From 3418ba520dd6251738ba905df84a201121433ecd Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 1 Mar 2019 17:19:55 +0000 Subject: COMPMID-1058: Implement DeepSpeech as a graph example Adding DeepSpeech v0.4.1 example Change-Id: I2edd55f16dda448cfbc368e264a9ad8999c4a750 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/947 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Manuel Bottini Reviewed-by: Isabella Gottardi --- examples/graph_deepspeech_v0_4_1.cpp | 359 +++++++++++++++++++++++++++++++++++ 1 file changed, 359 insertions(+) create mode 100644 examples/graph_deepspeech_v0_4_1.cpp diff --git a/examples/graph_deepspeech_v0_4_1.cpp b/examples/graph_deepspeech_v0_4_1.cpp new file mode 100644 index 0000000000..f280393e5d --- /dev/null +++ b/examples/graph_deepspeech_v0_4_1.cpp @@ -0,0 +1,359 @@ +/* + * Copyright (c) 2019 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); + + // 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; + } + + // Checks + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); + + // 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"); + + // TODO(COMPMID-2103): Use sub stream for FC weights and bias in LSTM cells + // 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 << InputLayer(lstm_weights_descriptor, + // get_weights_accessor(data_path, "rnn_lstm_cell_kernel_transpose.npy", weights_layout)) + // .set_name("h5/transpose"); + //lstm_fc_bias << InputLayer(lstm_bias_descriptor, + // get_weights_accessor(data_path, "rnn_lstm_cell_MatMul_bias.npy")) + // .set_name("MatMul_3_bias"); + + // LSTM Block + std::pair new_state_1 = add_lstm_cell(data_path, unstack_nid, 0, previous_state, previous_state, add_y); + std::pair new_state_2 = add_lstm_cell(data_path, unstack_nid, 1, new_state_1.first, new_state_1.second, add_y); + std::pair new_state_3 = add_lstm_cell(data_path, unstack_nid, 2, new_state_2.first, new_state_2.second, add_y); + std::pair new_state_4 = add_lstm_cell(data_path, unstack_nid, 3, new_state_3.first, new_state_3.second, add_y); + std::pair new_state_5 = add_lstm_cell(data_path, unstack_nid, 4, new_state_4.first, new_state_4.second, add_y); + std::pair new_state_6 = add_lstm_cell(data_path, unstack_nid, 5, new_state_5.first, new_state_5.second, add_y); + std::pair new_state_7 = add_lstm_cell(data_path, unstack_nid, 6, new_state_6.first, new_state_6.second, add_y); + std::pair new_state_8 = add_lstm_cell(data_path, unstack_nid, 7, new_state_7.first, new_state_7.second, add_y); + std::pair new_state_9 = add_lstm_cell(data_path, unstack_nid, 8, new_state_8.first, new_state_8.second, add_y); + std::pair new_state_10 = add_lstm_cell(data_path, unstack_nid, 9, new_state_9.first, new_state_9.second, add_y); + std::pair new_state_11 = add_lstm_cell(data_path, unstack_nid, 10, new_state_10.first, new_state_10.second, add_y); + std::pair new_state_12 = add_lstm_cell(data_path, unstack_nid, 11, new_state_11.first, new_state_11.second, add_y); + std::pair new_state_13 = add_lstm_cell(data_path, unstack_nid, 12, new_state_12.first, new_state_12.second, add_y); + std::pair new_state_14 = add_lstm_cell(data_path, unstack_nid, 13, new_state_13.first, new_state_13.second, add_y); + std::pair new_state_15 = add_lstm_cell(data_path, unstack_nid, 14, new_state_14.first, new_state_14.second, add_y); + std::pair new_state_16 = add_lstm_cell(data_path, unstack_nid, 15, new_state_15.first, new_state_15.second, add_y); + + if(n_steps > 1) + { + // 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; + + 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 add_lstm_cell(const std::string &data_path, + NodeID unstack_nid, + unsigned int unstack_idx, + SubStream previous_state_c, + SubStream previous_state_h, + SubStream add_y) + // TODO(COMPMID-2103): Use sub streams for FC weights and bias + //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(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, + get_weights_accessor(data_path, "rnn_lstm_cell_kernel_transpose.npy", DataLayout::NHWC), + get_weights_accessor(data_path, "rnn_lstm_cell_MatMul_bias.npy")) + .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(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(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(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(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 mul_1_ss(graph); + mul_1_ss.forward_tail(sigmoid_1_nid); + mul_1_ss << EltwiseLayer(std::move(mul_1_ss), std::move(tanh_ss), EltwiseOperation::Mul) + .set_name(cell_name + "/mul_1"); + + SubStream tanh_1_ss(graph); + tanh_1_ss.forward_tail(add_nid); + tanh_1_ss << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)) + .set_name(cell_name + "/Sigmoid"); + tanh_1_ss << EltwiseLayer(std::move(tanh_1_ss), std::move(previous_state_c), EltwiseOperation::Mul) + .set_name(cell_name + "/mul"); + + tanh_1_ss << EltwiseLayer(std::move(tanh_1_ss), 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(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(argc, argv); +} -- cgit v1.2.1