--- /dev/null
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <unistd.h>
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+#include <limits>
+
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/activation_functor.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace bidirectional_sequence_lstm {
+
+// Input Tensors of size {max_time, n_batch, n_input}
+constexpr int kInputTensor = 0;
+
+// Forward LSTM cell tensors.
+// Input weight tensors of size: {n_cell, n_input}
+constexpr int kFwInputToInputWeightsTensor = 1; // Optional
+constexpr int kFwInputToForgetWeightsTensor = 2;
+constexpr int kFwInputToCellWeightsTensor = 3;
+constexpr int kFwInputToOutputWeightsTensor = 4;
+
+// Recurrent weight tensors of size {n_cell, n_output}
+constexpr int kFwRecurrentToInputWeightsTensor = 5; // Optional
+constexpr int kFwRecurrentToForgetWeightsTensor = 6;
+constexpr int kFwRecurrentToCellWeightsTensor = 7;
+constexpr int kFwRecurrentToOutputWeightsTensor = 8;
+
+// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+constexpr int kFwCellToInputWeightsTensor = 9; // Optional
+constexpr int kFwCellToForgetWeightsTensor = 10; // Optional
+constexpr int kFwCellToOutputWeightsTensor = 11; // Optional
+
+// Gates bias tensors of size {n_cell}
+constexpr int kFwInputGateBiasTensor = 12; // Optional
+constexpr int kFwForgetGateBiasTensor = 13;
+constexpr int kFwCellGateBiasTensor = 14;
+constexpr int kFwOutputGateBiasTensor = 15;
+
+// Projection weight tensor of size {n_output, n_cell}
+constexpr int kFwProjectionWeightsTensor = 16; // Optional
+// Projection bias tensor of size {n_output}
+constexpr int kFwProjectionBiasTensor = 17; // Optional
+
+// Backward LSTM cell tensors.
+// Input weight tensors of size: {n_cell, n_input}
+constexpr int kBwInputToInputWeightsTensor = 18; // Optional
+constexpr int kBwInputToForgetWeightsTensor = 19;
+constexpr int kBwInputToCellWeightsTensor = 20;
+constexpr int kBwInputToOutputWeightsTensor = 21;
+
+// Recurrent weight tensors of size {n_cell, n_output}
+constexpr int kBwRecurrentToInputWeightsTensor = 22; // Optional
+constexpr int kBwRecurrentToForgetWeightsTensor = 23;
+constexpr int kBwRecurrentToCellWeightsTensor = 24;
+constexpr int kBwRecurrentToOutputWeightsTensor = 25;
+
+// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+constexpr int kBwCellToInputWeightsTensor = 26; // Optional
+constexpr int kBwCellToForgetWeightsTensor = 27; // Optional
+constexpr int kBwCellToOutputWeightsTensor = 28; // Optional
+
+// Gates bias tensors of size {n_cell}
+constexpr int kBwInputGateBiasTensor = 29; // Optional
+constexpr int kBwForgetGateBiasTensor = 30;
+constexpr int kBwCellGateBiasTensor = 31;
+constexpr int kBwOutputGateBiasTensor = 32;
+
+// Projection weight tensor of size {n_output, n_cell}
+constexpr int kBwProjectionWeightsTensor = 33; // Optional
+// Projection bias tensor of size {n_output}
+constexpr int kBwProjectionBiasTensor = 34; // Optional
+
+// Output tensors.
+constexpr int kFwScratchBufferTensor = 0;
+constexpr int kFwOutputStateTensor = 1;
+constexpr int kFwCellStateTensor = 2;
+constexpr int kFwOutputTensor = 3;
+
+constexpr int kBwScratchBufferTensor = 4;
+constexpr int kBwOutputStateTensor = 5;
+constexpr int kBwCellStateTensor = 6;
+constexpr int kBwOutputTensor = 7;
+
+// Check that input tensor dimensions matches with each other.
+TfLiteStatus CheckLstmTensorDimensions(
+ TfLiteContext* context, TfLiteNode* node, int n_input, int n_output,
+ int n_cell, int input_to_input_weights_tensor,
+ int input_to_forget_weights_tensor, int input_to_cell_weights_tensor,
+ int input_to_output_weights_tensor, int recurrent_to_input_weights_tensor,
+ int recurrent_to_forget_weights_tensor,
+ int recurrent_to_cell_weights_tensor,
+ int recurrent_to_output_weights_tensor, int cell_to_input_weights_tensor,
+ int cell_to_forget_weights_tensor, int cell_to_output_weights_tensor,
+ int input_gate_bias_tensor, int forget_gate_bias_tensor,
+ int cell_gate_bias_tensor, int output_gate_bias_tensor,
+ int projection_weights_tensor, int projection_bias_tensor) {
+ auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
+
+ // Making sure clipping parameters have valid values.
+ // == 0 means no clipping
+ // > 0 means clipping
+ TF_LITE_ENSURE(context, params->cell_clip >= 0);
+ TF_LITE_ENSURE(context, params->proj_clip >= 0);
+
+ TfLiteTensor* input_to_input_weights =
+ GetOptionalInputTensor(context, node, input_to_input_weights_tensor);
+ if (input_to_input_weights) {
+ TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell);
+ TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
+ }
+
+ TfLiteTensor* input_to_forget_weights =
+ GetInput(context, node, input_to_forget_weights_tensor);
+ TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
+ TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
+
+ TfLiteTensor* input_to_cell_weights =
+ GetInput(context, node, input_to_cell_weights_tensor);
+ TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
+ TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input);
+
+ TfLiteTensor* recurrent_to_input_weights =
+ GetOptionalInputTensor(context, node, recurrent_to_input_weights_tensor);
+ if (recurrent_to_input_weights) {
+ TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0],
+ n_cell);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1],
+ n_output);
+ }
+
+ TfLiteTensor* recurrent_to_forget_weights =
+ GetInput(context, node, recurrent_to_forget_weights_tensor);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
+ n_cell);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
+ n_output);
+
+ TfLiteTensor* recurrent_to_cell_weights =
+ GetInput(context, node, recurrent_to_cell_weights_tensor);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
+ TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1],
+ n_output);
+
+ // We make sure the input-gate's parameters are either both present (regular
+ // LSTM) or not at all (CIFG-LSTM).
+ const bool cifg_weights_all_or_none =
+ ((input_to_input_weights != nullptr) &&
+ (recurrent_to_input_weights != nullptr)) ||
+ ((input_to_input_weights == nullptr) &&
+ (recurrent_to_input_weights == nullptr));
+ TF_LITE_ENSURE(context, cifg_weights_all_or_none == true);
+
+ TfLiteTensor* cell_to_input_weights =
+ GetOptionalInputTensor(context, node, cell_to_input_weights_tensor);
+ if (cell_to_input_weights) {
+ TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell);
+ }
+
+ TfLiteTensor* cell_to_forget_weights =
+ GetOptionalInputTensor(context, node, cell_to_forget_weights_tensor);
+ if (cell_to_forget_weights) {
+ TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell);
+ }
+
+ TfLiteTensor* cell_to_output_weights =
+ GetOptionalInputTensor(context, node, cell_to_output_weights_tensor);
+ if (cell_to_output_weights) {
+ TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell);
+ }
+
+ // Making sure the peephole weights are there all or none.
+ const bool use_cifg = (input_to_input_weights == nullptr);
+ const bool peephole_weights_all_or_none =
+ ((cell_to_input_weights != nullptr || use_cifg) &&
+ (cell_to_forget_weights != nullptr) &&
+ (cell_to_output_weights != nullptr)) ||
+ ((cell_to_input_weights == nullptr) &&
+ (cell_to_forget_weights == nullptr) &&
+ (cell_to_output_weights == nullptr));
+ TF_LITE_ENSURE(context, peephole_weights_all_or_none == true);
+
+ // Make sure the input gate bias is present only when not a CIFG-LSTM.
+ TfLiteTensor* input_gate_bias =
+ GetOptionalInputTensor(context, node, input_gate_bias_tensor);
+ if (use_cifg) {
+ TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr);
+ } else {
+ TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
+ }
+
+ TfLiteTensor* forget_gate_bias =
+ GetInput(context, node, forget_gate_bias_tensor);
+ TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
+
+ TfLiteTensor* cell_bias = GetInput(context, node, cell_gate_bias_tensor);
+ TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell);
+
+ TfLiteTensor* output_gate_bias =
+ GetInput(context, node, output_gate_bias_tensor);
+ TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
+
+ TfLiteTensor* projection_weights =
+ GetOptionalInputTensor(context, node, projection_weights_tensor);
+ if (projection_weights) {
+ TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output);
+ TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell);
+ }
+
+ TfLiteTensor* projection_bias =
+ GetOptionalInputTensor(context, node, projection_bias_tensor);
+ if (projection_bias) {
+ TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1);
+ TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output);
+ }
+
+ // Making sure the projection tensors are consistent:
+ // 1) If projection weight is not present, then projection bias should not be
+ // present.
+ // 2) If projection weight is present, then projection bias is optional.
+ // TODO(ghodrat): make sure this is correct.
+ const bool projecton_tensors_consistent =
+ ((projection_weights != nullptr) || (projection_bias == nullptr));
+ TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
+
+ return kTfLiteOk;
+}
+
+TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
+ TfLiteNode* node, int n_input,
+ int n_output, int n_cell) {
+ CheckLstmTensorDimensions(
+ context, node, n_input, n_output, n_cell, kFwInputToInputWeightsTensor,
+ kFwInputToForgetWeightsTensor, kFwInputToCellWeightsTensor,
+ kFwInputToOutputWeightsTensor, kFwRecurrentToInputWeightsTensor,
+ kFwRecurrentToForgetWeightsTensor, kFwRecurrentToCellWeightsTensor,
+ kFwRecurrentToOutputWeightsTensor, kFwCellToInputWeightsTensor,
+ kFwCellToForgetWeightsTensor, kFwCellToOutputWeightsTensor,
+ kFwInputGateBiasTensor, kFwForgetGateBiasTensor, kFwCellGateBiasTensor,
+ kFwOutputGateBiasTensor, kFwProjectionWeightsTensor,
+ kFwProjectionBiasTensor);
+
+ CheckLstmTensorDimensions(
+ context, node, n_input, n_output, n_cell, kBwInputToInputWeightsTensor,
+ kBwInputToForgetWeightsTensor, kBwInputToCellWeightsTensor,
+ kBwInputToOutputWeightsTensor, kBwRecurrentToInputWeightsTensor,
+ kBwRecurrentToForgetWeightsTensor, kBwRecurrentToCellWeightsTensor,
+ kBwRecurrentToOutputWeightsTensor, kBwCellToInputWeightsTensor,
+ kBwCellToForgetWeightsTensor, kBwCellToOutputWeightsTensor,
+ kBwInputGateBiasTensor, kBwForgetGateBiasTensor, kBwCellGateBiasTensor,
+ kBwOutputGateBiasTensor, kBwProjectionWeightsTensor,
+ kBwProjectionBiasTensor);
+
+ // Check if Forward and Backward tensors match along required dimensions.
+ return kTfLiteOk;
+}
+
+// Resize the output, state and scratch tensors based on the sizes of the input
+// tensors. Also check that the size of the input tensors match each other.
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+ // Check we have all the inputs and outputs we need.
+ TF_LITE_ENSURE_EQ(context, node->inputs->size, 35);
+ TF_LITE_ENSURE_EQ(context, node->outputs->size, 8);
+
+ // Inferring batch size, number of outputs and sequence length and
+ // number of cells from the input tensors.
+ TfLiteTensor* input = GetInput(context, node, kInputTensor);
+ TF_LITE_ENSURE(context, input->dims->size > 1);
+ const int max_time = input->dims->data[0];
+ const int n_batch = input->dims->data[1];
+ const int n_input = input->dims->data[2];
+
+ TfLiteTensor* fw_input_to_output_weights =
+ GetInput(context, node, kFwInputToOutputWeightsTensor);
+ const int n_fw_cell = fw_input_to_output_weights->dims->data[0];
+ TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->data[1],
+ n_input);
+
+ TfLiteTensor* fw_recurrent_to_output_weights =
+ GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
+ TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->data[0],
+ n_fw_cell);
+ const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1];
+
+ // Check that input tensor dimensions matches with each other.
+ CheckInputTensorDimensions(context, node, n_input, n_fw_output, n_fw_cell);
+
+ // Get the pointer to output, state and scratch buffer tensors.
+ TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor);
+ TfLiteTensor* fw_output_state =
+ GetOutput(context, node, kFwOutputStateTensor);
+ TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor);
+ // TODO(ghodrat): Modify this as soon as we have a finalized method for
+ // scratch buffers.
+ TfLiteTensor* fw_scratch_buffer =
+ GetOutput(context, node, kFwScratchBufferTensor);
+
+ // Resize the output and output_state tensors.
+ TfLiteIntArray* fw_output_size = TfLiteIntArrayCreate(3);
+ fw_output_size->data[0] = max_time;
+ fw_output_size->data[1] = n_batch;
+ fw_output_size->data[2] = n_fw_output;
+ TF_LITE_ENSURE_OK(context,
+ context->ResizeTensor(context, fw_output, fw_output_size));
+
+ TfLiteIntArray* fw_output_state_size = TfLiteIntArrayCreate(2);
+ fw_output_state_size->data[0] = n_batch;
+ fw_output_state_size->data[1] = n_fw_output;
+ TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_output_state,
+ fw_output_state_size));
+
+ // Resize the scratch buffer tensor.
+ TfLiteIntArray* fw_cell_size = TfLiteIntArrayCreate(2);
+ fw_cell_size->data[0] = n_batch;
+ fw_cell_size->data[1] = n_fw_cell;
+ TF_LITE_ENSURE_OK(
+ context, context->ResizeTensor(context, fw_cell_state, fw_cell_size));
+
+ // Mark state tensors as persistent tensors.
+ fw_output_state->allocation_type = kTfLiteArenaRwPersistent;
+ fw_cell_state->allocation_type = kTfLiteArenaRwPersistent;
+
+ TfLiteTensor* fw_input_to_input_weights =
+ GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor);
+ const bool fw_use_cifg = (fw_input_to_input_weights == nullptr);
+ TfLiteIntArray* fw_scratch_buffer_size = TfLiteIntArrayCreate(2);
+ fw_scratch_buffer_size->data[0] = n_batch;
+ if (fw_use_cifg) {
+ // Reserving space for Cell, Forget, Output gates
+ fw_scratch_buffer_size->data[1] = n_fw_cell * 3;
+ } else {
+ // Reserving space for Input, Cell, Forget, Output gates
+ fw_scratch_buffer_size->data[1] = n_fw_cell * 4;
+ }
+ TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_scratch_buffer,
+ fw_scratch_buffer_size));
+ // Same for the backward cell.
+ TfLiteTensor* bw_input_to_output_weights =
+ GetInput(context, node, kBwInputToOutputWeightsTensor);
+ const int n_bw_cell = bw_input_to_output_weights->dims->data[0];
+ TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->data[1],
+ n_input);
+
+ TfLiteTensor* bw_recurrent_to_output_weights =
+ GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
+ TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->size, 2);
+ TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->data[0],
+ n_bw_cell);
+ const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1];
+
+ // Check that input tensor dimensions matches with each other.
+ CheckInputTensorDimensions(context, node, n_input, n_bw_output, n_bw_cell);
+
+ // Get the pointer to output, state and scratch buffer tensors.
+ TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor);
+ TfLiteTensor* bw_output_state =
+ GetOutput(context, node, kBwOutputStateTensor);
+ TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor);
+ // TODO(ghodrat): Modify this as soon as we have a finalized method for
+ // scratch buffers.
+ TfLiteTensor* bw_scratch_buffer =
+ GetOutput(context, node, kBwScratchBufferTensor);
+
+ // Resize the output and output_state tensors.
+ TfLiteIntArray* bw_output_size = TfLiteIntArrayCreate(3);
+ bw_output_size->data[0] = max_time;
+ bw_output_size->data[1] = n_batch;
+ bw_output_size->data[2] = n_bw_output;
+ TF_LITE_ENSURE_OK(context,
+ context->ResizeTensor(context, bw_output, bw_output_size));
+
+ TfLiteIntArray* bw_output_state_size = TfLiteIntArrayCreate(2);
+ bw_output_state_size->data[0] = n_batch;
+ bw_output_state_size->data[1] = n_bw_output;
+ TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_output_state,
+ bw_output_state_size));
+
+ // Resize the scratch buffer tensor.
+ TfLiteIntArray* bw_cell_size = TfLiteIntArrayCreate(2);
+ bw_cell_size->data[0] = n_batch;
+ bw_cell_size->data[1] = n_bw_cell;
+ TF_LITE_ENSURE_OK(
+ context, context->ResizeTensor(context, bw_cell_state, bw_cell_size));
+
+ // Mark state tensors as persistent tensors.
+ bw_output_state->allocation_type = kTfLiteArenaRwPersistent;
+ bw_cell_state->allocation_type = kTfLiteArenaRwPersistent;
+
+ TfLiteTensor* bw_input_to_input_weights =
+ GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor);
+ const bool bw_use_cifg = (bw_input_to_input_weights == nullptr);
+ TfLiteIntArray* bw_scratch_buffer_size = TfLiteIntArrayCreate(2);
+ bw_scratch_buffer_size->data[0] = n_batch;
+ if (bw_use_cifg) {
+ // Reserving space for Cell, Forget, Output gates
+ bw_scratch_buffer_size->data[1] = n_bw_cell * 3;
+ } else {
+ // Reserving space for Input, Cell, Forget, Output gates
+ bw_scratch_buffer_size->data[1] = n_bw_cell * 4;
+ }
+ TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_scratch_buffer,
+ bw_scratch_buffer_size));
+ return kTfLiteOk;
+}
+
+// Performs an LSTM batch inference step for input specified by input_ptr_batch.
+// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
+// biases (*_bias_ptr), and buffers (*_scratch), along with additional
+// parameters:
+// - params: various LSTM params including activation, clipping, etc.,
+// - use_cifg: use coupled input forget gates,
+// - use_peephole: whether to use peephole connection or not,
+// - n_batch: size of batch,
+// - n_cell: number of cells (or units),
+// - n_input: the input size,
+// - n_output: the output size.
+//
+// The pointers to the hidden state and the output are updated as a result.
+//
+// The pointers with the suffix "_batch" point to data aligned in batch_major
+// order, and each step processes batch_size many inputs from input_ptr_batch,
+// and updates batch_size many outputs and hidden states.
+void LstmBatchStep(
+ const float* input_ptr_batch, const float* input_to_input_weights_ptr,
+ const float* input_to_forget_weights_ptr,
+ const float* input_to_cell_weights_ptr,
+ const float* input_to_output_weights_ptr,
+ const float* recurrent_to_input_weights_ptr,
+ const float* recurrent_to_forget_weights_ptr,
+ const float* recurrent_to_cell_weights_ptr,
+ const float* recurrent_to_output_weights_ptr,
+ const float* cell_to_input_weights_ptr,
+ const float* cell_to_forget_weights_ptr,
+ const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr,
+ const float* forget_gate_bias_ptr, const float* cell_bias_ptr,
+ const float* output_gate_bias_ptr, const float* projection_weights_ptr,
+ const float* projection_bias_ptr, const TfLiteLSTMParams* params,
+ bool use_cifg, bool use_peephole, int n_batch, int n_cell, int n_input,
+ int n_output, float* output_state_ptr, float* cell_state_ptr,
+ float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch,
+ float* output_gate_scratch, float* output_ptr_time) {
+ // Initialize scratch buffers with bias.
+ if (!use_cifg) {
+ tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch,
+ input_gate_scratch);
+ }
+ tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch,
+ forget_gate_scratch);
+ tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch,
+ cell_scratch);
+ tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch,
+ output_gate_scratch);
+
+ // For each batch and cell: compute input_weight * input.
+ if (!use_cifg) {
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+ input_gate_scratch, /*result_stride=*/1);
+ }
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+ forget_gate_scratch, /*result_stride=*/1);
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+ cell_scratch, /*result_stride=*/1);
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+ output_gate_scratch, /*result_stride=*/1);
+
+ // For each batch and cell: compute recurrent_weight * output_state.
+ if (!use_cifg) {
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr,
+ n_batch, input_gate_scratch,
+ /*result_stride=*/1);
+ }
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr,
+ n_batch, forget_gate_scratch,
+ /*result_stride=*/1);
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr,
+ n_batch, cell_scratch, /*result_stride=*/1);
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr,
+ n_batch, output_gate_scratch,
+ /*result_stride=*/1);
+
+ // For each batch and cell: update input gate.
+ if (!use_cifg) {
+ if (use_peephole) {
+ tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+ cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch,
+ input_gate_scratch);
+ }
+ tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
+ input_gate_scratch);
+ }
+
+ // For each batch and cell: update forget gate.
+ if (use_peephole) {
+ tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+ cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch,
+ forget_gate_scratch);
+ }
+ tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
+ forget_gate_scratch);
+
+ // For each batch and cell: update the cell.
+ tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
+ n_batch * n_cell, cell_state_ptr);
+ tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
+ params->activation, cell_scratch);
+ if (use_cifg) {
+ tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
+ forget_gate_scratch);
+ tensor_utils::VectorVectorCwiseProductAccumulate(
+ cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr);
+ } else {
+ tensor_utils::VectorVectorCwiseProductAccumulate(
+ cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr);
+ }
+ if (params->cell_clip > 0.0) {
+ tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell,
+ params->cell_clip, cell_state_ptr);
+ }
+
+ // For each batch and cell: update the output gate.
+ if (use_peephole) {
+ tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+ cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch,
+ output_gate_scratch);
+ }
+ tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
+ output_gate_scratch);
+ tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell,
+ params->activation, cell_scratch);
+ tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
+ n_batch * n_cell, output_gate_scratch);
+
+ // For each batch: update the projection and output_state.
+ const bool use_projection_weight = (projection_weights_ptr != nullptr);
+ const bool use_projection_bias = (projection_bias_ptr != nullptr);
+ if (use_projection_weight) {
+ if (use_projection_bias) {
+ tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output,
+ n_batch, output_ptr_time);
+ } else {
+ tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output);
+ }
+ tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+ projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch,
+ output_ptr_time, /*result_stride=*/1);
+ if (params->proj_clip > 0.0) {
+ tensor_utils::ClipVector(output_ptr_time, n_batch * n_output,
+ params->proj_clip, output_ptr_time);
+ }
+ } else {
+ tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
+ output_ptr_time);
+ }
+ tensor_utils::CopyVector(output_ptr_time, n_batch * n_output,
+ output_state_ptr);
+}
+
+// The LSTM Op engine.
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+ auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
+
+ // Input tensor.
+ TfLiteTensor* input = GetInput(context, node, kInputTensor);
+ const int max_time = input->dims->data[0];
+ const int n_batch = input->dims->data[1];
+ const int n_input = input->dims->data[2];
+
+ // Tensors for the forward cell.
+ TfLiteTensor* fw_input_to_input_weights =
+ GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor);
+ TfLiteTensor* fw_input_to_forget_weights =
+ GetInput(context, node, kFwInputToForgetWeightsTensor);
+ TfLiteTensor* fw_input_to_cell_weights =
+ GetInput(context, node, kFwInputToCellWeightsTensor);
+ TfLiteTensor* fw_input_to_output_weights =
+ GetInput(context, node, kFwInputToOutputWeightsTensor);
+
+ TfLiteTensor* fw_recurrent_to_input_weights =
+ GetOptionalInputTensor(context, node, kFwRecurrentToInputWeightsTensor);
+ TfLiteTensor* fw_recurrent_to_forget_weights =
+ GetInput(context, node, kFwRecurrentToForgetWeightsTensor);
+ TfLiteTensor* fw_recurrent_to_cell_weights =
+ GetInput(context, node, kFwRecurrentToCellWeightsTensor);
+ TfLiteTensor* fw_recurrent_to_output_weights =
+ GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
+
+ TfLiteTensor* fw_cell_to_input_weights =
+ GetOptionalInputTensor(context, node, kFwCellToInputWeightsTensor);
+ TfLiteTensor* fw_cell_to_forget_weights =
+ GetOptionalInputTensor(context, node, kFwCellToForgetWeightsTensor);
+ TfLiteTensor* fw_cell_to_output_weights =
+ GetOptionalInputTensor(context, node, kFwCellToOutputWeightsTensor);
+
+ TfLiteTensor* fw_input_gate_bias =
+ GetOptionalInputTensor(context, node, kFwInputGateBiasTensor);
+ TfLiteTensor* fw_forget_gate_bias =
+ GetInput(context, node, kFwForgetGateBiasTensor);
+ TfLiteTensor* fw_cell_bias = GetInput(context, node, kFwCellGateBiasTensor);
+ TfLiteTensor* fw_output_gate_bias =
+ GetInput(context, node, kFwOutputGateBiasTensor);
+
+ TfLiteTensor* fw_projection_weights =
+ GetOptionalInputTensor(context, node, kFwProjectionWeightsTensor);
+ TfLiteTensor* fw_projection_bias =
+ GetOptionalInputTensor(context, node, kFwProjectionBiasTensor);
+
+ TfLiteTensor* fw_output_state =
+ GetOutput(context, node, kFwOutputStateTensor);
+ TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor);
+ TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor);
+
+ // Tensors for the backward cell.
+ TfLiteTensor* bw_input_to_input_weights =
+ GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor);
+ TfLiteTensor* bw_input_to_forget_weights =
+ GetInput(context, node, kBwInputToForgetWeightsTensor);
+ TfLiteTensor* bw_input_to_cell_weights =
+ GetInput(context, node, kBwInputToCellWeightsTensor);
+ TfLiteTensor* bw_input_to_output_weights =
+ GetInput(context, node, kBwInputToOutputWeightsTensor);
+
+ TfLiteTensor* bw_recurrent_to_input_weights =
+ GetOptionalInputTensor(context, node, kBwRecurrentToInputWeightsTensor);
+ TfLiteTensor* bw_recurrent_to_forget_weights =
+ GetInput(context, node, kBwRecurrentToForgetWeightsTensor);
+ TfLiteTensor* bw_recurrent_to_cell_weights =
+ GetInput(context, node, kBwRecurrentToCellWeightsTensor);
+ TfLiteTensor* bw_recurrent_to_output_weights =
+ GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
+
+ TfLiteTensor* bw_cell_to_input_weights =
+ GetOptionalInputTensor(context, node, kBwCellToInputWeightsTensor);
+ TfLiteTensor* bw_cell_to_forget_weights =
+ GetOptionalInputTensor(context, node, kBwCellToForgetWeightsTensor);
+ TfLiteTensor* bw_cell_to_output_weights =
+ GetOptionalInputTensor(context, node, kBwCellToOutputWeightsTensor);
+
+ TfLiteTensor* bw_input_gate_bias =
+ GetOptionalInputTensor(context, node, kBwInputGateBiasTensor);
+ TfLiteTensor* bw_forget_gate_bias =
+ GetInput(context, node, kBwForgetGateBiasTensor);
+ TfLiteTensor* bw_cell_bias = GetInput(context, node, kBwCellGateBiasTensor);
+ TfLiteTensor* bw_output_gate_bias =
+ GetInput(context, node, kBwOutputGateBiasTensor);
+
+ TfLiteTensor* bw_projection_weights =
+ GetOptionalInputTensor(context, node, kBwProjectionWeightsTensor);
+ TfLiteTensor* bw_projection_bias =
+ GetOptionalInputTensor(context, node, kBwProjectionBiasTensor);
+
+ TfLiteTensor* bw_output_state =
+ GetOutput(context, node, kBwOutputStateTensor);
+ TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor);
+ TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor);
+
+ // n_cell and n_output will be the same size when there is no projection.
+ const int n_fw_cell = fw_input_to_output_weights->dims->data[0];
+ const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1];
+
+ // Since we have already checked that weights are all there or none, we can
+ // check the existense of only one to the get the condition.
+ const bool fw_use_cifg = (fw_input_to_input_weights == nullptr);
+ const bool fw_use_peephole = (fw_cell_to_output_weights != nullptr);
+
+ // Index the scratch buffers pointers to the global scratch buffer.
+ TfLiteTensor* fw_scratch_buffer =
+ GetOutput(context, node, kFwScratchBufferTensor);
+ float* fw_input_gate_scratch = nullptr;
+ float* fw_cell_scratch = nullptr;
+ float* fw_forget_gate_scratch = nullptr;
+ float* fw_output_gate_scratch = nullptr;
+ if (fw_use_cifg) {
+ fw_cell_scratch = fw_scratch_buffer->data.f;
+ fw_forget_gate_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch;
+ fw_output_gate_scratch =
+ fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch;
+ } else {
+ fw_input_gate_scratch = fw_scratch_buffer->data.f;
+ fw_cell_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch;
+ fw_forget_gate_scratch =
+ fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch;
+ fw_output_gate_scratch =
+ fw_scratch_buffer->data.f + 3 * n_fw_cell * n_batch;
+ }
+
+ // Check optional tensors, the respective pointers can be null.
+ const float* fw_input_to_input_weights_ptr =
+ (fw_use_cifg) ? nullptr : fw_input_to_input_weights->data.f;
+ const float* fw_recurrent_to_input_weights_ptr =
+ (fw_use_cifg) ? nullptr : fw_recurrent_to_input_weights->data.f;
+ const float* fw_input_gate_bias_ptr =
+ (fw_use_cifg) ? nullptr : fw_input_gate_bias->data.f;
+ const float* fw_cell_to_input_weights_ptr =
+ (fw_use_peephole && !fw_use_cifg) ? fw_cell_to_input_weights->data.f
+ : nullptr;
+ const float* fw_cell_to_forget_weights_ptr =
+ (fw_use_peephole) ? fw_cell_to_forget_weights->data.f : nullptr;
+ const float* fw_cell_to_output_weights_ptr =
+ (fw_use_peephole) ? fw_cell_to_output_weights->data.f : nullptr;
+ const float* fw_projection_weights_ptr = (fw_projection_weights == nullptr)
+ ? nullptr
+ : fw_projection_weights->data.f;
+ const float* fw_projection_bias_ptr =
+ (fw_projection_bias == nullptr) ? nullptr : fw_projection_bias->data.f;
+
+ // Loop through the sequence.
+ for (int t = 0; t < max_time; t++) {
+ const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
+ float* output_ptr_time = fw_output->data.f + t * n_batch * n_fw_output;
+
+ LstmBatchStep(
+ input_ptr_batch, fw_input_to_input_weights_ptr,
+ fw_input_to_forget_weights->data.f, fw_input_to_cell_weights->data.f,
+ fw_input_to_output_weights->data.f, fw_recurrent_to_input_weights_ptr,
+ fw_recurrent_to_forget_weights->data.f,
+ fw_recurrent_to_cell_weights->data.f,
+ fw_recurrent_to_output_weights->data.f, fw_cell_to_input_weights_ptr,
+ fw_cell_to_forget_weights_ptr, fw_cell_to_output_weights_ptr,
+ fw_input_gate_bias_ptr, fw_forget_gate_bias->data.f,
+ fw_cell_bias->data.f, fw_output_gate_bias->data.f,
+ fw_projection_weights_ptr, fw_projection_bias_ptr, params, fw_use_cifg,
+ fw_use_peephole, n_batch, n_fw_cell, n_input, n_fw_output,
+ fw_output_state->data.f, fw_cell_state->data.f, fw_input_gate_scratch,
+ fw_forget_gate_scratch, fw_cell_scratch, fw_output_gate_scratch,
+ output_ptr_time);
+ }
+
+ // n_cell and n_output will be the same size when there is no projection.
+ const int n_bw_cell = bw_input_to_output_weights->dims->data[0];
+ const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1];
+
+ // Since we have already checked that weights are all there or none, we can
+ // check the existense of only one to the get the condition.
+ const bool bw_use_cifg = (bw_input_to_input_weights == nullptr);
+ const bool bw_use_peephole = (bw_cell_to_output_weights != nullptr);
+
+ // Index the scratch buffers pointers to the global scratch buffer.
+ TfLiteTensor* bw_scratch_buffer =
+ GetOutput(context, node, kBwScratchBufferTensor);
+ float* bw_input_gate_scratch = nullptr;
+ float* bw_cell_scratch = nullptr;
+ float* bw_forget_gate_scratch = nullptr;
+ float* bw_output_gate_scratch = nullptr;
+ if (bw_use_cifg) {
+ bw_cell_scratch = bw_scratch_buffer->data.f;
+ bw_forget_gate_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch;
+ bw_output_gate_scratch =
+ bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch;
+ } else {
+ bw_input_gate_scratch = bw_scratch_buffer->data.f;
+ bw_cell_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch;
+ bw_forget_gate_scratch =
+ bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch;
+ bw_output_gate_scratch =
+ bw_scratch_buffer->data.f + 3 * n_bw_cell * n_batch;
+ }
+
+ // Check optional tensors, the respective pointers can be null.
+ const float* bw_input_to_input_weights_ptr =
+ (bw_use_cifg) ? nullptr : bw_input_to_input_weights->data.f;
+ const float* bw_recurrent_to_input_weights_ptr =
+ (bw_use_cifg) ? nullptr : bw_recurrent_to_input_weights->data.f;
+ const float* bw_input_gate_bias_ptr =
+ (bw_use_cifg) ? nullptr : bw_input_gate_bias->data.f;
+ const float* bw_cell_to_input_weights_ptr =
+ (bw_use_peephole && !bw_use_cifg) ? bw_cell_to_input_weights->data.f
+ : nullptr;
+ const float* bw_cell_to_forget_weights_ptr =
+ (bw_use_peephole) ? bw_cell_to_forget_weights->data.f : nullptr;
+ const float* bw_cell_to_output_weights_ptr =
+ (bw_use_peephole) ? bw_cell_to_output_weights->data.f : nullptr;
+ const float* bw_projection_weights_ptr = (bw_projection_weights == nullptr)
+ ? nullptr
+ : bw_projection_weights->data.f;
+ const float* bw_projection_bias_ptr =
+ (bw_projection_bias == nullptr) ? nullptr : bw_projection_bias->data.f;
+
+ // Loop through the sequence backwards.
+ for (int t = max_time - 1; t >= 0; t--) {
+ const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
+ float* output_ptr_time = bw_output->data.f + t * n_batch * n_bw_output;
+
+ LstmBatchStep(
+ input_ptr_batch, bw_input_to_input_weights_ptr,
+ bw_input_to_forget_weights->data.f, bw_input_to_cell_weights->data.f,
+ bw_input_to_output_weights->data.f, bw_recurrent_to_input_weights_ptr,
+ bw_recurrent_to_forget_weights->data.f,
+ bw_recurrent_to_cell_weights->data.f,
+ bw_recurrent_to_output_weights->data.f, bw_cell_to_input_weights_ptr,
+ bw_cell_to_forget_weights_ptr, bw_cell_to_output_weights_ptr,
+ bw_input_gate_bias_ptr, bw_forget_gate_bias->data.f,
+ bw_cell_bias->data.f, bw_output_gate_bias->data.f,
+ bw_projection_weights_ptr, bw_projection_bias_ptr, params, bw_use_cifg,
+ bw_use_peephole, n_batch, n_bw_cell, n_input, n_bw_output,
+ bw_output_state->data.f, bw_cell_state->data.f, bw_input_gate_scratch,
+ bw_forget_gate_scratch, bw_cell_scratch, bw_output_gate_scratch,
+ output_ptr_time);
+ }
+
+ // Backward step.
+ return kTfLiteOk;
+}
+
+} // namespace bidirectional_sequence_lstm
+
+TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM() {
+ static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
+ bidirectional_sequence_lstm::Prepare,
+ bidirectional_sequence_lstm::Eval};
+ return &r;
+}
+
+} // namespace builtin
+} // namespace ops
+} // namespace tflite
--- /dev/null
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// Unit test for TFLite Bidirectional LSTM op.
+
+#include <iomanip>
+#include <memory>
+#include <vector>
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+class BidirectionalLSTMOpModel : public SingleOpModel {
+ public:
+ BidirectionalLSTMOpModel(int n_batch, int n_input, int n_cell, int n_output,
+ int sequence_length, bool use_cifg,
+ bool use_peephole, bool use_projection_weights,
+ bool use_projection_bias, float cell_clip,
+ float proj_clip,
+ const std::vector<std::vector<int>>& input_shapes)
+ : n_batch_(n_batch),
+ n_input_(n_input),
+ n_fw_cell_(n_cell),
+ n_bw_cell_(n_cell),
+ n_fw_output_(n_output),
+ n_bw_output_(n_output),
+ sequence_length_(sequence_length) {
+ input_ = AddInput(TensorType_FLOAT32);
+
+ if (use_cifg) {
+ fw_input_to_input_weights_ = AddNullInput();
+ } else {
+ fw_input_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+
+ fw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ fw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+ fw_input_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+ if (use_cifg) {
+ fw_recurrent_to_input_weights_ = AddNullInput();
+ } else {
+ fw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+
+ fw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ fw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+ fw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+ if (use_peephole) {
+ if (use_cifg) {
+ fw_cell_to_input_weights_ = AddNullInput();
+ } else {
+ fw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+ fw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ fw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32);
+ } else {
+ fw_cell_to_input_weights_ = AddNullInput();
+ fw_cell_to_forget_weights_ = AddNullInput();
+ fw_cell_to_output_weights_ = AddNullInput();
+ }
+
+ if (use_cifg) {
+ fw_input_gate_bias_ = AddNullInput();
+ } else {
+ fw_input_gate_bias_ = AddInput(TensorType_FLOAT32);
+ }
+ fw_forget_gate_bias_ = AddInput(TensorType_FLOAT32);
+ fw_cell_bias_ = AddInput(TensorType_FLOAT32);
+ fw_output_gate_bias_ = AddInput(TensorType_FLOAT32);
+
+ if (use_projection_weights) {
+ fw_projection_weights_ = AddInput(TensorType_FLOAT32);
+ if (use_projection_bias) {
+ fw_projection_bias_ = AddInput(TensorType_FLOAT32);
+ } else {
+ fw_projection_bias_ = AddNullInput();
+ }
+ } else {
+ fw_projection_weights_ = AddNullInput();
+ fw_projection_bias_ = AddNullInput();
+ }
+
+ fw_scratch_buffer_ = AddOutput(TensorType_FLOAT32);
+ // TODO(ghodrat): Modify these states when we have a permanent solution for
+ // persistent buffer.
+ fw_output_state_ = AddOutput(TensorType_FLOAT32);
+ fw_cell_state_ = AddOutput(TensorType_FLOAT32);
+ fw_output_ = AddOutput(TensorType_FLOAT32);
+
+ if (use_cifg) {
+ bw_input_to_input_weights_ = AddNullInput();
+ } else {
+ bw_input_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+
+ bw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ bw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+ bw_input_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+ if (use_cifg) {
+ bw_recurrent_to_input_weights_ = AddNullInput();
+ } else {
+ bw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+
+ bw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ bw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+ bw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+ if (use_peephole) {
+ if (use_cifg) {
+ bw_cell_to_input_weights_ = AddNullInput();
+ } else {
+ bw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32);
+ }
+ bw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+ bw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32);
+ } else {
+ bw_cell_to_input_weights_ = AddNullInput();
+ bw_cell_to_forget_weights_ = AddNullInput();
+ bw_cell_to_output_weights_ = AddNullInput();
+ }
+
+ if (use_cifg) {
+ bw_input_gate_bias_ = AddNullInput();
+ } else {
+ bw_input_gate_bias_ = AddInput(TensorType_FLOAT32);
+ }
+ bw_forget_gate_bias_ = AddInput(TensorType_FLOAT32);
+ bw_cell_bias_ = AddInput(TensorType_FLOAT32);
+ bw_output_gate_bias_ = AddInput(TensorType_FLOAT32);
+
+ if (use_projection_weights) {
+ bw_projection_weights_ = AddInput(TensorType_FLOAT32);
+ if (use_projection_bias) {
+ bw_projection_bias_ = AddInput(TensorType_FLOAT32);
+ } else {
+ bw_projection_bias_ = AddNullInput();
+ }
+ } else {
+ bw_projection_weights_ = AddNullInput();
+ bw_projection_bias_ = AddNullInput();
+ }
+
+ bw_scratch_buffer_ = AddOutput(TensorType_FLOAT32);
+ // TODO(ghodrat): Modify these states when we have a permanent solution for
+ // persistent buffer.
+ bw_output_state_ = AddOutput(TensorType_FLOAT32);
+ bw_cell_state_ = AddOutput(TensorType_FLOAT32);
+ bw_output_ = AddOutput(TensorType_FLOAT32);
+
+ SetBuiltinOp(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
+ BuiltinOptions_LSTMOptions,
+ CreateLSTMOptions(builder_, ActivationFunctionType_TANH,
+ cell_clip, proj_clip)
+ .Union());
+ BuildInterpreter(input_shapes);
+ }
+
+ // Set weights in forward and backward cells to be the same.
+ void SetInputToInputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_input_to_input_weights_, f);
+ PopulateTensor(bw_input_to_input_weights_, f);
+ }
+
+ void SetInputToForgetWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_input_to_forget_weights_, f);
+ PopulateTensor(bw_input_to_forget_weights_, f);
+ }
+
+ void SetInputToCellWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_input_to_cell_weights_, f);
+ PopulateTensor(bw_input_to_cell_weights_, f);
+ }
+
+ void SetInputToOutputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_input_to_output_weights_, f);
+ PopulateTensor(bw_input_to_output_weights_, f);
+ }
+
+ void SetRecurrentToInputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_recurrent_to_input_weights_, f);
+ PopulateTensor(bw_recurrent_to_input_weights_, f);
+ }
+
+ void SetRecurrentToForgetWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_recurrent_to_forget_weights_, f);
+ PopulateTensor(bw_recurrent_to_forget_weights_, f);
+ }
+
+ void SetRecurrentToCellWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_recurrent_to_cell_weights_, f);
+ PopulateTensor(bw_recurrent_to_cell_weights_, f);
+ }
+
+ void SetRecurrentToOutputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_recurrent_to_output_weights_, f);
+ PopulateTensor(bw_recurrent_to_output_weights_, f);
+ }
+
+ void SetCellToInputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_cell_to_input_weights_, f);
+ PopulateTensor(bw_cell_to_input_weights_, f);
+ }
+
+ void SetCellToForgetWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_cell_to_forget_weights_, f);
+ PopulateTensor(bw_cell_to_forget_weights_, f);
+ }
+
+ void SetCellToOutputWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_cell_to_output_weights_, f);
+ PopulateTensor(bw_cell_to_output_weights_, f);
+ }
+
+ void SetInputGateBias(std::initializer_list<float> f) {
+ PopulateTensor(fw_input_gate_bias_, f);
+ PopulateTensor(bw_input_gate_bias_, f);
+ }
+
+ void SetForgetGateBias(std::initializer_list<float> f) {
+ PopulateTensor(fw_forget_gate_bias_, f);
+ PopulateTensor(bw_forget_gate_bias_, f);
+ }
+
+ void SetCellBias(std::initializer_list<float> f) {
+ PopulateTensor(fw_cell_bias_, f);
+ PopulateTensor(bw_cell_bias_, f);
+ }
+
+ void SetOutputGateBias(std::initializer_list<float> f) {
+ PopulateTensor(fw_output_gate_bias_, f);
+ PopulateTensor(bw_output_gate_bias_, f);
+ }
+
+ void SetProjectionWeights(std::initializer_list<float> f) {
+ PopulateTensor(fw_projection_weights_, f);
+ PopulateTensor(bw_projection_weights_, f);
+ }
+
+ void SetProjectionBias(std::initializer_list<float> f) {
+ PopulateTensor(fw_projection_bias_, f);
+ PopulateTensor(bw_projection_bias_, f);
+ }
+
+ void ResetFwOutputAndCellStates() {
+ const int zero_buffer_size = n_fw_cell_ * n_batch_;
+ std::unique_ptr<float[]> zero_buffer(new float[zero_buffer_size]);
+ memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float));
+ PopulateTensor(fw_output_state_, 0, zero_buffer.get(),
+ zero_buffer.get() + zero_buffer_size);
+ PopulateTensor(fw_cell_state_, 0, zero_buffer.get(),
+ zero_buffer.get() + zero_buffer_size);
+ }
+
+ void ResetBwOutputAndCellStates() {
+ const int zero_buffer_size = n_bw_cell_ * n_batch_;
+ std::unique_ptr<float[]> zero_buffer(new float[zero_buffer_size]);
+ memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float));
+ PopulateTensor(bw_output_state_, 0, zero_buffer.get(),
+ zero_buffer.get() + zero_buffer_size);
+ PopulateTensor(bw_cell_state_, 0, zero_buffer.get(),
+ zero_buffer.get() + zero_buffer_size);
+ }
+
+ void SetInput(int offset, float* begin, float* end) {
+ PopulateTensor(input_, offset, begin, end);
+ }
+
+ std::vector<float> GetFwOutput() { return ExtractVector<float>(fw_output_); }
+ std::vector<float> GetBwOutput() { return ExtractVector<float>(bw_output_); }
+
+ int num_inputs() { return n_input_; }
+ int num_fw_outputs() { return n_fw_output_; }
+ int num_bw_outputs() { return n_bw_output_; }
+ int num_fw_cells() { return n_fw_cell_; }
+ int num_bw_cells() { return n_bw_cell_; }
+ int num_batches() { return n_batch_; }
+ int sequence_length() { return sequence_length_; }
+
+ private:
+ int input_;
+ int fw_input_to_input_weights_;
+ int fw_input_to_forget_weights_;
+ int fw_input_to_cell_weights_;
+ int fw_input_to_output_weights_;
+
+ int fw_recurrent_to_input_weights_;
+ int fw_recurrent_to_forget_weights_;
+ int fw_recurrent_to_cell_weights_;
+ int fw_recurrent_to_output_weights_;
+
+ int fw_cell_to_input_weights_;
+ int fw_cell_to_forget_weights_;
+ int fw_cell_to_output_weights_;
+
+ int fw_input_gate_bias_;
+ int fw_forget_gate_bias_;
+ int fw_cell_bias_;
+ int fw_output_gate_bias_;
+
+ int fw_projection_weights_;
+ int fw_projection_bias_;
+
+ int bw_input_to_input_weights_;
+ int bw_input_to_forget_weights_;
+ int bw_input_to_cell_weights_;
+ int bw_input_to_output_weights_;
+
+ int bw_recurrent_to_input_weights_;
+ int bw_recurrent_to_forget_weights_;
+ int bw_recurrent_to_cell_weights_;
+ int bw_recurrent_to_output_weights_;
+
+ int bw_cell_to_input_weights_;
+ int bw_cell_to_forget_weights_;
+ int bw_cell_to_output_weights_;
+
+ int bw_input_gate_bias_;
+ int bw_forget_gate_bias_;
+ int bw_cell_bias_;
+ int bw_output_gate_bias_;
+
+ int bw_projection_weights_;
+ int bw_projection_bias_;
+
+ int fw_output_;
+ int fw_output_state_;
+ int fw_cell_state_;
+ int fw_scratch_buffer_;
+
+ int bw_output_;
+ int bw_output_state_;
+ int bw_cell_state_;
+ int bw_scratch_buffer_;
+
+ int n_batch_;
+ int n_input_;
+ int n_fw_cell_;
+ int n_bw_cell_;
+ int n_fw_output_;
+ int n_bw_output_;
+ int sequence_length_;
+};
+
+TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) {
+ const int n_batch = 1;
+ const int n_input = 2;
+ // n_cell and n_output have the same size when there is no projection.
+ const int n_cell = 4;
+ const int n_output = 4;
+ const int sequence_length = 3;
+
+ BidirectionalLSTMOpModel lstm(
+ n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false,
+ /*use_peephole=*/false, /*use_projection_weights=*/false,
+ /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+ {
+ {sequence_length, n_batch, n_input}, // input tensor
+
+ // Forward cell
+ {n_cell, n_input}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {n_cell, n_output}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {0}, // cell_to_input_weight tensor
+ {0}, // cell_to_forget_weight tensor
+ {0}, // cell_to_output_weight tensor
+
+ {n_cell}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {0, 0}, // projection_weight tensor
+ {0}, // projection_bias tensor
+
+ // Backward cell
+ {n_cell, n_input}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {n_cell, n_output}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {0}, // cell_to_input_weight tensor
+ {0}, // cell_to_forget_weight tensor
+ {0}, // cell_to_output_weight tensor
+
+ {n_cell}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {0, 0}, // projection_weight tensor
+ {0}, // projection_bias tensor
+ });
+
+ lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589,
+ -0.34550029, 0.04266912, -0.15680569,
+ -0.34856534, 0.43890524});
+
+ lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163,
+ -0.20583314, 0.44344562, 0.22077113,
+ -0.29909778});
+
+ lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935,
+ -0.31343272, -0.40032279, 0.44781327,
+ 0.01387155, -0.35593212});
+
+ lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829,
+ 0.40525138, 0.44272184, 0.03897077, -0.1556896,
+ 0.19487578});
+
+ lstm.SetInputGateBias({0., 0., 0., 0.});
+
+ lstm.SetCellBias({0., 0., 0., 0.});
+
+ lstm.SetForgetGateBias({1., 1., 1., 1.});
+
+ lstm.SetOutputGateBias({0., 0., 0., 0.});
+
+ lstm.SetRecurrentToInputWeights(
+ {-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});
+
+ lstm.SetRecurrentToCellWeights(
+ {-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});
+
+ lstm.SetRecurrentToForgetWeights(
+ {-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});
+
+ lstm.SetRecurrentToOutputWeights(
+ {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});
+
+ // Input should have n_input * sequence_length many values.
+ static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
+ static float lstm_fw_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};
+ static float lstm_bw_golden_output[] = {
+ -0.0806187, 0.139077, 0.400476, -0.197842,
+ -0.0332076, 0.123838, 0.309777, -0.17621,
+ -0.0490733, 0.0739237, 0.067706, -0.0208124};
+
+ // Resetting cell_state and output_state
+ lstm.ResetFwOutputAndCellStates();
+ lstm.ResetBwOutputAndCellStates();
+
+ float* batch0_start = lstm_input;
+ float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+ lstm.SetInput(0, batch0_start, batch0_end);
+
+ lstm.Invoke();
+
+ float* fw_golden_start = lstm_fw_golden_output;
+ float* fw_golden_end =
+ fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length();
+ std::vector<float> fw_expected;
+ fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end);
+ EXPECT_THAT(lstm.GetFwOutput(),
+ ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+ float* bw_golden_start = lstm_bw_golden_output;
+ float* bw_golden_end =
+ bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length();
+ std::vector<float> bw_expected;
+ bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end);
+ EXPECT_THAT(lstm.GetBwOutput(),
+ ElementsAreArray(ArrayFloatNear(bw_expected)));
+
+ // Check reversed inputs.
+ static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.};
+
+ // Resetting cell_state and output_state
+ lstm.ResetFwOutputAndCellStates();
+ lstm.ResetBwOutputAndCellStates();
+
+ batch0_start = lstm_input_reversed;
+ batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+ lstm.SetInput(0, batch0_start, batch0_end);
+
+ lstm.Invoke();
+
+ fw_expected.clear();
+ for (int s = 0; s < lstm.sequence_length(); s++) {
+ fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs();
+ fw_golden_end = fw_golden_start + lstm.num_fw_outputs();
+ fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end);
+ }
+ EXPECT_THAT(lstm.GetBwOutput(),
+ ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+ bw_expected.clear();
+ for (int s = 0; s < lstm.sequence_length(); s++) {
+ bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs();
+ bw_golden_end = bw_golden_start + lstm.num_bw_outputs();
+ bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end);
+ }
+ EXPECT_THAT(lstm.GetFwOutput(),
+ ElementsAreArray(ArrayFloatNear(bw_expected)));
+}
+
+TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) {
+ const int n_batch = 1;
+ const int n_input = 2;
+ // n_cell and n_output have the same size when there is no projection.
+ const int n_cell = 4;
+ const int n_output = 4;
+ const int sequence_length = 3;
+
+ BidirectionalLSTMOpModel lstm(
+ n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/true,
+ /*use_peephole=*/true, /*use_projection_weights=*/false,
+ /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+ {
+ {sequence_length, n_batch, n_input}, // input tensor
+
+ {0, 0}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {0, 0}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {0}, // cell_to_input_weight tensor
+ {n_cell}, // cell_to_forget_weight tensor
+ {n_cell}, // cell_to_output_weight tensor
+
+ {0}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {0, 0}, // projection_weight tensor
+ {0}, // projection_bias tensor
+
+ {0, 0}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {0, 0}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {0}, // cell_to_input_weight tensor
+ {n_cell}, // cell_to_forget_weight tensor
+ {n_cell}, // cell_to_output_weight tensor
+
+ {0}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {0, 0}, // projection_weight tensor
+ {0}, // projection_bias tensor
+ });
+
+ lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781,
+ 0.04717243, 0.48944736, -0.38535351,
+ -0.17212132});
+
+ lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988,
+ -0.3633365, -0.22755712, 0.28253698, 0.24407166,
+ 0.33826375});
+
+ lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593,
+ -0.09426838, -0.44257352, 0.54939759,
+ 0.01533556, 0.42751634});
+
+ lstm.SetCellBias({0., 0., 0., 0.});
+
+ lstm.SetForgetGateBias({1., 1., 1., 1.});
+
+ lstm.SetOutputGateBias({0., 0., 0., 0.});
+
+ lstm.SetRecurrentToCellWeights(
+ {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
+ 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
+ 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
+ 0.21193194});
+
+ lstm.SetRecurrentToForgetWeights(
+ {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
+ 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
+ -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349});
+
+ lstm.SetRecurrentToOutputWeights(
+ {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
+ -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
+ 0.50248802, 0.26114327, -0.43736315, 0.33149987});
+
+ lstm.SetCellToForgetWeights(
+ {0.47485286, -0.51955009, -0.24458408, 0.31544167});
+ lstm.SetCellToOutputWeights(
+ {-0.17135078, 0.82760304, 0.85573703, -0.77109635});
+
+ static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
+ static float lstm_fw_golden_output[] = {
+ -0.36444446, -0.00352185, 0.12886585, -0.05163646,
+ -0.42312205, -0.01218222, 0.24201041, -0.08124574,
+ -0.358325, -0.04621704, 0.21641694, -0.06471302};
+ static float lstm_bw_golden_output[] = {
+ -0.401685, -0.0232794, 0.288642, -0.123074, -0.42915, -0.00871577,
+ 0.20912, -0.103567, -0.166398, -0.00486649, 0.0697471, -0.0537578};
+
+ // Resetting cell_state and output_state
+ lstm.ResetFwOutputAndCellStates();
+ lstm.ResetBwOutputAndCellStates();
+
+ float* batch0_start = lstm_input;
+ float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+ lstm.SetInput(0, batch0_start, batch0_end);
+
+ lstm.Invoke();
+
+ float* fw_golden_start = lstm_fw_golden_output;
+ float* fw_golden_end =
+ fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length();
+ std::vector<float> fw_expected;
+ fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end);
+ EXPECT_THAT(lstm.GetFwOutput(),
+ ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+ float* bw_golden_start = lstm_bw_golden_output;
+ float* bw_golden_end =
+ bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length();
+ std::vector<float> bw_expected;
+ bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end);
+ EXPECT_THAT(lstm.GetBwOutput(),
+ ElementsAreArray(ArrayFloatNear(bw_expected)));
+
+ // Check reversed inputs.
+ static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.};
+
+ // Resetting cell_state and output_state
+ lstm.ResetFwOutputAndCellStates();
+ lstm.ResetBwOutputAndCellStates();
+
+ batch0_start = lstm_input_reversed;
+ batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+ lstm.SetInput(0, batch0_start, batch0_end);
+
+ lstm.Invoke();
+
+ fw_expected.clear();
+ for (int s = 0; s < lstm.sequence_length(); s++) {
+ fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs();
+ fw_golden_end = fw_golden_start + lstm.num_fw_outputs();
+ fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end);
+ }
+ EXPECT_THAT(lstm.GetBwOutput(),
+ ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+ bw_expected.clear();
+ for (int s = 0; s < lstm.sequence_length(); s++) {
+ bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs();
+ bw_golden_end = bw_golden_start + lstm.num_bw_outputs();
+ bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end);
+ }
+ EXPECT_THAT(lstm.GetFwOutput(),
+ ElementsAreArray(ArrayFloatNear(bw_expected)));
+}
+
+TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) {
+ const int n_batch = 2;
+ const int n_input = 5;
+ const int n_cell = 20;
+ const int n_output = 16;
+ const int sequence_length = 4;
+
+ BidirectionalLSTMOpModel lstm(
+ n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false,
+ /*use_peephole=*/true, /*use_projection_weights=*/true,
+ /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+ {
+ {sequence_length, n_batch, n_input}, // input tensor
+
+ {n_cell, n_input}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {n_cell, n_output}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {n_cell}, // cell_to_input_weight tensor
+ {n_cell}, // cell_to_forget_weight tensor
+ {n_cell}, // cell_to_output_weight tensor
+
+ {n_cell}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {n_output, n_cell}, // projection_weight tensor
+ {0}, // projection_bias tensor
+
+ {n_cell, n_input}, // input_to_input_weight tensor
+ {n_cell, n_input}, // input_to_forget_weight tensor
+ {n_cell, n_input}, // input_to_cell_weight tensor
+ {n_cell, n_input}, // input_to_output_weight tensor
+
+ {n_cell, n_output}, // recurrent_to_input_weight tensor
+ {n_cell, n_output}, // recurrent_to_forget_weight tensor
+ {n_cell, n_output}, // recurrent_to_cell_weight tensor
+ {n_cell, n_output}, // recurrent_to_output_weight tensor
+
+ {n_cell}, // cell_to_input_weight tensor
+ {n_cell}, // cell_to_forget_weight tensor
+ {n_cell}, // cell_to_output_weight tensor
+
+ {n_cell}, // input_gate_bias tensor
+ {n_cell}, // forget_gate_bias tensor
+ {n_cell}, // cell_bias tensor
+ {n_cell}, // output_gate_bias tensor
+
+ {n_output, n_cell}, // projection_weight tensor
+ {0}, // projection_bias tensor
+ });
+
+ lstm.SetInputToInputWeights(
+ {0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
+ 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
+ -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
+ -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
+ -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
+ -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
+ -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
+ 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
+ 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
+ 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
+ -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
+ 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
+ -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
+ -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
+ -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
+ 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
+ -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
+ -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
+ -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
+ -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677});
+
+ lstm.SetInputToForgetWeights(
+ {-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236,
+ -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505,
+ -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495,
+ 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323,
+ 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421,
+ -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887,
+ -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791,
+ 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059,
+ 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068,
+ 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905,
+ 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605,
+ -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464,
+ 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506,
+ -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063,
+ -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375,
+ 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553,
+ 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353,
+ 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717,
+ -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371,
+ 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496});
+
+ lstm.SetInputToCellWeights(
+ {-0.04580283, -0.09549462, -0.032418985, -0.06454633,
+ -0.043528453, 0.043018587, -0.049152344, -0.12418144,
+ -0.078985475, -0.07596889, 0.019484362, -0.11434962,
+ -0.0074034138, -0.06314844, -0.092981495, 0.0062155537,
+ -0.025034338, -0.0028890965, 0.048929527, 0.06235075,
+ 0.10665918, -0.032036792, -0.08505916, -0.10843358,
+ -0.13002433, -0.036816437, -0.02130134, -0.016518239,
+ 0.0047691227, -0.0025825808, 0.066017866, 0.029991534,
+ -0.10652836, -0.1037554, -0.13056071, -0.03266643,
+ -0.033702414, -0.006473424, -0.04611692, 0.014419339,
+ -0.025174323, 0.0396852, 0.081777506, 0.06157468,
+ 0.10210095, -0.009658194, 0.046511717, 0.03603906,
+ 0.0069369148, 0.015960095, -0.06507666, 0.09551598,
+ 0.053568836, 0.06408714, 0.12835667, -0.008714329,
+ -0.20211966, -0.12093674, 0.029450472, 0.2849013,
+ -0.029227901, 0.1164364, -0.08560263, 0.09941786,
+ -0.036999565, -0.028842626, -0.0033637602, -0.017012902,
+ -0.09720865, -0.11193351, -0.029155117, -0.017936034,
+ -0.009768936, -0.04223324, -0.036159635, 0.06505112,
+ -0.021742892, -0.023377212, -0.07221364, -0.06430552,
+ 0.05453865, 0.091149814, 0.06387331, 0.007518393,
+ 0.055960953, 0.069779344, 0.046411168, 0.10509911,
+ 0.07463894, 0.0075130584, 0.012850982, 0.04555431,
+ 0.056955688, 0.06555285, 0.050801456, -0.009862683,
+ 0.00826772, -0.026555609, -0.0073611983, -0.0014897042});
+
+ lstm.SetInputToOutputWeights(
+ {-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
+ -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
+ 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
+ -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
+ -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
+ 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
+ -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
+ -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
+ -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
+ -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
+ 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
+ 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
+ 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
+ -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
+ 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
+ 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
+ -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
+ 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
+ -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
+ -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956});
+
+ lstm.SetInputGateBias(
+ {0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216,
+ -0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339,
+ -0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818,
+ 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196});
+
+ lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696,
+ 0.11098921, 0.15378423, 0.09263801, 0.09790885,
+ 0.09508917, 0.061199076, 0.07665568, -0.015443159,
+ -0.03499149, 0.046190713, 0.08895977, 0.10899629,
+ 0.40694186, 0.06030037, 0.012413437, -0.06108739});
+
+ lstm.SetCellBias({-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
+ -0.1483596, -0.10639995, -0.091433935, 0.058573797,
+ -0.06809782, -0.07889636, -0.043246906, -0.09829136,
+ -0.4279842, 0.034901652, 0.18797937, 0.0075234566,
+ 0.016178843, 0.1749513, 0.13975595, 0.92058027});
+
+ lstm.SetOutputGateBias(
+ {0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795,
+ 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895,
+ 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149,
+ -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877});
+
+ lstm.SetRecurrentToInputWeights(
+ {-0.001374326, -0.078856036, 0.10672688, 0.029162422,
+ -0.11585556, 0.02557986, -0.13446963, -0.035785314,
+ -0.01244275, 0.025961924, -0.02337298, -0.044228926,
+ -0.055839065, -0.046598054, -0.010546039, -0.06900766,
+ 0.027239809, 0.022582639, -0.013296484, -0.05459212,
+ 0.08981, -0.045407712, 0.08682226, -0.06867011,
+ -0.14390695, -0.02916037, 0.000996957, 0.091420636,
+ 0.14283475, -0.07390571, -0.06402044, 0.062524505,
+ -0.093129106, 0.04860203, -0.08364217, -0.08119002,
+ 0.009352075, 0.22920375, 0.0016303885, 0.11583097,
+ -0.13732095, 0.012405723, -0.07551853, 0.06343048,
+ 0.12162708, -0.031923793, -0.014335606, 0.01790974,
+ -0.10650317, -0.0724401, 0.08554849, -0.05727212,
+ 0.06556731, -0.042729504, -0.043227166, 0.011683251,
+ -0.013082158, -0.029302018, -0.010899579, -0.062036745,
+ -0.022509435, -0.00964907, -0.01567329, 0.04260106,
+ -0.07787477, -0.11576462, 0.017356863, 0.048673786,
+ -0.017577527, -0.05527947, -0.082487635, -0.040137455,
+ -0.10820036, -0.04666372, 0.022746278, -0.07851417,
+ 0.01068115, 0.032956902, 0.022433773, 0.0026891115,
+ 0.08944216, -0.0685835, 0.010513544, 0.07228705,
+ 0.02032331, -0.059686817, -0.0005566496, -0.086984694,
+ 0.040414046, -0.1380399, 0.094208956, -0.05722982,
+ 0.012092817, -0.04989123, -0.086576, -0.003399834,
+ -0.04696032, -0.045747425, 0.10091314, 0.048676282,
+ -0.029037097, 0.031399418, -0.0040285117, 0.047237843,
+ 0.09504992, 0.041799378, -0.049185462, -0.031518843,
+ -0.10516937, 0.026374253, 0.10058866, -0.0033195973,
+ -0.041975245, 0.0073591834, 0.0033782164, -0.004325073,
+ -0.10167381, 0.042500053, -0.01447153, 0.06464186,
+ -0.017142897, 0.03312627, 0.009205989, 0.024138335,
+ -0.011337001, 0.035530265, -0.010912711, 0.0706555,
+ -0.005894094, 0.051841937, -0.1401738, -0.02351249,
+ 0.0365468, 0.07590991, 0.08838724, 0.021681072,
+ -0.10086113, 0.019608743, -0.06195883, 0.077335775,
+ 0.023646897, -0.095322326, 0.02233014, 0.09756986,
+ -0.048691444, -0.009579111, 0.07595467, 0.11480546,
+ -0.09801813, 0.019894179, 0.08502348, 0.004032281,
+ 0.037211012, 0.068537936, -0.048005626, -0.091520436,
+ -0.028379958, -0.01556313, 0.06554592, -0.045599163,
+ -0.01672207, -0.020169014, -0.011877351, -0.20212261,
+ 0.010889619, 0.0047078193, 0.038385306, 0.08540671,
+ -0.017140968, -0.0035865551, 0.016678626, 0.005633034,
+ 0.015963363, 0.00871737, 0.060130805, 0.028611384,
+ 0.10109069, -0.015060172, -0.07894427, 0.06401885,
+ 0.011584063, -0.024466386, 0.0047652307, -0.09041358,
+ 0.030737216, -0.0046374933, 0.14215417, -0.11823516,
+ 0.019899689, 0.006106124, -0.027092824, 0.0786356,
+ 0.05052217, -0.058925, -0.011402121, -0.024987547,
+ -0.0013661642, -0.06832946, -0.015667673, -0.1083353,
+ -0.00096863037, -0.06988685, -0.053350925, -0.027275559,
+ -0.033664223, -0.07978348, -0.025200296, -0.017207067,
+ -0.058403496, -0.055697463, 0.005798788, 0.12965427,
+ -0.062582195, 0.0013350133, -0.10482091, 0.0379771,
+ 0.072521195, -0.0029455067, -0.13797039, -0.03628521,
+ 0.013806405, -0.017858358, -0.01008298, -0.07700066,
+ -0.017081132, 0.019358726, 0.0027079724, 0.004635139,
+ 0.062634714, -0.02338735, -0.039547626, -0.02050681,
+ 0.03385117, -0.083611414, 0.002862572, -0.09421313,
+ 0.058618143, -0.08598433, 0.00972939, 0.023867095,
+ -0.053934585, -0.023203006, 0.07452513, -0.048767887,
+ -0.07314807, -0.056307215, -0.10433547, -0.06440842,
+ 0.04328182, 0.04389765, -0.020006588, -0.09076438,
+ -0.11652589, -0.021705797, 0.03345259, -0.010329105,
+ -0.025767034, 0.013057034, -0.07316461, -0.10145612,
+ 0.06358255, 0.18531723, 0.07759293, 0.12006465,
+ 0.1305557, 0.058638252, -0.03393652, 0.09622831,
+ -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845,
+ -0.005644518, 0.06857898, -0.12598175, -0.035084512,
+ 0.03156317, -0.12794146, -0.031963028, 0.04692781,
+ 0.030070418, 0.0071660685, -0.095516115, -0.004643372,
+ 0.040170413, -0.062104587, -0.0037324072, 0.0554317,
+ 0.08184801, -0.019164372, 0.06791302, 0.034257166,
+ -0.10307039, 0.021943003, 0.046745934, 0.0790918,
+ -0.0265588, -0.007824208, 0.042546265, -0.00977924,
+ -0.0002440307, -0.017384544, -0.017990116, 0.12252321,
+ -0.014512694, -0.08251313, 0.08861942, 0.13589665,
+ 0.026351685, 0.012641483, 0.07466548, 0.044301085,
+ -0.045414884, -0.051112458, 0.03444247, -0.08502782,
+ -0.04106223, -0.028126027, 0.028473156, 0.10467447});
+
+ lstm.SetRecurrentToForgetWeights(
+ {-0.057784554, -0.026057621, -0.068447545, -0.022581743,
+ 0.14811787, 0.10826372, 0.09471067, 0.03987225,
+ -0.0039523416, 0.00030638507, 0.053185795, 0.10572994,
+ 0.08414449, -0.022036452, -0.00066928595, -0.09203576,
+ 0.032950465, -0.10985798, -0.023809856, 0.0021431844,
+ -0.02196096, -0.00326074, 0.00058621005, -0.074678116,
+ -0.06193199, 0.055729095, 0.03736828, 0.020123724,
+ 0.061878487, -0.04729229, 0.034919553, -0.07585433,
+ -0.04421272, -0.044019096, 0.085488975, 0.04058006,
+ -0.06890133, -0.030951202, -0.024628663, -0.07672815,
+ 0.034293607, 0.08556707, -0.05293577, -0.033561368,
+ -0.04899627, 0.0241671, 0.015736353, -0.095442444,
+ -0.029564252, 0.016493602, -0.035026584, 0.022337519,
+ -0.026871363, 0.004780428, 0.0077918363, -0.03601621,
+ 0.016435321, -0.03263031, -0.09543275, -0.047392778,
+ 0.013454138, 0.028934088, 0.01685226, -0.086110644,
+ -0.046250615, -0.01847454, 0.047608484, 0.07339695,
+ 0.034546845, -0.04881143, 0.009128804, -0.08802852,
+ 0.03761666, 0.008096139, -0.014454086, 0.014361001,
+ -0.023502491, -0.0011840804, -0.07607001, 0.001856849,
+ -0.06509276, -0.006021153, -0.08570962, -0.1451793,
+ 0.060212336, 0.055259194, 0.06974018, 0.049454916,
+ -0.027794661, -0.08077226, -0.016179763, 0.1169753,
+ 0.17213494, -0.0056326236, -0.053934924, -0.0124349,
+ -0.11520337, 0.05409887, 0.088759385, 0.0019655675,
+ 0.0042065294, 0.03881498, 0.019844765, 0.041858196,
+ -0.05695512, 0.047233116, 0.038937137, -0.06542224,
+ 0.014429736, -0.09719407, 0.13908425, -0.05379757,
+ 0.012321099, 0.082840554, -0.029899208, 0.044217527,
+ 0.059855383, 0.07711018, -0.045319796, 0.0948846,
+ -0.011724666, -0.0033288454, -0.033542685, -0.04764985,
+ -0.13873616, 0.040668588, 0.034832682, -0.015319203,
+ -0.018715994, 0.046002675, 0.0599172, -0.043107376,
+ 0.0294216, -0.002314414, -0.022424703, 0.0030315618,
+ 0.0014641669, 0.0029166266, -0.11878115, 0.013738511,
+ 0.12375372, -0.0006038222, 0.029104086, 0.087442465,
+ 0.052958444, 0.07558703, 0.04817258, 0.044462286,
+ -0.015213451, -0.08783778, -0.0561384, -0.003008196,
+ 0.047060397, -0.002058388, 0.03429439, -0.018839769,
+ 0.024734668, 0.024614193, -0.042046934, 0.09597743,
+ -0.0043254104, 0.04320769, 0.0064070094, -0.0019131786,
+ -0.02558259, -0.022822596, -0.023273505, -0.02464396,
+ -0.10991725, -0.006240552, 0.0074488563, 0.024044557,
+ 0.04383914, -0.046476185, 0.028658995, 0.060410924,
+ 0.050786525, 0.009452605, -0.0073054377, -0.024810238,
+ 0.0052906186, 0.0066939713, -0.0020913032, 0.014515517,
+ 0.015898481, 0.021362653, -0.030262267, 0.016587038,
+ -0.011442813, 0.041154444, -0.007631438, -0.03423484,
+ -0.010977775, 0.036152758, 0.0066366293, 0.11915515,
+ 0.02318443, -0.041350313, 0.021485701, -0.10906167,
+ -0.028218046, -0.00954771, 0.020531068, -0.11995105,
+ -0.03672871, 0.024019798, 0.014255957, -0.05221243,
+ -0.00661567, -0.04630967, 0.033188973, 0.10107534,
+ -0.014027541, 0.030796422, -0.10270911, -0.035999842,
+ 0.15443139, 0.07684145, 0.036571592, -0.035900835,
+ -0.0034699554, 0.06209149, 0.015920248, -0.031122351,
+ -0.03858649, 0.01849943, 0.13872518, 0.01503974,
+ 0.069941424, -0.06948533, -0.0088794185, 0.061282158,
+ -0.047401894, 0.03100163, -0.041533746, -0.10430945,
+ 0.044574402, -0.01425562, -0.024290353, 0.034563623,
+ 0.05866852, 0.023947537, -0.09445152, 0.035450947,
+ 0.02247216, -0.0042998926, 0.061146557, -0.10250651,
+ 0.020881841, -0.06747029, 0.10062043, -0.0023941975,
+ 0.03532124, -0.016341697, 0.09685456, -0.016764693,
+ 0.051808182, 0.05875331, -0.04536488, 0.001626336,
+ -0.028892258, -0.01048663, -0.009793449, -0.017093895,
+ 0.010987891, 0.02357273, -0.00010856845, 0.0099760275,
+ -0.001845119, -0.03551521, 0.0018358806, 0.05763657,
+ -0.01769146, 0.040995963, 0.02235177, -0.060430344,
+ 0.11475477, -0.023854522, 0.10071741, 0.0686208,
+ -0.014250481, 0.034261297, 0.047418304, 0.08562733,
+ -0.030519066, 0.0060542435, 0.014653856, -0.038836084,
+ 0.04096551, 0.032249358, -0.08355519, -0.026823482,
+ 0.056386515, -0.010401743, -0.028396193, 0.08507674,
+ 0.014410365, 0.020995233, 0.17040324, 0.11511526,
+ 0.02459721, 0.0066619175, 0.025853224, -0.023133837,
+ -0.081302024, 0.017264642, -0.009585969, 0.09491168,
+ -0.051313367, 0.054532815, -0.014298593, 0.10657464,
+ 0.007076659, 0.10964551, 0.0409152, 0.008275321,
+ -0.07283536, 0.07937492, 0.04192024, -0.1075027});
+
+ lstm.SetRecurrentToCellWeights(
+ {-0.037322544, 0.018592842, 0.0056175636, -0.06253426,
+ 0.055647098, -0.05713207, -0.05626563, 0.005559383,
+ 0.03375411, -0.025757805, -0.088049285, 0.06017052,
+ -0.06570978, 0.007384076, 0.035123326, -0.07920549,
+ 0.053676967, 0.044480428, -0.07663568, 0.0071805613,
+ 0.08089997, 0.05143358, 0.038261272, 0.03339287,
+ -0.027673481, 0.044746667, 0.028349208, 0.020090483,
+ -0.019443132, -0.030755889, -0.0040000007, 0.04465846,
+ -0.021585021, 0.0031670958, 0.0053199246, -0.056117613,
+ -0.10893326, 0.076739706, -0.08509834, -0.027997585,
+ 0.037871376, 0.01449768, -0.09002357, -0.06111149,
+ -0.046195522, 0.0422062, -0.005683705, -0.1253618,
+ -0.012925729, -0.04890792, 0.06985068, 0.037654128,
+ 0.03398274, -0.004781977, 0.007032333, -0.031787455,
+ 0.010868644, -0.031489216, 0.09525667, 0.013939797,
+ 0.0058680447, 0.0167067, 0.02668468, -0.04797466,
+ -0.048885044, -0.12722108, 0.035304096, 0.06554885,
+ 0.00972396, -0.039238118, -0.05159735, -0.11329045,
+ 0.1613692, -0.03750952, 0.06529313, -0.071974665,
+ -0.11769596, 0.015524369, -0.0013754242, -0.12446318,
+ 0.02786344, -0.014179351, 0.005264273, 0.14376344,
+ 0.015983658, 0.03406988, -0.06939408, 0.040699873,
+ 0.02111075, 0.09669095, 0.041345075, -0.08316494,
+ -0.07684199, -0.045768797, 0.032298047, -0.041805092,
+ 0.0119405, 0.0061010392, 0.12652606, 0.0064572375,
+ -0.024950314, 0.11574242, 0.04508852, -0.04335324,
+ 0.06760663, -0.027437469, 0.07216407, 0.06977076,
+ -0.05438599, 0.034033038, -0.028602652, 0.05346137,
+ 0.043184172, -0.037189785, 0.10420091, 0.00882477,
+ -0.054019816, -0.074273005, -0.030617684, -0.0028467078,
+ 0.024302477, -0.0038869337, 0.005332455, 0.0013399826,
+ 0.04361412, -0.007001822, 0.09631092, -0.06702025,
+ -0.042049985, -0.035070654, -0.04103342, -0.10273396,
+ 0.0544271, 0.037184782, -0.13150354, -0.0058036847,
+ -0.008264958, 0.042035464, 0.05891794, 0.029673764,
+ 0.0063542654, 0.044788733, 0.054816857, 0.062257513,
+ -0.00093483756, 0.048938446, -0.004952862, -0.007730018,
+ -0.04043371, -0.017094059, 0.07229206, -0.023670016,
+ -0.052195564, -0.025616996, -0.01520939, 0.045104615,
+ -0.007376126, 0.003533447, 0.006570588, 0.056037236,
+ 0.12436656, 0.051817212, 0.028532185, -0.08686856,
+ 0.11868599, 0.07663395, -0.07323171, 0.03463402,
+ -0.050708205, -0.04458982, -0.11590894, 0.021273347,
+ 0.1251325, -0.15313013, -0.12224372, 0.17228661,
+ 0.023029093, 0.086124025, 0.006445803, -0.03496501,
+ 0.028332196, 0.04449512, -0.042436164, -0.026587414,
+ -0.006041347, -0.09292539, -0.05678812, 0.03897832,
+ 0.09465633, 0.008115513, -0.02171956, 0.08304309,
+ 0.071401566, 0.019622514, 0.032163795, -0.004167056,
+ 0.02295182, 0.030739572, 0.056506045, 0.004612461,
+ 0.06524936, 0.059999723, 0.046395954, -0.0045512207,
+ -0.1335546, -0.030136576, 0.11584653, -0.014678886,
+ 0.0020118146, -0.09688814, -0.0790206, 0.039770417,
+ -0.0329582, 0.07922767, 0.029322514, 0.026405897,
+ 0.04207835, -0.07073373, 0.063781224, 0.0859677,
+ -0.10925287, -0.07011058, 0.048005477, 0.03438226,
+ -0.09606514, -0.006669445, -0.043381985, 0.04240257,
+ -0.06955775, -0.06769346, 0.043903265, -0.026784198,
+ -0.017840602, 0.024307009, -0.040079936, -0.019946516,
+ 0.045318738, -0.12233574, 0.026170589, 0.0074471775,
+ 0.15978073, 0.10185836, 0.10298046, -0.015476589,
+ -0.039390966, -0.072174534, 0.0739445, -0.1211869,
+ -0.0347889, -0.07943156, 0.014809798, -0.12412325,
+ -0.0030663363, 0.039695457, 0.0647603, -0.08291318,
+ -0.018529687, -0.004423833, 0.0037507233, 0.084633216,
+ -0.01514876, -0.056505352, -0.012800942, -0.06994386,
+ 0.012962922, -0.031234352, 0.07029052, 0.016418684,
+ 0.03618972, 0.055686004, -0.08663945, -0.017404709,
+ -0.054761406, 0.029065743, 0.052404847, 0.020238016,
+ 0.0048197987, -0.0214882, 0.07078733, 0.013016777,
+ 0.06262858, 0.009184685, 0.020785125, -0.043904778,
+ -0.0270329, -0.03299152, -0.060088247, -0.015162964,
+ -0.001828936, 0.12642565, -0.056757294, 0.013586685,
+ 0.09232601, -0.035886683, 0.06000002, 0.05229691,
+ -0.052580316, -0.082029596, -0.010794592, 0.012947712,
+ -0.036429964, -0.085508935, -0.13127148, -0.017744139,
+ 0.031502828, 0.036232427, -0.031581745, 0.023051167,
+ -0.05325106, -0.03421577, 0.028793324, -0.034633752,
+ -0.009881397, -0.043551125, -0.018609839, 0.0019097115,
+ -0.008799762, 0.056595087, 0.0022273948, 0.055752404});
+
+ lstm.SetRecurrentToOutputWeights({
+ 0.025825322, -0.05813119, 0.09495884, -0.045984812, -0.01255415,
+ -0.0026479573, -0.08196161, -0.054914974, -0.0046604523, -0.029587349,
+ -0.044576716, -0.07480124, -0.082868785, 0.023254942, 0.027502948,
+ -0.0039728214, -0.08683098, -0.08116779, -0.014675607, -0.037924774,
+ -0.023314456, -0.007401714, -0.09255757, 0.029460307, -0.08829125,
+ -0.005139627, -0.08989442, -0.0555066, 0.13596267, -0.025062224,
+ -0.048351806, -0.03850004, 0.07266485, -0.022414139, 0.05940088,
+ 0.075114764, 0.09597592, -0.010211725, -0.0049794707, -0.011523867,
+ -0.025980417, 0.072999895, 0.11091378, -0.081685916, 0.014416728,
+ 0.043229222, 0.034178585, -0.07530371, 0.035837382, -0.085607,
+ -0.007721233, -0.03287832, -0.043848954, -0.06404588, -0.06632928,
+ -0.073643476, 0.008214239, -0.045984086, 0.039764922, 0.03474462,
+ 0.060612556, -0.080590084, 0.049127717, 0.04151091, -0.030063879,
+ 0.008801774, -0.023021035, -0.019558564, 0.05158114, -0.010947698,
+ -0.011825728, 0.0075720972, 0.0699727, -0.0039981045, 0.069350146,
+ 0.08799282, 0.016156472, 0.035502106, 0.11695009, 0.006217345,
+ 0.13392477, -0.037875112, 0.025745004, 0.08940699, -0.00924166,
+ 0.0046702605, -0.036598757, -0.08811812, 0.10522024, -0.032441203,
+ 0.008176899, -0.04454919, 0.07058152, 0.0067963637, 0.039206743,
+ 0.03259838, 0.03725492, -0.09515802, 0.013326398, -0.052055415,
+ -0.025676316, 0.03198509, -0.015951829, -0.058556724, 0.036879618,
+ 0.043357447, 0.028362012, -0.05908629, 0.0059240665, -0.04995891,
+ -0.019187413, 0.0276265, -0.01628143, 0.0025863599, 0.08800015,
+ 0.035250366, -0.022165963, -0.07328642, -0.009415526, -0.07455109,
+ 0.11690406, 0.0363299, 0.07411125, 0.042103454, -0.009660886,
+ 0.019076364, 0.018299393, -0.046004917, 0.08891175, 0.0431396,
+ -0.026327137, -0.051502608, 0.08979574, -0.051670972, 0.04940282,
+ -0.07491107, -0.021240504, 0.022596184, -0.034280192, 0.060163025,
+ -0.058211457, -0.051837247, -0.01349775, -0.04639988, -0.035936575,
+ -0.011681591, 0.064818054, 0.0073146066, -0.021745546, -0.043124277,
+ -0.06471268, -0.07053354, -0.029321948, -0.05330136, 0.016933719,
+ -0.053782392, 0.13747959, -0.1361751, -0.11569455, 0.0033329215,
+ 0.05693899, -0.053219706, 0.063698, 0.07977434, -0.07924483,
+ 0.06936997, 0.0034815092, -0.007305279, -0.037325785, -0.07251102,
+ -0.033633437, -0.08677009, 0.091591336, -0.14165086, 0.021752775,
+ 0.019683983, 0.0011612234, -0.058154266, 0.049996935, 0.0288841,
+ -0.0024567875, -0.14345716, 0.010955264, -0.10234828, 0.1183656,
+ -0.0010731248, -0.023590032, -0.072285876, -0.0724771, -0.026382286,
+ -0.0014920527, 0.042667855, 0.0018776858, 0.02986552, 0.009814309,
+ 0.0733756, 0.12289186, 0.018043943, -0.0458958, 0.049412545,
+ 0.033632483, 0.05495232, 0.036686596, -0.013781798, -0.010036754,
+ 0.02576849, -0.08307328, 0.010112348, 0.042521734, -0.05869831,
+ -0.071689695, 0.03876447, -0.13275425, -0.0352966, -0.023077697,
+ 0.10285965, 0.084736146, 0.15568255, -0.00040734606, 0.027835453,
+ -0.10292561, -0.032401145, 0.10053256, -0.026142767, -0.08271222,
+ -0.0030240538, -0.016368777, 0.1070414, 0.042672627, 0.013456989,
+ -0.0437609, -0.022309763, 0.11576483, 0.04108048, 0.061026827,
+ -0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949,
+ 0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819,
+ -0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954,
+ 0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228,
+ -0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001,
+ -0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939,
+ -0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556,
+ -0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718,
+ 0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893,
+ 0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974,
+ -0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485,
+ 0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856,
+ 0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853,
+ -0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019,
+ 0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024,
+ 0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994,
+ 0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621,
+ });
+
+ lstm.SetCellToInputWeights(
+ {0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458,
+ -0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174,
+ -0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047,
+ 0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175});
+
+ lstm.SetCellToForgetWeights(
+ {-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276,
+ -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
+ -0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774,
+ 0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355});
+
+ lstm.SetCellToOutputWeights(
+ {0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764,
+ -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544,
+ -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817,
+ 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733});
+
+ lstm.SetProjectionWeights(
+ {-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832,
+ 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683,
+ -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931,
+ -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476,
+ 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067,
+ 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787,
+ 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588,
+ 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285,
+ -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949,
+ -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768,
+ -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929,
+ 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504,
+ 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946,
+ 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117,
+ 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253,
+ 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456,
+ -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552,
+ 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797,
+ -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272,
+ 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165,
+ -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922,
+ -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548,
+ 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786,
+ -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722,
+ 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318,
+ -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776,
+ -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307,
+ 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969,
+ -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593,
+ -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515,
+ -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288,
+ 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723,
+ 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097,
+ -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209,
+ 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268,
+ 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139,
+ 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707,
+ 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871,
+ 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553,
+ -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702,
+ -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615,
+ 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187,
+ -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388,
+ -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709,
+ 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263,
+ 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777,
+ 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935,
+ -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641,
+ -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996,
+ -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318,
+ 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437,
+ -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079,
+ 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237,
+ 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415,
+ -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124,
+ -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943,
+ -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311,
+ 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013,
+ -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364,
+ -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543,
+ -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102,
+ 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906,
+ 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955,
+ 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656});
+
+ static float lstm_input[][20] = {
+ {// Batch0: 4 (input_sequence_size) * 5 (n_input)
+ 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386,
+ 0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199,
+ 0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339},
+
+ {// Batch1: 4 (input_sequence_size) * 5 (n_input)
+ 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260,
+ 0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485,
+ 0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}};
+
+ static float lstm_fw_golden_output[][64] = {
+ {// Batch0: 4 (input_sequence_size) * 16 (n_output)
+ -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576,
+ -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004,
+ -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147,
+ 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363,
+ -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322,
+ -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308,
+ 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794,
+ 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474,
+ 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827,
+ 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512,
+ -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407,
+ -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193,
+ 0.0286833, 0.00824207, 0.0264887, 0.0305169},
+ {// Batch1: 4 (input_sequence_size) * 16 (n_output)
+ -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926,
+ -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232,
+ 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954,
+ 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507,
+ -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039,
+ -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233,
+ 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378,
+ 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034,
+ 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789,
+ 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855,
+ -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679,
+ -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181,
+ 0.0412031, 0.0118723, 0.0239643, 0.0394009}};
+
+ static float lstm_combined_golden_output[][64] = {
+ {
+ -0.022014, 0.073544, -0.002235, 0.040068, -0.037136, -0.052788,
+ 0.075325, -0.029378, 0.024298, -0.07733 , -0.030674, -0.060229,
+ 0.040599, 0.011608, 0.042005, 0.045977, -0.039225, 0.076294,
+ 0.000735, 0.032852, -0.069869, -0.053312, 0.073527, -0.028136,
+ 0.021585, -0.102679, -0.004327, -0.043304, 0.072861, 0.027077,
+ 0.034558, 0.068292, -0.036292, 0.069832, -0.003032, 0.053829,
+ -0.043821, -0.072713, 0.085029, -0.040374, 0.020014, -0.104521,
+ -0.034504, -0.059759, 0.062569, 0.025652, 0.049306, 0.061189,
+ -0.025146, 0.079643, -0.005188, 0.033080, -0.048079, -0.048082,
+ 0.069369, -0.028900, 0.024572, -0.077547, -0.022517, -0.054477,
+ 0.038857, 0.013336, 0.043234, 0.044788},
+ {
+ -0.039186, 0.070792, -0.005913, 0.02642, -0.068274, -0.05022,
+ 0.061444, -0.031241, 0.014996, -0.094544, -0.004146, -0.03464,
+ 0.058981, 0.026097, 0.039781, 0.058408, -0.031887, 0.069252,
+ 0.00576, 0.054062, -0.042801, -0.059974, 0.085272, -0.034453,
+ 0.026097, -0.0959, -0.031164, -0.058699, 0.06839, 0.020512,
+ 0.044727, 0.063609, -0.039863, 0.084819, -0.003909, 0.028666,
+ -0.075677, -0.045125, 0.070379, -0.033895, 0.022111, -0.097184,
+ -0.004921, -0.040851, 0.062316, 0.017435, 0.041437, 0.064568,
+ -0.039656, 0.060726, -0.003402, 0.036854, -0.056503, -0.058554,
+ 0.068588, -0.034879, 0.01352, -0.09962, -0.01434, -0.039505,
+ 0.065133, 0.024321, 0.038473, 0.062438
+ }};
+
+ // Resetting cell_state and output_state
+ lstm.ResetFwOutputAndCellStates();
+ lstm.ResetBwOutputAndCellStates();
+
+ for (int i = 0; i < lstm.sequence_length(); i++) {
+ float* batch0_start = lstm_input[0] + i * lstm.num_inputs();
+ float* batch0_end = batch0_start + lstm.num_inputs();
+
+ lstm.SetInput(2 * i * lstm.num_inputs(), batch0_start, batch0_end);
+
+ float* batch1_start = lstm_input[1] + i * lstm.num_inputs();
+ float* batch1_end = batch1_start + lstm.num_inputs();
+ lstm.SetInput((2 * i + 1) * lstm.num_inputs(), batch1_start, batch1_end);
+ }
+
+ lstm.Invoke();
+
+ std::vector<float> expected;
+ for (int i = 0; i < lstm.sequence_length(); i++) {
+ float* golden_start_batch0 =
+ lstm_fw_golden_output[0] + i * lstm.num_fw_outputs();
+ float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs();
+ float* golden_start_batch1 =
+ lstm_fw_golden_output[1] + i * lstm.num_fw_outputs();
+ float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs();
+ expected.insert(expected.end(), golden_start_batch0, golden_end_batch0);
+ expected.insert(expected.end(), golden_start_batch1, golden_end_batch1);
+ }
+ EXPECT_THAT(lstm.GetFwOutput(), ElementsAreArray(ArrayFloatNear(expected)));
+
+ // Check if the sum of forward backward matches the golden.
+ expected.clear();
+ for (int i = 0; i < lstm.sequence_length(); i++) {
+ float* golden_start_batch0 =
+ lstm_combined_golden_output[0] + i * lstm.num_fw_outputs();
+ float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs();
+ float* golden_start_batch1 =
+ lstm_combined_golden_output[1] + i * lstm.num_fw_outputs();
+ float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs();
+ expected.insert(expected.end(), golden_start_batch0, golden_end_batch0);
+ expected.insert(expected.end(), golden_start_batch1, golden_end_batch1);
+ }
+
+ std::vector<float> combined;
+ for (int i = 0; i < lstm.GetFwOutput().size(); ++i) {
+ combined.push_back(lstm.GetFwOutput()[i] + lstm.GetBwOutput()[i]);
+ }
+ EXPECT_THAT(combined, ElementsAreArray(ArrayFloatNear(expected)));
+}
+
+} // namespace
+} // namespace tflite
+
+int main(int argc, char** argv) {
+ ::tflite::LogToStderr();
+ ::testing::InitGoogleTest(&argc, argv);
+ return RUN_ALL_TESTS();
+}
projects / platform / upstream / tensorflow.git / commitdiff