From 82de6c7ebcffc8911df241a3d379c71464936be4 Mon Sep 17 00:00:00 2001 From: zhanlijun Date: Mon, 13 Dec 2021 18:40:31 +0800 Subject: [PATCH] add testcase --- tests/ut/python/ops/test_tensor_check.py | 513 +++++++++++++++++++++++ 1 file changed, 513 insertions(+) create mode 100644 tests/ut/python/ops/test_tensor_check.py diff --git a/tests/ut/python/ops/test_tensor_check.py b/tests/ut/python/ops/test_tensor_check.py new file mode 100644 index 00000000000..bc1f767e6cc --- /dev/null +++ b/tests/ut/python/ops/test_tensor_check.py @@ -0,0 +1,513 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +""" test control ops """ +import os +import numpy as np +import pytest + +import mindspore as ms +from mindspore import Tensor +from mindspore import context +from mindspore import nn +from mindspore.common import dtype as mstype +from mindspore.ops import composite as C +from mindspore.ops import operations as P +from mindspore.common.parameter import Parameter + +context.set_context(mode=context.GRAPH_MODE) + +grad_by_list = C.GradOperation(get_by_list=True) +grad_all = C.GradOperation(get_all=True) +grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True) + + +def if_compile_test(x_init, y_init): + """ + Feature: if compile test. + Description: if compile test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self): + """""" + super(Net, self).__init__() + self.square = P.Square() + self.add = P.Add() + self.value = Tensor(3, dtype=ms.float32) + self.switch = P.GeSwitch() + self.merge = P.Merge() + self.less = P.Less() + + def construct(self, x, y): + cond = self.less(x, y) + ret = self.value + if cond: + ret = self.add(x, ret) + ret = self.add(y, ret) + else: + ret = self.square(self.value) + return ret + + x = Tensor(x_init, dtype=ms.float32) + y = Tensor(y_init, dtype=ms.float32) + net = Net() + output = net(x, y) + return output + + +def test_if_nested_compile(): + """ + Feature: if nested compile test. + Description: if nested compile test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self, auto_prefix=True): + """""" + super().__init__(auto_prefix=auto_prefix) + self.squre = P.Square() + self.value = Tensor(3, dtype=ms.float32) + + def construct(self, x, y): + res = self.value + if x <= y: + res = x + res + res = y + res + else: + if x == y: + res = self.squre(self.value * y) + else: + res = self.squre(self.value) + return res + + x = Tensor(1.0, dtype=ms.float32) + y = Tensor(2.0, dtype=ms.float32) + net = Net() + net(x, y) + + +def test_if_inside_for(): + """ + Feature: if inside test. + Description: if inside test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self, auto_prefix=True): + """""" + super().__init__(auto_prefix=auto_prefix) + self.squre = P.Square() + self.value = Tensor(3, dtype=ms.float32) + self.count = 4 + + def construct(self, x, y): + res = 0 + for i in range(self.count): + if i == x: + res = res + x + else: + res = res - y + return res + + c1 = Tensor(1, dtype=ms.int32) + c2 = Tensor(1, dtype=ms.int32) + net = Net() + net(c1, c2) + + +def test_while_with_weight_in_condition(): + """ + Feature: while with weight in condition test. + Description: while with weight in condition test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self): + """""" + super(Net, self).__init__() + self.loop = Parameter(Tensor(1, dtype=ms.float32), name="loop") + + def construct(self, x): + while self.loop < 5: + self.loop += 1 + x += 1 + return x + + net = Net() + x = Tensor(-1, dtype=ms.float32) + grad_all(net)(x) + + +def test_while_add(): + """ + Feature: while add test. + Description: while add test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self, data): + """""" + super(Net, self).__init__() + self.start = Tensor(0, dtype=mstype.int32) + self.end = Tensor(2, dtype=mstype.int32) + self.out = Tensor(np.zeros([2, 3], dtype=np.float32)) + self.add = P.Add() + + def construct(self, inputs): + idx = self.start + end = self.end + out = self.out + while idx < end: + xi = inputs[idx, :, :] + out = self.add(out, xi) + idx = idx + 1 + return out + + x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32)) + net = Net(x) + net(x) + + +def test_tensor_all_construct_lack_branch(): + """ + Feature: tensor all construct lack test. + Description: tensor all construct lack test + Expectation: compile done without error. + """ + class NetConditionLackBranch(nn.Cell): + def __init__(self): + """""" + super(NetConditionLackBranch, self).__init__() + self.logicaland = P.LogicalAnd() + self.logicalor = P.LogicalOr() + + def construct(self, input1, input2): + if input1.all(): + return self.logicaland(input1, input2) + while input1.any(): + return self.logicalor(input1, input2) + # NOTICE: here missing return statement, default return None + + input_np_1 = np.random.choice([True], size=(2, 3, 4, 5)) + input_tensor_1 = Tensor(input_np_1) + input_np_2 = np.random.choice([True, False], size=(2, 3, 4, 5)) + input_tensor_2 = Tensor(input_np_2) + net = NetConditionLackBranch() + with pytest.raises(Exception): + net(input_tensor_1, input_tensor_2) + + +def test_parser_switch_layer_func_primitive(): + """ + Feature: parser switch layer func primitive test. + Description: parser switch layer func primitive test + Expectation: compile done without error. + """ + class FinalNet(nn.Cell): + def __init__(self, funcs): + """""" + super().__init__() + self.funcs = funcs + + def construct(self, i, input1): + x = self.funcs[i](input1) + return x + + func1 = P.ReLU() + func2 = P.Softmax() + funcs = (func1, func2) + net = FinalNet(funcs) + + input1 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32)) + i = Tensor(1, mstype.int32) + + with pytest.raises(ValueError): + net(i, input1) + + +def test_large_for_loop(): + """ + Feature: large for loop test. + Description: large for loop test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self): + """""" + super(Net, self).__init__() + self.flatten = P.ReLU() # nn.Flatten() + + def construct(self, x): + for elem in range(1, 1900): + x = self.flatten(x + elem) + return x + + t = Tensor(np.ones([2, 3], dtype=np.float32)) + net = Net() + os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' + old_max_call_depth = context.get_context('max_call_depth') + context.set_context(max_call_depth=60) + with pytest.raises(RuntimeError) as err: + net(t) + context.set_context(max_call_depth=old_max_call_depth) + os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' + assert 'Exceed function call depth limit 60' in str(err.value) + + +def test_large_for_loop_with_continue_break(): + """ + Feature: large for loop with continue break test. + Description: large for loop with continue break test + Expectation: compile done without error. + """ + class Net(nn.Cell): + def __init__(self): + """""" + super(Net, self).__init__() + self.flatten = P.ReLU() # nn.Flatten() + + def construct(self, x): + idx = 0 + for elem1 in range(200): + idx = idx + 1 + if idx < 10: + x = x + 0.5 + continue + if idx > 500: + break + x = self.flatten(x + elem1) + return x + + os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' + old_max_call_depth = context.get_context('max_call_depth') + context.set_context(max_call_depth=2000) + t = Tensor(np.ones([2, 3], dtype=np.float32)) + net = Net() + net(t) + os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' + context.set_context(max_call_depth=old_max_call_depth) + + +def test_recursive_call(): + """ + Feature: recursive call test. + Description: recursive call test + Expectation: compile done without error. + """ + class Net(nn.Cell): + """ Net definition """ + def __init__(self): + """""" + super(Net, self).__init__() + self.fc = nn.Dense(10, 10) # padding=0 + # self.net2 = Net2() + + def construct(self, x): + net2 = Net2() + x = net2(x) + out = self.fc(x) + return out + + class Net2(nn.Cell): + def __init__(self): + super(Net2, self).__init__() + self.net = Net() + self.fc = nn.Dense(10, 10) + + def construct(self, x): + x = self.net(x) + out = self.fc(x) + return out + + context.set_context(mode=context.GRAPH_MODE) + os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' + old_max_call_depth = context.get_context('max_call_depth') + context.set_context(max_call_depth=80) + input_data = Tensor(np.identity(10).astype(np.float32)) + net = Net2() + with pytest.raises(RuntimeError): + net(input_data) + os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' + context.set_context(max_call_depth=old_max_call_depth) + + +def test_pow(): + """ + Feature: pow test. + Description: pow test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[2, 2], [3, 3]])) + power = Tensor(np.array(3.0, np.int64)) + testpow = P.Pow() + expect = np.array([[8, 8], [27, 27]]) + result = testpow(input_tensor, power) + assert np.all(result.asnumpy() == expect) + + +def test_pow1(): + """ + Feature: pow one test. + Description: pow one test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[2, 2], [2, 2]])) + power = Tensor(np.array(3.0, np.int64)) + testpow = P.Pow() + expect = np.array([[8, 8], [8, 8]]) + result = testpow(input_tensor, power) + assert np.all(result.asnumpy() == expect) + + +def test_pow2(): + """ + Feature: pow two test. + Description: pow two test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[1, 1], [2, 2]])) + power = Tensor(np.array(3.0, np.int64)) + testpow = P.Pow() + expect = np.array([[1, 1], [8, 8]]) + result = testpow(input_tensor, power) + assert np.all(result.asnumpy() == expect) + + +def test_pow3(): + """ + Feature: pow three test. + Description: pow three test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[2, 2], [1, 1]])) + power = Tensor(np.array(3.0, np.int64)) + testpow = P.Pow() + expect = np.array([[8, 8], [1, 1]]) + result = testpow(input_tensor, power) + assert np.all(result.asnumpy() == expect) + + +def test_exp(): + """ + Feature: exp test. + Description: exp test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[2, 2], [3, 3]])) + testexp = P.Exp() + result = testexp(input_tensor) + expect = np.exp(np.array([[2, 2], [3, 3]])) + assert np.all(result.asnumpy() == expect) + + +def test_exp1(): + """ + Feature: exp one test. + Description: exp one test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[2, 2], [3, 3]])) + testexp = P.Exp() + result = testexp(input_tensor) + expect = np.exp(np.array([[2, 2], [3, 3]])) + assert np.all(result.asnumpy() == expect) + + +def test_realdiv(): + """ + Feature: realdiv test. + Description: realdiv test + Expectation: compile done without error. + """ + x = Tensor(2048.0) + y = Tensor(128.0) + div = P.RealDiv() + result = div(x, y) + x = x.asnumpy() + y = y.asnumpy() + expect = x / y + assert np.all(result.asnumpy() == expect) + + +def test_realdiv1(): + """ + Feature: realdiv one test. + Description: realdiv one test + Expectation: compile done without error. + """ + x = Tensor(256.0) + y = Tensor(128.0) + div = P.RealDiv() + result = div(x, y) + x = x.asnumpy() + y = y.asnumpy() + expect = x / y + assert np.all(result.asnumpy() == expect) + + +def test_eye(): + """ + Feature: eye test. + Description: eye test + Expectation: compile done without error. + """ + x = np.arange(3) + expect = np.ones_like(x) + expect = np.diag(expect) + eye = P.Eye() + eye_output = eye(3, 3, ms.float32) + assert np.all(eye_output.asnumpy() == expect) + + +def test_sub(): + """ + Feature: sub test. + Description: sub test + Expectation: compile done without error. + """ + input_x = Tensor(np.ones(shape=[3])) + input_y = Tensor(np.zeros(shape=[3])) + + sub = P.Sub() + result = sub(input_x, input_y) + expect = np.ones(shape=[3]) + assert np.all(result.asnumpy() == expect) + + +def test_square(): + """ + Feature: square test. + Description: square test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]])) + square = P.Square() + result = square(input_tensor) + expect = np.array([[1, 4, 9], [16, 25, 36]]) + assert np.all(result.asnumpy() == expect) + + +def test_sqrt(): + """ + Feature: sqrt test. + Description: sqrt test + Expectation: compile done without error. + """ + input_tensor = Tensor(np.array([[4, 4], [9, 9]])) + + sqrt = P.Sqrt() + expect = np.array([[2, 2], [3, 3]]) + result = sqrt(input_tensor) + assert np.all(result.asnumpy() == expect)