forked from mindspore-Ecosystem/mindspore
543 lines
19 KiB
Python
543 lines
19 KiB
Python
# Copyright 2020 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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""" test_pynative_hook_grad """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore.nn import Cell
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from mindspore import context
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from mindspore.common.tensor import Tensor
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from mindspore.ops.composite import GradOperation
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from mindspore.common import ParameterTuple
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class MetaFactory:
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def __init__(self):
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self.device_target = context.get_context('device_target')
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self.rank_size = None
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self.device_id = None
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self.global_rank_id = None
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class HookBase(MetaFactory):
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def __init__(self):
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super().__init__()
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MetaFactory.__init__(self)
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self.grad_input_list = []
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self.grad_output_list = []
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def ms_record_hook(self, cell_id, grad_input, grad_output):
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for grad in grad_input:
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self.grad_input_list.append(grad)
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for grad in grad_output:
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self.grad_output_list.append(grad)
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def ms_change_grad_double_hook(self, cell_id, grad_input, grad_output):
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y = Tensor(np.array([2.0]).astype(np.float32))
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mul = P.Mul()
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grad = grad_output[0]
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output = mul(grad, y)
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return (output,)
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class FinalNet(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.conv = nn.Conv2d(1, 3, 3)
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self.relu = nn.ReLU()
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def construct(self, x, flag):
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if flag:
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x = self.conv(x)
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else:
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x = self.relu(x)
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return self.relu(x)
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class _Grad(Cell):
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def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
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super().__init__()
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self.network = network
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self.grad = grad
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self.sens_param = self.grad.sens_param
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self.wrt_params = wrt_params
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self.real_inputs_count = real_inputs_count
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if self.wrt_params:
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self.params = ParameterTuple(self.network.trainable_params())
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def construct(self, *inputs):
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if self.wrt_params:
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if self.real_inputs_count is None or self.sens_param is False:
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return self.grad(self.network, self.params)(*inputs)
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real_inputs = inputs[:self.real_inputs_count]
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sense_param_inputs = inputs[self.real_inputs_count:]
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return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
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if self.real_inputs_count is None or self.sens_param is False:
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return self.grad(self.network)(*inputs)
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real_inputs = inputs[:self.real_inputs_count]
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sense_param_inputs = inputs[self.real_inputs_count:]
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return self.grad(self.network)(*real_inputs, sense_param_inputs)
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class GradOfAllInputs(_Grad):
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def __init__(self, network, sens_param=True, real_inputs_count=None):
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super().__init__(grad=GradOperation(get_all=True, sens_param=sens_param),
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network=network, real_inputs_count=real_inputs_count)
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class MsMul4(nn.Cell):
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def construct(self, input_mul):
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out = input_mul * 2
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return out
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class MsMul(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul()
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def construct(self, x, y):
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x = self.mul(x, y)
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return x
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class MsAdd4(nn.Cell):
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def construct(self, input_add):
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out = input_add + 4
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return out
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class MsOneInputNet(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.add = MsAdd4()
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self.mul = MsMul4()
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self.relu = nn.ReLU()
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def construct(self, x):
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x = self.add(x)
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x = self.mul(x)
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out = self.relu(x)
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return out
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class MsMultiInputNet(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.mul1 = MsMul()
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self.mul2 = MsMul4()
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def construct(self, x, y):
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a = self.mul1(x, y)
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b = self.mul2(x)
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output = self.mul1(a, b)
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return output
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class MsNetWithParameter(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.conv1 = nn.Conv2d(2, 4, kernel_size=(1, 1), has_bias=True,
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weight_init=Tensor(np.ones([4, 2, 1, 1]).astype(np.float32)),
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bias_init=Tensor(np.ones([4]).astype(np.float32)))
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self.conv2 = nn.Conv2d(4, 8, kernel_size=(1, 1), has_bias=True,
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weight_init=Tensor(np.ones([8, 4, 1, 1]).astype(np.float32)),
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bias_init=Tensor(np.ones([8]).astype(np.float32)))
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def construct(self, x):
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x = self.conv1(x)
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output = self.conv2(x)
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return output
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class MsNetWithCellinCell(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.net1 = MsOneInputNet()
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self.mul = MsMul4()
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def construct(self, x):
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x = self.net1(x)
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output = self.mul(x)
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return output
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class MsSingleOpNetWithBprop(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.op = nn.ReLU()
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def construct(self, x):
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return self.op(x)
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def bprop(self, x, out, dout):
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y = Tensor(np.array([5.0]).astype(np.float32))
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mul = P.Mul()
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return mul(x, y)
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class MsNetHasBpropInChild(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.add = MsAdd4()
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self.bprop_net = MsSingleOpNetWithBprop()
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def construct(self, x):
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x = self.add(x)
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return self.bprop_net(x)
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class MsMultiOpNetWithBprop(nn.Cell, HookBase):
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def __init__(self):
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super().__init__()
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HookBase.__init__(self)
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self.mul = MsMul4()
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self.relu = nn.ReLU()
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def construct(self, x):
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x = self.mul(x)
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return self.relu(x)
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def bprop(self, x, out, dout):
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y = Tensor(np.array([5.0]).astype(np.float32))
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mul = P.Mul()
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return mul(x, y)
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def _count_unequal_element(data_expected, data_me, rtol, atol):
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assert data_expected.shape == data_me.shape
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total_count = len(data_expected.flatten())
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error = np.abs(data_expected - data_me)
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greater = np.greater(error, atol + np.abs(data_me)*rtol)
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loss_count = np.count_nonzero(greater)
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assert (loss_count/total_count) < rtol,\
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"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".\
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format(data_expected[greater], data_me[greater], error[greater])
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def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
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if np.any(np.isnan(data_expected)):
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assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
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elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
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_count_unequal_element(data_expected, data_me, rtol, atol)
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else:
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assert True
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def pynative_hook_diff_hook():
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input_np = np.ones([1, 1, 224, 224]).astype(np.float32)
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ms_net = FinalNet()
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ms_net.set_grad()
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ms_net.conv.register_backward_hook(ms_net.ms_record_hook)
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ms_net.relu.register_backward_hook(ms_net.ms_change_grad_double_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms, Tensor(1))
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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grad_net(input_ms, Tensor(1), out_ms)
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def pynative_hook_outermost_cell_not_change_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsOneInputNet()
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ms_net.set_grad()
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ms_net.register_backward_hook(ms_net.ms_record_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input_ms, out_ms)
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#input grad
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input_torch_grad = np.array([[20, 20], [20, 20]])
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allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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#hook record grad
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torch_net_grad_output = np.array([[10, 10], [10, 10]])
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torch_net_grad_input = np.array([[20, 20], [20, 20]])
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allclose_nparray(torch_net_grad_output, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_input, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
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def pynative_hook_all_cell_record_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsOneInputNet()
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ms_net.set_grad()
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ms_net.mul.register_backward_hook(ms_net.ms_record_hook)
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ms_net.add.register_backward_hook(ms_net.ms_record_hook)
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ms_net.relu.register_backward_hook(ms_net.ms_record_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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grad_net(input_ms, out_ms)
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torch_net_grad_input0 = np.array([[10, 10], [10, 10]])
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torch_net_grad_output0 = np.array([[10, 10], [10, 10]])
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torch_net_grad_input1 = np.array([[20, 20], [20, 20]])
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torch_net_grad_output1 = np.array([[10, 10], [10, 10]])
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allclose_nparray(torch_net_grad_input0, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_output0, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_input1, ms_net.grad_output_list[1].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_output1, ms_net.grad_input_list[1].asnumpy(), 0.001, 0.001)
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torch_net_grad_input3 = np.array([[20, 20], [20, 20]])
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torch_net_grad_output2 = np.array([[20, 20], [20, 20]])
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allclose_nparray(torch_net_grad_input3, ms_net.grad_output_list[2].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_output2, ms_net.grad_input_list[2].asnumpy(), 0.001, 0.001)
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def pynative_hook_mul_change_input_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsOneInputNet()
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ms_net.set_grad()
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ms_net.mul.register_backward_hook(ms_net.ms_change_grad_double_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input_ms, out_ms)
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#input grad
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input_torch_grad = np.array([[40, 40], [40, 40]])
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allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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def pynative_hook_mul2_change_input_grad():
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input1_np = np.array([2.0, 3.0, 4.0]).astype(np.float32)
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input2_np = np.array([2.0, 3.0, 4.0]).astype(np.float32)
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ms_net = MsMultiInputNet()
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ms_net.set_grad()
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ms_net.mul2.register_backward_hook(ms_net.ms_change_grad_double_hook)
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input1_ms = Tensor(input1_np)
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input2_ms = Tensor(input2_np)
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out_ms = ms_net(input1_ms, input2_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input1_ms, input2_ms, out_ms)
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#input grad
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input1_torch_grad = np.array([384, 2916, 12288])
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input2_torch_grad = np.array([128, 972, 4096])
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allclose_nparray(input1_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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allclose_nparray(input2_torch_grad, input_ms_grad[1].asnumpy(), 0.001, 0.001)
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def pynative_hook_outermost_cell_change_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsNetWithCellinCell()
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ms_net.set_grad()
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ms_net.register_backward_hook(ms_net.ms_change_grad_double_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input_ms, out_ms)
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#input grad
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out_torch = np.array([[20, 20], [20, 20]])
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input_torch_grad = np.array([[160, 160], [160, 160]])
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allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
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allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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def pynative_hook_outermost_cell_record_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsSingleOpNetWithBprop()
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ms_net.set_grad()
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ms_net.bprop_debug = True
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ms_net.register_backward_hook(ms_net.ms_record_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input_ms, out_ms)
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if ms_net.grad_output_list or ms_net.grad_input_list:
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assert False
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#input grad
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out_torch = np.array([[1, 1], [1, 1]])
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input_torch_grad = np.array([[5, 5], [5, 5]])
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allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
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allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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def pynative_hook_bprop_outermost_cell_record_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsNetHasBpropInChild()
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ms_net.set_grad()
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ms_net.bprop_net.bprop_debug = True
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ms_net.register_backward_hook(ms_net.ms_record_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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input_ms_grad = grad_net(input_ms, out_ms)
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if len(ms_net.grad_output_list) != len(ms_net.grad_input_list) or not ms_net.grad_output_list:
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assert False
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#input grad
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out_torch = np.array([[5, 5], [5, 5]])
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input_torch_grad = np.array([[25, 25], [25, 25]])
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allclose_nparray(out_torch, out_ms.asnumpy(), 0.001, 0.001)
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allclose_nparray(input_torch_grad, input_ms_grad[0].asnumpy(), 0.001, 0.001)
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#hook record grad
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torch_net_grad_output = np.array([[5, 5], [5, 5]])
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torch_net_grad_input = np.array([[25, 25], [25, 25]])
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allclose_nparray(torch_net_grad_output, ms_net.grad_input_list[0].asnumpy(), 0.001, 0.001)
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allclose_nparray(torch_net_grad_input, ms_net.grad_output_list[0].asnumpy(), 0.001, 0.001)
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def pynative_hook_child_cell_record_grad():
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input_np = np.ones([2, 2]).astype(np.float32)
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ms_net = MsMultiOpNetWithBprop()
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ms_net.set_grad()
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ms_net.bprop_debug = True
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ms_net.relu.register_backward_hook(ms_net.ms_record_hook)
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ms_net.mul.register_backward_hook(ms_net.ms_record_hook)
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input_ms = Tensor(input_np)
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out_ms = ms_net(input_ms)
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grad_net = GradOfAllInputs(ms_net)
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grad_net.set_train()
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grad_net(input_ms, out_ms)
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if ms_net.grad_output_list or ms_net.grad_input_list:
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assert False
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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.env_onecard
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def test_pynative_hook_diff_hook_ascend():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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pynative_hook_diff_hook()
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@pytest.mark.level1
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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_hook_diff_hook_gpu():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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pynative_hook_diff_hook()
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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.env_onecard
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def test_pynative_hook_outermost_cell_not_change_grad_ascend():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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pynative_hook_outermost_cell_not_change_grad()
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|
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@pytest.mark.level1
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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_hook_outermost_cell_not_change_grad_gpu():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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pynative_hook_outermost_cell_not_change_grad()
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|
|
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@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
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|
def test_pynative_hook_all_cell_record_grad_ascend():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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pynative_hook_all_cell_record_grad()
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|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_all_cell_record_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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pynative_hook_all_cell_record_grad()
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|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_mul_change_input_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_mul_change_input_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_mul_change_input_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_mul_change_input_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_mul2_change_input_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_mul2_change_input_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_mul2_change_input_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_mul2_change_input_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_outermost_cell_change_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_outermost_cell_change_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_outermost_cell_change_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_outermost_cell_change_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_outermost_cell_record_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_outermost_cell_record_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_outermost_cell_record_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_outermost_cell_record_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_bprop_outermost_cell_record_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_bprop_outermost_cell_record_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_bprop_outermost_cell_record_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_bprop_outermost_cell_record_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_child_cell_record_grad_ascend():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
pynative_hook_child_cell_record_grad()
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_pynative_hook_child_cell_record_grad_gpu():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
pynative_hook_child_cell_record_grad()
|