unified nar.py into ar_nar.py
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@ -233,34 +233,13 @@ This script aims to implement everything as required per VALL-E agnostically, to
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## `models/ar_nar.py`
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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.
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By default, this is the default model, but is used through `cfg.model.capabilities = ["ar", "nar"]`.
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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.
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* Since one model can be trained AR-ly and NAR-ly, RVQ-level 0 can also be trained non-autoregressively with diffusion-like masking.
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For training, this model handles preparing the batch provided through the dataloader according to a randomly sampled targetted RVQ-level.
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For inferencing, this will dynamically inference depending on the arguments provided.
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## `models/ar.py`
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This script implements VALL-E as a pure autoregressive (AR) model.
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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.
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This model serves as an experiment that failed, and might be revisited in the future.
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Use of this is governed through `cfg.model.capabilities = ["ar"]`
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## `models/nar.py`
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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+.
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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).
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This model serves as an experiment that failed, and might be revisited in the future.
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Use of this is governed through `cfg.model.capabilities = ["nar"]`
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## `models/experimental.py`
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This script implements VALL-E as a mostly-HuggingFace compatible model, where it handles processing tokens as a uniform sequence of IDs.
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@ -255,13 +255,13 @@ class ModelExperimentalSettings:
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# it just seems like a bitch to try and train something worthwhile with it, since there's crackles every other token
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# RetNet's chunked inferencing might be a better place for this
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len_train_p: float = 0.05 # odds of injecting a "len" task within the model for NAR-len
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# to-to: just incorporate this as a task instead
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masking_train_p: float = 0.0 # odds of training with masking
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masking_train_rvq_levels: list = field(default_factory=lambda: [0,0]) # determines which levels to do mask training on
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# classifier-free guidance shit
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cfg_cond_dropout_p: float = 0.2 # probability to drop out text and audio during training
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cfg_text_dropout_p: float = 0.0 # probability to drop out input audio prompt during training
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cfg_prom_dropout_p: float = 0.3 # probability to drop out input audio prompt during training
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cfg_cond_dropout_p: float = 0.0 # 0.2 # probability to drop out text and audio during training
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cfg_text_dropout_p: float = 0.0 # 0.0 # probability to drop out input audio prompt during training
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cfg_prom_dropout_p: float = 0.0 # 0.3 # probability to drop out input audio prompt during training
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layerskip: bool = False # layerskip compatible model (or training for)
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#layerskip_rvq_levels: list = field(default_factory=lambda: []) # RVQ levels to train / inference layerskip for (to-do: implement, see if it matters)
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@ -757,6 +757,7 @@ class Config(BaseConfig):
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device: str = "cuda" # target device
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mode: str = "training" # "inferencing"
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experimental: bool = False # debug flag
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silent_errors: bool = False # if False, raise exceptions on errors that could silently lead to problems, if True ignore them
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dataset: Dataset = field(default_factory=lambda: Dataset)
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models: dict | list | None = field(default_factory=lambda: [])
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@ -879,7 +880,12 @@ class Config(BaseConfig):
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if data_parent.exists():
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return [ path.parent / child.name for child in Path(data_parent).glob(path.name) ]
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return path
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# return an empty list
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if self.silent_errors:
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return []
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# raise an error to avoid headaches
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raise Exception(f'Cannot unglob requested path: {path}')
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def format( self, training=True ):
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@ -957,10 +963,6 @@ class Config(BaseConfig):
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model["experimental"]["rvq_levels_p"] = model["experimental"]["p_rvq_levels"]
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del model["experimental"]["p_rvq_levels"]
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if "p_len_train" in model["experimental"]:
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model["experimental"]["len_train_p"] = model["experimental"]["p_len_train"]
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del model["experimental"]["p_len_train"]
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self.models = [ Model(**model) if isinstance(model, dict) else model for model in self.models ]
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self.loras = [ LoRA(**lora) if isinstance(lora, dict) else lora for lora in self.loras ]
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@ -999,22 +1001,17 @@ class Config(BaseConfig):
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if self.tokenizer == "naive":
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self.tokenizer = NaiveTokenizer()
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else:
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# ick...
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try:
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from transformers import PreTrainedTokenizerFast
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from transformers import PreTrainedTokenizerFast
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tokenizer_path = self.rel_path / self.tokenizer_path
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if tokenizer_path and not tokenizer_path.exists():
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tokenizer_path = Path("./data/") / self.tokenizer_path
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if tokenizer_path and tokenizer_path.exists():
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self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(tokenizer_path))
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else:
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self.tokenizer = NaiveTokenizer()
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except Exception as e:
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self.tokenizer = NaiveTokenizer()
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_logger.warning(f"Error while parsing tokenizer: {str(e)}")
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pass
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tokenizer_path = self.rel_path / self.tokenizer_path
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# deduce path if a local copy is not provided
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if not tokenizer_path.exists():
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tokenizer_path = Path("./data/") / self.tokenizer_path
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if not self.silent_errors and not tokenizer_path.exists():
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raise Exception(f'Tokenizer path not found: {tokenizer_path}')
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self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=str(tokenizer_path))
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# Preserves the old behavior
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@ -1071,8 +1068,9 @@ cfg = Config.from_cli()
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try:
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cfg.format()
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except Exception as e:
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if not cfg.silent_errors:
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raise e # throw an error because I'm tired of silent errors messing things up for me
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_logger.error(f"Error while parsing config YAML: {str(e)}")
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raise e # throw an error because I'm tired of silent errors messing things up for me
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if __name__ == "__main__":
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print(cfg)
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@ -1199,6 +1199,10 @@ class Dataset(_Dataset):
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task
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]
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# Duration prediction (<text><prompt> => len(<resp>))
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elif task == "len":
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proms = self.sample_prompts(spkr_name, reference=path)
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# noise suppression (<text>? <resp+noise> => <resp>)
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# speech removal (<text>?<resp+noise> => <noise>)
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elif task == "ns" or task == "sr":
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@ -193,7 +193,7 @@ def load_engines(training=True, **model_kwargs):
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("text_emb.weight", model.config.text_tokens ),
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("tasks_emb.weight", model.config.tasks ),
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("langs_emb.weight", model.config.langs ),
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("rvq_l_emb.weight", model.config.resp_levels + (1 if "len" in model.config.capabilities else 0) ),
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("rvq_l_emb.weight", model.config.resp_levels ),
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("resps_emb.embeddings.0.weight", model.config.audio_tokens + uses_stop_token ),
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("model.embed_tokens.weight", model.config.audio_tokens + uses_stop_token ),
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("classifiers.proj.0.weight" if model.config.experimental.split_classifiers else 'classifier.weight', model.config.audio_tokens + uses_stop_token ),
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@ -49,11 +49,8 @@ class TTS():
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else:
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raise Exception(f"Unknown config passed: {config}")
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try:
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cfg.format( training=False )
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cfg.dataset.use_hdf5 = False # could use cfg.load_hdf5(), but why would it ever need to be loaded for inferencing
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except Exception as e:
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raise e # throw an error because I'm tired of silent errors messing things up for me
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cfg.format( training=False )
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cfg.dataset.use_hdf5 = False # could use cfg.load_hdf5(), but why would it ever need to be loaded for inferencing
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if amp is None:
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amp = cfg.inference.amp
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@ -268,7 +265,7 @@ class TTS():
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with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
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if model_ar is not None:
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text_list = model_ar(
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text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], max_steps=max_ar_steps,
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text_list=None, proms_list=[resp], lang_list=[lang], resps_list=[resp], max_steps=max_ar_steps, task_list=["stt"],
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sampling_temperature=ar_temp,
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sampling_min_temperature=min_ar_temp,
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sampling_top_p=top_p, sampling_top_k=top_k, sampling_min_p=min_p,
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@ -318,7 +315,7 @@ class TTS():
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with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
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if model_ar is not None:
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resps_list = model_ar(
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text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps,
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text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps, task_list=["tts"],
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input_prompt_prefix=input_prompt_prefix,
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prefix_silence=prefix_silence,
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sampling_temperature=ar_temp,
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@ -343,7 +340,7 @@ class TTS():
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use_lora=use_lora,
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)
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resps_list = model_nar(
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text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list,
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text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list, task_list=["tts"],
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input_prompt_prefix=input_prompt_prefix,
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max_levels=max_nar_levels,
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sampling_temperature=nar_temp,
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@ -359,8 +356,8 @@ class TTS():
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use_lora=use_lora,
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)
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elif model_len is not None:
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len_list = model_len( text_list=[phns], proms_list=[prom], max_steps=5, disable_tqdm=not tqdm ) # don't need more than that
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len_list = [ clamp(1, max_ar_steps, l) for l in len_list ]
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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
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len_list = [ clamp(l, 1, max_ar_steps) for l in len_list ]
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kwargs = {}
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@ -375,7 +372,7 @@ class TTS():
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kwargs["resps_list"] = [ resp[:, :1] ]
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resps_list = model_nar( text_list=[phns], proms_list=[prom], len_list=len_list,
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resps_list = model_nar( text_list=[phns], proms_list=[prom], len_list=len_list, task_list=["tts"],
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max_steps=max_ar_steps,
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max_levels=max_nar_levels,
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sampling_temperature=nar_temp,
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@ -60,25 +60,7 @@ def download_model( save_path=DEFAULT_MODEL_PATH, chunkSize = 1024 ):
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def get_model(config, training=True, **model_kwargs):
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name = config.name
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if "len" in config.capabilities:
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from .nar import NAR
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model = NAR(
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n_text_tokens=config.text_tokens,
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n_audio_tokens=config.audio_tokens,
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d_model=config.dim,
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n_heads=config.heads,
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n_layers=config.layers,
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n_experts=config.experts,
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p_dropout=config.dropout,
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l_padding = config.input_alignment,
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training = training,
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config = config,
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**model_kwargs
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)
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elif config.experimental.hf:
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if config.experimental.hf:
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from .experimental import Model as Experimental
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model = Experimental(
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n_text_tokens=config.text_tokens,
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@ -1,638 +0,0 @@
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"""
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# an AR model that (should) handle:
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* handling all RVQ levels, but does it in an autoregressive manner
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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.
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"""
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from .base import Base, list_to_tensor, Categorical
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from ..config import cfg
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import torch
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from torch.nn.utils.rnn import pad_sequence
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import random
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import math
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from einops import rearrange
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from torch import Tensor
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from tqdm import trange
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import logging
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_logger = logging.getLogger(__name__)
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from ..utils import clamp
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from ..emb.qnt import trim, encode_as_embedding
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from .lora import enable_lora
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class AR(Base):
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def forward(
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self,
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text_list: list[Tensor],
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proms_list: list[Tensor],
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resps_list: list[Tensor] | None = None,
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task_list: list[Tensor] | None = None,
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lang_list: list[Tensor] | None = None,
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tone_list: list[Tensor] | None = None,
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len_list: list[Tensor] | None = None,
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training: bool | int | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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input_prompt_prefix: bool = False,
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prefix_silence: float = 1.0,
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sampling_temperature: float = 1.0,
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sampling_min_temperature: float = -1.0,
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sampling_top_k: int = -100,
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sampling_top_p: float = 1.0,
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sampling_min_p: float = 0.0,
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sampling_repetition_penalty: float = 1.0,
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sampling_repetition_penalty_decay: float = 0.0,
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sampling_length_penalty: float = 0.0,
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sampling_beam_width: int = 0,
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sampling_mirostat_tau: float = 0.0,
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sampling_mirostat_eta: float = 0.1,
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sampling_dry_multiplier=0.0,
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sampling_dry_base=1.75,
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sampling_dry_allowed_length=2,
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sampling_entropix=False,
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sampling_layer_skip: bool = False,
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sampling_layer_skip_exit_layer: int = -1,
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sampling_layer_skip_entropy_threshold: float = -1,
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sampling_layer_skip_varentropy_threshold: float = -1,
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sampling_refine_on_stop: bool = False,
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disable_tqdm=False,
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use_lora=None,
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):
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text_task = [ "stt" ]
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if text_list is not None:
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default_task = "tts"
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device = text_list[0].device
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batch_size = len(text_list)
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else:
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default_task = "stt"
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device = resps_list[0].device
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batch_size = len(resps_list)
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# generate task list if not provided
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if task_list is None:
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task_list = [ default_task for _ in range(batch_size) ]
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has_none = resps_list is None or text_list is None
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if not has_none:
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for i, task in enumerate( task_list ):
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if resps_list[i] is None or text_list[i] is None:
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has_none = True
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break
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# is training or NAR
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if not has_none:
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n_levels_set = {r.shape[-1] for r in resps_list}
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n_levels = next(iter(n_levels_set))
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# implicit
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if training is None:
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training = 0 if n_levels == self.n_resp_levels else None
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# is training
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if training is not None:
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# specifies how to sample probabilities of which RVQ levels to train against
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rvq_levels_p = self.config.experimental.rvq_levels_p if self.config is not None else "equal"
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# determines which RVQ level to target per batch
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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 ]
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# rate to perform token dropout errors
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token_dropout_error = self.config.experimental.token_dropout_error
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# RVQ levels to apply token dropout on
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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# implicitly set it to all levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels - 1]
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# allow passing a specific distribution of RVQ levels
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rvq_levels_p = rvq_levels_p if isinstance(rvq_levels_p, list) else []
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if not rvq_levels_p:
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lo, hi = quant_level_range[0], quant_level_range[1] + 1
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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if rvq_levels_p == "equal":
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rvq_levels_p = [ i for i in range( lo, hi ) ]
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else:
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# yuck
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rvq_levels_p = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
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# input RVQ levels
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quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
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for i, task in enumerate( task_list ):
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if task in text_task:
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quant_levels[i] = 0 # self.n_resp_levels - 1
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# trim resps to only contain all levels below the target level
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resps_list = [r if t in text_task else r[..., :l+1] for r, l, t in zip(resps_list, quant_levels, task_list)]
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# tensor to cat for RVQ level 0
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text_stop_sequence = torch.tensor([[2] * 1], device=device, dtype=torch.int16)
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audio_stop_sequence = torch.tensor([[self.stop_token] * 1], device=device, dtype=torch.int16)
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# I hate python's value/reference semantics so much
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for i, quant_level, resps, proms, task in zip(range(batch_size), quant_levels, resps_list, proms_list, task_list):
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# cap quant_level if it exceeds its corresponding resp/prom
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if quant_level >= resps.shape[-1]:
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quant_levels[i] = resps.shape[-1] - 1
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||||
|
||||
# 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()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -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.time_emb = TimeEmbedding(d_model) if "len" in self.capabilities else None
|
||||
self.len_emb = Embedding(11, d_model)
|
||||
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)
|
||||
quant_levels[i] = 0
|
||||
inputs[i].append( ( "quant_level", torch.tensor([ self.n_resp_levels ], device=device, dtype=torch.int16) ) )
|
||||
inputs[i].append( ( "quant_level", torch.tensor([ quant_level ], 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 = 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)
|
||||
causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities) or (task_name in ["len", "stt"])
|
||||
|
||||
if causal:
|
||||
l = self.causal_size
|
||||
|
|
@ -1487,14 +1456,8 @@ class Base(nn.Module):
|
|||
|
||||
logit = logits[i][it:it+seq_len]
|
||||
it += seq_len + 1 # +1 to incorporate the separator
|
||||
|
||||
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)
|
||||
|
||||
causal = (quant_level == 0 and "ar" in self.capabilities) or ("nar" not in self.capabilities) or (task_name in ["len", "stt"])
|
||||
|
||||
# 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 = [
|
||||
[ F.softmax(logit[i, :], dim=-1)[token].item() for i, token in enumerate(tokens) ]
|
||||
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)
|
||||
]
|
||||
scores = [
|
||||
[ F.softmax(logit[i, :], dim=-1)[token].item() for i, token in enumerate(tokens) ]
|
||||
for logit, tokens in zip(logits, res)
|
||||
]
|
||||
|
||||
return Sampled(res, logits, scores, entropy)
|
||||
|
|
@ -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()
|
||||
|
|
@ -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)
|
||||
end = clamp(0, seq_len - 1, end)
|
||||
start = clamp(start, 0, seq_len - 1)
|
||||
end = clamp(end, 0, seq_len - 1)
|
||||
for j in range( start, end ):
|
||||
distance = j - i
|
||||
logits[j, token] /= factor * (distance ** decay)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user