Mapped unidirectional gru & unit test
Signed-off-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
Chen Xin
Sven
parent
58395cf7a7
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/****************************************************************************
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*
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* Copyright (c) 2022 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#ifndef TIM_VX_OPS_UNIDIRECTIONAL_SEQUENCE_GRU_H_
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#define TIM_VX_OPS_UNIDIRECTIONAL_SEQUENCE_GRU_H_
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#include <array>
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#include "tim/vx/direct_map_op.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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/**
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* ## UnidirectionalSequenceGRU
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*
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* - num_units : dimensionality of the output space.
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* - activation : Activation function to use.
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* - recurrent_activation : Activation function to use for the recurrent step.
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* - reset_after : whether to apply reset gate after or before matrix multiplication.
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* False = "before", True = "after".
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* - return_sequences : Whether to return the last output in the output sequence,
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* or the full sequence. Default: False.
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* - time_major : If True, the inputs and outputs will be in shape [feature, batch, timesteps],
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* in the False case, it will be [feature, timesteps, batch].
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*/
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class UnidirectionalSequenceGRU : public DirectMapOp {
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public:
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enum ActivationType {
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kNONE = 0,
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kRELU = 1,
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kRELU6 = 3,
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kTANH = 4,
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kSIGMOID = 6,
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kHARDSIGMOID = 31, /* temporary use 31 */
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};
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UnidirectionalSequenceGRU(
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Graph* graph, uint32_t num_units,
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ActivationType activation = ActivationType::kTANH,
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ActivationType recurrent_activation = ActivationType::kSIGMOID,
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vsi_bool reset_after = TRUE,
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vsi_bool return_sequences = FALSE, /*False: only return last state*/
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vsi_bool time_major = TRUE);
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std::shared_ptr<Operation> Clone(
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std::shared_ptr<Graph>& graph) const override;
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protected:
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const uint32_t num_units_;
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const ActivationType activation_;
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const ActivationType recurrent_activation_;
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const int32_t reset_after_;
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const int32_t return_sequences_;
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const int32_t time_major_;
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};
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} // namespace ops
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} // namespace vx
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} // namespace tim
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#endif /* TIM_VX_OPS_UNIDIRECTIONAL_SEQUENCE_GRU_H_ */
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@ -112,7 +112,7 @@ GroupedConv1d|GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://tensorflow.
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|ROI_Align|ROI_ALIGN|Mapped|[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)
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|TopK|TOPK|Mapped (limited support)|[tf.math.top_k](https://tensorflow.google.cn/api_docs/python/tf/math/top_k)
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|GRUCell|GRUCELL_OVXLIB|Mapped|[tf.keras.layers.GRUCell](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
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|UnidirectionalSequenceGRU|GRU_OVXLIB|Planned 22Q3|[tf.keras.layers.GRU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
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|UnidirectionalSequenceGRU|GRU_OVXLIB|Mapped|[tf.keras.layers.GRU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
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|UnidirectionalSequenceRNN|UNIDIRECTIONAL_SEQUENCE_RNN|Planned 22Q3|[ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0ae11aa1d461d2abaa117f6ee2cb503dd8)
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|BidirectionalSequenceRNN|BIDIRECTIONAL_SEQUENCE_RNN|Planned 22Q3|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a487fc5ae247de828f13e62b99f259f3c)
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|BidirectionalSequenceLSTM|BIDIRECTIONAL_SEQUENCE_LSTM|Mapped|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a492a71cb7aa50b9a1a834a3cb269d778)
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/****************************************************************************
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*
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* Copyright (c) 2022 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/ops/unidirectional_sequence_gru.h"
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#include "direct_map_op_impl.h"
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#include "type_utils.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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UnidirectionalSequenceGRU::UnidirectionalSequenceGRU(
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Graph* graph, uint32_t num_units, ActivationType activation,
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ActivationType recurrent_activation, vsi_bool reset_after,
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vsi_bool return_sequences, vsi_bool time_major)
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: DirectMapOp(graph, VSI_NN_OP_GRU),
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num_units_(num_units),
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activation_(activation),
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recurrent_activation_(recurrent_activation),
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reset_after_(reset_after),
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return_sequences_(return_sequences),
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time_major_(time_major) {
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this->impl()->node()->nn_param.gru.num_units = num_units;
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this->impl()->node()->nn_param.gru.activation = activation;
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this->impl()->node()->nn_param.gru.recurrent_activation = recurrent_activation;
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this->impl()->node()->nn_param.gru.reset_after = reset_after;
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this->impl()->node()->nn_param.gru.return_sequences = return_sequences;
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this->impl()->node()->nn_param.gru.time_major = time_major;
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}
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std::shared_ptr<Operation> UnidirectionalSequenceGRU::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<UnidirectionalSequenceGRU>(
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this->num_units_, this->activation_, this->recurrent_activation_,
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this->reset_after_, this->return_sequences_, this->time_major_);
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}
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} // namespace ops
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} // namespace vx
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} // namespace tim
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@ -0,0 +1,129 @@
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/****************************************************************************
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*
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* Copyright (c) 2022 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/unidirectional_sequence_gru.h"
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#include "gtest/gtest.h"
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#include "test_utils.h"
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std::shared_ptr<tim::vx::Tensor> make_empty_tensor(
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std::shared_ptr<tim::vx::Graph> graph, const tim::vx::ShapeType& shape,
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const tim::vx::TensorAttribute& role);
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TEST(UnidirectionalSequenceGRU, unit_3) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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const int timesteps = 1;
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const int batchs = 2;
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const int feature = 4;
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const int num_units = 2;
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tim::vx::ShapeType in_shape({feature, batchs, timesteps});
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tim::vx::ShapeType hstate_shape({num_units, batchs});
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tim::vx::ShapeType out_shape({num_units, batchs, timesteps});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape,
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tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec hstate_spec(tim::vx::DataType::FLOAT32, hstate_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec kernel_i_spec(tim::vx::DataType::FLOAT32,
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tim::vx::ShapeType({feature, num_units}),
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec kernel_r_spec(tim::vx::DataType::FLOAT32,
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tim::vx::ShapeType({num_units, num_units}),
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tim::vx::TensorAttribute::CONSTANT);
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std::vector<float> kernel_i2z = {-0.1201707124710083, 0.051147401332855225,
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-0.02161085605621338, 0.2582472562789917,
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-0.7641150951385498, 0.27272117137908936,
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0.4013441801071167, -0.43467071652412415};
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std::vector<float> kernel_i2r = {-0.34522661566734314, 0.11888366937637329,
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0.6542353630065918, 0.6331415176391602,
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-0.2489457130432129, -0.47332942485809326,
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-0.7532100081443787, 0.46069061756134033};
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std::vector<float> kernel_i2h = {
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-0.0012096166610717773, -0.05206263065338135, -0.418102502822876,
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-0.20800292491912842, -0.5549647808074951, -0.1337134838104248,
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0.14222955703735352, -0.21347862482070923};
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std::vector<float> kernel_r2z = {-0.49559473991394043, -0.10428445041179657,
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0.39165210723876953, 0.38152191042900085};
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std::vector<float> kernel_r2r = {0.03387263044714928, -0.39444485306739807,
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0.4542817771434784, -0.4098765254020691};
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std::vector<float> kernel_r2h = {-0.5441233515739441, -0.35663682222366333,
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-0.3120974004268646, 0.6267299056053162};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto hstate_tensor = graph->CreateTensor(hstate_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto hstate_out = graph->CreateTensor(
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hstate_spec.SetAttribute(tim::vx::TensorAttribute::OUTPUT));
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auto kernel_i2z_tensor =
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graph->CreateTensor(kernel_i_spec, kernel_i2z.data());
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auto kernel_i2r_tensor =
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graph->CreateTensor(kernel_i_spec, kernel_i2r.data());
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auto kernel_i2h_tensor =
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graph->CreateTensor(kernel_i_spec, kernel_i2h.data());
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auto kernel_r2z_tensor =
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graph->CreateTensor(kernel_r_spec, kernel_r2z.data());
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auto kernel_r2r_tensor =
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graph->CreateTensor(kernel_r_spec, kernel_r2r.data());
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auto kernel_r2h_tensor =
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graph->CreateTensor(kernel_r_spec, kernel_r2h.data());
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std::vector<float> in_data = {1, 2, 3, 4, 1, 2, 3, 4};
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std::vector<float> hstate = {0, 0};
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std::vector<float> golden = {-0.2719525, -0.5766771, -0.2719525, -0.5766771};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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EXPECT_TRUE(
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hstate_tensor->CopyDataToTensor(hstate.data(), hstate.size() * 4));
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auto op =
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graph->CreateOperation<tim::vx::ops::UnidirectionalSequenceGRU>(num_units);
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(*op)
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.BindInputs({
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input_tensor,
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hstate_tensor, //h_state
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kernel_i2z_tensor, //KERNEL_I2
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kernel_i2r_tensor, //KERNEL_I2
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kernel_i2h_tensor, //KERNEL_I2
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kernel_r2z_tensor, //KERNEL_R2
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kernel_r2r_tensor, //KERNEL_R2
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kernel_r2h_tensor, //KERNEL_R2
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})
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.BindOutputs({output_tensor, hstate_out});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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