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Mul int8 support.

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PiperOrigin-RevId: 234382662
This commit is contained in:
Shashi Shekhar 2019-02-17 10:31:47 -08:00 committed by TensorFlower Gardener
parent 355cc566ef
commit 130aebaa1e
6 changed files with 231 additions and 43 deletions
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1
tensorflow/lite/kernels/internal/BUILD
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@ -312,6 +312,7 @@ cc_library(
"reference/integer_ops/dequantize.h",
"reference/integer_ops/fully_connected.h",
"reference/integer_ops/logistic.h",
"reference/integer_ops/mul.h",
"reference/integer_ops/pooling.h",
"reference/integer_ops/softmax.h",
"reference/integer_ops/tanh.h",

130
tensorflow/lite/kernels/internal/reference/integer_ops/mul.h Normal file
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@ -0,0 +1,130 @@
/* Copyright 2019 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_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
#include "public/gemmlowp.h"
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void MulElementwise(int size, const ArithmeticParams& params,
const int8_t* input1_data, const int8_t* input2_data,
int8_t* output_data) {
for (int i = 0; i < size; ++i) {
const int32 input1_val = params.input1_offset + input1_data[i];
const int32 input2_val = params.input2_offset + input2_data[i];
const int32 unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplierSmallerThanOneExp(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32 clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[i] = static_cast<int8_t>(clamped_output);
}
}
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int8_t* input1_data,
const RuntimeShape& input2_shape, const int8_t* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
gemmlowp::ScopedProfilingLabel label("Mul/8bit");
const int flat_size =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
MulElementwise(flat_size, params, input1_data, input2_data, output_data);
}
// Mul with 16 bit inputs and int8_t outputs.
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int16* input1_data,
const RuntimeShape& input2_shape, const int16* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
gemmlowp::ScopedProfilingLabel label("Mul/Int16Int8");
int32 output_offset = params.output_offset;
int32 output_activation_min = params.quantized_activation_min;
int32 output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int flat_size =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
// F0 uses 0 integer bits, range [-1, 1].
using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
F0 unclamped_result =
F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]);
int16 rescaled_result =
gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8);
int16 clamped_result =
std::min<int16>(output_activation_max - output_offset, rescaled_result);
clamped_result =
std::max<int16>(output_activation_min - output_offset, clamped_result);
output_data[i] = output_offset + clamped_result;
}
}
inline void BroadcastMul4DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int8_t* input1_data,
const RuntimeShape& input2_shape,
const int8_t* input2_data,
const RuntimeShape& output_shape,
int8_t* output_data) {
gemmlowp::ScopedProfilingLabel label("BroadcastMul4DSlow/8bit");
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
// The input shapes are extended as part of NdArrayDesc initialization.
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
const int32 input1_val =
params.input1_offset +
input1_data[SubscriptToIndex(desc1, b, y, x, c)];
const int32 input2_val =
params.input2_offset +
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
const int32 unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplierSmallerThanOneExp(
input1_val * input2_val, params.output_multiplier,
params.output_shift);
const int32 clamped_output = std::min(
params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[Offset(extended_output_shape, b, y, x, c)] =
static_cast<int8_t>(clamped_output);
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_

62
tensorflow/lite/kernels/mul.cc
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@ -12,6 +12,7 @@ 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.
==============================================================================*/
#include "tensorflow/lite/kernels/internal/reference/integer_ops/mul.h"
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
@ -87,8 +88,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
&data->output_activation_min,
&data->output_activation_max);
}
if (output->type == kTfLiteInt8) {
CalculateActivationRangeInt8(params->activation, output,
&data->output_activation_min,
&data->output_activation_max);
}
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) {
if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
output->type == kTfLiteInt16) {
double real_multiplier =
input1->params.scale * input2->params.scale / output->params.scale;
QuantizeMultiplierSmallerThanOneExp(
@ -151,8 +158,8 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteMulParams* params, const OpData* data,
const TfLiteTensor* input1,
const TfLiteTensor* input2, TfLiteTensor* output) {
if (input1->type == kTfLiteUInt8 && input2->type == kTfLiteUInt8 &&
output->type == kTfLiteUInt8) {
if (input1->type == input2->type && input1->type == output->type &&
(input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8)) {
tflite::ArithmeticParams op_params;
SetActivationParams(data->output_activation_min,
data->output_activation_max, &op_params);
@ -163,23 +170,31 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
op_params.output_shift = data->output_shift;
bool need_broadcast = optimized_ops::ProcessBroadcastShapes(
GetTensorShape(input1), GetTensorShape(input2), &op_params);
#define TF_LITE_MUL(type, opname) \
#define TF_LITE_MUL(type, opname, dtype) \
type::opname(op_params, GetTensorShape(input1), \
GetTensorData<uint8_t>(input1), GetTensorShape(input2), \
GetTensorData<uint8_t>(input2), GetTensorShape(output), \
GetTensorData<uint8_t>(output))
GetTensorData<dtype>(input1), GetTensorShape(input2), \
GetTensorData<dtype>(input2), GetTensorShape(output), \
GetTensorData<dtype>(output))
if (input1->type == kTfLiteInt8) {
if (need_broadcast) {
TF_LITE_MUL(reference_integer_ops, BroadcastMul4DSlow, int8_t);
} else {
TF_LITE_MUL(reference_integer_ops, Mul, int8_t);
}
} else {
// type == kTfLiteUInt8
if (kernel_type == kReference) {
if (need_broadcast) {
TF_LITE_MUL(reference_ops, BroadcastMul4DSlow);
TF_LITE_MUL(reference_ops, BroadcastMul4DSlow, uint8_t);
} else {
TF_LITE_MUL(reference_ops, Mul);
TF_LITE_MUL(reference_ops, Mul, uint8_t);
}
} else {
if (need_broadcast) {
TF_LITE_MUL(optimized_ops, BroadcastMulFivefold);
TF_LITE_MUL(optimized_ops, BroadcastMulFivefold, uint8_t);
} else {
TF_LITE_MUL(optimized_ops, Mul);
TF_LITE_MUL(optimized_ops, Mul, uint8_t);
}
}
}
#undef TF_LITE_MUL
@ -198,8 +213,8 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
}
#undef TF_LITE_MUL
} else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
output->type == kTfLiteUInt8) {
#define TF_LITE_MUL(type, opname) \
(output->type == kTfLiteUInt8 || output->type == kTfLiteInt8)) {
#define TF_LITE_MUL(type, opname, output_dtype) \
tflite::ArithmeticParams op_params; \
SetActivationParams(data->output_activation_min, \
data->output_activation_max, &op_params); \
@ -207,11 +222,15 @@ TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
type::opname(op_params, GetTensorShape(input1), \
GetTensorData<int16_t>(input1), GetTensorShape(input2), \
GetTensorData<int16_t>(input2), GetTensorShape(output), \
GetTensorData<uint8_t>(output))
if (kernel_type == kReference) {
TF_LITE_MUL(reference_ops, Mul);
GetTensorData<output_dtype>(output))
if (output->type == kTfLiteInt8) {
TF_LITE_MUL(reference_integer_ops, Mul, int8_t);
} else {
TF_LITE_MUL(optimized_ops, Mul);
if (kernel_type == kReference) {
TF_LITE_MUL(reference_ops, Mul, uint8_t);
} else {
TF_LITE_MUL(optimized_ops, Mul, uint8_t);
}
}
#undef TF_LITE_MUL
} else {
@ -233,14 +252,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) {
EvalMul<kernel_type>(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) {
} else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
output->type == kTfLiteInt16) {
TF_LITE_ENSURE_OK(
context, EvalQuantized<kernel_type>(context, node, params, data, input1,
input2, output));
} else {
context->ReportError(context,
"Mul only supports FLOAT32, INT32 and quantized UINT8 "
"and INT16 now, got %d.",
"Mul only supports FLOAT32, INT32 and quantized UINT8,"
" INT8 and INT16 now, got %d.",
output->type);
return kTfLiteError;
}

65
tensorflow/lite/kernels/mul_test.cc
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@ -73,8 +73,9 @@ class QuantizedMulOpModel : public BaseMulOpModel {
public:
using BaseMulOpModel::BaseMulOpModel;
template <typename integer_dtype>
std::vector<float> GetDequantizedOutput() {
return Dequantize<uint8_t>(ExtractVector<uint8_t>(output_),
return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
GetScale(output_), GetZeroPoint(output_));
}
@ -191,19 +192,28 @@ TEST(IntegerMulOpTest, WithBroadcast) {
}
}
TEST(QuantizedMulOpTest, NoActivation) {
QuantizedMulOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0},
{TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0},
{TensorType_UINT8, {}, -1.0, 1.0},
template <TensorType tensor_type, typename integer_dtype>
void NoActivation() {
QuantizedMulOpModel 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<uint8_t>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
m.QuantizeAndPopulate<uint8_t>(m.input2(), {0.6, 0.4, 0.9, 0.8});
m.QuantizeAndPopulate<integer_dtype>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.6, 0.4, 0.9, 0.8});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput(),
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
kQuantizedTolerance)));
}
TEST(QuantizedMulOpTest, NoActivationUInt8) {
NoActivation<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedMulOpTest, NoActivationInt8) {
NoActivation<TensorType_INT8, int8_t>();
}
TEST(QuantizedMulOpTest, NoActivationInt16) {
const float kMin = -1.f;
const float kMax = 32767.f / 32768.f;
@ -219,23 +229,32 @@ TEST(QuantizedMulOpTest, NoActivationInt16) {
kQuantizedToleranceInt16)));
}
TEST(QuantizedMulOpTest, NoActivationInt16WithUint8Output) {
template <TensorType tensor_type, typename integer_dtype>
void NoActivationInt16With8BitOutput() {
const float kMinInt16 = -1.f;
const float kMaxInt16 = 32767.f / 32768.f;
const float kMinUint8 = -1.f;
const float kMaxUint8 = 127.f / 128.f;
QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16},
{TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16},
{TensorType_UINT8, {}, kMinUint8, kMaxUint8},
{tensor_type, {}, kMinUint8, kMaxUint8},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<int16_t>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
m.QuantizeAndPopulate<int16_t>(m.input2(), {0.6, 0.4, 0.9, 0.8});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput(),
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
kQuantizedTolerance)));
}
TEST(QuantizedMulOpTest, NoActivationInt16WithUint8Output) {
NoActivationInt16With8BitOutput<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedMulOpTest, NoActivationInt16Withint8Output) {
NoActivationInt16With8BitOutput<TensorType_INT8, int8_t>();
}
// for quantized Mul, the error shouldn't exceed 2*step
float GetTolerance(int min, int max) {
float kQuantizedStep = (max - min) / 255.0;
@ -243,25 +262,35 @@ float GetTolerance(int min, int max) {
return kQuantizedTolerance;
}
TEST(QuantizedMulOpTest, WithBroadcast) {
template <TensorType tensor_type, typename integer_dtype>
void WithBroadcast() {
float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
for (int i = 0; i < test_shapes.size(); ++i) {
QuantizedMulOpModel m({TensorType_UINT8, test_shapes[i], -3.0, 3.0},
{TensorType_UINT8, {}, -3.0, 3.0}, // always a scalar
{TensorType_UINT8, {}, -3.0, 3.0},
QuantizedMulOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
{tensor_type, {}, -3.0, 3.0}, // always a scalar
{tensor_type, {}, -3.0, 3.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<uint8_t>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.QuantizeAndPopulate<uint8_t>(m.input2(), {0.1});
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});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput(),
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(
{-0.2, 0.02, 0.07, 0.08, 0.11, 0.2}, kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedMulOpTest, WithBroadcastUInt8) {
WithBroadcast<TensorType_UINT8, uint8_t>();
}
TEST(QuantizedMulOpTest, WithBroadcastInt8) {
WithBroadcast<TensorType_INT8, int8_t>();
}
} // namespace
} // namespace tflite

6
tensorflow/lite/toco/tflite/operator.cc
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@ -764,6 +764,12 @@ class Mul : public BuiltinOperator<MulOperator, ::tflite::MulOptions,
}
int GetVersion(const OperatorSignature& op_signature) const override {
const string& input_name = op_signature.op->inputs[0];
const Array& input_array = op_signature.model->GetArray(input_name);
// Version 2 supports signed int8 input types.
if (input_array.data_type == ArrayDataType::kInt8) {
return 2;
}
return 1;
}
};

2
tensorflow/lite/toco/tflite/operator_test.cc
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@ -841,6 +841,8 @@ TEST_F(OperatorTest, VersioningAddTest) { SimpleVersioningTest<AddOperator>(); }
TEST_F(OperatorTest, VersioningSubTest) { SimpleVersioningTest<SubOperator>(); }
TEST_F(OperatorTest, VersioningMulTest) { SimpleVersioningTest<MulOperator>(); }
TEST_F(OperatorTest, VersioningPadTest) { SimpleVersioningTest<PadOperator>(); }
TEST_F(OperatorTest, VersioningPadV2Test) {