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553 lines
20 KiB
553 lines
20 KiB
# 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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import os
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import re
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import subprocess
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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.ops.operations as P
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from mindspore.nn import Cell
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from mindspore.nn import ReLU, BatchNorm2d, Conv2d, ParameterUpdate
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from mindspore.nn import Momentum
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from mindspore.nn import SoftmaxCrossEntropyWithLogits
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from mindspore import amp
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from mindspore import context, Tensor
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from mindspore.common import ParameterTuple
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from mindspore.common.parameter import Parameter
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from mindspore.ops.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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.real_inputs_count is None or self.sens_param is False:
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if self.wrt_params:
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return self.grad(self.network, self.params)(*inputs)
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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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if self.wrt_params:
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return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
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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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'''
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get grads of all inputs
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'''
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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 GradOfAllInputsAndParams(_Grad):
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'''
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get grads of all inputs and params
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'''
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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, get_by_list=True, sens_param=sens_param),
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network=network, wrt_params=True, real_inputs_count=real_inputs_count)
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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,
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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 clear_files():
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os.system("rm verbose_ir_files/*")
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def find_files(file, para):
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output = subprocess.check_output(
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["grep '%s' verbose_ir_files/%s | wc -l" % (para, file)],
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shell=True)
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out = str(output, 'utf-8').strip()
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return out
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class SideEffectCastAll(Cell):
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def __init__(self):
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super().__init__()
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self.cast = P.Cast()
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self.dtype = ms.float16
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np.random.seed(5)
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inputs1 = np.random.randn(5, 5)
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inputs2 = np.random.randn(5, 5)
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self.parameter_a = Parameter(Tensor(inputs1, ms.float32), name="a")
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self.parameter_b = Parameter(Tensor(inputs2, ms.float32), name="b")
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self.assign = P.Assign()
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def construct(self, x, y):
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self.assign(self.parameter_a, x)
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self.assign(self.parameter_b, y)
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out_a = self.cast(self.parameter_a, self.dtype)
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out_b = self.cast(self.parameter_b, self.dtype)
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return out_a, out_b
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def test_side_effect_castall():
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clear_files()
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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net = SideEffectCastAll()
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inputs1 = np.random.randn(5, 5)
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inputs2 = np.random.randn(5, 5)
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net(Tensor(inputs1, ms.float32), Tensor(inputs2, ms.float32))
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result = find_files('hwopt*cast_all*.ir', 'CastAll')
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assert result == '2'
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class SideEffectControlFlowAssignDependWhileNet(Cell):
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def __init__(self):
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super().__init__()
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self.parameter1 = Parameter(
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Tensor([199.0], ms.float32), name="parameter1")
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self.assign = P.Assign()
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self.assignadd = P.AssignAdd()
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self.addn = P.AddN()
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def construct(self, x, y, z):
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self.assign(self.parameter1, x)
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while self.parameter1 < y:
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x = self.addn((x, x))
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self.assignadd(self.parameter1, z)
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return x
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def grad_mindspore_impl(self, params1, params2, params3, grad_ys):
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grad_net = GradOfAllInputsAndParams(self)
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grad_net.set_train()
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grad_out = grad_net(params1, params2, params3, grad_ys)
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return grad_out
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_side_effect_control_flow_assign_depend_while_net():
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net = SideEffectControlFlowAssignDependWhileNet()
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context.set_context(mode=context.GRAPH_MODE)
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out1 = net(Tensor([9.0], ms.float32), Tensor(
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[99.0], ms.float32), Tensor([1.0], ms.float32))
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net = SideEffectControlFlowAssignDependWhileNet()
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context.set_context(mode=context.PYNATIVE_MODE)
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out2 = net(Tensor([9.0], ms.float32), Tensor(
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[99.0], ms.float32), Tensor([1.0], ms.float32))
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allclose_nparray(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
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class Addn(Cell):
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def __init__(self):
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super().__init__()
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self.parameter3 = Parameter(Tensor([1.0], ms.float32),
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name="parameter3")
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self.parameter4 = Parameter(Tensor([3.0], ms.float32),
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name="parameter4")
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self.addn = P.AddN()
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def construct(self, inputs):
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out = self.addn((inputs, self.parameter3, self.parameter4))
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return out
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class Relu(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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def construct(self, inputs):
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out = self.relu(inputs)
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return out
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class SideEffectTwoAssignTwoAddnDependencyNet(Cell):
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def __init__(self):
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super().__init__()
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self.parameter1 = Parameter(Tensor([1.0], ms.float32),
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name="parameter1")
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self.parameter2 = Parameter(Tensor([3.0], ms.float32),
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name="parameter2")
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self.assign = P.Assign()
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self.addN = P.AddN()
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def construct(self, inputs):
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self.assign(self.parameter1, inputs)
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out = self.addN((inputs, self.parameter1, self.parameter2))
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self.assign(self.parameter2, inputs)
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out = self.addN((out, self.parameter1, self.parameter2))
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return out
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def grad_mindspore_impl(self, params, grad_ys):
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grad_net = GradOfAllInputsAndParams(self)
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grad_net.set_train()
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grad_out = grad_net(params, grad_ys)
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return grad_out
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ctrl_while_by_while_and_if_in_first_while():
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class Net(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.sigmoid = P.Sigmoid()
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self.tanh = P.Tanh()
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self.add = P.Add()
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a = np.full((1,), 5, dtype=np.float32)
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self.a = Parameter(Tensor(a), name="a")
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b = np.full((1,), 4, dtype=np.float32)
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self.b = Parameter(Tensor(b), name="b")
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c = np.full((1,), 7, dtype=np.float32)
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self.c = Parameter(Tensor(c), name="c")
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def construct(self, x):
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out = x
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while self.a < 7:
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if self.a < self.c:
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out = self.relu(x)
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self.a += 1
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while self.c > 5:
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out = self.add(out, out)
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self.c -= 1
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return out
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context.set_context(mode=context.GRAPH_MODE)
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input_np_a = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me_a = Tensor(input_np_a)
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net = Net()
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net(input_me_a)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ctrl_while_by_while_and_while_in_first_while():
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class Net(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.sigmoid = P.Sigmoid()
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self.tanh = P.Tanh()
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self.add = P.Add()
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a = np.full((1,), 5, dtype=np.float32)
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self.a = Parameter(Tensor(a), name="a")
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b = np.full((1,), 4, dtype=np.float32)
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self.b = Parameter(Tensor(b), name="b")
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c = np.full((1,), 7, dtype=np.float32)
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self.c = Parameter(Tensor(c), name="c")
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def construct(self, x):
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out = x
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while self.a < self.c:
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out = self.relu(x)
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while self.b > 1:
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self.b -= 1
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self.a += 1
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while self.c > 5:
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out = self.add(out, out)
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self.c -= 1
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return out
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context.set_context(mode=context.GRAPH_MODE)
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input_np_a = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me_a = Tensor(input_np_a)
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net = Net()
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net(input_me_a)
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class InplaceNet(Cell):
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def __init__(self):
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super().__init__()
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self.bn1 = BatchNorm2d(num_features=4, eps=1e-4,
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momentum=0.9, gamma_init=1, beta_init=0,
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moving_mean_init=0, moving_var_init=1, data_format="NHWC")
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self.bn2 = BatchNorm2d(num_features=4, eps=1e-4,
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momentum=0.9, gamma_init=1, beta_init=0,
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moving_mean_init=0, moving_var_init=1, data_format="NHWC")
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self.add = P.Add()
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self.relu = ReLU()
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self.conv2d1 = Conv2d(in_channels=4, out_channels=4,
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kernel_size=2, data_format="NHWC")
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self.conv2d2 = Conv2d(in_channels=4, out_channels=4,
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kernel_size=2, data_format="NHWC")
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self.conv2d3 = Conv2d(in_channels=4, out_channels=4,
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kernel_size=2, data_format="NHWC")
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self.conv2d4 = Conv2d(in_channels=4, out_channels=4,
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kernel_size=2, data_format="NHWC")
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def construct(self, input_x):
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tmp_c1 = self.conv2d1(input_x)
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tmp_c2 = self.conv2d2(input_x)
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tmp_x = self.bn1(tmp_c1)
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tmp_y = self.bn2(tmp_c2)
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tmp_w = self.add(tmp_x, tmp_y)
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tmp_w = self.relu(tmp_w)
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tmp_c1 = self.conv2d3(tmp_w)
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tmp_c2 = self.conv2d4(tmp_w)
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output = self.add(tmp_c1, tmp_c2)
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return output
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def test_ir_fusion_inplace_bn_conv_conv():
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clear_files()
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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input_np = np.random.uniform(0.0, 255.0,
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size=[4, 4, 4, 4]).astype(np.float32)
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label = np.ones([4, 4, 4, 4]).astype(np.float32)
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net = InplaceNet()
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loss = SoftmaxCrossEntropyWithLogits(sparse=False)
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opt = Momentum(learning_rate=0.01, momentum=0.9,
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params=filter(lambda x: x.requires_grad, net.get_parameters()))
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net = amp.build_train_network(net, opt, loss, level="O2",
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keep_batchnorm_fp32=False)
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net.set_train()
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net(Tensor(input_np), Tensor(label))
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find_accum = find_files("hwopt*cudnn_inplace*ir",
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"inplace_algo: accumulation")
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find_cover = find_files("hwopt*cudnn_inplace*ir",
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"inplace_algo: cover")
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assert find_accum == '1'
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assert find_cover == '1'
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def clean_all_ir_files(folder_path):
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if os.path.exists(folder_path):
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for file_name in os.listdir(folder_path):
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if file_name.endswith('.ir') or file_name.endswith('.dot') or \
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file_name.endswith('.dat'):
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os.remove(os.path.join(folder_path, file_name))
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def find_newest_validateir_file(folder_path):
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ckpt_files = map(lambda f: os.path.join(folder_path, f),
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filter(lambda f: re.match(r'\d+_validate_\d+.ir', f),
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os.listdir(folder_path)))
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return max(ckpt_files, key=os.path.getctime)
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def read_file():
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filename = find_newest_validateir_file('./')
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with open((os.path.join(filename)), 'r') as f:
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content = f.read()
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clean_all_ir_files('./')
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return content
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class Add(Cell):
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def __init__(self):
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super().__init__()
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self.add = P.Add()
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def construct(self, x, y):
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return self.add(x, y)
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class MixControlNet(Cell):
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def __init__(self, in_channel, x):
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super().__init__()
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#self._save_graphs(save_graph_flag=True, save_graph_path=".")
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self.biasadd = P.BiasAdd()
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self.equal = P.Equal()
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self.addn = P.AddN()
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self.conv = Conv2d(in_channels=in_channel, out_channels=in_channel,
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kernel_size=1, stride=1, has_bias=False,
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weight_init='ones', pad_mode='same')
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self.bn = BatchNorm2d(num_features=in_channel)
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self.assignadd = P.AssignAdd()
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self.assign = P.Assign()
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self.relu = ReLU()
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self.mean = P.ReduceMean(keep_dims=False)
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self.bias = Parameter(
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Tensor(np.random.randint(2, size=(3,)).astype((np.float32))),
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name="bias")
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self.bias2 = Parameter(Tensor(np.ones([3]).astype(np.float32)),
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name="bias2")
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self.parameterupdate = ParameterUpdate(self.bias)
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self.value = Tensor(np.random.randn(*(3,)), ms.float32)
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self.x = x
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def construct(self, input_x):
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x = self.x
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z = self.x
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out = self.biasadd(input_x, self.bias)
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while x < 20:
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update = self.parameterupdate(self.bias2)
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out = self.biasadd(out, update)
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if x < 10:
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out = self.addn((input_x, out))
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while z < 20:
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out = self.conv(out)
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z = z + 1
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if x < 20:
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out = self.biasadd(out, self.bias)
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if x % 2 == 0:
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self.assignadd(self.bias, self.value)
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out = self.biasadd(out, self.bias)
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out = self.bn(out)
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else:
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out = self.conv(out)
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x = x + 1
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out = self.addn((out, out))
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out = self.mean(out, (2, 3))
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return out
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def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
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label, cast_num,
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sparse=False,
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loss_flag=True,
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**kwargs):
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opt = Momentum(learning_rate=0.0001, momentum=0.009,
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params=network.trainable_params())
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loss = None
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if loss_flag:
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loss = SoftmaxCrossEntropyWithLogits(sparse=sparse, reduction='mean')
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train_network = ms.amp.build_train_network(network, opt, loss, level=level,
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**kwargs)
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out_me = train_network(input_x, label)
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if context.get_context("mode") == 0:
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content = read_file()
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castnum = re.findall('Cast', content)
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assert len(castnum) == cast_num
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return out_me
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def test_auto_mixed_precision_controlflow_auto():
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context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
|
|
net = MixControlNet(3, 5)
|
|
input_x = Tensor(
|
|
np.random.randint(2, size=(1, 3, 2, 2)).astype((np.float32)))
|
|
label = Tensor(np.zeros([1, 3]).astype(np.float32))
|
|
if ms.context.get_context("device_target") == "Ascend":
|
|
cast_num = 77
|
|
if ms.context.get_context("device_target") == "GPU":
|
|
cast_num = 73
|
|
use_build_train_network_controlflow_check_cast_num(net, "auto", input_x,
|
|
label, cast_num)
|
|
|
|
|
|
def test_updatestate_between_assigns():
|
|
class UpdateState_Assigns(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(3, dtype=ms.int32), name='para2')
|
|
|
|
def construct(self, value1, value2):
|
|
self.para1 = value1
|
|
self.para2 = value2
|
|
return self.para2
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
|
input_x = Tensor(10, dtype=ms.int32)
|
|
input_y = Tensor(30, dtype=ms.int32)
|
|
expect = Tensor(30, dtype=ms.int32)
|
|
net = UpdateState_Assigns()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
if ms.context.get_context('mode') == 0:
|
|
content = read_file()
|
|
updatestate_num = re.findall('UpdateState', content)
|
|
assert len(updatestate_num) == 1
|
|
|
|
|
|
def test_updatestate_between_maketuple_assign():
|
|
class UpdateState_MakeTuple_Assign(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(3, dtype=ms.int32), name='para2')
|
|
self.para3 = Parameter(Tensor(5, dtype=ms.int32), name='para3')
|
|
|
|
def construct(self, value1, value2, value3):
|
|
(self.para1, self.para2) = (value1, value2)
|
|
self.para3 = value3
|
|
return self.para3
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
|
input_x = Tensor(10, dtype=ms.int32)
|
|
input_y = Tensor(30, dtype=ms.int32)
|
|
input_z = Tensor(50, dtype=ms.int32)
|
|
expect = Tensor(50, dtype=ms.int32)
|
|
net = UpdateState_MakeTuple_Assign()
|
|
out = net(input_x, input_y, input_z)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
if ms.context.get_context('mode') == 0:
|
|
content = read_file()
|
|
updatestate_num = re.findall('UpdateState', content)
|
|
assert len(updatestate_num) == 1
|
|
|
|
|
|
def test_updatestate_between_assign_maketuple():
|
|
class UpdateState_Assign_MakeTuple(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(3, dtype=ms.int32), name='para2')
|
|
self.para3 = Parameter(Tensor(5, dtype=ms.int32), name='para3')
|
|
|
|
def construct(self, value1, value2, value3):
|
|
self.para1 = value1
|
|
(self.para2, self.para3) = (value2, value3)
|
|
return self.para3
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
|
input_x = Tensor(10, dtype=ms.int32)
|
|
input_y = Tensor(30, dtype=ms.int32)
|
|
input_z = Tensor(50, dtype=ms.int32)
|
|
expect = Tensor(50, dtype=ms.int32)
|
|
net = UpdateState_Assign_MakeTuple()
|
|
out = net(input_x, input_y, input_z)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
if ms.context.get_context('mode') == 0:
|
|
content = read_file()
|
|
updatestate_num = re.findall('UpdateState', content)
|
|
assert len(updatestate_num) == 1
|