209 lines
11 KiB
C++
209 lines
11 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2021 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_lstm.h"
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#include "gtest/gtest.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) //, const float& default_value) {
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{
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tim::vx::TensorSpec spec(tim::vx::DataType::FLOAT32, shape, role);
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auto tensor = graph->CreateTensor(spec);
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uint32_t count = 1;
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for (auto dim : shape) {count *= dim;}
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std::vector<float> default_value(count);
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for(auto& i: default_value) {
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i = 0.f;
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}
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tensor->CopyDataToTensor(default_value.data(), count*sizeof(float));
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return tensor;
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}
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TEST(LSTM_CELL, shape_in_2_cell_4_out_4_float32) {
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// NoCifg_NoPeephole_NoProjection_NoLayerNorm
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auto ctx = tim::vx::Context::Create();
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auto g = ctx->CreateGraph();
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uint32_t n_batch, n_step, n_cell, n_input, n_output;
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n_batch = 1, n_step = 1, n_cell = 4, n_input = 2, n_output = 4;
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tim::vx::ShapeType input_shape, cell_shape, state_shape;
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input_shape = {n_batch, n_step, n_input}; // non-time-major
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tim::vx::TensorSpec input_tensor_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_step, n_batch}), tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec i2i_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2i_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2c_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2c_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2f_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2f_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2o_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_input, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec i2o_bias_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_step, n_batch}), tim::vx::TensorAttribute::OUTPUT);
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// tim::vx::TensorSpec hstate_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_batch, n_output}), tim::vx::TensorAttribute::OUTPUT);
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// tim::vx::TensorSpec cstate_spec (tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_batch, n_cell}), tim::vx::TensorAttribute::OUTPUT);
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auto output_tensor = g->CreateTensor(output_spec);
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std::vector<float> input2input_weights, input2cell_weights, input2forget_weights, input2output_weights;
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// tim::vx::Tensor input2input_weights_tensor, input2cell_weights_tensor, input2forget_weights_tensor, input2output_weights_tensor;
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input2input_weights = {-0.45018822, -0.02338299, -0.0870589, -0.34550029,
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0.04266912, -0.15680569, -0.34856534, 0.43890524};
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std::vector<float> input_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto input2input_weights_tensor = g->CreateTensor(i2i_weight_spec, input2input_weights.data());
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auto input_gate_bias_tensor = g->CreateTensor(i2i_bias_spec, input_gate_bias.data());
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input2cell_weights = {-0.50013041, 0.1370284, 0.11810488, 0.2013163,
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-0.20583314, 0.44344562, 0.22077113, -0.29909778};
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std::vector<float> cell_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto cell_gate_bias_tensor = g->CreateTensor(i2c_bias_spec, cell_gate_bias.data());
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auto input2cell_weights_tensor = g->CreateTensor(i2c_weight_spec, input2cell_weights.data());
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input2forget_weights = {0.09701663, 0.20334584, -0.50592935,
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-0.31343272, -0.40032279, 0.44781327,
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0.01387155, -0.35593212};
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std::vector<float> forget_gate_bias = {1., 1., 1., 1.};
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auto input2forget_weights_tensor = g->CreateTensor(i2f_weight_spec, input2forget_weights.data());
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auto forget_gate_bias_tensor = g->CreateTensor(i2f_bias_spec, forget_gate_bias.data());
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input2output_weights = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
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0.44272184, 0.03897077, -0.1556896, 0.19487578};
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std::vector<float> output_gate_bias = {0.0, 0.0, 0.0, 0.0};
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auto input2output_weights_tensor = g->CreateTensor(i2o_weight_spec, input2output_weights.data());
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auto output_gate_bias_tensor = g->CreateTensor(i2o_bias_spec, output_gate_bias.data());
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tim::vx::TensorSpec r2i_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2c_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2f_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec r2o_weight_spec(tim::vx::DataType::FLOAT32, tim::vx::ShapeType({n_output, n_cell}), tim::vx::TensorAttribute::CONSTANT);
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std::vector<float> recurrent_to_input_weights = {
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-0.0063535, -0.2042388, 0.31454784, -0.35746509,
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0.28902304, 0.08183324, -0.16555229, 0.02286911,
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-0.13566875, 0.03034258, 0.48091322, -0.12528998,
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0.24077177, -0.51332325, -0.33502164, 0.10629296};
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std::vector<float> recurrent_to_cell_weights = {
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-0.3407414, 0.24443203, -0.2078532, 0.26320225,
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0.05695659, -0.00123841, -0.4744786, -0.35869038,
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-0.06418842, -0.13502428, -0.501764, 0.22830659,
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-0.46367589, 0.26016325, -0.03894562, -0.16368064};
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std::vector<float> recurrent_to_forget_weights = {
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-0.48684245, -0.06655136, 0.42224967, 0.2112639,
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0.27654213, 0.20864892, -0.07646349, 0.45877004,
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0.00141793, -0.14609534, 0.36447752, 0.09196436,
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0.28053468, 0.01560611, -0.20127171, -0.01140004};
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std::vector<float> recurrent_to_output_weights = {
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0.43385774, -0.17194885, 0.2718237, 0.09215671,
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0.24107647, -0.39835793, 0.18212086, 0.01301402,
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0.48572797, -0.50656658, 0.20047462, -0.20607421,
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-0.51818722, -0.15390486, 0.0468148, 0.39922136};
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auto r2i_weight_tensor = g->CreateTensor(r2i_weight_spec, recurrent_to_input_weights.data());
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auto r2c_weight_tensor = g->CreateTensor(r2c_weight_spec, recurrent_to_cell_weights.data());
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auto r2f_weight_tensor = g->CreateTensor(r2f_weight_spec, recurrent_to_forget_weights.data());
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auto r2o_weight_tensor = g->CreateTensor(r2o_weight_spec, recurrent_to_output_weights.data());
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// std::vector<std::vector<float>> golden_output = {
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// {{-0.02973187, 0.1229473, 0.20885126, -0.15358765}},
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// {{-0.03716109, 0.12507336, 0.41193449, -0.20860538}},
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// {{-0.15053082, 0.09120187, 0.24278517, -0.12222792}}};
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std::vector<float> input = {2,3};
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auto input_tensor = g->CreateTensor(input_tensor_spec, input.data());
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auto lstm_cell_op = g->CreateOperation<tim::vx::ops::UnidirectionalSequenceLstm>(
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0.0, 0.0, tim::vx::ops::UnidirectionalSequenceLstm::ActivationType::kTANH, 0.0, false,
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tim::vx::ops::UnidirectionalSequenceLstm::kNONE, true);
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(*lstm_cell_op)
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.BindInputs({
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input_tensor,
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g->CreateTensorPlaceHolder(), /*h_state*/
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g->CreateTensorPlaceHolder(), /*c_state*/
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input2input_weights_tensor,
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input2forget_weights_tensor,
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input2cell_weights_tensor,
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input2output_weights_tensor,
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r2i_weight_tensor,
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r2c_weight_tensor,
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r2f_weight_tensor,
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r2o_weight_tensor,
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g->CreateTensorPlaceHolder(), /*weight_c2i*/
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g->CreateTensorPlaceHolder(), /*weight_c2f*/
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g->CreateTensorPlaceHolder(), /*weight_c2o*/
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input_gate_bias_tensor,
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forget_gate_bias_tensor,
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cell_gate_bias_tensor,
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output_gate_bias_tensor,
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// optional for projection
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/*weight_prj*/
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/*bias_prj*/
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// Layer norm IFCO
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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// AUX weight_i2i|i2f|i2c|i2o
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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// g->CreateTensorPlaceHolder(),
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})
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.BindOutputs({
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output_tensor,
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make_empty_tensor(
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g, tim::vx::ShapeType({n_output, n_batch}),
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tim::vx::TensorAttribute::OUTPUT), // Output_H_STATE
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make_empty_tensor(
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g, tim::vx::ShapeType({n_cell, n_batch}),
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tim::vx::TensorAttribute::OUTPUT), // output_C_State
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});
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g->Compile();
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g->Run();
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std::vector<float> golden = {{-0.02973187, 0.1229473, 0.20885126, -0.15358765}};
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std::vector<float> real(golden.size());
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output_tensor->CopyDataFromTensor(real.data());
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for(uint32_t i = 0; i < golden.size(); ++i) {
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EXPECT_NEAR(golden[i], real[i], 0.001f) << "Failed at " << i << "th item";
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}
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} |