forked from mindspore-Ecosystem/mindspore
198 lines
6.8 KiB
Python
198 lines
6.8 KiB
Python
# Copyright 2020-2021 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 json
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import sys
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import time
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import shutil
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import glob
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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.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 Momentum
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from mindspore.nn import TrainOneStepCell
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from mindspore.nn import WithLossCell
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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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def construct(self, x_, y_):
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return self.add(x_, y_)
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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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def change_current_dump_json(file_name, dump_path):
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with open(file_name, 'r+') as f:
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data = json.load(f)
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data["common_dump_settings"]["path"] = dump_path
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with open(file_name, 'w') as f:
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json.dump(data, f)
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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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def test_async_dump():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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pwd = os.getcwd()
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dump_path = pwd + "/async_dump"
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change_current_dump_json('async_dump.json', dump_path)
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os.environ['MINDSPORE_DUMP_CONFIG'] = pwd + "/async_dump.json"
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dump_file_path = dump_path + '/rank_0/Net/0/0/'
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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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add(Tensor(x), Tensor(y))
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time.sleep(5)
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assert len(os.listdir(dump_file_path)) == 1
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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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pwd = os.getcwd()
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dump_path = pwd + '/e2e_dump'
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change_current_dump_json('e2e_dump.json', dump_path)
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os.environ['MINDSPORE_DUMP_CONFIG'] = pwd + '/e2e_dump.json'
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dump_file_path = dump_path + '/rank_0/Net/0/0/'
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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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add(Tensor(x), Tensor(y))
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time.sleep(5)
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assert len(os.listdir(dump_file_path)) == 5
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if context.get_context("device_target") == "Ascend":
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output_name = "Add.Add-op1.0.0.*.output.0.DefaultFormat.npy"
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else:
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output_name = "Add.Add-op3.0.0.*.output.0.DefaultFormat.npy"
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output_path = glob.glob(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 output.dtype == expect.dtype
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assert np.array_equal(output, expect)
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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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def test_e2e_dump():
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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.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_cpu_e2e_dump():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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run_e2e_dump()
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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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def search_path(path, keyword):
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content = os.listdir(path)
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for each in content:
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each_path = path + os.sep + each
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if keyword in each:
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return each_path
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read_write = os.access(each_path, os.W_OK) and os.access(each_path, os.R_OK)
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if not read_write:
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continue
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if os.path.isdir(each_path):
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search_path(each_path, keyword)
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return None
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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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def test_async_dump_net_multi_layer_mode1():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_name = "test_async_dump_net_multi_layer_mode1"
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json_file = os.path.join(os.getcwd(), "{}.json".format(test_name))
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rank_id = 0
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dump_full_path = os.path.join("/tmp/async_dump/", "{}_{}".format(test_name, rank_id))
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os.system("rm -rf {}/*".format(dump_full_path))
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os.environ["MINDSPORE_DUMP_CONFIG"] = json_file
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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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optimizer = Momentum(learning_rate=0.1, momentum=0.1,
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params=filter(lambda x: x.requires_grad, net.get_parameters()))
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer)
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train_network.set_train()
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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_dict = train_network(inputs, label)
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dump_path = "/tmp/async_dump/{}/rank_{}/test/0/0/".format(test_name, rank_id)
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dump_file = os.listdir(dump_path)
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dump_file_name = ""
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for file in dump_file:
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if "SoftmaxCrossEntropyWithLogits" in file:
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dump_file_name = file
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dump_file_full_path = os.path.join(dump_path, dump_file_name)
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npy_path = os.path.join(os.getcwd(), "./{}".format(test_name))
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if os.path.exists(npy_path):
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shutil.rmtree(npy_path)
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os.mkdir(npy_path)
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tool_path = search_path('/usr/local/Ascend', 'msaccucmp.pyc')
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if tool_path:
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cmd = "python {0} convert -d {1} -out {2}".format(tool_path, dump_file_full_path, npy_path)
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os.system(cmd)
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npy_file_list = os.listdir(npy_path)
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dump_result = {}
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for file in npy_file_list:
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if "output.0.npy" in file:
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dump_result["output0"] = np.load(os.path.join(npy_path, file))
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for index, value in enumerate(net_dict):
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assert value.asnumpy() == dump_result["output0"][index]
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else:
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print('not find convert tools msaccucmp.pyc')
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