Add qk norm optionally before attention calculation

examples/models/llama/TARGETS

@@ -16,6 +16,7 @@ runtime.python_library(
         "rope.py",
         "attention.py",
         "model_args.py",
+        "norm.py",
     ],
     _is_external_target = True,
     base_module = "executorch.examples.models.llama",

examples/models/llama/attention.py

@@ -5,6 +5,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from executorch.examples.models.llama.model_args import ModelArgs
+from executorch.examples.models.llama.norm import RMSNorm
 from executorch.examples.models.llama.rope import Rope
 
 
@@ -176,6 +177,14 @@ def __init__(self, args: ModelArgs, layer_id: int, rope: Rope):
         self.max_context_len = args.max_context_len
         self.dim = args.dim
         self.attention_qkv_bias = args.attention_qkv_bias
+        self.use_qk_norm = args.use_qk_norm
+
+        if self.use_qk_norm:
+            q_norm_dim = self.head_dim
+            k_norm_dim = self.head_dim
+            self.q_norm_fn = RMSNorm(q_norm_dim, eps=args.norm_eps)
+            self.k_norm_fn = RMSNorm(k_norm_dim, eps=args.norm_eps)
+
         self.wq = nn.Linear(
             self.dim, self.n_heads * self.head_dim, bias=self.attention_qkv_bias
         )
@@ -241,6 +250,10 @@ def forward(
         k = k.transpose(1, 2)
         v = v.transpose(1, 2)
 
+        if self.use_qk_norm:
+            q = self.q_norm_fn(q)
+            k = self.k_norm_fn(k)
+
         if self.use_kv_cache:
             assert input_pos is not None
             k, v = self.kv_cache.update(input_pos, k, v)

examples/models/llama/llama_transformer.py

@@ -18,59 +18,11 @@
 )
 
 from executorch.examples.models.llama.model_args import ModelArgs
-
+from executorch.examples.models.llama.norm import RMSNorm
 from executorch.examples.models.llama.rope import Rope
-
 from torch import nn
 
 
-class RMSNorm(torch.nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6):
-        """
-        Initialize the RMSNorm normalization layer.
-
-        Args:
-            dim (int): The dimension of the input tensor.
-            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-
-        Attributes:
-            eps (float): A small value added to the denominator for numerical stability.
-            weight (nn.Parameter): Learnable scaling parameter.
-
-        """
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
-
-        Args:
-            x (torch.Tensor): The input tensor.
-
-        Returns:
-            torch.Tensor: The normalized tensor.
-
-        """
-        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        """
-        Forward pass through the RMSNorm layer.
-
-        Args:
-            x (torch.Tensor): The input tensor.
-
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
-
-        """
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
-
-
 class FeedForward(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()

examples/models/llama/model_args.py

@@ -37,6 +37,7 @@ class ModelArgs:
     output_prune_map: Optional[Dict[int, int]] = None
     apply_embedding: bool = True  # Use embedding inside the transformer
     apply_output: bool = True  # Use output layer (unembedding) inside the transformer
+    use_qk_norm: bool = False  # apply normalization to q and k in the attention
     use_hf_rope: bool = False  # Use HuggingFace's RoPE implementation
     partial_rotary_factor: float = 1.0
     rope_theta: Optional[float] = (

examples/models/llama/norm.py

@@ -0,0 +1,51 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+import torch
+from torch import nn
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight

examples/models/llama/static_attention.py

@@ -209,7 +209,9 @@ def __init__(self, config: ModelArgs, layer_id: int, rope: Rope):
         self.head_dim = config.head_dim
         self.inv_scale = 1.0 / (float(self.head_dim) ** 0.5)
         self.attention_qkv_bias = config.attention_qkv_bias
+        self.use_qk_norm = config.use_qk_norm
 
+        assert not self.use_qk_norm, "QK norm not supported in static attention yet"
         self.wqs = nn.ModuleList(
             [
                 nn.Linear(self.dim, self.head_dim, bias=self.attention_qkv_bias)
@@ -258,7 +260,6 @@ def forward(
         new_vs = [self.wvs[i](x) for i in range(self.n_kv_heads)]
         new_qs = [self.rope(q, freqs_cos, freqs_sin) for q in new_qs]
         new_ks = [self.rope(k, freqs_cos, freqs_sin) for k in new_ks]
-
         all_ks = []
         all_vs = []
         for i in range(self.n_kv_heads):