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