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