chore: import upstream snapshot with attribution

tests/python/relax/test_op_statistical.py

@@ -0,0 +1,501 @@
+# 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.
+from collections.abc import Callable
+
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import relax, tirx
+from tvm.ir import Op, VDevice
+from tvm.script import relax as R
+
+
+def test_op_correctness():
+    x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    assert relax.op.max(x).op == Op.get("relax.max")
+    assert relax.op.mean(x).op == Op.get("relax.mean")
+    assert relax.op.min(x).op == Op.get("relax.min")
+    assert relax.op.prod(x).op == Op.get("relax.prod")
+    assert relax.op.std(x).op == Op.get("relax.std")
+    assert relax.op.sum(x).op == Op.get("relax.sum")
+    assert relax.op.variance(x).op == Op.get("relax.variance")
+    assert relax.op.median(x).op == Op.get("relax.median")
+
+
+def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
+    ret = bb.normalize(call)
+    tvm.ir.assert_structural_equal(ret.ty, expected_ty)
+
+
+def test_statistical_infer_ty():
+    bb = relax.BlockBuilder()
+    vdev0 = VDevice("llvm")
+    x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    x1 = relax.Var("x", R.Tensor("float32", ndim=4))
+    x2 = relax.Var("x", R.Tensor("float32"))
+    x3 = relax.Var("x", R.Tensor((2, 3, 4, 5)))
+    x4 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32", vdev0))
+
+    _check_inference(bb, relax.op.sum(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
+    _check_inference(bb, relax.op.sum(x4, axis=[1, 2]), relax.TensorType((2, 5), "float32", vdev0))
+    _check_inference(
+        bb,
+        relax.op.sum(x0, axis=[1, 2], keepdims=True),
+        relax.TensorType((2, 1, 1, 5), "float32"),
+    )
+    _check_inference(bb, relax.op.sum(x0, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.sum(x0, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), "float32"),
+    )
+    _check_inference(bb, relax.op.mean(x1, axis=[1, 2]), relax.TensorType(dtype="float32", ndim=2))
+    _check_inference(
+        bb,
+        relax.op.mean(x1, axis=[1, 2], keepdims=True),
+        relax.TensorType(dtype="float32", ndim=4),
+    )
+    _check_inference(bb, relax.op.mean(x1, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.mean(x1, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), "float32"),
+    )
+    _check_inference(bb, relax.op.variance(x2, axis=[1, 2]), relax.TensorType(dtype="float32"))
+    _check_inference(
+        bb,
+        relax.op.variance(x2, axis=[1, 2], keepdims=True),
+        relax.TensorType(dtype="float32"),
+    )
+    _check_inference(bb, relax.op.variance(x2, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.variance(x2, axis=None, keepdims=True),
+        relax.TensorType(dtype="float32"),
+    )
+    _check_inference(bb, relax.op.max(x3, axis=[1, 2]), relax.TensorType((2, 5), dtype=""))
+    _check_inference(
+        bb,
+        relax.op.max(x3, axis=[1, 2], keepdims=True),
+        relax.TensorType((2, 1, 1, 5), dtype=""),
+    )
+    _check_inference(bb, relax.op.max(x3, axis=None), relax.TensorType((), dtype=""))
+    _check_inference(
+        bb,
+        relax.op.max(x3, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), dtype=""),
+    )
+    _check_inference(bb, relax.op.prod(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
+    _check_inference(
+        bb,
+        relax.op.prod(x0, axis=[1, 2], keepdims=True),
+        relax.TensorType((2, 1, 1, 5), "float32"),
+    )
+    _check_inference(bb, relax.op.std(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
+    _check_inference(
+        bb,
+        relax.op.std(x0, axis=[1, 2], keepdims=True),
+        relax.TensorType((2, 1, 1, 5), "float32"),
+    )
+    _check_inference(bb, relax.op.sum(x0, axis=[-1, -4]), relax.TensorType((3, 4), "float32"))
+    _check_inference(bb, relax.op.sum(x0, axis=[]), relax.TensorType((2, 3, 4, 5), "float32"))
+
+
+def test_statistical_infer_ty_shape_symbolic():
+    bb = relax.BlockBuilder()
+    a = tirx.Var("a", "int64")
+    b = tirx.Var("b", "int64")
+    c = tirx.Var("c", "int64")
+    d = tirx.Var("d", "int64")
+    x = relax.Var("x", R.Tensor((a, b, c, d), "float32"))
+
+    _check_inference(bb, relax.op.min(x, axis=[1, 2]), relax.TensorType((a, d), "float32"))
+    _check_inference(
+        bb,
+        relax.op.min(x, axis=[1, 2], keepdims=True),
+        relax.TensorType((a, 1, 1, d), "float32"),
+    )
+    _check_inference(bb, relax.op.min(x, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.min(x, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), "float32"),
+    )
+
+
+def test_statistical_infer_ty_shape_var():
+    bb = relax.BlockBuilder()
+    s0 = relax.Var("s", relax.ShapeType(ndim=4))
+    s1 = relax.Var("s", relax.ShapeType())
+    x0 = relax.Var("x", relax.TensorType(s0, "float32"))
+    x1 = relax.Var("x", relax.TensorType(s1, "float32"))
+
+    _check_inference(bb, relax.op.max(x0), relax.TensorType((), dtype="float32"))
+    _check_inference(
+        bb, relax.op.max(x0, keepdims=True), relax.TensorType((1, 1, 1, 1), dtype="float32")
+    )
+    _check_inference(bb, relax.op.max(x0, axis=[2, 3]), relax.TensorType(dtype="float32", ndim=2))
+    _check_inference(
+        bb,
+        relax.op.max(x0, axis=[2, 3], keepdims=True),
+        relax.TensorType(dtype="float32", ndim=4),
+    )
+    _check_inference(bb, relax.op.max(x1), relax.TensorType((), dtype="float32"))
+    _check_inference(bb, relax.op.max(x1, keepdims=True), relax.TensorType(dtype="float32"))
+    _check_inference(bb, relax.op.max(x1, axis=[2, 3]), relax.TensorType(dtype="float32"))
+    _check_inference(
+        bb, relax.op.max(x1, axis=[2, 3], keepdims=True), relax.TensorType(dtype="float32")
+    )
+
+
+def test_statistical_infer_ty_more_input_dtype():
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float16"))
+    x1 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int8"))
+
+    _check_inference(bb, relax.op.sum(x0), relax.TensorType((), "float16"))
+    _check_inference(bb, relax.op.sum(x1), relax.TensorType((), "int8"))
+
+
+def test_statistical_infer_ty_axis_out_of_range_repetitive():
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    x1 = relax.Var("x", R.Tensor("float32", ndim=4))
+
+    with pytest.raises(ValueError):
+        bb.normalize(relax.op.mean(x0, axis=[4]))
+    with pytest.raises(ValueError):
+        bb.normalize(relax.op.mean(x1, axis=[3, 3]))
+    with pytest.raises(ValueError):
+        bb.normalize(relax.op.mean(x0, axis=[-1, 3]))
+    with pytest.raises(ValueError):
+        bb.normalize(relax.op.mean(x1, axis=[-4, -4]))
+    with pytest.raises(ValueError):
+        bb.normalize(relax.op.mean(x0, axis=[-5]))
+
+
+def test_statistical_infer_ty_wrong_input_type():
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", relax.ShapeType((2, 3, 4, 5)))
+    x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3, 4, 5), "float32")))
+
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.variance(x0))
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.variance(x1))
+
+
+scan_ops = [
+    relax.op.cumprod,
+    relax.op.cumsum,
+]
+
+
+@pytest.mark.parametrize("scan_op", scan_ops)
+def test_scan_op_infer_ty(scan_op: Callable):
+    bb = relax.BlockBuilder()
+    vdev0 = VDevice("llvm")
+    x0 = relax.Var("x", R.Tensor((2, 10, 4), "float32"))
+    x1 = relax.Var("x", R.Tensor("float32", ndim=3))
+    x2 = relax.Var("x", R.Tensor("float32"))
+    x3 = relax.Var("x", R.Tensor((2, 10, 4)))
+    x4 = relax.Var("x", R.Tensor(ndim=3))
+    x5 = relax.Var("x", R.Tensor())
+    x6 = relax.Var("x", R.Tensor((2, 10, 4), "float32", vdev0))
+
+    _check_inference(bb, scan_op(x0, axis=1), relax.TensorType((2, 10, 4), "float32"))
+    _check_inference(bb, scan_op(x6, axis=1), relax.TensorType((2, 10, 4), "float32", vdev0))
+    _check_inference(bb, scan_op(x1, axis=1), relax.TensorType(dtype="float32", ndim=3))
+    _check_inference(bb, scan_op(x2, axis=1), relax.TensorType(dtype="float32"))
+    _check_inference(bb, scan_op(x3, axis=1), relax.TensorType((2, 10, 4), dtype=""))
+    _check_inference(bb, scan_op(x4, axis=1), relax.TensorType(dtype="", ndim=3))
+    _check_inference(bb, scan_op(x5, axis=1), relax.TensorType(dtype=""))
+    _check_inference(bb, scan_op(x0), relax.TensorType((80,), "float32"))
+    _check_inference(bb, scan_op(x0, axis=1, dtype="int32"), relax.TensorType((2, 10, 4), "int32"))
+
+
+@pytest.mark.parametrize("scan_op", scan_ops)
+def test_scan_op_infer_ty_shape_symbolic(scan_op: Callable):
+    bb = relax.BlockBuilder()
+    a = tirx.Var("a", "int64")
+    b = tirx.Var("b", "int64")
+    c = tirx.Var("c", "int64")
+    x = relax.Var("x", R.Tensor((a, b, c), "float32"))
+
+    _check_inference(bb, scan_op(x, axis=1), relax.TensorType((a, b, c), "float32"))
+    _check_inference(bb, scan_op(x), relax.TensorType((a * b * c,), "float32"))
+
+
+@pytest.mark.parametrize("scan_op", scan_ops)
+def test_scan_op_infer_ty_more_input_dtype(scan_op: Callable):
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", R.Tensor((2, 3, 4), "float16"))
+    x1 = relax.Var("x", R.Tensor((2, 3, 4), "int8"))
+
+    _check_inference(bb, scan_op(x0, axis=1), relax.TensorType((2, 3, 4), "float16"))
+    _check_inference(bb, scan_op(x1, axis=1), relax.TensorType((2, 3, 4), "int8"))
+
+
+@pytest.mark.parametrize("scan_op", scan_ops)
+def test_scan_op_wrong_input_number(scan_op: Callable):
+    x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
+    y = relax.Var("y", R.Tensor((2, 3, 4), "float32"))
+
+    with pytest.raises(TypeError):
+        scan_op(x, y)
+
+
+@pytest.mark.parametrize("scan_op", scan_ops)
+def test_scan_opinfer_ty_wrong_input_type(scan_op: Callable):
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", relax.ShapeType((2, 3, 4, 5)))
+    x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3, 4, 5), "float32")))
+
+    with pytest.raises(TypeError):
+        bb.normalize(scan_op(x0, axis=1))
+    with pytest.raises(TypeError):
+        bb.normalize(scan_op(x1, axis=1))
+
+
+def test_statistical_ext_infer_ty():
+    bb = relax.BlockBuilder()
+    vdev0 = VDevice("llvm")
+    x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
+    x1 = relax.Var("x", R.Tensor("float32", ndim=4))
+    x2 = relax.Var("x", R.Tensor("float32"))
+    x3 = relax.Var("x", R.Tensor((2, 3, 4, 5)))
+    x4 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32", vdev0))
+
+    _check_inference(
+        bb,
+        relax.op.median(x0, axis=[1]),
+        relax.TupleType(
+            [
+                relax.TensorType((2, 4, 5), "float32"),
+                relax.TensorType((2, 4, 5), "int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x0, axis=[1], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType((2, 1, 4, 5), "float32"),
+                relax.TensorType((2, 1, 4, 5), "int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x1, axis=[1]),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32", ndim=3),
+                relax.TensorType(dtype="int64", ndim=3),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x1, axis=[1], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32", ndim=4),
+                relax.TensorType(dtype="int64", ndim=4),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x1, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), "float32"),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x2, axis=[1]),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32"),
+                relax.TensorType(dtype="int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x2, axis=[1], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32"),
+                relax.TensorType(dtype="int64"),
+            ]
+        ),
+    )
+    _check_inference(bb, relax.op.median(x2, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.median(x3, axis=[1], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType((2, 1, 4, 5), dtype=""),
+                relax.TensorType((2, 1, 4, 5), dtype="int64"),
+            ]
+        ),
+    )
+    _check_inference(bb, relax.op.median(x3, axis=None), relax.TensorType((), dtype=""))
+    _check_inference(
+        bb,
+        relax.op.median(x3, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), dtype=""),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x4, axis=[1]),
+        relax.TupleType(
+            [
+                relax.TensorType((2, 4, 5), "float32", vdev0),
+                relax.TensorType((2, 4, 5), "int64", vdev0),
+            ]
+        ),
+    )
+
+
+def test_statistical_ext_infer_ty_shape_symbolic():
+    bb = relax.BlockBuilder()
+    a = tirx.Var("a", "int64")
+    b = tirx.Var("b", "int64")
+    c = tirx.Var("c", "int64")
+    d = tirx.Var("d", "int64")
+    x = relax.Var("x", R.Tensor((a, b, c, d), "float32"))
+
+    _check_inference(
+        bb,
+        relax.op.median(x, axis=[1]),
+        relax.TupleType(
+            [
+                relax.TensorType((a, c, d), "float32"),
+                relax.TensorType((a, c, d), "int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x, axis=[1], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType((a, 1, c, d), "float32"),
+                relax.TensorType((a, 1, c, d), "int64"),
+            ]
+        ),
+    )
+    _check_inference(bb, relax.op.median(x, axis=None), relax.TensorType((), "float32"))
+    _check_inference(
+        bb,
+        relax.op.median(x, axis=None, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), "float32"),
+    )
+
+
+def test_statistical_ext_infer_ty_shape_var():
+    bb = relax.BlockBuilder()
+    s0 = relax.Var("s", relax.ShapeType(ndim=4))
+    s1 = relax.Var("s", relax.ShapeType())
+    x0 = relax.Var("x", relax.TensorType(s0, "float32"))
+    x1 = relax.Var("x", relax.TensorType(s1, "float32"))
+
+    _check_inference(bb, relax.op.median(x0), relax.TensorType((), dtype="float32"))
+    _check_inference(
+        bb,
+        relax.op.median(x0, keepdims=True),
+        relax.TensorType((1, 1, 1, 1), dtype="float32"),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x0, axis=[2]),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32", ndim=3),
+                relax.TensorType(dtype="int64", ndim=3),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x0, axis=[2], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32", ndim=4),
+                relax.TensorType(dtype="int64", ndim=4),
+            ]
+        ),
+    )
+    _check_inference(bb, relax.op.median(x1), relax.TensorType((), dtype="float32"))
+    _check_inference(
+        bb,
+        relax.op.median(x1, keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32"),
+                relax.TensorType(dtype="int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x1, axis=[2]),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32"),
+                relax.TensorType(dtype="int64"),
+            ]
+        ),
+    )
+    _check_inference(
+        bb,
+        relax.op.median(x1, axis=[2], keepdims=True),
+        relax.TupleType(
+            [
+                relax.TensorType(dtype="float32"),
+                relax.TensorType(dtype="int64"),
+            ]
+        ),
+    )
+
+
+def test_statistical_ext_infer_ty_more_input_dtype():
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float16"))
+    x1 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int8"))
+
+    _check_inference(bb, relax.op.median(x0), relax.TensorType((), "float16"))
+    _check_inference(bb, relax.op.median(x1), relax.TensorType((), "int8"))
+
+
+def test_statistical_ext_infer_ty_wrong_input_type():
+    bb = relax.BlockBuilder()
+    x0 = relax.Var("x", relax.ShapeType((2, 3, 4, 5)))
+    x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3, 4, 5), "float32")))
+
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.median(x0))
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.median(x1))
+
+
+if __name__ == "__main__":
+    tvm.testing.main()