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