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android13/external/ComputeLibrary/examples/graph_deepspeech_v0_4_1.cpp

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/*
* Copyright (c) 2019-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/graph.h"
#include "arm_compute/graph/Types.h"
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
using namespace arm_compute::utils;
using namespace arm_compute::graph;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement DeepSpeech v0.4.1's network using the Compute Library's graph API */
class GraphDeepSpeechExample : public Example
{
public:
GraphDeepSpeechExample()
: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "DeepSpeech v0.4.1")
{
}
bool do_setup(int argc, char **argv) override
{
// Parse arguments
cmd_parser.parse(argc, argv);
cmd_parser.validate();
// Consume common parameters
common_params = consume_common_graph_parameters(common_opts);
// Return when help menu is requested
if(common_params.help)
{
cmd_parser.print_help(argv[0]);
return false;
}
// Print parameter values
std::cout << common_params << std::endl;
// Get trainable parameters data path
std::string data_path = common_params.data_path;
const std::string model_path = "/cnn_data/deepspeech_model/";
if(!data_path.empty())
{
data_path += model_path;
}
// How many timesteps to process at once, higher values mean more latency
// Notice that this corresponds to the number of LSTM cells that will be instantiated
const unsigned int n_steps = 16;
// ReLU clipping value for non-recurrent layers
const float cell_clip = 20.f;
// Create input descriptor
const TensorShape tensor_shape = permute_shape(TensorShape(26U, 19U, n_steps, 1U), DataLayout::NHWC, common_params.data_layout);
TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
// Set weights trained layout
const DataLayout weights_layout = DataLayout::NHWC;
graph << common_params.target
<< common_params.fast_math_hint
<< InputLayer(input_descriptor,
get_weights_accessor(data_path, "input_values_x" + std::to_string(n_steps) + ".npy", weights_layout))
.set_name("input_node");
if(common_params.data_layout == DataLayout::NCHW)
{
graph << PermuteLayer(PermutationVector(2U, 0U, 1U), common_params.data_layout).set_name("permute_to_nhwc");
}
graph << ReshapeLayer(TensorShape(494U, n_steps)).set_name("Reshape_input")
// Layer 1
<< FullyConnectedLayer(
2048U,
get_weights_accessor(data_path, "h1_transpose.npy", weights_layout),
get_weights_accessor(data_path, "MatMul_bias.npy"))
.set_name("fc0")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, cell_clip))
.set_name("Relu")
// Layer 2
<< FullyConnectedLayer(
2048U,
get_weights_accessor(data_path, "h2_transpose.npy", weights_layout),
get_weights_accessor(data_path, "MatMul_1_bias.npy"))
.set_name("fc1")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, cell_clip))
.set_name("Relu_1")
// Layer 3
<< FullyConnectedLayer(
2048U,
get_weights_accessor(data_path, "h3_transpose.npy", weights_layout),
get_weights_accessor(data_path, "MatMul_2_bias.npy"))
.set_name("fc2")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, cell_clip))
.set_name("Relu_2")
// Layer 4
<< ReshapeLayer(TensorShape(2048U, 1U, n_steps)).set_name("Reshape_1");
// Unstack Layer (using SplitLayerNode)
NodeParams unstack_params = { "unstack", graph.hints().target_hint };
NodeID unstack_nid = GraphBuilder::add_split_node(graph.graph(), unstack_params, { graph.tail_node(), 0 }, n_steps, 2);
// Create input state descriptor
TensorDescriptor state_descriptor = TensorDescriptor(TensorShape(2048U), common_params.data_type).set_layout(common_params.data_layout);
SubStream previous_state(graph);
SubStream add_y(graph);
// Initial state for LSTM is all zeroes for both state_h and state_c, therefore only one input is created
previous_state << InputLayer(state_descriptor,
get_weights_accessor(data_path, "zeros.npy"))
.set_name("previous_state_c_h");
add_y << InputLayer(state_descriptor,
get_weights_accessor(data_path, "ones.npy"))
.set_name("add_y");
// Create LSTM Fully Connected weights and bias descriptors
TensorDescriptor lstm_weights_descriptor = TensorDescriptor(TensorShape(4096U, 8192U), common_params.data_type).set_layout(common_params.data_layout);
TensorDescriptor lstm_bias_descriptor = TensorDescriptor(TensorShape(8192U), common_params.data_type).set_layout(common_params.data_layout);
SubStream lstm_fc_weights(graph);
SubStream lstm_fc_bias(graph);
lstm_fc_weights << ConstantLayer(lstm_weights_descriptor,
get_weights_accessor(data_path, "rnn_lstm_cell_kernel_transpose.npy", weights_layout))
.set_name("h5/transpose");
lstm_fc_bias << ConstantLayer(lstm_bias_descriptor,
get_weights_accessor(data_path, "rnn_lstm_cell_MatMul_bias.npy"))
.set_name("MatMul_3_bias");
// LSTM Block
std::pair<SubStream, SubStream> new_state_1 = add_lstm_cell(unstack_nid, 0, previous_state, previous_state, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_2 = add_lstm_cell(unstack_nid, 1, new_state_1.first, new_state_1.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_3 = add_lstm_cell(unstack_nid, 2, new_state_2.first, new_state_2.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_4 = add_lstm_cell(unstack_nid, 3, new_state_3.first, new_state_3.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_5 = add_lstm_cell(unstack_nid, 4, new_state_4.first, new_state_4.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_6 = add_lstm_cell(unstack_nid, 5, new_state_5.first, new_state_5.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_7 = add_lstm_cell(unstack_nid, 6, new_state_6.first, new_state_6.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_8 = add_lstm_cell(unstack_nid, 7, new_state_7.first, new_state_7.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_9 = add_lstm_cell(unstack_nid, 8, new_state_8.first, new_state_8.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_10 = add_lstm_cell(unstack_nid, 9, new_state_9.first, new_state_9.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_11 = add_lstm_cell(unstack_nid, 10, new_state_10.first, new_state_10.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_12 = add_lstm_cell(unstack_nid, 11, new_state_11.first, new_state_11.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_13 = add_lstm_cell(unstack_nid, 12, new_state_12.first, new_state_12.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_14 = add_lstm_cell(unstack_nid, 13, new_state_13.first, new_state_13.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_15 = add_lstm_cell(unstack_nid, 14, new_state_14.first, new_state_14.second, add_y, lstm_fc_weights, lstm_fc_bias);
std::pair<SubStream, SubStream> new_state_16 = add_lstm_cell(unstack_nid, 15, new_state_15.first, new_state_15.second, add_y, lstm_fc_weights, lstm_fc_bias);
// Concatenate new states on height
const int axis = 1;
graph << StackLayer(axis,
std::move(new_state_1.second),
std::move(new_state_2.second),
std::move(new_state_3.second),
std::move(new_state_4.second),
std::move(new_state_5.second),
std::move(new_state_6.second),
std::move(new_state_7.second),
std::move(new_state_8.second),
std::move(new_state_9.second),
std::move(new_state_10.second),
std::move(new_state_11.second),
std::move(new_state_12.second),
std::move(new_state_13.second),
std::move(new_state_14.second),
std::move(new_state_15.second),
std::move(new_state_16.second))
.set_name("concat");
graph << FullyConnectedLayer(
2048U,
get_weights_accessor(data_path, "h5_transpose.npy", weights_layout),
get_weights_accessor(data_path, "MatMul_3_bias.npy"))
.set_name("fc3")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, cell_clip))
.set_name("Relu3")
<< FullyConnectedLayer(
29U,
get_weights_accessor(data_path, "h6_transpose.npy", weights_layout),
get_weights_accessor(data_path, "MatMul_4_bias.npy"))
.set_name("fc3")
<< SoftmaxLayer().set_name("logits");
graph << OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
config.num_threads = common_params.threads;
config.use_tuner = common_params.enable_tuner;
config.tuner_file = common_params.tuner_file;
config.convert_to_uint8 = (common_params.data_type == DataType::QASYMM8);
graph.finalize(common_params.target, config);
return true;
}
void do_run() override
{
// Run graph
graph.run();
}
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
CommonGraphParams common_params;
Stream graph;
Status set_node_params(Graph &g, NodeID nid, NodeParams &params)
{
INode *node = g.node(nid);
ARM_COMPUTE_RETURN_ERROR_ON(!node);
node->set_common_node_parameters(params);
return Status{};
}
std::pair<SubStream, SubStream> add_lstm_cell(NodeID unstack_nid,
unsigned int unstack_idx,
SubStream previous_state_c,
SubStream previous_state_h,
SubStream add_y,
SubStream lstm_fc_weights,
SubStream lstm_fc_bias)
{
const std::string cell_name("rnn/lstm_cell_" + std::to_string(unstack_idx));
const DataLayoutDimension concat_dim = (common_params.data_layout == DataLayout::NHWC) ? DataLayoutDimension::CHANNEL : DataLayoutDimension::WIDTH;
// Concatenate result of Unstack with previous_state_h
NodeParams concat_params = { cell_name + "/concat", graph.hints().target_hint };
NodeID concat_nid = graph.graph().add_node<ConcatenateLayerNode>(2, concat_dim);
graph.graph().add_connection(unstack_nid, unstack_idx, concat_nid, 0);
graph.graph().add_connection(previous_state_h.tail_node(), 0, concat_nid, 1);
set_node_params(graph.graph(), concat_nid, concat_params);
graph.forward_tail(concat_nid);
graph << FullyConnectedLayer(
8192U,
lstm_fc_weights,
lstm_fc_bias)
.set_name(cell_name + "/BiasAdd");
// Split Layer
const unsigned int num_splits = 4;
const unsigned int split_axis = 0;
NodeParams split_params = { cell_name + "/split", graph.hints().target_hint };
NodeID split_nid = GraphBuilder::add_split_node(graph.graph(), split_params, { graph.tail_node(), 0 }, num_splits, split_axis);
NodeParams sigmoid_1_params = { cell_name + "/Sigmoid_1", graph.hints().target_hint };
NodeParams add_params = { cell_name + "/add", graph.hints().target_hint };
NodeParams sigmoid_2_params = { cell_name + "/Sigmoid_2", graph.hints().target_hint };
NodeParams tanh_params = { cell_name + "/Tanh", graph.hints().target_hint };
// Sigmoid 1 (first split)
NodeID sigmoid_1_nid = graph.graph().add_node<ActivationLayerNode>(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
graph.graph().add_connection(split_nid, 0, sigmoid_1_nid, 0);
set_node_params(graph.graph(), sigmoid_1_nid, sigmoid_1_params);
// Tanh (second split)
NodeID tanh_nid = graph.graph().add_node<ActivationLayerNode>(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
graph.graph().add_connection(split_nid, 1, tanh_nid, 0);
set_node_params(graph.graph(), tanh_nid, tanh_params);
SubStream tanh_ss(graph);
tanh_ss.forward_tail(tanh_nid);
// Add (third split)
NodeID add_nid = graph.graph().add_node<EltwiseLayerNode>(descriptors::EltwiseLayerDescriptor{ EltwiseOperation::Add });
graph.graph().add_connection(split_nid, 2, add_nid, 0);
graph.graph().add_connection(add_y.tail_node(), 0, add_nid, 1);
set_node_params(graph.graph(), add_nid, add_params);
// Sigmoid 2 (fourth split)
NodeID sigmoid_2_nid = graph.graph().add_node<ActivationLayerNode>(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
graph.graph().add_connection(split_nid, 3, sigmoid_2_nid, 0);
set_node_params(graph.graph(), sigmoid_2_nid, sigmoid_2_params);
SubStream sigmoid_1_ss(graph);
sigmoid_1_ss.forward_tail(sigmoid_1_nid);
SubStream mul_1_ss(sigmoid_1_ss);
mul_1_ss << EltwiseLayer(std::move(sigmoid_1_ss), std::move(tanh_ss), EltwiseOperation::Mul)
.set_name(cell_name + "/mul_1");
SubStream tanh_1_ss_tmp(graph);
tanh_1_ss_tmp.forward_tail(add_nid);
tanh_1_ss_tmp << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC))
.set_name(cell_name + "/Sigmoid");
SubStream tanh_1_ss_tmp2(tanh_1_ss_tmp);
tanh_1_ss_tmp2 << EltwiseLayer(std::move(tanh_1_ss_tmp), std::move(previous_state_c), EltwiseOperation::Mul)
.set_name(cell_name + "/mul");
SubStream tanh_1_ss(tanh_1_ss_tmp2);
tanh_1_ss << EltwiseLayer(std::move(tanh_1_ss_tmp2), std::move(mul_1_ss), EltwiseOperation::Add)
.set_name(cell_name + "/new_state_c");
SubStream new_state_c(tanh_1_ss);
tanh_1_ss << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f))
.set_name(cell_name + "/Tanh_1");
SubStream sigmoid_2_ss(graph);
sigmoid_2_ss.forward_tail(sigmoid_2_nid);
graph << EltwiseLayer(std::move(sigmoid_2_ss), std::move(tanh_1_ss), EltwiseOperation::Mul)
.set_name(cell_name + "/new_state_h");
SubStream new_state_h(graph);
return std::pair<SubStream, SubStream>(new_state_c, new_state_h);
}
};
/** Main program for DeepSpeech v0.4.1
*
* Model is based on:
* https://arxiv.org/abs/1412.5567
* "Deep Speech: Scaling up end-to-end speech recognition"
* Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng
*
* Provenance: https://github.com/mozilla/DeepSpeech
*
* @note To list all the possible arguments execute the binary appended with the --help option
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments
*
* @return Return code
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphDeepSpeechExample>(argc, argv);
}
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