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Copy pathtest_op_binary.py
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343 lines (281 loc) · 15.3 KB
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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: F841
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), "float32"))
y = relax.Var("y", R.Tensor((2, 3), "float32"))
assert relax.op.add(x, y).op == Op.get("relax.add")
assert relax.op.divide(x, y).op == Op.get("relax.divide")
assert relax.op.floor_divide(x, y).op == Op.get("relax.floor_divide")
assert relax.op.multiply(x, y).op == Op.get("relax.multiply")
assert relax.op.power(x, y).op == Op.get("relax.power")
assert relax.op.atan2(x, y).op == Op.get("relax.atan2")
assert relax.op.subtract(x, y).op == Op.get("relax.subtract")
assert relax.op.mod(x, y).op == Op.get("relax.mod")
assert relax.op.floor_mod(x, y).op == Op.get("relax.floor_mod")
assert relax.op.equal(x, y).op == Op.get("relax.equal")
assert relax.op.greater(x, y).op == Op.get("relax.greater")
assert relax.op.greater_equal(x, y).op == Op.get("relax.greater_equal")
assert relax.op.less(x, y).op == Op.get("relax.less")
assert relax.op.less_equal(x, y).op == Op.get("relax.less_equal")
assert relax.op.not_equal(x, y).op == Op.get("relax.not_equal")
x = relax.Var("x", R.Tensor((2, 3), "int32"))
y = relax.Var("y", R.Tensor((2, 3), "int32"))
assert relax.op.bitwise_and(x, y).op == Op.get("relax.bitwise_and")
assert relax.op.bitwise_or(x, y).op == Op.get("relax.bitwise_or")
assert relax.op.bitwise_xor(x, y).op == Op.get("relax.bitwise_xor")
assert relax.op.left_shift(x, y).op == Op.get("relax.left_shift")
assert relax.op.right_shift(x, y).op == Op.get("relax.right_shift")
x = relax.Var("x", R.Tensor((2, 3), "bool"))
y = relax.Var("y", R.Tensor((2, 3), "bool"))
assert relax.op.logical_and(x, y).op == Op.get("relax.logical_and")
assert relax.op.logical_or(x, y).op == Op.get("relax.logical_or")
assert relax.op.logical_xor(x, y).op == Op.get("relax.logical_xor")
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)
binary_arith_ops = [
(relax.op.add, tirx.Add),
(relax.op.divide, tirx.Div),
(relax.op.floor_divide, tirx.FloorDiv),
(relax.op.multiply, tirx.Mul),
(relax.op.power, tirx.pow),
(relax.op.atan2, tirx.atan2),
(relax.op.subtract, tirx.Sub),
(relax.op.maximum, tirx.Max),
(relax.op.minimum, tirx.Min),
(relax.op.mod, tirx.Mod),
(relax.op.floor_mod, tirx.FloorMod),
]
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_arith_infer_ty(binary_arith_op: Callable):
bb = relax.BlockBuilder()
vdevice0 = VDevice("llvm")
vdevice1 = VDevice("cuda", 0)
x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
x1 = relax.Var("x", R.Tensor((1, 3), "float32"))
x2 = relax.Var("x", R.Tensor((3, 2, 3), "float32"))
x3 = relax.Var("x", R.Tensor((3, 1, 3), "float32"))
x4 = relax.Var("x", R.Tensor("float32", ndim=2))
x5 = relax.Var("x", R.Tensor())
x6 = relax.Var("x", R.Tensor("float32", ndim=2, vdevice=vdevice0))
x7 = relax.Var("x", R.Tensor((2, 3), "float32", vdevice0))
y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
y1 = relax.Var("y", R.Tensor((4, 3, 2, 1), "float32"))
y2 = relax.Var("y", R.Tensor("float32", ndim=2))
y3 = relax.Var("y", R.Tensor("float32", ndim=-1))
y4 = relax.Var("y", R.Tensor((2, 3), "float32", vdevice0))
y5 = relax.Var("y", R.Tensor("float32", ndim=2, vdevice=vdevice0))
_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, binary_arith_op(x1, y0), relax.TensorType((2, 3), "float32"))
_check_inference(bb, binary_arith_op(x1, y1), relax.TensorType((4, 3, 2, 3), "float32"))
_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType(dtype="float32", ndim=3))
_check_inference(bb, binary_arith_op(x3, y2), relax.TensorType(dtype="float32", ndim=3))
_check_inference(bb, binary_arith_op(x4, y0), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x4, y1), relax.TensorType(dtype="float32", ndim=4))
_check_inference(bb, binary_arith_op(x4, y2), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x4, y3), relax.TensorType(dtype="float32", ndim=-1))
_check_inference(bb, binary_arith_op(x5, y0), relax.TensorType(dtype="", ndim=-1))
_check_inference(
bb,
binary_arith_op(x6, y5),
relax.TensorType(dtype="float32", ndim=2, vdevice=vdevice0),
)
_check_inference(
bb,
binary_arith_op(x6, y2),
relax.TensorType(dtype="float32", ndim=2, vdevice=vdevice0),
)
_check_inference(bb, binary_arith_op(x7, y4), relax.TensorType((2, 3), "float32", vdevice0))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_infer_ty_binary_arith_prim_value_with_tensor(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Tensor((2, 3), "float32"))
y = relax.Var("y", R.Prim("float32"))
_check_inference(bb, binary_arith_op(x, y), relax.TensorType((2, 3), "float32"))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_infer_ty_binary_arith_prim_value_with_prim_value(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Prim("float32"))
y = relax.Var("y", R.Prim("float32"))
_check_inference(bb, binary_arith_op(x, y), tvm.ir.PrimType("float32"))
@pytest.mark.parametrize("binary_arith_op,tir_arith_op", binary_arith_ops)
@pytest.mark.xfail(reason="Not yet implemented")
def test_infer_ty_binary_arith_known_prim_value_with_prim_value(
binary_arith_op: Callable, tir_arith_op
):
bb = relax.BlockBuilder()
tir_x = tirx.Var("tir_x", "float32")
tir_y = tirx.Var("tir_y", "float32")
x = relax.Var("x", R.Prim(value=tir_x))
y = relax.Var("y", R.Prim(value=tir_y))
_check_inference(bb, binary_arith_op(x, y), tvm.ir.PrimType("float32"))
_check_inference(bb, binary_arith_op(y, x), tvm.ir.PrimType("float32"))
binary_cmp_ops = [
(relax.op.equal, tirx.EQ),
(relax.op.greater, tirx.GT),
(relax.op.greater_equal, tirx.GE),
(relax.op.less, tirx.LT),
(relax.op.less_equal, tirx.LE),
(relax.op.not_equal, tirx.NE),
]
@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
def test_binary_cmp_infer_ty(binary_cmp_op: Callable):
bb = relax.BlockBuilder()
vdev0 = VDevice("llvm")
x = relax.Var("x", R.Tensor((2, 3), "float32"))
y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
y1 = relax.Var("y", R.Tensor((2, 3), "int32"))
y2 = relax.Var("y", R.Tensor((2, 3), "float32", vdev0))
_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(x, y2), relax.TensorType((2, 3), "bool", vdev0))
@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
def test_infer_ty_binary_cmp_prim_value_to_tensor(binary_cmp_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Tensor((2, 3), "float32"))
y = relax.Var("y", R.Prim("float32"))
_check_inference(bb, binary_cmp_op(x, y), relax.TensorType((2, 3), "bool"))
_check_inference(bb, binary_cmp_op(y, x), relax.TensorType((2, 3), "bool"))
@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
def test_infer_ty_binary_cmp_prim_value_to_prim_value(binary_cmp_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Prim("float32"))
y = relax.Var("y", R.Prim("float32"))
_check_inference(bb, binary_cmp_op(x, y), tvm.ir.PrimType("bool"))
_check_inference(bb, binary_cmp_op(y, x), tvm.ir.PrimType("bool"))
@pytest.mark.parametrize("binary_cmp_op,tir_cmp_op", binary_cmp_ops)
@pytest.mark.xfail(reason="Not yet implemented")
def test_infer_ty_binary_cmp_known_prim_value_to_prim_value(binary_cmp_op: Callable, tir_cmp_op):
bb = relax.BlockBuilder()
tir_x = tirx.Var("tir_x", "float32")
tir_y = tirx.Var("tir_y", "float32")
x = relax.Var("x", R.Prim(value=tir_x))
y = relax.Var("y", R.Prim(value=tir_y))
_check_inference(bb, binary_cmp_op(x, y), tvm.ir.PrimType("bool"))
_check_inference(bb, binary_cmp_op(y, x), tvm.ir.PrimType("bool"))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_infer_ty_shape_symbolic(binary_arith_op: Callable):
bb = relax.BlockBuilder()
m = tirx.Var("m", "int64")
n = tirx.Var("n", "int64")
k = tirx.Var("k", "int64")
x0 = relax.Var("x", R.Tensor((m, n), "float32"))
x1 = relax.Var("x", R.Tensor((1, n), "float32"))
x2 = relax.Var("x", R.Tensor((k, n, m), "float32"))
x3 = relax.Var("x", R.Tensor((3, 1, n), "float32"))
x4 = relax.Var("x", R.Tensor("float32", ndim=2))
y0 = relax.Var("y", R.Tensor((m, n), "float32"))
y1 = relax.Var("y", R.Tensor((m, n + 2), "float32"))
y2 = relax.Var("y", R.Tensor((4, k, m, 1), "float32"))
y3 = relax.Var("y", R.Tensor("float32", ndim=2))
y4 = relax.Var("y", R.Tensor("float32", ndim=-1))
_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((m, n), "float32"))
_check_inference(bb, binary_arith_op(x0, y1), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x1, y0), relax.TensorType((m, n), "float32"))
_check_inference(bb, binary_arith_op(x1, y2), relax.TensorType((4, k, m, n), "float32"))
_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType(dtype="float32", ndim=4))
_check_inference(bb, binary_arith_op(x2, y3), relax.TensorType(dtype="float32", ndim=3))
_check_inference(bb, binary_arith_op(x3, y3), relax.TensorType(dtype="float32", ndim=3))
_check_inference(bb, binary_arith_op(x4, y0), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x4, y2), relax.TensorType(dtype="float32", ndim=4))
_check_inference(bb, binary_arith_op(x4, y3), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x4, y4), relax.TensorType(dtype="float32", ndim=-1))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_infer_ty_shape_var(binary_arith_op: Callable):
bb = relax.BlockBuilder()
s0 = relax.Var("s0", relax.ShapeType(ndim=2))
s1 = relax.Var("s1", relax.ShapeType(ndim=2))
s2 = relax.Var("s2", relax.ShapeType(ndim=4))
s3 = relax.Var("s3", relax.ShapeType(ndim=1))
s4 = relax.Var("s4", relax.ShapeType())
x = relax.Var("x", relax.TensorType(s0, "float32"))
y0 = relax.Var("y", relax.TensorType(s0, "float32"))
y1 = relax.Var("y", relax.TensorType(s1, "float32"))
y2 = relax.Var("y", relax.TensorType(s2, "float32"))
y3 = relax.Var("y", relax.TensorType(s3, "float32"))
y4 = relax.Var("y", relax.TensorType(s4, "float32"))
_check_inference(bb, binary_arith_op(x, y0), relax.TensorType(s0, "float32"))
_check_inference(bb, binary_arith_op(x, y1), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x, y2), relax.TensorType(dtype="float32", ndim=4))
_check_inference(bb, binary_arith_op(x, y3), relax.TensorType(dtype="float32", ndim=2))
_check_inference(bb, binary_arith_op(x, y4), relax.TensorType(dtype="float32"))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_arith_infer_ty_more_input_dtype(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
y0 = relax.Var("y", R.Tensor((2, 3), "float64"))
x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
y1 = relax.Var("y", R.Tensor((2, 3), "int8"))
x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
y2 = relax.Var("y", R.Tensor((2, 3), "int64"))
_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((2, 3), "float64"))
_check_inference(bb, binary_arith_op(x1, y1), relax.TensorType((2, 3), "int8"))
_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType((2, 3), "int64"))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_infer_ty_shape_unequal_const_int(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
y0 = relax.Var("y", R.Tensor((2, 4), "float32"))
with pytest.raises(ValueError):
bb.normalize(binary_arith_op(x0, y0))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_arith_infer_ty_dtype_mismatch(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Tensor((2, 3), "float32"))
y = relax.Var("y", R.Tensor((2, 3), "int32"))
with pytest.raises(TypeError):
bb.normalize(binary_arith_op(x, y))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_arith_infer_ty_vdevice_mismatch(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x = relax.Var("x", R.Tensor((2, 3), "float32", VDevice("llvm")))
y = relax.Var("y", R.Tensor((2, 3), "int32", VDevice("cuda")))
with pytest.raises(TypeError):
bb.normalize(binary_arith_op(x, y))
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_wrong_input_number(binary_arith_op: Callable):
x = relax.Var("x", R.Tensor((2, 3), "float32"))
with pytest.raises(TypeError):
binary_arith_op(x, x, x)
with pytest.raises(TypeError):
binary_arith_op(x)
with pytest.raises(TypeError):
binary_arith_op(x, x, x, x)
@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
def test_binary_infer_ty_wrong_input_type(binary_arith_op: Callable):
bb = relax.BlockBuilder()
x0 = relax.Var("x", relax.ShapeType((2, 3)))
x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
y = relax.Var("y", R.Tensor((2, 3), "float32"))
with pytest.raises(TypeError):
bb.normalize(binary_arith_op(x0, y))
with pytest.raises(TypeError):
bb.normalize(binary_arith_op(x1, y))
if __name__ == "__main__":
tvm.testing.main()
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