forked from mindspore-Ecosystem/mindspore
219 lines
6.8 KiB
Python
219 lines
6.8 KiB
Python
# Copyright 2022 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.
|
|
# ============================================================================
|
|
"""smoke tests for Sparse control flow cases"""
|
|
|
|
import pytest
|
|
import numpy as np
|
|
|
|
from mindspore import Tensor, CSRTensor, Parameter, nn, context
|
|
from mindspore.common import dtype as mstype
|
|
import mindspore.ops.operations as P
|
|
|
|
from .sparse_utils import compare_csr, get_csr_tensor, csr_add, get_csr_components, get_csr_from_scalar, \
|
|
forward_grad_net
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_while_tensor_as_condition_forward_and_backward():
|
|
"""
|
|
Feature: Test CSRTensor in while.
|
|
Description: Test CSRTensor computation in while loop.
|
|
Expectation: Success.
|
|
"""
|
|
class Net(nn.Cell):
|
|
|
|
def construct(self, x, y):
|
|
out = y
|
|
while x < 1:
|
|
out = csr_add(out, out.values)
|
|
x += 1
|
|
return out
|
|
|
|
x = Tensor(-2, dtype=mstype.int32)
|
|
y = get_csr_tensor()
|
|
net = Net()
|
|
|
|
csr1, grad_py = forward_grad_net(net, x, y, mode=context.PYNATIVE_MODE)
|
|
csr2, grad_graph = forward_grad_net(net, x, y, mode=context.GRAPH_MODE)
|
|
|
|
# Compare results
|
|
compare_csr(csr1, csr2)
|
|
assert (csr1.values.asnumpy() == np.array([8, 16], dtype=np.float32)).all()
|
|
assert len(grad_py) == 2
|
|
assert len(grad_graph) == 2
|
|
assert isinstance(grad_graph[1], CSRTensor)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_control_flow_while_if_continue_not_relevant_gt():
|
|
"""
|
|
Feature: Test CSRTensor in while.
|
|
Description: Test CSRTensor computation in while loop.
|
|
Expectation: Success.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.addn = P.AddN()
|
|
|
|
def construct(self, x):
|
|
s = x
|
|
t = csr_add(x, 1)
|
|
tensor_list = [x.values, x.values]
|
|
while len(tensor_list) < 4:
|
|
tensor_list.append(x.values)
|
|
a = self.addn(tensor_list)
|
|
x = csr_add(x, 1)
|
|
if t.values in tensor_list:
|
|
continue
|
|
s = csr_add(s, a)
|
|
return s
|
|
|
|
num = Tensor(-2, dtype=mstype.float32)
|
|
x = get_csr_from_scalar(num)
|
|
net = Net()
|
|
|
|
csr1, grad_py = forward_grad_net(net, x, mode=context.PYNATIVE_MODE)
|
|
csr2, grad_graph = forward_grad_net(net, x, mode=context.GRAPH_MODE)
|
|
|
|
# Compare results
|
|
compare_csr(csr1, csr2)
|
|
assert (csr1.values.asnumpy() == np.array([-8], dtype=np.float32)).all()
|
|
assert len(grad_py) == 1
|
|
assert len(grad_graph) == 1
|
|
assert isinstance(grad_graph[0], CSRTensor)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_control_flow_for_while_return_in_while_no():
|
|
"""
|
|
Feature: Test CSRTensor in while.
|
|
Description: Test CSRTensor computation in while loop.
|
|
Expectation: Success.
|
|
"""
|
|
class Net(nn.Cell):
|
|
|
|
def construct(self, x, y):
|
|
out = y
|
|
for _ in range(3):
|
|
out = csr_add(out, y.values)
|
|
while x < 5:
|
|
out = csr_add(out, out.values)
|
|
if x > 1:
|
|
return out
|
|
x += 1
|
|
return out
|
|
|
|
x = Tensor(-2, dtype=mstype.int32)
|
|
y = get_csr_tensor()
|
|
net = Net()
|
|
|
|
csr1, grad_py = forward_grad_net(net, x, y, mode=context.PYNATIVE_MODE)
|
|
csr2, grad_graph = forward_grad_net(net, x, y, mode=context.GRAPH_MODE)
|
|
|
|
# Compare results
|
|
compare_csr(csr1, csr2)
|
|
assert (csr1.values.asnumpy() == np.array([128, 256], dtype=np.float32)).all()
|
|
assert len(grad_py) == 2
|
|
assert len(grad_graph) == 2
|
|
assert isinstance(grad_graph[1], CSRTensor)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_control_flow_for_enumerate_if_continue():
|
|
"""
|
|
Feature: Test CSRTensor in while.
|
|
Description: Test CSRTensor computation in while loop.
|
|
Expectation: Success.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, t1, t2):
|
|
super().__init__()
|
|
self.p1 = Parameter(Tensor(t1, mstype.float32), name="a")
|
|
self.p2 = Parameter(Tensor(t2, mstype.float32), name="b")
|
|
self.assignadd = P.AssignAdd()
|
|
|
|
def construct(self, x):
|
|
plist = [self.p1, self.p2]
|
|
out = x
|
|
for i, t in enumerate(plist):
|
|
if t > 2:
|
|
continue
|
|
self.assignadd(t, 1)
|
|
out = csr_add(out, i * t)
|
|
return out
|
|
|
|
t1 = 1
|
|
t2 = 2
|
|
x = get_csr_tensor()
|
|
|
|
csr1, grad_py = forward_grad_net(Net(t1, t2), x, mode=context.PYNATIVE_MODE)
|
|
csr2, grad_graph = forward_grad_net(Net(t1, t2), x, mode=context.GRAPH_MODE)
|
|
|
|
# Compare results
|
|
compare_csr(csr1, csr2)
|
|
assert (csr1.values.asnumpy() == np.array([4, 5], dtype=np.float32)).all()
|
|
assert len(grad_py) == 1
|
|
assert len(grad_graph) == 1
|
|
assert isinstance(grad_graph[0], CSRTensor)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_multi_csr_in_if_else():
|
|
"""
|
|
Feature: Test multiple CSRTensors in if-else.
|
|
Description: Test CSRTensor computation in control flow.
|
|
Expectation: Success.
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, shape):
|
|
super().__init__()
|
|
self.shape = shape
|
|
|
|
def construct(self, indptr, indices, values, a, b):
|
|
x = CSRTensor(indptr, indices, values, self.shape)
|
|
if a > b:
|
|
x1 = x.abs()
|
|
x2 = x.astype(mstype.float16)
|
|
x3 = x.to_tuple()
|
|
else:
|
|
x1 = x.abs()
|
|
x2 = x.astype(mstype.float16)
|
|
x3 = x.to_tuple()
|
|
return x1, x2, x3
|
|
|
|
a = Tensor(1, mstype.float32)
|
|
b = Tensor(0, mstype.float32)
|
|
indptr, indices, values, shape = get_csr_components()
|
|
net = Net(shape)
|
|
|
|
forward_grad_net(net, indptr, indices, values, a, b, mode=context.PYNATIVE_MODE)
|
|
forward_grad_net(net, indptr, indices, values, a, b, mode=context.GRAPH_MODE)
|