205 lines
9.5 KiB
C++
205 lines
9.5 KiB
C++
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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.h"
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#include "tim/transform/layout_inference.h"
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#include "permute_vector.h"
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#include "test_utils.h"
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#include <algorithm>
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#include "gtest/gtest.h"
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TEST(RNNCell, layout_infer_align) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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uint32_t input_size = 3, batch_size = 2, num_units = 4;
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tim::vx::ShapeType input_shape({input_size, batch_size});
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tim::vx::ShapeType weights_shape({input_size, num_units});
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tim::vx::ShapeType recurrent_weights_shape({num_units, num_units});
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tim::vx::ShapeType bias_shape({num_units});
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tim::vx::ShapeType state_in_shape({num_units, batch_size});
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tim::vx::ShapeType output_shape({num_units, batch_size});
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tim::vx::ShapeType state_out_shape({num_units, batch_size});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 0);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_spec(tim::vx::DataType::FLOAT32, weights_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec recurrent_weights_spec(
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tim::vx::DataType::FLOAT32, recurrent_weights_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec state_in_spec(tim::vx::DataType::FLOAT32, state_in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec state_out_spec(tim::vx::DataType::UINT8, state_out_shape,
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tim::vx::TensorAttribute::OUTPUT, quant);
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std::vector<float> in_data = {
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0.12609188, 0.46347019, 0.89598465, 0.35867718, 0.36897406, 0.73463392,
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};
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std::vector<float> weights_data = {
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0.12609188, 0.46347019, 0.89598465, 0.35867718, 0.36897406, 0.73463392,
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0.12609188, 0.46347019, 0.89598465, 0.35867718, 0.36897406, 0.73463392,
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};
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std::vector<float> recurrent_weights_data = {
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-0.31930989, 0.37613347, 0.27901134, 0.36137494, -1.36916667, 0.38031587,
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0.21580373, 0.27072677, 1.01580888, 0.14943552, 1.15465137, 0.09784451,
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-1.02702999, 1.39296314, 0.15785322, 0.21931258,
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};
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std::vector<float> bias_data = {
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0.01580888,
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0.14943552,
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0.15465137,
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0.09784451,
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};
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std::vector<float> state_in_data = {0, 0, 0, 0, 0, 0, 0, 0};
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std::vector<float> output_golden = {0.781534, 0.771447, 0.830002, 0.749713,
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0.711524, 0.74155, 0.77355, 0.717427};
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std::vector<uint8_t> state_out_golden = {
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217, 214, 231, 208, 198, 206, 215, 199,
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};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weights_tensor = graph->CreateTensor(weights_spec, weights_data.data());
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auto recurrent_weights_tensor = graph->CreateTensor(
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recurrent_weights_spec, recurrent_weights_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto state_in_tensor = graph->CreateTensor(state_in_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto state_out_tensor = graph->CreateTensor(state_out_spec);
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auto op = graph->CreateOperation<tim::vx::ops::RNNCell>(
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tim::vx::ops::RNNCell::ActivationType::kSIGMOID);
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(*op)
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.BindInputs({input_tensor, weights_tensor, bias_tensor, state_in_tensor,
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recurrent_weights_tensor})
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.BindOutputs({output_tensor, state_out_tensor});
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auto transform = tim::transform::LayoutInference(graph, ctx);
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auto infer_graph = transform.first;
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EXPECT_TRUE(infer_graph->Compile());
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auto graph_io_map = transform.second;
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auto infer_input = graph_io_map[graph->InputsTensor()[0]];
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auto infer_input_state = graph_io_map[graph->InputsTensor()[1]];
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auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
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auto infer_output_state = graph_io_map[graph->OutputsTensor()[1]];
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infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
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infer_input_state->CopyDataToTensor(state_in_data.data(),
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state_in_data.size() * sizeof(float));
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EXPECT_TRUE(infer_graph->Run());
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std::vector<float> output(output_golden.size());
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std::vector<uint8_t> state_out(state_out_golden.size());
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EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(infer_output_state->CopyDataFromTensor(state_out.data()));
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EXPECT_TRUE(ArraysMatch(output_golden, output, 1e-5f));
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EXPECT_EQ(state_out_golden, state_out);
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}
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TEST(RNNCell, layout_infer_notalign) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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uint32_t input_size = 3, batch_size = 2, num_units = 4;
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tim::vx::ShapeType input_shape({batch_size, input_size}); //input_pv={1,0}
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tim::vx::ShapeType weights_shape({input_size,num_units});
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tim::vx::ShapeType recurrent_weights_shape({num_units, num_units});
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tim::vx::ShapeType bias_shape({num_units});
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tim::vx::ShapeType state_in_shape({num_units, batch_size});
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tim::vx::ShapeType output_shape({num_units, batch_size});
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tim::vx::ShapeType state_out_shape({num_units, batch_size});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 0);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_spec(tim::vx::DataType::FLOAT32, weights_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec recurrent_weights_spec(
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tim::vx::DataType::FLOAT32, recurrent_weights_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec state_in_spec(tim::vx::DataType::FLOAT32, state_in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec state_out_spec(tim::vx::DataType::UINT8, state_out_shape,
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tim::vx::TensorAttribute::OUTPUT, quant);
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std::vector<float> in_data = {
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0.12609188, 0.35867718, 0.46347019, 0.36897406, 0.89598465, 0.73463392,
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};
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std::vector<float> weights_data = {
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0.12609188, 0.46347019, 0.89598465, 0.35867718, 0.36897406, 0.73463392,
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0.12609188, 0.46347019, 0.89598465, 0.35867718, 0.36897406, 0.73463392,
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};
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std::vector<float> recurrent_weights_data = {
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-0.31930989, 0.37613347, 0.27901134, 0.36137494, -1.36916667, 0.38031587,
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0.21580373, 0.27072677, 1.01580888, 0.14943552, 1.15465137, 0.09784451,
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-1.02702999, 1.39296314, 0.15785322, 0.21931258,
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};
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std::vector<float> bias_data = {
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0.01580888,
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0.14943552,
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0.15465137,
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0.09784451,
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};
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std::vector<float> state_in_data = {0, 0, 0, 0, 0, 0, 0, 0};
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std::vector<float> output_golden = {0.781534, 0.771447, 0.830002, 0.749713,
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0.711524, 0.74155, 0.77355, 0.717427};
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std::vector<uint8_t> state_out_golden = {
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217, 214, 231, 208, 198, 206, 215, 199,
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};
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weights_tensor = graph->CreateTensor(weights_spec, weights_data.data());
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auto recurrent_weights_tensor = graph->CreateTensor(
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recurrent_weights_spec, recurrent_weights_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto state_in_tensor = graph->CreateTensor(state_in_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto state_out_tensor = graph->CreateTensor(state_out_spec);
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std::map<std::shared_ptr<tim::vx::Tensor>,
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std::shared_ptr<tim::transform::IPermuteVector>>
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tensor_pv_map;
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std::shared_ptr<tim::transform::IPermuteVector> pv =
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std::make_shared<tim::transform::PermuteVector<2>>(
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std::initializer_list<uint32_t>({1U, 0U}));
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tensor_pv_map.insert({input_tensor, pv});
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auto op = graph->CreateOperation<tim::vx::ops::RNNCell>(
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tim::vx::ops::RNNCell::ActivationType::kSIGMOID);
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(*op)
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.BindInputs({input_tensor, weights_tensor, bias_tensor, state_in_tensor,
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recurrent_weights_tensor})
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.BindOutputs({output_tensor, state_out_tensor});
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auto transform = tim::transform::LayoutInference(graph, ctx, tensor_pv_map);
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auto infer_graph = transform.first;
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EXPECT_TRUE(infer_graph->Compile());
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auto graph_io_map = transform.second;
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auto infer_input = graph_io_map[graph->InputsTensor()[0]];
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auto infer_input_state = graph_io_map[graph->InputsTensor()[1]];
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auto infer_output = graph_io_map[graph->OutputsTensor()[0]];
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auto infer_output_state = graph_io_map[graph->OutputsTensor()[1]];
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infer_input->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float));
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infer_input_state->CopyDataToTensor(state_in_data.data(),
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state_in_data.size() * sizeof(float));
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EXPECT_TRUE(infer_graph->Run());
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std::vector<float> output(output_golden.size());
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std::vector<uint8_t> state_out(state_out_golden.size());
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EXPECT_TRUE(infer_output->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(infer_output_state->CopyDataFromTensor(state_out.data()));
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EXPECT_TRUE(ArraysMatch(output_golden, output, 1e-5f));
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EXPECT_EQ(state_out_golden, state_out);
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}
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