8, 0.2, 0.4, 0.7, 0.1, 0.0},
|
|
{-0.8, 0.2, 0.7, 0.3, 0.9, 0.1}};
|
|
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.3, 0.1, -0.1, 0.3},
|
|
{0.6, 0.4, 0.5, -0.8, 0.0, -1.0},
|
|
{0.6, 0.4, -0.8, 0.5, -0.9, 0.1}};
|
|
std::vector<std::vector<float>> results = {{0.7, 0.6, 0.6, 0.5, 0.8, 1.0},
|
|
{-0.2, 0.6, 0.9, -0.1, 0.1, -1.0},
|
|
{-0.2, 0.6, -0.1, 0.8, 0.0, 0.2}};
|
|
for (size_t i = 0; i < inputs1.size(); ++i) {
|
|
QuantizedAddOpModel m({TensorType_INT16, {1, 2, 3, 1}, -1.0, 1.0},
|
|
{TensorType_INT16, {1, 2, 3, 1}, -1.0, 1.0},
|
|
{TensorType_INT16, {}, -1.0, 1.0},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<int16_t>(m.input1(), inputs1[i]);
|
|
m.QuantizeAndPopulate<int16_t>(m.input2(), inputs2[i]);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
m.GetDequantizedOutputInt16(),
|
|
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
|
|
<< "With test number " << i;
|
|
}
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedTestsActivationRELU_N1_TO_1() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-1.0, 1.0);
|
|
std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
|
|
{-0.8, 0.2, 0.7, 0.3}};
|
|
std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
|
|
{0.6, 0.4, -0.8, 0.5}};
|
|
std::vector<std::vector<float>> results = {{-0.2, 0.6, 1.0, -0.1},
|
|
{-0.2, 0.6, -0.1, 0.8}};
|
|
for (size_t i = 0; i < inputs1.size(); ++i) {
|
|
QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
|
|
{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
|
|
{tensor_type, {}, -1.0, 1.0},
|
|
ActivationFunctionType_RELU_N1_TO_1);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
m.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
|
|
<< "With test number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1UInt8) {
|
|
QuantizedTestsActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1Int8) {
|
|
QuantizedTestsActivationRELU_N1_TO_1<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedVariousInputShapes() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.0, 3.0);
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedAddOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
|
|
{tensor_type, test_shapes[i], -3.0, 3.0},
|
|
{tensor_type, {}, -3.0, 3.0},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input1(),
|
|
{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input2(),
|
|
{0.1, 0.3, 0.3, 0.5, 1.1, 0.1});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear({-1.9, 0.5, 1.0, 1.3, 2.2, 2.1},
|
|
kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedVariousInputShapesUInt8) {
|
|
QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedVariousInputShapesInt8) {
|
|
QuantizedVariousInputShapes<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedWithScalarBroadcast() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.f, 3.f);
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedAddOpModel model_fixture(
|
|
{tensor_type, test_shapes[i], -3.f, 3.f}, {tensor_type, {}, -3.f, 3.f},
|
|
{tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input1(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input2(),
|
|
{0.1f});
|
|
ASSERT_EQ(model_fixture.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
|
|
kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
// Re-run with exchanged inputs.
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedAddOpModel model_fixture(
|
|
{tensor_type, {}, -3.f, 3.f}, {tensor_type, test_shapes[i], -3.f, 3.f},
|
|
{tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input1(),
|
|
{0.1f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input2(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
|
|
ASSERT_EQ(model_fixture.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
|
|
kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastUInt8) {
|
|
QuantizedWithScalarBroadcast<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt8) {
|
|
QuantizedWithScalarBroadcast<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt16) {
|
|
QuantizedWithScalarBroadcast<TensorType_INT16, int16_t>();
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedWithMixedBroadcast() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.f, 3.f);
|
|
const std::vector<int> base_shape = {2, 3, 1, 2};
|
|
std::vector<std::vector<int>> test_shapes = {
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{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
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std::vector<std::vector<float>> test_outputs = {
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{-0.1f, 2.6f, -0.7f, 2.8f, 0.7f, 3.0f, 1.1f, 0.8f, 0.5f,
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1.0f, 1.9f, 1.4f, 1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
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1.4f, 3.0f, 0.8f, 3.0f, 2.2f, 3.0f, -1.4f, 0.3f, -2.0f,
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0.5f, -0.6f, 0.9f, 0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
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{-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.0f, -2.0f, 0.5f, 1.7f,
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-1.3f},
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{-0.1f, 2.5f, 0.0f, 2.6f, -0.7f, 1.9f, 1.1f, 0.7f, 1.2f,
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0.8f, 0.5f, 0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
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1.7f, 3.0f, 2.2f, 3.0f, 2.1f, 3.0f, -1.1f, 0.5f, -0.6f,
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1.0f, -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
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{-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.0f, -0.6f, 1.0f, 1.6f,
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-1.3f}};
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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QuantizedAddOpModel model_fixture({tensor_type, base_shape, -3.f, 3.f},
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{tensor_type, test_shapes[i], -3.f, 3.f},
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|
{tensor_type, {}, -3.f, 3.f},
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|
ActivationFunctionType_NONE);
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|
model_fixture.QuantizeAndPopulate<integer_dtype>(
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|
model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
|
|
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input2(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
|
|
ASSERT_EQ(model_fixture.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
// Re-run with exchanged inputs.
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedAddOpModel model_fixture({tensor_type, test_shapes[i], -3.f, 3.f},
|
|
{tensor_type, base_shape, -3.f, 3.f},
|
|
{tensor_type, {}, -3.f, 3.f},
|
|
ActivationFunctionType_NONE);
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input1(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
|
|
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
|
|
ASSERT_EQ(model_fixture.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastUInt8) {
|
|
QuantizedWithMixedBroadcast<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt8) {
|
|
QuantizedWithMixedBroadcast<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt16) {
|
|
QuantizedWithMixedBroadcast<TensorType_INT16, int16_t>();
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedWithGenericBroadcast() {
|
|
float kQuantizedTolerance = GetTolerance<integer_dtype>(-3.0, 3.0);
|
|
std::vector<int> test_shape1 = {1, 3, 1};
|
|
std::vector<int> test_shape2 = {2, 1, 2};
|
|
QuantizedAddOpModel m({tensor_type, test_shape1, -3.0, 3.0},
|
|
{tensor_type, test_shape2, -3.0, 3.0},
|
|
{tensor_type, {}, -3.0, 3.0},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input1(), {0.1, 0.2, 0.3});
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1, -0.2, 0.3, -0.4});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear({0.2, -0.1, 0.3, 0., 0.4, 0.1,
|
|
0.4, -0.3, 0.5, -0.2, 0.6, -0.1},
|
|
kQuantizedTolerance)));
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithGenericBroadcastUInt8) {
|
|
QuantizedWithGenericBroadcast<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt8) {
|
|
QuantizedWithGenericBroadcast<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt16) {
|
|
QuantizedWithGenericBroadcast<TensorType_INT16, int16_t>();
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|