chore: 更新项目名称

astrai/model/transformer.py

@@ -0,0 +1,152 @@
+import torch
+import torch.nn as nn
+
+from torch import Tensor
+from typing import Any, Mapping, Optional, Tuple
+from astrai.config.model_config import ModelConfig
+from astrai.model.module import (
+    Embedding,
+    DecoderBlock,
+    Linear,
+    RMSNorm,
+    RotaryEmbedding,
+)
+
+
+def process_attention_mask(
+    seq_mask: Tensor,
+    input_tensor: Tensor,
+    start_pos: int = 0,
+    is_causal: bool = False,
+) -> Tensor:
+    """
+    Create attention mask for GQA
+    Args:
+        seq_mask (Tensor): A tensor indicating whether each position is valid or not.
+        input_tensor (Tensor): The input tensor.
+        start_pos (int): The starting position of the sequence.
+        is_causal (bool): Whether the attention is causal or not.
+    Returns:
+        Tensor: The attention mask tensor.
+    """
+    device = input_tensor.device
+    dtype = input_tensor.dtype
+    seq_len = input_tensor.size(1)
+
+    if seq_mask is None:
+        if start_pos != 0:
+            # for single prompt chat
+            seq_mask = torch.ones((1, seq_len), dtype=torch.bool, device=device)
+        else:
+            return None
+
+    if seq_mask.dim() > 2:
+        # shape (bsz, seq_len) or (bsz,n_heads, seq_len, seq_len + start_pos)
+        # if ndim > 2, it's 4D tensor
+        return seq_mask
+
+    batch_size = seq_mask.size(0)
+    seq_mask = seq_mask[:, : start_pos + seq_len].to(device=device, dtype=torch.bool)
+    # (bsz, start_pos + seq_len)
+    expanded_mask = seq_mask.unsqueeze(1).expand(
+        batch_size, seq_len, start_pos + seq_len
+    )
+    # (bsz, seq_len, start_pos + seq_len)
+
+    if is_causal:
+        expanded_mask = torch.tril(expanded_mask, diagonal=start_pos)
+
+    attention_mask = torch.zeros_like(expanded_mask, dtype=dtype, device=device)
+    attention_mask = attention_mask.masked_fill_(
+        ~expanded_mask, -torch.finfo(dtype).max / 2
+    ).unsqueeze(1)
+    # (bsz, 1, seq_len, seq_len + start_pos)
+
+    return attention_mask
+
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.config = config
+        self.rotary_embeding = RotaryEmbedding(
+            config.dim // config.n_heads, config.max_len
+        )
+        self.embed_tokens = Embedding(config.vocab_size, config.dim)
+
+        self.layers = nn.ModuleList(
+            [
+                DecoderBlock(
+                    config.dim,
+                    config.n_heads,
+                    config.dim_ffn,
+                    config.n_kv_heads,
+                    config.norm_eps,
+                    config.use_qk_norm,
+                    config.use_gated_attention,
+                    layer_id,
+                )
+                for layer_id in range(config.n_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(config.dim, config.norm_eps)
+        self.lm_head = Linear(config.dim, config.vocab_size)
+
+        if self.config.tie_weight == True:
+            self.lm_head.weight = self.embed_tokens.weight
+
+        self._init_parameters()
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict=True, assign=False):
+        lm_head_key = "lm_head.weight"
+        embed_key = "embed_tokens.weight"
+
+        if self.config.tie_weight == True:
+            # same tensor
+            state_dict[lm_head_key] = state_dict[embed_key]
+        else:
+            if lm_head_key not in state_dict and embed_key in state_dict:
+                # use clone to avoid sharing the same tensor
+                state_dict[lm_head_key] = torch.clone(state_dict[embed_key])
+
+        return super().load_state_dict(state_dict, strict, assign)
+
+    def state_dict(self, destination=None, prefix="", keep_vars=False):
+        state_dict = super().state_dict(
+            destination=destination, prefix=prefix, keep_vars=keep_vars
+        )
+
+        if self.config.tie_weight == True:
+            lm_head_key = prefix + "lm_head.weight"
+            if lm_head_key in state_dict:
+                del state_dict[lm_head_key]
+
+        return state_dict
+
+    def _init_parameters(self):
+        for param in self.parameters():
+            if param.dim() > 1:
+                nn.init.normal_(param, mean=0.0, std=0.006)
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        input_mask: Optional[Tensor] = None,
+        persistent_key_values: Optional[Tuple[Tensor, Tensor]] = None,
+        start_pos: int = 0,
+    ) -> Tensor:
+        assert input_ids.ndim == 2
+
+        x = self.embed_tokens(input_ids)
+        rotary_emb = self.rotary_embeding(x, start_pos)
+
+        attn_mask = process_attention_mask(input_mask, x, start_pos, is_causal=True)
+
+        for layer in self.layers:
+            x = layer(x, rotary_emb, attn_mask, persistent_key_values, start_pos)
+
+        hidden_states = self.norm(x)
+        logits = self.lm_head(hidden_states)
+
+        return {"logits": logits, "hidden_states": hidden_states}