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 +1,141 @@
+# 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 WA`RRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import tvm
+import tvm.testing
+from tvm import IRModule, relax
+from tvm.script.parser import relax as R
+
+
+def _check(
+    parsed: relax.Function | IRModule,
+    expect: relax.Function | IRModule | None,
+):
+    test = parsed.script(show_meta=True)
+    roundtrip_mod = tvm.script.from_source(test)
+    tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
+    if expect:
+        tvm.ir.assert_structural_equal(parsed, expect)
+
+
+def test_nll_loss_backward():
+    @R.function
+    def foo(
+        output_grad: R.Tensor((3, 10, 10), dtype="float32"),
+        predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
+        targets: R.Tensor((3, 10, 10), dtype="int64"),
+        weights: R.Tensor((5,), dtype="float32"),
+    ) -> R.Tensor((3, 5, 10, 10), dtype="float32"):
+        gv: R.Tensor((3, 5, 10, 10), dtype="float32") = R.grad.nll_loss_backward(
+            output_grad, predictions, targets, weights, "mean", -1
+        )
+        return gv
+
+    output_grad = relax.Var("output_grad", R.Tensor((3, 10, 10), "float32"))
+    predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
+    targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
+    weights = relax.Var("weights", R.Tensor((5,), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [output_grad, predictions, targets, weights]):
+        gv = bb.emit(
+            relax.op.grad.nll_loss_backward(
+                output_grad, predictions, targets, weights, reduction="mean", ignore_index=-1
+            )
+        )
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_nll_loss_backward_no_weights():
+    @R.function
+    def foo(
+        output_grad: R.Tensor((3, 10, 10), dtype="float32"),
+        predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
+        targets: R.Tensor((3, 10, 10), dtype="int64"),
+    ) -> R.Tensor((3, 5, 10, 10), dtype="float32"):
+        gv: R.Tensor((3, 5, 10, 10), dtype="float32") = R.grad.nll_loss_backward(
+            output_grad, predictions, targets, reduction="mean", ignore_index=-1
+        )
+        return gv
+
+    output_grad = relax.Var("output_grad", R.Tensor((3, 10, 10), "float32"))
+    predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
+    targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [output_grad, predictions, targets]):
+        gv = bb.emit(
+            relax.op.grad.nll_loss_backward(
+                output_grad, predictions, targets, reduction="mean", ignore_index=-1
+            )
+        )
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_max_pool2d_backward():
+    @R.function
+    def foo(
+        output_grad: R.Tensor((3, 2, 6, 5), "float32"), data: R.Tensor((3, 2, 10, 10), "float32")
+    ):
+        gv = R.grad.max_pool2d_backward(
+            output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
+        )
+        return gv
+
+    output_grad = relax.Var("output_grad", R.Tensor((3, 2, 6, 5), "float32"))
+    data = relax.Var("data", R.Tensor((3, 2, 10, 10), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [output_grad, data]):
+        gv = bb.emit(
+            relax.op.grad.max_pool2d_backward(
+                output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
+            )
+        )
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+def test_avg_pool2d_backward():
+    @R.function
+    def foo(
+        output_grad: R.Tensor((3, 2, 6, 5), "float32"), data: R.Tensor((3, 2, 10, 10), "float32")
+    ):
+        gv = R.grad.avg_pool2d_backward(
+            output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
+        )
+        return gv
+
+    output_grad = relax.Var("output_grad", R.Tensor((3, 2, 6, 5), "float32"))
+    data = relax.Var("data", R.Tensor((3, 2, 10, 10), "float32"))
+    bb = relax.BlockBuilder()
+    with bb.function("foo", [output_grad, data]):
+        gv = bb.emit(
+            relax.op.grad.avg_pool2d_backward(
+                output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
+            )
+        )
+        bb.emit_func_output(gv)
+
+    _check(foo, bb.get()["foo"])
+
+
+if __name__ == "__main__":
+    tvm.testing.main()