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Add map for LogSoftmax 

Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia 2021-05-26 10:39:14 +08:00 committed by Kainan Cha
parent 37f686c34d
commit a1ba85691a
4 changed files with 259 additions and 1 deletions
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55
include/tim/vx/ops/logsoftmax.h Normal file
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@ -0,0 +1,55 @@
/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#ifndef TIM_VX_OPS_LOG_SOFTMAX_H_
#define TIM_VX_OPS_LOG_SOFTMAX_H_
#include "tim/vx/operation.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## LogSoftmax
*
* Computes the log softmax activation on the input tensor element-wise, per batch.
*
* ```
* logsoftmax = logits - log(reduce_sum(exp(logits), axis))
* ```
*/
class LogSoftmax : public Operation {
public:
LogSoftmax(Graph* graph, int32_t axis, float beta = 1.f);
protected:
int32_t axis_;
float beta_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_LOG_SOFTMAX_H_ */

2
src/tim/vx/ops/README.md
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@ -119,7 +119,7 @@ ArgMin|ARGMIN|Mapped|[tf.math.argmin](https://tensorflow.google.cn/api_docs/pyth
||GENERATE_PROPOSALS|Unmapped|[ANEURALNETWORKS_GENERATE_PROPOSALS](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a72484020f2c41c814de0a7bf93dbbfd4) ||GENERATE_PROPOSALS|Unmapped|[ANEURALNETWORKS_GENERATE_PROPOSALS](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a72484020f2c41c814de0a7bf93dbbfd4)
||DETECTION_POSTPROCESS|Unmapped|[ANEURALNETWORKS_DETECTION_POSTPROCESSING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0abd6365933837275bb1f5cde1fd9b8234) ||DETECTION_POSTPROCESS|Unmapped|[ANEURALNETWORKS_DETECTION_POSTPROCESSING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0abd6365933837275bb1f5cde1fd9b8234)
||RANDOM_MULTINOMIAL|Unmapped|[ANEURALNETWORKS_RANDOM_MULTINOMIAL](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6cb5032c09d3c4b542d18495c247b5b4) ||RANDOM_MULTINOMIAL|Unmapped|[ANEURALNETWORKS_RANDOM_MULTINOMIAL](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6cb5032c09d3c4b542d18495c247b5b4)
||LOG_SOFTMAX|Unmapped|[tf.nn.log_softmax](https://tensorflow.google.cn/api_docs/python/tf/nn/log_softmax) LogSoftmax|LOG_SOFTMAX|Mapped|[tf.nn.log_softmax](https://tensorflow.google.cn/api_docs/python/tf/nn/log_softmax)
||RELU_KERAS|Unmapped|[tf.keras.layers.ReLU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/ReLU) ||RELU_KERAS|Unmapped|[tf.keras.layers.ReLU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/ReLU)
||GRU_OVXLIB|Unmapped ||GRU_OVXLIB|Unmapped
||GRUCELL_OVXLIB|Unmapped ||GRUCELL_OVXLIB|Unmapped

41
src/tim/vx/ops/logsoftmax.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include "tim/vx/ops/logsoftmax.h"
#include "operation_private.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
LogSoftmax::LogSoftmax(Graph* graph, int32_t axis, float beta)
: Operation(graph, VSI_NN_OP_LOG_SOFTMAX), axis_(axis), beta_(beta) {
this->impl()->node()->nn_param.log_softmax.betaValue = beta_;
this->impl()->node()->nn_param.log_softmax.axis = axis_;
}
} // namespace ops
} // namespace vx
} // namespace tim

162
src/tim/vx/ops/logsoftmax_test.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/logsoftmax.h"
#include "gtest/gtest.h"
namespace {
template<typename T>
::testing::AssertionResult ArraysMatch(const std::vector<T>& expected,
const std::vector<T>& actual,
T abs_error){
for (size_t i = 0; i < expected.size(); ++i){
EXPECT_NEAR(expected[i], actual[i], abs_error) << "at index:" << i;
}
return ::testing::AssertionSuccess();
}
}
TEST(LogSoftmax, shape_6_1_float_axis_0) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({6, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
2, 3, 4, 5, 6, 7
};
std::vector<float> golden = {
-5.4562, -4.4562, -3.4562, -2.4562, -1.4562, -0.4562,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::LogSoftmax>(0);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size() * sizeof(float));
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(LogSoftmax, shape_3_6_1_float_axis_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({3, 6, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
-2.0000, 0.0000, 2.0000,
-3.0000, 0.0000, 3.0000,
-4.0000, 0.0000, 4.0000,
-5.0000, 0.0000, 5.0000,
-6.0000, 0.0000, 6.0000,
-7.0000, 0.0000, 7.0000,
};
std::vector<float> golden = {
-0.4561933, -1.7917595, -5.4561934,
-1.4561933, -1.7917595, -4.4561934,
-2.4561934, -1.7917595, -3.4561934,
-3.4561934, -1.7917595, -2.4561934,
-4.4561934, -1.7917595, -1.4561933,
-5.4561934, -1.7917595, -0.4561933,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::LogSoftmax>(1);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size() * sizeof(float));
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(LogSoftmax, shape_3_6_1_uint8_axis_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({3, 6, 1});
tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 2);
tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1.7917595, 2);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
io_shape, tim::vx::TensorAttribute::INPUT, input_quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
io_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data = {
0, 2, 4,
0, 2, 4,
0, 2, 4,
0, 2, 4,
0, 2, 4,
0, 2, 4,
};
std::vector<uint8_t> golden = {
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
1, 1, 1,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
auto op = graph->CreateOperation<tim::vx::ops::LogSoftmax>(1);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}