forked from mindspore-Ecosystem/mindspore
487 lines
16 KiB
Python
487 lines
16 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.ops as P
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from mindspore import Tensor
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from mindspore.nn.optim import Momentum
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from mindspore.common.api import jit
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from mindspore.common import Parameter, ParameterTuple
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import mindspore.context as context
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context.set_context(mode=context.PYNATIVE_MODE)
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@jit
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def ConvBnReLU(x):
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conv = nn.Conv2d(1, 2, kernel_size=2, stride=1, padding=0, weight_init="ones", pad_mode="valid")
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bn = nn.BatchNorm2d(2, momentum=0.99, eps=0.00001, gamma_init="ones")
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relu = nn.ReLU()
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x = conv(x)
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x = bn(x)
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x = relu(x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_call_single_func():
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inputs = Tensor(np.ones([1, 1, 2, 2]).astype(np.float32))
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out = ConvBnReLU(inputs)
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assert np.allclose(out[0][0][0][0].asnumpy(), 3.9999797, 0.0001, 0.0001)
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assert np.allclose(out[0][1][0][0].asnumpy(), 3.9999797, 0.0001, 0.0001)
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grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=False)
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out_grad = grad(ConvBnReLU)(inputs)
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assert np.allclose(out_grad[0][0][0][0][0][0].asnumpy(), 1.99998, 0.0001, 0.0001)
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class CellConvBnReLU(nn.Cell):
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def __init__(self):
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super(CellConvBnReLU, self).__init__()
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self.conv = nn.Conv2d(1, 2, kernel_size=2, stride=1, padding=0, weight_init="ones", pad_mode="valid")
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self.bn = nn.BatchNorm2d(2, momentum=0.99, eps=0.00001, gamma_init="ones")
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self.relu = nn.ReLU()
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@jit
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def construct(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_call_single_cell():
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inputs = Tensor(np.ones([1, 1, 2, 2]).astype(np.float32))
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# run forward
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net = CellConvBnReLU()
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out = net(inputs)
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assert np.allclose(out[0][0][0][0].asnumpy(), 3.9999797, 0.0001, 0.0001)
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assert np.allclose(out[0][1][0][0].asnumpy(), 3.9999797, 0.0001, 0.0001)
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# run grad twice
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grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=False)
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
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grad_first = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_first[0][0][0][0][0][0].asnumpy(), 1.99998, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][0][0][0][0][0].asnumpy(), 0.99999, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][1][0].asnumpy(), 3.99997, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][2][0].asnumpy(), 1.00000, 0.0001, 0.0001)
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optimizer(grad_first[1])
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grad_second = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_second[0][0][0][0][0][0].asnumpy(), 1.07999, 0.0001, 0.0001)
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assert np.allclose(grad_second[1][0][0][0][0][0].asnumpy(), 0.59999, 0.0001, 0.0001)
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assert np.allclose(grad_second[1][1][0].asnumpy(), 3.59998, 0.0001, 0.0001)
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assert np.allclose(grad_second[1][2][0].asnumpy(), 1.00000, 0.0001, 0.0001)
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class AddMulMul(nn.Cell):
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def __init__(self):
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super(AddMulMul, self).__init__()
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self.param = Parameter(Tensor(0.5, ms.float32))
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@jit
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def construct(self, x):
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x = x + x
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x = x * self.param
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x = x * x
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return x
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class CellCallSingleCell(nn.Cell):
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def __init__(self):
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super(CellCallSingleCell, self).__init__()
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self.conv = nn.Conv2d(1, 2, kernel_size=2, stride=1, padding=0, weight_init="ones", pad_mode="valid")
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self.bn = nn.BatchNorm2d(2, momentum=0.99, eps=0.00001, gamma_init="ones")
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self.relu = nn.ReLU()
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self.add_mul_mul = AddMulMul()
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def construct(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.add_mul_mul(x)
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x = self.relu(x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cell_call_cell():
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inputs = Tensor(np.ones([1, 1, 2, 2]).astype(np.float32))
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# run forward
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net = CellCallSingleCell()
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out = net(inputs)
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assert np.allclose(out[0][0][0][0].asnumpy(), 15.9998, 0.0001, 0.0001)
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assert np.allclose(out[0][1][0][0].asnumpy(), 15.9998, 0.0001, 0.0001)
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# run grad twice
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grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=False)
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
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grad_first = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_first[0][0][0][0][0][0].asnumpy(), 16.0, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][0][0][0][0][0].asnumpy(), 8.0, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][1][0].asnumpy(), 3.1999e+01, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][2][0].asnumpy(), 7.9999e+00, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][3].asnumpy(), 127.999, 0.0001, 0.0001)
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optimizer(grad_first[1])
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grad_second = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_second[0][0][0][0][0][0].asnumpy(), 2.726e+03, 1, 1)
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assert np.allclose(grad_second[1][0][0][0][0][0].asnumpy(), 6.816e+03, 1, 1)
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assert np.allclose(grad_second[1][1][0].asnumpy(), -2.477e+03, 1, 1)
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assert np.allclose(grad_second[1][2][0].asnumpy(), -3.097e+03, 1, 1)
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assert np.allclose(grad_second[1][3].asnumpy(), -1289, 1, 1)
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class Mul(nn.Cell):
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def __init__(self):
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super(Mul, self).__init__()
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self.param = Parameter(Tensor(1.5, ms.float32))
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@jit
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def construct(self, x):
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x = x * self.param
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return x
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class CallSameFunc(nn.Cell):
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def __init__(self):
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super(CallSameFunc, self).__init__()
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self.conv_bn_relu = CellConvBnReLU()
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self.mul = Mul()
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def construct(self, x):
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x = self.mul(x)
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x = self.mul(x)
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x = self.mul(x)
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x = self.conv_bn_relu(x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_call_same_func():
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inputs = Tensor(np.ones([1, 1, 2, 2]).astype(np.float32))
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# run forward
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net = CallSameFunc()
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out = net(inputs)
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assert np.allclose(out[0][0][0][0].asnumpy(), 13.4999, 0.0001, 0.0001)
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assert np.allclose(out[0][1][0][0].asnumpy(), 13.4999, 0.0001, 0.0001)
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# run grad twice
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grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=False)
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
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grad_first = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_first[0][0][0][0][0][0].asnumpy(), 6.75, 0.01, 0.01)
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assert np.allclose(grad_first[1][0][0][0][0][0].asnumpy(), 3.375, 0.001, 0.001)
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assert np.allclose(grad_first[1][1][0].asnumpy(), 13.4999, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][2][0].asnumpy(), 1.0000, 0.0001, 0.0001)
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assert np.allclose(grad_first[1][3].asnumpy(), 54.0000, 0.0001, 0.0001)
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optimizer(grad_first[1])
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grad_second = grad(net, ParameterTuple(net.trainable_params()))(inputs)
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assert np.allclose(grad_second[0][0][0][0][0][0].asnumpy(), 27.5, 0.1, 0.1)
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assert np.allclose(grad_second[1][0][0][0][0][0].asnumpy(), 20.76, 0.01, 0.01)
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assert np.allclose(grad_second[1][1][0].asnumpy(), -157, 1, 1)
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assert np.allclose(grad_second[1][2][0].asnumpy(), 1.0000, 0.0001, 0.0001)
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assert np.allclose(grad_second[1][3].asnumpy(), -84.6, 0.1, 0.1)
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function():
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class MsFunctionCell(nn.Cell):
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def __init__(self):
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super().__init__()
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self.param = Parameter(Tensor(1, ms.float32))
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@jit
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def construct(self, x):
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x = self.param * x
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return x
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class NetA(nn.Cell):
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def __init__(self):
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super().__init__()
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self.param = Parameter(Tensor(1, ms.float32))
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def construct(self, x):
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x = self.param * x
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x = x + x
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return x
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class NetB(nn.Cell):
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def __init__(self):
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super().__init__()
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self.ms_function_net = MsFunctionCell()
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def construct(self, x):
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x = self.ms_function_net(x)
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x = x + x
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return x
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net_a = NetA()
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params_a = ParameterTuple(net_a.trainable_params())
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net_b = NetB()
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params_b = ParameterTuple(net_b.trainable_params())
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input_data = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
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# The first net run
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grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=False)
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out_a = grad(net_a, params_a)(input_data)
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out_b = grad(net_b, params_b)(input_data)
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assert np.allclose(out_a[0][0].asnumpy(), out_b[0][0].asnumpy(), 0.0001, 0.0001)
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assert np.allclose(out_a[1][0].asnumpy(), out_b[1][0].asnumpy(), 0.0001, 0.0001)
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# The second net run
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out_a = grad(net_a, params_a)(input_data)
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out_b = grad(net_b, params_b)(input_data)
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assert np.allclose(out_a[0][0].asnumpy(), out_b[0][0].asnumpy(), 0.0001, 0.0001)
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assert np.allclose(out_a[1][0].asnumpy(), out_b[1][0].asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_mix_execute():
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"""
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Feature: PyNative ms_function.
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Description: Mixed execution of PyNative and ms_function.
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Expectation: The calculation result is correct.
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"""
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class Net(nn.Cell):
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@jit
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def test_ms_function(self, x, y):
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return x * y
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def construct(self, x, y):
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z = x * y
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return self.test_ms_function(z, x)
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net = Net()
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a = Tensor(2)
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b = Tensor(2)
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output = net(a, b)
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assert output == 8
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_empty_graph():
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"""
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Feature: PyNative ms_function.
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Description: Empty ms_function graph.
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Expectation: The calculation result is correct.
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"""
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class Net(nn.Cell):
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def __init__(self, x, y):
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super().__init__()
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self.x = x
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self.y = y
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self.relu = P.ReLU()
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@jit
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def max(self):
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if self.x > self.y:
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return self.x
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return self.y
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def construct(self):
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a = self.max()
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return self.relu(a)
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net = Net(Tensor(5, ms.float32), Tensor(10, ms.float32))
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output = net()
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assert output.asnumpy() == 10
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_control_flow_if_break():
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"""
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Feature: PyNative ms_function.
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Description: PyNative ms_function with control flow.
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Expectation: The calculation result is correct.
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"""
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.relu = P.ReLU()
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self.add = P.TensorAdd()
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@jit
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def construct(self, x, y, z):
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out = z
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for _ in range(5):
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if 2 * x < y:
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if 3 * x < y:
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out = self.add(out, out)
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x = x + 1
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out = self.relu(out)
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if x + 6 == y:
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break
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out = self.relu(out)
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return out
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net = Net()
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x = Tensor(2, ms.int32)
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y = Tensor(10, ms.int32)
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z = Tensor(np.ones([4, 4, 4]), ms.float32)
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output = net(x, y, z)
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assert (output.asnumpy() == z.asnumpy() * 4).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_with_dynamic_shape():
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"""
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Feature: PyNative ms_function.
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Description: PyNative ms_function with dynamic shape.
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Expectation: The calculation result is correct.
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"""
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@jit()
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def test(x):
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return ms.numpy.unique(x, return_inverse=True)
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x = Tensor([[1, 1, 2], [3, 3, 5]], ms.int32)
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output = test(x)
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assert (output[0].asnumpy() == np.array([1, 2, 3, 5])).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_with_tuple_inputs():
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"""
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Feature: PyNative ms_function.
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Description: PyNative ms_function with tuple inputs.
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Expectation: The calculation result is correct.
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"""
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.enable_tuple_broaden = True
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@jit
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def construct(self, grads):
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new_grads = []
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for grad in grads:
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new_grads.append(grad + 1)
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return new_grads
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x = Tensor(np.ones([2, 2]), dtype=ms.int32)
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y = Tensor(np.ones([2, 2]), dtype=ms.int32)
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net = Net()
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out = net((x, y))
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assert (out[0].asnumpy() == np.ones([2, 2]) + 1).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_ms_function_with_optional_inputs():
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"""
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Feature: PyNative ms_function.
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Description: PyNative ms_function with optional inputs.
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Expectation: The calculation result is correct.
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"""
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@jit
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def foo(x, y=1):
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return x + y
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a = Tensor(3, dtype=ms.int32)
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assert foo(a).asnumpy() == 4
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assert foo(a, 2).asnumpy() == 5
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assert foo(a, y=3).asnumpy() == 6
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assert foo(x=a, y=4).asnumpy() == 7
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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|
@pytest.mark.platform_arm_ascend_training
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|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
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|
@pytest.mark.env_onecard
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|
def test_pynative_ms_function_with_args_inputs():
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|
"""
|
|
Feature: PyNative ms_function.
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|
Description: PyNative ms_function with *args.
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|
Expectation: The calculation result is correct.
|
|
"""
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|
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|
@jit
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|
def foo(x, *args):
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return x + args[0] + args[1]
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|
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x = Tensor(3, dtype=ms.int32)
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assert foo(x, 1, 2).asnumpy() == 6
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|
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@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
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|
def test_pynative_ms_function_with_kwargs_inputs():
|
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"""
|
|
Feature: PyNative ms_function.
|
|
Description: PyNative ms_function with **kwargs.
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Expectation: Raise expected exception
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|
"""
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|
|
|
@jit
|
|
def foo(x, **kwargs):
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return x + kwargs.get('y')
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|
|
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with pytest.raises(ValueError):
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x = Tensor(3, dtype=ms.int32)
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|
data = {"y": 1}
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foo(x, **data)
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