File size: 5,451 Bytes
8192381 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# MIT_LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
from pathlib import Path
import torch
from fairseq2.models.nllb.tokenizer import NllbTokenizer
from seamless_communication.cli.m4t.finetune import dataloader, dist_utils, trainer
from seamless_communication.models.unity import (
UnitTokenizer,
UnitYModel,
load_unity_model,
load_unity_text_tokenizer,
load_unity_unit_tokenizer,
)
logging.basicConfig(
level=logging.INFO,
format=f"%(asctime)s %(levelname)s -- %(name)s.{os.getpid()}: %(message)s",
)
logger = logging.getLogger("finetune")
def init_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Example finetuning script for M4T models"
)
parser.add_argument(
"--train_dataset",
type=Path,
required=True,
help="Path to manifest with train samples",
)
parser.add_argument(
"--eval_dataset",
type=Path,
required=True,
help="Path to manifest with eval samples",
)
parser.add_argument(
"--model_name",
type=str,
default="seamlessM4T_medium",
help="Base model name (`seamlessM4T_medium`, `seamlessM4T_large`)",
)
parser.add_argument(
"--save_model_to",
type=Path,
required=True,
help="Path to save best finetuned model",
)
parser.add_argument(
"--seed",
type=int,
default=2343,
help="Randomizer seed value",
)
parser.add_argument(
"--batch_size",
type=int,
default=5,
help="Batch size for training and evaluation",
)
parser.add_argument(
"--patience",
type=int,
default=3,
help=(
"Set early termination after `patience` number of evaluations "
"without eval loss improvements"
),
)
parser.add_argument(
"--max_epochs",
type=int,
default=10,
help=("Max number of training epochs"),
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-7,
help=("Finetuning learning rate"),
)
parser.add_argument(
"--warmup_steps",
type=int,
default=100,
help=("Number of steps with linearly increasing learning rate"),
)
parser.add_argument(
"--eval_steps",
type=int,
default=50,
help=("Get eval loss after each `eval_steps` training steps "),
)
parser.add_argument(
"--log_steps",
type=int,
default=10,
help=("Log inner loss after each `log_steps` training steps"),
)
parser.add_argument(
"--mode",
type=trainer.FinetuneMode,
choices=list(trainer.FinetuneMode),
default=trainer.FinetuneMode.SPEECH_TO_TEXT,
help=(
"* `SPEECH_TO_SPEECH` -- finetune S2T and T2U parts of the model; "
"* `TEXT_TO_SPEECH` -- finetune only T2U; "
"* `SPEECH_TO_TEXT` -- finetune only S2T"
),
)
return parser
def main() -> None:
args = init_parser().parse_args()
dist_utils.init_distributed([logger, trainer.logger])
device = torch.device("cuda")
text_tokenizer: NllbTokenizer = load_unity_text_tokenizer(args.model_name)
unit_tokenizer: UnitTokenizer = load_unity_unit_tokenizer(args.model_name)
finetune_params = trainer.FinetuneParams(
finetune_mode=args.mode,
save_model_path=args.save_model_to,
device=device,
train_batch_size=args.batch_size,
eval_batch_size=args.batch_size,
patience=args.patience,
max_epochs=args.max_epochs,
learning_rate=args.learning_rate,
warmup_steps=args.warmup_steps,
eval_steps=args.eval_steps,
log_steps=args.log_steps,
)
logger.info(f"Finetune params: {finetune_params}")
model: UnitYModel = load_unity_model(
args.model_name, device=finetune_params.device, dtype=torch.float16
)
logger.info(f"Model {model}")
assert model.target_vocab_info == text_tokenizer.vocab_info
assert model.t2u_model is not None
assert model.t2u_model.target_vocab_info == unit_tokenizer.vocab_info
train_dataloader = dataloader.UnitYDataLoader(
text_tokenizer=text_tokenizer,
unit_tokenizer=unit_tokenizer,
batching_config=dataloader.BatchingConfig(
batch_size=finetune_params.train_batch_size,
rank=dist_utils.get_rank(),
world_size=dist_utils.get_world_size(),
),
dataset_manifest_path=args.train_dataset,
)
eval_dataloader = dataloader.UnitYDataLoader(
text_tokenizer=text_tokenizer,
unit_tokenizer=unit_tokenizer,
batching_config=dataloader.BatchingConfig(
batch_size=finetune_params.eval_batch_size,
rank=dist_utils.get_rank(),
world_size=dist_utils.get_world_size(),
),
dataset_manifest_path=args.eval_dataset,
)
finetune = trainer.UnitYFinetune(
model=model,
params=finetune_params,
train_data_loader=train_dataloader,
eval_data_loader=eval_dataloader,
)
finetune.run()
if __name__ == "__main__":
main()
|