new implementation tweaks and fixes to make it actually better (there were a lot of badwrong things being done that harmed the output quality, will evaluate the model further)
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98d1d8cb1e
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@ -152,6 +152,14 @@ These settings should be avoided:
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* however, it seems there's a regression that caused this to stop working consistently
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* disabling this falls back to explicitly training a `len` task (like the old implementation)
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## Samplers
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To-do: Remember what I was going to jot down here
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Sampling code is effectively the same, with the twist of instead outputting the logits for all codebooks at `dim=0`.
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The NAR-demasking step will account for this automatically, and has dials and knobs to adjust whether to mask off independent of other codebook levels, or for all codebook levels at a given timestep.
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## Benefits and Caveats
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To be evaluated thoroughly.
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@ -296,9 +296,14 @@ class AR_NAR(Base):
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cfg_rescale = sampling_kwargs.pop("cfg_rescale", 0.75)
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start_noise = sampling_kwargs.get("denoise_start", 0.0)
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end_noise = sampling_kwargs.get("denoise_end", 1.0)
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remasking = sampling_kwargs.get("remasking", True)
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max_steps = math.floor(max_steps * (end_noise - start_noise))
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largest_score = 1.0
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smallest_score = 0.0 # -float("inf")
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score_masked_only = sampling_kwargs.pop("sampling_scores_masked_only", False)
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remasking = sampling_kwargs.get("sampling_scores_remask", False)
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# to specify the initial mask used
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vc_list = sampling_kwargs.pop("vc_list", None)
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vc_threshold = sampling_kwargs.pop("vc_threshold", 0.25)
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@ -326,7 +331,7 @@ class AR_NAR(Base):
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# gen masking ratio
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noise_p = math.cos( start_noise * math.pi * 0.5 )
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# generate scoring mask (because the above mask will get masked off per the scores, so we do not need to mask beforehand)
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scores = [ torch.tensor( [ 1.0 if random.random() < noise_p else 0.0 for _ in range( seq_len ) ], dtype=torch.float32, device=device ) for seq_len in len_list ]
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scores = [ torch.tensor( [ smallest_score if random.random() < noise_p else largest_score for _ in range( seq_len ) ], dtype=torch.float32, device=device ) for seq_len in len_list ]
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else:
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# fill with masked tokens (even though they get masked anyways)
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resps_list = [ torch.ones((seq_len,), dtype=torch.int16, device=device) * self.mask_token for seq_len in len_list ]
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@ -350,7 +355,7 @@ class AR_NAR(Base):
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remask_p = 1.0 / (max_steps * 2) if remasking else 0
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mask_p = noise_p + remask_p
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# pick the worst scoring tokens to mask off
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masked_indices = [ score.topk( clamp( int( mask_p * seq_len ), 1, seq_len - step), dim=-1 ).indices for score, seq_len in zip(scores, len_list) ]
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masked_indices = [ score.topk( clamp( int( mask_p * seq_len ), 1, seq_len - step), dim=-1, largest=False ).indices for score, seq_len in zip(scores, len_list) ]
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# normal masking
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if vc_list is None or timestep >= vc_threshold:
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@ -450,15 +455,11 @@ class AR_NAR(Base):
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sampled_ids = filtered_sampled.ids
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# keep unmasked tokens
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resps_list = [ torch.where( masked, input_ids, resps ).to(torch.int16) for masked, input_ids, resps in zip( is_masked, sampled_ids, resps_list ) ]
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# get probability scores
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scores = [
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# conjugate to have worse scoring tokens picked for topk
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1.0 -
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# only keep scores of tokens we are predicting (and ignore the tokens previously finalized)
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torch.where( masked, torch.tensor([score for index, score in enumerate(scores)], device=device), torch.ones(masked.shape, device=device) )
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# use unmodified logit scores for this, as it offers better stability
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for scores, masked in zip( unfiltered_sampled.scores, is_masked )
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]
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# update scores, only updating tokens that were masked off, and force keeping unmasked tokens
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if score_masked_only:
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scores = [ torch.where( masked, scores.t(), smallest_score ) for masked, scores in zip( is_masked, sampled.scores ) ]
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else:
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scores = [ scores for scores in sampled.scores ]
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return resps_list
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@ -255,9 +255,15 @@ class AR_NAR_V2(Base_V2):
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cfg_rescale = sampling_kwargs.pop("cfg_rescale", 0.75)
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start_noise = sampling_kwargs.get("denoise_start", 0.0)
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end_noise = sampling_kwargs.get("denoise_end", 1.0)
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remasking = sampling_kwargs.get("remasking", True)
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max_steps = math.floor(max_steps * (end_noise - start_noise))
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largest_score = 1.0
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smallest_score = 0.0 # -float("inf")
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score_masked_only = sampling_kwargs.pop("sampling_scores_masked_only", False)
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score_flatten = sampling_kwargs.pop("sampling_scores_flatten", False)
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remasking = sampling_kwargs.get("sampling_scores_remask", False)
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# to specify the initial mask used
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vc_list = sampling_kwargs.pop("vc_list", None)
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vc_threshold = sampling_kwargs.pop("vc_threshold", 0.25)
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@ -291,7 +297,7 @@ class AR_NAR_V2(Base_V2):
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remask_p = 1.0 / (max_steps * 2) if remasking else 0
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mask_p = noise_p + remask_p
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# pick the worst scoring tokens to mask off
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masked_indices = [ score.topk( clamp( int( mask_p * seq_len ), 1, seq_len - step), dim=0 ).indices for score, seq_len in zip(scores, len_list) ]
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masked_indices = [ score.topk( clamp( int( mask_p * seq_len ), 1, seq_len - step), dim=0, largest=False ).indices for score, seq_len in zip(scores, len_list) ]
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# normal masking
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# mask off inputs
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@ -353,8 +359,15 @@ class AR_NAR_V2(Base_V2):
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# update resps, filling in the masked tokens with the new tokens
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resps_list = [ torch.where( masked, ids.t(), resps ).to(torch.int16) for masked, ids, resps in zip( is_masked, sampled.ids, resps_list ) ]
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# update scores, filling in the
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scores = [ 1.0 - torch.where( masked, scores.t(), 1 ) for masked, scores in zip( is_masked, sampled.scores ) ]
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# update scores, only updating tokens that were masked off, and force keeping unmasked tokens
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if score_masked_only:
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scores = [ torch.where( masked, scores.t(), smallest_score ) for masked, scores in zip( is_masked, sampled.scores ) ]
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else:
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scores = [ scores.t() for scores in sampled.scores ]
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# drop all levels at the timestep instead
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if score_flatten:
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scores = [ score.mean(dim=0).repeat( score.shape[0], 1 ) for score in scores ]
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return resps_list
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@ -222,15 +222,15 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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parser.add_argument("--split-text-by", type=str, default=kwargs["split-text-by"])
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parser.add_argument("--context-history", type=int, default=kwargs["context-history"])
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parser.add_argument("--input-prompt-length", type=float, default=kwargs["input-prompt-length"])
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parser.add_argument("--input-prompt-prefix", action='store_true', default=kwargs["input-prompt-prefix"])
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#parser.add_argument("--input-prompt-prefix", action='store_true', default=kwargs["input-prompt-prefix"])
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parser.add_argument("--max-duration", type=int, default=int(kwargs["max-duration"]*cfg.dataset.frames_per_second))
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parser.add_argument("--max-levels", type=int, default=kwargs["max-levels"])
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#parser.add_argument("--max-levels", type=int, default=kwargs["max-levels"])
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parser.add_argument("--max-steps", type=int, default=kwargs["max-steps"])
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parser.add_argument("--ar-temperature", type=float, default=kwargs["ar-temperature"])
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parser.add_argument("--nar-temperature", type=float, default=kwargs["nar-temperature"])
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parser.add_argument("--min-ar-temperature", type=float, default=kwargs["min-ar-temperature"])
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parser.add_argument("--min-nar-temperature", type=float, default=kwargs["min-nar-temperature"])
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parser.add_argument("--prefix-silence", type=float, default=kwargs["prefix-silence"])
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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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@ -238,6 +238,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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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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parser.add_argument("--length-penalty", type=float, default=kwargs["length-penalty"])
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"""
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parser.add_argument("--beam-width", type=int, default=kwargs["beam-width"])
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parser.add_argument("--mirostat-tau", type=float, default=kwargs["mirostat-tau"])
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parser.add_argument("--mirostat-eta", type=float, default=kwargs["mirostat-eta"])
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@ -249,10 +250,16 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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parser.add_argument("--layer-skip-exit-layer", type=int, default=kwargs["layer-skip-exit-layer"])
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parser.add_argument("--layer-skip-entropy-threshold", type=int, default=kwargs["layer-skip-entropy-threshold"])
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parser.add_argument("--layer-skip-varentropy-threshold", type=int, default=kwargs["layer-skip-varentropy-threshold"])
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"""
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parser.add_argument("--refine-on-stop", action="store_true")
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parser.add_argument("--denoise-start", type=float, default=0.0)
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parser.add_argument("--cfg-strength", type=float, default=kwargs['cfg-strength'])
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parser.add_argument("--cfg-rescale", type=float, default=kwargs['cfg-rescale'])
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parser.add_argument("--sampling-scores-masked-only", action="store_true")
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parser.add_argument("--sampling-scores-flatten", action="store_true")
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parser.add_argument("--sampling-scores-remask", action="store_true")
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args, unknown = parser.parse_known_args()
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if is_windows:
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@ -280,6 +287,15 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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if kwargs.pop("play", False):
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args.play = True
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if kwargs.pop("sampling-scores-masked-only", False):
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args.sampling_scores_masked_only = True
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if kwargs.pop("sampling-scores-flatten", False):
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args.sampling_scores_flatten = True
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if kwargs.pop("sampling-scores-remask", False):
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args.sampling_scores_remask = True
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if args.split_text_by == "lines":
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args.split_text_by = "\n"
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elif args.split_text_by == "none":
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@ -298,28 +314,32 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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phonemize=not args.no_phonemize,
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voice_convert=args.voice_convert,
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max_steps=args.max_steps,
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max_levels=args.max_levels,
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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, 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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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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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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#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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#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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prefix_silence=args.prefix_silence,
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input_prompt_prefix=args.input_prompt_prefix,
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#prefix_silence=args.prefix_silence,
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#input_prompt_prefix=args.input_prompt_prefix,
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input_prompt_length=args.input_prompt_length,
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cfg_strength=args.cfg_strength,
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cfg_rescale=args.cfg_rescale,
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sampling_scores_masked_only=args.sampling_scores_masked_only,
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sampling_scores_flatten=args.sampling_scores_flatten,
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sampling_scores_remask=args.sampling_scores_remask,
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)
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with timer("Inferenced in", callback=lambda msg: gr.Info( msg )) as t:
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@ -497,7 +517,12 @@ with ui:
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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.")
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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.")
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layout["inference_tts"]["inputs"]["length-penalty"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Length Penalty", info="(AR only) Modifies the probability of a stop token based on the current length of the sequence.")
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with gr.Row():
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layout["inference_tts"]["inputs"]["sampling-scores-masked-only"] = gr.Checkbox(label="Sampled Scores: Masked Only", info="(NAR-len only) Update scores for newly generated tokens only")
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layout["inference_tts"]["inputs"]["sampling-scores-flattened"] = gr.Checkbox(label="Sampled Scores: Flattened", info="(NAR-len only) Flattens the scores for all codebook levels")
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layout["inference_tts"]["inputs"]["sampling-scores-remask"] = gr.Checkbox(label="Sampled Scores: Remask", info="(NAR-len only) Remasks P%% of existing tokens randomly after each step.")
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# These settings are pretty much not supported anyways
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"""
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with gr.Tab("Experimental Settings", visible=cfg.experimental):
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with gr.Row():
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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.")
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layout["inference_tts"]["inputs"]["layer-skip-exit-layer"] = gr.Slider(value=11, minimum=0, maximum=11, step=1, label="Layer Skip Exit Layer", info="Maximum model layer to exit early from.")
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layout["inference_tts"]["inputs"]["layer-skip-entropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Entropy Threshold", info="Entropy threshold for early-exit")
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layout["inference_tts"]["inputs"]["layer-skip-varentropy-threshold"] = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="Layer Skip Varentropy Threshold", info="Varentropy threshold for early-exit")
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"""
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layout["inference_tts"]["buttons"]["inference"].click(
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fn=do_inference_tts,
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@ -565,6 +591,7 @@ with ui:
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layout["inference_stt"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
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layout["inference_stt"]["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.")
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layout["inference_stt"]["inputs"]["length-penalty"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Length Penalty", info="(AR only) Modifies the probability of a stop token based on the current length of the sequence.")
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"""
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with gr.Row():
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layout["inference_stt"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
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layout["inference_stt"]["inputs"]["mirostat-tau"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="Mirostat τ (Tau)", info="The \"surprise\" value when performing mirostat sampling. 0 to disable.")
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@ -573,6 +600,7 @@ with ui:
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layout["inference_stt"]["inputs"]["dry-multiplier"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="DRY Multiplier", info="The multiplying factor for the DRY score penalty (0 to disable DRY sampling).")
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layout["inference_stt"]["inputs"]["dry-base"] = gr.Slider(value=1.75, minimum=0.0, maximum=8.0, step=0.05, label="DRY Base", info="The base of the exponent in the DRY score penalty")
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layout["inference_stt"]["inputs"]["dry-allowed-length"] = gr.Slider(value=2, minimum=0, maximum=75, step=1, label="Allowed Length", info="The maximimum length a token can be to perform DRY penalty with.")
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"""
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layout["inference_stt"]["buttons"]["inference"].click(
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fn=do_inference_stt,
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