added experimental training setting to perform token dropout to MAYBE compensate for errors from the preceding RVQ level (two types: token error offset, token dropout embedding replace)
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@ -215,6 +215,11 @@ class ModelExperimentalSettings:
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unified_position_ids: bool = True # False will generate position IDs partitioned for each section
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tie_classifier_to_embedding: bool = False # Ties the classifier output to their respective embeddings, this does not seem to do anything good in testing
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# performs token dropout to compensate for errors
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token_dropout_error: float = 0.0 # probability to nudge a token by ±1
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token_dropout_rate: float = 0.0 # probability to randomly set a token to a special dropout value
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token_dropout_rvq_levels: list = field(default_factory=lambda: [1,8]) # determines which levels to do dropout, by default do not do dropout on RVQ level 0
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# I really need to clean this up
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@dataclass()
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class Model:
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@ -22,6 +22,9 @@ from ..emb.qnt import trim, encode_as_embedding
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from .lora import enable_lora
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def clamp(n, lo, hi):
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return max(lo, min(n, hi))
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class AR_NAR(Base):
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@property
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def capabilities(self) -> list[str]:
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@ -139,6 +142,11 @@ class AR_NAR(Base):
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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 ]
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token_dropout_error = self.config.experimental.token_dropout_error
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels]
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if p_rvq_levels == "equal":
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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quant_levels = [ random.randint(quant_level_range[0], quant_level_range[1] - 1) for i in range(batch_size) ]
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@ -165,39 +173,49 @@ class AR_NAR(Base):
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quant_levels = [ random.choice( pool ) for i in range(batch_size) ]
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# these two are techinically equivalent if the audio embeddings handle things properly
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"""
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resps_list = [r[..., 0] if l == 0 else r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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stop_sequence = torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16)
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"""
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resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
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stop_sequence = torch.Tensor([[self.stop_token] * 1]).to(device=device, dtype=torch.int16)
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"""
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for i in range(batch_size):
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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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# cap quant_level if it exceeds its corresponding resp/prom
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if quant_levels[i] >= resps_list[i].shape[-1]:
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quant_levels[i] = resps_list[i].shape[-1] - 1
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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_list[i] could be a Tensor, list[Tensor], or None
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if isinstance( proms_list[i], torch.Tensor ):
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if quant_levels[i] >= proms_list[i].shape[-1]:
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quant_levels[i] = proms_list[i].shape[-1] - 1
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# proms could be a Tensor, list[Tensor], or None
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if isinstance( proms, torch.Tensor ):
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if quant_level >= proms.shape[-1]:
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quant_levels[i] = proms.shape[-1] - 1
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elif isinstance( proms_list[i], list ):
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for j, prom in enumerate( proms_list[i] ):
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elif isinstance( proms, list ):
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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_levels[i] >= prom.shape[-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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# only apply stop token for RVQ level 0
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if quant_levels[i] > 0:
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continue
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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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steps = resps.shape[0]
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for l in range( quant_level ):
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for t in range( steps ):
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token = resps[t, l].item()
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if random.random() < token_dropout_error:
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offset = 1 * ( 1 if random.random() < 0.5 else -1 )
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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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if quant_level <= 0:
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# append stop tokens for AR
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# could technically do it in the .inputs call
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resps_list[i] = torch.cat([ resps_list[i], stop_sequence ])
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resps_list[i] = torch.cat([ resps, stop_sequence ])
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inputs = self.inputs(
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text_list=text_list,
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@ -12,7 +12,7 @@ Additional functionality (preparing inputs, generating full audio) should be del
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import math
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import torch
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import torch.nn.functional as F
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import traceback
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import random
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import numpy as np
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import re
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@ -440,6 +440,10 @@ class Base(nn.Module):
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self.rvq_l_emb = None
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self.len_emb = None
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# it would be nicer for these to be a token or live inside an embedding
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self.sep = nn.Parameter(torch.randn(d_model))
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self.dropout_token = nn.Parameter(torch.zeros(d_model)) # zeros sounds nicer than randn for a special value
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if self.version == 1: # legacy
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n_audio_tokens += (self.n_tasks - 1) # old models have the task tokens in the prom
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self.proms_emb = MultiEmbedding(self.n_resp_levels, n_audio_tokens, d_model)
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@ -484,9 +488,6 @@ class Base(nn.Module):
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# experimental NAR-only mode
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self.len_emb = Embedding(11, d_model) if "len" in self.capabilities else None
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# this would be nicer to be a stop token or live inside an embedding
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self.sep = nn.Parameter(torch.randn(d_model))
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# ick, there has to be a better way
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hf_attention = self.config.attention if self.config is not None else None
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@ -970,6 +971,16 @@ class Base(nn.Module):
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return self.proms_emb( input if input.dim() == 1 else input[:, : 1 if quant_level == 0 else quant_level], offset = 0 )
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# yuck
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token_dropout_rate = self.config.experimental.token_dropout_rate if self.config else 0.0
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels if self.config else None
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if self.dropout_token is None or not self.training:
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token_dropout_rate = 0.0
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [1, self.resp_levels]
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x_list = []
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for batch_index, batch_input in enumerate(inputs):
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batch = []
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@ -1018,6 +1029,16 @@ class Base(nn.Module):
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input if input.dim() == 1 or quant_level == 0 else input[:, :quant_level],
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offset = 0 if quant_level == 0 or "len" in self.capabilities else 1
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)
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# apply token dropout
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if token_dropout_rate > 0.0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
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steps = embedding.shape[0] - (1 if quant_level == 0 else 0) # do not mess with stop token
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for i in range( steps ):
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if random.random() > token_dropout_rate:
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continue
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embedding[i] = self.dropout_token
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elif name == "len" and self.len_emb is not None:
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embedding = self.len_emb( input )
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else:
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