def vc_s3_sweep_4(): sweep = prod([ flag("pipeline", ['sc']), flag("dataset", ['voxceleb']), flag("dataset.clip_length", [3]), flag("model", ['s3']), flag("model.d_model", [128]), flag("model.layer.d_state", [64]), # flag("model.pool.pool", [4]), # flag("model.pool.expand", [2]), flag("model.n_layers", [8]), flag("model.prenorm", [True]), flag("model.dropout", [0.1]), flag("+encoder._name_", ['conv1d']), lzip([ flag("+encoder.kernel_size", [8]), flag("+encoder.stride", [8]), flag("+encoder.padding", [0]), flag("loader.batch_size", [32]), ]), flag("optimizer.lr", [4e-3]), flag("model.norm", ['layer', 'batch']), ]) return sweep
def youtube_ablationssm_3_resume(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.path", ['/home/workspace/hippo/data/youtube_mix/']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), lzip([ flag("model.layer.trainable.A", [1]), flag("model.layer.trainable.B", [1]), flag("model.layer.trainable.P", [1]), flag("model.layer.trainable.Q", [1]), flag("+model.layer.tied_lr", [True]), flag("+model.layer.hurwitz", [True]), ]), flag("model.layer.trainable.C", [1]), flag("model.layer.trainable.dt", [0]), flag("model.expand", [2]), flag("model.ff", [2]), flag("model.n_layers", [2]), flag("loader.batch_size", [4]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [20]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), flag("trainer.resume_from_checkpoint", ['/home/workspace/hippo/outputs/2022-02-02/22-47-27/checkpoints/last.ckpt']) ]) return sweep
def youtube_isotropic_new(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.path", ['/home/workspace/projects/hippo/data/youtube_mix/']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), flag("model.expand", [0]), flag("model.ff", [4]), flag("model.pool", [[]]), flag("model.d_model", [256]), flag("loader.batch_size", [1]), lzip([ flag("model.n_layers", [4, 8]), flag("dataset.sample_len", [65536, 32768]), ]), flag("model.layer.trainable.A", [True]), flag("model.layer.trainable.B", [True]), flag("model.layer.trainable.P", [True]), flag("model.layer.trainable.dt", [True]), flag("model.layer.postact", ['glu']), flag("model.layer.hurwitz", [True]), flag("model.layer.tie_state", [True]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [10]), flag("dataset.drop_last", [False]), flag("decoder.mode", ['ragged']), ]) return sweep
def youtube_isotropic_resume_2(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.path", ['/home/workspace/projects/hippo/data/youtube_mix/']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), flag("model.expand", [0]), flag("model.ff", [4]), flag("model.pool", [[]]), flag("model.d_model", [256]), flag("loader.batch_size", [1]), lzip([ flag("model.n_layers", [4]), flag("dataset.sample_len", [65536]), flag( "trainer.resume_from_checkpoint", [ '/home/workspace/projects/hippo/outputs/2022-01-22/20-14-07/checkpoints/val/loss-v2.ckpt', ] ) ]), flag("model.layer.trainable.A", [2]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [10]), flag("dataset.drop_last", [False]), flag("decoder.mode", ['ragged']), ]) return sweep
def beethoven_8s_A_1(): """Run this later -- doesn't run on a V100.""" sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset.path", ['/home/workspace/projects/hippo/data/beethoven/']), lzip([ flag("dataset.sample_len", [128000]), flag("loader.batch_size", [1]), flag("model.layer.trainable.A", [1]), flag("model.layer.trainable.B", [1]), flag("model.layer.trainable.P", [1]), flag("model.layer.trainable.Q", [1]), flag("+model.layer.tied_lr", [True]), ]), flag("dataset.quantization", ['linear']), flag("model", ['snet']), flag("model.expand", [2]), flag("model.ff", [2]), flag("model.pool", [[4, 4]]), flag("model.n_layers", [8]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), ]) return sweep
def beethoven_shorter_resume(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset.path", ['/home/workspace/projects/hippo/data/beethoven/']), lzip([ flag("dataset.sample_len", [16000]), flag("loader.batch_size", [8]), flag( "trainer.resume_from_checkpoint", [ '/home/workspace/projects/hippo/outputs/2022-01-22/20-19-30/checkpoints/val/loss-v9.ckpt', ] ) ]), flag("dataset.quantization", ['linear']), flag("model", ['snet']), flag("model.expand", [2]), flag("model.ff", [2]), flag("model.pool", [[4, 4]]), flag("model.n_layers", [8]), flag("model.layer.trainable.A", [2]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), ]) return sweep
def youtube_snet_actglu_prenorm_pool(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.sample_len", [65536]), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), flag("model.n_layers", [8]), flag("model.ff", [2]), flag("model.expand", [2]), lzip([ flag("model.pool", [[4, 4, 4], [4, 4], [4, 4]]), flag("model.layer.postact", ['null', 'glu', 'null']), flag("model.prenorm", [True, True, False]), flag("loader.batch_size", [2, 2, 2]), ]), flag("model.layer.trainable.A", [2]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [10]), flag("dataset.path", ['/home/workspace/projects/hippo/data/youtube_mix/']) ]) return sweep
def snet_sc09(): sweep = prod([ flag("experiment", ['s4-sc09']), lzip([ flag("model.n_layers", [2, 4, 8]), flag("loader.batch_size", [32, 16, 8]), ]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("callbacks.model_checkpoint.save_top_k", [10]), ]) return sweep
def sc09(): sweep = prod([ flag("experiment", ['samplernn-qautomusic']), flag("dataset", ['sc09']), flag("dataset.quantization", ['mu-law']), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), lzip([ flag("model.n_rnn", [1, 2]), flag("model.frame_sizes", [[8, 2, 2], [16, 4]]), flag("train.state.overlap_len", [32, 64]), ]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), ]) return sweep
def s4_youtube_snetbigsweep(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), lzip([ flag("model.expand", [ 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, ]), flag("model.n_layers", [ 2, 4, 4, 6, 8, 2, 4, 6, 8, 2, ]), flag("model.pool", [ [4, 4, 4], [4, 4, 4], [4, 4], [4, 4], [4, 4], [4, 4, 4, 4], [4, 4, 4, 4], [4, 4, 4, 4], [4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4], ]), flag("model.ff", [ 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, ]), flag("loader.batch_size", [ 2, 1, 2, 1, 1, 4, 2, 2, 1, 4, ]), ]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [10]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), ]) return sweep
def beethoven(): sweep = prod([ flag("experiment", ['samplernn-qautomusic']), flag("dataset.path", ['/home/workspace/hippo/data/beethoven/']), flag("dataset.sample_len", [128000]), flag("dataset.quantization", ['linear']), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), lzip([ flag("model.n_rnn", [1, 2]), flag("model.frame_sizes", [[8, 2, 2], [16, 4]]), flag("train.state.overlap_len", [32, 64]), ]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), ]) return sweep
def s4_youtube_smalltwo_datavariations(): sweep = prod([ flag("experiment", ['s4-qautomusic']), lzip([ flag("dataset", ['youtubemix', 'youtubemix', 'youtubemix-hires']), flag("dataset.bits", [10, 12, 8]), flag("loader.batch_size", [1, 1, 1]), flag("model", ['unet', 'unet', 'snet']), flag("model.d_model", [64, 32, 64]), ]), flag("dataset.quantization", ['mu-law']), flag("model.expand", [2]), flag("model.n_layers", [2]), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [-1]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), ]) return sweep
def youtubemix_2(): sweep = prod([ flag("experiment", ['samplernn-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.path", ['/home/workspace/projects/hippo/data/youtube_mix/']), flag("dataset.quantization", ['mu-law']), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), lzip([ flag("model.n_rnn", [1, 2]), flag("model.frame_sizes", [[8, 2, 2], [16, 4]]), flag("train.state.overlap_len", [32, 64]), ]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), flag("loader.batch_size", [32]), ]) return sweep
def beethoven_shorter_all_A_1(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset.path", ['/home/workspace/projects/hippo/data/beethoven/']), lzip([ flag("dataset.sample_len", [64000, 32000]), flag("loader.batch_size", [2, 4]), ]), flag("dataset.quantization", ['linear']), flag("model", ['snet']), flag("model.expand", [2]), flag("model.ff", [2]), flag("model.pool", [[4, 4]]), flag("model.n_layers", [8]), flag("model.layer.trainable.A", [1]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("trainer.max_epochs", [500]), flag("callbacks.model_checkpoint.save_top_k", [10]), ]) return sweep
def youtube_snet_sweep_2(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), flag("model.n_layers", [2]), lzip([ flag("model.expand", [4, 4, 2]), flag("model.ff", [4, 4, 2]), flag("model.pool", [[4, 4], [8, 8, 2], [4, 4]]), flag("loader.batch_size", [2, 1, 2]), ]), flag("model.act_pool", ['glu']), flag("model.layer.trainable.A", [2]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [10]), flag("dataset.path", ['/home/workspace/projects/hippo/data/youtube_mix/']) ]) return sweep
def youtube_snet_paramsweep(): sweep = prod([ flag("experiment", ['s4-qautomusic']), flag("dataset", ['youtubemix']), flag("dataset.quantization", ['mu-law']), flag("model", ['snet']), lzip([ flag("model.layer.trainable.A", [1, 2, 2, 1, 1, 1]), flag("model.layer.trainable.C", [2, 1, 2, 1, 1, 1]), flag("model.layer.trainable.dt", [0, 0, 0, 1, 1, 0]), flag("model.layer.dt_min", [0.001, 0.001, 0.001, 0.001, 0.0001, 0.0001]), ]), flag("model.expand", [2]), flag("model.n_layers", [2]), flag("loader.batch_size", [4]), flag("model.ff", [2]), flag("trainer.max_epochs", [1000]), flag("optimizer.lr", [0.004]), flag("scheduler.patience", [20]), flag("callbacks.model_checkpoint.save_top_k", [20]), flag("task.metrics", [['bpb', 'accuracy', 'accuracy@3', 'accuracy@5', 'accuracy@10']]), ]) return sweep