forked from mindspore-Ecosystem/mindspore
240 lines
11 KiB
Python
240 lines
11 KiB
Python
# Copyright 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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"""
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Watchpoints test script for offline debugger APIs.
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"""
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import os
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import json
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import time
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import tempfile
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import numpy as np
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import pytest
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import mindspore.offline_debug.dbg_services as d
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from tests.security_utils import security_off_wrap
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from dump_test_utils import build_dump_structure, write_watchpoint_to_json
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GENERATE_GOLDEN = False
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watchpoint_hits_json = []
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def run_watchpoints(is_sync):
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if is_sync:
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test_name = "sync_watchpoints"
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else:
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test_name = "async_watchpoints"
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name1 = "Conv2D.Conv2D-op369.0.0.1"
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tensor1 = np.array([[[-1.2808e-03, 7.7629e-03, 1.9241e-02],
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[-1.3931e-02, 8.9359e-04, -1.1520e-02],
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[-6.3248e-03, 1.8749e-03, 1.0132e-02]],
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[[-2.5520e-03, -6.0005e-03, -5.1918e-03],
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[-2.7866e-03, 2.5487e-04, 8.4782e-04],
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[-4.6310e-03, -8.9111e-03, -8.1778e-05]],
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[[1.3914e-03, 6.0844e-04, 1.0643e-03],
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[-2.0966e-02, -1.2865e-03, -1.8692e-03],
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[-1.6647e-02, 1.0233e-03, -4.1313e-03]]], np.float32)
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info1 = d.TensorInfo(node_name="Default/network-WithLossCell/_backbone-AlexNet/conv1-Conv2d/Conv2D-op369",
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slot=1, iteration=2, rank_id=0, root_graph_id=0, is_output=False)
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name2 = "Parameter.fc2.bias.0.0.2"
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tensor2 = np.array([-5.0167350e-06, 1.2509107e-05, -4.3148934e-06, 8.1415592e-06,
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2.1177532e-07, 2.9952851e-06], np.float32)
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info2 = d.TensorInfo(node_name="Default/network-WithLossCell/_backbone-AlexNet/fc3-Dense/"
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"Parameter[6]_11/fc2.bias",
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slot=0, iteration=2, rank_id=0, root_graph_id=0, is_output=True)
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tensor3 = np.array([2.9060817e-07, -5.1009415e-06, -2.8662325e-06, 2.6036503e-06,
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-5.1546101e-07, 6.0798648e-06], np.float32)
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info3 = d.TensorInfo(node_name="Default/network-WithLossCell/_backbone-AlexNet/fc3-Dense/"
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"Parameter[6]_11/fc2.bias",
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slot=0, iteration=3, rank_id=0, root_graph_id=0, is_output=True)
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tensor_info = [info1, info2, info3]
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tensor_name = [name1, name2, name2]
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tensor_list = [tensor1, tensor2, tensor3]
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pwd = os.getcwd()
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with tempfile.TemporaryDirectory(dir=pwd) as tmp_dir:
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temp_dir = build_dump_structure(tmp_dir, tensor_name, tensor_list, "Test", tensor_info)
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debugger_backend = d.DbgServices(dump_file_path=temp_dir)
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debugger_backend.initialize(net_name="Test", is_sync_mode=is_sync)
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# NOTES:
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# -> watch_condition=6 is MIN_LT
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# -> watch_condition=18 is CHANGE_TOO_LARGE
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# test 1: watchpoint set and hit (watch_condition=6)
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param1 = d.Parameter(name="param", disabled=False, value=0.0)
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debugger_backend.add_watchpoint(watchpoint_id=1, watch_condition=6,
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check_node_list={"Default/network-WithLossCell/_backbone-AlexNet/"
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"conv1-Conv2d/Conv2D-op369":
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{"rank_id": [0], "root_graph_id": [0], "is_output": False
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}}, parameter_list=[param1])
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watchpoint_hits_test_1 = debugger_backend.check_watchpoints(iteration=2)
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assert len(watchpoint_hits_test_1) == 1
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if GENERATE_GOLDEN:
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print_watchpoint_hits(watchpoint_hits_test_1, 0, False, test_name)
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else:
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compare_expect_actual_result(watchpoint_hits_test_1, 0, test_name)
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# test 2: watchpoint remove and ensure it's not hit
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debugger_backend.remove_watchpoint(watchpoint_id=1)
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watchpoint_hits_test_2 = debugger_backend.check_watchpoints(iteration=2)
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assert not watchpoint_hits_test_2
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# test 3: watchpoint set and not hit, then remove
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param2 = d.Parameter(name="param", disabled=False, value=-1000.0)
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debugger_backend.add_watchpoint(watchpoint_id=2, watch_condition=6,
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check_node_list={"Default/network-WithLossCell/_backbone-AlexNet/"
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"conv1-Conv2d/Conv2D-op369":
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{"rank_id": [0], "root_graph_id": [0], "is_output": False
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}}, parameter_list=[param2])
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watchpoint_hits_test_3 = debugger_backend.check_watchpoints(iteration=2)
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assert not watchpoint_hits_test_3
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_ = debugger_backend.remove_watchpoint(watchpoint_id=2)
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# test 4: weight change watchpoint set and hit
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param_abs_mean_update_ratio_gt = d.Parameter(
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name="abs_mean_update_ratio_gt", disabled=False, value=0.0)
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param_epsilon = d.Parameter(name="epsilon", disabled=True, value=0.0)
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debugger_backend.add_watchpoint(watchpoint_id=3, watch_condition=18,
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check_node_list={"Default/network-WithLossCell/_backbone-AlexNet/fc3-Dense/"
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"Parameter[6]_11/fc2.bias":
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{"rank_id": [0], "root_graph_id": [0], "is_output": True
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}}, parameter_list=[param_abs_mean_update_ratio_gt,
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param_epsilon])
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watchpoint_hits_test_4 = debugger_backend.check_watchpoints(iteration=3)
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assert len(watchpoint_hits_test_4) == 1
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if GENERATE_GOLDEN:
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print_watchpoint_hits(watchpoint_hits_test_4, 1, True, test_name)
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else:
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compare_expect_actual_result(watchpoint_hits_test_4, 1, test_name)
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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_sync_watchpoints():
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run_watchpoints(True)
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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_watchpoints():
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run_watchpoints(False)
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def run_overflow_watchpoint(is_overflow):
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test_name = "overflow_watchpoint"
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tensor = np.array([65504, 65504], np.float16)
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task_id = 2
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stream_id = 7
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pwd = os.getcwd()
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with tempfile.TemporaryDirectory(dir=pwd) as tmp_dir:
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path = os.path.join(tmp_dir, "rank_0", "Add", "0", "0")
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os.makedirs(path, exist_ok=True)
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add_file = os.path.join(path, "Add.Default_Add-op0." + str(task_id) + "." + str(stream_id) + "."
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+ str(int(round(time.time() * 1000000))))
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with open(add_file, 'wb') as add_f:
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add_f.write(b'1')
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add_f.seek(8)
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add_f.write(b'\n\x032.0\x10\x83\xf7\xef\x9f\x99\xc8\xf3\x02\x1a\x10\x08\x02\x10\x02\x1a\x03')
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add_f.write(b'\n\x01\x020\x04:\x03\n\x01\x022\x0f')
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add_f.write(b'Default/Add-op0')
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add_f.write(tensor)
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overflow_file = os.path.join(path, "Opdebug.Node_OpDebug." + str(task_id) + "." + str(stream_id) +
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"." + str(int(round(time.time() * 1000000))))
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with open(overflow_file, 'wb') as f:
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f.seek(321, 0)
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byte_list = []
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for i in range(256):
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if i == 16:
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byte_list.append(stream_id)
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elif i == 24:
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if is_overflow:
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byte_list.append(task_id)
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else:
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# wrong task_id, should not generate overflow watchpoint hit
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byte_list.append(task_id + 1)
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else:
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byte_list.append(0)
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new_byte_array = bytearray(byte_list)
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f.write(bytes(new_byte_array))
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debugger_backend = d.DbgServices(dump_file_path=tmp_dir)
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debugger_backend.initialize(net_name="Add", is_sync_mode=False)
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debugger_backend.add_watchpoint(watchpoint_id=1, watch_condition=2,
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check_node_list={"Default/Add-op0":
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{"rank_id": [0], "root_graph_id": [0], "is_output": True
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}}, parameter_list=[])
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watchpoint_hits_test = debugger_backend.check_watchpoints(iteration=0)
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if is_overflow:
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assert len(watchpoint_hits_test) == 1
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if GENERATE_GOLDEN:
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print_watchpoint_hits(watchpoint_hits_test, 0, True, test_name)
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else:
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compare_expect_actual_result(watchpoint_hits_test, 0, test_name)
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else:
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assert not watchpoint_hits_test
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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_overflow_watchpoints_hit():
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"""
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Feature: Offline Debugger CheckWatchpoint
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Description: Test check overflow watchpoint hit
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Expectation: Overflow watchpoint is hit
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"""
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run_overflow_watchpoint(True)
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def compare_expect_actual_result(watchpoint_hits_list, test_index, test_name):
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"""Compare actual result with golden file."""
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pwd = os.getcwd()
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golden_file = os.path.realpath(os.path.join(pwd, "golden", test_name + "_expected.json"))
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with open(golden_file) as f:
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expected_list = json.load(f)
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for x, watchpoint_hits in enumerate(watchpoint_hits_list):
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test_id = "watchpoint_hit" + str(test_index + x + 1)
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expect_wp = expected_list[x + test_index][test_id]
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actual_wp = write_watchpoint_to_json(watchpoint_hits)
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assert actual_wp == expect_wp
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def print_watchpoint_hits(watchpoint_hits_list, test_index, is_print, test_name):
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"""Print watchpoint hits."""
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for x, watchpoint_hits in enumerate(watchpoint_hits_list):
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watchpoint_hit = "watchpoint_hit" + str(test_index + x + 1)
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wp = write_watchpoint_to_json(watchpoint_hits)
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watchpoint_hits_json.append({watchpoint_hit: wp})
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if is_print:
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with open(test_name + "_expected.json", "w") as dump_f:
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json.dump(watchpoint_hits_json, dump_f, indent=4, separators=(',', ': '))
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