[FlexAttention] Remove Old Constraint on last dim strides

drisspg · drisspg · commit 155a6533a476 · 2025-04-22T22:38:24.000-07:00
ghstack-source-id: 287e517 Pull Request resolved: #151959
diff --git a/test/inductor/test_flex_attention.py b/test/inductor/test_flex_attention.py
@@ -96,7 +96,7 @@ def temp_float32_matmul_precision(precision: str):
 
 def skip_on_cpu(test_func):
     """Decorator to skip tests that are not supported on CPU."""
-    decorated_func = skipCPUIf(True, "Not supported on CUDA")(test_func)
+    decorated_func = skipCPUIf(True, "Not supported on CPU")(test_func)
     return decorated_func
 
 
@@ -2842,6 +2842,7 @@ def test_strided_backwards(self):
             (1, 0, 2, 3),  # Reverse order
             (0, 2, 1, 3),  # Mixed order
             (2, 0, 1, 3),  # Another mixed order
+            (0, 1, 3, 2),  # Non contiguous last dim
         ],
     )
     @common_utils.parametrize("shape", [(2, 1, 128, 16), (4, 2, 64, 16)])
@@ -2890,12 +2891,7 @@ def test_flex_attention_stride_ordering(self, device, mode, permute_order, shape
     @common_utils.parametrize("mode", ["eager", "inductor"])
     @common_utils.parametrize(
         "permute_order",
-        [
-            (0, 1, 2, 3),
-            (1, 0, 2, 3),
-            (0, 2, 1, 3),
-            (2, 0, 1, 3),
-        ],
+        [(0, 1, 2, 3), (1, 0, 2, 3), (0, 2, 1, 3), (2, 0, 1, 3), (0, 1, 3, 2)],
     )
     @common_utils.parametrize("shape", [(2, 5, 128, 16), (4, 2, 64, 16)])
     def test_flex_attention_backward_stride_ordering(
@@ -2939,6 +2935,69 @@ def test_flex_attention_backward_stride_ordering(
                 f"Mode: {mode}, Stride order mismatch for {name}: grad {input_stride_order}, input {orig_stride_order}.",
             )
 
+    @supported_platform
+    def test_non_contiguous_last_dim(self, device):
+        """Test flex_attention with tensors having non contiguous last dimension."""
+        B, H, D = 4, 8, 64
+        dtype = torch.float16 if device == "cuda" else torch.float32
+        for S in [16, 64]:
+
+            def column_major_tensor():
+                tensor = torch.randn(
+                    (B, H, S, D),
+                    dtype=dtype,
+                    device=device,
+                )
+                # Column major in last 2 dims
+                return tensor.transpose(-1, -2).contiguous().transpose(-1, -2)
+
+            q = column_major_tensor()
+            k = column_major_tensor()
+            v = column_major_tensor()
+
+            requires_grad = device in DEVICE_SUPPORTS_BACKWARDS
+            if requires_grad:
+                q.requires_grad_(True)
+                k.requires_grad_(True)
+                v.requires_grad_(True)
+
+            self.assertNotEqual(q.stride()[-1], 1)
+            self.assertNotEqual(k.stride()[-1], 1)
+            self.assertNotEqual(v.stride()[-1], 1)
+
+            q_ref, k_ref, v_ref = query_key_value_clones(q, k, v)
+            q_gold, k_gold, v_gold = query_key_value_clones(q, k, v, torch.float64)
+
+            golden_out = flex_attention(q_gold, k_gold, v_gold)
+            ref_out = flex_attention(q_ref, k_ref, v_ref)
+
+            flex_compiled = torch.compile(flex_attention, fullgraph=True, dynamic=True)
+            compiled_out = flex_compiled(q, k, v)
+
+            self._check_out(golden_out, ref_out, compiled_out)
+
+            if requires_grad:
+                backward_grad = torch.randn_like(ref_out)
+
+                golden_out.backward(backward_grad.to(torch.float64))
+                ref_out.backward(backward_grad)
+                compiled_out.backward(backward_grad)
+
+                self._check_out_and_grad(
+                    golden_out,
+                    ref_out,
+                    compiled_out,
+                    q_gold,
+                    q_ref,
+                    q,
+                    k_gold,
+                    k_ref,
+                    k,
+                    v_gold,
+                    v_ref,
+                    v,
+                )
+
     @supported_platform
     @common_utils.parametrize("compile", [True, False])
     def test_fully_masked_out_rows_0_check(self, device, compile: bool):
diff --git a/torch/_inductor/kernel/flex_attention.py b/torch/_inductor/kernel/flex_attention.py
@@ -930,6 +930,15 @@ def check_cpu_supported():
     return supported
 
 
+def contiguous_last_dim(x):
+    """Ensure that realized IR node has a contigous stride in the last dimension."""
+    strides = x.maybe_get_stride()
+    if strides and strides[-1] != 1:
+        contiguous_stride_order = list(reversed(range(len(x.get_size()))))
+        return ExternKernel.require_stride_order(x, contiguous_stride_order)
+    return x
+
+
 def lower_cpu(
     query,
     key,
@@ -1092,6 +1101,9 @@ def convert_mask_graph_module(mask_graph):
         if isinstance(item, TensorBox):
             fake_buffers.append(item.data.data)  # type: ignore[attr-defined]
 
+    # CPU kernel requires last dim to be contiguous
+    query, key, value = map(contiguous_last_dim, [query, key, value])
+
     (
         query,
         key,
@@ -1258,7 +1270,6 @@ def set_head_dim_values(
     )
 
 
-# TODO: We probably also need a layout constraint?
 @register_lowering(torch.ops.higher_order.flex_attention, type_promotion_kind=None)
 def flex_attention(
     query,
@@ -1413,11 +1424,9 @@ def flex_attention(
     else:
         kernel_options.setdefault("IS_DIVISIBLE", True)
 
-    # Reuse query strides for output layout despite different last dimension.
-    # This works because only the last dim differs and we check it is contiguous.
+    # NB it is okay that the v_head_dim is different
+    # We are using these to match fill order of the output.
     q_strides = query.get_stride()
-    assert q_strides[-1] == 1, "Query must be contiguous in the last dimension"
-
     # Construct output layout with strides matching the query.
     out_size = [B, Hq, seq_len_q, v_head_dim]
     out_strides = infer_dense_strides(out_size, q_strides)
