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apache--tvm/tests/python/relax/test_op_create.py
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chore: import upstream snapshot with attribution
2026-07-13 13:36:25 +08:00

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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.
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
from tvm.script import tirx as T
def test_op_correctness():
x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
fill_value = relax.Var("fill_value", R.Tensor((), "float32"))
assert relax.op.full((2, 3), fill_value).op == Op.get("relax.full")
assert relax.op.full_like(x, fill_value).op == Op.get("relax.full_like")
assert relax.op.ones((2, 3), "float32").op == Op.get("relax.ones")
assert relax.op.ones_like(x).op == Op.get("relax.ones_like")
assert relax.op.zeros((2, 3), "float32").op == Op.get("relax.zeros")
assert relax.op.zeros_like(x).op == Op.get("relax.zeros_like")
assert relax.op.arange(3, 4, 1, "float32").op == Op.get("relax.arange")
assert relax.op.tril(x).op == Op.get("relax.tril")
assert relax.op.triu(x).op == Op.get("relax.triu")
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_full_infer_ty():
bb = relax.BlockBuilder()
vdev0 = VDevice("llvm")
v0 = relax.Var("v", R.Tensor((), "float32"))
v1 = relax.Var("v", R.Tensor("float32", ndim=0))
v2 = relax.Var("v", R.Tensor(()))
v3 = relax.Var("v", R.Tensor(ndim=0))
v4 = relax.Var("v", R.Tensor((), "float32", vdev0))
s0 = relax.ShapeExpr((2, 3))
s1 = relax.Var("s", relax.ShapeType((2, 3)))
s2 = relax.Var("s", relax.ShapeType(ndim=2))
s3 = relax.Var("s", relax.ShapeType())
_check_inference(bb, relax.op.full((2, 3), v0, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full(s0, v0, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full(s0, v0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full(s0, v4), relax.TensorType((2, 3), "float32", vdev0))
_check_inference(bb, relax.op.full(s1, v0, "float16"), relax.TensorType(s1, "float16"))
_check_inference(bb, relax.op.full(s1, v0), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full(s2, v0, "float16"), relax.TensorType(s2, "float16"))
_check_inference(bb, relax.op.full(s2, v0), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.full(s3, v0, "float16"), relax.TensorType(s3, "float16"))
_check_inference(bb, relax.op.full(s3, v0), relax.TensorType(s3, "float32"))
_check_inference(bb, relax.op.full((2, 3), v1, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v1), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full(s0, v1, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full(s0, v1), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full(s1, v1, "float16"), relax.TensorType(s1, "float16"))
_check_inference(bb, relax.op.full(s1, v1), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full(s2, v1, "float16"), relax.TensorType(s2, "float16"))
_check_inference(bb, relax.op.full(s2, v1), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.full(s3, v1, "float16"), relax.TensorType(s3, "float16"))
_check_inference(bb, relax.op.full(s3, v1), relax.TensorType(s3, "float32"))
_check_inference(bb, relax.op.full((2, 3), v2, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v2), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full(s0, v2, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full(s0, v2), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full(s1, v2, "float16"), relax.TensorType(s1, "float16"))
_check_inference(bb, relax.op.full(s1, v2), relax.TensorType(s1, dtype=None))
_check_inference(bb, relax.op.full(s2, v2, "float16"), relax.TensorType(s2, "float16"))
_check_inference(bb, relax.op.full(s2, v2), relax.TensorType(s2, dtype=None))
_check_inference(bb, relax.op.full(s3, v2, "float16"), relax.TensorType(s3, "float16"))
_check_inference(bb, relax.op.full(s3, v2), relax.TensorType(s3, dtype=None))
_check_inference(bb, relax.op.full((2, 3), v3, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v3), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full(s0, v3, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full(s0, v3), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full(s1, v3, "float16"), relax.TensorType(s1, "float16"))
_check_inference(
bb,
relax.op.full(
s1,
v3,
),
relax.TensorType(s1, dtype=None),
)
_check_inference(bb, relax.op.full(s2, v3, "float16"), relax.TensorType(s2, "float16"))
_check_inference(
bb,
relax.op.full(
s2,
v3,
),
relax.TensorType(s2, dtype=None),
)
_check_inference(bb, relax.op.full(s3, v3, "float16"), relax.TensorType(s3, "float16"))
_check_inference(bb, relax.op.full(s3, v3), relax.TensorType(s3, dtype=None))
def test_full_infer_ty_shape_symbolic():
bb = relax.BlockBuilder()
a = tirx.Var("a", "int64")
v = relax.Var("v", R.Tensor((), "float32"))
s0 = relax.ShapeExpr((a, 3))
s1 = relax.Var("s", relax.ShapeType((a, 3)))
_check_inference(bb, relax.op.full((a, 3), v, "float16"), relax.TensorType((a, 3), "float16"))
_check_inference(bb, relax.op.full((a, 3), v), relax.TensorType((a, 3), "float32"))
_check_inference(bb, relax.op.full(s0, v, "float16"), relax.TensorType((a, 3), "float16"))
_check_inference(bb, relax.op.full(s0, v), relax.TensorType((a, 3), "float32"))
_check_inference(bb, relax.op.full(s1, v, "float16"), relax.TensorType(s1, "float16"))
_check_inference(bb, relax.op.full(s1, v), relax.TensorType(s1, "float32"))
def test_full_infer_ty_shape_var():
bb = relax.BlockBuilder()
s0 = relax.Var("s", relax.ShapeType(()))
s1 = relax.Var("s", relax.ShapeType(ndim=0))
v0 = relax.Var("v", relax.TensorType(s0, "float32"))
v1 = relax.Var("v", relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full((2, 3), v0, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v1, "float16"), relax.TensorType((2, 3), "float16"))
def test_full_infer_ty_more_input_dtype():
bb = relax.BlockBuilder()
v0 = relax.Var("v", R.Tensor((), "float16"))
v1 = relax.Var("v", R.Tensor((), "int8"))
v2 = relax.Var("v", R.Tensor((), "int32"))
_check_inference(bb, relax.op.full((2, 3), v0, "float32"), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full((2, 3), v0), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full((2, 3), v1, "int32"), relax.TensorType((2, 3), "int32"))
_check_inference(bb, relax.op.full((2, 3), v1), relax.TensorType((2, 3), "int8"))
_check_inference(bb, relax.op.full((2, 3), v2, "int8"), relax.TensorType((2, 3), "int8"))
_check_inference(bb, relax.op.full((2, 3), v2), relax.TensorType((2, 3), "int32"))
def test_full_infer_ty_fill_value_not_scalar_tensor():
bb = relax.BlockBuilder()
s0 = relax.Var("s", relax.ShapeType((1,)))
s1 = relax.Var("s", relax.ShapeType(ndim=1))
s2 = relax.Var("s", relax.ShapeType())
v0 = relax.Var("v", R.Tensor((1,), "float32"))
v1 = relax.Var("v", R.Tensor("float32", ndim=1))
v2 = relax.Var("v", R.Tensor("float32"))
v3 = relax.Var("v", relax.TensorType(s0, "float32"))
v4 = relax.Var("v", relax.TensorType(s1, "float32"))
v5 = relax.Var("v", relax.TensorType(s2, "float32"))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v0))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v1))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v2))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v3))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v4))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v5))
def test_full_shape_not_tuple():
m = tirx.Var("m", "int64")
v = relax.Var("v", R.Tensor((), "float32"))
with pytest.raises(TypeError):
relax.op.full(4, v)
with pytest.raises(TypeError):
relax.op.full(m, v)
def test_full_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
v0 = relax.Var("v", R.Tensor((), "float32"))
v1 = relax.Var("v", relax.ShapeType(()))
v2 = relax.Var("v", relax.FuncType([], R.Tensor((), "float32")))
s = relax.Var("s", R.Tensor((2, 3)))
with pytest.raises(TypeError):
bb.normalize(relax.op.full(s, v0))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v1))
with pytest.raises(ValueError):
bb.normalize(relax.op.full((2, 3), v2))
def test_full_like_infer_ty():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
x1 = relax.Var("x", R.Tensor("float32", ndim=2))
x2 = relax.Var("x", R.Tensor("float32"))
x3 = relax.Var("x", R.Tensor((2, 3)))
x4 = relax.Var("x", R.Tensor(ndim=2))
x5 = relax.Var("x", R.Tensor())
v0 = relax.Var("v", R.Tensor((), "float16"))
v1 = relax.Var("v", R.Tensor("float16", ndim=0))
v2 = relax.Var("v", R.Tensor(()))
v3 = relax.Var("v", R.Tensor(ndim=0))
_check_inference(bb, relax.op.full_like(x0, v0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x0, v1), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x0, v2), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x0, v3), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x1, v0), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, relax.op.full_like(x1, v1), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, relax.op.full_like(x1, v2), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, relax.op.full_like(x1, v3), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, relax.op.full_like(x2, v0), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.full_like(x2, v1), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.full_like(x2, v2), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.full_like(x2, v3), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.full_like(x3, v0), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full_like(x3, v1), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full_like(x3, v2), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full_like(x3, v3), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.full_like(x4, v0), relax.TensorType(dtype=None, ndim=2))
_check_inference(bb, relax.op.full_like(x4, v1), relax.TensorType(dtype=None, ndim=2))
_check_inference(bb, relax.op.full_like(x4, v2), relax.TensorType(dtype=None, ndim=2))
_check_inference(bb, relax.op.full_like(x4, v3), relax.TensorType(dtype=None, ndim=2))
_check_inference(bb, relax.op.full_like(x5, v0), relax.TensorType(dtype=None))
_check_inference(bb, relax.op.full_like(x5, v1), relax.TensorType(dtype=None))
_check_inference(bb, relax.op.full_like(x5, v2), relax.TensorType(dtype=None))
_check_inference(bb, relax.op.full_like(x5, v3), relax.TensorType(dtype=None))
_check_inference(
bb, relax.op.full_like(x0, v0, dtype="float16"), relax.TensorType((2, 3), "float16")
)
_check_inference(
bb, relax.op.full_like(x0, v2, dtype="float16"), relax.TensorType((2, 3), "float16")
)
_check_inference(
bb, relax.op.full_like(x3, v0, dtype="float16"), relax.TensorType((2, 3), "float16")
)
_check_inference(
bb, relax.op.full_like(x3, v2, dtype="float16"), relax.TensorType((2, 3), "float16")
)
def test_full_like_infer_ty_shape_symbolic():
bb = relax.BlockBuilder()
m = tirx.Var("m", "int64")
n = tirx.Var("n", "int64")
x0 = relax.Var("x", R.Tensor((m, n), "float32"))
x1 = relax.Var("x", R.Tensor((m, n)))
v = relax.Var("v", R.Tensor((), "float16"))
_check_inference(bb, relax.op.full_like(x0, v), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.full_like(x1, v), relax.TensorType((m, n), dtype=None))
def test_full_like_infer_ty_shape_var():
bb = relax.BlockBuilder()
vdev0 = VDevice("llvm")
s0 = relax.Var("s", relax.ShapeType((2, 3)))
s1 = relax.Var("s", relax.ShapeType(ndim=2))
s2 = relax.Var("s", relax.ShapeType())
x0 = relax.Var("x", relax.TensorType(s0, "float32"))
x1 = relax.Var("x", relax.TensorType(s1, "float32"))
x2 = relax.Var("x", relax.TensorType(s2, "float32"))
x3 = relax.Var("x", R.Tensor((2, 3), "float32"))
x4 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))
sv0 = relax.Var("sv", relax.ShapeType(()))
sv1 = relax.Var("sv", relax.ShapeType(ndim=0))
v0 = relax.Var("v", relax.TensorType(sv0, "float16"))
v1 = relax.Var("v", relax.TensorType(sv1, "float16"))
v2 = relax.Var("v", R.Tensor((), "float16"))
v3 = relax.Var("v", relax.TensorType(sv1, "float16", vdev0))
_check_inference(bb, relax.op.full_like(x0, v0), relax.TensorType(s0, "float32"))
_check_inference(bb, relax.op.full_like(x0, v1), relax.TensorType(s0, "float32"))
_check_inference(bb, relax.op.full_like(x0, v2), relax.TensorType(s0, "float32"))
_check_inference(bb, relax.op.full_like(x1, v0), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full_like(x1, v1), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full_like(x1, v2), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.full_like(x2, v0), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.full_like(x2, v1), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.full_like(x2, v2), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.full_like(x3, v0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x3, v1), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.full_like(x4, v3), relax.TensorType((2, 3), "float32", vdev0))
def test_full_like_infer_ty_more_input_dtype():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float16"))
x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
v0 = relax.Var("v", R.Tensor((), "int32"))
v1 = relax.Var("v", R.Tensor((), "float64"))
_check_inference(bb, relax.op.full_like(x0, v0), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full_like(x0, v1), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.full_like(x1, v0), relax.TensorType((2, 3), "int8"))
_check_inference(bb, relax.op.full_like(x1, v1), relax.TensorType((2, 3), "int8"))
def test_full_like_infer_ty_fill_value_not_scalar_tensor():
bb = relax.BlockBuilder()
x = relax.Var("x", R.Tensor((2, 3), "float32"))
s0 = relax.Var("s", relax.ShapeType((1,)))
s1 = relax.Var("s", relax.ShapeType(ndim=1))
s2 = relax.Var("s", relax.ShapeType())
v0 = relax.Var("v", R.Tensor((1,), "float32"))
v1 = relax.Var("v", R.Tensor("float32", ndim=1))
v2 = relax.Var("v", R.Tensor("float32"))
v3 = relax.Var("v", relax.TensorType(s0, "float32"))
v4 = relax.Var("v", relax.TensorType(s1, "float32"))
v5 = relax.Var("v", relax.TensorType(s2, "float32"))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v0))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v1))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v2))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v3))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v4))
with pytest.raises(ValueError):
bb.normalize(relax.op.full_like(x, v5))
def test_full_like_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
x0 = relax.Var("x", relax.ShapeType((2, 3)))
x1 = relax.Var("x", relax.FuncType([], R.Tensor((), "float32")))
x2 = relax.Var("x", R.Tensor((2, 3)))
v0 = relax.Var("v", R.Tensor(()))
v1 = relax.Var("v", relax.ShapeType(()))
with pytest.raises(TypeError):
bb.normalize(relax.op.full_like(x0, v0))
with pytest.raises(TypeError):
bb.normalize(relax.op.full_like(x1, v0))
with pytest.raises(TypeError):
bb.normalize(relax.op.full_like(x2, v1))
def test_ones_zeros_infer_ty():
bb = relax.BlockBuilder()
s0 = relax.ShapeExpr((2, 3))
s1 = relax.Var("s", relax.ShapeType((2, 3)))
s2 = relax.Var("s", relax.ShapeType(ndim=2))
s3 = relax.Var("s", relax.ShapeType())
_check_inference(bb, relax.op.ones((2, 3), "float32"), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.ones(s0, "float32"), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.ones(s1, "float32"), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.ones(s2, "float32"), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.ones(s3, "float32"), relax.TensorType(s3, "float32"))
_check_inference(bb, relax.op.zeros((2, 3), "float32"), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.zeros(s0, "float32"), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.zeros(s1, "float32"), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.zeros(s2, "float32"), relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.zeros(s3, "float32"), relax.TensorType(s3, "float32"))
def test_ones_zeros_infer_ty_shape_symbolic():
bb = relax.BlockBuilder()
m = tirx.Var("m", "int64")
n = tirx.Var("n", "int64")
s0 = relax.ShapeExpr((m, n))
s1 = relax.Var("s", relax.ShapeType((m, n)))
_check_inference(bb, relax.op.ones((m, n), "float32"), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.ones(s0, "float32"), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.ones(s1, "float32"), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.zeros((m, n), "float32"), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.zeros(s0, "float32"), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.zeros(s1, "float32"), relax.TensorType(s1, "float32"))
def test_ones_zeros_infer_ty_more_input_dtype():
bb = relax.BlockBuilder()
s0 = relax.ShapeExpr((2, 3))
s1 = relax.Var("s", relax.ShapeType((2, 3)))
s2 = relax.Var("s", relax.ShapeType(ndim=2))
s3 = relax.Var("s", relax.ShapeType())
_check_inference(bb, relax.op.ones(s0, "float16"), relax.TensorType((2, 3), "float16"))
_check_inference(bb, relax.op.ones(s1, "int8"), relax.TensorType(s1, "int8"))
_check_inference(bb, relax.op.zeros(s2, "int32"), relax.TensorType(s2, "int32"))
_check_inference(bb, relax.op.zeros(s3, "float64"), relax.TensorType(s3, "float64"))
def test_ones_zeros_shape_not_tuple():
m = tirx.Var("m", "int64")
with pytest.raises(TypeError):
relax.op.ones(10, "float32")
with pytest.raises(TypeError):
relax.op.zeros(m, "float32")
def test_ones_zeros_wrong_dtype():
with pytest.raises(TypeError):
relax.op.ones((2, 3))
with pytest.raises(TypeError):
relax.op.zeros((2, 3))
def test_ones_zeros_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
s0 = relax.Var("s", R.Tensor((2, 3)))
s1 = relax.Var("s", relax.FuncType([], R.Tensor((2, 3))))
with pytest.raises(TypeError):
bb.normalize(relax.op.ones(s0, "float32"))
with pytest.raises(TypeError):
bb.normalize(relax.op.zeros(s1, "float32"))
def test_ones_like_zeros_like_infer_ty():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
x1 = relax.Var("x", R.Tensor("float32", ndim=2))
x2 = relax.Var("x", R.Tensor("float32"))
x3 = relax.Var("x", R.Tensor((2, 3)))
x4 = relax.Var("x", R.Tensor(ndim=2))
x5 = relax.Var("x", R.Tensor())
_check_inference(bb, relax.op.ones_like(x0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, relax.op.zeros_like(x1), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, relax.op.ones_like(x2), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.zeros_like(x3), relax.TensorType((2, 3), dtype=None))
_check_inference(bb, relax.op.ones_like(x4), relax.TensorType(dtype=None, ndim=2))
_check_inference(bb, relax.op.zeros_like(x5), relax.TensorType(dtype=None))
_check_inference(
bb, relax.op.ones_like(x0, dtype="float16"), relax.TensorType((2, 3), "float16")
)
_check_inference(
bb, relax.op.zeros_like(x3, dtype="float16"), relax.TensorType((2, 3), "float16")
)
def test_ones_like_zeros_like_infer_ty_shape_symbolic():
bb = relax.BlockBuilder()
m = tirx.Var("m", "int64")
n = tirx.Var("n", "int64")
x0 = relax.Var("x", R.Tensor((m, n), "float32"))
x1 = relax.Var("x", R.Tensor((m, n)))
_check_inference(bb, relax.op.ones_like(x0), relax.TensorType((m, n), "float32"))
_check_inference(bb, relax.op.zeros_like(x1), relax.TensorType((m, n), dtype=None))
def test_ones_like_zeros_like_infer_ty_shape_var():
bb = relax.BlockBuilder()
s0 = relax.Var("s", relax.ShapeType((2, 3)))
s1 = relax.Var("s", relax.ShapeType(ndim=2))
s2 = relax.Var("s", relax.ShapeType())
x0 = relax.Var("x", relax.TensorType(s0, "float32"))
x1 = relax.Var("x", relax.TensorType(s1, "float32"))
x2 = relax.Var("x", relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.ones_like(x0), relax.TensorType(s0, "float32"))
_check_inference(bb, relax.op.zeros_like(x1), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.zeros_like(x2), relax.TensorType(s2, "float32"))
def test_ones_like_zeros_like_infer_ty_more_input_dtype():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
_check_inference(bb, relax.op.ones_like(x0), relax.TensorType((2, 3), "float64"))
_check_inference(bb, relax.op.zeros_like(x1), relax.TensorType((2, 3), "int8"))
def test_ones_like_zeros_like_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
x0 = relax.Var("x", relax.ShapeType((2, 3)))
x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
with pytest.raises(TypeError):
bb.normalize(relax.op.ones_like(x0))
with pytest.raises(TypeError):
bb.normalize(relax.op.zeros_like(x1))
def test_eye_infer_ty():
bb = relax.BlockBuilder()
_check_inference(bb, relax.op.eye(3), relax.TensorType((3, 3), "float32"))
_check_inference(bb, relax.op.eye(2, 4), relax.TensorType((2, 4), "float32"))
_check_inference(bb, relax.op.eye(3, dtype="int64"), relax.TensorType((3, 3), "int64"))
_check_inference(bb, relax.op.eye(3, 5, k=1), relax.TensorType((3, 5), "float32"))
_check_inference(bb, relax.op.eye(3, 5, k=-2), relax.TensorType((3, 5), "float32"))
def test_eye_infer_ty_symbolic():
bb = relax.BlockBuilder()
n = tirx.Var("n", "int64")
m = tirx.Var("m", "int64")
k = tirx.Var("k", "int64")
_check_inference(bb, relax.op.eye(n), relax.TensorType((n, n), "float32"))
_check_inference(bb, relax.op.eye(n, m), relax.TensorType((n, m), "float32"))
_check_inference(bb, relax.op.eye(n, k=k), relax.TensorType((n, n), "float32"))
def test_eye_like_infer_ty():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((3, 4), "float32"))
x1 = relax.Var("x", R.Tensor((2, 5), "int64"))
x2 = relax.Var("x", R.Tensor((3, 3)))
_check_inference(bb, relax.op.eye_like(x0), relax.TensorType((3, 4), "float32"))
_check_inference(bb, relax.op.eye_like(x1), relax.TensorType((2, 5), "int64"))
_check_inference(bb, relax.op.eye_like(x2), relax.TensorType((3, 3), dtype=None))
_check_inference(bb, relax.op.eye_like(x0, k=1), relax.TensorType((3, 4), "float32"))
_check_inference(
bb, relax.op.eye_like(x1, dtype="float32"), relax.TensorType((2, 5), "float32")
)
def test_eye_like_infer_ty_symbolic():
bb = relax.BlockBuilder()
n = tirx.Var("n", "int64")
m = tirx.Var("m", "int64")
x = relax.Var("x", R.Tensor((n, m), "float32"))
k = tirx.Var("k", "int64")
_check_inference(bb, relax.op.eye_like(x), relax.TensorType((n, m), "float32"))
_check_inference(bb, relax.op.eye_like(x, k=k), relax.TensorType((n, m), "float32"))
def test_eye_like_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
x0 = relax.Var("x", relax.ShapeType((2, 3)))
x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
with pytest.raises(TypeError):
bb.normalize(relax.op.eye_like(x0))
with pytest.raises(TypeError):
bb.normalize(relax.op.eye_like(x1))
def test_arange_infer_ty():
bb = relax.BlockBuilder()
_check_inference(bb, relax.op.arange(10), relax.TensorType((10,), "int64"))
_check_inference(bb, relax.op.arange(1, 10), relax.TensorType((9,), "int64"))
_check_inference(bb, relax.op.arange(0, 10, 2), relax.TensorType((5,), "int64"))
_check_inference(bb, relax.op.arange(1, 10, 2), relax.TensorType((5,), "int64"))
_check_inference(bb, relax.op.arange(10.0), relax.TensorType((10,), "float32"))
_check_inference(bb, relax.op.arange(1.0, 10), relax.TensorType((9,), "float32"))
_check_inference(bb, relax.op.arange(0, 20, 2.5), relax.TensorType((8,), "float32"))
_check_inference(bb, relax.op.arange(1, 10, 2.3), relax.TensorType((4,), "float32"))
def test_arange_infer_ty_shape_var():
bb = relax.BlockBuilder()
start = tirx.Var("start", "int64")
stop = tirx.Var("stop", "int64")
step = tirx.Var("step", "int64")
_check_inference(bb, relax.op.arange(stop), relax.TensorType((stop,), "int64"))
_check_inference(bb, relax.op.arange(1, stop), relax.TensorType((stop - 1,), "int64"))
_check_inference(bb, relax.op.arange(start, stop), relax.TensorType((stop - start,), "int64"))
_check_inference(
bb,
relax.op.arange(start, stop, 2),
relax.TensorType(((stop + 1 - start) // 2,), "int64"),
)
_check_inference(
bb,
relax.op.arange(start, stop, step),
relax.TensorType(((stop + step - start - 1) // step,), "int64"),
)
start = tirx.Var("start", "float32")
stop = tirx.Var("stop", "float32")
step = tirx.Var("step", "float32")
_check_inference(
bb,
relax.op.arange(stop),
relax.TensorType((T.cast(T.ceil(stop), "int64"),), "float32"),
)
_check_inference(
bb,
relax.op.arange(1, stop),
relax.TensorType((T.cast(T.ceil(stop - 1.0), "int64"),), "float32"),
)
_check_inference(
bb,
relax.op.arange(start, stop),
relax.TensorType((T.cast(T.ceil(stop - start), "int64"),), "float32"),
)
_check_inference(
bb,
relax.op.arange(start, stop, 2),
relax.TensorType((T.cast(T.ceil((stop - start) / 2), "int64"),), "float32"),
)
_check_inference(
bb,
relax.op.arange(start, stop, step),
relax.TensorType((T.cast(T.ceil((stop - start) / step), "int64"),), "float32"),
)
def test_tril_triu_infer_ty():
bb = relax.BlockBuilder()
vdev0 = VDevice("llvm")
x0 = relax.Var("x", R.Tensor((2, 3, 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, 3, 4)))
x4 = relax.Var("x", R.Tensor(ndim=3))
x5 = relax.Var("x", R.Tensor())
x6 = relax.Var("x", R.Tensor((2, 3, 4), "float32", vdev0))
_check_inference(bb, relax.op.tril(x0, k=1), relax.TensorType((2, 3, 4), "float32"))
_check_inference(bb, relax.op.triu(x0, k=0), relax.TensorType((2, 3, 4), "float32"))
_check_inference(bb, relax.op.tril(x0), relax.TensorType((2, 3, 4), "float32"))
_check_inference(bb, relax.op.triu(x1), relax.TensorType(dtype="float32", ndim=3))
_check_inference(bb, relax.op.tril(x2), relax.TensorType(dtype="float32"))
_check_inference(bb, relax.op.triu(x3), relax.TensorType((2, 3, 4), dtype=None))
_check_inference(bb, relax.op.tril(x4), relax.TensorType(dtype=None, ndim=3))
_check_inference(bb, relax.op.triu(x5), relax.TensorType(dtype=None))
_check_inference(bb, relax.op.tril(x6), relax.TensorType((2, 3, 4), "float32", vdev0))
def test_tril_triu_infer_ty_shape_symbolic():
bb = relax.BlockBuilder()
vdev0 = VDevice("llvm")
a = tirx.Var("a", "int64")
b = tirx.Var("b", "int64")
c = tirx.Var("c", "int64")
x0 = relax.Var("x", R.Tensor((a, b, c), "float32"))
x1 = relax.Var("x", R.Tensor((a, b, c)))
x2 = relax.Var("x", R.Tensor((a, b, c), "float32", vdev0))
x3 = relax.Var("x", R.Tensor((16, 32, 64)))
# Dynamic tensor, static offset
_check_inference(bb, relax.op.tril(x0), relax.TensorType((a, b, c), "float32"))
_check_inference(bb, relax.op.triu(x1), relax.TensorType((a, b, c), dtype=None))
_check_inference(bb, relax.op.tril(x2), relax.TensorType((a, b, c), "float32", vdev0))
# Static tensor, dynamic offset
_check_inference(bb, relax.op.tril(x3, a), relax.TensorType((16, 32, 64), dtype=None))
# Dynamic tensor, dynamic offset
_check_inference(bb, relax.op.tril(x0, a), relax.TensorType((a, b, c), "float32"))
def test_tril_triu_infer_ty_shape_var():
bb = relax.BlockBuilder()
s0 = relax.Var("s", relax.ShapeType((2, 3, 4)))
s1 = relax.Var("s", relax.ShapeType(ndim=3))
s2 = relax.Var("s", relax.ShapeType())
x0 = relax.Var("x", relax.TensorType(s0, "float32"))
x1 = relax.Var("x", relax.TensorType(s1, "float32"))
x2 = relax.Var("x", relax.TensorType(s2, "float32"))
_check_inference(bb, relax.op.tril(x0), relax.TensorType(s0, "float32"))
_check_inference(bb, relax.op.triu(x1), relax.TensorType(s1, "float32"))
_check_inference(bb, relax.op.tril(x2), relax.TensorType(s2, "float32"))
def test_tril_triu_infer_ty_more_input_dtype():
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3, 4), "float16"))
x1 = relax.Var("x", R.Tensor((2, 3, 4), "int8"))
x2 = relax.Var("x", R.Tensor((2, 3, 4), "int32"))
_check_inference(bb, relax.op.triu(x0), relax.TensorType((2, 3, 4), "float16"))
_check_inference(bb, relax.op.tril(x1), relax.TensorType((2, 3, 4), "int8"))
_check_inference(bb, relax.op.triu(x2), relax.TensorType((2, 3, 4), "int32"))
def test_tril_triu_infer_ty_less_than_two_ndim():
bb = relax.BlockBuilder()
s0 = relax.Var("s", relax.ShapeType((2,)))
s1 = relax.Var("s", relax.ShapeType(()))
s2 = relax.Var("s", relax.ShapeType(ndim=1))
s3 = relax.Var("s", relax.ShapeType(ndim=0))
x0 = relax.Var("x", R.Tensor((2,), "float32"))
x1 = relax.Var("x", R.Tensor((), "float32"))
x2 = relax.Var("x", R.Tensor("float32", ndim=1))
x3 = relax.Var("x", R.Tensor("float32", ndim=0))
x4 = relax.Var("x", relax.TensorType(s0, "float32"))
x5 = relax.Var("x", relax.TensorType(s1, "float32"))
x6 = relax.Var("x", relax.TensorType(s2, "float32"))
x7 = relax.Var("x", relax.TensorType(s3, "float32"))
with pytest.raises(ValueError):
bb.normalize(relax.op.tril(x0))
with pytest.raises(ValueError):
bb.normalize(relax.op.triu(x1))
with pytest.raises(ValueError):
bb.normalize(relax.op.tril(x2))
with pytest.raises(ValueError):
bb.normalize(relax.op.triu(x3))
with pytest.raises(ValueError):
bb.normalize(relax.op.tril(x4))
with pytest.raises(ValueError):
bb.normalize(relax.op.triu(x5))
with pytest.raises(ValueError):
bb.normalize(relax.op.tril(x6))
with pytest.raises(ValueError):
bb.normalize(relax.op.triu(x7))
def test_tril_triu_infer_ty_wrong_input_type():
bb = relax.BlockBuilder()
x0 = relax.Var("x", relax.ShapeType((2, 3, 4)))
x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3, 4), "float32")))
with pytest.raises(TypeError):
bb.normalize(relax.op.tril(x0))
with pytest.raises(TypeError):
bb.normalize(relax.op.triu(x1))
if __name__ == "__main__":
tvm.testing.main()
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