| """GPT Blocks used for the GPT Model.""" |
| from typing import Any, Optional |
| import torch |
| import torch.nn as nn |
| from .fc import FC_CLASS_REGISTRY |
| try: |
| import transformer_engine.pytorch as te |
| except: |
| te = None |
|
|
| class MPTMLP(nn.Module): |
|
|
| def __init__(self, d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None): |
| super().__init__() |
| fc_kwargs = {} |
| if fc_type != 'te': |
| fc_kwargs['device'] = device |
| self.up_proj = FC_CLASS_REGISTRY[fc_type](d_model, expansion_ratio * d_model, **fc_kwargs) |
| self.act = nn.GELU(approximate='none') |
| self.down_proj = FC_CLASS_REGISTRY[fc_type](expansion_ratio * d_model, d_model, **fc_kwargs) |
| self.down_proj._is_residual = True |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| return self.down_proj(self.act(self.up_proj(x))) |
| FFN_CLASS_REGISTRY = {'mptmlp': MPTMLP} |
| if te is not None: |
| te.LayerNormMLP._has_norm = True |
| FFN_CLASS_REGISTRY['te_ln_mlp'] = te.LayerNormMLP |
|
|
| def build_ffn(d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, **kwargs: Any) -> nn.Module: |
| ffn_type = kwargs.pop('ffn_type') |
| if ffn_type == 'mptmlp': |
| if len(kwargs) > 0: |
| raise ValueError(f'MPTMLP got an unexpected keyword argument: {kwargs}') |
| return MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, device=device) |
| elif ffn_type == 'te_ln_mlp': |
| assert te is not None |
| return te.LayerNormMLP(hidden_size=d_model, ffn_hidden_size=d_model * expansion_ratio, **kwargs) |
| raise ValueError(f'ffn_type={ffn_type!r} not recognized.') |
