# Copyright 2024 The YourMT3 Authors. # # 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 # # Please see the details in the LICENSE file. """ test.py """ import os import pprint import argparse import torch from utils.data_modules import AMTDataModule from utils.task_manager import TaskManager from model.init_train import initialize_trainer, update_config from model.ymt3 import YourMT3 from config.data_presets import data_preset_single_cfg, data_preset_multi_cfg from config.vocabulary import drum_vocab_presets from utils.utils import str2bool # yapf: disable parser = argparse.ArgumentParser(description="YourMT3") # General parser.add_argument('exp_id', type=str, help='A unique identifier for the experiment is used to resume training. The "@" symbol can be used to load a specific checkpoint.') parser.add_argument('-p', '--project', type=str, default='ymt3', help='project name') parser.add_argument('-d', '--data-preset', type=str, default='musicnet_thickstun_ext_em', help='dataset preset (default=musicnet_thickstun_ext_em). See config/data.py for more options.') # Audio configurations parser.add_argument('-ac', '--audio-codec', type=str, default=None, help='audio codec (default=None). {"spec", "melspec"}. If None, default value defined in config.py will be used.') parser.add_argument('-hop', '--hop-length', type=int, default=None, help='hop length in frames (default=None). {128, 300} 128 for MT3, 300 for PerceiverTFIf None, default value defined in config.py will be used.') parser.add_argument('-nmel', '--n-mels', type=int, default=None, help='number of mel bins (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-if', '--input-frames', type=int, default=None, help='number of audio frames for input segment (default=None). If None, default value defined in config.py will be used.') # Model configurations parser.add_argument('-sqr', '--sca-use-query-residual', type=str2bool, default=None, help='sca use query residual flag. Default follows config.py') parser.add_argument('-enc', '--encoder-type', type=str, default=None, help="Encoder type. 't5' or 'perceiver-tf' or 'conformer'. Default is 't5', following config.py.") parser.add_argument('-dec', '--decoder-type', type=str, default=None, help="Decoder type. 't5' or 'multi-t5'. Default is 't5', following config.py.") parser.add_argument('-preenc', '--pre-encoder-type', type=str, default='default', help="Pre-encoder type. None or 'conv' or 'default'. By default, t5_enc:None, perceiver_tf_enc:conv, conformer:None") parser.add_argument('-predec', '--pre-decoder-type', type=str, default='default', help="Pre-decoder type. {None, 'linear', 'conv1', 'mlp', 'group_linear'} or 'default'. Default is {'t5': None, 'perceiver-tf': 'linear', 'conformer': None}.") parser.add_argument('-cout', '--conv-out-channels', type=int, default=None, help='Number of filters for pre-encoder conv layer. Default follows "model_cfg" of config.py.') parser.add_argument('-tenc', '--task-cond-encoder', type=str2bool, default=True, help='task conditional encoder (default=True). True or False') parser.add_argument('-tdec', '--task-cond-decoder', type=str2bool, default=True, help='task conditional decoder (default=True). True or False') parser.add_argument('-df', '--d-feat', type=int, default=None, help='Audio feature will be projected to this dimension for Q,K,V of T5 or K,V of Perceiver (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-pt', '--pretrained', type=str2bool, default=False, help='pretrained T5(default=False). True or False') parser.add_argument('-b', '--base-name', type=str, default="google/t5-v1_1-small", help='base model name (default="google/t5-v1_1-small")') parser.add_argument('-epe', '--encoder-position-encoding-type', type=str, default='default', help="Positional encoding type of encoder. By default, pre-defined PE for T5 or Perceiver-TF encoder in config.py. For T5: {'sinusoidal', 'trainable'}, conformer: {'rotary', 'trainable'}, Perceiver-TF: {'trainable', 'rope', 'alibi', 'alibit', 'None', '0', 'none', 'tkd', 'td', 'tk', 'kdt'}.") parser.add_argument('-dpe', '--decoder-position-encoding-type', type=str, default='default', help="Positional encoding type of decoder. By default, pre-defined PE for T5 in config.py. {'sinusoidal', 'trainable'}.") parser.add_argument('-twe', '--tie-word-embedding', type=str2bool, default=None, help='tie word embedding (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-el', '--event-length', type=int, default=None, help='event length (default=None). If None, default value defined in model cfg of config.py will be used.') # Perceiver-TF configurations parser.add_argument('-dl', '--d-latent', type=int, default=None, help='Latent dimension of Perceiver. On T5, this will be ignored (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-nl', '--num-latents', type=int, default=None, help='Number of latents of Perceiver. On T5, this will be ignored (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-dpm', '--perceiver-tf-d-model', type=int, default=None, help='Perceiver-TF d_model (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-npb', '--num-perceiver-tf-blocks', type=int, default=None, help='Number of blocks of Perceiver-TF. On T5, this will be ignored (default=None). If None, default value defined in config.py.') parser.add_argument('-npl', '--num-perceiver-tf-local-transformers-per-block', type=int, default=None, help='Number of local layers per block of Perceiver-TF. On T5, this will be ignored (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-npt', '--num-perceiver-tf-temporal-transformers-per-block', type=int, default=None, help='Number of temporal layers per block of Perceiver-TF. On T5, this will be ignored (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-atc', '--attention-to-channel', type=str2bool, default=None, help='Attention to channel flag of Perceiver-TF. On T5, this will be ignored (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-ln', '--layer-norm-type', type=str, default=None, help='Layer normalization type (default=None). {"layer_norm", "rms_norm"}. If None, default value defined in config.py will be used.') parser.add_argument('-ff', '--ff-layer-type', type=str, default=None, help='Feed forward layer type (default=None). {"mlp", "moe", "gmlp"}. If None, default value defined in config.py will be used.') parser.add_argument('-wf', '--ff-widening-factor', type=int, default=None, help='Feed forward layer widening factor for MLP/MoE/gMLP (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-nmoe', '--moe-num-experts', type=int, default=None, help='Number of experts for MoE (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-kmoe', '--moe-topk', type=int, default=None, help='Top-k for MoE (default=None). If None, default value defined in config.py will be used.') parser.add_argument('-act', '--hidden-act', type=str, default=None, help='Hidden activation function (default=None). {"gelu", "silu", "relu", "tanh"}. If None, default value defined in config.py will be used.') parser.add_argument('-rt', '--rotary-type', type=str, default=None, help='Rotary embedding type expressed in three letters. e.g. ppl: "pixel" for SCA and latents, "lang" for temporal transformer. If None, use config.') parser.add_argument('-rk', '--rope-apply-to-keys', type=str2bool, default=None, help='Apply rope to keys (default=None). If None, use config.') parser.add_argument('-rp', '--rope-partial-pe', type=str2bool, default=None, help='Whether to apply RoPE to partial positions (default=None). If None, use config.') # Decoder configurations parser.add_argument('-dff', '--decoder-ff-layer-type', type=str, default=None, help='Feed forward layer type of decoder (default=None). {"mlp", "moe", "gmlp"}. If None, default value defined in config.py will be used.') parser.add_argument('-dwf', '--decoder-ff-widening-factor', type=int, default=None, help='Feed forward layer widening factor for decoder MLP/MoE/gMLP (default=None). If None, default value defined in config.py will be used.') # Task and Evaluation configurations parser.add_argument('-tk', '--task', type=str, default='mt3_full_plus', help='tokenizer type (default=mt3_full_plus). See config/task.py for more options.') parser.add_argument('-epv', '--eval-program-vocab', type=str, default=None, help='evaluation vocabulary (default=None). If None, default vocabulary of the data preset will be used.') parser.add_argument('-edv', '--eval-drum-vocab', type=str, default=None, help='evaluation vocabulary for drum (default=None). If None, default vocabulary of the data preset will be used.') parser.add_argument('-etk', '--eval-subtask-key', type=str, default='default', help='evaluation subtask key (default=default). See config/task.py for more options.') parser.add_argument('-t', '--onset-tolerance', type=float, default=0.05, help='onset tolerance (default=0.05).') parser.add_argument('-os', '--test-octave-shift', type=str2bool, default=False, help='test optimal octave shift (default=False). True or False') parser.add_argument('-w', '--write-model-output', type=str2bool, default=False, help='write model test output to file (default=False). True or False') # Trainer configurations parser.add_argument('-pr','--precision', type=str, default="bf16-mixed", help='precision (default="bf16-mixed") {32, 16, bf16, bf16-mixed}') parser.add_argument('-st', '--strategy', type=str, default='auto', help='strategy (default=auto). auto or deepspeed or ddp') parser.add_argument('-n', '--num-nodes', type=int, default=1, help='number of nodes (default=1)') parser.add_argument('-g', '--num-gpus', type=str, default='auto', help='number of gpus (default="auto")') parser.add_argument('-wb', '--wandb-mode', type=str, default=None, help='wandb mode for logging (default=None). "disabled" or "online" or "offline". If None, default value defined in config.py will be used.') # Debug parser.add_argument('-debug', '--debug-mode', type=str2bool, default=False, help='debug mode (default=False). True or False') parser.add_argument('-tps', '--test-pitch-shift', type=int, default=None, help='use pitch shift when testing. debug-purpose only. (default=None). semitone in int.') args = parser.parse_args() # yapf: enable if torch.__version__ >= "1.13": torch.set_float32_matmul_precision("high") args.epochs = None # Initialize trainer trainer, wandb_logger, dir_info, shared_cfg = initialize_trainer(args, stage='test') # Update config with args, including augmentation settings shared_cfg, audio_cfg, model_cfg = update_config(args, shared_cfg, stage='test') def main(): # Data preset if args.data_preset in data_preset_single_cfg: # convert single preset into multi preset format data_preset = { "presets": [args.data_preset], "eval_vocab": data_preset_single_cfg[args.data_preset]["eval_vocab"], } for k in data_preset_single_cfg[args.data_preset].keys(): if k in ["eval_drum_vocab", "add_pitch_class_metric"]: data_preset[k] = data_preset_single_cfg[args.data_preset][k] elif args.data_preset in data_preset_multi_cfg: data_preset = data_preset_multi_cfg[args.data_preset] else: raise ValueError("Invalid data preset") eval_drum_vocab = data_preset.get("eval_drum_vocab", None) if args.eval_drum_vocab != None: # override eval_drum_vocab eval_drum_vocab = drum_vocab_presets[args.eval_drum_vocab] # Task manager tm = TaskManager(task_name=args.task, max_shift_steps=int(shared_cfg["TOKENIZER"]["max_shift_steps"]), debug_mode=args.debug_mode) print(f"Task: {tm.task_name}, Max Shift Steps: {tm.max_shift_steps}") results = [] for i, preset in enumerate(data_preset["presets"]): # sdp: unpacking multi preset as a list of single presets sdp = { "presets": [preset], "eval_vocab": [data_preset["eval_vocab"][i]], "eval_drum_vocab": eval_drum_vocab, } for k in data_preset.keys(): if k not in ["presets", "eval_vocab"]: sdp[k] = data_preset[k] dm = AMTDataModule(data_preset_multi=sdp, task_manager=tm, audio_cfg=audio_cfg) model = YourMT3( audio_cfg=audio_cfg, model_cfg=model_cfg, shared_cfg=shared_cfg, optimizer=None, task_manager=tm, # tokenizer is a member of task_manager eval_subtask_key=args.eval_subtask_key, eval_vocab=args.eval_program_vocab if args.eval_program_vocab != None else sdp["eval_vocab"], eval_drum_vocab=sdp["eval_drum_vocab"], write_output_dir=dir_info["lightning_dir"] if args.write_model_output or args.test_octave_shift else None, onset_tolerance=float(args.onset_tolerance), add_pitch_class_metric=sdp.get("add_pitch_class_metric", None), test_optimal_octave_shift=args.test_octave_shift, test_pitch_shift_layer=args.test_pitch_shift) # load checkpoint & drop pitchshift from state_dict checkpoint = torch.load(dir_info["last_ckpt_path"]) state_dict = checkpoint['state_dict'] new_state_dict = {k: v for k, v in state_dict.items() if 'pitchshift' not in k} model.load_state_dict(new_state_dict, strict=False) # if args.test_pitch_shift is None: # new_state_dict = {k: v for k, v in state_dict.items() if 'pitchshift' not in k} # model.load_state_dict(new_state_dict, strict=False) # else: # model.load_state_dict(state_dict, strict=False) results.append("-----------------------------------------------------------------") results.append(sdp) results.append(trainer.test(model, datamodule=dm)) # TODO: directly load checkpoint including hyperparmeters https://lightning.ai/docs/pytorch/1.6.2/common/hyperparameters.html # save result pp = pprint.PrettyPrinter(indent=4) results_str = pp.pformat(results) result_file = os.path.join(dir_info["lightning_dir"], f"result_{args.task}_{args.eval_subtask_key}_{args.data_preset}.json") with open(result_file, 'w') as f: f.write(results_str) print(f"Result is saved to {result_file}") if __name__ == "__main__": main()