| """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, no_bias: bool=False, **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, bias=not no_bias) |
| 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, bias=not no_bias, **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) |
