| """GPT Blocks used for the GPT Model.""" | |
| from typing import Any, Dict, Optional, Tuple | |
| import torch | |
| import torch.nn as nn | |
| from .attention import ATTN_CLASS_REGISTRY | |
| from .ffn import FFN_CLASS_REGISTRY, build_ffn | |
| from .norm import NORM_CLASS_REGISTRY | |
| class MPTBlock(nn.Module): | |
| def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Optional[Dict]=None, ffn_config: Optional[Dict]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, **kwargs: Any): | |
| if attn_config is None: | |
| attn_config = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8} | |
| if ffn_config is None: | |
| ffn_config = {'ffn_type': 'mptmlp'} | |
| del kwargs | |
| super().__init__() | |
| norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] | |
| assert isinstance(attn_config['attn_type'], str) | |
| attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']] | |
| args_to_exclude_in_attn_class = {'attn_type', 'prefix_lm', 'alibi', 'attn_uses_sequence_id', 'alibi_bias_max'} | |
| attn_config_subset_for_attn_class = {k: v for (k, v) in attn_config.items() if k not in args_to_exclude_in_attn_class} | |
| self.norm_1 = norm_class(d_model, device=device) | |
| self.attn = attn_class(d_model=d_model, n_heads=n_heads, fc_type=fc_type, device=device, **attn_config_subset_for_attn_class) | |
| self.norm_2 = None | |
| if not getattr(FFN_CLASS_REGISTRY[ffn_config['ffn_type']], '_has_norm', False): | |
| self.norm_2 = norm_class(d_model, device=device) | |
| self.ffn = build_ffn(d_model=d_model, expansion_ratio=expansion_ratio, device=device, **ffn_config) | |
| self.resid_attn_dropout = nn.Dropout(resid_pdrop) | |
| self.resid_ffn_dropout = nn.Dropout(resid_pdrop) | |
| def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: | |
| a = self.norm_1(x) | |
| (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions) | |
| x = x + self.resid_attn_dropout(b) | |
| m = x | |
| if self.norm_2 is not None: | |
| m = self.norm_2(x) | |
| n = self.ffn(m) | |
| x = x + self.resid_ffn_dropout(n) | |
| return (x, attn_weights, past_key_value) |