[Feature] Support Reduce operators for bitwise and/or/xor

examples/flash_attention/example_gqa_bwd_tma_reduce.py

@@ -443,7 +443,8 @@ def maybe_contiguous(x):
             dk = torch.empty(shape_k, dtype=torch.float16, device=q.device)
             dv = torch.empty(shape_v, dtype=torch.float16, device=q.device)
             kernel(q, k, v, do, lse, delta, dq, dk, dv)
-            dq = mod_post(dq)
+            dq, _, _ = mod_post(dq, torch.zeros_like(k, dtype=torch.float32),
+                                torch.zeros_like(v, dtype=torch.float32))
             dk, dv = dk.sum(0), dv.sum(0)
 
         return dq, dk, dv, None, None, None

src/op/reduce.cc

@@ -70,6 +70,19 @@ PrimExpr ReduceOpNode::MakeInitValue() const {
     }
   } else if (type->isAbsMax()) {
     return make_const(dst->dtype, 0);
+  } else if (type->isBitAnd()) {
+    if (is_int) {
+      return make_const(dst->dtype, -1);
+    } else if (is_uint) {
+      return make_const(dst->dtype, (1 << bits) - 1);
+    } else {
+      // Should not arrive here
+      return make_const(dst->dtype, -INFINITY);
+    }
+  } else if (type->isBitOr()) {
+    return make_zero(dst->dtype);
+  } else if (type->isBitXor()) {
+    return make_zero(dst->dtype);
   } else {
     LOG(FATAL) << "Unsupported reduce type: " << type->type;
   }
@@ -91,6 +104,12 @@ PrimExpr ReduceOpNode::MakeReduce(const PrimExpr &lhs,
     return Min(lhs, rhs);
   } else if (type->isAbsMax()) {
     return Max(Max(lhs, rhs), -Min(lhs, rhs));
+  } else if (type->isBitAnd()) {
+    return lhs & rhs;
+  } else if (type->isBitOr()) {
+    return lhs | rhs;
+  } else if (type->isBitXor()) {
+    return lhs ^ rhs;
   } else {
     LOG(FATAL) << "Unsupported reduce type: " << type->type;
   }
@@ -107,6 +126,12 @@ std::string ReduceOpNode::MakeCodegenReducer() const {
     return "tl::MinOp";
   } else if (type->isAbsMax()) {
     return "tl::MaxOp";
+  } else if (type->isBitAnd()) {
+    return "tl::BitAndOp";
+  } else if (type->isBitOr()) {
+    return "tl::BitOrOp";
+  } else if (type->isBitXor()) {
+    return "tl::BitXorOp";
   } else {
     LOG(FATAL) << "Unsupported reduce type: " << type->type;
     return "";
@@ -195,6 +220,12 @@ Stmt ReduceOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
     require_init = true;
   } else if (this->type->isAbsSum()) {
     require_init = true;
+  } else if (this->type->isBitAnd()) {
+    require_init = true;
+  } else if (this->type->isBitOr()) {
+    require_init = true;
+  } else if (this->type->isBitXor()) {
+    require_init = true;
   }
 
   Buffer clear_buffer = dst_buffer;
@@ -203,6 +234,12 @@ Stmt ReduceOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
     need_duplicate = true;
   } else if (this->type->isAbsSum() && !this->clear) {
     need_duplicate = true;
+  } else if (this->type->isBitAnd()) {
+    need_duplicate = true;
+  } else if (this->type->isBitOr() && !this->clear) {
+    need_duplicate = true;
+  } else if (this->type->isBitXor() && !this->clear) {
+    need_duplicate = true;
   }
 
   if (need_duplicate) {
@@ -213,9 +250,10 @@ Stmt ReduceOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
   }
 
   // make reduce-init stmt
-  if (require_init)
+  if (require_init) {
     stmts.push_back(
         BufferStore(clear_buffer, this->MakeInitValue(), dst_indices));
+  }
 
   // make thread-local reduce
   Array<PrimExpr> src_indice_compressed;
@@ -298,6 +336,29 @@ Stmt ReduceOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
                                   Add(BufferLoad(dst_buffer, dst_indices),
                                       BufferLoad(clear_buffer, dst_indices)),
                                   dst_indices));
+    } else if (this->type->isBitAnd()) {
+      if (!this->clear) {
+        stmts.push_back(
+            BufferStore(dst_buffer,
+                        bitwise_and(BufferLoad(dst_buffer, dst_indices),
+                                    BufferLoad(clear_buffer, dst_indices)),
+                        dst_indices));
+      } else {
+        stmts.push_back(BufferStore(
+            dst_buffer, BufferLoad(clear_buffer, dst_indices), dst_indices));
+      }
+    } else if (this->type->isBitOr()) {
+      stmts.push_back(
+          BufferStore(dst_buffer,
+                      bitwise_or(BufferLoad(dst_buffer, dst_indices),
+                                 BufferLoad(clear_buffer, dst_indices)),
+                      dst_indices));
+    } else if (this->type->isBitXor()) {
+      stmts.push_back(
+          BufferStore(dst_buffer,
+                      bitwise_xor(BufferLoad(dst_buffer, dst_indices),
+                                  BufferLoad(clear_buffer, dst_indices)),
+                      dst_indices));
     } else {
       ICHECK(false) << "Unsupported reduce type: " << this->type->type;
     }

src/op/reduce.h

@@ -21,6 +21,9 @@ enum class ReduceTypeEnum : uint8_t {
   kMax,    ///< Maximum value reduction
   kMin,    ///< Minimum value reduction
   kAbsMax, ///< Maximum absolute value reduction
+  kBitAnd, ///< Bitwise and reduction
+  kBitOr,  ///< Bitwise or reduction
+  kBitXor, ///< Bitwise xor reduction
 };
 
 /// Node class representing a reduction type
@@ -50,6 +53,9 @@ class ReduceTypeNode : public Object {
   bool isMax() const { return type == int(ReduceTypeEnum::kMax); }
   bool isMin() const { return type == int(ReduceTypeEnum::kMin); }
   bool isAbsMax() const { return type == int(ReduceTypeEnum::kAbsMax); }
+  bool isBitAnd() const { return type == int(ReduceTypeEnum::kBitAnd); }
+  bool isBitOr() const { return type == int(ReduceTypeEnum::kBitOr); }
+  bool isBitXor() const { return type == int(ReduceTypeEnum::kBitXor); }
 };
 
 /// Wrapper class for reduction type with string-based construction
@@ -68,6 +74,12 @@ class ReduceType : public ObjectRef {
       node->type = int(ReduceTypeEnum::kAbsMax);
     } else if (type == "min") {
       node->type = int(ReduceTypeEnum::kMin);
+    } else if (type == "bitand") {
+      node->type = int(ReduceTypeEnum::kBitAnd);
+    } else if (type == "bitor") {
+      node->type = int(ReduceTypeEnum::kBitOr);
+    } else if (type == "bitxor") {
+      node->type = int(ReduceTypeEnum::kBitXor);
     } else {
       LOG(FATAL) << "Invalid reduce type: " << type;
     }

src/tl_templates/cuda/reduce.h

@@ -22,6 +22,24 @@ struct MinOp {
   }
 };
 
+struct BitAndOp {
+  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
+    return x & y;
+  }
+};
+
+struct BitOrOp {
+  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
+    return x | y;
+  }
+};
+
+struct BitXorOp {
+  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
+    return x ^ y;
+  }
+};
+
 template <class Reducer, int threads, int scale, int thread_offset = 0,
           int all_threads = threads>
 struct AllReduce {

testing/python/math/test_math_bitwise_reduce.py

@@ -0,0 +1,115 @@
+import tilelang
+import tilelang.language as T
+import torch
+import tilelang.testing
+
+
+@tilelang.jit(
+    out_idx=[-1],
+    pass_configs={
+        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: False,
+    },
+)
+def bitwise_reduce(
+    M,
+    N,
+    block_M,
+    block_N,
+    name,
+    func,
+    clear=True,
+):
+
+    @T.prim_func
+    def reduce_func(
+            A: T.Tensor((M, N), "int32"),
+            B: T.Tensor((M), "int32"),
+            Output: T.Tensor((M), "int32"),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
+            A_shared = T.alloc_shared((block_M, block_N), "int32")
+            A_fragment = T.alloc_fragment((block_M, block_N), "int32")
+            B_shared = T.alloc_shared((block_M,), "int32")
+            B_fragment = T.alloc_fragment((block_M), "int32")
+            T.copy(A[by * block_M, bx * block_N], A_shared)
+            T.copy(A_shared, A_fragment)
+            T.copy(B[by * block_M], B_shared)
+            T.copy(B_shared, B_fragment)
+            func(A_fragment, B_fragment, clear=clear)
+            T.copy(B_fragment, Output[by * block_M])
+
+    return reduce_func
+
+
+def run_single_bitwise_reduce(
+    name,
+    func,
+    clear=True,
+):
+    M, N = 32, 32
+    block_M, block_N = 32, 32
+    kernel = bitwise_reduce(M, N, block_M, block_N, name, func, clear)
+
+    # Generate test data that exercises all bit patterns for robust bitwise reduce testing
+    a = torch.zeros((M, N), device="cuda", dtype=torch.int32)
+
+    # Fill with patterns that will produce meaningful results for bitwise operations:
+    # - Different bit patterns across rows/columns
+    # - Mix of 0s and 1s in various positions
+    # - Some all-1s and all-0s patterns for edge cases
+    for i in range(M):
+        for j in range(N):
+            # Create varied bit patterns:
+            # Row-based pattern: alternating bits based on row index
+            row_pattern = (i & 0xF) << (i % 4)  # 4-bit patterns shifted by row
+
+            # Column-based pattern: different bit positions set based on column
+            col_pattern = (1 << (j % 31))  # Single bit set at different positions
+
+            # Combine patterns with XOR to create diverse bit distributions
+            # Add some deterministic "noise" based on position
+            position_factor = (i * N + j) % 256
+
+            # Final value combines all patterns
+            a[i, j] = (row_pattern ^ col_pattern ^ position_factor) & 0xFFFFFFFF
+
+            if i % 4 == 0:
+                a[i, j] &= ~(0x1 << (i // 4))
+            elif i % 2 == 0:
+                a[i, j] |= (0x1 << (i // 2))
+
+    if name == "reduce_bitand":
+        expected = torch.full((M,), -1, device="cuda", dtype=torch.int32)
+    elif name == "reduce_bitor" or name == "reduce_bitxor":
+        expected = torch.full((M,), 0, device="cuda", dtype=torch.int32)
+    else:
+        raise ValueError("Invalid name: {}".format(name))
+
+    output = kernel(a, expected)
+
+    for i in range(M):
+        for j in range(N):
+            if name == "reduce_bitand":
+                expected[i] = expected[i] & a[i, j]
+            elif name == "reduce_bitor":
+                expected[i] = expected[i] | a[i, j]
+            elif name == "reduce_bitxor":
+                expected[i] = expected[i] ^ a[i, j]
+            else:
+                raise ValueError("Invalid name: {}".format(name))
+    assert torch.all(output == expected)
+    print("✓ {} with clear={} test passed".format(name, clear))
+
+
+@tilelang.testing.requires_cuda
+def test_bitwise_reduce_ops():
+    run_single_bitwise_reduce("reduce_bitand", T.reduce_bitand, clear=True)
+    run_single_bitwise_reduce("reduce_bitor", T.reduce_bitor, clear=True)
+    run_single_bitwise_reduce("reduce_bitxor", T.reduce_bitxor, clear=True)
+    run_single_bitwise_reduce("reduce_bitand", T.reduce_bitand, clear=False)
+    run_single_bitwise_reduce("reduce_bitor", T.reduce_bitor, clear=False)
+    run_single_bitwise_reduce("reduce_bitxor", T.reduce_bitxor, clear=False)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/language/__init__.py

@@ -58,6 +58,9 @@
     reduce_sum,  # noqa: F401
     reduce_abssum,  # noqa: F401
     reduce_absmax,  # noqa: F401
+    reduce_bitand,  # noqa: F401
+    reduce_bitor,  # noqa: F401
+    reduce_bitxor,  # noqa: F401
     cumsum,  # noqa: F401
     finalize_reducer,  # noqa: F401
 )

tilelang/language/reduce.py

@@ -139,6 +139,51 @@ def reduce_absmax(buffer: tir.Buffer, out: tir.Buffer, dim: int = -1, clear: boo
     return reduce(buffer, out, "absmax", dim, clear)
 
 
+def reduce_bitand(buffer: tir.Buffer, out: tir.Buffer, dim: int = -1, clear: bool = True):
+    """Perform reduce bitwise-and on input buffer, store the result to output buffer.
+
+    Args:
+        buffer (tir.Buffer): The input buffer
+        out (tir.Buffer): The output buffer
+        dim (int): The dimension to perform reduce on
+
+    Returns:
+        tir.Call: Handle to the reduction operation
+    """
+    dim = _legalize_dim(buffer, dim)
+    return reduce(buffer, out, "bitand", dim, clear)
+
+
+def reduce_bitor(buffer: tir.Buffer, out: tir.Buffer, dim: int = -1, clear: bool = True):
+    """Perform reduce bitwise-or on input buffer, store the result to output buffer.
+
+    Args:
+        buffer (tir.Buffer): The input buffer
+        out (tir.Buffer): The output buffer
+        dim (int): The dimension to perform reduce on
+
+    Returns:
+        tir.Call: Handle to the reduction operation
+    """
+    dim = _legalize_dim(buffer, dim)
+    return reduce(buffer, out, "bitor", dim, clear)
+
+
+def reduce_bitxor(buffer: tir.Buffer, out: tir.Buffer, dim: int = -1, clear: bool = True):
+    """Perform reduce bitwise-xor on input buffer, store the result to output buffer.
+
+    Args:
+        buffer (tir.Buffer): The input buffer
+        out (tir.Buffer): The output buffer
+        dim (int): The dimension to perform reduce on
+
+    Returns:
+        tir.Call: Handle to the reduction operation
+    """
+    dim = _legalize_dim(buffer, dim)
+    return reduce(buffer, out, "bitxor", dim, clear)
+
+
 @macro
 def cumsum_fragment(src: tir.Buffer, dst: tir.Buffer, dim: int, reverse: bool) -> tir.PrimExpr:
     cumsum_smem = alloc_shared(src.shape, src.dtype, "shared.dyn")