chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,109 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""codegen related to bool types"""
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm.script import ir as I
+from tvm.script import tirx as T
+
+
+@pytest.mark.gpu
+@pytest.mark.parametrize("target", ["llvm", "cuda", "rocm", "vulkan", "metal", "opencl"])
+def test_cmp_load_store(target):
+    if not tvm.testing.device_enabled(target):
+        pytest.skip(f"{target} not enabled")
+
+    @I.ir_module(s_tir=True)
+    class GPUModule:
+        @T.prim_func(s_tir=True)
+        def main(
+            A: T.Buffer((32,), "float32"),
+            B: T.Buffer((32,), "float32"),
+            D: T.Buffer((32,), "float32"),
+        ):
+            T.func_attr({"tirx.noalias": True})
+            C = T.sblock_alloc_buffer((32,), "bool")
+            for i0_0 in T.thread_binding(8, thread="blockIdx.x"):
+                for i0_1 in T.thread_binding(4, thread="blockIdx.x"):
+                    with T.sblock("C"):
+                        v_i0 = T.axis.spatial(32, i0_0 * 4 + i0_1)
+                        T.reads(B[v_i0], A[v_i0])
+                        T.writes(C[v_i0])
+                        C[v_i0] = B[v_i0] < A[v_i0]
+            for i0_0 in T.thread_binding(8, thread="blockIdx.x"):
+                for i0_1 in T.thread_binding(4, thread="blockIdx.x"):
+                    with T.sblock("D"):
+                        v_i0 = T.axis.spatial(32, i0_0 * 4 + i0_1)
+                        T.reads(C[v_i0], A[v_i0])
+                        T.writes(D[v_i0])
+                        D[v_i0] = T.Cast("float32", C[v_i0] and T.float32(1.0) < A[v_i0])
+
+    @I.ir_module(s_tir=True)
+    class CPUModule:
+        @T.prim_func(s_tir=True)
+        def main(
+            A: T.Buffer((32,), "float32"),
+            B: T.Buffer((32,), "float32"),
+            D: T.Buffer((32,), "float32"),
+        ):
+            T.func_attr({"tirx.noalias": True})
+            C = T.sblock_alloc_buffer((32,), "bool")
+            for i0 in range(32):
+                with T.sblock("C"):
+                    v_i0 = T.axis.spatial(32, i0)
+                    T.reads(B[v_i0], A[v_i0])
+                    T.writes(C[v_i0])
+                    C[v_i0] = B[v_i0] < A[v_i0]
+            for i0 in range(32):
+                with T.sblock("D"):
+                    v_i0 = T.axis.spatial(32, i0)
+                    T.reads(C[v_i0], A[v_i0])
+                    T.writes(D[v_i0])
+                    D[v_i0] = T.Cast("float32", C[v_i0] and T.float32(1.0) < A[v_i0])
+
+    arr_size = 32
+    is_gpu = tvm.target.Target(target).kind.name != "llvm"
+    mod = GPUModule if is_gpu else CPUModule
+
+    f = tvm.compile(mod, target=target)
+
+    a_np = np.random.uniform(size=arr_size).astype("float32")
+    b_np = np.random.uniform(size=arr_size).astype("float32")
+
+    def run_and_check():
+        dev = tvm.device(target)
+        a = tvm.runtime.tensor(a_np, dev)
+        b = tvm.runtime.tensor(b_np, dev)
+        d = tvm.runtime.tensor(np.zeros(arr_size, dtype="float32"), dev)
+        f(a, b, d)
+        np.testing.assert_equal(
+            d.numpy(),
+            np.logical_and(a_np > b_np, a_np > 1).astype("float32"),
+        )
+
+    if is_gpu:
+        tvm.testing.run_with_gpu_lock(run_and_check)
+    else:
+        run_and_check()
+
+
+if __name__ == "__main__":
+    tvm.testing.main()