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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2018 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 "LSTMLayer.h"
#include "OperationUtils.h"
#include <cker/operation/LSTM.h>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace ops
{
namespace
{
template <typename T>
T *getOptionalOutputBuffer(onert::backend::IPortableTensor *tensor, std::vector<uint8_t> *temp_vec,
size_t total_size)
{
if (tensor == nullptr)
{
temp_vec->reserve(total_size);
return reinterpret_cast<T *>(temp_vec->data());
}
else
{
assert(tensor->total_size() == total_size);
return getBuffer<T>(tensor);
}
}
inline void initializeStateBuffer(const onert::backend::IPortableTensor *tensor_in, void *buffer,
bool needs_memcpy)
{
assert(tensor_in != nullptr);
assert(buffer != nullptr);
if (needs_memcpy)
memcpy(buffer, tensor_in->buffer(), tensor_in->total_size());
else
memset(buffer, 0, tensor_in->total_size());
}
} // namespace
void LSTMLayer::LSTMFloat()
{
auto in_shape = _input->getShape();
assert(in_shape.rank() >= 2 && in_shape.rank() <= 3);
int max_time, n_batch;
if (in_shape.rank() == 3)
{
max_time = (_time_major) ? in_shape.dim(0) : in_shape.dim(1);
n_batch = (_time_major) ? in_shape.dim(1) : in_shape.dim(0);
}
else
{
max_time = 1;
n_batch = in_shape.dim(0);
}
const int n_input = in_shape.dim(_input->getShape().rank() - 1);
const int aux_input_size = 0;
// n_cell and n_output will be the same size when there is no projection.
const int n_cell = _input_to_output_weights->getShape().dim(0);
const int n_output = _recurrent_to_output_weights->getShape().dim(1);
// Since we have already checked that weights are all there or none, we can
// check the existence of only one to the get the condition.
const bool use_cifg = (_input_to_input_weights == nullptr);
// Optional outputs
float *output_state_buf = getOptionalOutputBuffer<float>(_output_state, &_output_state_vec,
_output_state_in->total_size());
float *cell_state_buf =
getOptionalOutputBuffer<float>(_cell_state, &_cell_state_vec, _cell_state_in->total_size());
initializeStateBuffer(_output_state_in, output_state_buf, _has_output_state_data);
initializeStateBuffer(_cell_state_in, cell_state_buf, _has_cell_state_data);
// Index the scratch buffers pointers to the global scratch buffer.
float *scratch_buffer_buf = getOptionalOutputBuffer<float>(
_scratch_buffer, &_scratch_vec, n_batch * n_cell * (use_cifg ? 3 : 4) * sizeof(float));
float *input_gate_scratch = nullptr;
float *cell_gate_scratch = nullptr;
float *forget_gate_scratch = nullptr;
float *output_gate_scratch = nullptr;
if (use_cifg)
{
cell_gate_scratch = scratch_buffer_buf;
forget_gate_scratch = scratch_buffer_buf + n_cell * n_batch;
output_gate_scratch = scratch_buffer_buf + 2 * n_cell * n_batch;
}
else
{
input_gate_scratch = scratch_buffer_buf;
cell_gate_scratch = scratch_buffer_buf + n_cell * n_batch;
forget_gate_scratch = scratch_buffer_buf + 2 * n_cell * n_batch;
output_gate_scratch = scratch_buffer_buf + 3 * n_cell * n_batch;
}
auto optional_tensor_ptr = [](const IPortableTensor *tensor) {
// If tensor is not given or the tensor size is 0, consider it was not given
return (tensor && tensor->total_size() > 0) ? getBuffer<float>(tensor) : nullptr;
};
// Optional inputs
const float *input_to_input_weights_ptr = optional_tensor_ptr(_input_to_input_weights);
const float *recurrent_to_input_weights_ptr = optional_tensor_ptr(_recurrent_to_input_weights);
const float *cell_to_input_weights_ptr = optional_tensor_ptr(_cell_to_input_weights);
const float *cell_to_forget_weights_ptr = optional_tensor_ptr(_cell_to_forget_weights);
const float *cell_to_output_weights_ptr = optional_tensor_ptr(_cell_to_output_weights);
const float *input_gate_bias_ptr = optional_tensor_ptr(_input_gate_bias);
const float *projection_weights_ptr = optional_tensor_ptr(_projection_weights);
const float *projection_bias_ptr = optional_tensor_ptr(_projection_bias);
const float *input_layer_norm_coefficients_ptr =
optional_tensor_ptr(_input_layer_norm_coefficients);
const float *forget_layer_norm_coefficients_ptr =
optional_tensor_ptr(_forget_layer_norm_coefficients);
const float *cell_layer_norm_coefficients_ptr =
optional_tensor_ptr(_cell_layer_norm_coefficients);
const float *output_layer_norm_coefficients_ptr =
optional_tensor_ptr(_output_layer_norm_coefficients);
// Copy out the LSTM specific params so they can be passed in the function.
nnfw::cker::LSTMParams lstm_params;
lstm_params.activation = convertActivationType(_params.activation);
lstm_params.cell_clip = _params.cell_threshold;
lstm_params.proj_clip = _params.projection_threshold;
auto out_shape = _output->getShape();
const int output_batch_leading_dim = out_shape.dim(out_shape.rank() - 1);
if (_time_major)
{
// Loop through the sequence.
const int input_step = n_batch * n_input;
const int output_step = n_batch * output_batch_leading_dim;
for (int t = 0; t < max_time; t++)
{
// If this is the forward_sequence, step forward, otherwise step
// backwards.
const int t_rel = _forward_sequence ? t : max_time - t - 1;
const float *input_ptr = getBuffer<float>(_input) + t_rel * input_step;
const float *aux_input_ptr = nullptr;
if (_aux_input)
{
aux_input_ptr = getBuffer<float>(_aux_input) + t_rel * input_step;
}
float *output_ptr = getBuffer<float>(_output) + t_rel * output_step + _output_offset;
LstmStepFloat(
input_ptr, input_to_input_weights_ptr, getBuffer<float>(_input_to_forget_weights),
getBuffer<float>(_input_to_cell_weights), getBuffer<float>(_input_to_output_weights),
aux_input_ptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, recurrent_to_input_weights_ptr,
getBuffer<float>(_recurrent_to_forget_weights),
getBuffer<float>(_recurrent_to_cell_weights),
getBuffer<float>(_recurrent_to_output_weights), cell_to_input_weights_ptr,
cell_to_forget_weights_ptr, cell_to_output_weights_ptr, input_layer_norm_coefficients_ptr,
forget_layer_norm_coefficients_ptr, cell_layer_norm_coefficients_ptr,
output_layer_norm_coefficients_ptr, input_gate_bias_ptr,
getBuffer<float>(_forget_gate_bias), getBuffer<float>(_cell_gate_bias),
getBuffer<float>(_output_gate_bias), projection_weights_ptr, projection_bias_ptr,
&lstm_params, n_batch, n_cell, n_input, aux_input_size, n_output, output_batch_leading_dim,
output_state_buf, cell_state_buf, input_gate_scratch, forget_gate_scratch,
cell_gate_scratch, output_gate_scratch, output_ptr);
}
}
else
{
for (int b = 0; b < n_batch; b++)
{
const int input_step = n_input;
const int output_step = output_batch_leading_dim;
for (int t = 0; t < max_time; t++)
{
// If this is the forward_sequence, step forward, otherwise step
// backwards.
const int t_rel = _forward_sequence ? t : max_time - t - 1;
const int time_offset = b * max_time + t_rel;
const float *input_ptr = getBuffer<float>(_input) + time_offset * input_step;
const float *aux_input_ptr = nullptr;
if (_aux_input)
{
aux_input_ptr = getBuffer<float>(_aux_input) + time_offset * input_step;
}
float *output_ptr = getBuffer<float>(_output) + time_offset * output_step + _output_offset;
// Offset the {output,cell}_state pointers to the right batch.
float *output_state_ptr = output_state_buf + b * output_batch_leading_dim;
float *cell_state_ptr = cell_state_buf + b * n_cell;
// Offset the scratch pointers to the right batch.
float *input_gate_scratch_ptr =
input_gate_scratch ? input_gate_scratch + b * n_cell : nullptr;
float *forget_gate_scratch_ptr = forget_gate_scratch + b * n_cell;
float *cell_gate_scratch_ptr = cell_gate_scratch + b * n_cell;
float *output_gate_scratch_ptr = output_gate_scratch + b * n_cell;
LstmStepFloat(
input_ptr, input_to_input_weights_ptr, getBuffer<float>(_input_to_forget_weights),
getBuffer<float>(_input_to_cell_weights), getBuffer<float>(_input_to_output_weights),
aux_input_ptr,
/*aux_input_to_input_weights=*/nullptr,
/*aux_input_to_forget_weights=*/nullptr,
/*aux_input_to_cell_weights=*/nullptr,
/*aux_input_to_output_weights=*/nullptr, recurrent_to_input_weights_ptr,
getBuffer<float>(_recurrent_to_forget_weights),
getBuffer<float>(_recurrent_to_cell_weights),
getBuffer<float>(_recurrent_to_output_weights), cell_to_input_weights_ptr,
cell_to_forget_weights_ptr, cell_to_output_weights_ptr, input_layer_norm_coefficients_ptr,
forget_layer_norm_coefficients_ptr, cell_layer_norm_coefficients_ptr,
output_layer_norm_coefficients_ptr, input_gate_bias_ptr,
getBuffer<float>(_forget_gate_bias), getBuffer<float>(_cell_gate_bias),
getBuffer<float>(_output_gate_bias), projection_weights_ptr, projection_bias_ptr,
&lstm_params, /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output,
output_batch_leading_dim, output_state_ptr, cell_state_ptr, input_gate_scratch_ptr,
forget_gate_scratch_ptr, cell_gate_scratch_ptr, output_gate_scratch_ptr, output_ptr);
}
}
}
}
void LSTMLayer::configure(
const IPortableTensor *input, const IPortableTensor *input_to_input_weights,
const IPortableTensor *input_to_forget_weights, const IPortableTensor *input_to_cell_weights,
const IPortableTensor *input_to_output_weights, const IPortableTensor *recurrent_to_input_weights,
const IPortableTensor *recurrent_to_forget_weights,
const IPortableTensor *recurrent_to_cell_weights,
const IPortableTensor *recurrent_to_output_weights, const IPortableTensor *cell_to_input_weights,
const IPortableTensor *cell_to_forget_weights, const IPortableTensor *cell_to_output_weights,
const IPortableTensor *input_layer_norm_weights, const IPortableTensor *forget_layer_norm_weights,
const IPortableTensor *cell_layer_norm_weights, const IPortableTensor *output_layer_norm_weights,
const IPortableTensor *aux_input, const IPortableTensor *aux_input_to_input_weights,
const IPortableTensor *aux_input_to_forget_weights,
const IPortableTensor *aux_input_to_cell_weights,
const IPortableTensor *aux_input_to_output_weights, const IPortableTensor *input_gate_bias,
const IPortableTensor *forget_gate_bias, const IPortableTensor *cell_gate_bias,
const IPortableTensor *output_gate_bias, const IPortableTensor *projection_weights,
const IPortableTensor *projection_bias, const IPortableTensor *output_state_in,
const IPortableTensor *cell_state_in, const ir::operation::LSTM::Param ¶ms,
bool forward_sequence, bool time_major, int output_offset, IPortableTensor *scratch_buffer,
IPortableTensor *output_state, IPortableTensor *cell_state, IPortableTensor *output,
bool has_output_state_data, bool has_cell_state_data)
{
_input = input;
_input_to_input_weights = input_to_input_weights;
_input_to_forget_weights = input_to_forget_weights;
_input_to_cell_weights = input_to_cell_weights;
_input_to_output_weights = input_to_output_weights;
_recurrent_to_input_weights = recurrent_to_input_weights;
_recurrent_to_forget_weights = recurrent_to_forget_weights;
_recurrent_to_cell_weights = recurrent_to_cell_weights;
_recurrent_to_output_weights = recurrent_to_output_weights;
_cell_to_input_weights = cell_to_input_weights;
_cell_to_forget_weights = cell_to_forget_weights;
_cell_to_output_weights = cell_to_output_weights;
_input_layer_norm_coefficients = input_layer_norm_weights;
_forget_layer_norm_coefficients = forget_layer_norm_weights;
_cell_layer_norm_coefficients = cell_layer_norm_weights;
_output_layer_norm_coefficients = output_layer_norm_weights;
_aux_input = aux_input, _aux_input_to_input_weights = aux_input_to_input_weights,
_aux_input_to_forget_weights = aux_input_to_forget_weights,
_aux_input_to_cell_weights = aux_input_to_cell_weights,
_aux_input_to_output_weights = aux_input_to_output_weights, _input_gate_bias = input_gate_bias;
_forget_gate_bias = forget_gate_bias;
_cell_gate_bias = cell_gate_bias;
_output_gate_bias = output_gate_bias;
_projection_weights = projection_weights;
_projection_bias = projection_bias;
_output_state_in = output_state_in;
_cell_state_in = cell_state_in;
_params = params;
_forward_sequence = forward_sequence;
_time_major = time_major;
_output_offset = output_offset;
_scratch_buffer = scratch_buffer;
_output_state = output_state;
_cell_state = cell_state;
_output = output;
_has_output_state_data = has_output_state_data;
_has_cell_state_data = has_cell_state_data;
}
void LSTMLayer::run()
{
if (_input->data_type() == OperandType::FLOAT32)
{
LSTMFloat();
}
else
{
throw std::runtime_error{"LSTMLayer: unsupported data type"};
}
}
} // namespace ops
} // namespace cpu
} // namespace backend
} // namespace onert
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