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import logging
from typing import Tuple
import torch
import torch.nn as nn
from torch.nn.utils.parametrizations import weight_norm
from modeling_llama import LlamaModel, LlamaConfig
class GPT(nn.Module):
def __init__(
self,
gpt_config: dict,
num_audio_tokens: int = 626,
num_text_tokens: int = 21178,
num_vq=4,
use_flash_attn=False,
device=torch.device("cpu"),
logger=logging.getLogger(__name__),
):
super().__init__()
self.logger = logger
self.device = device
self.device_gpt = device if "mps" not in str(device) else torch.device("cpu")
self.num_vq = num_vq
self.num_audio_tokens = num_audio_tokens
self.use_flash_attn = use_flash_attn
self.gpt, self.llama_config = self._build_llama(gpt_config, self.device_gpt)
self.is_te_llama = False
self.model_dim = int(self.gpt.config.hidden_size)
self.emb_code = nn.ModuleList(
[
nn.Embedding(
num_audio_tokens,
self.model_dim,
device=self.device_gpt,
)
for _ in range(num_vq)
],
)
self.emb_text = nn.Embedding(
num_text_tokens, self.model_dim, device=self.device_gpt
)
self.head_text = weight_norm(
nn.Linear(
self.model_dim,
num_text_tokens,
bias=False,
device=device,
),
name="weight",
)
self.head_code = nn.ModuleList(
[
weight_norm(
nn.Linear(
self.model_dim,
num_audio_tokens,
bias=False,
device=device,
),
name="weight",
)
for _ in range(self.num_vq)
],
)
def from_pretrained(self, file_path: str):
self.load_state_dict(
torch.load(file_path, weights_only=True, mmap=True), strict=False
)
def _build_llama(
self,
config: dict,
device: torch.device,
) -> Tuple[LlamaModel, LlamaConfig]:
llama_config = LlamaConfig(**config)
model = LlamaModel(llama_config)
del model.embed_tokens
return model.to(device), llama_config
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