wehub-resource-sync
283 lines
9.2 KiB
Python
283 lines
9.2 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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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((3, 4, 5), "float32"))
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assert relax.op.sort(x, axis=1).op == Op.get("relax.sort")
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assert relax.op.argsort(x, axis=1).op == Op.get("relax.argsort")
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assert relax.op.topk(x, k=1, axis=1).op == Op.get("relax.topk")
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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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def test_sort_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, 10, 4), "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"))
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x3 = relax.Var("x", R.Tensor((2, 10, 4)))
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x4 = relax.Var("x", R.Tensor(ndim=3))
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x5 = relax.Var("x", R.Tensor())
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x6 = relax.Var("x", R.Tensor((2, 10, 4), "float32", vdev0))
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_check_inference(bb, relax.op.sort(x0, axis=1), relax.TensorType((2, 10, 4), "float32"))
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_check_inference(bb, relax.op.sort(x6, axis=1), relax.TensorType((2, 10, 4), "float32", vdev0))
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_check_inference(bb, relax.op.sort(x1, axis=1), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, relax.op.sort(x2, axis=1), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.sort(x3, axis=1), relax.TensorType((2, 10, 4), dtype=""))
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_check_inference(bb, relax.op.sort(x4, axis=1), relax.TensorType(dtype="", ndim=3))
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_check_inference(bb, relax.op.sort(x5, axis=1), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.sort(x0), relax.TensorType((2, 10, 4), "float32"))
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_check_inference(
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bb,
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relax.op.sort(x0, axis=1, descending=False),
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relax.TensorType((2, 10, 4), "float32"),
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)
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def test_sort_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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a = tirx.Var("a", "int64")
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b = tirx.Var("b", "int64")
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c = tirx.Var("c", "int64")
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x = relax.Var("x", R.Tensor((a, b, c), "float32"))
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_check_inference(bb, relax.op.sort(x, axis=1), relax.TensorType((a, b, c), "float32"))
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_check_inference(bb, relax.op.sort(x), relax.TensorType((a, b, c), "float32"))
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def test_sort_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3, 4), "float16"))
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x1 = relax.Var("x", R.Tensor((2, 3, 4), "int8"))
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_check_inference(bb, relax.op.sort(x0, axis=1), relax.TensorType((2, 3, 4), "float16"))
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_check_inference(bb, relax.op.sort(x1, axis=1), relax.TensorType((2, 3, 4), "int8"))
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def test_sort_wrong_input():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", relax.ShapeType((2, 3, 4, 5)))
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x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3, 4, 5), "float32")))
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x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
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y = relax.Var("y", R.Tensor((2, 3, 4), "float32"))
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with pytest.raises(TypeError):
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relax.op.sort(x, y)
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with pytest.raises(TypeError):
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bb.normalize(relax.op.sort(x0, axis=1))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.sort(x1, axis=1))
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def test_argsort_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, 10, 4), "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"))
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x3 = relax.Var("x", R.Tensor((2, 10, 4)))
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x4 = relax.Var("x", R.Tensor(ndim=3))
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x5 = relax.Var("x", R.Tensor())
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x6 = relax.Var("x", R.Tensor((2, 10, 4), "float32", vdev0))
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_check_inference(
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bb,
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relax.op.argsort(x0, axis=1, descending=False, dtype="int64"),
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relax.TensorType((2, 10, 4), "int64"),
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)
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_check_inference(bb, relax.op.argsort(x6, axis=1), relax.TensorType((2, 10, 4), "int32", vdev0))
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_check_inference(bb, relax.op.argsort(x1, axis=1), relax.TensorType(dtype="int32", ndim=3))
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_check_inference(
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bb, relax.op.argsort(x2, axis=1, dtype="float16"), relax.TensorType(dtype="float16")
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)
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_check_inference(bb, relax.op.argsort(x3, axis=1), relax.TensorType((2, 10, 4), dtype="int32"))
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_check_inference(bb, relax.op.argsort(x4, axis=1), relax.TensorType(dtype="int32", ndim=3))
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_check_inference(bb, relax.op.argsort(x5, axis=1), relax.TensorType(dtype="int32"))
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_check_inference(bb, relax.op.argsort(x0), relax.TensorType((2, 10, 4), "int32"))
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_check_inference(
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bb,
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relax.op.argsort(x0, axis=1, descending=False),
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relax.TensorType((2, 10, 4), "int32"),
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)
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def test_argsort_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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a = tirx.Var("a", "int64")
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b = tirx.Var("b", "int64")
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c = tirx.Var("c", "int64")
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x = relax.Var("x", R.Tensor((a, b, c), "float32"))
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_check_inference(bb, relax.op.argsort(x, axis=1), relax.TensorType((a, b, c), "int32"))
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_check_inference(bb, relax.op.argsort(x), relax.TensorType((a, b, c), "int32"))
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def test_topk_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, 10, 4), "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"))
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x3 = relax.Var("x", R.Tensor((2, 10, 4)))
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x4 = relax.Var("x", R.Tensor(ndim=3))
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x5 = relax.Var("x", R.Tensor())
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x6 = relax.Var("x", R.Tensor((2, 10, 4), "float32", vdev0))
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_check_inference(
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bb,
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relax.op.topk(x0, k=5, axis=1, ret_type="both", largest=False, dtype="int64"),
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relax.TupleType(
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[
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relax.TensorType((2, 5, 4), "float32"),
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relax.TensorType((2, 5, 4), "int64"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x6),
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relax.TupleType(
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[
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relax.TensorType((2, 10, 1), "float32", vdev0),
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relax.TensorType((2, 10, 1), "int32", vdev0),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x1, k=3, axis=1),
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relax.TupleType(
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[
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relax.TensorType(dtype="float32", ndim=3),
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relax.TensorType(dtype="int32", ndim=3),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x2),
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relax.TupleType([relax.TensorType(dtype="float32"), relax.TensorType(dtype="int32")]),
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)
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_check_inference(
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bb,
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relax.op.topk(x3, axis=0),
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relax.TupleType(
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[
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relax.TensorType((1, 10, 4), None),
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relax.TensorType((1, 10, 4), dtype="int32"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x4, axis=1),
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relax.TupleType(
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[
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relax.TensorType(ndim=3, dtype=None),
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relax.TensorType(dtype="int32", ndim=3),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x5, axis=1),
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relax.TupleType(
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[
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relax.TensorType(dtype=None),
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relax.TensorType(dtype="int32"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x0),
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relax.TupleType(
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[
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relax.TensorType((2, 10, 1), "float32"),
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relax.TensorType((2, 10, 1), "int32"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x0, k=-1),
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relax.TupleType(
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[
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relax.TensorType((2, 10, 4), "float32"),
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relax.TensorType((2, 10, 4), "int32"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x0, k=6),
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relax.TupleType(
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[
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relax.TensorType((2, 10, 4), "float32"),
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relax.TensorType((2, 10, 4), "int32"),
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]
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),
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)
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def test_topk_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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a = tirx.Var("a", "int64")
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b = tirx.Var("b", "int64")
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c = tirx.Var("c", "int64")
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x = relax.Var("x", R.Tensor((a, b, c), "float32"))
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_check_inference(
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bb,
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relax.op.topk(x, axis=1),
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relax.TupleType(
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[
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relax.TensorType((a, 1, c), "float32"),
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relax.TensorType((a, 1, c), "int32"),
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]
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),
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)
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_check_inference(
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bb,
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relax.op.topk(x, k=3),
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relax.TupleType(
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[
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relax.TensorType((a, b, 3), "float32"),
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relax.TensorType((a, b, 3), "int32"),
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]
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),
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)
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if __name__ == "__main__":
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tvm.testing.main()
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