diff --git a/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp b/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp
index a2d4012bf23e..00d921510167 100644
--- a/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp
+++ b/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp
@@ -106,8 +106,12 @@ warpsPerTileV3(DotOp dotOp, const ArrayRef<int64_t> shape, int numWarps,
                const SmallVector<unsigned, 3> &instrShape) {
   SetVector<Operation *> slices;
   mlir::getForwardSlice(dotOp.getResult(), &slices);
-  if (llvm::find_if(slices, [](Operation *op) { return isa<DotOp>(op); }) !=
-      slices.end())
+  // Contains a chained dot. We prefer to assign warps to one axis
+  // to facilitate use cases like flash attention, allowing reductions within
+  // the same warp.
+  if (llvm::find_if(slices, [](Operation *op) {
+        return op->hasTrait<OpTrait::DotLike>();
+      }) != slices.end())
     return {(unsigned)numWarps, 1};
 
   // For MMAv3, the smallest indivisible unit of warp shape is (4, 1).
diff --git a/test/TritonGPU/accelerate-matmul.mlir b/test/TritonGPU/accelerate-matmul.mlir
index 85b37f3ed3a9..2f9793e52f09 100644
--- a/test/TritonGPU/accelerate-matmul.mlir
+++ b/test/TritonGPU/accelerate-matmul.mlir
@@ -73,6 +73,33 @@ module attributes {"triton_gpu.target" = "cuda:80", "triton_gpu.num-ctas" = 1 :
 
 // -----
 
+// CHECK: #mma = #triton_gpu.nvidia_mma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [8, 1], instrShape = [16, 32, 16]}>
+// CHECK: #mma1 = #triton_gpu.nvidia_mma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [4, 2], instrShape = [16, 64, 16]}>
+#blocked = #triton_gpu.blocked<{sizePerThread = [4, 4], threadsPerWarp = [2, 16], warpsPerCTA = [8, 1], order = [1, 0]}>
+#blocked1 = #triton_gpu.blocked<{sizePerThread = [4, 4], threadsPerWarp = [1, 32], warpsPerCTA = [8, 1], order = [1, 0]}>
+#blocked2 = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [2, 16], warpsPerCTA = [8, 1], order = [1, 0]}>
+module attributes {"triton_gpu.target" = "cuda:90", "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 8 : i32, "triton_gpu.threads-per-warp" = 32 : i32} {
+  // CHECK-LABEL: chained_dot
+  tt.func public @chained_dot_wgmma(
+    %arg0: tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked}>>,
+    %arg1: tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked}>>,
+    %arg2: tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked1}>>) -> tensor<64x128xf32, #blocked1> {
+    %cst_0 = arith.constant dense<0.000000e+00> : tensor<64x64xf32, #blocked>
+    %cst_1 = arith.constant dense<0.000000e+00> : tensor<64x128xf32, #blocked1>
+  // CHECK: triton_nvidia_gpu.warp_group_dot {{.*}} -> tensor<64x64xf32, #mma>
+    %d = tt.dot %arg0, %arg1, %cst_0 :
+      tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked}>> * tensor<128x64xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked}>> -> tensor<64x64xf32, #blocked>
+    %t = arith.truncf %d : tensor<64x64xf32, #blocked> to tensor<64x64xf16, #blocked>
+    %c = triton_gpu.convert_layout %t : tensor<64x64xf16, #blocked> -> tensor<64x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked1}>>
+  // CHECK: triton_nvidia_gpu.warp_group_dot {{.*}} -> tensor<64x128xf32, #mma1>
+    %r = tt.dot %c, %arg2, %cst_1 :
+      tensor<64x64xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #blocked1}>> * tensor<64x128xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #blocked1}>> -> tensor<64x128xf32, #blocked1>
+    tt.return %r : tensor<64x128xf32, #blocked1>
+  }
+}
+
+// -----
+
 // CHECK: #[[$MMA:.+]] = #triton_gpu.nvidia_mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [4, 2], instrShape = [16, 8]}>
 #blocked = #triton_gpu.blocked<{sizePerThread = [4, 4], threadsPerWarp = [2, 16], warpsPerCTA = [8, 1], order = [1, 0]}>
 #blocked1 = #triton_gpu.blocked<{sizePerThread = [4, 4], threadsPerWarp = [1, 32], warpsPerCTA = [8, 1], order = [1, 0]}>