Fix SDPA vmap with GQA/MQA shapes (n_heads != n_kv_heads)

The ScaledDotProductAttention primitive relied on Custom::vmap which
re-vmapped the fallback lambda. That lambda captured n_q_heads and
n_kv_heads at creation time, causing shape mismatches (SIGSEGV/hang)
when vmap changed the array dimensions.

Add a dedicated vmap override that merges the vmap axis into the batch
dimension and re-invokes scaled_dot_product_attention, which recomputes
head counts from actual shapes. Falls back to Custom::vmap for sinks.

Author: Brooooooklyn

mlx/fast.cpp

@@ -50,6 +50,83 @@ std::pair<std::vector<array>, std::vector<int>> Custom::vmap(
   return {outputs, out_axes};
 }
 
+std::pair<std::vector<array>, std::vector<int>> ScaledDotProductAttention::vmap(
+    const std::vector<array>& inputs,
+    const std::vector<int>& axes) {
+  auto s = stream();
+
+  // Sinks require 1-D input; fall back to generic vmap for that case.
+  if (has_sinks_) {
+    return Custom::vmap(inputs, axes);
+  }
+
+  // Determine vmap size from the first mapped input.
+  int vmap_size = -1;
+  for (int i = 0; i < static_cast<int>(axes.size()); ++i) {
+    if (axes[i] != -1) {
+      vmap_size = inputs[i].shape(axes[i]);
+      break;
+    }
+  }
+
+  auto prepare = [&s, vmap_size](const array& x, int ax) -> array {
+    if (ax == -1) {
+      return repeat(expand_dims(x, 0, s), vmap_size, 0, s);
+    }
+    if (ax != 0) {
+      return moveaxis(x, ax, 0, s);
+    }
+    return x;
+  };
+
+  auto q = prepare(inputs[0], axes[0]);
+  auto k = prepare(inputs[1], axes[1]);
+  auto v = prepare(inputs[2], axes[2]);
+
+  // [V, B, H, L, D] -> [V*B, H, L, D]
+  auto merge_batch = [&s, vmap_size](const array& x) -> array {
+    auto shape = x.shape();
+    Shape new_shape = {vmap_size * shape[1]};
+    new_shape.insert(new_shape.end(), shape.begin() + 2, shape.end());
+    return reshape(x, std::move(new_shape), s);
+  };
+
+  q = merge_batch(q);
+  k = merge_batch(k);
+  v = merge_batch(v);
+
+  std::optional<array> mask_arr;
+  bool has_arr_mask = !do_causal_ && inputs.size() > 3;
+  if (has_arr_mask) {
+    mask_arr = merge_batch(prepare(inputs[3], axes[3]));
+  }
+  std::string mask_mode = do_causal_ ? "causal" : has_arr_mask ? "array" : "";
+
+  auto out = scaled_dot_product_attention(
+      q, k, v, scale_, mask_mode, mask_arr, std::nullopt, s);
+
+  // [V*B, H, L, D] -> [V, B, H, L, D]
+  auto split_batch = [&s, vmap_size](const array& x) -> array {
+    auto shape = x.shape();
+    Shape new_shape = {vmap_size, shape[0] / vmap_size};
+    new_shape.insert(new_shape.end(), shape.begin() + 1, shape.end());
+    return reshape(x, std::move(new_shape), s);
+  };
+
+  out = split_batch(out);
+
+  // The re-invoked SDPA may produce a logsumexp sibling when training.
+  if (output_logsumexp_) {
+    assert(
+        !out.siblings().empty() &&
+        "vmap'd SDPA expected logsumexp sibling output");
+    auto lse = split_batch(out.siblings()[0]);
+    return {{out, lse}, {0, 0}};
+  }
+
+  return {{out}, {0}};
+}
+
 array rms_norm(
     const array& x,
     const std::optional<array>& weight,

mlx/fast_primitives.h

@@ -244,6 +244,10 @@ class ScaledDotProductAttention : public Custom {
       const std::vector<int>& argnums,
       const std::vector<array>& outputs) override;
 
+  std::pair<std::vector<array>, std::vector<int>> vmap(
+      const std::vector<array>& inputs,
+      const std::vector<int>& axes) override;
+
   bool is_equivalent(const Primitive& other) const override;
 
   DEFINE_NAME(ScaledDotProductAttention);

python/tests/test_fast_sdpa.py

@@ -642,6 +642,46 @@ def test_sdpa_sliced(self):
                         tolerance = {"rtol": 1e-2, "atol": 1e-2}
                     self.assertTrue(mx.allclose(ref, out, **tolerance))
 
+    @unittest.skipIf(not mx.metal.is_available(), "Metal kernel required")
+    def test_sdpa_vmap_uses_fused_kernel(self):
+        """Verify vmap'd SDPA dispatches the fused Metal kernel, not the
+        decomposed fallback.  The fused kernel and the fallback use different
+        accumulation orders, producing distinguishable float16 results for
+        large enough shapes.  We check that vmap output matches the
+        non-vmapped (fused) output exactly."""
+        D = 64
+        L = 128  # L > 8 → sdpa_full kernel path
+        scale = 1.0 / math.sqrt(D)
+        B = 4
+
+        for n_q, n_kv in [(32, 32), (32, 8), (16, 4)]:
+            with self.subTest(n_q_heads=n_q, n_kv_heads=n_kv):
+                mx.random.seed(42)
+                q = mx.random.normal((B, n_q, L, D), dtype=mx.float16)
+                k = mx.random.normal((B, n_kv, L, D), dtype=mx.float16)
+                v = mx.random.normal((B, n_kv, L, D), dtype=mx.float16)
+                mx.eval(q, k, v)
+
+                # Non-vmapped SDPA — uses fused Metal kernel
+                kernel_out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale)
+
+                # Vmapped SDPA — should also use fused kernel
+                def f(qi, ki, vi):
+                    return mx.fast.scaled_dot_product_attention(
+                        qi[None], ki[None], vi[None], scale=scale
+                    )[0]
+
+                vmap_out = mx.vmap(f)(q, k, v)
+                mx.eval(kernel_out, vmap_out)
+
+                # Fused kernel output must match exactly (same kernel, same
+                # accumulation).  If vmap fell back to the decomposed path,
+                # float16 rounding differences would cause a mismatch.
+                self.assertTrue(
+                    mx.array_equal(kernel_out, vmap_out),
+                    f"vmap path did not use fused kernel for ({n_q},{n_kv})",
+                )
+
 
 if __name__ == "__main__":
     mlx_tests.MLXTestRunner(failfast=True)