[Executorch][llama] Allow custom sdpa op replacement pass to leverage attention mask

Differential Revision: D73222736

Pull Request resolved: #10285

Author: kimishpatel

examples/models/llama/source_transformation/sdpa.py

@@ -22,9 +22,15 @@ class SDPACustom(torch.nn.Module):
     def __init__(
         self,
         dim: int,
+        max_context_len,
+        enable_dynamic_shape,
+        use_attention_mask: bool = False,
     ):
         super().__init__()
         self.dim = dim
+        self.max_context_len = max_context_len
+        self.use_attention_mask = use_attention_mask
+        self.enable_dynamic_shape = enable_dynamic_shape
 
     def forward(
         self,
@@ -36,6 +42,16 @@ def forward(
         seqlen,
         mask,
     ):
+        if self.use_attention_mask:
+            if self.enable_dynamic_shape:
+                start_pos = input_pos[-1].item()
+                torch._check_is_size(start_pos)
+                torch._check(start_pos < self.max_context_len)
+                seq_length = q.size(2)
+                mask = mask.narrow(0, start_pos, seq_length)
+            else:
+                mask = mask[input_pos]
+
         q = q.transpose(1, 2)  # (bs, seqlen, n_local_heads, head_dim)
         k = k.transpose(1, 2)
         v = v.transpose(1, 2)
@@ -47,34 +63,54 @@ def forward(
         k = k.to(dtype=torch.float)
         v = v.to(dtype=torch.float)
 
-        output = torch.ops.llama.custom_sdpa(
-            q,
-            k,
-            v,
-            input_pos[0].item(),
-            None,  # Attention mask
-            0,  # dropout probability. Ignored by the code
-            True,  # is_causal
-        )
+        if self.use_attention_mask:
+            output = torch.ops.llama.custom_sdpa(
+                q,
+                k,
+                v,
+                input_pos[0].item(),
+                mask,  # Attention mask
+                0,  # dropout probability. Ignored by the code
+                False,  # is_causal
+            )
+        else:
+            output = torch.ops.llama.custom_sdpa(
+                q,
+                k,
+                v,
+                input_pos[0].item(),
+                None,  # Attention mask
+                0,  # dropout probability. Ignored by the code
+                True,  # is_causal
+            )
         return output.view(bsz, seqlen, self.dim).to(dtype=input_dtype)
 
 
-def _replace_sdpa_with_custom_op(module: torch.nn.Module):
+def _replace_sdpa_with_custom_op(
+    module: torch.nn.Module, use_attention_mask: bool = False
+):
     for name, child in module.named_children():
         if isinstance(child, SDPA):
             setattr(
                 module,
                 name,
-                SDPACustom(child.dim),
+                SDPACustom(
+                    child.dim,
+                    child.max_context_len,
+                    child.enable_dynamic_shape,
+                    use_attention_mask=use_attention_mask,
+                ),
             )
         else:
-            _replace_sdpa_with_custom_op(child)
+            _replace_sdpa_with_custom_op(child, use_attention_mask=use_attention_mask)
 
 
-def replace_sdpa_with_custom_op(module: torch.nn.Module) -> torch.nn.Module:
+def replace_sdpa_with_custom_op(
+    module: torch.nn.Module, use_attention_mask: bool = False
+) -> torch.nn.Module:
     from executorch.extension.llm.custom_ops import custom_ops  # noqa
 
-    _replace_sdpa_with_custom_op(module)
+    _replace_sdpa_with_custom_op(module, use_attention_mask=use_attention_mask)
     return module
 
 

examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py

@@ -71,8 +71,8 @@ def test_simple(self, is_dynamic_shape=False):
         self.seq_len = 3
         self._init_cache()
         q, k_val, v_val = self._init_kv()
-        self.float_sdpa = SDPACustom(self.dim)
-        self.quantized_sdpa = SDPACustom(self.dim)
+        self.float_sdpa = SDPACustom(self.dim, self.max_context_len, True)
+        self.quantized_sdpa = SDPACustom(self.dim, self.max_context_len, True)
         k, v = self.custom_kv_cache.update(input_pos, k_val, v_val)
         float_out = self.float_sdpa(input_pos, q, k, v, 1, self.seq_len, None)
         k, v = self.quantized_kv_cache.update(input_pos, k_val, v_val)