unified nar.py into ar_nar.py

docs/models.md

@@ -233,34 +233,13 @@ This script aims to implement everything as required per VALL-E agnostically, to
 
 ## `models/ar_nar.py`
 
-This script implements VALL-E as a unified autoregressive and non-autoregressive model, where RVQ-level 0 is inferenced autoregressively, the remaining levels are infereneced non-autoregressively.
+This script implements VALL-E as a unified autoregressive and non-autoregressive model, where RVQ-level 0 is inferenced autoregressively, the remaining levels are infereneced non-autoregressively, if requested.
-
+* Since one model can be trained AR-ly and NAR-ly, RVQ-level 0 can also be trained non-autoregressively with diffusion-like masking.
-By default, this is the default model, but is used through `cfg.model.capabilities = ["ar", "nar"]`.
 
 For training, this model handles preparing the batch provided through the dataloader according to a randomly sampled targetted RVQ-level.
 
 For inferencing, this will dynamically inference depending on the arguments provided.
 
-## `models/ar.py`
-
-This script implements VALL-E as a pure autoregressive (AR) model.
-
-If `cfg.model.experimental.interleave=True`, this makes use of interleaving its audio codes, instead of inferencing per-codebook level. If not, this simply attends to RVQ level 0.
-
-This model serves as an experiment that failed, and might be revisited in the future.
-
-Use of this is governed through `cfg.model.capabilities = ["ar"]`
-
-## `models/nar.py`
-
-This script implements VALL-E as a mostly-pure non-autoregresive model, where it infers the duration autoregressively (if `"len" in cfg.model.capabilities`). If not, this simply attends to RVQ levels 1+.
-
-This makes use of training an additional `len` task that can infer the duration of a requested input, as well as (maybe) using special tokens as the initial input for RVQ-level 0 (the level the AR attends to).
-
-This model serves as an experiment that failed, and might be revisited in the future.
-
-Use of this is governed through `cfg.model.capabilities = ["nar"]`
-
 ## `models/experimental.py`
 
 This script implements VALL-E as a mostly-HuggingFace compatible model, where it handles processing tokens as a uniform sequence of IDs.

vall_e/config.py

@@ -255,13 +255,13 @@ class ModelExperimentalSettings:
 	# it just seems like a bitch to try and train something worthwhile with it, since there's crackles every other token
 	# RetNet's chunked inferencing might be a better place for this
 
-	len_train_p: float = 0.05 # odds of injecting a "len" task within the model for NAR-len
+	masking_train_p: float = 0.0 # odds of training with masking
-	# to-to: just incorporate this as a task instead
+	masking_train_rvq_levels: list = field(default_factory=lambda: [0,0]) # determines which levels to do mask training on
 
 	# classifier-free guidance shit
-	cfg_cond_dropout_p: float = 0.2 # probability to drop out text and audio during training
+	cfg_cond_dropout_p: float = 0.0 # 0.2 # probability to drop out text and audio during training
-	cfg_text_dropout_p: float = 0.0  # probability to drop out input audio prompt during training
+	cfg_text_dropout_p: float = 0.0 # 0.0  # probability to drop out input audio prompt during training
-	cfg_prom_dropout_p: float = 0.3  # probability to drop out input audio prompt during training
+	cfg_prom_dropout_p: float = 0.0 # 0.3  # probability to drop out input audio prompt during training
 
 	layerskip: bool = False # layerskip compatible model (or training for)
 	#layerskip_rvq_levels: list = field(default_factory=lambda: []) # RVQ levels to train / inference layerskip for (to-do: implement, see if it matters)
@@ -757,6 +757,7 @@ class Config(BaseConfig):
 	device: str = "cuda" # target device
 	mode: str = "training" # "inferencing"
 	experimental: bool = False # debug flag
+	silent_errors: bool = False # if False, raise exceptions on errors that could silently lead to problems, if True ignore them
 
 	dataset: Dataset = field(default_factory=lambda: Dataset)
 	models: dict | list | None = field(default_factory=lambda: [])
@@ -879,7 +880,12 @@ class Config(BaseConfig):
 		if data_parent.exists():
 			return [ path.parent / child.name for child in Path(data_parent).glob(path.name) ]
 		
-		return path
+		# return an empty list
+		if self.silent_errors:
+			return []
+
+		# raise an error to avoid headaches
+		raise Exception(f'Cannot unglob requested path: {path}')
 
 
 	def format( self, training=True ):
@@ -957,10 +963,6 @@ class Config(BaseConfig):
 				model["experimental"]["rvq_levels_p"] = model["experimental"]["p_rvq_levels"]
 				del model["experimental"]["p_rvq_levels"]
 
-			if "p_len_train" in model["experimental"]:
-				model["experimental"]["len_train_p"] = model["experimental"]["p_len_train"]
-				del model["experimental"]["p_len_train"]
-
 		self.models = [ Model(**model) if isinstance(model, dict) else model for model in self.models ]
 		self.loras = [ LoRA(**lora)  if isinstance(lora, dict) else lora for lora in self.loras ]
 
@@ -999,22 +1001,17 @@ class Config(BaseConfig):
 		if self.tokenizer == "naive":
 			self.tokenizer = NaiveTokenizer()
 		else:
-			# ick...
+			from transformers import PreTrainedTokenizerFast
-			try:
-				from transformers import PreTrainedTokenizerFast
 
-				tokenizer_path = self.rel_path / self.tokenizer_path
+			tokenizer_path = self.rel_path / self.tokenizer_path
-				if tokenizer_path and not tokenizer_path.exists():
+			# deduce path if a local copy is not provided
-					tokenizer_path = Path("./data/") / self.tokenizer_path
+			if not tokenizer_path.exists():
+				tokenizer_path = Path("./data/") / self.tokenizer_path
 			
-				if tokenizer_path and tokenizer_path.exists():
+			if not self.silent_errors and not tokenizer_path.exists():
-					self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(tokenizer_path))
+				raise Exception(f'Tokenizer path not found: {tokenizer_path}')
-				else:
+
-					self.tokenizer = NaiveTokenizer()
+			self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(tokenizer_path))
-			except Exception as e:
-				self.tokenizer = NaiveTokenizer()
-				_logger.warning(f"Error while parsing tokenizer: {str(e)}")
-				pass
 
 
 # Preserves the old behavior
@@ -1071,8 +1068,9 @@ cfg = Config.from_cli()
 try:
 	cfg.format()
 except Exception as e:
+	if not cfg.silent_errors:
+		raise e # throw an error because I'm tired of silent errors messing things up for me
 	_logger.error(f"Error while parsing config YAML: {str(e)}")
-	raise e # throw an error because I'm tired of silent errors messing things up for me
 
 if __name__ == "__main__":
 	print(cfg)

vall_e/data.py

@@ -1199,6 +1199,10 @@ class Dataset(_Dataset):
 				task
 			]
 
+		# Duration prediction (<text><prompt> => len(<resp>))
+		elif task == "len":
+			proms = self.sample_prompts(spkr_name, reference=path)
+
 		# noise suppression (<text>? <resp+noise> => <resp>)
 		# speech removal (<text>?<resp+noise> => <noise>)
 		elif task == "ns" or task == "sr":

vall_e/engines/__init__.py

@@ -193,7 +193,7 @@ def load_engines(training=True, **model_kwargs):
 					("text_emb.weight", model.config.text_tokens ),
 					("tasks_emb.weight", model.config.tasks ),
 					("langs_emb.weight", model.config.langs ),
-					("rvq_l_emb.weight", model.config.resp_levels + (1 if "len" in model.config.capabilities else 0) ),
+					("rvq_l_emb.weight", model.config.resp_levels ),
 					("resps_emb.embeddings.0.weight", model.config.audio_tokens + uses_stop_token ),
 					("model.embed_tokens.weight", model.config.audio_tokens + uses_stop_token ),
 					("classifiers.proj.0.weight" if model.config.experimental.split_classifiers else 'classifier.weight', model.config.audio_tokens + uses_stop_token ),

vall_e/inference.py

@@ -49,11 +49,8 @@ class TTS():
 		else:
 			raise Exception(f"Unknown config passed: {config}")		
 
-		try:
+		cfg.format( training=False )
-			cfg.format( training=False )
+		cfg.dataset.use_hdf5 = False # could use cfg.load_hdf5(), but why would it ever need to be loaded for inferencing
-			cfg.dataset.use_hdf5 = False # could use cfg.load_hdf5(), but why would it ever need to be loaded for inferencing
-		except Exception as e:
-			raise e # throw an error because I'm tired of silent errors messing things up for me
 
 		if amp is None:
 			amp = cfg.inference.amp
@@ -268,7 +265,7 @@ class TTS():
 			with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
 				if model_ar is not None:
 					text_list = model_ar(
-						text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], max_steps=max_ar_steps,
+						text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], max_steps=max_ar_steps, task_list=["stt"],
 						sampling_temperature=ar_temp,
 						sampling_min_temperature=min_ar_temp,
 						sampling_top_p=top_p, sampling_top_k=top_k, sampling_min_p=min_p,
@@ -318,7 +315,7 @@ class TTS():
 			with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
 				if model_ar is not None:
 					resps_list = model_ar(
-						text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps,
+						text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps, task_list=["tts"],
 						input_prompt_prefix=input_prompt_prefix,
 						prefix_silence=prefix_silence,
 						sampling_temperature=ar_temp,
@@ -343,7 +340,7 @@ class TTS():
 						use_lora=use_lora,
 					)
 					resps_list = model_nar(
-						text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list,
+						text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list, task_list=["tts"],
 						input_prompt_prefix=input_prompt_prefix,
 						max_levels=max_nar_levels,
 						sampling_temperature=nar_temp,
@@ -359,8 +356,8 @@ class TTS():
 						use_lora=use_lora,
 					)
 				elif model_len is not None:
-					len_list = model_len( text_list=[phns], proms_list=[prom], max_steps=5, disable_tqdm=not tqdm ) # don't need more than that
+					len_list = model_len( text_list=[phns], proms_list=[prom], task_list=["len"], max_steps=5, disable_tqdm=not tqdm ) # don't need more than that
-					len_list = [ clamp(1, max_ar_steps, l) for l in len_list ]
+					len_list = [ clamp(l, 1, max_ar_steps) for l in len_list ]
 					
 					kwargs = {}
 					
@@ -375,7 +372,7 @@ class TTS():
 
 						kwargs["resps_list"] = [ resp[:, :1] ]
 
-					resps_list = model_nar( text_list=[phns], proms_list=[prom], len_list=len_list,
+					resps_list = model_nar( text_list=[phns], proms_list=[prom], len_list=len_list, task_list=["tts"],
 						max_steps=max_ar_steps,
 						max_levels=max_nar_levels,
 						sampling_temperature=nar_temp,

vall_e/models/__init__.py

@@ -60,25 +60,7 @@ def download_model( save_path=DEFAULT_MODEL_PATH, chunkSize = 1024 ):
 def get_model(config, training=True, **model_kwargs):
 	name = config.name
 
-	if "len" in config.capabilities:
+	if config.experimental.hf:
-		from .nar import NAR
-		model = NAR(
-			n_text_tokens=config.text_tokens,
-			n_audio_tokens=config.audio_tokens,
-			d_model=config.dim,
-			n_heads=config.heads,
-			n_layers=config.layers,
-			n_experts=config.experts,
-			
-			p_dropout=config.dropout,
-			
-			l_padding = config.input_alignment,
-			
-			training = training,
-			config = config,
-			**model_kwargs
-		)
-	elif config.experimental.hf:
 		from .experimental import Model as Experimental
 		model = Experimental(
 			n_text_tokens=config.text_tokens,

vall_e/models/ar.py

@@ -1,638 +0,0 @@
-"""
-# an AR model that (should) handle:
-* handling all RVQ levels, but does it in an autoregressive manner
-
-It's in a mess of a state, because I want this to be an interleaved model, but it just seems better to use the vall_e.models.experimental model.
-"""
-from .base import Base, list_to_tensor, Categorical
-from ..config import cfg
-
-import torch
-from torch.nn.utils.rnn import pad_sequence
-
-import random
-import math
-from einops import rearrange
-from torch import Tensor
-from tqdm import trange
-import logging
-
-_logger = logging.getLogger(__name__)
-
-from ..utils import clamp
-from ..emb.qnt import trim, encode_as_embedding
-from .lora import enable_lora
-
-class AR(Base):
-	def forward(
-		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor] | None = None,
-		
-		task_list: list[Tensor] | None = None,
-		lang_list: list[Tensor] | None = None,
-		tone_list: list[Tensor] | None = None,
-		len_list: list[Tensor] | None = None,
-
-		training: bool | int | None = None,
-
-		max_steps: int = 1000,
-		max_levels: int = 0,
-
-		input_prompt_prefix: bool = False,
-		prefix_silence: float = 1.0,
-
-		sampling_temperature: float = 1.0,
-		sampling_min_temperature: float = -1.0,
-		sampling_top_k: int = -100,
-		sampling_top_p: float = 1.0,
-		sampling_min_p: float = 0.0,
-		sampling_repetition_penalty: float = 1.0,
-		sampling_repetition_penalty_decay: float = 0.0,
-		sampling_length_penalty: float = 0.0,
-		sampling_beam_width: int = 0,
-		sampling_mirostat_tau: float = 0.0,
-		sampling_mirostat_eta: float = 0.1,
-		sampling_dry_multiplier=0.0,
-		sampling_dry_base=1.75,
-		sampling_dry_allowed_length=2,
-		sampling_entropix=False,
-		
-		sampling_layer_skip: bool = False,
-		sampling_layer_skip_exit_layer: int = -1,
-		sampling_layer_skip_entropy_threshold: float = -1,
-		sampling_layer_skip_varentropy_threshold: float = -1,
-
-		sampling_refine_on_stop: bool = False,
-
-		disable_tqdm=False,
-		use_lora=None,
-	):
-		text_task = [ "stt" ]
-
-		if text_list is not None:
-			default_task = "tts"
-			device = text_list[0].device
-			batch_size = len(text_list)
-		else:
-			default_task = "stt"
-			device = resps_list[0].device
-			batch_size = len(resps_list)
-
-		# generate task list if not provided
-		if task_list is None:
-			task_list = [ default_task for _ in range(batch_size) ]
-
-		has_none = resps_list is None or text_list is None
-		if not has_none:
-			for i, task in enumerate( task_list ):
-				if resps_list[i] is None or text_list[i] is None:
-					has_none = True
-					break
-
-		# is training or NAR
-		if not has_none:
-			n_levels_set = {r.shape[-1] for r in resps_list}
-			n_levels = next(iter(n_levels_set))
-
-			# implicit
-			if training is None:
-				training = 0 if n_levels == self.n_resp_levels else None
-
-			# is training
-			if training is not None:
-				# specifies how to sample probabilities of which RVQ levels to train against
-				rvq_levels_p = self.config.experimental.rvq_levels_p if self.config is not None else "equal"
-				# determines which RVQ level to target per batch
-				quant_level_range = self.config.experimental.rvq_level_range if self.config is not None and self.config.experimental.rvq_level_range else [ 0 if self.causal else 1, self.n_resp_levels - 1 ]
-				# rate to perform token dropout errors
-				token_dropout_error = self.config.experimental.token_dropout_error
-				# RVQ levels to apply token dropout on
-				token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
-				# implicitly set it to all levels
-				if not token_dropout_rvq_levels:
-					token_dropout_rvq_levels = [0, self.resp_levels - 1]
-				# allow passing a specific distribution of RVQ levels
-				rvq_levels_p = rvq_levels_p if isinstance(rvq_levels_p, list) else []
-				if not rvq_levels_p:
-					lo, hi = quant_level_range[0], quant_level_range[1] + 1
-					# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
-					if rvq_levels_p == "equal":
-						rvq_levels_p = [ i for i in range( lo, hi ) ]
-					else:
-						# yuck
-						rvq_levels_p = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
-
-				# input RVQ levels
-				quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
-				for i, task in enumerate( task_list ):
-					if task in text_task:
-						quant_levels[i] = 0 # self.n_resp_levels - 1
-				
-				# trim resps to only contain all levels below the target level
-				resps_list = [r if t in text_task else r[..., :l+1] for r, l, t in zip(resps_list, quant_levels, task_list)]
-
-				# tensor to cat for RVQ level 0
-				text_stop_sequence = torch.tensor([[2] * 1], device=device, dtype=torch.int16)
-				audio_stop_sequence = torch.tensor([[self.stop_token] * 1], device=device, dtype=torch.int16)
-				# I hate python's value/reference semantics so much
-				for i, quant_level, resps, proms, task in zip(range(batch_size), quant_levels, resps_list, proms_list, task_list):
-					# cap quant_level if it exceeds its corresponding resp/prom
-					if quant_level >= resps.shape[-1]:
-						quant_levels[i] = resps.shape[-1] - 1
-
-					# proms could be a Tensor, list[Tensor], or None
-					if isinstance( proms, torch.Tensor ):
-						if quant_level >= proms.shape[-1]:
-							quant_levels[i] = proms.shape[-1] - 1
-
-					elif isinstance( proms, list ):
-						for j, prom in enumerate( proms ):
-							if not isinstance( prom, torch.Tensor ):
-								continue
-							if quant_level >= prom.shape[-1]:
-								quant_levels[i] = prom.shape[-1] - 1
-
-					# apply token dropout error compensation
-					if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
-						steps = resps.shape[0]
-						for l in range( quant_level ):
-							for t in range( steps ):
-								token = resps[t, l].item()
-
-								if random.random() < token_dropout_error:								
-									offset = 1 * ( 1 if random.random() < 0.5  else -1 )
-									resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
-
-					# only apply stop token for RVQ level 0
-					if quant_level <= 0:
-						# append stop tokens for AR
-						if task in text_task:
-							#text_list[i] = torch.cat([ resps, text_stop_sequence ])
-							...
-						else:
-							resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
-
-				inputs = self.inputs(
-					text_list=text_list,
-					proms_list=proms_list,
-					resps_list=resps_list,
-					lang_list=lang_list,
-					tone_list=tone_list,
-					task_list=task_list,
-
-					quant_levels=quant_levels,
-				)
-
-				return super().forward(
-					inputs=inputs,
-					quant_levels=quant_levels, # could technically just grab this from the above inputs since they're included as an RVQ level token
-				)
-		
-		# is AR
-		if cfg.lora is not None:
-			enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
-
-		# STT
-		start_slice = [ 0 for _ in range(batch_size) ]
-		sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
-		stopped = torch.zeros(batch_size, device=device).bool()
-		
-		audio_stop_token = self.stop_token
-		text_stop_token = 2
-
-		state = None
-		mirostat = [
-			{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
-		] * batch_size if sampling_mirostat_tau > 0.0 else None
-
-		scores = [ 1.0 ] * sampling_beam_width
-		metrics = []
-
-		# ick
-		"""
-		low_temperature = False # sampling_temperature < 0.6 # sampling_repetition_penalty == 1.0 and sampling_temperature == 0.0 #
-		low_temperature_range = cfg.dataset.frames_per_second * 5
-		
-		original_sampling_temperature = sampling_temperature 
-		original_sampling_repetition_penalty = sampling_repetition_penalty 
-		original_sampling_repetition_penalty_decay = sampling_repetition_penalty_decay
-		"""
-
-		sampling_layer_skip_variables = {} if sampling_layer_skip else None
-
-		if sampling_layer_skip:
-			if sampling_layer_skip_entropy_threshold >= 0:
-				sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
-			if sampling_layer_skip_varentropy_threshold >= 0:
-				sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
-			if sampling_layer_skip_exit_layer >= 0:
-				sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
-
-		for i, sequence in enumerate( sequence_list ):
-			# add <bos> to text for STT
-			if task_list[i] in text_task:
-				start_slice[i] = 1
-				sequence_list[i] = torch.cat([sequence_list[i], torch.tensor([1], dtype=torch.int16, device=device)])
-			# treat input prompt as initial resp (by prefixing with the prompt instead)
-			elif input_prompt_prefix:
-				start_slice[i] = proms_list[i].shape[0]
-				sequence_list[i], proms_list[i] = proms_list[i][:, 0], sequence_list[i]
-			elif prefix_silence > 0:
-				sequence_list[i] = get_silence(prefix_silence, device=sequence_list[i].device)
-				sequence_list[i] = sequence_list[i][:, 0]
-				# start_slice[i] = sequence_list[i].shape[0]
-
-		# get next in sequence
-		for n in trange(max_steps // max(1, self.causal_size), desc="AR", disable=disable_tqdm):
-			# it would technically be faster to just append the new token's embedding to the inputs, but there's a VERY small performance gain from doing it, so it's not worth it
-			text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
-			resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
-
-			# greedy sampling in the AR *does* work, but requires some quasi-exotic sampling to work around the initial burst of garbage from polluting the rest of the sequence
-			# naturally, rep pen wrangles this initial burst of noise, but naively relying on rep_pen is no good, as it fails after ~6 seconds of audio
-			# however, switching to a default sampling temperature with "clean greedy sampled codes" will make the rest of sequence sound as if it were greedy sampled
-			# to-do: tune these values, maybe have it factor based on confidence scores or something
-			"""
-			if low_temperature:
-				enabled = n < low_temperature_range
-				sampling_repetition_penalty = 1.125 if enabled else 1.25
-				#sampling_repetition_penalty_decay = 0.0 if enabled else original_sampling_repetition_penalty_decay
-				#sampling_temperature = original_sampling_temperature if enabled else 1.0
-			"""
-
-			inputs = self.inputs(
-				text_list=text_list,
-				proms_list=proms_list,
-				resps_list=resps_list,
-				lang_list=lang_list,
-				tone_list=tone_list,
-				len_list=len_list,
-				task_list=task_list,
-				quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
-			)
-
-			# to-do: find an elegant way to write this
-			output = super().forward(
-				inputs=inputs,
-				state=state,
-				
-				layer_skip_variables=sampling_layer_skip_variables,
-
-				output_attentions=sampling_entropix,
-			)
-			logits, state = output.logits, output.state
-
-			sampled = super().sample(
-				logits=logits,
-				prev_list=None if sampling_repetition_penalty == 1.0 and sampling_length_penalty == 0.0 else [ resps_list[i] if task not in text_task else text_list[i] for i, task in enumerate( task_list ) ],
-
-				temperature=sampling_temperature,
-				min_temperature=sampling_min_temperature,
-				top_p=sampling_top_p,
-				top_k=sampling_top_k,
-				min_p=sampling_min_p,
-				repetition_penalty=sampling_repetition_penalty,
-				repetition_penalty_decay=sampling_repetition_penalty_decay,
-				length_penalty=sampling_length_penalty,
-				beam_width=sampling_beam_width,
-
-				mirostat=mirostat,
-
-				dry_multiplier=sampling_dry_multiplier,
-				dry_base=sampling_dry_base,
-				dry_allowed_length=sampling_dry_allowed_length,
-
-				attentions=output.attentions if sampling_entropix else None,
-			)
-
-			r = sampled[0]
-
-			if cfg.experimental:
-				if sampled.entropy:
-					metrics.append( sampled.entropy )
-				elif sampled.scores:
-					metrics.append( [ { "p": p[0], "exited_layer": output.exited_layer } for p in sampled.scores ] )
-
-			if mirostat is not None:
-				mirostat = sampled.scores
-			elif sampling_beam_width > 0:
-				# expand tuple
-				s = sampled.scores
-				# first step, expand batch
-				if batch_size == 1:
-					batch_size = sampling_beam_width
-					text_list = text_list * sampling_beam_width
-					proms_list = proms_list * sampling_beam_width
-					sequence_list = sequence_list * sampling_beam_width
-					task_list = task_list * sampling_beam_width
-					start_slice = start_slice * sampling_beam_width
-					stopped = torch.zeros(batch_size, device=device).bool()
-
-				scores = [ scores[i] + score for i, score in enumerate(s) ]
-
-			# append tokens
-			for i, ri in enumerate(r):
-				task = task_list[i]
-				stop_token = audio_stop_token if task not in text_task else text_stop_token
-				if stop_token in ri:
-					stopped[i] = True
-				sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
-
-			# stop token found
-			# stopped |= r == stop_token
-			if stopped.all().item():
-				break
-
-		# to-do for layerskip / speculative sampling: rerun the last sequence again at max depth
-
-		if metrics:
-			from ..plot import plot_sample_metrics
-			filename = "metrics"
-			if sampling_entropix:
-				filename += f'[entropix]'
-			if sampling_layer_skip_exit_layer >= 0:
-				filename += f'[{sampling_layer_skip_exit_layer+1}]'
-
-			plot_sample_metrics( metrics, filename=f'{filename}.png' )
-
-		# pick the best scoring candidate
-		# desu this is always going to be candidate 0
-		if sampling_beam_width:
-			sequence_list = sequence_list[:1]
-			task_list = task_list[:1]
-
-		# remove stop token
-		sequence_list = [self._prune(r, audio_stop_token if task_list[i] not in text_task else text_stop_token) for i, r in enumerate(sequence_list)]
-		# remove <bos>
-		sequence_list = [ sequence_list[i][start_slice[i]:] for i, task in enumerate( task_list ) ]
-
-		if sampling_refine_on_stop:
-			# get how much we need to slice from the end
-			slice_lengths = [ sequence.shape[-1] for sequence in sequence_list ]
-			# -1 for the stop token
-			logits = [ logit[-length-1:-1] for logit, length in zip(logits, slice_lengths) ]
-			# greedy sample from the sequence
-			refined_list = [ logit.argmax(dim=-1) for logit in logits ]
-			# to-do: compare scores
-			# set the "refined" list as the output
-			sequence_list = refined_list	
-
-		return sequence_list
-
-
-def example_usage():
-	cfg.trainer.backend = "local"
-	cfg.hyperparameters.gradient_accumulation_steps = 1
-	if cfg.audio_backend == "dac":
-		cfg.sample_rate = 44_100
-
-	from functools import partial
-	from einops import repeat
-	from tqdm import tqdm
-
-	from ..emb.qnt import decode_to_file, unload_model, trim_random, repeat_extend_audio, concat_audio, merge_audio
-	from ..engines import Engine, Engines
-	from ..utils import wrapper as ml
-	
-	import numpy as np
-	import re
-
-	device = "cuda"
-	
-	# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
-	"""
-	if "mamba" in cfg.model.arch_type:
-		cfg.model.resp_levels = 1
-	"""
-	# cfg.model.loss_factors = {}
-
-	def tokenize(content):
-		return torch.tensor( cfg.tokenizer.encode(content) )
-
-	def _load_quants(path) -> Tensor:
-		qnt = np.load(path, allow_pickle=True)[()]
-		return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.resp_levels, :].t().to(torch.int16)
-
-	qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
-	noise = _load_quants(f"./data/noise.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
-
-	text_list = [
-		tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
-		#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
-	]
-	proms_list = [
-		qnt[:cfg.dataset.frames_per_second, :].to(device),
-		#qnt[:cfg.dataset.frames_per_second, :].to(device),
-	]
-	resps_list = [
-		qnt[:, :].to(device),
-		#qnt[:cfg.dataset.frames_per_second, :].to(device),
-	]
-
-	text_list = text_list[:1]
-	proms_list = proms_list[:1]
-	resps_list = resps_list[:1]
-
-	batch_size = len(text_list)
-
-	# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
-	kwargs = {
-		'n_text_tokens': 256,
-		'n_audio_tokens': 1024,
-
-		'd_model': 1024, # 256, # 1024, # 1536
-		'n_heads': 16, # 4, # 16, # 24
-		'n_layers': 12, # 32
-		'n_experts': 1,
-
-		'p_dropout': 0.1,
-
-		'l_padding': 8 if cfg.optimizations.fp8 else 0,
-
-		'config': cfg.model
-	}
-	
-	"""
-	try:
-		kwargs['config'] = cfg.model
-	except Exception as e:
-		pass
-	"""
-
-	bos_id, space_id, eos_id = cfg.tokenizer.encode( " " )
-	tasks = cfg.dataset.tasks_list
-
-	model = AR(**kwargs).to(device)
-	steps = 75 * len(tasks) * cfg.model.experimental.causal_size
-
-	optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
-	scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
-	learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None
-
-	if cfg.optimizations.dadaptation:
-		# do not combine the two
-		if scheduler == "schedulefree":
-			scheduler = ""
-
-		learning_rate = 1.0
-	
-	if optimizer == "prodigy":
-		if learning_rate is None:
-			learning_rate = 1.0
-
-		optimizer = ml.Prodigy
-	elif optimizer == "adagrad":
-		if learning_rate is None:
-			learning_rate = 1.0e-2
-
-		optimizer = ml.Adagrad
-	elif optimizer == "adamw":
-		if learning_rate is None:
-			learning_rate = 1.0e-4
-
-		optimizer = ml.AdamW
-	elif optimizer == "sdg":
-		if learning_rate is None:
-			learning_rate = 1.0e-4
-
-		optimizer = ml.SGD
-	else:
-		raise ValueError(f"Unrecognized optimizer: {optimizer}")
-
-	_logger.info(f"Optimizer: {optimizer}\tLearning rate: {learning_rate}")
-
-	optimizer = optimizer(model.parameters(), lr=learning_rate)
-
-	if scheduler == "schedulefree":
-		if isinstance(optimizer, ml.AdamW):
-			scheduler = ml.schedulefree.AdamWScheduleFree
-		elif isinstance(optimizer, ml.SGD):
-			scheduler = ml.schedulefree.SGDScheduleFree
-		else:
-			scheduler = None
-
-		if scheduler is not None:
-			_logger.info(f"Scheduler: {scheduler}")
-			optimizer = scheduler( model.parameters(), lr = learning_rate )
-
-	if cfg.optimizations.replace and cfg.optimizations.linear:
-		model = ml.replace_linear( model )
-		
-	if cfg.optimizations.replace and cfg.optimizations.embedding:
-		model = ml.replace_embedding( model )
-
-	"""
-	cfg.optimizations.model_offloading = {
-		"devices": ["cuda:0", "cpu"],
-	#	"limits": [ 0.9, -1 ],
-		"assign": [[ f'layers.{i}.' for i in range(0,10) ], [ f'layers.{i}.' for i in range(11,12) ] + [ "model.norm" ]],
-	#	"limits": [ 256 * (1024 ** 2), -1 ]
-	}
-	"""
-	
-	engine = Engine(model=model, optimizer=optimizer)
-	engines = Engines({"ar": engine})
-	engines.setup()
-	
-	"""
-	if cfg.optimizations.model_offloading:
-		model = ml.offload_model( model, policy=cfg.optimizations.model_offloading )
-	"""
-
-	"""
-	torch.save( {
-		'module': model.state_dict()
-	}, f"./data/{cfg.model.arch_type}.pth" )
-	"""
-
-	_logger.info(f"AR ({cfg.model.arch_type}, {cfg.audio_backend}) parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
-
-	@torch.no_grad()
-	def sample_data(task=None):
-		texts = []
-		proms = []
-		resps = []
-
-		for i in range(batch_size):
-			if task is None:
-				task = random.choice(tasks)
-
-			text = text_list[i]
-			prom = proms_list[i]
-			resp = resps_list[i]
-
-			# do nothing
-			if task == "tts":
-				...
-			elif task == "tts-c":
-				trim_length = int(random.uniform(cfg.dataset.prompt_duration_range[0], cfg.dataset.prompt_duration_range[1]) * cfg.dataset.frames_per_second)
-
-				prom = resp[:trim_length]
-				resp = resp[trim_length:]
-			elif task == "ns" or task == "sr":
-				# extend the noise to fill the target audio
-				noise_ext = repeat_extend_audio( noise, resp.shape[0] )
-				# create the input prompt by merging the target audio with the noise
-				prom = merge_audio( resp.cpu(), noise_ext, scale=[1, cfg.dataset.noise_scale], device=cfg.dataset.reencode_device )
-				# set the target to just be the noise if <sr>
-				if task == "sr":
-					resp = noise_ext
-
-				# set the text prompt to empty to train without a guided text prompt
-				if random.random() < 0.5:
-					text = torch.tensor([bos_id, eos_id], device=device, dtype=torch.uint8)
-
-			texts.append( text.to(device) )
-			proms.append( prom.to(device) )
-			resps.append( resp.to(device) )
-
-		return texts, proms, resps
-
-	@torch.inference_mode()
-	def sample( name, steps=1000, task=None ):
-		engine.eval()
-
-		texts, proms, resps = sample_data( task )
-
-		resps = engine( texts, proms, max_steps=steps, sampling_temperature=0.95 )
-
-		for i, o in enumerate(resps):
-			_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{task}.{name}.wav", device=device)
-
-		unload_model()
-
-	def train():
-		engine.train()
-		t = trange(steps)
-		for i in t:
-			texts, proms, resps = sample_data()
-
-			stats = {"step": i}
-			stats |= engine.traverse(text_list=texts, proms_list=proms, resps_list=resps)
-			stats |= {"grad_norm": engine.get_global_grad_norm()}
-
-			tqdm.write(f"{stats}")
-
-		"""
-		torch.save( {
-			'module': model.state_dict()
-		}, f"./data/{cfg.model.arch_type}.pth" )
-		"""
-
-	#sample("init", 5)
-	train()
-
-	"""
-	if cfg.optimizations.compile:
-		model = ml.compile_model(model, backend=cfg.optimizations.compile)
-	"""
-	
-	for task in tasks:
-		sample("final", task=task)
-
-	engines.quit()
-
-if __name__ == "__main__":
-	example_usage()

vall_e/models/base.py

@@ -246,9 +246,6 @@ class AudioEmbedding(nn.Module):
 			# prom
 			if self.capabilities is None:
 				offset = 0
-			# resp
-			#elif "len" in self.capabilities:
-			#	offset = 1
 			elif "nar" not in self.capabilities:
 				offset = 0
 			elif quant_level > 0:
@@ -492,16 +489,6 @@ class Base(nn.Module):
 			# +1 to include the stop or mask token
 			n_resp_tokens = n_audio_tokens + ( 1 if self.causal_size > 0 else 0 )
 			l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
-		"""
-		elif "len" not in self.capabilities:
-			# +1 to include the stop token
-			n_resp_tokens = n_audio_tokens + ( 1 if self.causal_size > 0 else 0 )
-			l_tokens = [n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1)
-		# NAR-len model
-		else:
-			n_resp_tokens = n_audio_tokens
-			l_tokens = [n_resp_tokens] * (self.n_resp_levels)
-		"""
 
 		self.unified_position_ids = unified_position_ids
 		self.interleave = interleave
@@ -561,11 +548,11 @@ class Base(nn.Module):
 		# this ***might*** let me also unify the proms_emb and resps_embedding
 		if self.version >= 5:
 			# "len" RVQ level-0 gets an additional token
-			self.rvq_l_emb = Embedding(self.n_resp_levels + (1 if "len" in self.capabilities else 0), d_model)
+			self.rvq_l_emb = Embedding(self.n_resp_levels, d_model)
 		
 			# experimental NAR-only mode
-			self.len_emb = Embedding(11, d_model) if "len" in self.capabilities else None
+			self.len_emb = Embedding(11, d_model)
-			self.time_emb = TimeEmbedding(d_model) if "len" in self.capabilities else None
+			self.time_emb = TimeEmbedding(d_model)
 
 		if attention_backend == "auto":
 			attention_backend = "sdpa"
@@ -645,7 +632,7 @@ class Base(nn.Module):
 					use_reentrant=False
 				))
 		elif self.arch_type == "llama":
-			LlamaClass = LlamaModel_Adapted if (self.layerskip or "len" in self.capabilities) else LlamaModel
+			LlamaClass = LlamaModel_Adapted # if (self.layerskip or "len" in self.capabilities) else LlamaModel
 
 			if n_experts <= 1:
 				self.model = LlamaClass(LlamaConfig(
@@ -668,12 +655,6 @@ class Base(nn.Module):
 				# replace with desired attention
 				if attention_backend not in HF_ATTENTIONS:
 					self.model = ml.replace_attention( self.model, klass=LlamaAttention_Adapted, target=LlamaAttention, mode=attention_backend )
-
-				# replace with modified Llama
-				"""
-				if "len" in self.capabilities:
-					self.model = ml.replace_attention( self.model, klass=LlamaDecoderLayer_Adapted, target=LlamaDecoderLayer, mode=attention_backend )
-				"""
 			else:
 				self.model = MixtralModel(MixtralConfig(
 					vocab_size =n_resp_tokens,
@@ -1012,6 +993,7 @@ class Base(nn.Module):
 		for i in range(batch_size):
 			quant_level = quant_levels[i] if quant_levels is not None else 0
 			task_type = task_list[i] if task_list is not None else "tts"
+			timestep = time_list[i] if time_list is not None else None
 
 			# insert task type as a string
 			inputs[i].append( ( "task", task_type ) )
@@ -1023,12 +1005,6 @@ class Base(nn.Module):
 			# Sequence: <text><sep><rvq lvl><sep><prom><sep><resp>
 			# prom /may/ include <task> tokens inside to help guide things, per SpeechX
 			if f'<{task_type}>' in get_task_symmap() and task_type not in self.special_tasks:
-				# pick a random timestep
-				if "len" in self.capabilities and quant_level == 0:
-					timestep = random.random()
-				else:
-					timestep = 1.0
-
 				# insert the text prompt
 				if text_list is not None and text_list[i] is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
@@ -1045,7 +1021,7 @@ class Base(nn.Module):
 				if "tone" in self.capabilities and tone_list is not None and tone_list[i] is not None:
 					inputs[i].append( ( "tone", tone_list[i] ) )
 				# insert timestep token
-				if "len" in self.capabilities and quant_level == 0:
+				if timestep is not None:
 					# store timestep information
 					inputs[i].append( ("timestep", torch.tensor([timestep], device=device, dtype=self.time_emb.mlp[0].weight.dtype) ) )
 				# insert the current output response
@@ -1053,7 +1029,7 @@ class Base(nn.Module):
 					inputs[i].append( ( "resp", resps_list[i] ) )
 
 					# store dropout mask
-					if "len" in self.capabilities and quant_level == 0:
+					if timestep is not None:
 						dropout_mask = _dropout_mask( resps_list[i], p=math.cos(timestep * math.pi * 0.5) )
 						inputs[i].append( ("dropout_mask", dropout_mask ) )
 		
@@ -1072,9 +1048,7 @@ class Base(nn.Module):
 					inputs[i].append( ( "lang", lang_list[i] ) )
 				# technically will always be level 0 but for the sake of keeing the input formatting coherent...
 				if self.rvq_l_emb is not None:
-					# override to 0 (I don't know if this change propagates, I'm not familiar with when python passes by (copied) value or reference)
+					inputs[i].append( ( "quant_level", torch.tensor([ quant_level ], device=device, dtype=torch.int16) ) )
-					quant_levels[i] = 0
-					inputs[i].append( ( "quant_level", torch.tensor([ self.n_resp_levels ], device=device, dtype=torch.int16) ) )
 				# insert input audio prompt
 				if proms_list is not None and proms_list[i] is not None:
 					inputs[i].append( ( "prom", proms_list[i] ) )
@@ -1195,7 +1169,7 @@ class Base(nn.Module):
 						embedding = _interleave_sequence_reshape( embeddings )
 
 					# if training NAR-len RVQ level 0
-					elif "len" in self.capabilities and quant_level == 0 and dropout_mask is not None:
+					elif dropout_mask is not None:
 						embedding = self.resps_emb(
 							# if masked use masked token, else original token
 							torch.where( dropout_mask, self.stop_token, input if input.dim() == 1 else input[:, 0] ),
@@ -1220,10 +1194,6 @@ class Base(nn.Module):
 							)
 						else:
 							offset = 0
-							"""
-							if "len" in self.capabilities:
-								offset = 1
-							"""
 							if "nar" not in self.capabilities:
 								offset = 0
 							elif quant_level > 0:
@@ -1264,14 +1234,21 @@ class Base(nn.Module):
 		name,
 		at=None,
 	):
+		find_all = at is None
+		res = [] if at is None else None
+		
 		for batch_index, batch_input in enumerate(inputs):
-			if at is not None and batch_index != at:
+			if not find_all and batch_index != at:
 				continue
 
 			for n, input in batch_input:
-				if n == name:
+				if n != name:
+					continue
+				if not find_all:
 					return input
-		return None
+				res.append( input )
+		
+		return res
 
 	# creates position ids from a given input list
 	# if not unified_position_ids, then each input segment will have its own sequence
@@ -1401,15 +1378,7 @@ class Base(nn.Module):
 			for i in range(batch_size):
 				quant_level = quant_levels[i]
 				task_name = task_list[i]
-
+				causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities) or (task_name in ["len", "stt"])
-				causal = False
-
-				if "len" in self.capabilities:
-					causal = task_name == "len"
-					if quant_level >= self.n_resp_levels:
-						quant_level = 0
-				else:
-					causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities)
 
 				if causal:
 					l = self.causal_size
@@ -1488,13 +1457,7 @@ class Base(nn.Module):
 				logit = logits[i][it:it+seq_len]
 				it += seq_len + 1 # +1 to incorporate the separator
 
-				causal = False
+				causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities) or (task_name in ["len", "stt"])				
-				if "len" in self.capabilities:
-					causal = task_name == "len"
-					if quant_level >= self.n_resp_levels:
-						quant_level = 0
-				else:
-					causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities)
 				
 				# for the AR, shift sequence so that it predicts the next token
 				#	 (the NAR predicts the next token in place, so it's not necessary to do any modifications for it)
@@ -1854,15 +1817,9 @@ class Base(nn.Module):
 				res = [ Categorical(logits=logit).sample() for logit in logits ]
 
 			# calculate token probabilities
-			if "len" in self.capabilities:
+			scores = [
-				scores = [
+				[ F.softmax(logit[i, :], dim=-1)[token].item() for i, token in enumerate(tokens) ]
-					[ F.softmax(logit[i, :], dim=-1)[token].item() for i, token in enumerate(tokens) ]
+				for logit, tokens in zip(logits, res)
-					for logit, tokens in zip(logits, res)
+			]
-				]
-			else:
-				scores = [ 
-					[ F.softmax(logit[-1, :], dim=-1)[token].item() for token in tokens ]
-					for logit, tokens in zip(logits, res)
-				]
 
 		return Sampled(res, logits, scores, entropy)

vall_e/models/nar.py

@@ -1,672 +0,0 @@
-"""
-A (mostly) NAR model that handles inferencing all RVQ levels in parallel (NAR).
-I believe Meta's Voicebox does this too (predict the utterance length, then decode in parallel)
-It *does* have to inference the initial length in an autoregresssive-ish manner (it can technically also be done in parallel)
-
-Initial experiments show this only really "works" for the a few brief seconds before going to silence. I imagine I need to read more papers or just need to train longer.
-"""
-
-
-import random
-import math
-import numpy as np
-import logging
-import torch
-from torch.nn.utils.rnn import pad_sequence
-
-from einops import rearrange
-from torch import Tensor
-from tqdm import trange
-
-from .base import Base, list_to_tensor, Categorical, _dropout_mask
-from ..config import cfg
-from ..emb.qnt import trim, repeat_extend_audio
-from ..utils import clamp
-
-_logger = logging.getLogger(__name__)
-
-class NAR(Base):
-	def forward(
-		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor] | None = None,
-		
-		task_list: list[Tensor] | None = None,
-		lang_list: list[Tensor] | None = None,
-		tone_list: list[Tensor] | None = None,
-		len_list: list[Tensor] | None = None,
-
-		training: bool | int | None = None,
-
-		max_steps: int = 1000,
-		max_levels: int = 0,
-
-		input_prompt_prefix: bool = False,
-		prefix_silence: float = 1.0,
-		denoise_start: float = 0.0,
-
-		sampling_temperature: float = 1.0,
-		sampling_min_temperature: float = -1.0,
-		sampling_top_k: int = -100,
-		sampling_top_p: float = 1.0,
-		sampling_min_p: float = 0.0,
-		sampling_repetition_penalty: float = 1.0,
-		sampling_repetition_penalty_decay: float = 0.0,
-		sampling_length_penalty: float = 0.0,
-		sampling_beam_width: int = 0,
-		sampling_mirostat_tau: float = 0.0,
-		sampling_mirostat_eta: float = 0.1,
-		sampling_dry_multiplier=0.0,
-		sampling_dry_base=1.75,
-		sampling_dry_allowed_length=2,
-		sampling_entropix=False,
-		
-		sampling_layer_skip: bool = False,
-		sampling_layer_skip_exit_layer: int = -1,
-		sampling_layer_skip_entropy_threshold: float = -1,
-		sampling_layer_skip_varentropy_threshold: float = -1,
-
-		sampling_refine_on_stop: bool = False,
-
-		disable_tqdm=False,
-		use_lora=None,
-	):
-		text_task = [ "stt" ]
-
-		if text_list is not None:
-			default_task = "tts"
-			device = text_list[0].device
-			batch_size = len(text_list)
-		else:
-			default_task = "stt"
-			device = resps_list[0].device
-			batch_size = len(resps_list)
-
-		# generate task list if not provided
-		if task_list is None:
-			task_list = [ default_task for _ in range(batch_size) ]
-
-		has_none = resps_list is None or text_list is None
-		if not has_none:
-			for i, task in enumerate( task_list ):
-				if resps_list[i] is None or text_list[i] is None:
-					has_none = True
-					break
-
-		# is training or NAR
-		if not has_none:
-			n_levels_set = {r.shape[-1] for r in resps_list}
-			n_levels = next(iter(n_levels_set))
-
-			# implicit
-			if training is None:
-				training = 0 if n_levels == self.n_resp_levels else None
-
-			# is training
-			if training is not None:
-				len_train_p = self.config.experimental.len_train_p if self.config is not None else 0.05
-
-				n_levels_set = {r.shape[-1] for r in resps_list}
-				n_levels = next(iter(n_levels_set))
-
-				# assert n_levels == self.n_resp_levels
-
-				# to-do: make this YAML configurable
-				def sample_task():
-					return "len" if random.random() < len_train_p else "tts"
-
-				# generate task list to train against
-				task_list = [ sample_task() for _ in range(batch_size) ]
-
-				# specifies how to sample probabilities of which RVQ levels to train against
-				rvq_levels_p = self.config.experimental.rvq_levels_p if self.config is not None else "equal"
-				# determines which RVQ level to target per batch
-				quant_level_range = self.config.experimental.rvq_level_range if self.config is not None and self.config.experimental.rvq_level_range else [ 0 if self.causal else 1, self.n_resp_levels - 1 ]
-				# rate to perform token dropout errors
-				token_dropout_error = self.config.experimental.token_dropout_error
-				# RVQ levels to apply token dropout on
-				token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
-				# CFG
-				cfg_text_dropout_p = self.config.experimental.cfg_text_dropout_p if self.config is not None else 0.0
-				cfg_cond_dropout_p = self.config.experimental.cfg_cond_dropout_p if self.config is not None else 0.0
-				cfg_prom_dropout_p = self.config.experimental.cfg_prom_dropout_p if self.config is not None else 0.0
-				# implicitly set it to all levels
-				if not token_dropout_rvq_levels:
-					token_dropout_rvq_levels = [0, self.resp_levels - 1]
-				# allow passing a specific distribution of RVQ levels
-				rvq_levels_p = rvq_levels_p if isinstance(rvq_levels_p, list) else []
-				if not rvq_levels_p:
-					lo, hi = quant_level_range[0], quant_level_range[1] + 1
-					# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
-					if rvq_levels_p == "equal":
-						rvq_levels_p = [ i for i in range( lo, hi ) ]
-					else:
-						# yuck
-						rvq_levels_p = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
-
-				# input RVQ levels
-				quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
-				for i, task in enumerate( task_list ):
-					if task in text_task:
-						quant_levels[i] = 0 # self.n_resp_levels - 1
-				
-				# trim resps to only contain all levels below the target level
-				resps_list = [r if t in text_task else r[..., :l+1] for r, l, t in zip(resps_list, quant_levels, task_list)]
-				# empty string for CFG
-				text_start_stop_sequence = torch.tensor([1, 2], device=device, dtype=torch.int16)
-				# I hate python's value/reference semantics so much
-				for i, quant_level, text, resps, proms, task in zip(range(batch_size), quant_levels, text_list, resps_list, proms_list, task_list):
-					# cap quant_level if it exceeds its corresponding resp/prom
-					if quant_level >= resps.shape[-1]:
-						quant_levels[i] = resps.shape[-1] - 1
-
-					# proms could be a Tensor, list[Tensor], or None
-					if isinstance( proms, torch.Tensor ):
-						if quant_level >= proms.shape[-1]:
-							quant_levels[i] = proms.shape[-1] - 1
-
-					elif isinstance( proms, list ):
-						for j, prom in enumerate( proms ):
-							if not isinstance( prom, torch.Tensor ):
-								continue
-							if quant_level >= prom.shape[-1]:
-								quant_levels[i] = prom.shape[-1] - 1
-
-					# apply token dropout error compensation
-					if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
-						steps = resps.shape[0]
-						for l in range( quant_level ):
-							for t in range( steps ):
-								token = resps[t, l].item()
-
-								if random.random() < token_dropout_error:								
-									offset = 1 * ( 1 if random.random() < 0.5  else -1 )
-									resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
-
-					# only apply stop token for RVQ level 0
-					if quant_level <= 0:
-						# append stop tokens for AR
-						if task in text_task:
-							#text_list[i] = torch.cat([ resps, text_stop_sequence ])
-							...
-						else:
-							#resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
-							...
-
-					# apply CFG (should probably only apply to NAR quant level 0)
-					if task not in text_task + ["len"]:
-						drop_text = False
-						drop_audio = False
-
-						if random.random() < cfg_prom_dropout_p:
-							drop_audio = True
-						
-						if random.random() < cfg_cond_dropout_p:
-							drop_audio = True
-							drop_text = True
-
-						if drop_text:
-							text_list[i] = text_start_stop_sequence
-
-						if drop_audio:
-							proms_list[i] = None
-
-				inputs = self.inputs(
-					text_list=text_list,
-					proms_list=proms_list,
-					resps_list=resps_list,
-					lang_list=lang_list,
-					tone_list=tone_list,
-					task_list=task_list,
-
-					quant_levels=quant_levels,
-				)
-
-				return super().forward(
-					inputs=inputs,
-					quant_levels=quant_levels,
-				)
-
-
-		if len_list is not None:
-			sampling_layer_skip_variables = {} if sampling_layer_skip else None
-			
-			if max_levels == 0:
-				max_levels = self.n_max_levels - 1
-
-			if sampling_layer_skip:
-				if sampling_layer_skip_entropy_threshold >= 0:
-					sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
-				if sampling_layer_skip_varentropy_threshold >= 0:
-					sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
-				if sampling_layer_skip_exit_layer >= 0:
-					sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
-
-			# initial condition
-			"""
-			print( len_list )
-			len_list = [ clamp(1, max_steps, l) for l in len_list ]
-			print( len_list )
-			"""
-			metrics = []
-
-			mask_token = torch.tensor([self.stop_token], dtype=torch.int16, device=device)
-			prev_list = [ torch.concat([ mask_token for _ in range( resp_len ) ]) for resp_len in len_list ]
-
-			# special "scheduling" to inference RVQ-level 0
-			level = 0
-			if cfg.lora is not None:
-				enable_lora( self, cfg.lora.active_level( level ) if use_lora is None else use_lora )
-
-			def log(x, eps = 1e-20):
-				return torch.log(x.clamp(min = eps))
-
-			def gumbel_sample(x, temperature = 1., dim = -1):
-				return ((x / max(temperature, 1e-10)) + -log(-log(torch.zeros_like(x).uniform_(0, 1)))).argmax(dim = dim)
-
-			_super = super()
-			def demask_sampling( batch_index, seq_len ):
-				# overrides
-				max_steps = 10
-				temperature = 0.3
-				cfg_strength = 1.0
-				sampling_repetition_penalty = 1.0 # force rep pen off, because this caused false positives due to how rep pen was being naively applied......
-				sampling_top_p = 0.9 # a lot of demasking samplers use a top-k of seq_len * 0.9
-
-				# if we're denoising from an existing sequence
-				if denoise_start > 0.0 and resps_list is not None:
-					start_noise = denoise_start
-					noise_p = math.cos( start_noise * math.pi * 0.5 )
-					mask = torch.tensor( [ random.random() < noise_p for _ in range( seq_len ) ], dtype=torch.bool, device=device )
-					input_ids = torch.where( mask, self.stop_token, resps_list[batch_index][:, 0] )
-				else:
-					input_ids = torch.ones((seq_len,), dtype=torch.int16, device=device) * self.stop_token
-				
-				scores = torch.zeros((seq_len,), dtype=torch.float32, device=device)
-
-				quant_levels = [ level for _ in range(batch_size) ]
-				prev_list = [ input_ids ]
-
-				start_temperature = temperature
-				start_noise = 0.0
-				end_noise = 1.0
-
-				null_text = torch.tensor([1, 2], device=device, dtype=torch.int16)
-				null_prom = None
-
-				for timestep, steps_until_x0 in zip(torch.linspace(start_noise, end_noise, max_steps), reversed(range(max_steps))):
-					# anneal temperature
-					temperature = start_temperature * (steps_until_x0 / max_steps) 
-					# get noise level, per cosine scheduling
-					noise_p = math.cos( timestep * math.pi * 0.5 )
-					# number of tokens to mask off to "noise" the input sequence
-					masked_tokens_n = max(int( noise_p * seq_len ), 1)
-					# pick the worst scoring tokens to mask off
-					masked_indices = scores.topk( masked_tokens_n, dim=-1 ).indices
-					# mask off inputs
-					input_ids = input_ids.scatter(0, masked_indices, self.stop_token)
-					# boolean mask
-					is_masked = input_ids == self.stop_token
-					# setup inputs
-
-					inputs = _super.inputs(
-						text_list=text_list,
-						proms_list=proms_list,
-						resps_list=[ input_ids ],
-						lang_list=lang_list,
-						tone_list=tone_list,
-						time_list=[ timestep ],
-						quant_levels=quant_levels,
-					)
-					output = _super.forward(
-						inputs=inputs,
-						quant_levels=quant_levels,
-						layer_skip_variables=sampling_layer_skip_variables,
-					)
-
-					logits = output.logits
-
-					if cfg_strength > 0:
-						null_inputs = _super.inputs(
-							text_list=[ null_text ],
-							proms_list=[ null_prom ],
-							resps_list=[ input_ids ],
-							lang_list=lang_list,
-							tone_list=tone_list,
-							time_list=[ timestep ],
-							quant_levels=quant_levels,
-						)
-						null_output = _super.forward(
-							inputs=null_inputs,
-							quant_levels=quant_levels,
-							layer_skip_variables=sampling_layer_skip_variables,
-						)
-						for logit, null_logits in zip(output.logits, null_output.logits):
-							logit[-seq_len:] = logit[-seq_len:] + ( logit[-seq_len:] - null_logits[-seq_len:] ) * cfg_strength
-
-					# sample with sampler settings
-					filtered_sampled = _super.sample(
-						logits=logits,
-						prev_list=prev_list,
-						quant_levels=quant_levels,
-
-						temperature=temperature,
-						min_temperature=sampling_min_temperature,
-						top_p=sampling_top_p,
-						top_k=sampling_top_k,
-						min_p=sampling_min_p,
-						repetition_penalty=sampling_repetition_penalty,
-						repetition_penalty_decay=sampling_repetition_penalty_decay,
-						length_penalty=sampling_length_penalty,
-					)
-
-					# retrieves unfiltered logits
-					unfiltered_sampled = _super.sample(
-						logits=logits,
-						prev_list=prev_list,
-						quant_levels=quant_levels,
-						temperature=0.0,
-					)
-					# update previous list of tokens
-					prev_list = [ input_ids ]
-
-					# extract logits
-					filtered_logits = filtered_sampled.logits[0]
-					unfiltered_logits = unfiltered_sampled.logits[0]
-
-					# extract scores
-					filtered_scores = filtered_sampled.scores[0]
-					unfiltered_scores = unfiltered_sampled.scores[0]
-
-					# extract sampled tokens
-					filtered_tokens = filtered_sampled[0][0]
-					unfiltered_tokens = unfiltered_sampled[0][0]
-
-					# sample with gumbelnoise
-					# I actually feel like this doesn't matter? it's hard to judge with a partially trained NAR-len model
-					sampled_ids = gumbel_sample( filtered_logits, temperature=temperature, dim=-1 )
-					#sampled_ids = filtered_tokens
-
-					# keep unmasked tokens
-					input_ids = torch.where( is_masked, sampled_ids, input_ids )
-					# update scores (conjugated to put the worst scores at the top)
-					scores = 1.0 - torch.tensor([score for score in unfiltered_scores], device=device)
-
-				if cfg.experimental:
-					print( timestep, steps_until_x0, noise_p, masked_tokens_n, input_ids, scores )
-
-				return input_ids
-
-			# perform demasked sampling (mock diffusion)
-			prev_list = [ demask_sampling( batch_index=i, seq_len=l ) for i, l in enumerate( len_list ) ]
-
-			# expand if given a raw 1D tensor
-			for i, resp in enumerate(prev_list):
-				if resp.dim() == 1:
-					prev_list[i] = resp.unsqueeze(-1)
-			
-			for n in trange( max_levels, desc="NAR", disable=disable_tqdm ):				
-				level = prev_list[0].shape[-1]
-				if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
-					break
-
-				if cfg.lora is not None:
-					enable_lora( self, cfg.lora.active_level( level ) if use_lora is None else use_lora )
-
-				quant_levels = [ level for _ in range(batch_size) ] # torch.full((len(text_list),), level)
-
-				inputs = self.inputs(
-					text_list=text_list,
-					proms_list=proms_list,
-					resps_list=prev_list,
-					lang_list=lang_list,
-					tone_list=tone_list,
-					quant_levels=quant_levels,
-				)
-
-				output = super().forward(
-					inputs=inputs,
-					quant_levels=quant_levels,
-
-					layer_skip_variables=sampling_layer_skip_variables,
-				)
-				logits, state = output.logits, output.state
-
-				sampled = super().sample(
-					logits=logits,
-					prev_list=prev_list,
-					quant_levels=quant_levels,
-
-					temperature=0.0, # sampling_temperature,
-					#min_temperature=sampling_min_temperature,
-					#top_p=sampling_top_p,
-					#top_k=sampling_top_k,
-					#min_p=sampling_min_p,
-					#repetition_penalty=sampling_repetition_penalty,
-					#repetition_penalty_decay=sampling_repetition_penalty_decay,
-					#length_penalty=sampling_length_penalty,
-					#beam_width=sampling_beam_width,
-					#mirostat=mirostat,
-				)
-
-				resps_list = sampled[0]
-				prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device=device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
-
-			return prev_list
-		
-		# is AR
-		if cfg.lora is not None:
-			enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
-
-		sequence_list = [ torch.tensor([0], device=device,dtype=torch.int16) for _ in range(batch_size) ]
-		stopped = torch.zeros(batch_size, device=device).bool()
-		
-		stop_token = 10
-		task_list = [ "len" for _ in range(batch_size) ]
-
-		for n in trange(10, desc="AR", disable=disable_tqdm):
-			len_list = sequence_list
-
-			inputs = self.inputs(
-				text_list=text_list,
-				proms_list=proms_list,
-				resps_list=resps_list,
-				lang_list=lang_list,
-				tone_list=tone_list,
-				len_list=len_list,
-				task_list=task_list,
-				quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
-			)
-
-			output = super().forward(
-				inputs=inputs,
-			)
-			logits = output.logits
-
-			r = [ logit[-1:].argmax(dim=1) for logit in logits ]
-			# sanitize
-			for i, token in enumerate(r):
-				if token > 10:
-					r[i][0] = stop_token
-
-			# append tokens
-			for i, ri in enumerate(r):
-				if stop_token in ri:
-					stopped[i] = True
-				sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
-
-			# stop token found
-			stopped |= r == stop_token
-			if stopped.all().item():
-				break
-
-		# convert tokens into int
-		return [ int("".join([ str(token.item()) for token in r if token != stop_token ])) for r in sequence_list ]
-
-
-def example_usage():
-	cfg.trainer.backend = "local"
-	cfg.hyperparameters.gradient_accumulation_steps = 1
-	if cfg.audio_backend == "dac":
-		cfg.sample_rate = 44_100
-
-	from functools import partial
-	from einops import repeat
-	from tqdm import tqdm
-
-	from ..emb.qnt import decode_to_file, unload_model
-	from ..engines import Engine
-	from ..utils import wrapper as ml
-	
-	import numpy as np
-	import re
-
-	device = "cuda"
-
-	def load_artifact( path ):
-		artifact = np.load(path, allow_pickle=True)[()]
-
-		text = torch.tensor( cfg.tokenizer.encode( artifact["metadata"]["phonemes"] ) ).to(dtype=torch.uint8, device=device)
-		audio = torch.from_numpy(artifact["codes"].astype(np.int16))[0, :, :].t().to(dtype=torch.int16, device=device)
-
-		return text, audio
-
-	text, audio = load_artifact(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
-
-	text_list = [ text ]
-	proms_list = [ audio[:cfg.dataset.frames_per_second, :] ]
-	resps_list = [ audio ]
-
-	# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
-	kwargs = {
-		'n_text_tokens': 256,
-		'n_audio_tokens': 1024,
-
-		'd_model': 1024, # 256, # 1024, # 1536
-		'n_heads': 16, # 4, # 16, # 24
-		'n_layers': 12, # 32
-		'n_experts': 1,
-
-		'p_dropout': 0.1,
-
-		'l_padding': 8 if cfg.optimizations.fp8 else 0,
-
-		'config': cfg.model
-	}
-	
-	"""
-	try:
-		kwargs['config'] = cfg.model
-	except Exception as e:
-		pass
-	"""
-
-	model = NAR(**kwargs).to(device)
-	steps = 250 
-
-	optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
-	scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
-	learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None
-
-	if cfg.optimizations.dadaptation:
-		# do not combine the two
-		if scheduler == "schedulefree":
-			scheduler = ""
-
-		learning_rate = 1.0
-	
-	if optimizer == "prodigy":
-		if learning_rate is None:
-			learning_rate = 1.0
-
-		optimizer = ml.Prodigy
-	elif optimizer == "adagrad":
-		if learning_rate is None:
-			learning_rate = 1.0e-2
-
-		optimizer = ml.Adagrad
-	elif optimizer == "adamw":
-		if learning_rate is None:
-			learning_rate = 1.0e-4
-
-		optimizer = ml.AdamW
-	elif optimizer == "sdg":
-		if learning_rate is None:
-			learning_rate = 1.0e-4
-
-		optimizer = ml.SGD
-	else:
-		raise ValueError(f"Unrecognized optimizer: {optimizer}")
-
-	_logger.info(f"Optimizer: {optimizer}\tLearning rate: {learning_rate}")
-
-	optimizer = optimizer(model.parameters(), lr=learning_rate)
-
-	if scheduler == "schedulefree":
-		if isinstance(optimizer, ml.AdamW):
-			scheduler = ml.schedulefree.AdamWScheduleFree
-		elif isinstance(optimizer, ml.SGD):
-			scheduler = ml.schedulefree.SGDScheduleFree
-		else:
-			scheduler = None
-
-		if scheduler is not None:
-			_logger.info(f"Scheduler: {scheduler}")
-			optimizer = scheduler( model.parameters(), lr = learning_rate )
-
-	if cfg.optimizations.replace and cfg.optimizations.linear:
-		model = ml.replace_linear( model )
-		
-	if cfg.optimizations.replace and cfg.optimizations.embedding:
-		model = ml.replace_embedding( model )
-	
-	engine = Engine(model=model, optimizer=optimizer)
-
-	"""
-	torch.save( {
-		'module': model.state_dict()
-	}, f"./data/{cfg.model.arch_type}.pth" )
-	"""
-
-	_logger.info(f"NAR parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
-
-	@torch.inference_mode()
-	def sample( name, steps=1000 ):
-		if cfg.audio_backend == "dac" and name == "init":
-			return
-
-		engine.eval()
-
-		len_list = engine(text_list, proms_list, max_steps=steps, sampling_temperature=0.95 )
-		resps_list = engine( text_list, proms_list, len_list=len_list, sampling_temperature=0.2 )
-
-		len_list = [ min(l, 500) for l in len_list ]
-
-		for i, o in enumerate(resps_list):
-			_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{name}.wav", device=device)
-
-		unload_model()
-
-	def train():
-		engine.train()
-		t = trange(steps)
-		for i in t:
-			stats = {"step": i}
-			stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
-			stats |= {"grad_norm": engine.get_global_grad_norm()}
-
-			tqdm.write(f"{stats}")
-
-		"""
-		torch.save( {
-			'module': model.state_dict()
-		}, f"./data/{cfg.model.arch_type}.pth" )
-		"""
-
-	#sample("init", 5)
-	train()
-	sample("final")
-
-if __name__ == "__main__":
-	example_usage()

vall_e/samplers.py

@@ -47,8 +47,8 @@ def reptition_penalize( logits, previous=None, factor=1.0, decay=0.0, one_time=F
 			start = i + 1
 			# apply either up to limit tokens, or to the end
 			end = start + limit if limit > 0 else seq_len
-			start = clamp(0, seq_len - 1, start)
+			start = clamp(start, 0, seq_len - 1)
-			end   = clamp(0, seq_len - 1, end)
+			end   = clamp(end, 0, seq_len - 1)
 			for j in range( start, end ):
 				distance = j - i
 				logits[j, token] /= factor * (distance ** decay)