Upload blocks.py

blocks.py

@@ -0,0 +1,55 @@
+"""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
+try:
+    from flash_attn.bert_padding import unpad_input, pad_input
+except:
+    (unpad_input, pad_input) = (None, None)
+attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'qk_gn': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'sliding_window_size': -1, 'alibi': False, 'alibi_bias_max': 8, 'rope': False, 'rope_theta': 10000, 'rope_impl': 'dail', 'rope_dail_config': {'type': 'original', 'pos_idx_in_fp32': True, 'xpos_scale_base': 512}, 'rope_hf_config': {'type': 'no_scaling', 'factor': 1.0}}
+
+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, use_pad_tok_in_ffn: bool=True, **kwargs: Any):
+        if attn_config is None:
+            attn_config = attn_config_defaults
+        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', 'rope', 'rope_theta', 'rope_impl', 'rope_dail_config', 'rope_hf_config'}
+        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)
+        self.use_pad_tok_in_ffn = use_pad_tok_in_ffn
+
+    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[Dict]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False, alibi_slopes: Optional[torch.Tensor]=None, flash_attn_padding_info: Optional[dict[str, torch.Tensor]]=None) -> 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, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions, alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info)
+        x = x + self.resid_attn_dropout(b)
+        m = x
+        if self.norm_2 is not None:
+            m = self.norm_2(x)
+        (batch_size, seq_len) = m.size()[:2]
+        indices = None
+        if not self.use_pad_tok_in_ffn:
+            assert unpad_input is not None
+            (m, indices, _, _) = unpad_input(m, attention_mask)
+        n = self.ffn(m)
+        if not self.use_pad_tok_in_ffn:
+            assert pad_input is not None
+            n = pad_input(n, indices, batch_size, seq_len)
+        x = x + self.resid_ffn_dropout(n)
+        return (x, attn_weights, past_key_value)