forked from huawei/mindspore2022
732 lines
28 KiB
Python
732 lines
28 KiB
Python
# Copyright 2020-2022 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 importlib import import_module
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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.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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from mindspore.ops import operations as P, constexpr
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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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from dump_test_utils import generate_dump_json, generate_dump_json_with_overflow, \
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generate_statistic_dump_json, check_dump_structure, find_nth_pos
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from tests.security_utils import security_off_wrap
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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 run_async_dump(test_name):
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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')
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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', '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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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)
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assert len(os.listdir(dump_file_path)) == 1
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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")
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Expectation: dump data are generated as protobuf file format (suffix with timestamp)
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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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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', '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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if context.get_context("device_target") == "Ascend":
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assert len(os.listdir(dump_file_path)) == 3
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output_name = "Add.Add-op*.0.0.*.output.0.DefaultFormat.npy"
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elif context.get_context("device_target") == "CPU":
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assert len(os.listdir(dump_file_path)) == 5
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output_name = "Add.Add-op*.0.0.*.output.0.DefaultFormat.npy"
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else:
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assert len(os.listdir(dump_file_path)) == 3
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output_name = "Add.Add-op*.0.0.*.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.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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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)
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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_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_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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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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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@security_off_wrap
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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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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@security_off_wrap
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def test_cpu_e2e_dump_with_hccl_set():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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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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@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_dump():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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run_e2e_dump()
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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_dump_with_hccl_set():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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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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')
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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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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_file_path = os.path.join(dump_path, 'rank_0', 'test', '0', '0')
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dump_file_name = list(Path(dump_file_path).rglob("*SoftmaxCrossEntropyWithLogits*"))[0]
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dump_file_full_path = os.path.join(dump_file_path, dump_file_name)
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npy_path = os.path.join(dump_path, "npy_files")
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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_list = list(Path('/usr/local/Ascend').rglob('msaccucmp.py*'))
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if tool_path_search_list:
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converter = import_module("mindspore.offline_debug.convert_async")
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converter.AsyncDumpConverter([dump_file_full_path], npy_path).convert_files()
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npy_result_file = list(Path(npy_path).rglob("*output.0.*.npy"))[0]
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dump_result = np.load(os.path.join(npy_path, npy_result_file))
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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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else:
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print('Failed to find hisi convert tools: msaccucmp.py or msaccucmp.pyc.')
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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_diagnostic_path():
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"""
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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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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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add(Tensor(x), Tensor(y))
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assert len(os.listdir(dump_file_path)) == 3
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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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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_x86_gpu_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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"""Test dump with execution graph on GPU."""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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run_e2e_dump_execution_graph()
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def run_overflow_dump():
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"""Run async dump and generate overflow"""
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if sys.platform != 'linux':
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return
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overflow_x = np.array([60000, 60000]).astype(np.float16)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'overflow_dump')
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dump_config_path = os.path.join(tmp_dir, 'overflow_dump.json')
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generate_dump_json_with_overflow(dump_path, dump_config_path, 'test_async_dump', 3)
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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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add(Tensor(overflow_x), Tensor(overflow_x))
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exe_graph_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
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for _ in range(5):
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if not os.path.exists(exe_graph_path):
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time.sleep(2)
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check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
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# check if overflow dump generate exact two files, and the naming format
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assert len(os.listdir(exe_graph_path)) == 2
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output_path = glob.glob(os.path.join(exe_graph_path, "Add.Default_Add-op0.*.*.*"))[0]
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overflow_path = glob.glob(os.path.join(exe_graph_path, "Opdebug.Node_OpDebug.*.*.*"))[0]
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assert output_path
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assert overflow_path
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# check if generated files have matching task and stream id
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output_file_name = os.path.split(output_path)
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overflow_file_name = os.path.split(overflow_path)
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output_second_dot_pos = find_nth_pos(output_file_name[1], ".", 2)
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output_third_dot_pos = find_nth_pos(output_file_name[1], ".", 3)
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output_fourth_dot_pos = find_nth_pos(output_file_name[1], ".", 4)
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output_task_id = output_file_name[1][output_second_dot_pos+1:output_third_dot_pos]
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output_stream_id = output_file_name[1][output_third_dot_pos+1:output_fourth_dot_pos]
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overflow_second_dot_pos = find_nth_pos(overflow_file_name[1], ".", 2)
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overflow_third_dot_pos = find_nth_pos(overflow_file_name[1], ".", 3)
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overflow_fourth_dot_pos = find_nth_pos(overflow_file_name[1], ".", 4)
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overflow_task_id = overflow_file_name[1][overflow_second_dot_pos+1:overflow_third_dot_pos]
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overflow_stream_id = overflow_file_name[1][overflow_third_dot_pos+1:overflow_fourth_dot_pos]
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assert output_task_id == overflow_task_id
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assert output_stream_id == overflow_stream_id
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# check if overflow dump file contains same task and stream id as file name
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with open(overflow_path, 'rb') as f:
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f.seek(321, 0)
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raw_data = f.read()
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task_id_infile = int.from_bytes(raw_data[24:25], 'little')
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stream_id_infile = int.from_bytes(raw_data[16:17], 'little')
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assert output_task_id == str(task_id_infile)
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assert output_stream_id == str(stream_id_infile)
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del os.environ['MINDSPORE_DUMP_CONFIG']
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def run_not_overflow_dump():
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"""Run async dump and not generate overflow"""
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if sys.platform != 'linux':
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return
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overflow_x = np.array([60000, 60000]).astype(np.float16)
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overflow_y = np.array([2, 2]).astype(np.float16)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'overflow_dump')
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dump_config_path = os.path.join(tmp_dir, 'overflow_dump.json')
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generate_dump_json_with_overflow(dump_path, dump_config_path, 'test_async_dump', 3)
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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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add(Tensor(overflow_x), Tensor(overflow_y))
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exe_graph_path = os.path.join(dump_path, 'rank_0', 'Net', '0', '0')
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# check no overflow is happening, and path should not be generated
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assert not os.path.exists(exe_graph_path)
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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_ascend_overflow_dump():
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"""
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Feature: Overflow Dump
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Description: Test overflow dump
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Expectation: Overflow is occurred, and overflow dump file is in correct format
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
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run_overflow_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_not_overflow_dump():
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"""
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Feature: Overflow Dump
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Description: Test overflow dump
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Expectation: Overflow is not occurred, and overflow dump file is not generated
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
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run_not_overflow_dump()
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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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add(Tensor(x), Tensor(y))
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def run_saved_data_dump_test(scenario, saved_data):
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"""Run e2e dump on scenario, testing statistic dump"""
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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, 'test_saved_data')
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dump_config_path = os.path.join(tmp_dir, 'test_saved_data.json')
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generate_statistic_dump_json(dump_path, dump_config_path, scenario, saved_data)
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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', '0', '0')
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if os.path.isdir(dump_path):
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shutil.rmtree(dump_path)
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exec_network_cmd = 'cd {0}; python -c "from test_data_dump import run_train; run_train()"'.format(os.getcwd())
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_ = os.system(exec_network_cmd)
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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)
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if saved_data in ('statistic', 'full'):
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check_statistic_dump(dump_file_path)
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if saved_data in ('tensor', 'full'):
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check_data_dump(dump_file_path)
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if saved_data == 'statistic':
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# assert only file is statistic.csv, tensor data is not saved
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assert len(os.listdir(dump_file_path)) == 1
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elif saved_data == 'tensor':
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# assert only tensor data is saved, not statistics
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stat_path = os.path.join(dump_file_path, 'statistic.csv')
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assert not os.path.isfile(stat_path)
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del os.environ['MINDSPORE_DUMP_CONFIG']
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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_statistic_dump():
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"""
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Feature: GPU Statistics Dump
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Description: Test GPU statistics dump
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Expectation: Statistics are stored in statistic.csv files
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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run_saved_data_dump_test('test_gpu_e2e_dump', 'statistic')
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|
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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_tensor_dump():
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"""
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Feature: GPU Tensor Dump
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Description: Test GPU tensor dump
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Expectation: Tensor data are stored in npy files
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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run_saved_data_dump_test('test_gpu_e2e_dump', 'tensor')
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|
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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_full_dump():
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"""
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Feature: GPU Full Dump
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Description: Test GPU full dump
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Expectation: Tensor are stored in npy files and their statistics stored in statistic.csv
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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run_saved_data_dump_test('test_gpu_e2e_dump', 'full')
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|
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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_stat_dump_nulls():
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"""
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Feature: GPU Statistics Dump
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Description: Test GPU statistics dump when printing tensors full with NaNs and Infs
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Expectation: Min, Max, Avg Values stored in statistic.csv show null for such tensors
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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if sys.platform != 'linux':
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return
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empty_x = np.array([]).astype(np.float16)
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with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
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dump_path = os.path.join(tmp_dir, 'test_saved_data')
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dump_config_path = os.path.join(tmp_dir, 'test_saved_data.json')
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generate_statistic_dump_json(dump_path, dump_config_path, 'test_gpu_e2e_dump', 'statistic')
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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', '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(empty_x), Tensor(empty_x))
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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 dumped data
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output_path = glob.glob(os.path.join(dump_file_path, 'statistic.csv'))[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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[output] = list(reader)
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assert output['IO'] == 'output'
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assert output['Min Value'] == 'null'
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assert output['Max Value'] == 'null'
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assert output['Avg Value'] == 'null'
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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
|
|
@security_off_wrap
|
|
def test_ascend_statistic_dump():
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"""
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|
Feature: Ascend Statistics Dump
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Description: Test Ascend statistics dump
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Expectation: Statistics are stored in statistic.csv files
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_saved_data_dump_test('test_async_dump', 'statistic')
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|
|
|
|
|
@pytest.mark.level0
|
|
@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_statistic_dump_kernel_by_kernel():
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"""
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|
Feature: Ascend Statistics Dump in kernel by kernel (mindRT) mode
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Description: Test Ascend statistics dump
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Expectation: Statistics are stored in statistic.csv files
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"""
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# set env `GRAPH_OP_RUN`` to enable kernel-by-kernel mode.
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os.environ['GRAPH_OP_RUN'] = "1"
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_saved_data_dump_test('test_async_dump', 'statistic')
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del os.environ['GRAPH_OP_RUN']
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|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_tensor_dump():
|
|
"""
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|
Feature: Ascend Tensor Dump
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|
Description: Test Ascend tensor dump
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|
Expectation: Tensors are stored in npy files
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"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_saved_data_dump_test('test_async_dump', 'tensor')
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|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_full_dump():
|
|
"""
|
|
Feature: Ascend Full Dump
|
|
Description: Test Ascend full dump
|
|
Expectation: Tensors are stored in npy files and their statistics stored in statistic.csv
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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|
run_saved_data_dump_test('test_async_dump', 'full')
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|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_ascend_full_dump_kernel_by_kernel():
|
|
"""
|
|
Feature: Ascend Full Dump in kernel-by-kernel (MindRT) mode
|
|
Description: Test Ascend full dump
|
|
Expectation: Tensors are stored in npy files and their statistics stored in statistic.csv
|
|
"""
|
|
os.environ['GRAPH_OP_RUN'] = "1"
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|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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run_saved_data_dump_test('test_async_dump', 'full')
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|
del os.environ['GRAPH_OP_RUN']
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|
|
|
|
|
@constexpr
|
|
def construct_tensor(cst):
|
|
return Tensor(np.array(cst))
|
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|
|
|
|
class ConstantNet(nn.Cell):
|
|
def __init__(self):
|
|
super(ConstantNet, self).__init__()
|
|
self.relu = ops.ReLU()
|
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|
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def construct(self, x_):
|
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return self.relu(construct_tensor(ops.shape(x_)))
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|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_constant_async_ascend_dump():
|
|
"""
|
|
Feature: Constant async dump
|
|
Description: Test async constant dump in Ascend
|
|
Expectation: constant dump folder is created, dump file has expected tensor info
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'constant_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'constant_dump.json')
|
|
generate_dump_json(dump_path, dump_config_path, 'test_async_dump')
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
net = ConstantNet()
|
|
tensor = Tensor(np.random.random([1, 2, 3]))
|
|
expect = net(tensor)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
constant_path = os.path.join(dump_path, 'rank_0', 'Net', '0', 'constants')
|
|
assert os.path.exists(constant_path)
|
|
assert len(os.listdir(constant_path)) == 1
|
|
|
|
output_name = "Parameter.data-*.0.0.*.DefaultFormat.npy"
|
|
output_path = glob.glob(os.path.join(constant_path, output_name))[0]
|
|
real_path = os.path.realpath(output_path)
|
|
output = np.load(real_path)
|
|
assert np.array_equal(output, expect)
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
def run_constant_e2e_dump():
|
|
if sys.platform != 'linux':
|
|
return
|
|
with tempfile.TemporaryDirectory(dir='/tmp') as tmp_dir:
|
|
dump_path = os.path.join(tmp_dir, 'constant_dump')
|
|
dump_config_path = os.path.join(tmp_dir, 'constant_dump.json')
|
|
generate_dump_json(dump_path, dump_config_path, 'test_e2e_dump')
|
|
os.environ['MINDSPORE_DUMP_CONFIG'] = dump_config_path
|
|
if os.path.isdir(dump_path):
|
|
shutil.rmtree(dump_path)
|
|
net = ConstantNet()
|
|
tensor = Tensor(np.random.random([1, 2, 3]))
|
|
expect = net(tensor)
|
|
check_dump_structure(dump_path, dump_config_path, 1, 1, 1)
|
|
constant_path = os.path.join(dump_path, 'rank_0', 'Net', '0', 'constants')
|
|
assert os.path.exists(constant_path)
|
|
assert len(os.listdir(constant_path)) == 1
|
|
|
|
output_name = "Parameter.data-*.0.0.*.DefaultFormat.npy"
|
|
output_path = glob.glob(os.path.join(constant_path, output_name))[0]
|
|
real_path = os.path.realpath(output_path)
|
|
output = np.load(real_path)
|
|
assert np.array_equal(output, expect)
|
|
del os.environ['MINDSPORE_DUMP_CONFIG']
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_constant_gpu_e2e_dump():
|
|
"""
|
|
Feature: Constant sync dump
|
|
Description: Test constant sync dump in GPU
|
|
Expectation: constant dump folder is created, dump file has expected tensor info
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
run_constant_e2e_dump()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
@security_off_wrap
|
|
def test_constant_ascend_e2e_dump():
|
|
"""
|
|
Feature: Constant sync dump
|
|
Description: Test constant sync dump in Ascend
|
|
Expectation: constant dump folder is created, dump file has expected tensor info
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
run_constant_e2e_dump()
|