forked from mrq/ai-voice-cloning
Added option to disable bitsandbytesoptimizations for systems that do not support it (systems without a Turing-onward Nvidia card), saves use of float16 and bitsandbytes for training into the config json
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25
src/train.py
25
src/train.py
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@ -1,7 +1,7 @@
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import os
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import sys
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import argparse
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import yaml
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"""
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if 'BITSANDBYTES_OVERRIDE_LINEAR' not in os.environ:
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@ -14,6 +14,21 @@ if 'BITSANDBYTES_OVERRIDE_ADAMW' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '1'
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"""
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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args.opt = " ".join(args.opt) # absolutely disgusting
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with open(args.opt, 'r') as file:
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opt_config = yaml.safe_load(file)
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if "ext" in opt_config and "bitsandbytes" in opt_config["ext"] and not opt_config["ext"]["bitsandbytes"]:
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os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '0'
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# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
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# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
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@ -70,13 +85,9 @@ if __name__ == "__main__":
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import torch_intermediary
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if torch_intermediary.OVERRIDE_ADAM:
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print("Using BitsAndBytes ADAMW optimizations")
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else:
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print("NOT using BitsAndBytes ADAMW optimizations")
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except Exception as e:
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pass
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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args.opt = " ".join(args.opt) # absolutely disgusting
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train(args.opt, args.launcher)
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26
src/utils.py
26
src/utils.py
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@ -731,7 +731,7 @@ EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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def schedule_learning_rate( iterations ):
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return [int(iterations * d) for d in EPOCH_SCHEDULE]
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def optimize_training_settings( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
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def optimize_training_settings( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, bnb, voice ):
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name = f"{voice}-finetune"
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dataset_name = f"{voice}-train"
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dataset_path = f"./training/{voice}/train.txt"
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@ -778,6 +778,9 @@ def optimize_training_settings( epochs, learning_rate, learning_rate_schedule, b
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if not os.path.exists(get_halfp_model_path()):
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convert_to_halfp()
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if bnb:
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messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")
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messages.append(f"For {epochs} epochs with {lines} lines in batches of {batch_size}, iterating for {iterations} steps ({int(iterations / epochs)} steps per epoch)")
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return (
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@ -791,7 +794,7 @@ def optimize_training_settings( epochs, learning_rate, learning_rate_schedule, b
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messages
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)
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def save_training_settings( iterations=None, learning_rate=None, learning_rate_schedule=None, batch_size=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None ):
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def save_training_settings( iterations=None, learning_rate=None, learning_rate_schedule=None, batch_size=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None, bnb=None ):
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settings = {
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"iterations": iterations if iterations else 500,
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"batch_size": batch_size if batch_size else 64,
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@ -809,7 +812,8 @@ def save_training_settings( iterations=None, learning_rate=None, learning_rate_s
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'resume_state': f"resume_state: '{resume_path}'",
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'pretrain_model_gpt': f"pretrain_model_gpt: './models/tortoise/autoregressive{'_half' if half_p else ''}.pth'",
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'float16': 'true' if half_p else 'false'
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'float16': 'true' if half_p else 'false',
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'bitsandbytes': 'true' if bnb else 'false',
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}
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if resume_path:
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@ -1038,6 +1042,9 @@ def setup_args():
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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'output-volume': 1,
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'training-default-halfp': False,
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'training-default-bnb': True,
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}
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if os.path.isfile('./config/exec.json'):
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@ -1067,6 +1074,9 @@ def setup_args():
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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
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parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
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parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
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parser.add_argument("--os", default="unix", help="Specifies which OS, easily")
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args = parser.parse_args()
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@ -1093,7 +1103,7 @@ def setup_args():
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return args
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, training_default_halfp, training_default_bnb ):
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global args
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args.listen = listen
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@ -1113,10 +1123,13 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.concurrency_count = concurrency_count
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args.output_sample_rate = output_sample_rate
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args.output_volume = output_volume
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args.training_default_halfp = training_default_halfp
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args.training_default_bnb = training_default_bnb
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save_args_settings()
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def save_args_settings():
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global args
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settings = {
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'listen': None if args.listen else args.listen,
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'share': args.share,
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@ -1137,8 +1150,13 @@ def save_args_settings():
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'output-volume': args.output_volume,
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'training-default-halfp': args.training_default_halfp,
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'training-default-bnb': args.training_default_bnb,
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}
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print(settings)
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os.makedirs('./config/', exist_ok=True)
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with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(settings, indent='\t') )
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26
src/webui.py
26
src/webui.py
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@ -200,11 +200,12 @@ def optimize_training_settings_proxy( *args, **kwargs ):
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"\n".join(tup[7])
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)
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def import_training_settings_proxy( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
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def import_training_settings_proxy( voice ):
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indir = f'./training/{voice}/'
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outdir = f'./training/{voice}-finetune/'
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in_config_path = f"{indir}/train.yaml"
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out_configs = []
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if os.path.isdir(outdir):
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out_configs = sorted([d[:-5] for d in os.listdir(outdir) if d[-5:] == ".yaml" ])
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if len(out_configs) > 0:
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@ -244,6 +245,12 @@ def import_training_settings_proxy( epochs, learning_rate, learning_rate_schedul
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resume_path = f'{statedir}/{resumes[-1]}.state'
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messages.append(f"Latest resume found: {resume_path}")
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half_p = config['fp16']
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bnb = True
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if "ext" in config and "bitsandbytes" in config["ext"]:
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bnb = config["ext"]["bitsandbytes"]
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messages = "\n".join(messages)
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return (
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@ -255,11 +262,13 @@ def import_training_settings_proxy( epochs, learning_rate, learning_rate_schedul
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print_rate,
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save_rate,
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resume_path,
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half_p,
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bnb,
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messages
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)
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def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, voice ):
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def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule, batch_size, mega_batch_factor, print_rate, save_rate, resume_path, half_p, bnb, voice ):
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name = f"{voice}-finetune"
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dataset_name = f"{voice}-train"
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dataset_path = f"./training/{voice}/train.txt"
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@ -297,6 +306,7 @@ def save_training_settings_proxy( epochs, learning_rate, learning_rate_schedule,
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output_name=f"{voice}/train.yaml",
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resume_path=resume_path,
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half_p=half_p,
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bnb=bnb,
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))
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return "\n".join(messages)
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@ -471,10 +481,12 @@ def setup_gradio():
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]
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training_settings = training_settings + [
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gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
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gr.Checkbox(label="Half Precision", value=False),
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]
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training_halfp = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
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training_bnb = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
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dataset_list = gr.Dropdown( get_dataset_list(), label="Dataset", type="value" )
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training_settings = training_settings + [ dataset_list ]
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training_settings = training_settings + [ training_halfp, training_bnb, dataset_list ]
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with gr.Row():
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refresh_dataset_list = gr.Button(value="Refresh Dataset List")
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import_dataset_button = gr.Button(value="Import Dataset")
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@ -558,6 +570,8 @@ def setup_gradio():
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outputs=None
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)
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exec_inputs = exec_inputs + [ training_halfp, training_bnb ]
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for i in exec_inputs:
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i.change( fn=update_args, inputs=exec_inputs )
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@ -731,8 +745,8 @@ def setup_gradio():
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outputs=training_settings[1:8] + [save_yaml_output] #console_output
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)
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import_dataset_button.click(import_training_settings_proxy,
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inputs=training_settings,
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outputs=training_settings[:8] + [save_yaml_output] #console_output
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inputs=dataset_list,
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outputs=training_settings[:10] + [save_yaml_output] #console_output
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)
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save_yaml_button.click(save_training_settings_proxy,
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inputs=training_settings,
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