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mindspore/tests/st/auto_monad/test_auto_monad_mindtester.py

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import pytest
import numpy as np
import mindspore as ms
import mindspore.ops.operations as P
from mindspore.nn import Cell
from mindspore import context, Tensor
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.train.model import Model
from mindspore.ops.composite import GradOperation
from mindspore.common import ParameterTuple
from tests.security_utils import security_off_wrap
context.set_context(mode=context.GRAPH_MODE)
class _Grad(Cell):
def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
super().__init__()
self.network = network
self.grad = grad
self.sens_param = self.grad.sens_param
self.wrt_params = wrt_params
self.real_inputs_count = real_inputs_count
if self.wrt_params:
self.params = ParameterTuple(self.network.trainable_params())
def construct(self, *inputs):
if self.real_inputs_count is None or self.sens_param is False:
if self.wrt_params:
return self.grad(self.network, self.params)(*inputs)
return self.grad(self.network)(*inputs)
real_inputs = inputs[:self.real_inputs_count]
sense_param_inputs = inputs[self.real_inputs_count:]
if self.wrt_params:
return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
return self.grad(self.network)(*real_inputs, sense_param_inputs)
class GradOfFirstInput(_Grad):
"""
get grad of first input
"""
def __init__(self, network, sens_param=True, real_inputs_count=None):
super().__init__(grad=GradOperation(sens_param=sens_param),
network=network, real_inputs_count=real_inputs_count)
class GradOfAllInputs(_Grad):
'''
get grads of all inputs
'''
def __init__(self, network, sens_param=True, real_inputs_count=None):
super().__init__(grad=GradOperation(get_all=True, sens_param=sens_param),
network=network, real_inputs_count=real_inputs_count)
class GradOfAllInputsAndParams(_Grad):
'''
get grads of all inputs and params
'''
def __init__(self, network, sens_param=True, real_inputs_count=None):
super().__init__(grad=GradOperation(get_all=True, get_by_list=True, sens_param=sens_param),
network=network, wrt_params=True, real_inputs_count=real_inputs_count)
def _count_unequal_element(data_expected, data_me, rtol, atol):
assert data_expected.shape == data_me.shape
total_count = len(data_expected.flatten())
error = np.abs(data_expected - data_me)
greater = np.greater(error, atol + np.abs(data_me) * rtol)
loss_count = np.count_nonzero(greater)
assert (loss_count / total_count) < rtol, \
"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
format(data_expected[greater], data_me[greater], error[greater])
def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
if np.any(np.isnan(data_expected)):
assert np.allclose(data_expected, data_me, rtol,
atol, equal_nan=equal_nan)
elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
_count_unequal_element(data_expected, data_me, rtol, atol)
else:
assert True
class ControlGraphSupportNotEqual(Cell):
def construct(self, x, y, z, input_data):
if x != y:
out = input_data + input_data
else:
out = input_data - input_data
if x == z:
out2 = input_data * input_data
else:
out2 = input_data / input_data
if x == z:
out3_f = (lambda a: a + a)
out3 = out3_f(input_data)
else:
out3_f = (lambda a: a + a + a)
out3 = out3_f(input_data)
return out, out2, out3
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ctrl_if_while_graph_support_not_equal_true():
x = np.array(0).astype(np.float32)
y = np.array(3).astype(np.float32)
input_shape = (512, 512, 7, 7)
input_data = np.random.randn(*input_shape).astype(np.float32)
net = ControlGraphSupportNotEqual()
model = Model(net)
out_me = model.predict(Tensor(x), Tensor(y), Tensor(x), Tensor(input_data))
out = input_data + input_data
out2 = input_data * input_data
out3 = input_data + input_data
allclose_nparray(out, out_me[0].asnumpy(), 0.0001, 0.0001)
allclose_nparray(out2, out_me[1].asnumpy(), 0.0001, 0.0001)
allclose_nparray(out3, out_me[2].asnumpy(), 0.0001, 0.0001)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ctrl_if_while_graph_support_not_equal_false():
x = np.array(0).astype(np.float32)
y = np.array(0).astype(np.float32)
z = np.array(3).astype(np.float32)
input_shape = (512, 512, 7, 7)
input_data = np.random.randn(*input_shape).astype(np.float32)
net = ControlGraphSupportNotEqual()
model = Model(net)
out_me = model.predict(Tensor(x), Tensor(y), Tensor(z), Tensor(input_data))
out = input_data - input_data
out2 = input_data / input_data
out3 = input_data + input_data + input_data
allclose_nparray(out, out_me[0].asnumpy(), 0.0001, 0.0001)
allclose_nparray(out2, out_me[1].asnumpy(), 0.0001, 0.0001)
allclose_nparray(out3, out_me[2].asnumpy(), 0.0001, 0.0001)
class ControlBprop(Cell):
def construct(self, x, y, z, input_data):
if x != y:
out = input_data + input_data
else:
out = input_data - input_data
if x == z:
out2 = input_data * input_data
else:
out2 = input_data / input_data
if x == z:
out3_f = (lambda a: a + a)
out3 = out3_f(input_data)
else:
out3_f = (lambda a: a + a + a)
out3 = out3_f(input_data)
return out, out2, out3
def bprop(self, x, y, z, input_data, out, dout):
return x * 2, y * 3, z, input_data * 5.1
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ctrl_if_while_bprop_true():
x = np.array(0).astype(np.float32)
y = np.array(3).astype(np.float32)
input_shape = (512, 512, 7, 7)
input_data = np.random.randn(*input_shape).astype(np.float32)
net = ControlBprop()
grad_net = GradOfAllInputs(net, sens_param=False)
grad_net.set_train()
grads = grad_net(Tensor(x), Tensor(y), Tensor(x), Tensor(input_data))
allclose_nparray(x * 2, grads[0].asnumpy(), 0.0000, 0.0000)
allclose_nparray(y * 3, grads[1].asnumpy(), 0.0000, 0.0000)
allclose_nparray(x, grads[2].asnumpy(), 0.0000, 0.0000)
allclose_nparray(input_data * 5.1, grads[3].asnumpy(), 0.0000, 0.0000)
class TwoInput(Cell):
def __init__(self):
super().__init__()
self.op = P.Mul()
def construct(self, x, y):
x = self.op(x, y)
return x
class InlineBpropTwoInput1(Cell):
def __init__(self):
super().__init__()
self.f = TwoInput()
self.f.set_grad()
self.grad = GradOfAllInputs(self.f, sens_param=False)
def construct(self, x, y):
if x > y:
x = self.f(x, y)
else:
x = self.f(x, y)
return x
def bprop(self, x, y, out, dout):
if x > y:
grads = self.grad(x, y)
else:
grads = self.grad(x, y)
return grads[0] * 2, grads[1] * 2
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ctrl_if_while_bprop_inlinebprop_twoinput():
net = InlineBpropTwoInput1()
input1 = Tensor(np.array(2).astype(np.float32))
input2 = Tensor(np.array(1).astype(np.float32))
grad_net = GradOfAllInputs(net, sens_param=False)
grad_net.set_train()
grads = grad_net(input1, input2)
allclose_nparray(input1.asnumpy() * 2, grads[1].asnumpy(), 0, 0)
allclose_nparray(input2.asnumpy() * 2, grads[0].asnumpy(), 0, 0)
class ControlOneIfOneParaOneAddn(Cell):
def __init__(self, input_shape):
super().__init__()
self.addn = P.AddN()
self.assign = P.Assign()
self.inputdata = Parameter(initializer(
1, input_shape, ms.float32), name="global_step")
def construct(self, x, y, input_data):
if x > y:
out = self.inputdata
else:
out = self.addn([input_data, input_data, input_data])
if x > y:
out = self.assign(self.inputdata, input_data)
return out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_ctrl_if_para_addn_true():
x = Tensor(1, ms.float32)
y = Tensor(0, ms.float32)
input_shape = (1024, 512, 7, 7)
input_data = np.random.randn(*input_shape).astype(np.float32)
net = ControlOneIfOneParaOneAddn(input_shape)
out = net(x, y, Tensor(input_data))
allclose_nparray(input_data[0], out.asnumpy()[0], 0.0001, 0.0001)
class AddnCell(Cell):
def __init__(self):
super().__init__()
self.addn = P.AddN()
def construct(self, x):
x = self.addn((x, x))
return x
class SideEffectMemoryCellAddnNet(Cell):
def __init__(self):
super().__init__()
self.para = Parameter(Tensor([1.0], ms.float32), name="para")
self.assign = P.Assign()
self.addn = P.AddN()
self.addn1 = AddnCell()
def construct(self, x):
x = self.addn1(x)
self.assign(self.para, x)
out = self.addn((self.para, x))
return out
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_grad_memory_addn():
net = SideEffectMemoryCellAddnNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
net.grad_mindspore_impl(inputs, grad_ys)
class SideEffectIOCellAddnNet(Cell):
def __init__(self):
super().__init__()
self.para1 = Parameter(Tensor([1.0], ms.float32), name="para1")
self.para2 = Parameter(Tensor([3.0], ms.float32), name="para2")
self.print = P.Print()
self.addn = AddnCell()
def construct(self, x):
self.print("para1:", self.para1)
self.print("para2:", self.para2)
x = self.addn(x)
return x
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@security_off_wrap
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_grad_io_addn():
net = SideEffectIOCellAddnNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
net.grad_mindspore_impl(inputs, grad_ys)
class SideEffectReturnParameterNet(Cell):
def __init__(self):
super().__init__()
self.para = Parameter(Tensor([1.0], ms.float32), name="para")
self.assign = P.Assign()
self.addn = P.AddN()
self.relu = P.ReLU()
def construct(self, inputs):
p1 = self.assign(self.para, inputs)
out = self.addn((inputs, inputs, inputs))
out = self.relu(out)
return p1
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_grad_read_dependency_return_parameter():
net = SideEffectReturnParameterNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
net.grad_mindspore_impl(inputs, grad_ys)
class SideEffectAssignAddnReluReturnParNet(Cell):
def __init__(self):
super().__init__()
self.parameter1 = Parameter(
Tensor([1.0], ms.float32), name="parameter1")
self.assign = P.Assign()
self.addN = P.AddN()
self.relu = P.ReLU()
def construct(self, inputs):
p1 = self.assign(self.parameter1, inputs)
out = self.addN((inputs, inputs, inputs))
out = self.relu(out)
return p1
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_side_effect_grad_read_dependency_assign_addn_relu_return_parameter():
net = SideEffectAssignAddnReluReturnParNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
out1 = net.grad_mindspore_impl(inputs, grad_ys)
net = SideEffectAssignAddnReluReturnParNet()
try:
context.set_context(mode=context.PYNATIVE_MODE)
out2 = net.grad_mindspore_impl(inputs, grad_ys)
allclose_nparray(out1[0][0].asnumpy(), out2[0]
[0].asnumpy(), 0.001, 0.001)
allclose_nparray(out1[1][0].asnumpy(), out2[1]
[0].asnumpy(), 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
class SideEffectPrintInHighOrdeAddnNet(Cell):
def __init__(self):
super().__init__()
self.parameter1 = Parameter(
Tensor([1.0], ms.float32), name="parameter1")
self.parameter2 = Parameter(
Tensor([3.0], ms.float32), name="parameter2")
self.assign = P.Assign()
self.addn = P.AddN()
self.mul = P.Mul()
self.print = P.Print()
def construct(self, x):
self.high_order_func()
out = self.addn((self.parameter1, x, self.parameter2))
return out
def high_order_func(self):
self.print("parameter1: ", self.parameter1)
self.print("parameter2: ", self.parameter2)
return True
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@security_off_wrap
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_side_effect_high_order_print_in_high_order_net():
print_file = os.getcwd() + "/test_side_effect_high_order_print_in_high_order_net.data"
context.set_context(print_file_path=print_file)
net = SideEffectPrintInHighOrdeAddnNet()
out1 = net(Tensor([9.0], ms.float32))
net = SideEffectPrintInHighOrdeAddnNet()
try:
context.set_context(mode=context.PYNATIVE_MODE)
out2 = net(Tensor([9.0], ms.float32))
allclose_nparray(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
class SideEffectControlFlowAssignDependTwoIfNet(Cell):
def __init__(self):
super().__init__()
self.parameter1 = Parameter(
Tensor([3.0], ms.float32), name="parameter1")
self.assign = P.Assign()
self.mul = P.Mul()
self.addn = P.AddN()
self.depend = P.Depend()
def construct(self, x, y):
self.assign(self.parameter1, x)
if self.parameter1 > y:
x = self.mul(x, x)
p2 = self.assign(self.parameter1, x)
if self.parameter1 > y:
x = self.addn((x, self.parameter1))
p3 = self.assign(self.parameter1, x)
self.depend(p3, p2)
return x
def grad_mindspore_impl(self, params1, params2, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params1, params2, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_side_effect_grad_control_flow_assign_depend_of_two_if():
net = SideEffectControlFlowAssignDependTwoIfNet()
grad_ys = Tensor([18.0], ms.float32)
inputs1 = Tensor([9.0], ms.float32)
inputs2 = Tensor([6.0], ms.float32)
net.grad_mindspore_impl(inputs1, inputs2, grad_ys)
class SideEffectTwoAddnSwitchNet(Cell):
def __init__(self):
super().__init__()
self.addN = P.AddN()
def construct(self, x):
y = x
x = self.addN((x, x, x))
y = self.addN((y, y))
if x > y:
return x
return y
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_side_effect_grad_two_addn_switch():
net = SideEffectTwoAddnSwitchNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
out1 = net.grad_mindspore_impl(inputs, grad_ys)
net = SideEffectTwoAddnSwitchNet()
try:
expect = 54.0
allclose_nparray(out1[0][0].asnumpy(), expect, 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
class SideEffectGradIfNet(Cell):
def __init__(self):
super().__init__()
self.relu = P.ReLU()
a = np.full((1,), 5, dtype=np.float32)
self.a = Parameter(Tensor(a), name="a")
b = np.full((1,), 4, dtype=np.float32)
self.b = Parameter(Tensor(b), name="b")
def construct(self, x):
if self.a > self.b:
x = self.relu(x)
out = x
else:
out = x + 2
return out
def grad_mindspore_impl(self, params, grad_ys):
grad_net = GradOfFirstInput(self)
grad_net.set_train()
grad_out = grad_net(params, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_side_effect_grad_if():
context.set_context(mode=context.GRAPH_MODE)
net = SideEffectGradIfNet()
grad_ys = Tensor([18.0], ms.float32)
inputs = Tensor([9.0], ms.float32)
out1 = net.grad_mindspore_impl(inputs, grad_ys)
net = SideEffectGradIfNet()
try:
expect = 18.0
allclose_nparray(out1.asnumpy(), expect, 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
class OneInputBprop(Cell):
def __init__(self):
super().__init__()
self.op = P.ReLU()
def construct(self, x):
return self.op(x)
def bprop(self, x, out, dout):
return (5 * x,)
class HighGrad(Cell):
def __init__(self, network, grad_list, sens_param=False, real_inputs_count=None):
super().__init__()
self.grads = [network]
for i in range(len(grad_list) - 1):
_grad = grad_list[i](self.grads[i], sens_param=False)
self.grads.append(_grad)
self.final_grad = grad_list[-1](self.grads[-1],
sens_param=sens_param, real_inputs_count=real_inputs_count)
def construct(self, *inputs):
return self.final_grad(*inputs)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_highgrad_one_input_sec_grad():
net = OneInputBprop()
x = Tensor(np.array([2, 2]).astype(np.float32))
grad_net = HighGrad(net, [GradOfFirstInput, GradOfFirstInput])
dxdx = grad_net(x)
assert (dxdx.asnumpy() == np.array([5, 5]).astype(np.float32)).all()
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_highgrad_one_input_third_grad():
net = OneInputBprop()
x = Tensor(np.array([2, 2]).astype(np.float32))
grad_net = HighGrad(
net, [GradOfFirstInput, GradOfFirstInput, GradOfFirstInput])
third_grad = grad_net(x)
assert (third_grad.asnumpy() == np.array([0, 0]).astype(np.float32)).all()
class SideEffectControlFlowAssignDependWhileNet(Cell):
def __init__(self):
super().__init__()
self.parameter1 = Parameter(Tensor([199.0], ms.float32), name="parameter1")
self.assign = P.Assign()
self.assignadd = P.AssignAdd()
self.addn = P.AddN()
self.depend = P.Depend()
def construct(self, x, y, z):
p1 = self.assign(self.parameter1, x)
while self.parameter1 < y:
x = self.addn((x, x))
p2 = self.assignadd(self.parameter1, z)
self.depend(p2, p1)
return x
def grad_mindspore_impl(self, params1, params2, params3, grad_ys):
grad_net = GradOfAllInputsAndParams(self)
grad_net.set_train()
grad_out = grad_net(params1, params2, params3, grad_ys)
return grad_out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_side_effect_grad_control_flow_assign_depend_while_net():
context.set_context(mode=context.GRAPH_MODE)
net = SideEffectControlFlowAssignDependWhileNet()
grad_ys = Tensor([18.0], ms.float32)
inputs1 = Tensor([9.0], ms.float32)
inputs2 = Tensor([6.0], ms.float32)
inputs3 = Tensor([3.0], ms.float32)
out1 = net.grad_mindspore_impl(inputs1, inputs2, inputs3, grad_ys)
try:
expect1 = 18.0
expect2 = 0
allclose_nparray(out1[0][0].asnumpy(), expect1, 0.001, 0.001)
allclose_nparray(out1[1][0].asnumpy(), expect2, 0.001, 0.001)
finally:
context.set_context(mode=context.GRAPH_MODE)
class AssignInZipLoop(Cell):
def __init__(self):
super().__init__()
self.conv1 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.conv2 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.params1 = self.conv1.trainable_params()
self.params2 = self.conv2.trainable_params()
def construct(self, x):
for p1, p2 in zip(self.params1, self.params2):
P.Assign()(p2, p1 + x)
out = 0
for p1, p2 in zip(self.params1, self.params2):
out = p1 + p2
print(p1)
print(p2)
return out
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_assign_in_zip_loop():
"""
Feature: Auto-monad load grouping and merge.
Description: Assign/Load inside a zip loop.
Expectation: 'p1 + p2' should be executed after Assign, and out is 1.
"""
x = Tensor.from_numpy(np.ones([1], np.float32))
net = AssignInZipLoop()
out = net(x)
assert np.all(out.asnumpy() == 1)
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