Adds kernel impl for dequantizing per-channel quantized tensors to float
PiperOrigin-RevId: 299955689 Change-Id: I03c7b9693a2b86518b4b8dd9303b231fc4c025ee
This commit is contained in:
Sachin Joglekar
TensorFlower Gardener
parent
0008a03e43
commit
185dd2376b
@ -1028,6 +1028,17 @@ cc_test(
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],
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)
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cc_test(
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name = "per_channel_dequantize_test",
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srcs = ["per_channel_dequantize_test.cc"],
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deps = [
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":reference_base",
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":types",
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"//tensorflow/lite/kernels:test_util",
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"@com_google_googletest//:gtest_main",
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],
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)
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exports_files(["optimized/eigen_tensor_reduced_instantiations_oss.h"])
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filegroup(
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121
tensorflow/lite/kernels/internal/per_channel_dequantize_test.cc
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121
tensorflow/lite/kernels/internal/per_channel_dequantize_test.cc
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@ -0,0 +1,121 @@
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/* Copyright 2020 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 <cstdint>
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#include <vector>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/kernels/internal/reference/dequantize.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/test_util.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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TEST(PerChannelDequantize, TestInt8ToFloat_2D) {
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const std::vector<float> scales = {0.5, 0.25};
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const std::vector<int> zero_points = {-1, -1};
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const int quantized_dimension = 0;
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const RuntimeShape shape({2, 5});
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const std::vector<int8_t> input = {-128, -127, -126, -125, -124,
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123, 124, 125, 126, 127};
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std::vector<float> output(10, -1);
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PerChannelDequantizationParams op_params;
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op_params.zero_point = zero_points.data();
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op_params.scale = scales.data();
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op_params.quantized_dimension = quantized_dimension;
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reference_ops::PerChannelDequantize(op_params, shape, input.data(), shape,
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output.data());
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EXPECT_THAT(output,
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ElementsAreArray(ArrayFloatNear({-63.5, -63, -62.5, -62, -61.5,
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31, 31.25, 31.5, 31.75, 32})));
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}
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TEST(PerChannelDequantize, TestInt8ToFloat_3D) {
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const std::vector<float> scales = {0.5, 0.25, 0.5, 0.25, 1.0};
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const std::vector<int> zero_points = {-1, 1, -1, 1, 0};
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const int quantized_dimension = 2;
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const RuntimeShape shape({1, 2, 5});
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const std::vector<int8_t> input = {-128, -127, -126, -125, -124,
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123, 124, 125, 126, 127};
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std::vector<float> output(10, -1);
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PerChannelDequantizationParams op_params;
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op_params.zero_point = zero_points.data();
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op_params.scale = scales.data();
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op_params.quantized_dimension = quantized_dimension;
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reference_ops::PerChannelDequantize(op_params, shape, input.data(), shape,
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output.data());
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EXPECT_THAT(output,
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ElementsAreArray(ArrayFloatNear({-63.5, -32, -62.5, -31.5, -124,
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62, 30.75, 63, 31.25, 127})));
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}
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TEST(PerChannelDequantize, TestInt8ToFloat_4DDim0) {
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const std::vector<float> scales = {0.5, 0.25};
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const std::vector<int> zero_points = {-1, 1};
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const int quantized_dimension = 0;
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RuntimeShape shape({2, 2, 5, 1});
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const std::vector<int8_t> input = {-128, -127, -126, -125, -124, 123, 124,
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125, 126, 127, -128, -127, -126, -125,
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-124, 123, 124, 125, 126, 127};
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std::vector<float> output(20, -1);
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PerChannelDequantizationParams op_params;
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op_params.zero_point = zero_points.data();
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op_params.scale = scales.data();
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op_params.quantized_dimension = quantized_dimension;
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reference_ops::PerChannelDequantize(op_params, shape, input.data(), shape,
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output.data());
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EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
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{-63.5, -63, -62.5, -62, -61.5, 62, 62.5,
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63, 63.5, 64, -32.25, -32, -31.75, -31.5,
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-31.25, 30.5, 30.75, 31, 31.25, 31.5})));
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}
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TEST(PerChannelDequantize, TestInt8ToFloat_4DDim3) {
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const std::vector<float> scales = {0.5, 0.25, 0.5, 0.25, 1.0};
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const std::vector<int> zero_points = {-1, 1, -1, 1, 0};
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const int quantized_dimension = 3;
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RuntimeShape shape({1, 2, 2, 5});
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const std::vector<int8_t> input = {-128, -127, -126, -125, -124, 123, 124,
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125, 126, 127, -128, -127, -126, -125,
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-124, 123, 124, 125, 126, 127};
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std::vector<float> output(20, -1);
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PerChannelDequantizationParams op_params;
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op_params.zero_point = zero_points.data();
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op_params.scale = scales.data();
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op_params.quantized_dimension = quantized_dimension;
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reference_ops::PerChannelDequantize(op_params, shape, input.data(), shape,
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output.data());
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EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
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{-63.5, -32, -62.5, -31.5, -124, 62, 30.75,
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63, 31.25, 127, -63.5, -32, -62.5, -31.5,
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-124, 62, 30.75, 63, 31.25, 127})));
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}
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} // namespace
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} // namespace tflite
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@ -17,6 +17,8 @@ limitations under the License.
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#include <limits.h>
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#include <vector>
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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@ -60,6 +62,35 @@ inline void DequantizeInteger(const tflite::DequantizationParams& op_params,
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}
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}
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// Dequantizes per-channel quantized tensor to float.
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template <typename T>
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inline void PerChannelDequantize(
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const tflite::PerChannelDequantizationParams& op_params,
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const RuntimeShape& input_shape, const T* input_data,
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const RuntimeShape& output_shape, float* output_data) {
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// Ensure flat size is same.
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MatchingFlatSize(input_shape, output_shape);
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const int32* zero_point = op_params.zero_point;
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const float* scale = op_params.scale;
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const int32 quantized_dimension = op_params.quantized_dimension;
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const int32 num_dims = input_shape.DimensionsCount();
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const int32* dims_data = input_shape.DimsData();
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std::vector<int> current_dim(num_dims, 0);
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do {
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size_t offset =
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ReducedOutputOffset(num_dims, reinterpret_cast<const int*>(dims_data),
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current_dim.data(), 0, nullptr);
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const int channel = current_dim[quantized_dimension];
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const int32 val = input_data[offset];
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const float result =
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static_cast<float>(scale[channel] * (val - zero_point[channel]));
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output_data[offset] = result;
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} while (NextIndex(num_dims, reinterpret_cast<const int*>(dims_data),
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current_dim.data()));
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}
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} // namespace reference_ops
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} // namespace tflite
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@ -863,6 +863,12 @@ struct DequantizationParams {
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int32 zero_point;
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};
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struct PerChannelDequantizationParams {
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const float* scale;
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const int32* zero_point;
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int32 quantized_dimension;
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};
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struct FakeQuantParams {
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MinMax minmax;
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int32 num_bits;
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