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Add SquaredDifference op in hexagon delegate. The op is not supported natively so we lower the op to Mul(Sub(a,b), Sub(a,b))

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PiperOrigin-RevId: 349301984
Change-Id: I19c184de2018ca6a417fd02da51885b1538f82bf
This commit is contained in:
Karim Nosir 2020-12-28 10:29:44 -08:00 committed by TensorFlower Gardener
parent 40dad0a283
commit ea9f1bf016
8 changed files with 222 additions and 0 deletions
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1
tensorflow/lite/delegates/hexagon/README.md
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@ -99,6 +99,7 @@ are verified in `IsNodeSupportedByHexagon`:
* SoftMax
* SpaceToDepth
* Split
* SquaredDifference
* Strided Slice
* Sub (Support relu activations)
* Tanh

1
tensorflow/lite/delegates/hexagon/builders/BUILD
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@ -35,6 +35,7 @@ cc_library(
"softmax_builder.cc",
"space_to_depth_builder.cc",
"split_builder.cc",
"squared_difference.cc",
"strided_slice_builder.cc",
"transpose_builder.cc",
"transpose_conv_2d_builder.cc",

2
tensorflow/lite/delegates/hexagon/builders/op_builder.cc
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@ -159,6 +159,8 @@ OpBuilder* GraphBuilder::CreateOpBuilderFromTfLiteOp(int op_type,
return CreatePackBuilder(this, OP_QuantizedPack_8);
case kTfLiteBuiltinStridedSlice:
return CreateStridedSliceBuilder(this, OP_QuantizedStridedSlice_8);
case kTfLiteBuiltinSquaredDifference:
return CreateSquaredDifferenceOpBuilder(this, OP_QuantizedSub_8p8to8);
default:
context_->ReportError(context_, "Op not supported: %d", op_type);
return nullptr;

2
tensorflow/lite/delegates/hexagon/builders/op_factory.h
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@ -60,6 +60,8 @@ OpBuilder* CreateSliceOpBuilder(GraphBuilder* graph_builder, int op_type);
OpBuilder* CreatePackBuilder(GraphBuilder* graph_builder, int op_type);
OpBuilder* CreateMatMulOpBuilder(GraphBuilder* graph_builder, int op_type);
OpBuilder* CreateStridedSliceBuilder(GraphBuilder* graph_builder, int op_type);
OpBuilder* CreateSquaredDifferenceOpBuilder(GraphBuilder* graph_builder,
int op_type);
} // namespace hexagon
} // namespace delegates

104
tensorflow/lite/delegates/hexagon/builders/squared_difference.cc Normal file
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@ -0,0 +1,104 @@
/* Copyright 2020 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.
==============================================================================*/
#include "tensorflow/lite/delegates/hexagon/builders/op_builder.h"
namespace tflite {
namespace delegates {
namespace hexagon {
// Builder for SquaredDifference op by computing Mul(Sub(A,B), Sub(A,B))
class SquaredDifferenceOpBuilder : public OpBuilder {
public:
explicit SquaredDifferenceOpBuilder(GraphBuilder* graph_builder, int op_type)
: OpBuilder(graph_builder, op_type) {}
TfLiteStatus PopulateSubGraph(const TfLiteIntArray* inputs,
const TfLiteIntArray* outputs,
TfLiteContext* context) override;
TfLiteStatus RegisterOutputs(const TfLiteIntArray* outputs,
TfLiteContext* context) override;
private:
TensorID node_output_;
};
TfLiteStatus SquaredDifferenceOpBuilder::PopulateSubGraph(
const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
TfLiteContext* context) {
// We model Squared Diff as Mul(Sub(a,b), Sub(a,b))
// Add first Sub op.
const int tensor_a_index = inputs->data[0];
const int tensor_b_index = inputs->data[1];
const auto& tensor_a = context->tensors[tensor_a_index];
const auto& tensor_b = context->tensors[tensor_b_index];
AddInput(graph_builder_->GetHexagonTensorId(tensor_a_index));
AddInput(graph_builder_->GetHexagonTensorId(tensor_b_index));
// Inputs min/max
TF_LITE_ENSURE_STATUS(ComputeAndAddMinAndMax(context, tensor_a));
TF_LITE_ENSURE_STATUS(ComputeAndAddMinAndMax(context, tensor_b));
// Output details.
float output_min = -1, output_max = -1;
TF_LITE_ENSURE_STATUS(ComputeMinAndMaxQuantValues(
context->tensors[outputs->data[0]], &output_min, &output_max));
auto* output_min_const = graph_builder_->AddConstNodeWithData(
kScalarShape, reinterpret_cast<char*>(&output_min), sizeof(output_min));
auto* output_max_const = graph_builder_->AddConstNodeWithData(
kScalarShape, reinterpret_cast<char*>(&output_max), sizeof(output_max));
int output_batch_size, output_height_size, output_width_size,
output_depth_size;
GetDims(&output_batch_size, &output_height_size, &output_width_size,
&output_depth_size, context->tensors[outputs->data[0]].dims);
auto sub_out = AddOutput(sizeof(uint8_t), 4,
{output_batch_size, output_height_size,
output_width_size, output_depth_size});
auto sub_min = AddOutput(sizeof(float), 4, kScalarShape);
auto sub_max = AddOutput(sizeof(float), 4, kScalarShape);
// Add Mul
auto* mul_op = graph_builder_->AddNode(GetTFLiteNodeID());
mul_op->SetOpType(OP_QuantizedMul_8x8to8);
mul_op->AddInput(sub_out);
mul_op->AddInput(sub_out);
mul_op->AddInput(sub_min);
mul_op->AddInput(sub_max);
mul_op->AddInput(sub_min);
mul_op->AddInput(sub_max);
mul_op->AddInput(TensorID(output_min_const->GetID(), 0));
mul_op->AddInput(TensorID(output_max_const->GetID(), 0));
node_output_ = mul_op->AddOutput(sizeof(uint8_t), 4,
{output_batch_size, output_height_size,
output_width_size, output_depth_size});
mul_op->AddOutput(sizeof(float), 4, kScalarShape);
mul_op->AddOutput(sizeof(float), 4, kScalarShape);
return kTfLiteOk;
}
TfLiteStatus SquaredDifferenceOpBuilder::RegisterOutputs(
const TfLiteIntArray* outputs, TfLiteContext* context) {
graph_builder_->AddTensorWithID(outputs->data[0], node_output_.first,
node_output_.second);
return kTfLiteOk;
}
OpBuilder* CreateSquaredDifferenceOpBuilder(GraphBuilder* graph_builder,
int op_type) {
return new SquaredDifferenceOpBuilder(graph_builder, op_type);
}
} // namespace hexagon
} // namespace delegates
} // namespace tflite

1
tensorflow/lite/delegates/hexagon/builders/tests/BUILD
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@ -49,6 +49,7 @@ hexagon_op_tests(
"softmax_test.cc",
"space_to_depth_test.cc",
"split_test.cc",
"squared_difference_test.cc",
"strided_slice_test.cc",
"transpose_conv_test.cc",
"transpose_test.cc",

106
tensorflow/lite/delegates/hexagon/builders/tests/squared_difference_test.cc Normal file
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@ -0,0 +1,106 @@
/* Copyright 2020 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.
==============================================================================*/
#include <gtest/gtest.h>
#include "tensorflow/lite/delegates/hexagon/builders/tests/hexagon_delegate_op_model.h"
namespace tflite {
using ::testing::ElementsAreArray;
class SquaredDifferenceOpModel : public SingleOpModelWithHexagon {
public:
SquaredDifferenceOpModel(const TensorData& input1, const TensorData& input2,
const TensorData& output) {
input1_ = AddInput(input1);
input2_ = AddInput(input2);
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_SQUARED_DIFFERENCE,
BuiltinOptions_SquaredDifferenceOptions,
CreateSquaredDifferenceOptions(builder_).Union());
BuildInterpreter({GetShape(input1_), GetShape(input2_)});
}
int input1() { return input1_; }
int input2() { return input2_; }
template <typename integer_dtype>
std::vector<float> GetDequantizedOutput() {
return Dequantize<int8_t>(ExtractVector<int8_t>(output_), GetScale(output_),
GetZeroPoint(output_));
}
protected:
int input1_;
int input2_;
int output_;
};
float GetTolerance(int min, int max) {
float kQuantizedStep = (max - min) / 255.0;
return kQuantizedStep;
}
TEST(QuantizedSquaredDifferenceOpTest, Quantized_SameShape) {
float kQuantizedTolerance = GetTolerance(0, 1);
SquaredDifferenceOpModel m({TensorType_INT8, {1, 2, 2, 1}, -1.2, 0.8},
{TensorType_INT8, {1, 2, 2, 1}, -1.5, 0.5},
{TensorType_INT8, {}, 0.0, 0.5});
m.QuantizeAndPopulate<int8_t>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
m.QuantizeAndPopulate<int8_t>(m.input2(), {0.5, 0.2, -1.5, 0.5});
m.ApplyDelegateAndInvoke();
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({0.49, 0.0, 0.09, 0.09},
kQuantizedTolerance)));
}
TEST(QuantizedSquaredDifferenceOpTest, Quantized_VariousInputShapes) {
// NOTE: the min/max are 0 and 9. We use larger threshold for accuracy
// issue in Hexagon.
float kQuantizedTolerance = GetTolerance(0, 10);
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) {
SquaredDifferenceOpModel m({TensorType_INT8, test_shapes[i], -2.0, 1.7},
{TensorType_INT8, test_shapes[i], -1.0, 1.0},
{TensorType_INT8, {}, 0.0, 9.0});
m.QuantizeAndPopulate<int8_t>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0});
m.QuantizeAndPopulate<int8_t>(m.input2(), {1.0, 0.2, 0.6, 0.4, -1.0, -0.0});
m.ApplyDelegateAndInvoke();
EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear(
{9.0, 0.0, 0.09, 0.16, 4.41, 4.0}, kQuantizedTolerance)))
<< "With shape number " << i;
}
}
TEST(QuantizedSquaredDifferenceOpTest, Quantized_WithBroadcast) {
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
float kQuantizedTolerance = GetTolerance(0, 1);
for (int i = 0; i < test_shapes.size(); ++i) {
SquaredDifferenceOpModel m({TensorType_INT8, test_shapes[i], -0.2, 1.1},
{TensorType_INT8, {}, 0.0, 0.1},
{TensorType_INT8, {}, 0.0, 1.0});
m.QuantizeAndPopulate<int8_t>(m.input1(), {-0.2, 0.2, 0.5, 0.8, 0.11, 1.1});
m.QuantizeAndPopulate<int8_t>(m.input2(), {0.1});
m.ApplyDelegateAndInvoke();
EXPECT_THAT(
m.GetDequantizedOutput<int8_t>(),
ElementsAreArray(ArrayFloatNear({0.09, 0.01, 0.16, 0.49, 0.0001, 1.0},
kQuantizedTolerance)))
<< "With shape number " << i;
}
}
} // namespace tflite

5
tensorflow/lite/delegates/hexagon/utils.cc
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@ -101,6 +101,7 @@ bool CheckOpVersion(const TfLiteRegistration* registration) {
case kTfLiteBuiltinTanh:
case kTfLiteBuiltinTranspose:
return registration->version <= 2;
case kTfLiteBuiltinSquaredDifference:
case kTfLiteBuiltinRelu:
return registration->version == 2;
case kTfLiteBuiltinConv2d:
@ -426,6 +427,10 @@ bool IsNodeSupportedByHexagon(const TfLiteRegistration* registration,
// Hexagon doesn't support ellipsis/new-axis masks.
return (params->ellipsis_mask == 0 && params->new_axis_mask == 0);
}
case kTfLiteBuiltinSquaredDifference: {
return InputsWithCorrectTypes(node, context,
{{kTfLiteInt8}, {kTfLiteInt8}});
}
default:
return false;
}