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"""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) |