!10423 add testcases of dump

From: @rainyhorse
Reviewed-by: @chujinjin,@kisnwang
Signed-off-by: @kisnwang
This commit is contained in:
mindspore-ci-bot 2020-12-26 16:46:39 +08:00 committed by Gitee
commit dafa401247
2 changed files with 90 additions and 0 deletions

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@ -0,0 +1,25 @@
{
"common_dump_settings":{
"dump_mode": 0,
"path": "/tmp/async_dump/test_async_dump_net_multi_layer_mode1",
"net_name": "test",
"iteration": 0,
"input_output": 2,
"kernels": [
"default/TensorAdd-op10",
"Gradients/Default/network-WithLossCell/_backbone-ReLUReduceMeanDenseRelu/dense-Dense/gradBiasAdd/BiasAddGrad-op8",
"Default/network-WithLossCell/_loss_fn-SoftmaxCrossEntropyWithLogits/SoftmaxCrossEntropyWithLogits-op5",
"Default/optimizer-Momentum/tuple_getitem-op29",
"Default/optimizer-Momentum/ApplyMomentum-op12"
],
"support_device": [0,1,2,3,4,5,6,7]
},
"async_dump_settings": {
"enable": true,
"op_debug_mode": 0
},
"e2e_dump_settings": {
"enable": false,
"trans_flag": false
}
}

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@ -22,6 +22,12 @@ import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.nn import Cell
from mindspore.nn import Dense
from mindspore.nn import SoftmaxCrossEntropyWithLogits
from mindspore.nn import Momentum
from mindspore.nn import TrainOneStepCell
from mindspore.nn import WithLossCell
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@ -81,3 +87,62 @@ def test_e2e_dump():
add(Tensor(x), Tensor(y))
time.sleep(5)
assert len(os.listdir(dump_file_path)) == 5
class ReluReduceMeanDenseRelu(Cell):
def __init__(self, kernel, bias, in_channel, num_class):
super().__init__()
self.relu = P.ReLU()
self.mean = P.ReduceMean(keep_dims=False)
self.dense = Dense(in_channel, num_class, kernel, bias)
def construct(self, x_):
x_ = self.relu(x_)
x_ = self.mean(x_, (2, 3))
x_ = self.dense(x_)
x_ = self.relu(x_)
return x_
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_async_dump_net_multi_layer_mode1():
test_name = "test_async_dump_net_multi_layer_mode1"
json_file = os.path.join(os.getcwd(), "{}.json".format(test_name))
device_id = context.get_context("device_id")
dump_full_path = os.path.join("/tmp/async_dump/", "{}_{}".format(test_name, device_id))
os.system("rm -rf {}/*".format(dump_full_path))
os.environ["MINDSPORE_DUMP_CONFIG"] = json_file
weight = Tensor(np.ones((1000, 2048)).astype(np.float32))
bias = Tensor(np.ones((1000,)).astype(np.float32))
net = ReluReduceMeanDenseRelu(weight, bias, 2048, 1000)
criterion = SoftmaxCrossEntropyWithLogits(sparse=False)
optimizer = Momentum(learning_rate=0.1, momentum=0.1, params=filter(lambda x: x.requires_grad, net.get_parameters()))
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(net_with_criterion, optimizer)
train_network.set_train()
inputs = Tensor(np.random.randn(32, 2048, 7, 7).astype(np.float32))
label = Tensor(np.zeros(shape=(32, 1000)).astype(np.float32))
net_dict = train_network(inputs, label)
dump_path = "/tmp/async_dump/{}/device_{}/test_graph_0/0/0/".format(test_name, device_id)
dump_file = os.listdir(dump_path)
dump_file_name = ""
for file in dump_file:
if "SoftmaxCrossEntropyWithLogits" in file:
dump_file_name = file
dump_file_full_path = os.path.join(dump_path, dump_file_name)
npy_path = os.path.join(os.getcwd(), "./{}".format(test_name))
if os.path.exists(npy_path):
shutil.rmtree(npy_path)
os.mkdir(npy_path)
cmd = "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/dump_data_conversion.pyc " \
"-type offline -target numpy -i {0} -o {1}".format(dump_file_full_path, npy_path)
os.system(cmd)
npy_file_list = os.listdir(npy_path)
dump_result = {}
for file in npy_file_list:
if "output.0.npy" in file:
dump_result["output0"] = np.load(os.path.join(npy_path, file))
for index, value in enumerate(net_dict):
assert value.asnumpy() == dump_result["output0"][index]