153 lines
4.7 KiB
Python
153 lines
4.7 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 astrai.config.model_config import ModelConfig
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from astrai.model.module import (
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Embedding,
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DecoderBlock,
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Linear,
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RMSNorm,
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RotaryEmbedding,
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)
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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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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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input_tensor (Tensor): The input tensor.
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start_pos (int): The starting position of the sequence.
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is_causal (bool): Whether the attention is causal or not.
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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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seq_len = input_tensor.size(1)
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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(
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batch_size, seq_len, start_pos + seq_len
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)
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# (bsz, seq_len, start_pos + seq_len)
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if is_causal:
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expanded_mask = torch.tril(expanded_mask, diagonal=start_pos)
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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_(
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~expanded_mask, -torch.finfo(dtype).max / 2
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).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: ModelConfig):
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super().__init__()
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self.config = config
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self.rotary_embeding = RotaryEmbedding(
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config.dim // config.n_heads, config.max_len
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)
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self.embed_tokens = Embedding(config.vocab_size, config.dim)
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self.layers = nn.ModuleList(
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[
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DecoderBlock(
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config.dim,
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config.n_heads,
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config.dim_ffn,
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config.n_kv_heads,
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config.norm_eps,
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config.use_qk_norm,
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config.use_gated_attention,
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layer_id,
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)
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for layer_id in range(config.n_layers)
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]
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)
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self.norm = RMSNorm(config.dim, config.norm_eps)
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self.lm_head = Linear(config.dim, config.vocab_size)
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if self.config.tie_weight == True:
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self.lm_head.weight = self.embed_tokens.weight
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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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lm_head_key = "lm_head.weight"
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embed_key = "embed_tokens.weight"
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if self.config.tie_weight == True:
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# same tensor
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state_dict[lm_head_key] = state_dict[embed_key]
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else:
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if lm_head_key not in state_dict and embed_key in state_dict:
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# use clone to avoid sharing the same tensor
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state_dict[lm_head_key] = torch.clone(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(
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destination=destination, prefix=prefix, keep_vars=keep_vars
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)
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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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x = self.embed_tokens(input_ids)
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rotary_emb = self.rotary_embeding(x, start_pos)
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attn_mask = process_attention_mask(input_mask, x, start_pos, is_causal=True)
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for layer in self.layers:
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x = layer(x, rotary_emb, 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 {"logits": logits, "hidden_states": hidden_states}
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