[Feat] Support warp reduce

src/op/builtin.cc

@@ -341,5 +341,30 @@ TIR_DEFINE_TL_BUILTIN(tcgen05_mma_arrive)
     .set_attr<TCallEffectKind>("TCallEffectKind",
                                Integer(CallEffectKind::kOpaque));
 
+TIR_DEFINE_TL_BUILTIN(warp_reduce_sum)
+    .set_num_inputs(1)
+    .set_attr<TCallEffectKind>("TCallEffectKind",
+                               Integer(CallEffectKind::kOpaque));
+
+TIR_DEFINE_TL_BUILTIN(warp_reduce_max)
+    .set_num_inputs(1)
+    .set_attr<TCallEffectKind>("TCallEffectKind",
+                               Integer(CallEffectKind::kOpaque));
+
+TIR_DEFINE_TL_BUILTIN(warp_reduce_min)
+    .set_num_inputs(1)
+    .set_attr<TCallEffectKind>("TCallEffectKind",
+                               Integer(CallEffectKind::kOpaque));
+
+TIR_DEFINE_TL_BUILTIN(warp_reduce_bitand)
+    .set_num_inputs(1)
+    .set_attr<TCallEffectKind>("TCallEffectKind",
+                               Integer(CallEffectKind::kOpaque));
+
+TIR_DEFINE_TL_BUILTIN(warp_reduce_bitor)
+    .set_num_inputs(1)
+    .set_attr<TCallEffectKind>("TCallEffectKind",
+                               Integer(CallEffectKind::kOpaque));
+
 } // namespace tl
 } // namespace tvm

src/op/builtin.h

@@ -571,6 +571,31 @@ TVM_DLL const Op &device_assert();
  */
 TVM_DLL const Op &device_assert_with_msg();
 
+/*!
+ * \brief tilelang intrinsic for warp reduction sum.
+ */
+TVM_DLL const Op &warp_reduce_sum();
+
+/*!
+ * \brief tilelang intrinsic for warp reduction max.
+ */
+TVM_DLL const Op &warp_reduce_max();
+
+/*!
+ * \brief tilelang intrinsic for warp reduction min.
+ */
+TVM_DLL const Op &warp_reduce_min();
+
+/*!
+ * \brief tilelang intrinsic for warp reduction bitand.
+ */
+TVM_DLL const Op &warp_reduce_bitand();
+
+/*!
+ * \brief tilelang intrinsic for warp reduction bitor.
+ */
+TVM_DLL const Op &warp_reduce_bitor();
+
 } // namespace tl
 } // namespace tvm
 

src/target/codegen_cuda.cc

@@ -2609,6 +2609,16 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) {
     std::string func_name = math_func(op->dtype, "fdiv", rounding_mode);
     os << func_name << "(" << PrintExpr(op->args[0]) << ", "
        << PrintExpr(op->args[1]) << ")";
+  } else if (op->op.same_as(tl::warp_reduce_sum())) {
+    os << "tl::warp_reduce_sum(" << PrintExpr(op->args[0]) << ")";
+  } else if (op->op.same_as(tl::warp_reduce_max())) {
+    os << "tl::warp_reduce_max(" << PrintExpr(op->args[0]) << ")";
+  } else if (op->op.same_as(tl::warp_reduce_min())) {
+    os << "tl::warp_reduce_min(" << PrintExpr(op->args[0]) << ")";
+  } else if (op->op.same_as(tl::warp_reduce_bitand())) {
+    os << "tl::warp_reduce_bitand(" << PrintExpr(op->args[0]) << ")";
+  } else if (op->op.same_as(tl::warp_reduce_bitor())) {
+    os << "tl::warp_reduce_bitor(" << PrintExpr(op->args[0]) << ")";
   } else {
     CodeGenC::VisitExpr_(op, os);
   }

src/tl_templates/cuda/reduce.h

@@ -250,4 +250,35 @@ template <int threads, int Axis = 0, bool reverse = false> struct CumSum2D {
   }
 };
 
+template <typename T, typename ReduceOp>
+TL_DEVICE T warp_reduce(T value, ReduceOp op) {
+  constexpr uint32_t mask = 0xffffffff;
+  value = op(value, __shfl_xor_sync(mask, value, 16));
+  value = op(value, __shfl_xor_sync(mask, value, 8));
+  value = op(value, __shfl_xor_sync(mask, value, 4));
+  value = op(value, __shfl_xor_sync(mask, value, 2));
+  value = op(value, __shfl_xor_sync(mask, value, 1));
+  return value;
+}
+
+template <typename T> TL_DEVICE T warp_reduce_sum(T value) {
+  return warp_reduce<T>(value, SumOp());
+}
+
+template <typename T> TL_DEVICE T warp_reduce_max(T value) {
+  return warp_reduce<T>(value, MaxOp());
+}
+
+template <typename T> TL_DEVICE T warp_reduce_min(T value) {
+  return warp_reduce<T>(value, MinOp());
+}
+
+template <typename T> TL_DEVICE T warp_reduce_bitand(T value) {
+  return warp_reduce<T>(value, BitAndOp());
+}
+
+template <typename T> TL_DEVICE T warp_reduce_bitor(T value) {
+  return warp_reduce<T>(value, BitOrOp());
+}
+
 } // namespace tl

testing/python/language/test_tilelang_language_warp_reduce.py

@@ -0,0 +1,83 @@
+import torch
+
+import tilelang
+import tilelang.testing
+import tilelang.language as T
+
+
+@tilelang.jit
+def get_kernel(reduce_op: str, dtype: str):
+
+    assert reduce_op in ["sum", "max", "min", "bitand", "bitor"]
+
+    @T.prim_func
+    def main(x: T.Tensor((32), dtype)):
+        with T.Kernel(1, threads=32):
+            tx = T.get_thread_binding(0)
+            local_val = T.alloc_local([1], dtype)
+            local_val[0] = x[tx]
+            reduced_val = T.alloc_local([1], dtype)
+            if reduce_op == "sum":
+                reduced_val[0] = T.warp_reduce_sum(local_val[0])
+            elif reduce_op == "max":
+                reduced_val[0] = T.warp_reduce_max(local_val[0])
+            elif reduce_op == "min":
+                reduced_val[0] = T.warp_reduce_min(local_val[0])
+            elif reduce_op == "bitand":
+                reduced_val[0] = T.warp_reduce_bitand(local_val[0])
+            elif reduce_op == "bitor":
+                reduced_val[0] = T.warp_reduce_bitor(local_val[0])
+            x[tx] = reduced_val[0]
+
+    return main
+
+
+def test_warp_reduce_sum():
+    a = torch.randn((32,), dtype=torch.float32, device='cuda')
+    kernel = get_kernel('sum', 'float32')
+    ref = torch.full_like(a, a.sum())
+    kernel(a)
+    torch.testing.assert_close(a, ref)
+
+
+def test_warp_reduce_max():
+    a = torch.randn((32,), dtype=torch.float32, device='cuda')
+    kernel = get_kernel("max", 'float32')
+    print(kernel.get_kernel_source())
+    ref = torch.full_like(a, a.max())
+    kernel(a)
+    torch.testing.assert_close(a, ref)
+
+
+def test_warp_reduce_min():
+    a = torch.randn((32,), dtype=torch.float32, device='cuda')
+    kernel = get_kernel("min", 'float32')
+    ref = torch.full_like(a, a.min())
+    kernel(a)
+    torch.testing.assert_close(a, ref)
+
+
+def test_warp_reduce_bitand():
+    a = torch.randint(0, 100, size=(32,), dtype=torch.int32, device='cuda')
+    kernel = get_kernel("bitand", 'int32')
+    ref_val = a[0]
+    for i in range(1, a.shape[0]):
+        ref_val = ref_val & a[i]
+    ref = torch.full_like(a, ref_val)
+    kernel(a)
+    torch.testing.assert_close(a, ref)
+
+
+def test_warp_reduce_bitor():
+    a = torch.randint(0, 100, size=(32,), dtype=torch.int32, device='cuda')
+    kernel = get_kernel("bitor", 'int32')
+    ref_val = a[0]
+    for i in range(1, a.shape[0]):
+        ref_val = ref_val | a[i]
+    ref = torch.full_like(a, ref_val)
+    kernel(a)
+    torch.testing.assert_close(a, ref)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/language/__init__.py

@@ -65,6 +65,11 @@
     reduce_bitxor,  # noqa: F401
     cumsum,  # noqa: F401
     finalize_reducer,  # noqa: F401
+    warp_reduce_sum,  # noqa: F401
+    warp_reduce_max,  # noqa: F401
+    warp_reduce_min,  # noqa: F401
+    warp_reduce_bitand,  # noqa: F401
+    warp_reduce_bitor,  # noqa: F401
 )
 from .print import print, device_assert  # noqa: F401
 from .customize import (

tilelang/language/reduce.py

@@ -325,3 +325,83 @@ def finalize_reducer(reducer: tir.Buffer):
         tir.op.Op.get("tl.finalize_reducer"),
         reducer.access_ptr("w"),
     )
+
+
+def warp_reduce_sum(value: tir.PrimExpr):
+    """Perform warp reduction sum on a register value.
+
+    This function reduces a value across all threads in a warp using shuffle operations.
+    Each thread provides a  register `value`, and after the reduction, all threads
+    will have the sum of all values across the warp.
+
+    Args:
+        value (tir.PrimExpr): The input register value to reduce
+
+    Returns:
+        tir.PrimExpr: The reduced sum value (same on all threads in the warp)
+    """
+    return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_sum"), value)
+
+
+def warp_reduce_max(value: tir.PrimExpr):
+    """Perform warp reduction max on a register value.
+
+    This function reduces a value across all threads in a warp using shuffle operations.
+    Each thread provides a  register `value`, and after the reduction, all threads
+    will have the max of all values across the warp.
+
+    Args:
+        value (tir.PrimExpr): The input register value to reduce
+
+    Returns:
+        tir.PrimExpr: The reduced max value (same on all threads in the warp)
+    """
+    return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_max"), value)
+
+
+def warp_reduce_min(value: tir.PrimExpr):
+    """Perform warp reduction min on a register value.
+
+    This function reduces a value across all threads in a warp using shuffle operations.
+    Each thread provides a  register `value`, and after the reduction, all threads
+    will have the min of all values across the warp.
+
+    Args:
+        value (tir.PrimExpr): The input register value to reduce
+
+    Returns:
+        tir.PrimExpr: The reduced min value (same on all threads in the warp)
+    """
+    return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_min"), value)
+
+
+def warp_reduce_bitand(value: tir.PrimExpr):
+    """Perform warp reduction bitwise-and on a register value.
+
+    This function reduces a value across all threads in a warp using shuffle operations.
+    Each thread provides a  register `value`, and after the reduction, all threads
+    will have the bitwise-and of all values across the warp.
+
+    Args:
+        value (tir.PrimExpr): The input register value to reduce
+
+    Returns:
+        tir.PrimExpr: The reduced bitwise-and value (same on all threads in the warp)
+    """
+    return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_bitand"), value)
+
+
+def warp_reduce_bitor(value: tir.PrimExpr):
+    """Perform warp reduction bitwise-or on a register value.
+
+    This function reduces a value across all threads in a warp using shuffle operations.
+    Each thread provides a  register `value`, and after the reduction, all threads
+    will have the bitwise-or of all values across the warp.
+
+    Args:
+        value (tir.PrimExpr): The input register value to reduce
+
+    Returns:
+        tir.PrimExpr: The reduced bitwise-or value (same on all threads in the warp)
+    """
+    return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_bitor"), value)