679 lines
23 KiB
Python
Executable File
679 lines
23 KiB
Python
Executable File
import math
|
||
import torch
|
||
import torch.nn.functional as F
|
||
import traceback
|
||
import numpy as np
|
||
|
||
from typing import Literal, overload
|
||
from functools import partial
|
||
from einops import rearrange
|
||
|
||
from torch import Tensor, einsum, nn
|
||
from torch.distributions import Categorical
|
||
from torch.nn.utils.rnn import pad_sequence
|
||
from torch.utils.checkpoint import checkpoint
|
||
from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
|
||
|
||
from ..samplers import reptition_penalize, length_penalize, top_k_top_p_filtering, dynamic_temperature, top_k_logits_list, mirostat_sample
|
||
|
||
try:
|
||
from .transformer import SinusoidalEmbedding, Block as TransformerBlock
|
||
except Exception as e:
|
||
print("Error importing `transformer` arch:", e)
|
||
pass
|
||
|
||
try:
|
||
from .retnet import RetNetDecoder, RetNetConfig
|
||
except Exception as e:
|
||
print("Error importing `retnet` arch:", e)
|
||
pass
|
||
|
||
try:
|
||
from transformers import LlamaModel, LlamaConfig
|
||
except Exception as e:
|
||
print("Error importing `llama` arch:", e)
|
||
pass
|
||
|
||
try:
|
||
from transformers import MixtralModel, MixtralConfig
|
||
from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func, MixtralSparseMoeBlock
|
||
|
||
# This is required because batch sizes > 1 throws errors
|
||
def Fixed_MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
""" """
|
||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||
hidden_states = hidden_states.reshape(-1, hidden_dim) # was view()
|
||
# router_logits: (batch * sequence_length, n_experts)
|
||
router_logits = self.gate(hidden_states)
|
||
|
||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||
# we cast back to the input dtype
|
||
routing_weights = routing_weights.to(hidden_states.dtype)
|
||
|
||
final_hidden_states = torch.zeros(
|
||
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
||
)
|
||
|
||
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
||
|
||
for expert_idx in range(self.num_experts):
|
||
expert_layer = self.experts[expert_idx]
|
||
idx, top_x = torch.where(expert_mask[expert_idx])
|
||
|
||
if top_x.shape[0] == 0:
|
||
continue
|
||
top_x_list = top_x.tolist()
|
||
idx_list = idx.tolist()
|
||
|
||
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
|
||
|
||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||
return final_hidden_states, router_logits
|
||
|
||
Original_MixtralSparseMoeBlock_forward = MixtralSparseMoeBlock.forward
|
||
MixtralSparseMoeBlock.forward = Fixed_MixtralSparseMoeBlock_forward
|
||
|
||
except Exception as e:
|
||
print("Error importing `mixtral` arch:", e)
|
||
|
||
def _create_mask(l, device):
|
||
"""1 is valid region and 0 is invalid."""
|
||
seq = torch.arange(max(l), device=device).unsqueeze(0) # (1 t)
|
||
stop = torch.tensor(l, device=device).unsqueeze(1) # (b 1)
|
||
return (seq < stop).float() # (b t)
|
||
|
||
def _join(x: tuple[Tensor], sep: Tensor):
|
||
"""
|
||
Args:
|
||
x: (k t d)
|
||
sep: (d)
|
||
"""
|
||
ret = x[0]
|
||
for i in range(1, len(x)):
|
||
ret = torch.cat((ret, sep[None], x[i]), dim=0)
|
||
return ret
|
||
|
||
def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
|
||
"""
|
||
Args:
|
||
x_list: [(t d)]
|
||
Returns:
|
||
x: (? ? ?)
|
||
m: (? ? ?), same as x
|
||
"""
|
||
l = list(map(len, x_list))
|
||
x = rearrange(pad_sequence(x_list), pattern)
|
||
m = _create_mask(l, x_list[0].device)
|
||
m = m.t().unsqueeze(-1) # (t b 1)
|
||
m = rearrange(m, pattern)
|
||
m = m.to(x)
|
||
return x, m
|
||
|
||
# automagically parses a batch-list and returns it as a list
|
||
class Embedding(nn.Embedding):
|
||
def forward(self, x_list: list[Tensor]) -> list[Tensor]:
|
||
if len(x_list) == 0:
|
||
return []
|
||
|
||
return super().forward(torch.cat(x_list)).split([*map(len, x_list)])
|
||
|
||
class MultiEmbedding(nn.Module):
|
||
"""
|
||
This embedding sums embeddings on different levels.
|
||
"""
|
||
|
||
def __init__(self, max_n_levels, n_tokens, token_dim, monolithic=False):
|
||
super().__init__()
|
||
self.monolithic = monolithic
|
||
self.max_n_levels = max_n_levels
|
||
self.n_tokens = n_tokens
|
||
self.weight = nn.Parameter(torch.randn(max_n_levels, n_tokens, token_dim))
|
||
|
||
# to-do: select quant level from given quant_levels tensor if given (i.e. through the resp_emb)
|
||
# I imagine this is an oversight in the NAR.
|
||
def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None) -> list[Tensor]:
|
||
if len(x_list) == 0:
|
||
return []
|
||
|
||
# this "strategy" will reserve the weight[0] for te AR and weight[1:] for the NAR
|
||
# the NAR cannot share RVQ-bin level 0 with the AR for the resp_emb
|
||
if self.monolithic:
|
||
w = self.weight[:1] if quant_levels is None else self.weight[1:]
|
||
else:
|
||
w = self.weight
|
||
|
||
padded_x_list = []
|
||
|
||
for i, xi in enumerate(x_list):
|
||
xi = F.one_hot(xi.to(torch.int64), num_classes=self.n_tokens) # t l' k
|
||
wi = w.shape[0] - xi.shape[1]
|
||
xi = F.pad(xi, (0, 0, 0, wi)) # t l k
|
||
padded_x_list.append(xi.to(w))
|
||
|
||
x = torch.cat(padded_x_list) # n l k
|
||
x = einsum("l k d, n l k -> n d", w, x)
|
||
|
||
x_list = x.split([*map(len, x_list)])
|
||
|
||
return x_list
|
||
|
||
# Embedding that sums each RVQ-bin level within a given input acoustic prompt
|
||
class AudioEmbedding(nn.Module):
|
||
def __init__(self, l_tokens, token_dim, levels=None):
|
||
super().__init__()
|
||
self.embeddings = nn.ModuleList([nn.Embedding(n_tokens, token_dim) for n_tokens in l_tokens])
|
||
self.weight = nn.ParameterList([nn.Parameter( torch.Tensor([1]) ) for i in range(levels)]) if levels is not None else None
|
||
|
||
def forward(self, x_list: list[Tensor], quant_levels: Tensor | None = None ) -> list[Tensor]:
|
||
res_list = []
|
||
|
||
for i, xi in enumerate(x_list):
|
||
# prom
|
||
if quant_levels is None and xi.shape[-1] > 1:
|
||
x = sum( [ self.embeddings[k]( xi[:, k] ) * (self.weight[k] if self.weight is not None else 1) for k in range(xi.shape[-1]) ] )
|
||
# AR resp
|
||
elif quant_levels is None or quant_levels[i] == 0:
|
||
x = self.embeddings[0]( xi[:, 0] )
|
||
# NAR resp
|
||
else:
|
||
x = sum( [ self.embeddings[k+1]( xi[:, k] ) * (self.weight[k+1] if self.weight is not None else 1) for k in range(xi.shape[-1]) ] )
|
||
res_list.append(x)
|
||
|
||
return res_list
|
||
|
||
class Base(nn.Module):
|
||
@property
|
||
def causal(self) -> bool:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def arch_type(self) -> str:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def norm_type(self):
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_prom_levels(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_resp_levels(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_max_levels(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_langs(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def n_tasks(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def recurrent_chunk_size(self) -> int:
|
||
raise NotImplementedError
|
||
|
||
@property
|
||
def rotary_embedding_base(self) -> float:
|
||
return 10000
|
||
|
||
@property
|
||
def interleave(self) -> bool:
|
||
return False
|
||
|
||
@property
|
||
def monolithic(self) -> bool:
|
||
return False
|
||
|
||
@property
|
||
def version(self) -> int:
|
||
return 1
|
||
|
||
@property
|
||
def stop_token(self):
|
||
if not self.causal:
|
||
raise ValueError("Not using stop token!")
|
||
return self.n_tokens
|
||
|
||
@property
|
||
def ignore_index(self):
|
||
return -100
|
||
|
||
@staticmethod
|
||
def _samplewise_merge_tensors(*l, sep: Tensor | None):
|
||
if sep is None:
|
||
cat = torch.cat
|
||
else:
|
||
cat = partial(_join, sep=sep)
|
||
|
||
l = [ x for x in l if x is not None ]
|
||
|
||
return [*map(cat, zip(*l))]
|
||
|
||
def __init__(
|
||
self,
|
||
n_tokens: int = 1024,
|
||
d_model: int = 512,
|
||
n_heads: int = 8,
|
||
n_layers: int = 12,
|
||
p_dropout: float = 0.1,
|
||
|
||
n_experts: int=1,
|
||
|
||
config = None,
|
||
):
|
||
super().__init__()
|
||
self.config = config
|
||
self.activation_checkpointing = self.config.activation_checkpointing if self.config is not None else True
|
||
|
||
self.n_tokens = n_tokens
|
||
self.d_model = d_model
|
||
self.n_heads = n_heads
|
||
self.n_layers = n_layers
|
||
self.n_experts = n_experts
|
||
|
||
# +1 to include the stop token
|
||
# to-do: undo this dogshit mistake; tasks tokens should be delegated to its own embedding
|
||
n_prom_tokens = n_tokens
|
||
n_resp_tokens = n_tokens + (1 if self.causal else 0) # AR requires a stop token to... know when to stop
|
||
|
||
self.text_emb = Embedding(n_tokens, d_model)
|
||
self.langs_emb = None
|
||
self.tasks_emb = None
|
||
|
||
if self.version == 1: # legacy
|
||
n_prom_tokens += (self.n_tasks - 1) # old models have the task tokens in the prom
|
||
self.proms_emb = MultiEmbedding(self.n_prom_levels, n_prom_tokens, d_model)
|
||
self.resps_emb = MultiEmbedding(self.n_resp_levels, n_resp_tokens, d_model, monolithic=self.monolithic)
|
||
else:
|
||
# [1024] * 8
|
||
self.proms_emb = AudioEmbedding([n_prom_tokens] * self.n_prom_levels, d_model, self.n_prom_levels if self.version > 3 else None)
|
||
# [1025] + [1024] * 8
|
||
self.resps_emb = AudioEmbedding([n_resp_tokens] + [n_resp_tokens - 1] * (self.n_resp_levels - 1), d_model, self.n_resp_levels if self.version > 3 else None)
|
||
|
||
|
||
if self.version >= 3:
|
||
self.langs_emb = Embedding(self.n_langs, d_model) if self.n_langs > 0 else None
|
||
self.tasks_emb = Embedding(self.n_tasks, d_model) if self.n_tasks > 0 else None
|
||
|
||
self.sep = nn.Parameter(torch.randn(d_model))
|
||
|
||
if self.arch_type == "transformer":
|
||
self.sin_emb = SinusoidalEmbedding(d_model)
|
||
self.blocks = nn.ModuleList([TransformerBlock(
|
||
d_model=d_model,
|
||
n_heads=n_heads,
|
||
p_dropout=p_dropout,
|
||
causal=self.causal,
|
||
norm_type=self.norm_type,
|
||
n_levels=self.n_resp_levels,
|
||
) for _ in range(n_layers) ])
|
||
elif self.arch_type == "llama":
|
||
if n_experts <= 1:
|
||
self.model = LlamaModel(LlamaConfig(
|
||
vocab_size=n_resp_tokens,
|
||
hidden_size=d_model,
|
||
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
||
intermediate_size=d_model*4,
|
||
num_hidden_layers=n_layers,
|
||
num_attention_heads=n_heads,
|
||
attention_dropout=p_dropout,
|
||
num_key_value_heads=n_heads,
|
||
hidden_act="gelu",
|
||
is_encoder_decoder=False,
|
||
is_decoder=True,
|
||
))
|
||
else:
|
||
self.model = MixtralModel(MixtralConfig(
|
||
vocab_size =n_resp_tokens,
|
||
hidden_size=d_model,
|
||
max_position_embeddings=75 * 60, # max-length of 60 seconds
|
||
intermediate_size=d_model*4,
|
||
num_hidden_layers=n_layers,
|
||
num_attention_heads=n_heads,
|
||
attention_dropout=p_dropout,
|
||
num_key_value_heads=n_heads,
|
||
hidden_act="gelu",
|
||
is_encoder_decoder=False,
|
||
is_decoder=True,
|
||
num_local_experts=n_experts,
|
||
num_experts_per_tok=min(2, n_experts),
|
||
))
|
||
elif self.arch_type == "retnet":
|
||
kwargs = dict(
|
||
vocab_size=n_resp_tokens,
|
||
decoder_embed_dim=d_model,
|
||
decoder_value_embed_dim =d_model * 2,
|
||
decoder_retention_heads=n_heads,
|
||
decoder_ffn_embed_dim=d_model * 4,
|
||
decoder_layers=n_layers,
|
||
dropout=p_dropout,
|
||
checkpoint_activations=self.activation_checkpointing,
|
||
activation_fn="gelu",
|
||
use_layernorm=True, # self.version < 3,
|
||
use_biases=True, # self.version < 3,
|
||
use_glu=False, # self.version >= 3,
|
||
|
||
chunkwise_recurrent=self.causal and self.recurrent_chunk_size > 0,
|
||
recurrent_chunkwise_size=self.recurrent_chunk_size if self.causal else 0,
|
||
no_output_layer=True,
|
||
decoder_normalize_before=True,
|
||
|
||
rotary_embedding_base=self.rotary_embedding_base, # 10000
|
||
)
|
||
|
||
if n_experts > 1:
|
||
kwargs.update(dict(
|
||
use_xmoe=True,
|
||
moe_freq=1,
|
||
moe_expert_count=n_experts,
|
||
moe_gating_use_fp32=False,
|
||
))
|
||
|
||
self.model = RetNetDecoder(RetNetConfig(**kwargs))
|
||
|
||
self.classifier = nn.Linear(d_model, n_resp_tokens)
|
||
|
||
self.accuracy_metric = MulticlassAccuracy(
|
||
n_resp_tokens,
|
||
top_k=10,
|
||
average="micro",
|
||
multidim_average="global",
|
||
ignore_index=self.ignore_index,
|
||
)
|
||
|
||
self.precision_metric = MulticlassPrecision(
|
||
n_resp_tokens,
|
||
top_k=10,
|
||
average="micro",
|
||
multidim_average="global",
|
||
ignore_index=self.ignore_index,
|
||
)
|
||
|
||
def forward(
|
||
self,
|
||
text_list: list[Tensor],
|
||
proms_list: list[Tensor],
|
||
resps_list: list[Tensor],
|
||
targ_list: list[Tensor] | None = None,
|
||
|
||
lang_list: list[Tensor] | None = None,
|
||
|
||
quant_levels: Tensor | None = None,
|
||
state: dict | None = None,
|
||
):
|
||
batch_size = len(text_list)
|
||
|
||
if self.langs_emb is None:
|
||
lang_list = None
|
||
|
||
x_list = self._samplewise_merge_tensors(
|
||
self.text_emb(text_list),
|
||
self.langs_emb(lang_list) if lang_list is not None else None,
|
||
self.proms_emb(proms_list),
|
||
self.resps_emb(resps_list, quant_levels),
|
||
sep=self.sep,
|
||
)
|
||
|
||
x, m = list_to_tensor(x_list)
|
||
aux_loss = None
|
||
|
||
device = x.device
|
||
|
||
if state is not None and self.arch_type == "retnet":
|
||
# prefill
|
||
if len(state) == 0:
|
||
prefill_size = x.shape[1]
|
||
# run the initial prompt to fill the KV cache
|
||
for n in range(prefill_size):
|
||
xi = x[:, n, :].unsqueeze(1)
|
||
self.model(xi, incremental_state=state, token_embeddings=xi, features_only=True)
|
||
|
||
# grab last token(s)
|
||
x = x[:, -1, :].unsqueeze(1)
|
||
# HF transformer derived model
|
||
elif self.arch_type == "llama":
|
||
kwargs = dict(
|
||
#attention_mask=m,
|
||
inputs_embeds=x,
|
||
)
|
||
if self.n_experts > 1:
|
||
kwargs["output_router_logits"] = True
|
||
|
||
t = self.model(**kwargs)
|
||
x = t[0]
|
||
|
||
if self.n_experts > 1:
|
||
router_logits = t[-1]
|
||
aux_loss = self.model.config.router_aux_loss_coef * load_balancing_loss_func( router_logits, self.model.config.num_local_experts, self.model.config.num_experts_per_tok )
|
||
elif self.arch_type == "transformer":
|
||
# ensures we specify a quant_level for the transformer implementation's AdaLN
|
||
l = torch.zeros((batch_size,), dtype=torch.int32) if quant_levels is None else quant_levels
|
||
l = l.to(device)
|
||
# inject position information
|
||
x = self.sin_emb.add_pe(x)
|
||
# pass our inputs through the transformer
|
||
for block in self.blocks:
|
||
x = block(x, m, l)
|
||
elif self.arch_type == "retnet":
|
||
# pass our inputs through the RetNet
|
||
x, _ = self.model(x, incremental_state=state, token_embeddings=x, features_only=True)
|
||
if _ is not None and "l_aux" in _ and self.n_experts > 1:
|
||
aux_loss = torch.sum(torch.stack([ t for t in _["l_aux"] if t is not None])) * 0.001
|
||
# output projection layer with masking
|
||
x = self.classifier(x) * m
|
||
|
||
# Remove padding
|
||
logits = [ hi[:li] for hi, li in zip(x, map(len, x_list)) ]
|
||
|
||
# compute loss if the target is given
|
||
if targ_list is not None:
|
||
|
||
target_list = self._samplewise_merge_tensors(
|
||
text_list,
|
||
lang_list,
|
||
[ torch.full_like(t[..., 0], self.ignore_index) for t in proms_list ], # create a tensor sequence with one RVQ-bin of the input prompt, but with `ignore_index`, as the prompt is not neeeded for computing the loss against
|
||
targ_list,
|
||
sep=torch.tensor(self.ignore_index, device=device)
|
||
)
|
||
|
||
# modify only for the AR so it can properly behave like a transformer
|
||
for i in range(len(target_list)):
|
||
if quant_levels is not None and quant_levels[i] > 0:
|
||
continue
|
||
|
||
logits[i] = logits[i][..., :-1, :] # shift the target so that token n...
|
||
target_list[i] = target_list[i][..., 1:] # predicts token n + 1
|
||
|
||
target = torch.cat( target_list )
|
||
inputs = torch.cat( logits )
|
||
|
||
self.loss = dict(
|
||
# "nll" was in the original implementation and should actually just be called something else
|
||
nll = F.cross_entropy( inputs, target, ignore_index=self.ignore_index )
|
||
)
|
||
self.stats = dict(
|
||
acc = self.accuracy_metric( inputs, target ),
|
||
# precision = self.precision_metric( inputs, target ),
|
||
)
|
||
|
||
if aux_loss is not None:
|
||
self.loss["nll"] += aux_loss
|
||
|
||
return logits
|
||
|
||
def sample(
|
||
self,
|
||
logits: list[Tensor],
|
||
resps_list: list[Tensor],
|
||
quant_levels: Tensor | None = None,
|
||
|
||
temperature: float = 1.0,
|
||
min_temperature: float = -1.0,
|
||
top_k: int = -100,
|
||
top_p: float = 1.0,
|
||
|
||
repetition_penalty: float = 1.0,
|
||
repetition_penalty_decay: float = 0.0,
|
||
|
||
length_penalty: float = 0.0,
|
||
|
||
beam_width: int = 0,
|
||
|
||
mirostat: list[dict] | None = None,
|
||
):
|
||
if min_temperature < 0:
|
||
min_temperature = temperature
|
||
# (NAR) return the entire generated response
|
||
if quant_levels is not None:
|
||
logits = [ logit[-l:] for logit, l in zip(logits, map(len, resps_list)) ]
|
||
# (AR chunkwise) return the last chunkwise piece
|
||
elif self.causal and self.recurrent_chunk_size > 0:
|
||
logits = [ logit[-l:] for logit, l in zip(logits, self.recurrent_chunk_size) ]
|
||
# (AR) return just the last code
|
||
else:
|
||
logits = [ logit[-1:] for logit in logits ]
|
||
|
||
devices = [ logit.device for logit in logits ]
|
||
logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
|
||
|
||
# perform repetition penalizing
|
||
logits = [ reptition_penalize(logit, previous=resps[:, -1], factor=repetition_penalty, decay=repetition_penalty_decay) for logit, resps in zip( logits, resps_list ) ]
|
||
|
||
# (AR) perform length penalizing
|
||
if quant_levels is None and self.causal:
|
||
logits = [ length_penalize(logit, length=l + 1, factor=length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, resps_list) ) ]
|
||
|
||
# perform top_k/top_p filtering of our logits
|
||
if top_k > 0 or top_p < 1.0:
|
||
logits = [ top_k_top_p_filtering(logit, top_k=top_k, top_p=top_p) for logit in logits ]
|
||
|
||
# trigger dynamic temperature sampling if the minimum temperature is not the same as the sampling temperature
|
||
# epsilon float comparison because I don't trust Python
|
||
if abs(temperature - min_temperature) >= 0.001:
|
||
logits = [ dynamic_temperature(logit, temperature=temperature, min_temperature=min_temperature) for logit in logits ]
|
||
else:
|
||
logits = [ logit / temperature for logit in logits ]
|
||
|
||
# do mirostat sampling
|
||
# currently incompatible with beam searching with the way the two are implemented, perhaps a night of brain bashing can make the two work
|
||
if mirostat is not None:
|
||
# mirostat sampling
|
||
return [ mirostat_sample(logit, state=state) for logit, state in zip(logits, mirostat) ]
|
||
|
||
# do beam search (naive implementation)
|
||
# picks the top-k across all batches, and re-batches those resultant tokens
|
||
# returns the logit scores as well to be P-concatted with the previous scores
|
||
# to-do: not naively implement beam searching
|
||
if beam_width > 1:
|
||
candidates = top_k_logits_list( logits, beam_width )
|
||
res = [ torch.tensor(token, dtype=torch.int16).unsqueeze(dim=-1) for batch, token in candidates ]
|
||
scores = [ logits[batch].flatten()[token] for batch, token in candidates ]
|
||
return res, scores
|
||
|
||
# and sample
|
||
return [ Categorical(logits=logit).sample() for logit in logits ]
|
||
|
||
def example_usage():
|
||
from ..config import cfg
|
||
cfg.trainer.backend = "local"
|
||
cfg.trainer.check_for_oom = False
|
||
|
||
from functools import partial
|
||
|
||
from einops import repeat
|
||
|
||
from ..emb.qnt import decode_to_file
|
||
from ..engines import Engine, Engines
|
||
from tqdm import tqdm, trange
|
||
from ..utils import wrapper as ml
|
||
|
||
from .ar import AR
|
||
from .nar import NAR
|
||
|
||
device = "cuda"
|
||
x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels)
|
||
symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
|
||
def tokenize(content, lang_marker="en"):
|
||
split = content.split(" ")
|
||
phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
|
||
return torch.tensor([*map(symmap.get, phones)]).to()
|
||
|
||
kwargs = {
|
||
'n_tokens': 1024,
|
||
'd_model': 1024,
|
||
'n_heads': 16,
|
||
'n_layers': 12,
|
||
}
|
||
models = { "ar": AR(**kwargs).to(device), "nar": NAR(**kwargs).to(device) }
|
||
|
||
for name, model in models.items():
|
||
print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
|
||
|
||
engines = Engines({ name: Engine(model=model, optimizer=ml.AdamW(model.parameters(), lr=1e-4)) for name, model in models.items() })
|
||
|
||
train = True
|
||
|
||
qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
|
||
text_list = [
|
||
tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
|
||
#tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
|
||
]
|
||
|
||
proms_list = [
|
||
qnt.to(device),
|
||
]
|
||
resps_list = [
|
||
qnt.to(device),
|
||
]
|
||
|
||
def sample( name, steps=600 ):
|
||
AR = None
|
||
NAR = None
|
||
|
||
engines.eval()
|
||
for name, engine in engines.items():
|
||
if name[:2] == "ar":
|
||
AR = engine
|
||
elif name[:3] == "nar":
|
||
NAR = engine
|
||
|
||
resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
|
||
resps_list = [r.unsqueeze(-1) for r in resps_list]
|
||
codes = NAR( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 )
|
||
|
||
decode_to_file(resps_list[0], f"./data/ar.{name}.wav", device=device)
|
||
decode_to_file(codes[0], f"./data/ar+nar.{name}.wav", device=device)
|
||
|
||
if train:
|
||
sample("init", 15)
|
||
|
||
engines.train()
|
||
t = trange(500)
|
||
for i in t:
|
||
stats = {"step": i}
|
||
"""
|
||
for name, engine in engines.items():
|
||
stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
|
||
"""
|
||
stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list})
|
||
tqdm.write(f"{stats}")
|
||
else:
|
||
for name, engine in engines.items():
|
||
engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))
|
||
|
||
sample("final")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
example_usage()
|