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Move the templatized integer implementation of Softmax to reference/softmax.h, make it work with uint8_t to replace the uint8 version there.

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PiperOrigin-RevId: 302070491
Change-Id: I3a1148604fbeae271891d2cc202e765aed5b5b9f
This commit is contained in:
Robert David 2020-03-20 11:51:05 -07:00 committed by TensorFlower Gardener
parent d0e21cd468
commit a794c690b4
7 changed files with 31 additions and 134 deletions
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1
tensorflow/lite/kernels/internal/BUILD
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@ -438,7 +438,6 @@ cc_library(
"reference/integer_ops/mean.h", "reference/integer_ops/mean.h",
"reference/integer_ops/mul.h", "reference/integer_ops/mul.h",
"reference/integer_ops/pooling.h", "reference/integer_ops/pooling.h",
"reference/integer_ops/softmax.h",
"reference/integer_ops/tanh.h", "reference/integer_ops/tanh.h",
"reference/integer_ops/transpose_conv.h", "reference/integer_ops/transpose_conv.h",
"reference/logistic.h", "reference/logistic.h",

107
tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h
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@ -1,107 +0,0 @@
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_SOFTMAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_SOFTMAX_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
// Quantized softmax with int8 input and int8/int16 output.
template <typename OutputT = int8_t>
inline void Softmax(const SoftmaxParams& params,
const RuntimeShape& input_shape, const int8* input_data,
const RuntimeShape& output_shape, OutputT* output_data) {
const int32_t input_beta_multiplier = params.input_multiplier;
const int32_t input_beta_left_shift = params.input_left_shift;
const int diff_min = params.diff_min;
// The representation chosen for the input to the exp() function is Q5.26.
// We need to leave extra space since values that we skip might be as large as
// -32 before multiplying by input_beta_multiplier, and therefore as large as
// -16 afterwards. Note that exp(-8) is definitely not insignificant to
// accumulation, but exp(-16) definitely is.
static const int kScaledDiffIntegerBits = 5;
static const int kAccumulationIntegerBits = 12;
using FixedPointScaledDiff =
gemmlowp::FixedPoint<int32_t, kScaledDiffIntegerBits>;
using FixedPointAccum =
gemmlowp::FixedPoint<int32_t, kAccumulationIntegerBits>;
using FixedPoint0 = gemmlowp::FixedPoint<int32_t, 0>;
const int trailing_dim = input_shape.DimensionsCount() - 1;
const int outer_size =
MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
const int depth =
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
for (int i = 0; i < outer_size; ++i) {
int8 max_in_row = -128;
for (int c = 0; c < depth; ++c) {
max_in_row = std::max(max_in_row, input_data[i * depth + c]);
}
FixedPointAccum sum_of_exps = FixedPointAccum::Zero();
for (int c = 0; c < depth; ++c) {
int32_t input_diff =
static_cast<int32_t>(input_data[i * depth + c]) - max_in_row;
if (input_diff >= diff_min) {
const int32_t input_diff_rescaled =
MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, input_beta_multiplier, input_beta_left_shift);
const FixedPointScaledDiff scaled_diff_f8 =
FixedPointScaledDiff::FromRaw(input_diff_rescaled);
sum_of_exps = sum_of_exps + gemmlowp::Rescale<kAccumulationIntegerBits>(
exp_on_negative_values(scaled_diff_f8));
}
}
int num_bits_over_unit;
FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal(
sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit));
for (int c = 0; c < depth; ++c) {
int32_t input_diff =
static_cast<int32_t>(input_data[i * depth + c]) - max_in_row;
if (input_diff >= diff_min) {
const int32_t input_diff_rescaled =
MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, input_beta_multiplier, input_beta_left_shift);
const FixedPointScaledDiff scaled_diff_f8 =
FixedPointScaledDiff::FromRaw(input_diff_rescaled);
FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8);
const int32_t unsat_output = gemmlowp::RoundingDivideByPOT(
(shifted_scale * exp_in_0).raw(),
num_bits_over_unit + 31 - (sizeof(OutputT) * 8));
// TODO(b/148494470): Handle int32 shifts properly:
const int32_t shifted_output =
unsat_output -
(static_cast<int32_t>(std::numeric_limits<OutputT>::max()) + 1);
output_data[i * depth + c] = static_cast<OutputT>(std::max(
std::min(shifted_output,
static_cast<int32_t>(std::numeric_limits<OutputT>::max())),
static_cast<int32_t>(std::numeric_limits<OutputT>::min())));
} else {
output_data[i * depth + c] = std::numeric_limits<OutputT>::min();
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_SOFTMAX_H_

25
tensorflow/lite/kernels/internal/reference/softmax.h
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@ -15,6 +15,8 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#include <limits>
#include "fixedpoint/fixedpoint.h" #include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h" #include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h" #include "tensorflow/lite/kernels/internal/quantization_util.h"
@ -59,9 +61,11 @@ inline void Softmax(const SoftmaxParams& params,
} }
} }
// Quantized softmax with int8/uint8 input and int8/uint8/int16 output.
template <typename InputT, typename OutputT>
inline void Softmax(const SoftmaxParams& params, inline void Softmax(const SoftmaxParams& params,
const RuntimeShape& input_shape, const uint8* input_data, const RuntimeShape& input_shape, const InputT* input_data,
const RuntimeShape& output_shape, uint8* output_data) { const RuntimeShape& output_shape, OutputT* output_data) {
const int32 input_beta_multiplier = params.input_multiplier; const int32 input_beta_multiplier = params.input_multiplier;
const int32 input_beta_left_shift = params.input_left_shift; const int32 input_beta_left_shift = params.input_left_shift;
const int diff_min = params.diff_min; const int diff_min = params.diff_min;
@ -84,7 +88,7 @@ inline void Softmax(const SoftmaxParams& params,
MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
for (int i = 0; i < outer_size; ++i) { for (int i = 0; i < outer_size; ++i) {
uint8 max_in_row = 0; InputT max_in_row = std::numeric_limits<InputT>::min();
for (int c = 0; c < depth; ++c) { for (int c = 0; c < depth; ++c) {
max_in_row = std::max(max_in_row, input_data[i * depth + c]); max_in_row = std::max(max_in_row, input_data[i * depth + c]);
} }
@ -120,14 +124,19 @@ inline void Softmax(const SoftmaxParams& params,
FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8);
int32 unsat_output = gemmlowp::RoundingDivideByPOT( int32 unsat_output = gemmlowp::RoundingDivideByPOT(
(shifted_scale * exp_in_0).raw(), num_bits_over_unit + 31 - 8); (shifted_scale * exp_in_0).raw(),
num_bits_over_unit + 31 - (sizeof(OutputT) * 8));
output_data[i * depth + c] = static_cast<uint8>( const int32 shifted_output =
std::max(std::min(unsat_output, static_cast<int32>(255)), unsat_output +
static_cast<int32>(0))); static_cast<int32>(std::numeric_limits<OutputT>::min());
output_data[i * depth + c] = static_cast<OutputT>(std::max(
std::min(shifted_output,
static_cast<int32>(std::numeric_limits<OutputT>::max())),
static_cast<int32>(std::numeric_limits<OutputT>::min())));
} else { } else {
output_data[i * depth + c] = 0; output_data[i * depth + c] = std::numeric_limits<OutputT>::min();
} }
} }
} }

1
tensorflow/lite/kernels/internal/softmax_quantized_test.cc
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@ -25,7 +25,6 @@ limitations under the License.
#include <gtest/gtest.h> #include <gtest/gtest.h>
#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h" #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h" #include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h"
#include "tensorflow/lite/kernels/internal/reference/reference_ops.h" #include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/lite/kernels/internal/test_util.h" #include "tensorflow/lite/kernels/internal/test_util.h"
#include "tensorflow/lite/string_type.h" #include "tensorflow/lite/string_type.h"

29
tensorflow/lite/micro/kernels/softmax.cc
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@ -19,7 +19,6 @@ limitations under the License.
#include "tensorflow/lite/c/common.h" #include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h" #include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h" #include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h" #include "tensorflow/lite/kernels/op_macros.h"
@ -116,13 +115,13 @@ void Softmax1DQuantized(const TfLiteTensor* input, TfLiteTensor* output,
GetTensorData<uint8_t>(output)); GetTensorData<uint8_t>(output));
} else { } else {
if (output->type == kTfLiteInt16) { if (output->type == kTfLiteInt16) {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(op_params, shape,
op_params, shape, GetTensorData<int8_t>(input), shape, GetTensorData<int8_t>(input), shape,
GetTensorData<int16_t>(output)); GetTensorData<int16_t>(output));
} else { } else {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(op_params, shape,
op_params, shape, GetTensorData<int8_t>(input), shape, GetTensorData<int8_t>(input), shape,
GetTensorData<int8_t>(output)); GetTensorData<int8_t>(output));
} }
} }
} }
@ -147,13 +146,13 @@ void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output,
GetTensorData<uint8_t>(output)); GetTensorData<uint8_t>(output));
} else { } else {
if (output->type == kTfLiteInt16) { if (output->type == kTfLiteInt16) {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(op_params, shape,
op_params, shape, GetTensorData<int8_t>(input), shape, GetTensorData<int8_t>(input), shape,
GetTensorData<int16_t>(output)); GetTensorData<int16_t>(output));
} else { } else {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(op_params, shape,
op_params, shape, GetTensorData<int8_t>(input), shape, GetTensorData<int8_t>(input), shape,
GetTensorData<int8_t>(output)); GetTensorData<int8_t>(output));
} }
} }
} }
@ -180,11 +179,11 @@ void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output,
GetTensorShape(output), GetTensorData<uint8_t>(output)); GetTensorShape(output), GetTensorData<uint8_t>(output));
} else { } else {
if (output->type == kTfLiteInt16) { if (output->type == kTfLiteInt16) {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(
op_params, GetTensorShape(input), GetTensorData<int8_t>(input), op_params, GetTensorShape(input), GetTensorData<int8_t>(input),
GetTensorShape(output), GetTensorData<int16_t>(output)); GetTensorShape(output), GetTensorData<int16_t>(output));
} else { } else {
tflite::reference_integer_ops::Softmax( tflite::reference_ops::Softmax(
op_params, GetTensorShape(input), GetTensorData<int8_t>(input), op_params, GetTensorShape(input), GetTensorData<int8_t>(input),
GetTensorShape(output), GetTensorData<int8_t>(output)); GetTensorShape(output), GetTensorData<int8_t>(output));
} }

1
tensorflow/lite/micro/kernels/xtensa_hifimini/softmax.cc
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@ -19,7 +19,6 @@ limitations under the License.
#include "tensorflow/lite/c/common.h" #include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h" #include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h" #include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h" #include "tensorflow/lite/kernels/op_macros.h"

1
tensorflow/lite/micro/tools/make/Makefile
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@ -149,7 +149,6 @@ tensorflow/lite/kernels/internal/reference/integer_ops/conv.h \
tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h \ tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h \
tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h \ tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h \
tensorflow/lite/kernels/internal/reference/integer_ops/mul.h \ tensorflow/lite/kernels/internal/reference/integer_ops/mul.h \
tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h \
tensorflow/lite/kernels/internal/reference/maximum_minimum.h \ tensorflow/lite/kernels/internal/reference/maximum_minimum.h \
tensorflow/lite/kernels/internal/reference/mul.h \ tensorflow/lite/kernels/internal/reference/mul.h \
tensorflow/lite/kernels/internal/reference/neg.h \ tensorflow/lite/kernels/internal/reference/neg.h \