wehub-resource-sync
225 lines
8.8 KiB
Python
225 lines
8.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from collections.abc import Callable
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import pytest
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import tvm
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import tvm.testing
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from tvm import relax, tirx
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from tvm.ir import Op, VDevice
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from tvm.script import relax as R
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def test_op_correctness():
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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assert relax.op.abs(x).op == Op.get("relax.abs")
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assert relax.op.acos(x).op == Op.get("relax.acos")
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assert relax.op.acosh(x).op == Op.get("relax.acosh")
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assert relax.op.asin(x).op == Op.get("relax.asin")
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assert relax.op.asinh(x).op == Op.get("relax.asinh")
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assert relax.op.atan(x).op == Op.get("relax.atan")
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assert relax.op.atanh(x).op == Op.get("relax.atanh")
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assert relax.op.ceil(x).op == Op.get("relax.ceil")
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assert relax.op.cos(x).op == Op.get("relax.cos")
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assert relax.op.cosh(x).op == Op.get("relax.cosh")
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assert relax.op.exp(x).op == Op.get("relax.exp")
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assert relax.op.floor(x).op == Op.get("relax.floor")
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assert relax.op.isfinite(x).op == Op.get("relax.isfinite")
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assert relax.op.isinf(x).op == Op.get("relax.isinf")
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assert relax.op.isnan(x).op == Op.get("relax.isnan")
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assert relax.op.log(x).op == Op.get("relax.log")
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assert relax.op.negative(x).op == Op.get("relax.negative")
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assert relax.op.round(x).op == Op.get("relax.round")
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assert relax.op.rsqrt(x).op == Op.get("relax.rsqrt")
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assert relax.op.sigmoid(x).op == Op.get("relax.sigmoid")
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assert relax.op.sin(x).op == Op.get("relax.sin")
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assert relax.op.sinh(x).op == Op.get("relax.sinh")
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assert relax.op.square(x).op == Op.get("relax.square")
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assert relax.op.sqrt(x).op == Op.get("relax.sqrt")
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assert relax.op.tan(x).op == Op.get("relax.tan")
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assert relax.op.tanh(x).op == Op.get("relax.tanh")
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assert relax.op.clip(x, 0, 6).op == Op.get("relax.clip")
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assert relax.op.erf(x).op == Op.get("relax.erf")
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x = relax.Var("x", R.Tensor((2, 3), "int32"))
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assert relax.op.bitwise_not(x).op == Op.get("relax.bitwise_not")
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x = relax.Var("x", R.Tensor((2, 3), "bool"))
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assert relax.op.logical_not(x).op == Op.get("relax.logical_not")
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def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
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ret = bb.normalize(call)
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tvm.ir.assert_structural_equal(ret.ty, expected_ty)
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unary_arith_ops = [
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(relax.op.abs, False),
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(relax.op.acos, True),
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(relax.op.acosh, True),
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(relax.op.asin, True),
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(relax.op.asinh, True),
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(relax.op.atan, True),
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(relax.op.atanh, True),
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(relax.op.ceil, False),
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(relax.op.cos, True),
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(relax.op.cosh, True),
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(relax.op.exp, True),
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(relax.op.floor, False),
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(relax.op.log, True),
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(relax.op.negative, False),
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(relax.op.round, False),
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(relax.op.rsqrt, True),
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(relax.op.sigmoid, True),
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(relax.op.sign, False),
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(relax.op.sin, True),
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(relax.op.sinh, True),
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(relax.op.square, False),
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(relax.op.sqrt, True),
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(relax.op.tan, True),
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(relax.op.tanh, True),
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]
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@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
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def test_unary_arith_infer_ty(unary_arith_op: Callable):
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bb = relax.BlockBuilder()
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vdev0 = VDevice("llvm")
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=3))
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x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
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x3 = relax.Var("x", R.Tensor((2, 3)))
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x4 = relax.Var("x", R.Tensor())
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x5 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))
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_check_inference(bb, unary_arith_op(x0), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, unary_arith_op(x5), relax.TensorType((2, 3), "float32", vdev0))
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_check_inference(bb, unary_arith_op(x1), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, unary_arith_op(x2), relax.TensorType(dtype="float32"))
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_check_inference(bb, unary_arith_op(x3), relax.TensorType((2, 3), dtype=""))
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_check_inference(bb, unary_arith_op(x4), relax.TensorType(dtype=""))
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@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
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def test_unary_arith_infer_ty_shape_symbolic(unary_arith_op: Callable):
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(bb, unary_arith_op(x0), relax.TensorType((m, n), "float32"))
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_check_inference(bb, unary_arith_op(x1), relax.TensorType((4, n), "float32"))
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@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
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def test_unary_arith_infer_ty_shape_var(unary_arith_op: Callable):
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=2))
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s1 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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_check_inference(bb, unary_arith_op(x0), relax.TensorType(s0, "float32"))
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_check_inference(bb, unary_arith_op(x1), relax.TensorType(s1, "float32"))
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@pytest.mark.parametrize("unary_arith_op,require_float_dtype", unary_arith_ops)
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def test_unary_arith_infer_ty_more_input_dtype(unary_arith_op: Callable, require_float_dtype: bool):
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if require_float_dtype:
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return
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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_check_inference(bb, unary_arith_op(x0), relax.TensorType((2, 3), "float64"))
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_check_inference(bb, unary_arith_op(x1), relax.TensorType((2, 3), "int8"))
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_check_inference(bb, unary_arith_op(x2), relax.TensorType((2, 3), "int64"))
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@pytest.mark.parametrize("unary_arith_op,require_float_dtype", unary_arith_ops)
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def test_unary_arith_infer_ty_invalid_input_dtype(
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unary_arith_op: Callable, require_float_dtype: bool
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):
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if not require_float_dtype:
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return
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int64"))
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with pytest.raises(TypeError):
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bb.normalize(unary_arith_op(x0))
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with pytest.raises(TypeError):
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bb.normalize(unary_arith_op(x1))
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@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
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def test_unary_arith_wrong_input_number(unary_arith_op: Callable):
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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unary_arith_op(x, x)
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with pytest.raises(TypeError):
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unary_arith_op(x, x, x)
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@pytest.mark.parametrize("unary_arith_op", [row[0] for row in unary_arith_ops])
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def test_unary_arith_infer_ty_wrong_input_type(unary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", relax.ShapeType((2, 3)))
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x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
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with pytest.raises(TypeError):
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bb.normalize(unary_arith_op(x0))
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with pytest.raises(TypeError):
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bb.normalize(unary_arith_op(x1))
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def test_clip_infer_ty():
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bb = relax.BlockBuilder()
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vdev0 = VDevice("llvm")
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=3))
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x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
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x3 = relax.Var("x", R.Tensor((2, 3)))
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x4 = relax.Var("x", R.Tensor())
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x5 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))
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_check_inference(bb, relax.op.clip(x0, 0, 6), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, relax.op.clip(x5, 0, 6), relax.TensorType((2, 3), "float32", vdev0))
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_check_inference(bb, relax.op.clip(x1, 0, 6), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, relax.op.clip(x2, 0, 6), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.clip(x3, 0, 6), relax.TensorType((2, 3), dtype=""))
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_check_inference(bb, relax.op.clip(x4, 0, 6), relax.TensorType(dtype=""))
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# Symbolic
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x5 = relax.Var("x", R.Tensor((m, n), "float32"))
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x6 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(bb, relax.op.clip(x5, 0, 6), relax.TensorType((m, n), "float32"))
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_check_inference(bb, relax.op.clip(x6, 0, 6), relax.TensorType((4, n), "float32"))
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if __name__ == "__main__":
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tvm.testing.main()
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