(self, params)
| 256 | |
| 257 | class Transformer(nn.Module): |
| 258 | def __init__(self, params): |
| 259 | super().__init__() |
| 260 | self.params = params |
| 261 | self.vocab_size = params.vocab_size |
| 262 | self.n_layers = params.n_layers |
| 263 | |
| 264 | self.wte = SharedEmbedding(params.vocab_size, params.d_model) |
| 265 | |
| 266 | self.blocks = torch.nn.ModuleList() |
| 267 | for layer_id in range(params.n_layers): |
| 268 | self.blocks.append(MPTBlock(layer_id, params)) |
| 269 | |
| 270 | self.norm_f = LPLayerNorm(params.d_model, eps=1e-6) |
| 271 | |
| 272 | @torch.inference_mode() |
| 273 | def forward(self, tokens: torch.Tensor, start_pos: int): |
nothing calls this directly
no test coverage detected