def log_basic_info(args) -> None: """ Log basic information like version number, arguments, etc. :param args: Arguments as returned by argparse. """ log_sockeye_version(logger) log_mxnet_version(logger) logger.info("Command: %s", " ".join(sys.argv)) logger.info("Arguments: %s", args)
def main(): params = argparse.ArgumentParser(description='Translate CLI') arguments.add_translate_cli_args(params) args = params.parse_args() if args.output is not None: global logger logger = setup_main_logger(__name__, file_logging=True, path="%s.%s" % (args.output, C.LOG_NAME)) if args.checkpoints is not None: check_condition( len(args.checkpoints) == len(args.models), "must provide checkpoints for each model") log_sockeye_version(logger) log_mxnet_version(logger) logger.info("Command: %s", " ".join(sys.argv)) logger.info("Arguments: %s", args) output_handler = sockeye.output_handler.get_output_handler( args.output_type, args.output, args.sure_align_threshold) with ExitStack() as exit_stack: context = _setup_context(args, exit_stack) bucket_source_width, bucket_target_width = args.bucket_width translator = sockeye.inference.Translator( context, args.ensemble_mode, bucket_source_width, bucket_target_width, sockeye.inference.LengthPenalty(args.length_penalty_alpha, args.length_penalty_beta), *sockeye.inference.load_models(context, args.max_input_len, args.beam_size, args.models, args.checkpoints, args.softmax_temperature, args.max_output_length_num_stds)) read_and_translate(translator, output_handler, args.input)
def main(): params = argparse.ArgumentParser(description='CLI to train sockeye sequence-to-sequence models.') arguments.add_train_cli_args(params) args = params.parse_args() # seed the RNGs np.random.seed(args.seed) random.seed(args.seed) mx.random.seed(args.seed) if args.use_fused_rnn: check_condition(not args.use_cpu, "GPU required for FusedRNN cells") check_condition(args.optimized_metric == C.BLEU or args.optimized_metric in args.metrics, "Must optimize either BLEU or one of tracked metrics (--metrics)") # Checking status of output folder, resumption, etc. # Create temporary logger to console only logger = setup_main_logger(__name__, file_logging=False, console=not args.quiet) output_folder = os.path.abspath(args.output) resume_training = False training_state_dir = os.path.join(output_folder, C.TRAINING_STATE_DIRNAME) if os.path.exists(output_folder): if args.overwrite_output: logger.info("Removing existing output folder %s.", output_folder) shutil.rmtree(output_folder) os.makedirs(output_folder) elif os.path.exists(training_state_dir): with open(os.path.join(output_folder, C.ARGS_STATE_NAME), "r") as fp: old_args = json.load(fp) arg_diffs = _dict_difference(vars(args), old_args) | _dict_difference(old_args, vars(args)) # Remove args that may differ without affecting the training. arg_diffs -= set(C.ARGS_MAY_DIFFER) # allow different device-ids provided their total count is the same if 'device_ids' in arg_diffs and len(old_args['device_ids']) == len(vars(args)['device_ids']): arg_diffs.discard('device_ids') if not arg_diffs: resume_training = True else: # We do not have the logger yet logger.error("Mismatch in arguments for training continuation.") logger.error("Differing arguments: %s.", ", ".join(arg_diffs)) sys.exit(1) elif os.path.exists(os.path.join(output_folder, C.PARAMS_BEST_NAME)): logger.error("Refusing to overwrite model folder %s as it seems to contain a trained model.", output_folder) sys.exit(1) else: logger.info("The output folder %s already exists, but no training state or parameter file was found. " "Will start training from scratch.", output_folder) else: os.makedirs(output_folder) logger = setup_main_logger(__name__, file_logging=True, console=not args.quiet, path=os.path.join(output_folder, C.LOG_NAME)) log_sockeye_version(logger) log_mxnet_version(logger) logger.info("Command: %s", " ".join(sys.argv)) logger.info("Arguments: %s", args) with open(os.path.join(output_folder, C.ARGS_STATE_NAME), "w") as fp: json.dump(vars(args), fp) with ExitStack() as exit_stack: # context if args.use_cpu: logger.info("Device: CPU") context = [mx.cpu()] else: num_gpus = get_num_gpus() check_condition(num_gpus >= 1, "No GPUs found, consider running on the CPU with --use-cpu " "(note: check depends on nvidia-smi and this could also mean that the nvidia-smi " "binary isn't on the path).") if args.disable_device_locking: context = expand_requested_device_ids(args.device_ids) else: context = exit_stack.enter_context(acquire_gpus(args.device_ids, lock_dir=args.lock_dir)) logger.info("Device(s): GPU %s", context) context = [mx.gpu(gpu_id) for gpu_id in context] # load existing or create vocabs if resume_training: vocab_source = vocab.vocab_from_json_or_pickle(os.path.join(output_folder, C.VOCAB_SRC_NAME)) vocab_target = vocab.vocab_from_json_or_pickle(os.path.join(output_folder, C.VOCAB_TRG_NAME)) else: num_words_source, num_words_target = args.num_words word_min_count_source, word_min_count_target = args.word_min_count # if the source and target embeddings are tied we build a joint vocabulary: if args.weight_tying and C.WEIGHT_TYING_SRC in args.weight_tying_type \ and C.WEIGHT_TYING_TRG in args.weight_tying_type: vocab_source = vocab_target = _build_or_load_vocab(args.source_vocab, [args.source, args.target], num_words_source, word_min_count_source) else: vocab_source = _build_or_load_vocab(args.source_vocab, [args.source], num_words_source, word_min_count_source) vocab_target = _build_or_load_vocab(args.target_vocab, [args.target], num_words_target, word_min_count_target) # write vocabularies vocab.vocab_to_json(vocab_source, os.path.join(output_folder, C.VOCAB_SRC_NAME) + C.JSON_SUFFIX) vocab.vocab_to_json(vocab_target, os.path.join(output_folder, C.VOCAB_TRG_NAME) + C.JSON_SUFFIX) vocab_source_size = len(vocab_source) vocab_target_size = len(vocab_target) logger.info("Vocabulary sizes: source=%d target=%d", vocab_source_size, vocab_target_size) # create data iterators max_seq_len_source, max_seq_len_target = args.max_seq_len batch_num_devices = 1 if args.use_cpu else sum(-di if di < 0 else 1 for di in args.device_ids) train_iter, eval_iter, config_data = data_io.get_training_data_iters(source=os.path.abspath(args.source), target=os.path.abspath(args.target), validation_source=os.path.abspath( args.validation_source), validation_target=os.path.abspath( args.validation_target), vocab_source=vocab_source, vocab_target=vocab_target, vocab_source_path=args.source_vocab, vocab_target_path=args.target_vocab, batch_size=args.batch_size, batch_by_words=args.batch_type == C.BATCH_TYPE_WORD, batch_num_devices=batch_num_devices, fill_up=args.fill_up, max_seq_len_source=max_seq_len_source, max_seq_len_target=max_seq_len_target, bucketing=not args.no_bucketing, bucket_width=args.bucket_width) # learning rate scheduling learning_rate_half_life = none_if_negative(args.learning_rate_half_life) # TODO: The loading for continuation of the scheduler is done separately from the other parts if not resume_training: lr_scheduler_instance = lr_scheduler.get_lr_scheduler(args.learning_rate_scheduler_type, args.checkpoint_frequency, learning_rate_half_life, args.learning_rate_reduce_factor, args.learning_rate_reduce_num_not_improved, args.learning_rate_schedule, args.learning_rate_warmup) else: with open(os.path.join(training_state_dir, C.SCHEDULER_STATE_NAME), "rb") as fp: lr_scheduler_instance = pickle.load(fp) # model configuration num_embed_source, num_embed_target = args.num_embed encoder_num_layers, decoder_num_layers = args.num_layers encoder_embed_dropout, decoder_embed_dropout = args.embed_dropout encoder_rnn_dropout_inputs, decoder_rnn_dropout_inputs = args.rnn_dropout_inputs encoder_rnn_dropout_states, decoder_rnn_dropout_states = args.rnn_dropout_states if encoder_embed_dropout > 0 and encoder_rnn_dropout_inputs > 0: logger.warning("Setting encoder RNN AND source embedding dropout > 0 leads to " "two dropout layers on top of each other.") if decoder_embed_dropout > 0 and decoder_rnn_dropout_inputs > 0: logger.warning("Setting encoder RNN AND source embedding dropout > 0 leads to " "two dropout layers on top of each other.") encoder_rnn_dropout_recurrent, decoder_rnn_dropout_recurrent = args.rnn_dropout_recurrent if encoder_rnn_dropout_recurrent > 0 or decoder_rnn_dropout_recurrent > 0: check_condition(args.rnn_cell_type == C.LSTM_TYPE, "Recurrent dropout without memory loss only supported for LSTMs right now.") encoder_transformer_preprocess, decoder_transformer_preprocess = args.transformer_preprocess encoder_transformer_postprocess, decoder_transformer_postprocess = args.transformer_postprocess config_conv = None if args.encoder == C.RNN_WITH_CONV_EMBED_NAME: config_conv = encoder.ConvolutionalEmbeddingConfig(num_embed=num_embed_source, max_filter_width=args.conv_embed_max_filter_width, num_filters=args.conv_embed_num_filters, pool_stride=args.conv_embed_pool_stride, num_highway_layers=args.conv_embed_num_highway_layers, dropout=args.conv_embed_dropout) if args.encoder in (C.TRANSFORMER_TYPE, C.TRANSFORMER_WITH_CONV_EMBED_TYPE): config_encoder = transformer.TransformerConfig( model_size=args.transformer_model_size, attention_heads=args.transformer_attention_heads, feed_forward_num_hidden=args.transformer_feed_forward_num_hidden, num_layers=encoder_num_layers, vocab_size=vocab_source_size, dropout_attention=args.transformer_dropout_attention, dropout_relu=args.transformer_dropout_relu, dropout_prepost=args.transformer_dropout_prepost, weight_tying=args.weight_tying and C.WEIGHT_TYING_SRC in args.weight_tying_type, positional_encodings=not args.transformer_no_positional_encodings, preprocess_sequence=encoder_transformer_preprocess, postprocess_sequence=encoder_transformer_postprocess, conv_config=config_conv) else: config_encoder = encoder.RecurrentEncoderConfig( vocab_size=vocab_source_size, num_embed=num_embed_source, embed_dropout=encoder_embed_dropout, rnn_config=rnn.RNNConfig(cell_type=args.rnn_cell_type, num_hidden=args.rnn_num_hidden, num_layers=encoder_num_layers, dropout_inputs=encoder_rnn_dropout_inputs, dropout_states=encoder_rnn_dropout_states, dropout_recurrent=encoder_rnn_dropout_recurrent, residual=args.rnn_residual_connections, first_residual_layer=args.rnn_first_residual_layer, forget_bias=args.rnn_forget_bias), conv_config=config_conv, reverse_input=args.rnn_encoder_reverse_input) decoder_weight_tying = args.weight_tying and C.WEIGHT_TYING_TRG in args.weight_tying_type \ and C.WEIGHT_TYING_SOFTMAX in args.weight_tying_type if args.decoder == C.TRANSFORMER_TYPE: config_decoder = transformer.TransformerConfig( model_size=args.transformer_model_size, attention_heads=args.transformer_attention_heads, feed_forward_num_hidden=args.transformer_feed_forward_num_hidden, num_layers=decoder_num_layers, vocab_size=vocab_target_size, dropout_attention=args.transformer_dropout_attention, dropout_relu=args.transformer_dropout_relu, dropout_prepost=args.transformer_dropout_prepost, weight_tying=decoder_weight_tying, positional_encodings=not args.transformer_no_positional_encodings, preprocess_sequence=decoder_transformer_preprocess, postprocess_sequence=decoder_transformer_postprocess, conv_config=None) else: attention_num_hidden = args.rnn_num_hidden if not args.attention_num_hidden else args.attention_num_hidden config_coverage = None if args.attention_type == C.ATT_COV: config_coverage = coverage.CoverageConfig(type=args.attention_coverage_type, num_hidden=args.attention_coverage_num_hidden, layer_normalization=args.layer_normalization) config_attention = attention.AttentionConfig(type=args.attention_type, num_hidden=attention_num_hidden, input_previous_word=args.attention_use_prev_word, rnn_num_hidden=args.rnn_num_hidden, layer_normalization=args.layer_normalization, config_coverage=config_coverage, num_heads=args.attention_mhdot_heads) config_decoder = decoder.RecurrentDecoderConfig( vocab_size=vocab_target_size, max_seq_len_source=max_seq_len_source, num_embed=num_embed_target, rnn_config=rnn.RNNConfig(cell_type=args.rnn_cell_type, num_hidden=args.rnn_num_hidden, num_layers=decoder_num_layers, dropout_inputs=decoder_rnn_dropout_inputs, dropout_states=decoder_rnn_dropout_states, dropout_recurrent=decoder_rnn_dropout_recurrent, residual=args.rnn_residual_connections, first_residual_layer=args.rnn_first_residual_layer, forget_bias=args.rnn_forget_bias), attention_config=config_attention, embed_dropout=decoder_embed_dropout, hidden_dropout=args.rnn_decoder_hidden_dropout, weight_tying=decoder_weight_tying, state_init=args.rnn_decoder_state_init, context_gating=args.rnn_context_gating, layer_normalization=args.layer_normalization, attention_in_upper_layers=args.attention_in_upper_layers) config_loss = loss.LossConfig(type=args.loss, vocab_size=vocab_target_size, normalize=args.normalize_loss, smoothed_cross_entropy_alpha=args.smoothed_cross_entropy_alpha) model_config = model.ModelConfig(config_data=config_data, max_seq_len_source=max_seq_len_source, max_seq_len_target=max_seq_len_target, vocab_source_size=vocab_source_size, vocab_target_size=vocab_target_size, config_encoder=config_encoder, config_decoder=config_decoder, config_loss=config_loss, lexical_bias=args.lexical_bias, learn_lexical_bias=args.learn_lexical_bias, weight_tying=args.weight_tying, weight_tying_type=args.weight_tying_type if args.weight_tying else None) model_config.freeze() # create training model training_model = training.TrainingModel(config=model_config, context=context, train_iter=train_iter, fused=args.use_fused_rnn, bucketing=not args.no_bucketing, lr_scheduler=lr_scheduler_instance) # We may consider loading the params in TrainingModule, for consistency # with the training state saving if resume_training: logger.info("Found partial training in directory %s. Resuming from saved state.", training_state_dir) training_model.load_params_from_file(os.path.join(training_state_dir, C.TRAINING_STATE_PARAMS_NAME)) elif args.params: logger.info("Training will initialize from parameters loaded from '%s'", args.params) training_model.load_params_from_file(args.params) lexicon_array = lexicon.initialize_lexicon(args.lexical_bias, vocab_source, vocab_target) if args.lexical_bias else None weight_initializer = initializer.get_initializer(args.weight_init, args.weight_init_scale, args.rnn_h2h_init, lexicon=lexicon_array) optimizer = args.optimizer optimizer_params = {'wd': args.weight_decay, "learning_rate": args.initial_learning_rate} if lr_scheduler_instance is not None: optimizer_params["lr_scheduler"] = lr_scheduler_instance clip_gradient = none_if_negative(args.clip_gradient) if clip_gradient is not None: optimizer_params["clip_gradient"] = clip_gradient if args.momentum is not None: optimizer_params["momentum"] = args.momentum if args.normalize_loss: # When normalize_loss is turned on we normalize by the number of non-PAD symbols in a batch which implicitly # already contains the number of sentences and therefore we need to disable rescale_grad. optimizer_params["rescale_grad"] = 1.0 else: # Making MXNet module API's default scaling factor explicit optimizer_params["rescale_grad"] = 1.0 / args.batch_size logger.info("Optimizer: %s", optimizer) logger.info("Optimizer Parameters: %s", optimizer_params) # Handle options that override training settings max_updates = args.max_updates max_num_checkpoint_not_improved = args.max_num_checkpoint_not_improved min_num_epochs = args.min_num_epochs # Fixed training schedule always runs for a set number of updates if args.learning_rate_schedule: max_updates = sum(num_updates for (_, num_updates) in args.learning_rate_schedule) max_num_checkpoint_not_improved = -1 min_num_epochs = 0 monitor_bleu = args.monitor_bleu # Turn on BLEU monitoring when the optimized metric is BLEU and it hasn't been enabled yet if args.optimized_metric == C.BLEU and monitor_bleu == 0: logger.info("You chose BLEU as the optimized metric, will turn on BLEU monitoring during training. " "To control how many validation sentences are used for calculating bleu use " "the --monitor-bleu argument.") monitor_bleu = -1 training_model.fit(train_iter, eval_iter, output_folder=output_folder, max_params_files_to_keep=args.keep_last_params, metrics=args.metrics, initializer=weight_initializer, max_updates=max_updates, checkpoint_frequency=args.checkpoint_frequency, optimizer=optimizer, optimizer_params=optimizer_params, optimized_metric=args.optimized_metric, max_num_not_improved=max_num_checkpoint_not_improved, min_num_epochs=min_num_epochs, monitor_bleu=monitor_bleu, use_tensorboard=args.use_tensorboard, mxmonitor_pattern=args.monitor_pattern, mxmonitor_stat_func=args.monitor_stat_func)