From c9b438e97b791f63c1eb3f98a5069398e01d6a0d Mon Sep 17 00:00:00 2001 From: "zhengbin (G)" Date: Thu, 24 Dec 2020 10:44:22 +0800 Subject: [PATCH] add dump testcase --- ...test_async_dump_net_multi_layer_mode1.json | 25 +++++++ tests/st/dump/test_data_dump.py | 65 +++++++++++++++++++ 2 files changed, 90 insertions(+) create mode 100644 tests/st/dump/test_async_dump_net_multi_layer_mode1.json diff --git a/tests/st/dump/test_async_dump_net_multi_layer_mode1.json b/tests/st/dump/test_async_dump_net_multi_layer_mode1.json new file mode 100644 index 0000000000..41b392adec --- /dev/null +++ b/tests/st/dump/test_async_dump_net_multi_layer_mode1.json @@ -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 + } +} \ No newline at end of file diff --git a/tests/st/dump/test_data_dump.py b/tests/st/dump/test_data_dump.py index 97b85dad72..7f7a1b54b5 100644 --- a/tests/st/dump/test_data_dump.py +++ b/tests/st/dump/test_data_dump.py @@ -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]