I guess I'll fall for the NAR-len meme again (I don't know where my previous weights are, so I need to train it again to test something)
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@ -36,6 +36,7 @@ Non-autoregressive trainng is performed by having the input tokens from the prev
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However, having a pure NAR is challenging, as you need to both explicitly provide the duration and provide a "good enough" starting sequence of tokens for the initial sequence.
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* The former problem is easily "solved" by training a `len` inferencing task, where the given input predicts the requested duration for a given utterance autoregressively.
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* The latter however proves to be a bit of a challenge, as this could be anything from random noise to a unique token.
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* The current implementation repeats the input prompt's RVQ level 0 as the initial condition, but inferencing fills with stop tokens. This *might* be the problem, but I do not have my `nar-len-llama-8` weights stored anywhere, sadly.
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* Testing showed that it's easy to predict the duration, but decoding the first RVQ level accurately proves to be a chore.
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* Initially, output seemed chaotic and unreliable, but further experiments showed the model will "work" for a brief moment before going silent.
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@ -48,6 +49,8 @@ One problem exhibited from a NAR is producing arfifacts ("crust") in the final w
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The "magic" of subjugating a transformer for audio use lies within the ensemble of the embeddings. This is necessary as each piece of a sequence is fundamentally different, but a HF-compatible model can geta way with treating each sequence as separate ranges within a total token sequence.
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While embeddings *can* be tied to the output head, testing showed that the model ***really*** does not like to do this, although my implementation could very well be flawed.
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### Text Embeddings
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The input text phonemes (or output for STT) are passed through an embedding head (`text`), similar to how a normal text LLM would. Nothing fancy is required, as it's very straightforward.
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@ -61,29 +61,50 @@ class AR(Base):
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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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device = text_list[0].device
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batch_size = len(text_list)
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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 = [ "tts" for _ in range(batch_size) ]
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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 resps_list is not None:
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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 = n_levels == self.n_resp_levels
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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:
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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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@ -107,16 +128,19 @@ class AR(Base):
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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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if not self.interleave:
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quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
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# trim resps to only contain all levels below the target level
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resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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else:
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quant_levels = [ 0 for i in range(batch_size) ]
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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 in zip(range(batch_size), quant_levels, resps_list, proms_list):
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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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@ -130,9 +154,8 @@ class AR(Base):
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for j, prom in enumerate( proms ):
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if not isinstance( prom, torch.Tensor ):
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continue
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if quant_level >= prom.shape[-1]:
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quant_levels[i] = prom.shape[-1] - 1
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if quant_level >= prom.shape[-1]:
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quant_levels[i] = prom.shape[-1] - 1
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# apply token dropout error compensation
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if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
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@ -146,9 +169,13 @@ class AR(Base):
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resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
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# only apply stop token for RVQ level 0
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stop_sequence = torch.tensor([[self.stop_token] * resps.shape[-1]], device=device, dtype=torch.int16)
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resps_list[i] = torch.cat([ resps, stop_sequence ])
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if quant_level <= 0:
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# append stop tokens for AR
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if task in text_task:
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#text_list[i] = torch.cat([ resps, text_stop_sequence ])
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...
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else:
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resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
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inputs = self.inputs(
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text_list=text_list,
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@ -157,21 +184,26 @@ class AR(Base):
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lang_list=lang_list,
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tone_list=tone_list,
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task_list=task_list,
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quant_levels=quant_levels,
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)
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return super().forward(
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inputs=inputs,
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quant_levels=quant_levels, # could technically just grab this from the above inputs since they're included as an RVQ level token
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)
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# is AR
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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
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# STT
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start_slice = [ 0 for _ in range(batch_size) ]
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sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
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stopped = torch.zeros(batch_size, device=device).bool()
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stop_token = self.stop_token
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audio_stop_token = self.stop_token
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text_stop_token = 2
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state = None
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mirostat = [
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@ -179,10 +211,59 @@ class AR(Base):
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] * batch_size if sampling_mirostat_tau > 0.0 else None
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scores = [ 1.0 ] * sampling_beam_width
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metrics = []
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# ick
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"""
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low_temperature = False # sampling_temperature < 0.6 # sampling_repetition_penalty == 1.0 and sampling_temperature == 0.0 #
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low_temperature_range = cfg.dataset.frames_per_second * 5
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original_sampling_temperature = sampling_temperature
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original_sampling_repetition_penalty = sampling_repetition_penalty
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original_sampling_repetition_penalty_decay = sampling_repetition_penalty_decay
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"""
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sampling_layer_skip_variables = {} if sampling_layer_skip else None
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if sampling_layer_skip:
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if sampling_layer_skip_entropy_threshold >= 0:
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sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
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if sampling_layer_skip_varentropy_threshold >= 0:
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sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
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if sampling_layer_skip_exit_layer >= 0:
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sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
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for i, sequence in enumerate( sequence_list ):
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# add <bos> to text for STT
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if task_list[i] in text_task:
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start_slice[i] = 1
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sequence_list[i] = torch.cat([sequence_list[i], torch.tensor([1], dtype=torch.int16, device=device)])
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# treat input prompt as initial resp (by prefixing with the prompt instead)
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elif input_prompt_prefix:
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start_slice[i] = proms_list[i].shape[0]
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sequence_list[i], proms_list[i] = proms_list[i][:, 0], sequence_list[i]
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elif prefix_silence > 0:
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sequence_list[i] = get_silence(prefix_silence, device=sequence_list[i].device)
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sequence_list[i] = sequence_list[i][:, 0]
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# start_slice[i] = sequence_list[i].shape[0]
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# get next in sequence
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for n in trange(max_steps // max(1, self.causal_size), desc="AR", disable=disable_tqdm):
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resps_list = [x.unsqueeze(dim=-1) for x in sequence_list]
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# 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
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text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
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resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
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# 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
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# 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
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# 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
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# to-do: tune these values, maybe have it factor based on confidence scores or something
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"""
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if low_temperature:
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enabled = n < low_temperature_range
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sampling_repetition_penalty = 1.125 if enabled else 1.25
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#sampling_repetition_penalty_decay = 0.0 if enabled else original_sampling_repetition_penalty_decay
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#sampling_temperature = original_sampling_temperature if enabled else 1.0
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"""
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inputs = self.inputs(
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text_list=text_list,
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@ -195,15 +276,20 @@ class AR(Base):
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quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
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)
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# to-do: find an elegant way to write this
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output = super().forward(
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inputs=inputs,
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state=state,
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layer_skip_variables=sampling_layer_skip_variables,
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output_attentions=sampling_entropix,
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)
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logits, state = output.logits, output.state
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sampled = super().sample(
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logits=logits,
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prev_list=resps_list,
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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 ) ],
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temperature=sampling_temperature,
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min_temperature=sampling_min_temperature,
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@ -220,47 +306,81 @@ class AR(Base):
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dry_multiplier=sampling_dry_multiplier,
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dry_base=sampling_dry_base,
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dry_allowed_length=sampling_dry_allowed_length,
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attentions=output.attentions if sampling_entropix else None,
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)
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r = sampled[0]
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if cfg.experimental:
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if sampled.entropy:
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metrics.append( sampled.entropy )
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elif sampled.scores:
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metrics.append( [ { "p": p[0], "exited_layer": output.exited_layer } for p in sampled.scores ] )
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if mirostat is not None:
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mirostat = sampled.scores
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elif sampling_beam_width > 0:
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# expand tuple
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scores = sampled.scores
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s = sampled.scores
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# first step, expand batch
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if batch_size == 1:
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batch_size = sampling_beam_width
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text_list = text_list * sampling_beam_width
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proms_list = proms_list * sampling_beam_width
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sequence_list = sequence_list * sampling_beam_width
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task_list = task_list * sampling_beam_width
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start_slice = start_slice * sampling_beam_width
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stopped = torch.zeros(batch_size, device=device).bool()
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scores = [ scores[i] + score for i, score in enumerate(scores) ]
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scores = [ scores[i] + score for i, score in enumerate(s) ]
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# append tokens
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for i, ri in enumerate(r):
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task = task_list[i]
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stop_token = audio_stop_token if task not in text_task else text_stop_token
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if stop_token in ri:
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stopped[i] = True
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sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
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# stop token found
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stopped |= r == stop_token
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# stopped |= r == stop_token
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if stopped.all().item():
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break
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# to-do for layerskip / speculative sampling: rerun the last sequence again at max depth
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if metrics:
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from ..plot import plot_sample_metrics
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filename = "metrics"
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if sampling_entropix:
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filename += f'[entropix]'
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if sampling_layer_skip_exit_layer >= 0:
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filename += f'[{sampling_layer_skip_exit_layer+1}]'
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plot_sample_metrics( metrics, filename=f'{filename}.png' )
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# pick the best scoring candidate
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# desu this is always going to be candidate 0
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if sampling_beam_width:
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sequence_list = [ sequence_list[0] ]
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sequence_list = sequence_list[:1]
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task_list = task_list[:1]
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sequence_list = [self._prune(r, stop_token) for r in sequence_list]
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# remove stop token
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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)]
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# remove <bos>
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sequence_list = [ sequence_list[i][start_slice[i]:] for i, task in enumerate( task_list ) ]
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for i, seq in enumerate( sequence_list ):
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steps = seq.shape[0] // self.n_resp_levels
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nearest_steps = steps * self.n_resp_levels
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sequence_list[i] = seq[:nearest_steps].view(( steps, self.n_resp_levels ))
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if sampling_refine_on_stop:
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# get how much we need to slice from the end
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slice_lengths = [ sequence.shape[-1] for sequence in sequence_list ]
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# -1 for the stop token
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logits = [ logit[-length-1:-1] for logit, length in zip(logits, slice_lengths) ]
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# greedy sample from the sequence
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refined_list = [ logit.argmax(dim=-1) for logit in logits ]
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# to-do: compare scores
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# set the "refined" list as the output
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sequence_list = refined_list
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return sequence_list
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@ -1126,25 +1126,27 @@ class Base(nn.Module):
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embedding = _interleave_sequence_reshape( embeddings )
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elif "len" in self.capabilities and quant_level == 0:
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if input_prom is not None:
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# fill with the prom as the initial condition
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repeat = (input.shape[0] // input_prom.shape[0]) + 1
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repeated = input_prom[:, :1].repeat((repeat, 1))[:input.shape[0], :1]
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assert input_prom is not None, "Guru mediating during training"
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# fill with the prom as the initial condition
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repeat = (input.shape[0] // input_prom.shape[0]) + 1
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repeated = input_prom[:, :1].repeat((repeat, 1))[:input.shape[0], :1]
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embedding = self.resps_emb(
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repeated,
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offset = 0,
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quant_level = 0,
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)
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"""
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# fill with "stop" token from the len layer for the NAR-only model
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filler_token = 12
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embedding = self.resps_emb(
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# self.dropout_token.repeat((input.shape[0], 1)),
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torch.full_like(input if input.dim() == 1 else input[..., 0], filler_token),
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offset = 0,
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quant_level = 0,
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)
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"""
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embedding = self.resps_emb(
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repeated,
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offset = 0,
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quant_level = 0,
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)
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else:
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# fill with "stop" token from the len layer for the NAR-only model
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filler_token = 12
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embedding = self.resps_emb(
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# self.dropout_token.repeat((input.shape[0], 1)),
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torch.full_like(input if input.dim() == 1 else input[..., 0], filler_token),
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offset = 0,
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quant_level = 0,
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)
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# cheat-y way to handle performing STT across all levels
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elif task_type in summed_embeddings_task:
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# we do a manual sum because I trained it to use the AR embeddings + NAR embeddings for STT......
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@ -21,6 +21,9 @@ from tqdm import trange
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from ..emb.qnt import trim
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import logging
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def clamp(n, lo, hi):
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return max(lo, min(n, hi))
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_logger = logging.getLogger(__name__)
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class NAR(Base):
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@ -58,119 +61,172 @@ class NAR(Base):
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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,
|
||||
|
||||
disable_tqdm=False,
|
||||
use_lora=None,
|
||||
):
|
||||
device = text_list[0].device
|
||||
batch_size = len(text_list)
|
||||
text_task = [ "stt" ]
|
||||
|
||||
# is training
|
||||
if resps_list is not None:
|
||||
len_train_p = self.config.experimental.len_train_p if self.config is not None else 0.05
|
||||
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))
|
||||
|
||||
# assert n_levels == self.n_resp_levels
|
||||
# implicit
|
||||
if training is None:
|
||||
training = 0 if n_levels == self.n_resp_levels else None
|
||||
|
||||
# to-do: make this YAML configurable
|
||||
def sample_task():
|
||||
return "len" if random.random() < len_train_p else "tts"
|
||||
# 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
|
||||
|
||||
# generate task list to train against
|
||||
task_list = [ sample_task() for _ in range(batch_size) ]
|
||||
n_levels_set = {r.shape[-1] for r in resps_list}
|
||||
n_levels = next(iter(n_levels_set))
|
||||
|
||||
# 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 ) ], [])
|
||||
# assert n_levels == self.n_resp_levels
|
||||
|
||||
# input RVQ levels
|
||||
quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
|
||||
# trim resps to only contain all levels below the target level
|
||||
resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
|
||||
# to-do: make this YAML configurable
|
||||
def sample_task():
|
||||
return "len" if random.random() < len_train_p else "tts"
|
||||
|
||||
# I hate python's value/reference semantics so much
|
||||
for i, quant_level, resps, proms in zip(range(batch_size), quant_levels, resps_list, proms_list):
|
||||
# cap quant_level if it exceeds its corresponding resp/prom
|
||||
if quant_level >= resps.shape[-1]:
|
||||
quant_levels[i] = resps.shape[-1] - 1
|
||||
# generate task list to train against
|
||||
task_list = [ sample_task() for _ in range(batch_size) ]
|
||||
|
||||
# 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
|
||||
# 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 ) ], [])
|
||||
|
||||
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
|
||||
# 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)]
|
||||
|
||||
# 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()
|
||||
# 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
|
||||
|
||||
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
|
||||
# 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
|
||||
|
||||
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,
|
||||
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
|
||||
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
# 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,
|
||||
)
|
||||
|
||||
return super().forward(
|
||||
inputs=inputs,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
|
||||
# NAR
|
||||
if len_list is not None:
|
||||
# is NAR
|
||||
if max_levels == 0:
|
||||
max_levels = self.n_resp_levels
|
||||
|
||||
# fill with mock tokens
|
||||
# to-do: repeat with the input prompt, as per training
|
||||
prev_list = [ torch.tensor([ self.stop_token for _ in range(resp_len) ], device=device, dtype=torch.int16) for resp_len in len_list ]
|
||||
|
||||
start = True
|
||||
# to-do: figure out why this fails when I copy some things from ar_nar
|
||||
for n in trange( max_levels, desc="NAR", disable=disable_tqdm ):
|
||||
level = 0 if n == 0 else 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(
|
||||
|
|
@ -185,19 +241,17 @@ class NAR(Base):
|
|||
output = super().forward(
|
||||
inputs=inputs,
|
||||
quant_levels=quant_levels,
|
||||
)
|
||||
logits = output.logits
|
||||
|
||||
"""
|
||||
resps_list = [ logit[-l:].argmax(dim=1) for logit, l in zip(logits, len_list) ]
|
||||
"""
|
||||
# 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=1.0 if n == 0 else sampling_temperature,
|
||||
temperature=sampling_temperature,
|
||||
min_temperature=sampling_min_temperature,
|
||||
top_p=sampling_top_p,
|
||||
top_k=sampling_top_k,
|
||||
|
|
@ -218,6 +272,9 @@ class NAR(Base):
|
|||
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()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user