diff --git a/include/tim/vx/ops/logsoftmax.h b/include/tim/vx/ops/logsoftmax.h new file mode 100644 index 0000000..f05de74 --- /dev/null +++ b/include/tim/vx/ops/logsoftmax.h @@ -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_ */ \ No newline at end of file diff --git a/src/tim/vx/ops/README.md b/src/tim/vx/ops/README.md index 77d0fe3..0cc429e 100644 --- a/src/tim/vx/ops/README.md +++ b/src/tim/vx/ops/README.md @@ -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) ||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) -||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) ||GRU_OVXLIB|Unmapped ||GRUCELL_OVXLIB|Unmapped diff --git a/src/tim/vx/ops/logsoftmax.cc b/src/tim/vx/ops/logsoftmax.cc new file mode 100644 index 0000000..5ea9130 --- /dev/null +++ b/src/tim/vx/ops/logsoftmax.cc @@ -0,0 +1,41 @@ +/**************************************************************************** +* +* 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 \ No newline at end of file diff --git a/src/tim/vx/ops/logsoftmax_test.cc b/src/tim/vx/ops/logsoftmax_test.cc new file mode 100644 index 0000000..e64f7ad --- /dev/null +++ b/src/tim/vx/ops/logsoftmax_test.cc @@ -0,0 +1,162 @@ +/**************************************************************************** +* +* 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 +::testing::AssertionResult ArraysMatch(const std::vector& expected, + const std::vector& 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 in_data = { + 2, 3, 4, 5, 6, 7 + }; + std::vector 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(0); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector 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 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 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(1); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector 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 in_data = { + 0, 2, 4, + 0, 2, 4, + 0, 2, 4, + 0, 2, 4, + 0, 2, 4, + 0, 2, 4, + }; + std::vector 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(1); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); +}