wehub-resource-sync
502 lines
17 KiB
Python
502 lines
17 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, 4, 5), "float32"))
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assert relax.op.max(x).op == Op.get("relax.max")
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assert relax.op.mean(x).op == Op.get("relax.mean")
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assert relax.op.min(x).op == Op.get("relax.min")
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assert relax.op.prod(x).op == Op.get("relax.prod")
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assert relax.op.std(x).op == Op.get("relax.std")
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assert relax.op.sum(x).op == Op.get("relax.sum")
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assert relax.op.variance(x).op == Op.get("relax.variance")
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assert relax.op.median(x).op == Op.get("relax.median")
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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_statistical_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, 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, relax.op.sum(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
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_check_inference(bb, relax.op.sum(x4, axis=[1, 2]), relax.TensorType((2, 5), "float32", vdev0))
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_check_inference(
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bb,
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relax.op.sum(x0, axis=[1, 2], keepdims=True),
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relax.TensorType((2, 1, 1, 5), "float32"),
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)
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_check_inference(bb, relax.op.sum(x0, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.sum(x0, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "float32"),
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)
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_check_inference(bb, relax.op.mean(x1, axis=[1, 2]), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(
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bb,
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relax.op.mean(x1, axis=[1, 2], keepdims=True),
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relax.TensorType(dtype="float32", ndim=4),
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)
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_check_inference(bb, relax.op.mean(x1, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.mean(x1, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "float32"),
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)
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_check_inference(bb, relax.op.variance(x2, axis=[1, 2]), relax.TensorType(dtype="float32"))
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_check_inference(
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bb,
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relax.op.variance(x2, axis=[1, 2], keepdims=True),
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relax.TensorType(dtype="float32"),
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)
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_check_inference(bb, relax.op.variance(x2, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.variance(x2, axis=None, keepdims=True),
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relax.TensorType(dtype="float32"),
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)
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_check_inference(bb, relax.op.max(x3, axis=[1, 2]), relax.TensorType((2, 5), dtype=""))
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_check_inference(
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bb,
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relax.op.max(x3, axis=[1, 2], keepdims=True),
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relax.TensorType((2, 1, 1, 5), dtype=""),
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)
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_check_inference(bb, relax.op.max(x3, axis=None), relax.TensorType((), dtype=""))
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_check_inference(
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bb,
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relax.op.max(x3, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), dtype=""),
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)
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_check_inference(bb, relax.op.prod(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
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_check_inference(
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bb,
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relax.op.prod(x0, axis=[1, 2], keepdims=True),
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relax.TensorType((2, 1, 1, 5), "float32"),
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)
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_check_inference(bb, relax.op.std(x0, axis=[1, 2]), relax.TensorType((2, 5), "float32"))
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_check_inference(
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bb,
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relax.op.std(x0, axis=[1, 2], keepdims=True),
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relax.TensorType((2, 1, 1, 5), "float32"),
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)
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_check_inference(bb, relax.op.sum(x0, axis=[-1, -4]), relax.TensorType((3, 4), "float32"))
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_check_inference(bb, relax.op.sum(x0, axis=[]), relax.TensorType((2, 3, 4, 5), "float32"))
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def test_statistical_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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d = tirx.Var("d", "int64")
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x = relax.Var("x", R.Tensor((a, b, c, d), "float32"))
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_check_inference(bb, relax.op.min(x, axis=[1, 2]), relax.TensorType((a, d), "float32"))
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_check_inference(
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bb,
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relax.op.min(x, axis=[1, 2], keepdims=True),
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relax.TensorType((a, 1, 1, d), "float32"),
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)
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_check_inference(bb, relax.op.min(x, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.min(x, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "float32"),
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)
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def test_statistical_infer_ty_shape_var():
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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, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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_check_inference(bb, relax.op.max(x0), relax.TensorType((), dtype="float32"))
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_check_inference(
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bb, relax.op.max(x0, keepdims=True), relax.TensorType((1, 1, 1, 1), dtype="float32")
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)
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_check_inference(bb, relax.op.max(x0, axis=[2, 3]), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(
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bb,
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relax.op.max(x0, axis=[2, 3], keepdims=True),
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relax.TensorType(dtype="float32", ndim=4),
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)
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_check_inference(bb, relax.op.max(x1), relax.TensorType((), dtype="float32"))
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_check_inference(bb, relax.op.max(x1, keepdims=True), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.max(x1, axis=[2, 3]), relax.TensorType(dtype="float32"))
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_check_inference(
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bb, relax.op.max(x1, axis=[2, 3], keepdims=True), relax.TensorType(dtype="float32")
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)
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def test_statistical_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, 5), "float16"))
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x1 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int8"))
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_check_inference(bb, relax.op.sum(x0), relax.TensorType((), "float16"))
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_check_inference(bb, relax.op.sum(x1), relax.TensorType((), "int8"))
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def test_statistical_infer_ty_axis_out_of_range_repetitive():
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bb = relax.BlockBuilder()
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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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with pytest.raises(ValueError):
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bb.normalize(relax.op.mean(x0, axis=[4]))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.mean(x1, axis=[3, 3]))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.mean(x0, axis=[-1, 3]))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.mean(x1, axis=[-4, -4]))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.mean(x0, axis=[-5]))
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def test_statistical_infer_ty_wrong_input_type():
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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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with pytest.raises(TypeError):
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bb.normalize(relax.op.variance(x0))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.variance(x1))
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scan_ops = [
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relax.op.cumprod,
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relax.op.cumsum,
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]
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@pytest.mark.parametrize("scan_op", scan_ops)
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def test_scan_op_infer_ty(scan_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, 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, scan_op(x0, axis=1), relax.TensorType((2, 10, 4), "float32"))
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_check_inference(bb, scan_op(x6, axis=1), relax.TensorType((2, 10, 4), "float32", vdev0))
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_check_inference(bb, scan_op(x1, axis=1), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, scan_op(x2, axis=1), relax.TensorType(dtype="float32"))
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_check_inference(bb, scan_op(x3, axis=1), relax.TensorType((2, 10, 4), dtype=""))
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_check_inference(bb, scan_op(x4, axis=1), relax.TensorType(dtype="", ndim=3))
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_check_inference(bb, scan_op(x5, axis=1), relax.TensorType(dtype=""))
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_check_inference(bb, scan_op(x0), relax.TensorType((80,), "float32"))
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_check_inference(bb, scan_op(x0, axis=1, dtype="int32"), relax.TensorType((2, 10, 4), "int32"))
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@pytest.mark.parametrize("scan_op", scan_ops)
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def test_scan_op_infer_ty_shape_symbolic(scan_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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x = relax.Var("x", R.Tensor((a, b, c), "float32"))
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_check_inference(bb, scan_op(x, axis=1), relax.TensorType((a, b, c), "float32"))
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_check_inference(bb, scan_op(x), relax.TensorType((a * b * c,), "float32"))
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@pytest.mark.parametrize("scan_op", scan_ops)
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def test_scan_op_infer_ty_more_input_dtype(scan_op: Callable):
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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, scan_op(x0, axis=1), relax.TensorType((2, 3, 4), "float16"))
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_check_inference(bb, scan_op(x1, axis=1), relax.TensorType((2, 3, 4), "int8"))
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@pytest.mark.parametrize("scan_op", scan_ops)
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def test_scan_op_wrong_input_number(scan_op: Callable):
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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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scan_op(x, y)
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@pytest.mark.parametrize("scan_op", scan_ops)
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def test_scan_opinfer_ty_wrong_input_type(scan_op: Callable):
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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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with pytest.raises(TypeError):
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bb.normalize(scan_op(x0, axis=1))
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with pytest.raises(TypeError):
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bb.normalize(scan_op(x1, axis=1))
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def test_statistical_ext_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, 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(
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bb,
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relax.op.median(x0, axis=[1]),
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relax.TupleType(
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[
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relax.TensorType((2, 4, 5), "float32"),
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relax.TensorType((2, 4, 5), "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.median(x0, axis=[1], keepdims=True),
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relax.TupleType(
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[
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relax.TensorType((2, 1, 4, 5), "float32"),
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relax.TensorType((2, 1, 4, 5), "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.median(x1, 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="int64", 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.median(x1, axis=[1], keepdims=True),
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relax.TupleType(
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[
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relax.TensorType(dtype="float32", ndim=4),
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relax.TensorType(dtype="int64", ndim=4),
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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.median(x1, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "float32"),
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)
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_check_inference(
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bb,
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relax.op.median(x2, axis=[1]),
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relax.TupleType(
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[
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relax.TensorType(dtype="float32"),
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relax.TensorType(dtype="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.median(x2, axis=[1], keepdims=True),
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relax.TupleType(
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[
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relax.TensorType(dtype="float32"),
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relax.TensorType(dtype="int64"),
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]
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),
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)
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_check_inference(bb, relax.op.median(x2, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.median(x3, axis=[1], keepdims=True),
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relax.TupleType(
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[
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relax.TensorType((2, 1, 4, 5), dtype=""),
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relax.TensorType((2, 1, 4, 5), dtype="int64"),
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]
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),
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)
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_check_inference(bb, relax.op.median(x3, axis=None), relax.TensorType((), dtype=""))
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_check_inference(
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bb,
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relax.op.median(x3, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), dtype=""),
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)
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_check_inference(
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bb,
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relax.op.median(x4, axis=[1]),
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relax.TupleType(
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[
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relax.TensorType((2, 4, 5), "float32", vdev0),
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relax.TensorType((2, 4, 5), "int64", vdev0),
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]
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),
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)
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def test_statistical_ext_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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d = tirx.Var("d", "int64")
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x = relax.Var("x", R.Tensor((a, b, c, d), "float32"))
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_check_inference(
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bb,
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relax.op.median(x, axis=[1]),
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relax.TupleType(
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[
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relax.TensorType((a, c, d), "float32"),
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relax.TensorType((a, c, d), "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.median(x, axis=[1], keepdims=True),
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relax.TupleType(
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[
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relax.TensorType((a, 1, c, d), "float32"),
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relax.TensorType((a, 1, c, d), "int64"),
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]
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),
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)
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_check_inference(bb, relax.op.median(x, axis=None), relax.TensorType((), "float32"))
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_check_inference(
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bb,
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relax.op.median(x, axis=None, keepdims=True),
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relax.TensorType((1, 1, 1, 1), "float32"),
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)
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def test_statistical_ext_infer_ty_shape_var():
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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, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
|
|
|
|
_check_inference(bb, relax.op.median(x0), relax.TensorType((), dtype="float32"))
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x0, keepdims=True),
|
|
relax.TensorType((1, 1, 1, 1), dtype="float32"),
|
|
)
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x0, axis=[2]),
|
|
relax.TupleType(
|
|
[
|
|
relax.TensorType(dtype="float32", ndim=3),
|
|
relax.TensorType(dtype="int64", ndim=3),
|
|
]
|
|
),
|
|
)
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x0, axis=[2], keepdims=True),
|
|
relax.TupleType(
|
|
[
|
|
relax.TensorType(dtype="float32", ndim=4),
|
|
relax.TensorType(dtype="int64", ndim=4),
|
|
]
|
|
),
|
|
)
|
|
_check_inference(bb, relax.op.median(x1), relax.TensorType((), dtype="float32"))
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x1, keepdims=True),
|
|
relax.TupleType(
|
|
[
|
|
relax.TensorType(dtype="float32"),
|
|
relax.TensorType(dtype="int64"),
|
|
]
|
|
),
|
|
)
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x1, axis=[2]),
|
|
relax.TupleType(
|
|
[
|
|
relax.TensorType(dtype="float32"),
|
|
relax.TensorType(dtype="int64"),
|
|
]
|
|
),
|
|
)
|
|
_check_inference(
|
|
bb,
|
|
relax.op.median(x1, axis=[2], keepdims=True),
|
|
relax.TupleType(
|
|
[
|
|
relax.TensorType(dtype="float32"),
|
|
relax.TensorType(dtype="int64"),
|
|
]
|
|
),
|
|
)
|
|
|
|
|
|
def test_statistical_ext_infer_ty_more_input_dtype():
|
|
bb = relax.BlockBuilder()
|
|
x0 = relax.Var("x", R.Tensor((2, 3, 4, 5), "float16"))
|
|
x1 = relax.Var("x", R.Tensor((2, 3, 4, 5), "int8"))
|
|
|
|
_check_inference(bb, relax.op.median(x0), relax.TensorType((), "float16"))
|
|
_check_inference(bb, relax.op.median(x1), relax.TensorType((), "int8"))
|
|
|
|
|
|
def test_statistical_ext_infer_ty_wrong_input_type():
|
|
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), "float32")))
|
|
|
|
with pytest.raises(TypeError):
|
|
bb.normalize(relax.op.median(x0))
|
|
with pytest.raises(TypeError):
|
|
bb.normalize(relax.op.median(x1))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|