forked from mindspore-Ecosystem/mindspore
328 lines
13 KiB
Python
328 lines
13 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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import sys
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import tempfile
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import time
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import shutil
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import glob
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import csv
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from pathlib import Path
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.nn import Cell
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from mindspore.nn import Dense
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from mindspore.nn import SoftmaxCrossEntropyWithLogits
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from mindspore.nn import WithLossCell
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from mindspore import dataset as ds
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from mindspore.train import Model
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from dump_test_utils import generate_dump_json, check_dump_structure
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from tests.security_utils import security_off_wrap
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def dataset_generator():
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for i in range(1, 10):
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yield np.ones((32, 2 * i), dtype=np.float32), np.ones((32, 2 * i), dtype=np.float32)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.add = P.Add()
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self.shape = P.TensorShape()
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self.reshape = P.Reshape()
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def construct(self, x_, y_):
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val = self.add(x_, y_)
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size = self.shape(val)
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res = self.reshape(val, size)
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return res
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def run_async_dump(test_name):
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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network = Net()
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dataset = ds.GeneratorDataset(dataset_generator, ['data1', 'data2'])
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t0 = Tensor(dtype=mindspore.float32, shape=[32, None])
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t1 = Tensor(dtype=mindspore.float32, shape=[32, None])
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network.set_inputs(t0, t1)
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model = Model(network)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'async_dump')
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dump_config_path = os.path.join(tmp_dir, 'async_dump.json')
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generate_dump_json(dump_path, dump_config_path, test_name)
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os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
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dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '1', '0')
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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model.train(10, dataset, dataset_sink_mode=True)
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for _ in range(3):
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if not os.path.exists(dump_file_path):
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time.sleep(2)
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check_dump_structure(dump_path, dump_config_path, 1, 2, 1, [1])
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assert os.listdir(dump_file_path)
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del os.environ['MINDSPORE_DUMP_CONFIG']
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_async_dump():
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"""
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Feature: async dump on Ascend
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Description: test async dump with default file_format value ("bin"), in fact, the tensor file is saved as npy.
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Expectation: dump data are generated as npy.
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"""
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run_async_dump("test_async_dump")
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def run_e2e_dump():
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if sys.platform != 'linux':
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return
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network = Net()
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dataset = ds.GeneratorDataset(dataset_generator, ['data1', 'data2'])
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t0 = Tensor(dtype=mindspore.float32, shape=[32, None])
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t1 = Tensor(dtype=mindspore.float32, shape=[32, None])
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network.set_inputs(t0, t1)
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model = Model(network)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'e2e_dump')
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dump_config_path = os.path.join(tmp_dir, 'e2e_dump.json')
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generate_dump_json(dump_path, dump_config_path, 'test_e2e_dump')
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os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
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dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '1', '0')
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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model.train(10, dataset, dataset_sink_mode=True)
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for _ in range(3):
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if not os.path.exists(dump_file_path):
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time.sleep(2)
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check_dump_structure(dump_path, dump_config_path, 1, 1, 1, [1])
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assert os.listdir(dump_file_path)
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output_name = "Add.Add-op*.0.*.*.output.0.DefaultFormat.npy"
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output_path = glob.glob(os.path.join(dump_file_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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output = np.load(real_path)
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expect = np.ones((32, 2), dtype=np.float32) * 2
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assert output.dtype == expect.dtype
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assert np.array_equal(output, expect)
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del os.environ['MINDSPORE_DUMP_CONFIG']
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_e2e_dump():
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"""
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Feature: e2e dump on Ascend.
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Description: test e2e dump.
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Expectation: dump data are generated as npy.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_e2e_dump()
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_e2e_dump_with_hccl_env():
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"""
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Feature: e2e dump on Ascend with hccl env.
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Description: test e2e dump.
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Expectation: dump data are generated as npy.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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os.environ["RANK_TABLE_FILE"] = "invalid_file.json"
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os.environ["RANK_ID"] = "4"
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run_e2e_dump()
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del os.environ['RANK_TABLE_FILE']
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del os.environ['RANK_ID']
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class ReluReduceMeanDenseRelu(Cell):
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def __init__(self, kernel, bias, in_channel, num_class):
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super().__init__()
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self.relu = P.ReLU()
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self.mean = P.ReduceMean(keep_dims=False)
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self.dense = Dense(in_channel, num_class, kernel, bias)
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def construct(self, x_):
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x_ = self.relu(x_)
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x_ = self.mean(x_, (2, 3))
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x_ = self.dense(x_)
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x_ = self.relu(x_)
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return x_
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_async_dump_net_multi_layer_mode1():
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"""
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Feature: e2e dump on Ascend.
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Description: test e2e dump with a multi_layer_model.
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Expectation: dump data are generated as npy.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'async_dump_net_multi_layer_mode1')
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json_file_path = os.path.join(tmp_dir, "test_async_dump_net_multi_layer_mode1.json")
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generate_dump_json(dump_path, json_file_path, 'test_async_dump_net_multi_layer_mode1_npy')
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os.environ['MINDSPORE_DUMP_CONFIG'] = json_file_path
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weight = Tensor(np.ones((1000, 2048)).astype(np.float32))
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bias = Tensor(np.ones((1000,)).astype(np.float32))
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net = ReluReduceMeanDenseRelu(weight, bias, 2048, 1000)
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criterion = SoftmaxCrossEntropyWithLogits(sparse=False)
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net_with_criterion = WithLossCell(net, criterion)
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input_dynamic = Tensor(shape=[None, 2048, 7, 7], dtype=mindspore.float32)
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label_dynamic = Tensor(shape=[None, 1000], dtype=mindspore.float32)
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inputs = Tensor(np.random.randn(32, 2048, 7, 7).astype(np.float32))
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label = Tensor(np.zeros(shape=(32, 1000)).astype(np.float32))
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net_with_criterion.set_inputs(input_dynamic, label_dynamic)
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net_dict = net_with_criterion(inputs, label)
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dump_file_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
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dump_file_name = list(Path(dump_file_path).rglob("*SoftmaxCrossEntropyWithLogits*.output.0.*.npy"))[0]
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dump_file_full_path = os.path.join(dump_file_path, dump_file_name)
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dump_result = np.load(dump_file_full_path)
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for index, value in enumerate(net_dict):
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assert value.asnumpy() == dump_result[index]
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del os.environ['MINDSPORE_DUMP_CONFIG']
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_dump_with_diagnostic_path():
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"""
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Feature: e2e dump on Ascend with path not set (set to empty).
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Description: test e2e dump when path is not set (set to empty) in dump json file and MS_DIAGNOSTIC_DATA_PATH is set.
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Expectation: Data is expected to be dumped into MS_DIAGNOSTIC_DATA_PATH/debug_dump.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_config_path = os.path.join(tmp_dir, 'e2e_dump.json')
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generate_dump_json('', dump_config_path, 'test_e2e_dump')
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os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
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diagnose_path = os.path.join(tmp_dir, 'e2e_dump')
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os.environ['MS_DIAGNOSTIC_DATA_PATH'] = diagnose_path
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dump_file_path = os.path.join(diagnose_path, 'debug_dump', 'rank_0', 'Net', '0', '0')
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if os.path.isdir(diagnose_path):
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shutil.rmtree(diagnose_path)
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add = Net()
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inputx_dynamic = Tensor(shape=[None, 100], dtype=mindspore.float32)
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inputy_dynamic = Tensor(shape=[None, 100], dtype=mindspore.float32)
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add.set_inputs(inputx_dynamic, inputy_dynamic)
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x = Tensor(np.random.randn(10, 100).astype(np.float32))
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y = Tensor(np.random.randn(10, 100).astype(np.float32))
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add(Tensor(x), Tensor(y))
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assert len(os.listdir(dump_file_path)) == 8
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del os.environ['MINDSPORE_DUMP_CONFIG']
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del os.environ['MS_DIAGNOSTIC_DATA_PATH']
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def run_e2e_dump_execution_graph():
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"""Run e2e dump and check execution order."""
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if sys.platform != 'linux':
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return
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'e2e_dump_exe_graph')
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dump_config_path = os.path.join(tmp_dir, 'e2e_dump.json')
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generate_dump_json(dump_path, dump_config_path, 'test_e2e_dump')
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os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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add = Net()
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inputx_dynamic = Tensor(shape=[None, 100], dtype=mindspore.float32)
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inputy_dynamic = Tensor(shape=[None, 100], dtype=mindspore.float32)
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add.set_inputs(inputx_dynamic, inputy_dynamic)
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x = Tensor(np.random.randn(10, 100).astype(np.float32))
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y = Tensor(np.random.randn(10, 100).astype(np.float32))
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add(Tensor(x), Tensor(y))
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exe_graph_path = os.path.join(dump_path, 'rank_0', 'execution_order')
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assert len(os.listdir(exe_graph_path)) == 2
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del os.environ['MINDSPORE_DUMP_CONFIG']
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_dump_with_execution_graph():
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"""
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Feature: e2e dump on Ascend saves execution graph.
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Description: test e2e dump saves execution graph.
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Expectation: ms_execution_order_graph_0.csv and ms_global_execution_order_graph_0.csv are expected to be dumped into
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folder execution_order.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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run_e2e_dump_execution_graph()
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def check_statistic_dump(dump_file_path):
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output_name = "statistic.csv"
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output_path = glob.glob(os.path.join(dump_file_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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with open(real_path) as f:
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reader = csv.DictReader(f)
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stats = list(reader)
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num_tensors = len(stats)
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assert num_tensors == 3
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for tensor in stats:
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if tensor['IO'] == 'input' and tensor['Slot'] == 0:
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assert tensor['Min Value'] == '1'
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assert tensor['Max Value'] == '6'
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elif tensor['IO'] == 'input' and tensor['Slot'] == 1:
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assert tensor['Min Value'] == '7'
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assert tensor['Max Value'] == '12'
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elif tensor['IO'] == 'output' and tensor['Slot'] == 0:
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assert tensor['Min Value'] == '8'
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assert tensor['Max Value'] == '18'
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def check_data_dump(dump_file_path):
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output_name = "Add.Add-op*.output.0.*.npy"
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output_path = glob.glob(os.path.join(dump_file_path, output_name))[0]
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real_path = os.path.realpath(output_path)
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output = np.load(real_path)
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expect = np.array([[8, 10, 12], [14, 16, 18]], np.float32)
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assert np.array_equal(output, expect)
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def run_train():
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add = Net()
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inputx_dynamic = Tensor(shape=[None, 3], dtype=mindspore.float32)
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inputy_dynamic = Tensor(shape=[None, 3], dtype=mindspore.float32)
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add.set_inputs(inputx_dynamic, inputy_dynamic)
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x = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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y = np.array([[7, 8, 9], [10, 11, 12]]).astype(np.float32)
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add(Tensor(x), Tensor(y))
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