Support QK norm in static attention

examples/models/llama/static_attention.py

@@ -212,7 +212,6 @@ def __init__(self, config: ModelArgs, layer_id: int, rope: Rope):
         self.use_qk_norm = config.use_qk_norm
         self.use_conv2d = False
 
-        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)
@@ -241,6 +240,13 @@ def __init__(self, config: ModelArgs, layer_id: int, rope: Rope):
         self.wo = nn.Linear(self.n_heads * self.head_dim, self.dim, bias=False)
         self.rope = _Rope(rope.params.use_hf_rope)
 
+        if self.use_qk_norm:
+            self.q_norm = torch.nn.RMSNorm(self.head_dim, config.norm_eps)
+            self.k_norm = torch.nn.RMSNorm(self.head_dim, config.norm_eps)
+        else:
+            self.q_norm = torch.nn.Identity()
+            self.k_norm = torch.nn.Identity()
+
     def forward(
         self,
         x: torch.Tensor,
@@ -275,6 +281,10 @@ def from_conv2ds(ts):
             new_ks = from_conv2ds(new_ks)
             new_vs = from_conv2ds(new_vs)
 
+        if self.use_qk_norm:
+            new_qs = [self.q_norm(q) for q in new_qs]
+            new_ks = [self.k_norm(k) for k in new_ks]
+
         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 = []
@@ -325,6 +335,13 @@ def load_weights_from_attention_mha(self, other: AttentionMHA):
 
         self.wo.weight.data.copy_(other.wo.weight)
 
+        if other.use_qk_norm:
+            self.use_qk_norm = True
+            self.q_norm = torch.nn.RMSNorm(other.q_norm_fn.dim, other.q_norm_fn.eps)
+            self.q_norm.load_state_dict(other.q_norm_fn.state_dict())
+            self.k_norm = torch.nn.RMSNorm(other.k_norm_fn.dim, other.k_norm_fn.eps)
+            self.k_norm.load_state_dict(other.k_norm_fn.state_dict())
+
     def linear_to_conv2d(self):
         def transfer_weight(linear, conv2d):
             conv2d.weight.data.copy_(linear.weight[:, :, None, None])

examples/models/llama/tests/test_static_attention.py

@@ -17,12 +17,13 @@ def setUp(self):
         torch.manual_seed(42)
 
     def test_without_cache(self):
-        def test(use_conv2d):
+        def test(use_qk_norm, use_conv2d):
             config = ModelArgs(
                 dim=64,
                 n_heads=4,
                 n_kv_heads=2,
                 max_seq_len=8,
+                use_qk_norm=use_qk_norm,
             )
             layer_id = 0
             rope = Rope(config)
@@ -47,8 +48,10 @@ def test(use_conv2d):
             )
             self.assertTrue(torch.isclose(y, expected, rtol=1e-3).all())
 
-        test(True)
-        test(False)
+        test(True, True)
+        test(True, False)
+        test(False, True)
+        test(False, False)
 
     def test_hf_rope_without_cache(self):
         config = ModelArgs(