forked from mindspore-Ecosystem/mindspore
1521 lines
49 KiB
Python
1521 lines
49 KiB
Python
# 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 re
|
|
import time
|
|
import pytest
|
|
import numpy as np
|
|
import mindspore as ms
|
|
import mindspore.ops.operations as P
|
|
import mindspore.nn as nn
|
|
from mindspore.nn import Cell
|
|
from mindspore.nn import ReLU, BatchNorm2d, Conv2d, ParameterUpdate
|
|
from mindspore.nn import Momentum, SoftmaxCrossEntropyWithLogits
|
|
from mindspore import context, Tensor
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.ops.primitive import constexpr
|
|
from capture import Capture, capture, check_output
|
|
from tests.security_utils import security_off_wrap
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
|
|
|
|
@pytest.fixture(name="pynative_save_graphs")
|
|
def _pynative_save_graphs():
|
|
context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
|
|
yield
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
|
|
clean_all_ir_files('./')
|
|
|
|
|
|
@pytest.fixture(name="with_save_graphs")
|
|
def _with_save_graphs():
|
|
context.set_context(save_graphs=True)
|
|
yield
|
|
context.set_context(save_graphs=False)
|
|
clean_all_ir_files('./')
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print():
|
|
class Print(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
|
|
def construct(self, x, y):
|
|
self.print("input_x:", x, "input_y:", y)
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
net = Print()
|
|
net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
|
|
patterns = {'input_x:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y:\nTensor(shape=[], dtype=Int32, value=4)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_add():
|
|
class Print_Add(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.add = P.Add()
|
|
|
|
def construct(self, x, y):
|
|
x = self.add(x, y)
|
|
self.print("input_x:", x, "input_y:", y)
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(7, dtype=ms.int32)
|
|
net = Print_Add()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {'input_x:\nTensor(shape=[], dtype=Int32, value=7)\n'
|
|
'input_y:\nTensor(shape=[], dtype=Int32, value=4)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_assign():
|
|
class Print_Assign(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x):
|
|
self.print("before:", self.para)
|
|
self.para = x
|
|
self.print("after:", self.para)
|
|
return self.para
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(3, dtype=ms.int32)
|
|
net = Print_Assign()
|
|
out = net(input_x)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {'before:\nTensor(shape=[], dtype=Int32, value=1)',
|
|
'after:\nTensor(shape=[], dtype=Int32, value=3)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_assign_add():
|
|
class Print_Assign_Add(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.add = P.Add()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
self.print("before:", self.para)
|
|
self.para = x
|
|
self.print("after:", self.para)
|
|
x = self.add(self.para, y)
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(7, dtype=ms.int32)
|
|
net = Print_Assign_Add()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {'before:\nTensor(shape=[], dtype=Int32, value=1)',
|
|
'after:\nTensor(shape=[], dtype=Int32, value=3)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_while():
|
|
class Print_While(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
|
|
def construct(self, x, y):
|
|
self.print("input_x before:", x, "input_y before:", y)
|
|
while x < y:
|
|
self.print("input_x after:", x, "input_y after:", y)
|
|
x = x + 1
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(1, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(4, dtype=ms.int32)
|
|
net = Print_While()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {'input_x before:\nTensor(shape=[], dtype=Int32, value=1)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=1)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_if():
|
|
class Print_If(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
|
|
def construct(self, x, y):
|
|
self.print("input_x before:", x, "input_y before:", y)
|
|
if x < y:
|
|
self.print("input_x after:", x, "input_y after:", y)
|
|
x = x + 1
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(4, dtype=ms.int32)
|
|
net = Print_If()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {'input_x before:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_assign_while():
|
|
class Print_Assign_While(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.para = Parameter(Tensor(0, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
self.print("input_x before:", x, "input_y before:",
|
|
y, "para before:", self.para)
|
|
while x < y:
|
|
self.para = x
|
|
x = self.para + 1
|
|
self.print("input_x after:", x, "input_y after:",
|
|
y, "para after:", self.para)
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(1, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(4, dtype=ms.int32)
|
|
net = Print_Assign_While()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {
|
|
'input_x before:\nTensor(shape=[], dtype=Int32, value=1)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para before:\nTensor(shape=[], dtype=Int32, value=0)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=1)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=2)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=3)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_assign_if():
|
|
class Print_Assign_If(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
self.print("input_x before:", x, "input_y before:",
|
|
y, "para before:", self.para)
|
|
self.para = x
|
|
if x < y:
|
|
x = self.para + 1
|
|
self.print("input_x after:", x, "input_y after:",
|
|
y, "para after:", self.para)
|
|
return x
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(4, dtype=ms.int32)
|
|
net = Print_Assign_If()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {
|
|
'input_x before:\nTensor(shape=[], dtype=Int32, value=3)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para before:\nTensor(shape=[], dtype=Int32, value=1)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=3)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign():
|
|
class Assign(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, value):
|
|
self.para = value
|
|
return self.para
|
|
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(3, dtype=ms.int32)
|
|
net = Assign()
|
|
out = net(input_x)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_implicit():
|
|
class Assign_Implicit(Cell):
|
|
def __init__(self):
|
|
super(Assign_Implicit, self).__init__()
|
|
self.b = Parameter(initializer(
|
|
1, [5], ms.float32), name="global_step")
|
|
|
|
def construct(self, w):
|
|
self.b = w
|
|
return self.b
|
|
|
|
input_data = Tensor(np.ones([5]).astype(np.int32))
|
|
net = Assign_Implicit()
|
|
out = net(input_data)
|
|
assert out.dtype == ms.float32
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_write_after_read():
|
|
class Assign_WAR(Cell):
|
|
def __init__(self):
|
|
super(Assign_WAR, self).__init__()
|
|
self.assign = P.Assign()
|
|
self.sub = P.Sub()
|
|
self.add = P.Add()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
self.weight = Parameter(Tensor(5, dtype=ms.int32), name='weight')
|
|
|
|
def construct(self, x, y):
|
|
# without auto_monad, execute order is wrong: Add - Assign - Sub - Assign
|
|
# expected execute order: Add - Assign - Assign - Sub
|
|
self.para = self.add(y, x)
|
|
self.assign(self.para, y)
|
|
return self.sub(self.para, self.weight)
|
|
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(-1, dtype=ms.int32)
|
|
net = Assign_WAR()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_read_after_write():
|
|
class Assign_RAW(Cell):
|
|
def __init__(self):
|
|
super(Assign_RAW, self).__init__()
|
|
self.assign_add = P.AssignAdd()
|
|
self.greater = P.Greater()
|
|
self.add = P.Add()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
# without auto_monad, execute order is wrong: Add - Assign - Greater - AssignAdd
|
|
# expected execute order: AssignAdd - Add - Assign
|
|
self.greater(x, y)
|
|
self.assign_add(self.para, x)
|
|
self.para = self.add(x, y)
|
|
return self.para
|
|
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(7, dtype=ms.int32)
|
|
net = Assign_RAW()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_if():
|
|
class Assign_If(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
if x < y:
|
|
self.para = x
|
|
else:
|
|
self.para = y
|
|
return self.para
|
|
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(3, dtype=ms.int32)
|
|
net = Assign_If()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if():
|
|
class If(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
|
|
def construct(self, x, y):
|
|
if x > y:
|
|
x = self.sub(x, y)
|
|
else:
|
|
x = self.add(x, y)
|
|
return x
|
|
|
|
input_x = Tensor(3, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(7, dtype=ms.int32)
|
|
net = If()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_while():
|
|
class While(Cell):
|
|
def construct(self, x, y):
|
|
y = y + 4
|
|
while x < y:
|
|
x = x + 1
|
|
x = x + 3
|
|
return x
|
|
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(14, dtype=ms.int32)
|
|
expect = Tensor(21, dtype=ms.int32)
|
|
net = While()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_while():
|
|
class Assign_While(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
y = y + 4
|
|
while x < y:
|
|
x = x + 1
|
|
self.para = x
|
|
self.para = x - 1
|
|
return self.para
|
|
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(14, dtype=ms.int32)
|
|
expect = Tensor(17, dtype=ms.int32)
|
|
net = Assign_While()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_for():
|
|
class For(Cell):
|
|
def construct(self, x, y):
|
|
y = x + y
|
|
for _ in range(20):
|
|
y = y + 1
|
|
return y
|
|
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(26, dtype=ms.int32)
|
|
net = For()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_for():
|
|
class Print_For(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
|
|
def construct(self, x, y):
|
|
y = x + y
|
|
self.print("input_x before:", x, "input_y before:", y)
|
|
for _ in range(3):
|
|
y = y + 1
|
|
self.print("input_x after:", x, "input_y after:", y)
|
|
return y
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(9, dtype=ms.int32)
|
|
net = Print_For()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {
|
|
'input_x before:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=6)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=7)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=8)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=9)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@security_off_wrap
|
|
def test_print_assign_for():
|
|
class Print_Assign_For(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.print = P.Print()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
y = x + y
|
|
self.print("input_x before:", x, "input_y before:",
|
|
y, "para before:", self.para)
|
|
for _ in range(3):
|
|
y = y + 1
|
|
self.para = x + y
|
|
self.print("input_x after:", x, "input_y after:",
|
|
y, "para after:", self.para)
|
|
return y
|
|
|
|
cap = Capture()
|
|
with capture(cap):
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(4, dtype=ms.int32)
|
|
expect = Tensor(9, dtype=ms.int32)
|
|
net = Print_Assign_For()
|
|
out = net(input_x, input_y)
|
|
time.sleep(0.1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
patterns = {
|
|
'input_x before:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y before:\nTensor(shape=[], dtype=Int32, value=6)\n'
|
|
'para before:\nTensor(shape=[], dtype=Int32, value=1)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=7)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=9)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=8)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=10)',
|
|
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
|
|
'input_y after:\nTensor(shape=[], dtype=Int32, value=9)\n'
|
|
'para after:\nTensor(shape=[], dtype=Int32, value=11)'}
|
|
check_output(cap.output, patterns)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_for():
|
|
class Assign_For(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
y = y + 4
|
|
for _ in range(5):
|
|
x = x + y
|
|
self.para = x
|
|
return self.para
|
|
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(37, dtype=ms.int32)
|
|
net = Assign_For()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@constexpr
|
|
def _check_shape(shape):
|
|
if len(shape) != 1:
|
|
raise ValueError(f"Invalid shape {shape}")
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_constexpr_check():
|
|
class ConstexprCheck(Cell):
|
|
def __init__(self):
|
|
super(ConstexprCheck, self).__init__()
|
|
self.shape = P.Shape()
|
|
|
|
def construct(self, x, y):
|
|
s = self.shape(x)
|
|
_check_shape(s)
|
|
x = x + y
|
|
return x
|
|
|
|
x = Tensor([2], dtype=ms.int32)
|
|
y = Tensor([3], dtype=ms.int32)
|
|
expect = Tensor(5, dtype=ms.int32)
|
|
net = ConstexprCheck()
|
|
# Input with valid shape.
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
# Input with wrong shape, exception is expected.
|
|
with pytest.raises(ValueError):
|
|
wrong_x = Tensor(np.ones((2, 2)), dtype=ms.int32)
|
|
out = net(wrong_x, y)
|
|
print(out)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_lambda():
|
|
class If_Lambda(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
out = x
|
|
if x < y:
|
|
x2 = (lambda a: a + a)
|
|
out = x2(self.para)
|
|
out = out + y
|
|
return out
|
|
|
|
input_x = Tensor(2, dtype=ms.int32)
|
|
input_y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(5, dtype=ms.int32)
|
|
net = If_Lambda()
|
|
out = net(input_x, input_y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_multi_assign():
|
|
class Multi_Assign(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.assign = P.Assign()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(2, dtype=ms.int32), name='para2')
|
|
self.para3 = Parameter(Tensor(3, dtype=ms.int32), name='para3')
|
|
|
|
def construct(self, x, y, z):
|
|
a = self.assign(self.para1, x)
|
|
a = self.assign(self.para2, y)
|
|
a = self.assign(self.para3, z)
|
|
return self.para1 + self.para2 + a
|
|
|
|
x = Tensor(4, dtype=ms.int32)
|
|
y = Tensor(5, dtype=ms.int32)
|
|
z = Tensor(6, dtype=ms.int32)
|
|
expect = Tensor(15, dtype=ms.int32)
|
|
net = Multi_Assign()
|
|
out = net(x, y, z)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_multi_assign_addn():
|
|
class Multi_Assign_Addn(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.addn = P.AddN()
|
|
self.assign = P.Assign()
|
|
self.para1 = Parameter(Tensor(1.0, dtype=ms.float32), name='para1')
|
|
self.para2 = Parameter(Tensor(3.0, dtype=ms.float32), name='para2')
|
|
|
|
def construct(self, inputs):
|
|
self.assign(self.para1, inputs)
|
|
out = self.addn((inputs, self.para1, self.para2))
|
|
self.assign(self.para2, inputs)
|
|
out = self.addn((out, self.para1, self.para2))
|
|
return out
|
|
|
|
x = Tensor(9.0, dtype=ms.float32)
|
|
expect = Tensor(39.0, dtype=ms.float32)
|
|
net = Multi_Assign_Addn()
|
|
out = net(x)
|
|
np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@security_off_wrap
|
|
def test_multi_assign_print():
|
|
class Multi_Assign_Print(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.pow = P.Pow()
|
|
self.print = P.Print()
|
|
self.assign = P.Assign()
|
|
self.exponent = Tensor([2.0], ms.float32)
|
|
self.para1 = Parameter(Tensor(1.0, dtype=ms.float32), name='para1')
|
|
self.para2 = Parameter(Tensor(3.0, dtype=ms.float32), name='para2')
|
|
|
|
def construct(self, inputs):
|
|
self.assign(self.para1, inputs)
|
|
self.assign(self.para2, self.pow(inputs, self.exponent))
|
|
self.print(inputs)
|
|
self.print(self.para1)
|
|
self.print(self.para2)
|
|
return inputs
|
|
|
|
x = Tensor(9.0, dtype=ms.float32)
|
|
expect = Tensor(9.0, dtype=ms.float32)
|
|
expect_para1 = Tensor(9.0, dtype=ms.float32)
|
|
expect_para2 = Tensor(81.00001, dtype=ms.float32)
|
|
net = Multi_Assign_Print()
|
|
out = net(x)
|
|
np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
|
|
np.testing.assert_almost_equal(
|
|
net.para1.data.asnumpy(), expect_para1.asnumpy())
|
|
np.testing.assert_almost_equal(
|
|
net.para2.data.asnumpy(), expect_para2.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_matmul_assign_biasadd():
|
|
class Matmul_Assign_Biasadd(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
inputs = np.array([[1, 1], [1, 1]])
|
|
self.parameter1 = Parameter(
|
|
Tensor(inputs, ms.float32), name="parameter1")
|
|
biasadd = np.array([0, -1])
|
|
self.parameter2 = Parameter(
|
|
Tensor(biasadd, ms.float32), name="biasadd")
|
|
self.assign = P.Assign()
|
|
self.matmul = P.MatMul()
|
|
self.biasadd = P.BiasAdd()
|
|
|
|
def construct(self, x):
|
|
self.assign(self.parameter1, x)
|
|
x = self.matmul(x, self.parameter1)
|
|
self.assign(self.parameter1, x)
|
|
x = self.biasadd(x, self.parameter2)
|
|
return x
|
|
|
|
net = Matmul_Assign_Biasadd()
|
|
inputs = np.array([[1, 2], [3, 4]])
|
|
out1 = net(Tensor(inputs, ms.float32))
|
|
net = Matmul_Assign_Biasadd()
|
|
try:
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
out2 = net(Tensor(inputs, ms.float32))
|
|
np.testing.assert_almost_equal(out1.asnumpy(), out2.asnumpy())
|
|
finally:
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_while_if():
|
|
class Assign_While_If(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.mul = P.Mul()
|
|
self.addn = P.AddN()
|
|
self.assign = P.Assign()
|
|
self.assign_sub = P.AssignSub()
|
|
self.para = Parameter(Tensor(1.0, dtype=ms.float32), name='para')
|
|
|
|
def construct(self, x, y, z, w):
|
|
self.assign(self.para, x)
|
|
if self.para > y:
|
|
self.assign(self.para, y)
|
|
x = self.mul(x, x)
|
|
while self.para > z:
|
|
x = self.addn((x, self.para))
|
|
self.assign_sub(self.para, w)
|
|
return x
|
|
|
|
x = Tensor(99.0, dtype=ms.float32)
|
|
y = Tensor(44.0, dtype=ms.float32)
|
|
z = Tensor(11.0, dtype=ms.float32)
|
|
w = Tensor(1.0, dtype=ms.float32)
|
|
expect = Tensor(10725.0, dtype=ms.float32)
|
|
net = Assign_While_If()
|
|
out = net(x, y, z, w)
|
|
np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_isolate_call():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(2, dtype=ms.int32), name='para2')
|
|
|
|
def construct(self, x, y):
|
|
self.setpara(x, y)
|
|
return self.para1 + self.para2
|
|
|
|
def setpara(self, x, y):
|
|
self.para1 = x
|
|
self.setpara2(y)
|
|
return x
|
|
|
|
def setpara2(self, y):
|
|
self.para2 = y
|
|
return y
|
|
|
|
x = Tensor(4, dtype=ms.int32)
|
|
y = Tensor(5, dtype=ms.int32)
|
|
expect = Tensor(9, dtype=ms.int32)
|
|
net = Net()
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign_return_true():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
if self.mycheck(x, y):
|
|
out = x + y
|
|
else:
|
|
out = x - y
|
|
out = self.para + out
|
|
return out
|
|
|
|
def mycheck(self, x, y):
|
|
self.setpara(x, y)
|
|
return True
|
|
|
|
def setpara(self, x, y):
|
|
self.para = x + y
|
|
return True
|
|
|
|
x = Tensor(2, dtype=ms.int32)
|
|
y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(10, dtype=ms.int32)
|
|
net = Net()
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_unpack_call():
|
|
class SetPara(Cell):
|
|
def __init__(self, para):
|
|
super(SetPara, self).__init__()
|
|
self.para = para
|
|
|
|
def construct(self, x, y):
|
|
self.para = x + y
|
|
return True
|
|
|
|
class MyNet(Cell):
|
|
def __init__(self):
|
|
super(MyNet, self).__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
self.set_para = SetPara(self.para)
|
|
|
|
def construct(self, *inputs):
|
|
self.call_func(self.set_para, *inputs)
|
|
out = self.para + 1
|
|
return out
|
|
|
|
def call_func(self, func, *inputs):
|
|
func(*inputs)
|
|
return True
|
|
|
|
x = Tensor(2, dtype=ms.int32)
|
|
y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(6, dtype=ms.int32)
|
|
net = MyNet()
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_tuple_of_tuple():
|
|
class SetPara(Cell):
|
|
def __init__(self, para):
|
|
super(SetPara, self).__init__()
|
|
self.para = para
|
|
|
|
def construct(self, x, y):
|
|
self.para = x + y
|
|
return True
|
|
|
|
class MyNet(Cell):
|
|
def __init__(self):
|
|
super(MyNet, self).__init__()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
self.set_para = SetPara(self.para)
|
|
|
|
def construct(self, x, y):
|
|
t1 = (self.set_para, x)
|
|
t2 = (t1, y)
|
|
t2[0][0](t2[1], t1[1])
|
|
out = self.para + 1
|
|
return out
|
|
|
|
def call_func(self, func, *inputs):
|
|
func(*inputs)
|
|
return True
|
|
|
|
x = Tensor(2, dtype=ms.int32)
|
|
y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(6, dtype=ms.int32)
|
|
net = MyNet()
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_write_read_write():
|
|
class MyNet(Cell):
|
|
def __init__(self):
|
|
super(MyNet, self).__init__()
|
|
self.para1 = Parameter(Tensor(1, dtype=ms.int32), name='para1')
|
|
self.para2 = Parameter(Tensor(2, dtype=ms.int32), name='para2')
|
|
|
|
def construct(self, x, y, x1, y1):
|
|
self.para1 = x
|
|
self.para2 = y
|
|
a = self.para1 + self.para2
|
|
self.para1 = x1
|
|
self.para2 = y1
|
|
return a + self.para1 + self.para2
|
|
|
|
x = Tensor(3, dtype=ms.int32)
|
|
y = Tensor(4, dtype=ms.int32)
|
|
x1 = Tensor(5, dtype=ms.int32)
|
|
y1 = Tensor(6, dtype=ms.int32)
|
|
expect = Tensor(18, dtype=ms.int32)
|
|
net = MyNet()
|
|
out = net(x, y, x1, y1)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_variable_from_outer_graph():
|
|
class MyNet(Cell):
|
|
def __init__(self):
|
|
super(MyNet, self).__init__()
|
|
self.cond = False
|
|
self.add = P.Add()
|
|
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
|
|
|
|
def construct(self, x, y):
|
|
b = self.para + x
|
|
a = self.para + b
|
|
if self.cond:
|
|
a = self.add(a, x)
|
|
else:
|
|
a = self.add(a, y)
|
|
return a + b
|
|
|
|
x = Tensor(2, dtype=ms.int32)
|
|
y = Tensor(3, dtype=ms.int32)
|
|
expect = Tensor(10, dtype=ms.int32)
|
|
net = MyNet()
|
|
out = net(x, y)
|
|
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_ctrl_while_by_while_and_if_in_first_while():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = P.ReLU()
|
|
self.sigmoid = P.Sigmoid()
|
|
self.tanh = P.Tanh()
|
|
self.add = P.Add()
|
|
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")
|
|
c = np.full((1,), 7, dtype=np.float32)
|
|
self.c = Parameter(Tensor(c), name="c")
|
|
|
|
def construct(self, x):
|
|
out = x
|
|
while self.a < 7:
|
|
if self.a < self.c:
|
|
out = self.relu(x)
|
|
self.a += 1
|
|
while self.c > 5:
|
|
out = self.add(out, out)
|
|
self.c -= 1
|
|
return out
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
input_np_a = np.random.randn(2, 3, 4, 5).astype(np.float32)
|
|
input_me_a = Tensor(input_np_a)
|
|
net = Net()
|
|
net(input_me_a)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_ctrl_if_by_while_and_while_in_first_if():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = P.ReLU()
|
|
self.sigmoid = P.Sigmoid()
|
|
self.tanh = P.Tanh()
|
|
self.add = P.Add()
|
|
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")
|
|
c = np.full((1,), 7, dtype=np.float32)
|
|
self.c = Parameter(Tensor(c), name="c")
|
|
|
|
def construct(self, x):
|
|
out = x
|
|
if self.a < self.c:
|
|
out = self.relu(x)
|
|
while self.a < 7:
|
|
self.a += 1
|
|
|
|
while self.c > 5:
|
|
out = self.add(out, out)
|
|
self.c -= 1
|
|
return out
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
input_np_a = np.random.randn(2, 3, 4, 5).astype(np.float32)
|
|
input_me_a = Tensor(input_np_a)
|
|
net = Net()
|
|
net(input_me_a)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_ctrl_while_by_while_and_while_in_first_while():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = P.ReLU()
|
|
self.sigmoid = P.Sigmoid()
|
|
self.tanh = P.Tanh()
|
|
self.add = P.Add()
|
|
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")
|
|
c = np.full((1,), 7, dtype=np.float32)
|
|
self.c = Parameter(Tensor(c), name="c")
|
|
|
|
def construct(self, x):
|
|
out = x
|
|
while self.a < self.c:
|
|
out = self.relu(x)
|
|
while self.b > 1:
|
|
self.b -= 1
|
|
self.a += 1
|
|
|
|
while self.c > 5:
|
|
out = self.add(out, out)
|
|
self.c -= 1
|
|
return out
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
input_np_a = np.random.randn(2, 3, 4, 5).astype(np.float32)
|
|
input_me_a = Tensor(input_np_a)
|
|
net = Net()
|
|
net(input_me_a)
|
|
|
|
|
|
def clear_json_info():
|
|
os.system("rm -rf ./kernel_meta/*.json")
|
|
os.system("rm -rf ./kernel_meta/*.info")
|
|
|
|
|
|
def find_json_info(file):
|
|
result = os.system("ls -al ./kernel_meta/%s" % (file))
|
|
return result
|
|
|
|
|
|
class MultiOutReluBywaySqrt(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = nn.ReLU()
|
|
self.sqrt = P.Sqrt()
|
|
|
|
def construct(self, x):
|
|
x = self.relu(x)
|
|
x = self.relu(x)
|
|
x1 = self.relu(x)
|
|
x = self.relu(x1)
|
|
y = self.sqrt(x1)
|
|
return x, y
|
|
|
|
|
|
class MultiOutReluSqrtBywaySqrt(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = nn.ReLU()
|
|
self.sqrt = P.Sqrt()
|
|
self.sin = P.Sin()
|
|
|
|
def construct(self, x):
|
|
x = self.relu(x)
|
|
x = self.sqrt(x)
|
|
x1 = self.relu(x)
|
|
x = self.sin(x1)
|
|
y = self.sqrt(x1)
|
|
return x, y
|
|
|
|
|
|
def clean_all_ir_files(folder_path):
|
|
if os.path.exists(folder_path):
|
|
for file_name in os.listdir(folder_path):
|
|
if file_name.endswith('.ir') or file_name.endswith('.dot') or \
|
|
file_name.endswith('.dat') or file_name.endswith('.pb') or \
|
|
file_name.startswith('trace_code_graph'):
|
|
os.remove(os.path.join(folder_path, file_name))
|
|
|
|
|
|
def find_newest_validateir_file(folder_path):
|
|
ckpt_files = map(lambda f: os.path.join(folder_path, f),
|
|
filter(lambda f: re.match(r'\d+_validate_\d+.ir', f),
|
|
os.listdir(folder_path)))
|
|
return max(ckpt_files, key=os.path.getctime)
|
|
|
|
|
|
def read_file():
|
|
filename = find_newest_validateir_file('./')
|
|
with open((os.path.join(filename)), 'r') as f:
|
|
content = f.read()
|
|
return content
|
|
|
|
|
|
def check_keep_batchnorm_fp32_false(kwargs, level):
|
|
if ms.context.get_context("device_target") == "GPU":
|
|
if level == "O2":
|
|
if "keep_batchnorm_fp32" in kwargs.keys() and (not kwargs["keep_batchnorm_fp32"]):
|
|
if "cast_model_type" not in kwargs.keys() or kwargs["cast_model_type"] == ms.float16:
|
|
return True
|
|
else:
|
|
if "cast_model_type" in kwargs.keys() and kwargs["cast_model_type"] == ms.float16:
|
|
if "keep_batchnorm_fp32" not in kwargs.keys() or (not kwargs["keep_batchnorm_fp32"]):
|
|
return True
|
|
return False
|
|
|
|
|
|
def use_build_train_network_check_cast_num(network, level, inputs, label, cast_num, loss_flag=True, **kwargs):
|
|
diff_cast = 0
|
|
if check_keep_batchnorm_fp32_false(kwargs, level):
|
|
diff_cast += 8
|
|
opt = Momentum(learning_rate=0.0001, momentum=0.009,
|
|
params=network.trainable_params())
|
|
loss = None
|
|
if loss_flag:
|
|
loss = SoftmaxCrossEntropyWithLogits(sparse=False, reduction='mean')
|
|
|
|
train_network = ms.amp.build_train_network(
|
|
network, opt, loss, level=level, **kwargs)
|
|
out_me = train_network(inputs, label)
|
|
if context.get_context("mode") == 0:
|
|
content = read_file()
|
|
castnum = re.findall('Cast', content)
|
|
assert len(castnum) == max(cast_num - diff_cast, 0)
|
|
return out_me
|
|
|
|
|
|
class AssignNet(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = ReLU()
|
|
self.mean = P.ReduceMean(keep_dims=False)
|
|
self.assign_sub = P.AssignSub()
|
|
self.input_data = Parameter(initializer(
|
|
1, [1, 3, 2, 2], ms.float32), name='value')
|
|
|
|
def construct(self, x):
|
|
x = self.assign_sub(self.input_data, x)
|
|
x = self.relu(x)
|
|
x = self.mean(x, (2, 3))
|
|
return x
|
|
|
|
@security_off_wrap
|
|
def test_auto_mixed_precision_train_1(pynative_save_graphs):
|
|
net = AssignNet()
|
|
input32 = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
|
|
label32 = Tensor(np.zeros([1, 3]).astype(np.float32))
|
|
use_build_train_network_check_cast_num(net, "O0", input32, label32, 0)
|
|
|
|
@security_off_wrap
|
|
def test_auto_mixed_precision_train_2(pynative_save_graphs):
|
|
net = AssignNet()
|
|
input32 = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
|
|
label32 = Tensor(np.zeros([1, 3]).astype(np.float32))
|
|
use_build_train_network_check_cast_num(net, "O2", input32, label32, 2)
|
|
|
|
|
|
class MixControlNet(Cell):
|
|
def __init__(self, in_channel, x):
|
|
super().__init__()
|
|
self.biasadd = P.BiasAdd()
|
|
self.equal = P.Equal()
|
|
self.addn = P.AddN()
|
|
self.conv = Conv2d(in_channels=in_channel, out_channels=in_channel,
|
|
kernel_size=1, stride=1, has_bias=False,
|
|
weight_init='ones', pad_mode='same')
|
|
self.bn = BatchNorm2d(num_features=in_channel)
|
|
self.assignadd = P.AssignAdd()
|
|
self.assign = P.Assign()
|
|
self.relu = ReLU()
|
|
self.mean = P.ReduceMean(keep_dims=False)
|
|
self.bias = Parameter(
|
|
Tensor(np.random.randint(2, size=(3,)).astype((np.float32))),
|
|
name="bias")
|
|
self.bias2 = Parameter(Tensor(np.ones([3]).astype(np.float32)),
|
|
name="bias2")
|
|
self.parameterupdate = ParameterUpdate(self.bias)
|
|
self.value = Tensor(np.random.randn(*(3,)), ms.float32)
|
|
self.x = x
|
|
|
|
def construct(self, input_x):
|
|
x = self.x
|
|
z = self.x
|
|
out = self.biasadd(input_x, self.bias)
|
|
while x < 20:
|
|
update = self.parameterupdate(self.bias2)
|
|
out = self.biasadd(out, update)
|
|
if x < 10:
|
|
out = self.addn((input_x, out))
|
|
while z < 20:
|
|
out = self.conv(out)
|
|
z = z + 1
|
|
if x < 20:
|
|
out = self.biasadd(out, self.bias)
|
|
if x % 2 == 0:
|
|
out = self.biasadd(out, self.bias)
|
|
self.assignadd(self.bias, self.value)
|
|
out = self.bn(out)
|
|
else:
|
|
out = self.conv(out)
|
|
x = x + 1
|
|
out = self.addn((out, out))
|
|
out = self.mean(out, (2, 3))
|
|
return out
|
|
|
|
|
|
def use_build_train_network_controlflow_check_cast_num(network, level, input_x,
|
|
label, cast_num,
|
|
sparse=False,
|
|
loss_flag=True,
|
|
**kwargs):
|
|
opt = Momentum(learning_rate=0.0001, momentum=0.009,
|
|
params=network.trainable_params())
|
|
loss = None
|
|
if loss_flag:
|
|
loss = SoftmaxCrossEntropyWithLogits(sparse=sparse, reduction='mean')
|
|
|
|
train_network = ms.amp.build_train_network(network, opt, loss, level=level,
|
|
**kwargs)
|
|
out_me = train_network(input_x, label)
|
|
if context.get_context("mode") == 0:
|
|
content = read_file()
|
|
castnum = re.findall('Cast', content)
|
|
assert len(castnum) == cast_num
|
|
return out_me
|
|
|
|
@security_off_wrap
|
|
def test_auto_mixed_precision_controlflow_auto(pynative_save_graphs):
|
|
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)
|
|
|
|
|
|
# op_cast should be located in order_list after abstract_specialize.
|
|
# Besides Ascend, it can work on CPU.
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_if_cast():
|
|
class Net(nn.Cell):
|
|
def __init__(self, cond1):
|
|
super().__init__()
|
|
self.cond1 = cond1
|
|
self.op_cast = P.Cast()
|
|
self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
|
|
|
|
def construct(self, beta1, beta2):
|
|
z_local = self.op_cast(self.z, ms.float16)
|
|
self.z = beta2
|
|
if self.cond1:
|
|
out = z_local + beta1
|
|
else:
|
|
out = z_local - beta1
|
|
|
|
return out
|
|
|
|
net = Net(True)
|
|
beta1 = Tensor(np.array([2]).astype(np.float32))
|
|
beta2 = Tensor(np.array([10]).astype(np.float32))
|
|
r1 = net(beta1, beta2)
|
|
expect = Tensor(np.array([3]).astype(np.float32))
|
|
np.testing.assert_array_equal(r1.asnumpy(), expect.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_while_forward():
|
|
class MyWhileNet(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.max = P.ReduceMax()
|
|
|
|
def construct(self, idx, end, x):
|
|
while idx < end:
|
|
part = x[idx, :, :]
|
|
max_num = self.max(part)
|
|
x[idx, :, 0:2] = max_num
|
|
idx = idx + 1
|
|
return x
|
|
|
|
net = MyWhileNet()
|
|
idx = Tensor(np.array(0), dtype=ms.int32)
|
|
end = Tensor(np.array(2), dtype=ms.int32)
|
|
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
|
|
output = net(idx, end, x)
|
|
expect = np.array([[[3, 3], [3, 3]], [[7, 7], [7, 7]]], dtype=np.int32)
|
|
assert np.allclose(output.asnumpy(), expect, 0.0001, 0.0001)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_multi_add_assign():
|
|
class Net(Cell):
|
|
def __init__(self, i1):
|
|
super(Net, self).__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.assign = P.Assign()
|
|
self.p = Parameter(i1, name='para')
|
|
|
|
def construct(self, a, d, e):
|
|
res1 = self.add(self.add(self.add(self.p, a), a), a)
|
|
mul = self.mul(d, e)
|
|
self.assign(self.p, mul)
|
|
res2 = self.sub(self.p, e)
|
|
return res2, res1
|
|
|
|
def numpy_out(p, a, d, e):
|
|
res1 = p + a + a + a
|
|
res_as = d * e
|
|
res2 = d * e - e
|
|
return res2, res1, res_as
|
|
|
|
p = (np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i0 = (np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i1 = (np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i2 = (np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
|
|
net = Net(Tensor(p))
|
|
r2, r1 = net(Tensor(i0), Tensor(i1), Tensor(i2))
|
|
|
|
outputs = [r2.asnumpy(), r1.asnumpy(), net.p.data.asnumpy()]
|
|
expects = numpy_out(p, i0, i1, i2)
|
|
np.testing.assert_array_equal(outputs, expects)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_multi_abs_add_assign():
|
|
class Net(Cell):
|
|
def __init__(self, para):
|
|
super(Net, self).__init__()
|
|
self.add = P.Add()
|
|
self.sub = P.Sub()
|
|
self.mul = P.Mul()
|
|
self.abs = P.Abs()
|
|
self.assign = P.Assign()
|
|
self.p = Parameter(para, name='para')
|
|
|
|
def construct(self, a, d, e):
|
|
tmp = self.abs(self.add(self.abs(a), self.abs(self.mul(a, a))))
|
|
res1 = self.add(self.p, tmp)
|
|
mul = self.mul(d, e)
|
|
self.assign(self.p, mul)
|
|
res2 = self.sub(self.p, e)
|
|
return res2, res1, tmp
|
|
|
|
def numpy_out(p, a, d, e):
|
|
tmp = np.abs(np.abs(a) + np.abs(a * a))
|
|
res1 = p + tmp
|
|
res_as = d * e
|
|
res2 = d * e - e
|
|
return res2, res1, res_as, tmp
|
|
|
|
p = -(np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i0 = -(np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i1 = -(np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
i2 = -(np.abs(np.random.normal(0, 1, [3])) + 1).astype(np.float32)
|
|
|
|
net = Net(Tensor(p))
|
|
r2, r1, tmp = net(Tensor(i0), Tensor(i1), Tensor(i2))
|
|
|
|
outputs = [r2.asnumpy(), r1.asnumpy(), net.p.data.asnumpy(), tmp.asnumpy()]
|
|
expects = numpy_out(p, i0, i1, i2)
|
|
np.testing.assert_array_equal(outputs, expects)
|