[GitHub] [tvm] MasterJH5574 commented on a diff in pull request #12825: [MetaSchedule] Relax conditions of rule Cross-Thread Reduction

[GitBox <gi...@apache.org>]

MasterJH5574 commented on code in PR #12825:
URL: https://github.com/apache/tvm/pull/12825#discussion_r973386916


##########
tests/python/unittest/test_meta_schedule_schedule_rule_cross_thread_reduction.py:
##########
@@ -663,8 +685,85 @@ def argmax_1(
     )
 
 
+def test_gpu_argmax_32():
+    @T.prim_func
+    def argmax_0(
+        idx: T.Buffer[(1, 32), "int32"],
+        val: T.Buffer[(1, 32), "float32"],
+        argmax_v0: T.Buffer[(1,), "int32"],
+        argmax_v1: T.Buffer[(1,), "float32"],
+    ) -> None:
+        # body
+        # with T.block("root")
+        for i0, i1 in T.grid(1, 32):
+            with T.block("argmax"):
+                i, k = T.axis.remap("SR", [i0, i1])
+                T.reads(idx[i, k], val[i, k])
+                T.writes(argmax_v0[i], argmax_v1[i])
+                with T.init():
+                    argmax_v0[i] = -1
+                    argmax_v1[i] = T.float32(-3.4028234663852886e38)
+                v_argmax_v0: T.int32 = T.Select(argmax_v1[i] >= val[i, k], argmax_v0[i], idx[i, k])
+                v_argmax_v1: T.float32 = T.Select(
+                    argmax_v1[i] >= val[i, k], argmax_v1[i], val[i, k]
+                )
+                argmax_v0[i] = v_argmax_v0
+                argmax_v1[i] = v_argmax_v1
+
+    @T.prim_func
+    def argmax_1(
+        idx: T.Buffer[(1, 32), "int32"],
+        val: T.Buffer[(1, 32), "float32"],
+        argmax_v0: T.Buffer[(1,), "int32"],
+        argmax_v1: T.Buffer[(1,), "float32"],
+    ) -> None:
+        # body
+        # with T.block("root")
+        for i0, i1_0 in T.grid(1, 1):
+            for i1_1 in T.thread_binding(64, thread="threadIdx.x"):
+                with T.block("argmax"):
+                    i = T.axis.spatial(1, i0)
+                    k = T.axis.reduce(32, i1_0 * 64 + i1_1)
+                    T.where(i1_0 * 64 + i1_1 < 32)
+                    T.reads(idx[i, k], val[i, k])
+                    T.writes(argmax_v0[i], argmax_v1[i])
+                    with T.init():
+                        argmax_v0[i] = -1
+                        argmax_v1[i] = T.float32(-3.4028234663852886e38)
+                    v_argmax_v0: T.int32 = T.Select(
+                        argmax_v1[i] >= val[i, k], argmax_v0[i], idx[i, k]
+                    )
+                    v_argmax_v1: T.float32 = T.Select(
+                        argmax_v1[i] >= val[i, k], argmax_v1[i], val[i, k]
+                    )
+                    argmax_v0[i] = v_argmax_v0
+                    argmax_v1[i] = v_argmax_v1
+
+    decision_0 = []  # type: ignore
+    decision_1 = [
+        ("SampleCategorical", 4),
+    ]
+
+    mod = argmax_32
+    actual = ms.TuneContext(
+        mod=mod,
+        target=Target("nvidia/geforce-rtx-3090", host="llvm"),
+        space_generator=ms.space_generator.PostOrderApply(),
+        sch_rules=get_rules("cuda", ms.schedule_rule.CrossThreadReduction),
+        task_name="test",
+    ).generate_design_space()
+    print(actual[1].mod.script())

Review Comment:
   Sorry my negligence 🤦‍♂️



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