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Running
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Running
on
Zero
File size: 4,863 Bytes
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from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
import warnings
import torch
from safetensors.torch import load_file
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from ..config import DiaConfig, load_config
from ..core.model import Dia2Model
from ..core.precision import Precision, resolve_precision
from ..audio import MimiCodec, DEFAULT_MIMI_MODEL_ID
from .state_machine import StateMachine, TokenIds
@dataclass
class RuntimeContext:
config: DiaConfig
model: Dia2Model
precision: Precision
tokenizer: PreTrainedTokenizerBase
mimi: MimiCodec
device: torch.device
machine: StateMachine
transformer_step: callable
depformer_step: callable
constants: TokenIds
audio_delays: list[int]
audio_delay_tensor: torch.Tensor
frame_rate: float
def build_runtime(
*,
config_path: str | Path,
weights_path: str | Path,
tokenizer_id: Optional[str],
repo_id: Optional[str],
mimi_id: Optional[str],
device: str,
dtype_pref: str,
) -> tuple[RuntimeContext, str, str]:
device_obj = torch.device(device)
if device_obj.type == "cuda":
cuda_matmul = torch.backends.cuda.matmul
if hasattr(cuda_matmul, "fp32_precision"):
cuda_matmul.fp32_precision = "tf32"
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="Please use the new API settings",
)
torch.backends.cuda.matmul.allow_tf32 = True
else: # pragma: no cover - compatibility with older PyTorch
torch.backends.cuda.matmul.allow_tf32 = True
# Handle cuDNN conv TF32 settings (check if conv attribute exists first)
if hasattr(torch.backends.cudnn, "conv"):
cudnn_conv = torch.backends.cudnn.conv
if hasattr(cudnn_conv, "fp32_precision"):
cudnn_conv.fp32_precision = "tf32"
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="Please use the new API settings",
)
torch.backends.cudnn.allow_tf32 = True
else:
torch.backends.cudnn.allow_tf32 = True
else:
# For older PyTorch versions without the conv attribute
torch.backends.cudnn.allow_tf32 = True
precision = resolve_precision(dtype_pref, device_obj)
config = load_config(config_path)
model = Dia2Model(config, precision)
state = load_file(str(weights_path))
model.load_state_dict(state)
model = model.to(device_obj)
tokenizer_ref = tokenizer_id or config.assets.tokenizer or repo_id
if tokenizer_ref is None:
raise ValueError("Tokenizer id is missing. Provide --tokenizer or add assets.tokenizer to the config.")
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_ref,
use_fast=False,
trust_remote_code=True,
)
mimi_ref = mimi_id or config.assets.mimi or DEFAULT_MIMI_MODEL_ID
mimi = MimiCodec.from_pretrained(mimi_ref, device=device_obj)
data_cfg = config.data
constants = TokenIds(
card=data_cfg.text_vocab_size,
new_word=data_cfg.text_new_word_token_id,
pad=data_cfg.text_pad_token_id,
bos=getattr(tokenizer, "bos_token_id", 1) or 1,
zero=data_cfg.text_zero_token_id,
spk1=tokenizer.convert_tokens_to_ids("[S1]") if "[S1]" in tokenizer.get_vocab() else data_cfg.text_new_word_token_id,
spk2=tokenizer.convert_tokens_to_ids("[S2]") if "[S2]" in tokenizer.get_vocab() else data_cfg.text_new_word_token_id,
audio_pad=data_cfg.audio_pad_token_id,
audio_bos=data_cfg.audio_bos_token_id,
)
machine = StateMachine(
token_ids=constants,
second_stream_ahead=data_cfg.second_stream_ahead,
max_padding=6,
initial_padding=0,
)
audio_delays = list(data_cfg.delay_pattern)
audio_delay_tensor = torch.tensor(audio_delays, device=device_obj, dtype=torch.long) if audio_delays else torch.empty(0, dtype=torch.long, device=device_obj)
frame_rate = getattr(mimi, "frame_rate", 75.0)
runtime = RuntimeContext(
config=config,
precision=precision,
model=model,
tokenizer=tokenizer,
mimi=mimi,
device=device_obj,
machine=machine,
constants=constants,
audio_delays=audio_delays,
audio_delay_tensor=audio_delay_tensor,
frame_rate=frame_rate,
transformer_step=model.transformer.forward_step,
depformer_step=model.depformer.forward_step,
)
return runtime, tokenizer_ref, mimi_ref
__all__ = [
"RuntimeContext",
"build_runtime",
]
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