| /* |
| * Copyright (c) 2019-2021 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.mlgo_file = common_params.mlgo_file; |
| config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type); |
| config.synthetic_type = common_params.data_type; |
| |
| 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 ¶ms) |
| { |
| 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); |
| } |