136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
import torch
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import torch.nn as nn
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from torch import Tensor
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from typing import Any, Mapping, Optional, Tuple
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from khaosz.config.model_config import TransformerConfig
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from khaosz.model.module import Embedding, DecoderBlock, Linear, RMSNorm, get_rotary_emb
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def process_attention_mask(
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seq_mask: Tensor,
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input_tensor: Tensor,
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start_pos: int = 0,
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seq_len: int = 0,
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is_causal: bool = False,
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) -> Tensor:
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"""
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Create attention mask for GQA
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Args:
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seq_mask (Tensor): A tensor indicating whether each position is valid or not.
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start_pos (int): The starting position of the sequence.
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seq_len (int): The length of the sequence.
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is_causal (bool): Whether the attention is causal or not.
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device (torch.device): The device to use.
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Returns:
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Tensor: The attention mask tensor.
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"""
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device = input_tensor.device
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dtype = input_tensor.dtype
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if seq_mask is None:
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if start_pos != 0:
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# for single prompt chat
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seq_mask = torch.ones((1, seq_len), dtype=torch.bool, device=device)
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else:
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return None
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if seq_mask.dim() > 2:
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# shape (bsz, seq_len) or (bsz,n_heads, seq_len, seq_len + start_pos)
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# if ndim > 2, it's 4D tensor
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return seq_mask
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batch_size = seq_mask.size(0)
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seq_mask = seq_mask[:, :start_pos + seq_len].to(device=device, dtype=torch.bool)
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# (bsz, start_pos + seq_len)
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expanded_mask = seq_mask.unsqueeze(1).expand(batch_size, seq_len, start_pos + seq_len)
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# (bsz, seq_len, start_pos + seq_len)
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if is_causal:
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causal_mask = torch.tril(
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torch.ones((seq_len, start_pos + seq_len), dtype=torch.bool, device=device),
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diagonal=start_pos
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)
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causal_mask = causal_mask.unsqueeze(0).expand(batch_size, seq_len, start_pos + seq_len)
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expanded_mask = expanded_mask & causal_mask
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attention_mask = torch.zeros_like(expanded_mask, dtype=dtype, device=device)
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attention_mask = attention_mask.masked_fill_(~expanded_mask, -torch.finfo(dtype).max / 2).unsqueeze(1)
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# (bsz, 1, seq_len, seq_len + start_pos)
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return attention_mask
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class Transformer(nn.Module):
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def __init__(self, config: TransformerConfig):
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super().__init__()
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self.config = config
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self.embed_tokens = Embedding(config.vocab_size, config.n_dim)
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lm_head_init_weight = self.embed_tokens.weight if config.tie_weight == True else None
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self.layers = nn.ModuleList([
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DecoderBlock(config.n_dim, config.n_head, config.d_ffn, config.n_kvhead, config.norm_eps, layer_id)
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for layer_id in range(config.n_layer)
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])
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self.norm = RMSNorm(config.n_dim, config.norm_eps)
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self.lm_head = Linear(config.n_dim, config.vocab_size, weight_param=lm_head_init_weight)
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self.freq_cis = get_rotary_emb(config.n_dim // config.n_head, config.m_len)
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self._init_parameters()
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def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
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if self.config.tie_weight == True:
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lm_head_key = 'lm_head.weight'
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embed_key = 'embed_tokens.weight'
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if lm_head_key not in state_dict and embed_key in state_dict:
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state_dict[lm_head_key] = state_dict[embed_key]
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return super().load_state_dict(state_dict, strict, assign)
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def state_dict(self, destination=None, prefix='', keep_vars=False):
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state_dict = super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
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if self.config.tie_weight == True:
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lm_head_key = prefix + 'lm_head.weight'
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if lm_head_key in state_dict:
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del state_dict[lm_head_key]
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return state_dict
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def _init_parameters(self):
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for param in self.parameters():
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if param.dim() > 1:
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nn.init.normal_(param, mean=0.0, std=0.006)
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def forward(
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self,
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input_ids: Tensor,
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input_mask: Optional[Tensor]=None,
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persistent_key_values: Optional[Tuple[Tensor, Tensor]]=None,
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start_pos: int = 0
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) -> Tensor:
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assert input_ids.ndim == 2
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seq_len = input_ids.size(-1)
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x = self.embed_tokens(input_ids)
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freqs_cis = self.freq_cis[start_pos:start_pos+seq_len].to(x.device)
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attn_mask = process_attention_mask(
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input_mask,
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x,
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start_pos=start_pos,
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seq_len=seq_len,
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is_causal=True
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)
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for layer in self.layers:
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x = layer(x, freqs_cis, attn_mask, persistent_key_values, start_pos)
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hidden_states = self.norm(x)
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logits = self.lm_head(hidden_states)
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return {
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"logits": logits,
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"hidden_states": hidden_states
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}
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