new meme sampler PogChamp new meme sampler PogChamp (it sort of helps?)
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@ -35,6 +35,7 @@ def main():
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parser.add_argument("--top-p", type=float, default=1.0)
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parser.add_argument("--top-k", type=int, default=0)
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parser.add_argument("--top-no", type=float, default=0.0)
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parser.add_argument("--min-p", type=float, default=0.0)
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parser.add_argument("--repetition-penalty", type=float, default=1.0)
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parser.add_argument("--repetition-penalty-decay", type=float, default=0.0)
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@ -83,7 +84,7 @@ def main():
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max_duration=args.max_duration,
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ar_temperature=args.ar_temperature, nar_temperature=args.nar_temperature,
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min_ar_temperature=args.min_ar_temperature, min_nar_temperature=args.min_nar_temperature,
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top_p=args.top_p, top_k=args.top_k, min_p=args.min_p,
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top_p=args.top_p, top_k=args.top_k, top_no=args.top_no,min_p=args.min_p,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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@ -1383,6 +1383,9 @@ class Dataset(_Dataset):
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def training_(self, value):
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self.training = value
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def index(self):
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return self.sampler.index() if self.sampler is not None else -1
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def __len__(self):
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if self.sampler_type == "group":
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return min(len(self.spkr_groups), self._head or len(self.spkr_groups))
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@ -57,37 +57,54 @@ def main():
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parser.add_argument("--language", type=str, default="en")
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parser.add_argument("--max-ar-steps", type=int, default=12 * cfg.dataset.frames_per_second)
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parser.add_argument("--max-nar-levels", type=int, default=7)
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parser.add_argument("--language", type=str, default="en")
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parser.add_argument("--task", type=str, default="tts")
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parser.add_argument("--out-path", type=Path, default=None)
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parser.add_argument("--ar-temp", type=float, default=0.0)
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parser.add_argument("--nar-temp", type=float, default=0.0)
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parser.add_argument("--min-ar-temp", type=float, default=-1.0)
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parser.add_argument("--min-nar-temp", type=float, default=-1.0)
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parser.add_argument("--input-prompt-length", type=float, default=0.0)
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parser.add_argument("--yaml", type=Path, default=None)
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parser.add_argument("--model", type=Path, default=None)
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parser.add_argument("--lora", type=Path, default=None)
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parser.add_argument("--max-duration", type=int, default=12 * cfg.dataset.frames_per_second)
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parser.add_argument("--max-steps", type=int, default=25)
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parser.add_argument("--max-levels", type=int, default=7)
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parser.add_argument("--ar-temperature", type=float, default=1.0)
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parser.add_argument("--nar-temperature", type=float, default=0.0)
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parser.add_argument("--min-ar-temperature", type=float, default=-1.0)
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parser.add_argument("--min-nar-temperature", type=float, default=-1.0)
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parser.add_argument("--input-prompt-length", type=float, default=3.0)
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parser.add_argument("--input-prompt-prefix", action="store_true")
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parser.add_argument("--prefix-silence", type=float, default=0.0)
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parser.add_argument("--cfg-strength", type=float, default=0.0)
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parser.add_argument("--top-p", type=float, default=1.0)
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parser.add_argument("--top-k", type=int, default=0)
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parser.add_argument("--top-no", type=float, default=0.0)
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parser.add_argument("--min-p", type=float, default=0.0)
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parser.add_argument("--repetition-penalty", type=float, default=1.125)
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parser.add_argument("--repetition-penalty", type=float, default=1.0)
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parser.add_argument("--repetition-penalty-decay", type=float, default=0.0)
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parser.add_argument("--length-penalty", type=float, default=0.0)
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parser.add_argument("--beam-width", type=int, default=0)
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parser.add_argument("--mirostat-tau", type=float, default=0)
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parser.add_argument("--mirostat-eta", type=float, default=0)
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parser.add_argument("--dry-multiplier", type=float, default=0)
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parser.add_argument("--dry-base", type=float, default=1.75)
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parser.add_argument("--dry-allowed-length", type=int, default=2)
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parser.add_argument("--entropix-sampling", action="store_true")
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parser.add_argument("--layer-skip", action="store_true")
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parser.add_argument("--layer-skip-exit-layer", type=int, default=None)
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parser.add_argument("--layer-skip-entropy-threshold", type=int, default=0.1)
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parser.add_argument("--layer-skip-varentropy-threshold", type=int, default=0.1)
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parser.add_argument("--refine-on-stop", action="store_true")
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# experimental settings
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parser.add_argument("--load-from-artifact", type=Path, default=None)
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parser.add_argument("--denoise-start", type=float, default=0.0)
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parser.add_argument("--seed", type=int, default=None)
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@ -135,19 +152,19 @@ def main():
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comparison_kwargs["titles"] = ["LoRA", "No LoRA"]
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comparison_kwargs["disabled"]["use_lora"] = True
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comparison_kwargs["disabled"]["ar_temp"] = 0.0
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comparison_kwargs["disabled"]["ar_temperature"] = 0.0
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comparison_kwargs["enabled"]["use_lora"] = False
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comparison_kwargs["enabled"]["ar_temp"] = 0.95
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comparison_kwargs["enabled"]["ar_temperature"] = 0.95
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elif args.comparison == "entropix-sampling":
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comparison_kwargs["suffix"] = "entropix_sampling"
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comparison_kwargs["titles"] = ["Without Entropix", "With Entropix"]
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comparison_kwargs["disabled"]["entropix_sampling"] = False
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comparison_kwargs["disabled"]["ar_temp"] = args.ar_temp
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comparison_kwargs["disabled"]["ar_temperature"] = args.ar_temperature
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comparison_kwargs["disabled"]["top_k"] = args.top_k
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comparison_kwargs["disabled"]["top_p"] = args.top_p
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comparison_kwargs["enabled"]["entropix_sampling"] = True
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comparison_kwargs["enabled"]["ar_temp"] = 0.666
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comparison_kwargs["enabled"]["ar_temperature"] = 0.666
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comparison_kwargs["enabled"]["top_k"] = 27
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comparison_kwargs["enabled"]["top_p"] = 0.9
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elif args.comparison == "layerskip":
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@ -163,14 +180,14 @@ def main():
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comparison_kwargs["disabled"]["refine_on_stop"] = False
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comparison_kwargs["enabled"]["refine_on_stop"] = True
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elif args.comparison == "ar-temp":
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current_temp = args.ar_temp
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other_temp = 1.0
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current_temperature = args.ar_temperature
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other_temperature = 1.0
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comparison_kwargs["suffix"] = "temperature"
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comparison_kwargs["titles"] = [f"Temp: {current_temp:.2f}", f"Temp: {other_temp:.2f}"]
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comparison_kwargs["titles"] = [f"Temp: {current_temperature:.2f}", f"Temp: {other_temperature:.2f}"]
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comparison_kwargs["disabled"]["ar_temp"] = current_temp
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comparison_kwargs["enabled"]["ar_temp"] = other_temp
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comparison_kwargs["disabled"]["ar_temperature"] = current_temperature
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comparison_kwargs["enabled"]["ar_temperature"] = other_temperature
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elif args.comparison == "input-prompt-length":
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current_length = args.input_prompt_length
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other_length = 3.0
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@ -209,21 +226,34 @@ def main():
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# read html template
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html = open(args.demo_dir / "index.template.html", "r", encoding="utf-8").read()
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# replace values in our template
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html = html.replace(r"${PREAMBLE}", args.preamble )
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html = html.replace(r"${SETTINGS}", str(dict(
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input_prompt_length=args.input_prompt_length,
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max_ar_steps=args.max_ar_steps, max_nar_levels=args.max_nar_levels,
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ar_temp=args.ar_temp, nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp, min_nar_temp=args.min_nar_temp,
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top_p=args.top_p, top_k=args.top_k, min_p=args.min_p,
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sampling_kwargs = dict(
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max_steps=args.max_steps,
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max_levels=args.max_levels,
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max_duration=args.max_duration,
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ar_temperature=args.ar_temperature, nar_temperature=args.nar_temperature,
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min_ar_temperature=args.min_ar_temperature, min_nar_temperature=args.min_nar_temperature,
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top_p=args.top_p, top_k=args.top_k, top_no=args.top_no,min_p=args.min_p,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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mirostat_tau=args.mirostat_tau, mirostat_eta=args.mirostat_eta,
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dry_multiplier=args.dry_multiplier, dry_base=args.dry_base, dry_allowed_length=args.dry_allowed_length,
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entropix_sampling=args.entropix_sampling,
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)) )
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layer_skip=args.layer_skip,
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layer_skip_exit_layer=args.layer_skip_exit_layer,
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layer_skip_entropy_threshold=args.layer_skip_entropy_threshold,
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layer_skip_varentropy_threshold=args.layer_skip_varentropy_threshold,
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refine_on_stop=args.refine_on_stop,
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denoise_start=args.denoise_start,
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input_prompt_length=args.input_prompt_length,
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input_prompt_prefix=args.input_prompt_prefix,
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prefix_silence=args.prefix_silence,
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cfg_strength=args.cfg_strength,
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)
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# replace values in our template
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html = html.replace(r"${PREAMBLE}", args.preamble )
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html = html.replace(r"${SETTINGS}", str(sampling_kwargs))
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# pull from provided samples
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samples_dirs = {
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@ -324,18 +354,9 @@ def main():
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references=[prompt],
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language=language,
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input_prompt_length=args.input_prompt_length,
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max_ar_steps=args.max_ar_steps, max_nar_levels=args.max_nar_levels,
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ar_temp=args.ar_temp, nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp, min_nar_temp=args.min_nar_temp,
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top_p=args.top_p, top_k=args.top_k,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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mirostat_tau=args.mirostat_tau, mirostat_eta=args.mirostat_eta,
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dry_multiplier=args.dry_multiplier, dry_base=args.dry_base, dry_allowed_length=args.dry_allowed_length,
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entropix_sampling=args.entropix_sampling,
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seed=seed,
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tqdm=False,
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**sampling_kwargs,
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)
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def safe_inference( out_path=out_path ):
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@ -31,6 +31,8 @@ from .lora import enable_lora
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text_task = [ "stt" ]
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class AR_NAR(Base):
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# parse inputs for training
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# a lot of this could be delegated back to the dataloader, but it's just easier to keep the task of the dataloader to provide sufficient data, and the model to process the data for training
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def forward_train(
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self,
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text_list: list[Tensor],
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@ -62,19 +64,17 @@ class AR_NAR(Base):
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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# RVQ levels to apply masking training on
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masking_train_rvq_levels = self.config.experimental.masking_train_rvq_levels
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# force set mask training
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if "len" not in self.capabilities:
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masking_train_rvq_levels = 0.0
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elif "ar" not in self.capabilities:
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masking_train_rvq_levels = 1.0
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# CFG
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cfg_text_dropout_p = self.config.experimental.cfg_text_dropout_p if self.config is not None else 0.0
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cfg_cond_dropout_p = self.config.experimental.cfg_cond_dropout_p if self.config is not None else 0.0
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cfg_prom_dropout_p = self.config.experimental.cfg_prom_dropout_p if self.config is not None else 0.0
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# rate to train RVQ level AR-ly or NAR-ly
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masking_train_p = self.config.experimental.masking_train_p if self.config is not None else 0.5
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# force set mask training
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if "len" not in self.capabilities:
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masking_train_p = 0.0
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elif "ar" not in self.capabilities:
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masking_train_p = 1.0
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# implicitly set it to all levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels - 1]
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@ -116,9 +116,11 @@ class AR_NAR(Base):
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text_stop_sequence = torch.tensor([2], device=device, dtype=torch.int16)
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text_start_stop_sequence = torch.tensor([1, 2], device=device, dtype=torch.int16)
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audio_stop_sequence = torch.tensor([[self.stop_token]], device=device, dtype=torch.int16)
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# I hate python's value/reference semantics so much
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# final validations and stuff
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for i, quant_level, resps, proms, task in zip(range(batch_size), quant_levels, resps_list, proms_list, task_list):
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# cap quant_level if it exceeds its corresponding resp/prom
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# this was needed for when my DAC-encoded audio was erroneously trimmed to 8 RVQ levels instead of 9
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if quant_level >= resps.shape[-1]:
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quant_levels[i] = resps.shape[-1] - 1
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@ -1706,7 +1706,9 @@ class Base(nn.Module):
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dry_multiplier = sampling_kwargs.get("dry_multiplier", 0.0)
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dry_base = sampling_kwargs.get("dry_base", 1.75)
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dry_allowed_length = sampling_kwargs.get("dry_allowed_length", 2)
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#
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top_no = sampling_kwargs.get("top_no", 1.0)
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#
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attentions = sampling_kwargs.get("attentions", None)
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batch_size = len( logits )
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@ -1792,6 +1794,10 @@ class Base(nn.Module):
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elif temperature > 0.0:
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logits = [ logit / temperature for logit in logits ]
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# do top-no logit processing
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if top_no > 0.0:
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logits = [ top_no_logits_processing(logit) for logit in logits ]
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# do DRY sampling
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if dry_multiplier > 0.0 and prev_list is not None:
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logits = [ dry_sampling(logit, previous=prevs, factor=dry_multiplier, base=dry_base, allowed_length=dry_allowed_length) for logit, prevs in zip( logits, prev_list ) ]
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@ -159,6 +159,19 @@ def top_k_logits_list( logits_list, k ):
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candidates[i] = tuple(t)
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return candidates
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# top-nσ logit processing
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# from https://arxiv.org/abs/2411.07641
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def top_no_logits_processing( logits, n = 1.0 ):
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M = torch.max(logits, dim=-1, keepdim=True).values
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σ = torch.std(logits, dim=-1, keepdim=True)
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mask = logits >= M - n * σ
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n_inf = torch.full_like( logits, -float("inf") )
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logits = torch.where( mask, logits, n_inf )
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return logits
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# Credit to: https://github.com/basusourya/mirostat/
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# performs mirostat-based sampling
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@ -44,6 +44,9 @@ class PoolSampler():
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def __call__(self, *args, **kwargs):
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return self.sample(*args, **kwargs)
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def index(self):
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return len(self.global_indices) - len(self.current_pool)
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def get_state(self):
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return { "length": self.length, "global_pool": self.global_pool, "global_indices": self.global_indices, "current_pool": self.current_pool }
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@ -72,6 +75,9 @@ class OrderedSampler(Sampler):
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yield self.position
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self.position += 1
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def index(self):
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return self.position
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def get_state(self):
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return { "position": self.position, "length": self.length }
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@ -125,6 +131,9 @@ class BatchedOrderedSampler(Sampler):
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yield self.batches[self.position]
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self.position += 1
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def index(self):
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return self.position
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def get_state(self):
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return { "position": self.position, "batches": self.batches }
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@ -154,6 +163,9 @@ class RandomSampler(Sampler):
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yield self.perm[self.position]
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self.position += 1
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def index(self):
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return self.position
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def get_state(self):
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return { "position": self.position, "length": self.length, "perm": self.perm, "generator": self.generator.get_state() }
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@ -104,7 +104,7 @@ def _non_blocking_input():
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def _make_infinite_epochs(dl):
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while True:
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#_logger.info("New epoch starts.")
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yield from tqdm(dl, "Epoch progress", dynamic_ncols=True, disable=not is_global_leader())
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yield from tqdm(dl, "Epoch progress", dynamic_ncols=True, disable=not is_global_leader()) # , initial=dl.dataset.index(), total=len(dl.dataset)) # to-do: figure out why this number jumps
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@local_leader_only(default=None)
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@ -216,6 +216,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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parser.add_argument("--prefix-silence", type=float, default=kwargs["prefix-silence"])
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parser.add_argument("--top-p", type=float, default=kwargs["top-p"])
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parser.add_argument("--top-k", type=int, default=kwargs["top-k"])
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parser.add_argument("--top-no", type=float, default=kwargs["top-no"])
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parser.add_argument("--min-p", type=float, default=kwargs["min-p"])
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parser.add_argument("--repetition-penalty", type=float, default=kwargs["repetition-penalty"])
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parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
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@ -265,7 +266,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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max_duration=args.max_duration,
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ar_temperature=args.ar_temperature, nar_temperature=args.nar_temperature,
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min_ar_temperature=args.min_ar_temperature, min_nar_temperature=args.min_nar_temperature,
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top_p=args.top_p, top_k=args.top_k, min_p=args.min_p,
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top_p=args.top_p, top_k=args.top_k, min_p=args.min_p, top_no=args.top_no,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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@ -434,8 +435,8 @@ with ui:
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with gr.Row():
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layout["inference_tts"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
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layout["inference_tts"]["inputs"]["top-k"] = gr.Slider(value=0, minimum=0, maximum=1024, step=1, label="Top K", info="Limits the samples to the top K of probabilities.")
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layout["inference_tts"]["inputs"]["top-no"] = gr.Slider(value=1, minimum=0, maximum=2, step=0.05, label="Top-nσ", info="Performs top-nσ logits processing.")
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layout["inference_tts"]["inputs"]["min-p"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Min P")
|
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layout["inference_tts"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
|
||||
with gr.Row():
|
||||
layout["inference_tts"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.0, minimum=0.0, maximum=5.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
|
||||
layout["inference_tts"]["inputs"]["repetition-penalty-decay"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty Length Decay", info="Modifies the reptition penalty based on how far back in time the token appeared in the sequence.")
|
||||
|
|
@ -451,10 +452,11 @@ with ui:
|
|||
with gr.Row():
|
||||
layout["inference_tts"]["inputs"]["max-steps"] = gr.Slider(value=25, minimum=1, maximum=500, step=1, label="Max NAR Steps", info="Limits how many steps to perform in the NAR (demask) pass.")
|
||||
layout["inference_tts"]["inputs"]["max-levels"] = gr.Slider(value=7, minimum=0, maximum=7, step=1, label="Max NAR Levels", info="Limits how many steps to perform in the NAR pass.")
|
||||
layout["inference_tts"]["inputs"]["input-prompt-prefix"] = gr.Checkbox(label="Input Prompt as Prefix", info="Treats the input prompt clip as the prefix of the generated sequence.")
|
||||
with gr.Row():
|
||||
layout["inference_tts"]["inputs"]["input-prompt-prefix"] = gr.Checkbox(label="Input Prompt as Prefix", info="Treats the input prompt clip as the prefix of the generated sequence.")
|
||||
layout["inference_tts"]["inputs"]["prefix-silence"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.0, step=0.05, label="Silence Prefix Duration", info="Amount of silence to prefix to the output response before beginning inference.")
|
||||
with gr.Row():
|
||||
layout["inference_tts"]["inputs"]["beam-width"] = gr.Slider(value=0, minimum=0, maximum=32, step=1, label="Beam Width", info="Number of branches to search through for beam search sampling.")
|
||||
layout["inference_tts"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
|
||||
layout["inference_tts"]["inputs"]["entropix-sampling"] = gr.Checkbox(label="Entropix Sampling", info="Dynamically samples based on entropy/varentropy values from the logits / attention scores.")
|
||||
with gr.Row():
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user