refactor-attention

examples/models/llama/llama_transformer.py

@@ -12,10 +12,7 @@
 import torch
 import torch.nn.functional as F
 
-from executorch.examples.models.llama.attention import (
-    ATTENTION_REGISTRY,
-    ForwardOptions,
-)
+from executorch.examples.models.llama.attention import Attention, ForwardOptions
 
 from executorch.examples.models.llama.model_args import ModelArgs
 from executorch.examples.models.llama.norm import RMSNorm
@@ -83,19 +80,13 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class TransformerBlock(nn.Module):
-    def __init__(self, layer_id: int, args: ModelArgs, rope: Rope):
+    def __init__(self, args: ModelArgs, attention: Attention):
         super().__init__()
         self.use_kv_cache = args.use_kv_cache
         self.n_heads = args.n_heads
         self.dim = args.dim
         self.head_dim = args.head_dim
-        if args.attention_type not in ATTENTION_REGISTRY:
-            raise ValueError(
-                f"Unknown attention type: {args.attention_type}. "
-                f"Available: {list(ATTENTION_REGISTRY.keys())}"
-            )
-        cls = ATTENTION_REGISTRY[args.attention_type]
-        self.attention = cls(args, layer_id, rope)
+        self.attention = attention
         if args.moe:
             self.block_sparse_moe = MOEFeedForward(args)
         else:
@@ -117,7 +108,7 @@ def forward(self, x, freqs_cos, freqs_sin, attn_options: ForwardOptions):  # x:
 
 
 class Transformer(nn.Module):
-    def __init__(self, params: ModelArgs):
+    def __init__(self, params: ModelArgs, layers: nn.ModuleList, rope: Rope):
         super().__init__()
         self.params = params
         self.vocab_size = params.vocab_size
@@ -130,10 +121,8 @@ def __init__(self, params: ModelArgs):
             if self.apply_embedding
             else None
         )
-        self.rope = Rope(params)
-        self.layers = torch.nn.ModuleList()
-        for layer_id in range(params.n_layers):
-            self.layers.append(TransformerBlock(layer_id, params, self.rope))
+        self.layers = layers
+        self.rope = rope
         self.norm = RMSNorm(params.dim, eps=params.norm_eps)
         self.output = (
             nn.Linear(params.dim, params.vocab_size, bias=False)

examples/models/llama/model.py

@@ -15,9 +15,13 @@
     get_checkpoint_dtype,
     get_default_model_resource_dir,
 )
-from executorch.examples.models.llama.llama_transformer import Transformer
-
+from executorch.examples.models.llama.attention import ATTENTION_REGISTRY
+from executorch.examples.models.llama.llama_transformer import (
+    Transformer,
+    TransformerBlock,
+)
 from executorch.examples.models.llama.model_args import ModelArgs
+from executorch.examples.models.llama.rope import Rope
 from torchao.utils import TorchAOBaseTensor
 
 try:
@@ -174,7 +178,24 @@ def __init__(self, **kwargs):
         # They possess all other metadata a tensor carries such as size, stride, requires_grad.
         with torch.device("meta"):
             # Model itself is loaded in default dtype, fp32.
-            self.model_ = Transformer(model_args)
+
+            # Construct attention layers.
+            rope = Rope(model_args)
+            if model_args.attention_type not in ATTENTION_REGISTRY:
+                raise ValueError(
+                    f"Unknown attention type: {model_args.attention_type}. "
+                    f"Available: {list(ATTENTION_REGISTRY.keys())}"
+                )
+            layers = torch.nn.ModuleList()
+            cls = ATTENTION_REGISTRY[model_args.attention_type]
+            for layer_id in range(model_args.n_layers):
+                attention = cls(model_args, layer_id, rope)
+                transformer_block = TransformerBlock(model_args, attention)
+                layers.append(transformer_block)
+
+            # Construct transformer model.
+            self.model_ = Transformer(model_args, layers, rope)
+
             # Get checkpoint dtype.
             if checkpoint:
                 self.model_.checkpoint_dtype = get_checkpoint_dtype(checkpoint)