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
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+# 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.
+# ruff: noqa: F401
+"""Tests to validate relax fast math tranform pass."""
+
+import pytest
+
+import tvm.testing
+from tvm import relax, topi
+from tvm.ir.base import assert_structural_equal
+from tvm.relax.transform import FastMathTransform
+from tvm.script import ir as I
+from tvm.script import relax as R
+
+
+def _run_pass_compare_output(Before, Expected):
+    fast_mod = FastMathTransform()(Before)
+    if not relax.analysis.check_well_formed(fast_mod):
+        print("IRModule is not well-formed")
+    assert_structural_equal(Expected, fast_mod)
+
+
+def test_optimize_transform_layout_pass_one_arg():
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(x: R.Tensor((16,), dtype="float32")) -> R.Tensor((16,), dtype="float32"):
+            lv1: R.Tensor((16,), dtype="float32") = R.nn.softmax(x)
+            lv2: R.Tensor((16,), dtype="float32") = R.exp(lv1)
+            lv3: R.Tensor((16,), dtype="float32") = R.erf(lv2)
+            lv4: R.Tensor((16,), dtype="float32") = R.tanh(lv3)
+            return lv4
+
+    bb = relax.BlockBuilder()
+    x = relax.Var("x", R.Tensor((16,), "float32"))
+    with bb.function("main", [x]):
+        lv1 = bb.emit_te(topi.nn.fast_softmax, x)
+        lv2 = bb.emit_te(topi.fast_exp, lv1)
+        lv3 = bb.emit_te(topi.fast_erf, lv2)
+        lv4 = bb.emit_te(topi.fast_tanh, lv3)
+        bb.emit_func_output(lv4)
+    Expected = bb.get()
+
+    _run_pass_compare_output(Before, Expected)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()