Support HuggingFace RoPE in static attention

Differential Revision: D69857290

Pull Request resolved: #8569

Author: sxu

examples/models/llama/static_attention.py

@@ -114,15 +114,30 @@ def update(
         return all_data, (out_k_cache, out_v_cache)
 
 
-def _apply_rotary_embedding(
-    x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor
-) -> torch.Tensor:
-    x_r, x_i = x[..., ::2], x[..., 1::2]
-    x_out_r = x_r * freqs_cos - x_i * freqs_sin
-    x_out_i = x_r * freqs_sin + x_i * freqs_cos
+class _Rope(nn.Module):
+    def __init__(self, use_hf_rope):
+        super().__init__()
+        self.use_hf_rope = use_hf_rope
+
+    def forward(
+        self, x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor
+    ) -> torch.Tensor:
+        if self.use_hf_rope:
+            if len(freqs_cos.shape) == 2:
+                freqs_cos = freqs_cos.unsqueeze(0)
+            if len(freqs_sin.shape) == 2:
+                freqs_sin = freqs_sin.unsqueeze(0)
+            x1 = x[..., : x.shape[-1] // 2]
+            x2 = x[..., x.shape[-1] // 2 :]
+            x_rotated = torch.cat((-x2, x1), dim=-1)
+            return x * freqs_cos + x_rotated * freqs_sin
+        else:
+            x_r, x_i = x[..., ::2], x[..., 1::2]
+            x_out_r = x_r * freqs_cos - x_i * freqs_sin
+            x_out_i = x_r * freqs_sin + x_i * freqs_cos
 
-    x_out = torch.cat([x_out_r, x_out_i], dim=-1)
-    return x_out
+            x_out = torch.cat([x_out_r, x_out_i], dim=-1)
+            return x_out
 
 
 @register_attention("static")
@@ -172,6 +187,7 @@ def __init__(self, config: ModelArgs, layer_id: int, rope: Rope):
             [StaticVCache(layer_id, i) for i in range(self.n_kv_heads)]
         )
         self.wo = nn.Linear(self.n_heads * self.head_dim, self.dim, bias=False)
+        self.rope = _Rope(rope.params.use_hf_rope)
 
     def forward(
         self,
@@ -191,8 +207,8 @@ def forward(
         new_qs = [self.wqs[i](x) for i in range(self.n_heads)]
         new_ks = [self.wks[i](x) for i in range(self.n_kv_heads)]
         new_vs = [self.wvs[i](x) for i in range(self.n_kv_heads)]
-        new_qs = [_apply_rotary_embedding(q, freqs_cos, freqs_sin) for q in new_qs]
-        new_ks = [_apply_rotary_embedding(k, freqs_cos, freqs_sin) 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 = []
         all_vs = []
@@ -211,7 +227,7 @@ def forward(
             kv_idx = i // self.n_heads_per_kv_group
             attn = new_qs[i] @ all_ks[kv_idx].transpose(-2, -1)
             attn = attn * self.inv_scale
-            attn = attn + mask  # pyre-ignore
+            attn = attn + mask
             attn = F.softmax(attn, dim=-1)
             heads.append(attn @ all_vs[kv_idx])
 

examples/models/llama/tests/test_static_attention.py

@@ -43,6 +43,35 @@ def test_without_cache(self):
         )
         self.assertTrue(torch.isclose(y, expected, rtol=1e-3).all())
 
+    def test_hf_rope_without_cache(self):
+        config = ModelArgs(
+            dim=64,
+            n_heads=4,
+            n_kv_heads=2,
+            max_seq_len=8,
+            use_hf_rope=True,
+        )
+        layer_id = 0
+        rope = Rope(config)
+        attn_mha = AttentionMHA(config, layer_id, rope).eval()
+        static_attn = StaticAttention(config, layer_id, rope).eval()
+        static_attn.load_weights_from_attention_mha(attn_mha)
+
+        x = torch.rand(1, config.max_seq_len, config.dim)
+        freqs_cos, freqs_sin = rope.get_freqs(None, config.max_seq_len)
+        expected, _ = attn_mha(x, freqs_cos, freqs_sin)
+        mask = torch.triu(
+            torch.full((1, config.max_seq_len, config.max_seq_len), float("-inf")),
+            diagonal=1,
+        )
+        y, _ = static_attn(
+            x,
+            freqs_cos.unsqueeze(0),
+            freqs_sin.unsqueeze(0),
+            mask=mask,
+        )
+        self.assertTrue(torch.isclose(y, expected, rtol=1e-3).all())
+
     def test_with_cache(self):
         config = ModelArgs(
             dim=64,