[Language] Support accumulative T.reduce_sum

src/op/reduce.cc

@@ -15,6 +15,7 @@
 
 #include "../layout/utils.h"
 #include "../transform/loop_partition.h"
+#include "tir/transforms/ir_utils.h"
 
 namespace tvm {
 namespace tl {
@@ -124,10 +125,33 @@ Stmt ReduceOp::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
 
   Array<Stmt> stmts;
 
+  bool require_init = this->clear;
+  // sum op must be cleared
+  if (this->type == ReduceType::kSum) {
+    require_init = true;
+  } else if (this->type == ReduceType::kAbsSum) {
+    require_init = true;
+  }
+
+  Buffer clear_buffer = dst_buffer;
+  bool need_duplicate = false;
+  if (this->type == ReduceType::kSum && !this->clear) {
+    need_duplicate = true;
+  } else if (this->type == ReduceType::kAbsSum && !this->clear) {
+    need_duplicate = true;
+  }
+
+  if (need_duplicate) {
+    // Create a new buffer with same shape and dtype as dst_buffer
+    clear_buffer = decl_buffer(dst_buffer->shape, dst_buffer->dtype,
+                               dst_buffer->name + "_clear",
+                               GetPtrStorageScope(dst_buffer->data));
+  }
+
   // make reduce-init stmt
-  if (this->clear)
+  if (require_init)
     stmts.push_back(
-        BufferStore(dst_buffer, this->MakeInitValue(), dst_indices));
+        BufferStore(clear_buffer, this->MakeInitValue(), dst_indices));
 
   // make thread-local reduce
   Array<PrimExpr> src_indice_compressed;
@@ -141,8 +165,8 @@ Stmt ReduceOp::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
     src_var_compressed.push_back(var);
   }
   Stmt reduce_local = BufferStore(
-      dst_buffer,
-      this->MakeReduce(BufferLoad(dst_buffer, dst_indices),
+      clear_buffer,
+      this->MakeReduce(BufferLoad(clear_buffer, dst_indices),
                        BufferLoad(src_buffer, src_indice_compressed)),
       dst_indices);
   for (int i = src_layout->OutputDim() - 1; i >= 0; i--) {
@@ -179,20 +203,37 @@ Stmt ReduceOp::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
            << reducing_threads << ", " << (*scale) << ">::run";
       }
       Array<PrimExpr> thread_reduce_args = {
-          StringImm(ss.str()), BufferLoad(dst_buffer, dst_indices)};
+          StringImm(ss.str()), BufferLoad(clear_buffer, dst_indices)};
       if (reducing_threads >= 32) {
         PrimExpr workspace = T.AddWorkspace(
-            *as_const_int(T.thread_bounds->extent), dst_buffer->dtype);
+            *as_const_int(T.thread_bounds->extent), clear_buffer->dtype);
         thread_reduce_args.push_back(workspace);
       }
       auto call =
-          Call(dst_buffer->dtype, builtin::call_extern(), thread_reduce_args);
-      stmts.push_back(BufferStore(dst_buffer, call, dst_indices));
+          Call(clear_buffer->dtype, builtin::call_extern(), thread_reduce_args);
+      stmts.push_back(BufferStore(clear_buffer, call, dst_indices));
     }
   }
-  Stmt reduce_interthread =
-      BufferStore(dst_buffer, BufferLoad(dst_buffer, dst_indices), dst_indices);
+  Stmt reduce_interthread = BufferStore(
+      clear_buffer, BufferLoad(clear_buffer, dst_indices), dst_indices);
 
+  // copy clear_buffer to dst_buffer
+  if (need_duplicate) {
+    // if is reduce sum, we should add a copy from clear_buffer to dst_buffer
+    if (this->type == ReduceType::kSum) {
+      stmts.push_back(BufferStore(dst_buffer,
+                                  Add(BufferLoad(dst_buffer, dst_indices),
+                                      BufferLoad(clear_buffer, dst_indices)),
+                                  dst_indices));
+    } else if (this->type == ReduceType::kAbsSum) {
+      stmts.push_back(BufferStore(dst_buffer,
+                                  Add(BufferLoad(dst_buffer, dst_indices),
+                                      BufferLoad(clear_buffer, dst_indices)),
+                                  dst_indices));
+    } else {
+      ICHECK(false) << "Unsupported reduce type: " << (int)this->type;
+    }
+  }
   // make the outer spatial loop
   Stmt body = stmts.size() > 1 ? SeqStmt(stmts) : stmts[0];
   for (int i = dst_layout->InputDim() - 1; i >= 0; i--) {
@@ -201,6 +242,10 @@ Stmt ReduceOp::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
   }
 
   body = PartitionLoop(Downcast<For>(body), T.thread_var, analyzer, dst_layout);
+  if (need_duplicate) {
+    body = Allocate(clear_buffer->data, clear_buffer->dtype,
+                    clear_buffer->shape, const_true(), body);
+  }
   return body;
 }
 

src/transform/pipeline_planning.cc

@@ -389,7 +389,6 @@ class PipelinePlanner : public StmtExprMutator {
     // Handle trailing unassigned copy stages:
     // These are typically final copy operations needing post-main-stage
     // insertion
-
     auto &head_pinfo = pipeline_stage_infos.at(0);
     int unassigned_order_elem = -1;
 
@@ -422,7 +421,7 @@ class PipelinePlanner : public StmtExprMutator {
       int copy_order_min = pipeline_stage_infos.size();
       int non_copy_order_max = 0;
       for (auto &pinfo : pipeline_stage_infos) {
-        if (pinfo.copy_stage) {
+        if (pinfo.copy_stage || pinfo.prepare_for_condition) {
           copy_stage_cnt++;
           copy_order_min = std::min(copy_order_min, pinfo.order);
         } else {
@@ -437,7 +436,7 @@ class PipelinePlanner : public StmtExprMutator {
       for (auto &pinfo : pipeline_stage_infos) { // move copy to the beginning
         pinfo.order =
             (pinfo.order + copy_stage_at_end) % pipeline_stage_infos.size();
-        if (!pinfo.copy_stage)
+        if (!pinfo.copy_stage && !pinfo.prepare_for_condition)
           pinfo.stage--;
       }
     }

testing/python/language/test_tilelang_language_reduce_max.py

@@ -50,5 +50,46 @@ def test_reduce_max():
     run_reduce_max(256, 256, "float16")
 
 
+def reduce_max_test_clear(M, N, dtype="float16"):
+    import tilelang.language as T
+
+    @T.prim_func
+    def main(
+            A: T.Tensor((M, N), dtype),
+            B: T.Tensor((M,), dtype),
+    ):
+        with T.Kernel(1, threads=32) as _:
+            A_local = T.alloc_fragment((M, N), dtype)
+            B_local = T.alloc_fragment((M,), dtype)
+
+            T.copy(A, A_local)
+            T.fill(B_local, -T.infinity(dtype))
+            T.reduce_max(A_local, B_local, dim=1, clear=False)
+            T.copy(B_local, B)
+
+    return main
+
+
+def run_reduce_max_clear(M, N, dtype="float16"):
+    program = reduce_max_test_clear(M, N, dtype)
+    jit_kernel = tl.compile(program, out_idx=-1)
+    print(jit_kernel.get_kernel_source())
+
+    def ref_program(A):
+        return A.max(dim=1).values
+
+    import torch
+    dummp_A = torch.randn((M, N), dtype=getattr(torch, dtype)).cuda()
+    ref_out = ref_program(dummp_A)
+    tl_out = jit_kernel(dummp_A)
+    print(tl_out)
+    print(ref_out)
+    torch.testing.assert_close(tl_out, ref_out, atol=1e-2, rtol=1e-2)
+
+
+def test_reduce_max_clear():
+    run_reduce_max_clear(256, 256, "float16")
+
+
 if __name__ == "__main__":
     tilelang.testing.main()

testing/python/language/test_tilelang_language_reduce_sum.py

@@ -0,0 +1,105 @@
+# Copyright (c) Tile-AI Corporation.
+# Licensed under the MIT License.
+
+from tilelang import tvm as tvm
+import tilelang.testing
+import tilelang as tl
+
+tilelang.testing.set_random_seed()
+
+
+def reduce_sum_test(M, N, dtype="float16"):
+    import tilelang.language as T
+
+    @T.prim_func
+    def main(
+            A: T.Tensor((M, N), dtype),
+            B: T.Tensor((M,), dtype),
+    ):
+        with T.Kernel(1) as _:
+            A_local = T.alloc_fragment((M, N), dtype)
+            B_local = T.alloc_fragment((M,), dtype)
+
+            # Copy input to local
+            T.copy(A, A_local)
+            # Perform reduce_sum operation
+            T.reduce_sum(A_local, B_local, dim=1)
+            # Copy result back
+            T.copy(B_local, B)
+
+    return main
+
+
+def run_reduce_sum(M, N, dtype="float16"):
+    program = reduce_sum_test(M, N, dtype)
+    jit_kernel = tl.compile(program, out_idx=-1)
+    profiler = jit_kernel.get_profiler()
+
+    def ref_program(A):
+        return A.sum(dim=1)
+
+    profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
+
+
+def test_reduce_sum():
+    # Test different sizes
+    run_reduce_sum(256, 256)
+    run_reduce_sum(512, 128)
+    run_reduce_sum(128, 512)
+
+    # Test different dtypes
+    run_reduce_sum(256, 256, "float32")
+    run_reduce_sum(256, 256, "float16")
+
+
+def reduce_sum_test_clear(M, N, dtype="float16"):
+    import tilelang.language as T
+
+    @T.prim_func
+    def main(
+            A: T.Tensor((M, N), dtype),
+            B: T.Tensor((M,), dtype),
+    ):
+        with T.Kernel(1, threads=32) as _:
+            A_local = T.alloc_fragment((M, N), dtype)
+            B_local = T.alloc_fragment((M,), dtype)
+
+            T.copy(A, A_local)
+            T.fill(B_local, 1)
+            T.reduce_sum(A_local, B_local, dim=1, clear=False)
+            T.copy(B_local, B)
+
+    return main
+
+
+def run_reduce_sum_clear(M, N, dtype="float16"):
+    program = reduce_sum_test_clear(M, N, dtype)
+    jit_kernel = tl.compile(
+        program,
+        out_idx=-1,
+        pass_configs={
+            "tl.disable_tma_lower": True,
+            "tl.disable_warp_specialized": True,
+        })
+    print(jit_kernel.get_kernel_source())
+
+    def ref_program(A):
+        return A.sum(dim=1) + 1
+
+    import torch
+    dummp_A = torch.randn((M, N), dtype=getattr(torch, dtype)).cuda()
+    ref_out = ref_program(dummp_A)
+    tl_out = jit_kernel(dummp_A)
+    print(tl_out)
+    print(ref_out)
+    torch.testing.assert_close(tl_out, ref_out, atol=1e-2, rtol=1e-2)
+
+
+def test_reduce_sum_clear():
+    run_reduce_sum_clear(256, 256, "float32")
+    run_reduce_sum_clear(512, 128, "float32")
+    run_reduce_sum_clear(128, 512, "float32")
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

testing/python/transform/test_tilelang_transform_annotate_device_regions.py

@@ -13,12 +13,12 @@ class BaseCompare(tilelang.testing.CompareBeforeAfter):
 class TestAnnotateThreadExtent(BaseCompare):
     """Annotation inserted at the "thread_extent" attribute"""
 
-    def before(A: T.Buffer(16, "float32")):
+    def before(A: T.Tensor(16, "float32")):
         T.func_attr({"target": T.target("cuda", host="llvm")})
         i = T.launch_thread("threadIdx.x", 16)
         A[i] = 0.0
 
-    def expected(A: T.Buffer(16, "float32")):
+    def expected(A: T.Tensor(16, "float32")):
         T.func_attr({"target": T.target("cuda", host="llvm")})
         T.attr(T.target("cuda"), "target", 0)
         i = T.launch_thread("threadIdx.x", 16)
@@ -28,12 +28,12 @@ def expected(A: T.Buffer(16, "float32")):
 class TestAnnotateDeviceScope(BaseCompare):
     """Annotation inserted at the "device_scope" attribute"""
 
-    def before(A: T.Buffer(1, "float32")):
+    def before(A: T.Tensor(1, "float32")):
         T.func_attr({"target": T.target("cuda", host="llvm")})
         T.attr(0, "device_scope", 0)
         A[0] = 0.0
 
-    def expected(A: T.Buffer(1, "float32")):
+    def expected(A: T.Tensor(1, "float32")):
         T.func_attr({"target": T.target("cuda", host="llvm")})
         T.attr(T.target("cuda"), "target", 0)
         T.attr(0, "device_scope", 0)

tilelang/language/reduce.py

@@ -68,18 +68,28 @@ def reduce_min(buffer: tir.Buffer, out: tir.Buffer, dim: int, clear: bool = True
     return reduce(buffer, out, "min", dim, clear)
 
 
-def reduce_sum(buffer: tir.Buffer, out: tir.Buffer, dim: int):
+def reduce_sum(buffer: tir.Buffer, out: tir.Buffer, dim: int, clear: bool = True):
     """Perform reduce sum 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
+        clear (bool, optional): If True, output buffer will be cleared before reduction.
+                              If False, results will be accumulated on existing values.
+                              Defaults to True.
+    Note: When clear=True, reduce_sum will not compute directly on the output buffer. This is because 
+          during warp reduction, the same value would be accumulated multiple times (number of threads 
+          in the warp). Therefore, the implementation with clear=True follows these steps:
+        1. create a temp buffer with same shape and dtype as out
+        2. copy out to temp buffer
+        3. call reduce_sum with temp buffer and out
+        4. Add temp buffer to out
 
     Returns:
         tir.Call: Handle to the reduction operation
     """
-    return reduce(buffer, out, "sum", dim, True)
+    return reduce(buffer, out, "sum", dim, clear)
 
 
 def reduce_abssum(buffer: tir.Buffer, out: tir.Buffer, dim: int):
@@ -96,7 +106,7 @@ def reduce_abssum(buffer: tir.Buffer, out: tir.Buffer, dim: int):
     return reduce(buffer, out, "abssum", dim, True)
 
 
-def reduce_absmax(buffer: tir.Buffer, out: tir.Buffer, dim: int):
+def reduce_absmax(buffer: tir.Buffer, out: tir.Buffer, dim: int, clear: bool = True):
     """Perform reduce absolute max on input buffer, store the result to output buffer.
 
     Args:
@@ -107,7 +117,7 @@ def reduce_absmax(buffer: tir.Buffer, out: tir.Buffer, dim: int):
     Returns:
         tir.Call: Handle to the reduction operation
     """
-    return reduce(buffer, out, "absmax", dim, True)
+    return reduce(buffer, out, "absmax", dim, clear)
 
 
 @macro