wehub-resource-sync
409 lines
19 KiB
Python
409 lines
19 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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cond = relax.Var("cond", R.Tensor((2, 3), "bool"))
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("x", R.Tensor((2, 3), "float32"))
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assert relax.op.where(cond, x, y).op == Op.get("relax.where")
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assert relax.op.argmax(x).op == Op.get("relax.argmax")
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assert relax.op.argmin(x).op == Op.get("relax.argmin")
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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_where_infer_ty():
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bb = relax.BlockBuilder()
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vdev0 = VDevice("llvm")
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cond0 = relax.Var("cond", R.Tensor((6, 5, 1, 3, 1), "bool"))
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cond1 = relax.Var("cond", R.Tensor("bool", ndim=5))
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cond2 = relax.Var("cond", R.Tensor("bool"))
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cond3 = relax.Var("cond", R.Tensor((6, 5, 1, 3, 1), "bool", vdev0))
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x0 = relax.Var("x", R.Tensor((5, 1, 3, 2), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=4))
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x2 = relax.Var("x", R.Tensor("float32"))
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x3 = relax.Var("x", R.Tensor((5, 1, 3, 2)))
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x4 = relax.Var("x", R.Tensor(ndim=4))
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x5 = relax.Var("x", R.Tensor())
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x6 = relax.Var("x", R.Tensor((5, 1, 3, 2), "float32", vdev0))
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y0 = relax.Var("y", R.Tensor((4, 3, 1), "float32"))
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y1 = relax.Var("y", R.Tensor("float32", ndim=3))
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y2 = relax.Var("y", R.Tensor("float32"))
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y3 = relax.Var("y", R.Tensor((4, 3, 1)))
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y4 = relax.Var("y", R.Tensor(ndim=3))
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y5 = relax.Var("y", R.Tensor())
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y6 = relax.Var("y", R.Tensor((4, 3, 1), "float32", vdev0))
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_check_inference(
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bb, relax.op.where(cond0, x0, y0), relax.TensorType((6, 5, 4, 3, 2), "float32")
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)
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_check_inference(
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bb, relax.op.where(cond3, x6, y6), relax.TensorType((6, 5, 4, 3, 2), "float32", vdev0)
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)
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_check_inference(bb, relax.op.where(cond0, x1, y0), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x2, y0), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond0, x3, y0), relax.TensorType((6, 5, 4, 3, 2), dtype=""))
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_check_inference(bb, relax.op.where(cond0, x4, y0), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x5, y0), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x1, y1), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x2, y1), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond0, x3, y1), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x4, y1), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x5, y1), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x2, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond0, x3, y2), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x4, y2), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x5, y2), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x3, y3), relax.TensorType((6, 5, 4, 3, 2), dtype=""))
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_check_inference(bb, relax.op.where(cond0, x4, y3), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x5, y3), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x4, y4), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x5, y4), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond0, x5, y5), relax.TensorType(dtype=""))
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_check_inference(bb, relax.op.where(cond1, x0, y0), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond1, x2, y0), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond2, x0, y0), relax.TensorType(dtype="float32"))
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def test_where_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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d0 = tirx.Var("d", "int64")
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d1 = tirx.Var("d", "int64")
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e = tirx.Var("e", "int64")
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cond = relax.Var("cond", R.Tensor((a, b, 1, d0, 1), "bool"))
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x0 = relax.Var("x", R.Tensor((b, 1, d0, e), "float32"))
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x1 = relax.Var("x", R.Tensor((b, 1, d1, e), "float32"))
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x2 = relax.Var("x", R.Tensor((b, 1, d0, e)))
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y0 = relax.Var("y", R.Tensor((c, d0, 1), "float32"))
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y1 = relax.Var("y", R.Tensor((c, d0, 1)))
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_check_inference(
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bb, relax.op.where(cond, x0, y0), relax.TensorType((a, b, c, d0, e), "float32")
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)
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_check_inference(bb, relax.op.where(cond, x1, y0), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond, x2, y0), relax.TensorType((a, b, c, d0, e), dtype=""))
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_check_inference(bb, relax.op.where(cond, x0, y1), relax.TensorType((a, b, c, d0, e), dtype=""))
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_check_inference(bb, relax.op.where(cond, x1, y1), relax.TensorType(dtype="", ndim=5))
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_check_inference(bb, relax.op.where(cond, x2, y1), relax.TensorType((a, b, c, d0, e), dtype=""))
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def test_where_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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scond0 = relax.Var("scond", relax.ShapeType((6, 5, 1, 3, 1)))
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scond1 = relax.Var("scond", relax.ShapeType(ndim=5))
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scond2 = relax.Var("scond", relax.ShapeType())
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sx0 = relax.Var("sx", relax.ShapeType((5, 1, 3, 2)))
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sx1 = relax.Var("sx", relax.ShapeType(ndim=4))
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sx2 = relax.Var("sx", relax.ShapeType())
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sy0 = relax.Var("sy", relax.ShapeType((4, 3, 1)))
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sy1 = relax.Var("sy", relax.ShapeType(ndim=3))
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sy2 = relax.Var("sy", relax.ShapeType())
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s0 = relax.Var("s", relax.ShapeType((6, 5, 4, 3, 2)))
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s1 = relax.Var("s", relax.ShapeType(ndim=5))
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s2 = relax.Var("s", relax.ShapeType())
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cond0 = relax.Var("cond", relax.TensorType(scond0, "bool"))
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cond1 = relax.Var("cond", relax.TensorType(scond1, "bool"))
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cond2 = relax.Var("cond", relax.TensorType(scond2, "bool"))
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cond3 = relax.Var("cond", relax.TensorType(s0, "bool"))
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cond4 = relax.Var("cond", relax.TensorType(s1, "bool"))
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cond5 = relax.Var("cond", relax.TensorType(s2, "bool"))
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x0 = relax.Var("x", relax.TensorType(sx0, "float32"))
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x1 = relax.Var("x", relax.TensorType(sx1, "float32"))
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x2 = relax.Var("x", relax.TensorType(sx2, "float32"))
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x3 = relax.Var("x", relax.TensorType(s0, "float32"))
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x4 = relax.Var("x", relax.TensorType(s1, "float32"))
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x5 = relax.Var("x", relax.TensorType(s2, "float32"))
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y0 = relax.Var("y", relax.TensorType(sy0, "float32"))
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y1 = relax.Var("y", relax.TensorType(sy1, "float32"))
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y2 = relax.Var("y", relax.TensorType(sy2, "float32"))
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y3 = relax.Var("y", relax.TensorType(s0, "float32"))
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y4 = relax.Var("y", relax.TensorType(s1, "float32"))
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y5 = relax.Var("y", relax.TensorType(s2, "float32"))
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_check_inference(bb, relax.op.where(cond0, x0, y0), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x0, y1), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x0, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond0, x1, y1), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond0, x1, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond0, x2, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond1, x1, y1), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond1, x1, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond1, x2, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond2, x2, y2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond3, x3, y3), relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.where(cond3, x3, y4), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond3, x4, y3), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond4, x3, y3), relax.TensorType(dtype="float32", ndim=5))
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_check_inference(bb, relax.op.where(cond4, x4, y4), relax.TensorType(s1, "float32"))
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_check_inference(bb, relax.op.where(cond4, x4, y5), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond4, x5, y4), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond5, x4, y4), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.where(cond5, x5, y5), relax.TensorType(s2, "float32"))
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def test_where_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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cond = relax.Var("cond", R.Tensor((6, 5, 1, 3, 1), "bool"))
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x0 = relax.Var("x", R.Tensor((5, 1, 3, 2), "float16"))
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y0 = relax.Var("y", R.Tensor((4, 3, 1), "float16"))
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x1 = relax.Var("x", R.Tensor((5, 1, 3, 2), "int8"))
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y1 = relax.Var("y", R.Tensor((4, 3, 1), "int8"))
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x2 = relax.Var("x", R.Tensor((5, 1, 3, 2), "int32"))
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y2 = relax.Var("y", R.Tensor((4, 3, 1), "int32"))
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_check_inference(bb, relax.op.where(cond, x0, y0), relax.TensorType((6, 5, 4, 3, 2), "float16"))
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_check_inference(bb, relax.op.where(cond, x1, y1), relax.TensorType((6, 5, 4, 3, 2), "int8"))
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_check_inference(bb, relax.op.where(cond, x2, y2), relax.TensorType((6, 5, 4, 3, 2), "int32"))
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def test_where_infer_ty_cond_not_boolean():
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bb = relax.BlockBuilder()
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cond0 = relax.Var("cond", R.Tensor((2, 3), "float32"))
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cond1 = relax.Var("cond", relax.TensorType(dtype="int32", ndim=2))
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("y", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond0, x, y))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond1, x, y))
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def test_where_infer_ty_shape_unequal_const_int():
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bb = relax.BlockBuilder()
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cond0 = relax.Var("cond", R.Tensor((6, 5, 1, 4, 1), "bool"))
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cond1 = relax.Var("cond", R.Tensor((6, 5, 1, 3, 1), "bool"))
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x0 = relax.Var("x", R.Tensor((5, 1, 4, 2), "float32"))
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x1 = relax.Var("x", R.Tensor((5, 1, 3, 2), "float32"))
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y0 = relax.Var("y", R.Tensor((4, 4, 1), "float32"))
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y1 = relax.Var("y", R.Tensor((4, 3, 1), "float32"))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.where(cond0, x1, y1))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.where(cond1, x0, y1))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.where(cond1, x1, y0))
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def test_where_infer_ty_dtype_mismatch():
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bb = relax.BlockBuilder()
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cond = relax.Var("cond", R.Tensor((2, 3), "bool"))
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", R.Tensor((2, 3), "float16"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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y1 = relax.Var("y", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond, x0, y0))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond, x1, y1))
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def test_where_infer_ty_wrong_input_type():
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bb = relax.BlockBuilder()
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cond0 = relax.Var("cond", relax.ShapeType((2, 3)))
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cond1 = relax.Var("cond", R.Tensor((2, 3), "bool"))
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x0 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
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x1 = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", relax.TupleType([R.Tensor((2, 3), "float32")]))
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y1 = relax.Var("y", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond0, x1, y1))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond1, x0, y1))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.where(cond1, x1, y0))
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argmax_argmin_ops = [relax.op.argmax, relax.op.argmin]
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@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
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def test_argmax_argmin_infer_ty(argmax_argmin_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, 4, 5), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=4))
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x2 = relax.Var("x", R.Tensor("float32"))
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x3 = relax.Var("x", R.Tensor((2, 3, 4, 5)))
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x4 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32", vdev0))
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_check_inference(bb, argmax_argmin_op(x0, axis=1), relax.TensorType((2, 4, 5), "int64"))
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_check_inference(bb, argmax_argmin_op(x4, axis=1), relax.TensorType((2, 4, 5), "int64", vdev0))
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_check_inference(
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bb,
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argmax_argmin_op(x0, axis=1, keepdims=True),
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relax.TensorType((2, 1, 4, 5), "int64"),
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)
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_check_inference(bb, argmax_argmin_op(x0, axis=None), relax.TensorType((), "int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x0, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "int64"),
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)
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_check_inference(bb, argmax_argmin_op(x1, axis=1), relax.TensorType(dtype="int64", ndim=3))
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_check_inference(
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bb,
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argmax_argmin_op(x1, axis=1, keepdims=True),
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relax.TensorType(dtype="int64", ndim=4),
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)
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_check_inference(bb, argmax_argmin_op(x1, axis=None), relax.TensorType((), "int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x1, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "int64"),
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)
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_check_inference(bb, argmax_argmin_op(x2, axis=1), relax.TensorType(dtype="int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x2, axis=1, keepdims=True),
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relax.TensorType(dtype="int64"),
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)
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_check_inference(bb, argmax_argmin_op(x2, axis=None), relax.TensorType((), "int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x2, axis=None, keepdims=True),
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relax.TensorType(dtype="int64"),
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)
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_check_inference(bb, argmax_argmin_op(x3, axis=1), relax.TensorType((2, 4, 5), dtype="int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x3, axis=1, keepdims=True),
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relax.TensorType((2, 1, 4, 5), dtype="int64"),
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)
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_check_inference(bb, argmax_argmin_op(x3, axis=None), relax.TensorType((), dtype="int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x3, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), dtype="int64"),
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)
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_check_inference(
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bb,
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argmax_argmin_op(x0, axis=1, keepdims=True),
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relax.TensorType((2, 1, 4, 5), "int64"),
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)
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_check_inference(bb, argmax_argmin_op(x0, axis=-1), relax.TensorType((2, 3, 4), "int64"))
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@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
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def test_argmax_argmin_infer_ty_shape_symbolic(argmax_argmin_op: Callable):
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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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d = tirx.Var("d", "int64")
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x = relax.Var("x", R.Tensor((a, b, c, d), "int64"))
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|
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_check_inference(bb, argmax_argmin_op(x, axis=1), relax.TensorType((a, c, d), "int64"))
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_check_inference(
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bb,
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argmax_argmin_op(x, axis=1, keepdims=True),
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relax.TensorType((a, 1, c, d), "int64"),
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)
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_check_inference(bb, argmax_argmin_op(x, axis=None), relax.TensorType((), "int64"))
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_check_inference(
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|
bb,
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argmax_argmin_op(x, axis=None, keepdims=True),
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|
relax.TensorType((1, 1, 1, 1), "int64"),
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|
)
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|
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@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
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def test_argmax_argmin_infer_ty_shape_var(argmax_argmin_op: Callable):
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=4))
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s1 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "int64"))
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x1 = relax.Var("x", relax.TensorType(s1, "int64"))
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|
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_check_inference(bb, argmax_argmin_op(x0), relax.TensorType((), dtype="int64"))
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|
_check_inference(
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|
bb, argmax_argmin_op(x0, keepdims=True), relax.TensorType((1, 1, 1, 1), dtype="int64")
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|
)
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|
_check_inference(bb, argmax_argmin_op(x0, axis=2), relax.TensorType(dtype="int64", ndim=3))
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|
_check_inference(
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|
bb,
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|
argmax_argmin_op(x0, axis=2, keepdims=True),
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|
relax.TensorType(dtype="int64", ndim=4),
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|
)
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|
_check_inference(bb, argmax_argmin_op(x1), relax.TensorType((), dtype="int64"))
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|
_check_inference(bb, argmax_argmin_op(x1, keepdims=True), relax.TensorType(dtype="int64"))
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|
_check_inference(bb, argmax_argmin_op(x1, axis=2), relax.TensorType(dtype="int64"))
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|
_check_inference(
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|
bb, argmax_argmin_op(x1, axis=2, keepdims=True), relax.TensorType(dtype="int64")
|
|
)
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|
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|
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@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
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|
def test_argmax_argmin_infer_ty_more_input_dtype(argmax_argmin_op: Callable):
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|
bb = relax.BlockBuilder()
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|
x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float16"))
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|
x1 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int8"))
|
|
|
|
_check_inference(bb, argmax_argmin_op(x0), relax.TensorType((), "int64"))
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|
_check_inference(bb, argmax_argmin_op(x1), relax.TensorType((), "int64"))
|
|
|
|
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|
@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
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|
def test_argmax_argmin_infer_ty_axis_out_of_range(argmax_argmin_op: Callable):
|
|
bb = relax.BlockBuilder()
|
|
x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int64"))
|
|
x1 = relax.Var("x", R.Tensor("int64", ndim=4))
|
|
|
|
with pytest.raises(ValueError):
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|
bb.normalize(argmax_argmin_op(x0, axis=4))
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|
with pytest.raises(ValueError):
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|
bb.normalize(argmax_argmin_op(x0, axis=-5))
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|
with pytest.raises(ValueError):
|
|
bb.normalize(argmax_argmin_op(x1, axis=4))
|
|
with pytest.raises(ValueError):
|
|
bb.normalize(argmax_argmin_op(x1, axis=-5))
|
|
|
|
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|
@pytest.mark.parametrize("argmax_argmin_op", argmax_argmin_ops)
|
|
def test_argmax_argmin_infer_ty_wrong_input_type(argmax_argmin_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), "int64")))
|
|
|
|
with pytest.raises(TypeError):
|
|
bb.normalize(argmax_argmin_op(x0))
|
|
with pytest.raises(TypeError):
|
|
bb.normalize(argmax_argmin_op(x1))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|