forked from mindspore-Ecosystem/mindspore
192 lines
7.2 KiB
Python
192 lines
7.2 KiB
Python
# Copyright 2021-2022 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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from os import path
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import tempfile
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import time
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import shutil
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import csv
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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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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 dump_test_utils import generate_dump_json
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from tests.security_utils import security_off_wrap
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class AddNet(Cell):
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def __init__(self):
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super(AddNet, self).__init__()
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self.add = P.TensorAdd()
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def construct(self, input_x, input_y):
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output_z = self.add(input_x, input_y)
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return output_z
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class NewAddNet(Cell):
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def __init__(self):
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super(NewAddNet, self).__init__()
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self.add = P.AddN()
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def construct(self, b1, b2, x, y):
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z = self.add([x, y, y])
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if b1 < b2:
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z = self.add([x, y, y])
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else:
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z = self.add([x, x, y])
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return z
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def train_addnet(epoch):
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net = AddNet()
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net2 = NewAddNet()
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output_list = []
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b1 = Tensor(np.array(1).astype(np.float32))
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b2 = Tensor(np.array(3).astype(np.float32))
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input_x = Tensor(np.ones([2, 1, 2, 1]).astype(np.float32))
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input_y = Tensor(np.ones([2, 1, 2, 1]).astype(np.float32))
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for _ in range(epoch):
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out_put = net(input_x, input_y)
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out2 = net2(b1, b2, out_put, input_x)
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output_list.append(out2.asnumpy())
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input_x = input_x + input_y
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b1 = b1+1
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return output_list
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def check_graph_structure(dump_file_path, execution_order_path, graph_id, expect_steps):
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dump_data_path = os.path.join(dump_file_path, graph_id)
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assert sorted(os.listdir(dump_data_path)) == expect_steps
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graph_history_file_path = os.path.join(
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execution_order_path, 'ms_global_execution_order_graph_{}.csv'.format(graph_id))
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assert path.exists(graph_history_file_path)
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with open(graph_history_file_path) as csvfile:
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history_graph = csv.reader(csvfile)
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iter_list_graph = [row[0] for row in history_graph]
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assert iter_list_graph == expect_steps
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def run_multi_root_graph_dump(device, dump_mode, test_name):
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"""Run dump for multi root graph script."""
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context.set_context(mode=context.GRAPH_MODE, device_target=device)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, dump_mode)
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dump_config_path = os.path.join(tmp_dir, dump_mode + ".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')
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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epoch = 3
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train_addnet(epoch)
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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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execution_order_path = os.path.join(dump_path, 'rank_0', 'execution_order')
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# Multi root graph script: check dump data dir and graph history files and see if iteration number is matched.
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if device == "GPU" or os.environ.get('GRAPH_OP_RUN') == "1":
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# In GPU or KernelByKernel, we have 4 kernel graphs folders under rank_0 dir.
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# In graph history dir, there are 2 files for each graph (ms_execution_order and ms_global_execution_order).
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assert len(os.listdir(dump_file_path)) == 4
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assert len(os.listdir(execution_order_path)) == 8
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check_graph_structure(dump_file_path, execution_order_path, '0', ['0', '2', '4'])
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check_graph_structure(dump_file_path, execution_order_path, '1', ['1', '3', '5'])
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check_graph_structure(dump_file_path, execution_order_path, '2', ['1', '3'])
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check_graph_structure(dump_file_path, execution_order_path, '3', ['5'])
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else:
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# In Ascend Super Kernel, we have 2 root graphs folders under rank_0 dir.
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# In graph history dir, there are 4 ms_execution_order files and 2 ms_global_execution_order files.
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# Each graph should have 3 iterations. Each graph was executed once per epoch.
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# Graph 0 was executed in even iterations, graph 1 was executed in odd iterations.
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assert len(os.listdir(dump_file_path)) == 2
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assert len(os.listdir(execution_order_path)) == 6
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check_graph_structure(dump_file_path, execution_order_path, '0', ['0', '2', '4'])
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check_graph_structure(dump_file_path, execution_order_path, '1', ['1', '3', '5'])
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_GPU_e2e_multi_root_graph_dump():
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"""
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Feature:
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Multi root graph e2e dump for GPU.
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Description:
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Test multi root graph e2e dump GPU.
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Expectation:
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Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
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"""
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run_multi_root_graph_dump("GPU", "e2e_dump", "test_GPU_e2e_multi_root_graph_dump")
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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_Ascend_e2e_multi_root_graph_dump():
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"""
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Feature:
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Multi root graph e2e dump for Ascend.
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Description:
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Test multi root graph e2e dump Ascend.
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Expectation:
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Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
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"""
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run_multi_root_graph_dump("Ascend", "e2e_dump", "test_Ascend_e2e_multi_root_graph_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_Ascend_async_multi_root_graph_dump():
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"""
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Feature:
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Multi root graph async dump for Ascend.
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Description:
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Test multi root graph async dump Ascend.
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Expectation:
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Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
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"""
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run_multi_root_graph_dump("Ascend", "async_dump", "test_Ascend_async_multi_root_graph_dump")
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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_ascend_multi_root_graph_dump_kernel_by_kernel():
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"""
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Feature:
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Multi root graph dump for Ascend kernel by kernel.
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Description:
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Test multi root graph dump in Ascend kernel by kernel.
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Expectation:
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Dump for two different graphs, graph 0 even iterations and graph 1 odd iterations.
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"""
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os.environ['GRAPH_OP_RUN'] = "1"
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run_multi_root_graph_dump("Ascend", "e2e_dump", "test_Ascend_e2e_multi_root_graph_dump")
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del os.environ['GRAPH_OP_RUN']
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