modelscope--ms-swift/tests/test_utils.py

#!/usr/bin/env python
# Copyright (c) ModelScope Contributors. All rights reserved.

import copy
import numpy as np
import os
import pickle
import requests
import shutil
import socket
import subprocess
import sys
import tarfile
import tempfile
import unittest
from collections import OrderedDict
from collections.abc import Mapping
from modelscope.hub.constants import DEFAULT_CREDENTIALS_PATH
from os.path import expanduser

TEST_LEVEL = 2
TEST_LEVEL_STR = 'TEST_LEVEL'

# for user citest and sdkdev
TEST_ACCESS_TOKEN1 = os.environ.get('TEST_ACCESS_TOKEN_CITEST', None)
TEST_ACCESS_TOKEN2 = os.environ.get('TEST_ACCESS_TOKEN_SDKDEV', None)

TEST_MODEL_CHINESE_NAME = '内部测试模型'
TEST_MODEL_ORG = 'citest'


def delete_credential():
    path_credential = expanduser(DEFAULT_CREDENTIALS_PATH)
    shutil.rmtree(path_credential, ignore_errors=True)


def test_level():
    global TEST_LEVEL
    if TEST_LEVEL_STR in os.environ:
        TEST_LEVEL = int(os.environ[TEST_LEVEL_STR])

    return TEST_LEVEL


def require_tf(test_case):
    test_case = unittest.skip('test requires TensorFlow')(test_case)
    return test_case


def require_torch(test_case):
    return test_case


def set_test_level(level: int):
    global TEST_LEVEL
    TEST_LEVEL = level


class DummyTorchDataset:

    def __init__(self, feat, label, num) -> None:
        self.feat = feat
        self.label = label
        self.num = num

    def __getitem__(self, index):
        import torch
        return {'feat': torch.Tensor(self.feat), 'labels': torch.Tensor(self.label)}

    def __len__(self):
        return self.num


def create_dummy_test_dataset(feat, label, num):
    return DummyTorchDataset(feat, label, num)


def download_and_untar(fpath, furl, dst) -> str:
    if not os.path.exists(fpath):
        r = requests.get(furl)
        with open(fpath, 'wb') as f:
            f.write(r.content)

    file_name = os.path.basename(fpath)
    root_dir = os.path.dirname(fpath)
    target_dir_name = os.path.splitext(os.path.splitext(file_name)[0])[0]
    target_dir_path = os.path.join(root_dir, target_dir_name)

    # untar the file
    t = tarfile.open(fpath)
    t.extractall(path=dst)

    return target_dir_path


def get_case_model_info():
    status_code, result = subprocess.getstatusoutput(
        'grep -rn "damo/" tests/  | grep -v ".pyc" | grep -v "Binary file" | grep -v run.py ')
    lines = result.split('\n')
    test_cases = OrderedDict()
    model_cases = OrderedDict()
    for line in lines:
        # "tests/msdatasets/test_ms_dataset.py:92:        model_id = 'damo/bert-base-sst2'"
        line = line.strip()
        elements = line.split(':')
        test_file = elements[0]
        model_pos = line.find('damo')
        left_quote = line[model_pos - 1]
        rquote_idx = line.rfind(left_quote)
        model_name = line[model_pos:rquote_idx]
        if test_file not in test_cases:
            test_cases[test_file] = set()
        model_info = test_cases[test_file]
        model_info.add(model_name)

        if model_name not in model_cases:
            model_cases[model_name] = set()
        case_info = model_cases[model_name]
        case_info.add(test_file.replace('tests/', '').replace('.py', '').replace('/', '.'))

    return model_cases


def compare_arguments_nested(print_content, arg1, arg2, rtol=1.e-3, atol=1.e-8, ignore_unknown_type=True):
    type1 = type(arg1)
    type2 = type(arg2)
    if type1.__name__ != type2.__name__:
        if print_content is not None:
            print(f'{print_content}, type not equal:{type1.__name__} and {type2.__name__}')
        return False

    if arg1 is None:
        return True
    elif isinstance(arg1, (int, str, bool, np.bool_, np.integer, np.str_)):
        if arg1 != arg2:
            if print_content is not None:
                print(f'{print_content}, arg1:{arg1}, arg2:{arg2}')
            return False
        return True
    elif isinstance(arg1, (float, np.floating)):
        if not np.isclose(arg1, arg2, rtol=rtol, atol=atol, equal_nan=True):
            if print_content is not None:
                print(f'{print_content}, arg1:{arg1}, arg2:{arg2}')
            return False
        return True
    elif isinstance(arg1, (tuple, list)):
        if len(arg1) != len(arg2):
            if print_content is not None:
                print(f'{print_content}, length is not equal:{len(arg1)}, {len(arg2)}')
            return False
        if not all([
                compare_arguments_nested(None, sub_arg1, sub_arg2, rtol=rtol, atol=atol)
                for sub_arg1, sub_arg2 in zip(arg1, arg2)
        ]):
            if print_content is not None:
                print(f'{print_content}')
            return False
        return True
    elif isinstance(arg1, Mapping):
        keys1 = arg1.keys()
        keys2 = arg2.keys()
        if len(keys1) != len(keys2):
            if print_content is not None:
                print(f'{print_content}, key length is not equal:{len(keys1)}, {len(keys2)}')
            return False
        if len(set(keys1) - set(keys2)) > 0:
            if print_content is not None:
                print(f'{print_content}, key diff:{set(keys1) - set(keys2)}')
            return False
        if not all([compare_arguments_nested(None, arg1[key], arg2[key], rtol=rtol, atol=atol) for key in keys1]):
            if print_content is not None:
                print(f'{print_content}')
            return False
        return True
    elif isinstance(arg1, np.ndarray):
        arg1 = np.where(np.equal(arg1, None), np.NaN, arg1).astype(dtype=float)
        arg2 = np.where(np.equal(arg2, None), np.NaN, arg2).astype(dtype=float)
        if not all(np.isclose(arg1, arg2, rtol=rtol, atol=atol, equal_nan=True).flatten()):
            if print_content is not None:
                print(f'{print_content}')
            return False
        return True
    else:
        if ignore_unknown_type:
            return True
        else:
            raise ValueError(f'type not supported: {type1}')


_DIST_SCRIPT_TEMPLATE = """
import ast
import argparse
import pickle
import torch
from torch import distributed as dist
from modelscope.utils.torch_utils import get_dist_info
import {}

parser = argparse.ArgumentParser()
parser.add_argument('--save_all_ranks', type=ast.literal_eval, help='save all ranks results')
parser.add_argument('--save_file', type=str, help='save file')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()


def main():
    results = {}.{}({})  # module.func(params)
    if args.save_all_ranks:
        save_file = args.save_file + str(dist.get_rank())
        with open(save_file, 'wb') as f:
            pickle.dump(results, f)
    else:
        rank, _ = get_dist_info()
        if rank == 0:
            with open(args.save_file, 'wb') as f:
                pickle.dump(results, f)


if __name__ == '__main__':
    main()
"""


class DistributedTestCase(unittest.TestCase):
    """Distributed TestCase for test function with distributed mode.
    Examples:
        >>> import torch
        >>> from torch import distributed as dist
        >>> from modelscope.utils.torch_utils import init_dist

        >>> def _test_func(*args, **kwargs):
        >>>     init_dist(launcher='pytorch')
        >>>     rank = dist.get_rank()
        >>>     if rank == 0:
        >>>         value = torch.tensor(1.0).cuda()
        >>>     else:
        >>>         value = torch.tensor(2.0).cuda()
        >>>     dist.all_reduce(value)
        >>>     return value.cpu().numpy()

        >>> class DistTest(DistributedTestCase):
        >>>     def test_function_dist(self):
        >>>         args = ()  # args should be python builtin type
        >>>         kwargs = {}  # kwargs should be python builtin type
        >>>         self.start(
        >>>             _test_func,
        >>>             num_gpus=2,
        >>>             assert_callback=lambda x: self.assertEqual(x, 3.0),
        >>>             *args,
        >>>             **kwargs,
        >>>         )
    """

    def _start(self, dist_start_cmd, func, num_gpus, assert_callback=None, save_all_ranks=False, *args, **kwargs):
        script_path = func.__code__.co_filename
        script_dir, script_name = os.path.split(script_path)
        script_name = os.path.splitext(script_name)[0]
        func_name = func.__qualname__

        func_params = []
        for arg in args:
            if isinstance(arg, str):
                arg = ('\'{}\''.format(arg))
            func_params.append(str(arg))

        for k, v in kwargs.items():
            if isinstance(v, str):
                v = ('\'{}\''.format(v))
            func_params.append('{}={}'.format(k, v))

        func_params = ','.join(func_params).strip(',')

        tmp_run_file = tempfile.NamedTemporaryFile(suffix='.py').name
        tmp_res_file = tempfile.NamedTemporaryFile(suffix='.pkl').name

        with open(tmp_run_file, 'w') as f:
            print('save temporary run file to : {}'.format(tmp_run_file))
            print('save results to : {}'.format(tmp_res_file))
            run_file_content = _DIST_SCRIPT_TEMPLATE.format(script_name, script_name, func_name, func_params)
            f.write(run_file_content)

        tmp_res_files = []
        if save_all_ranks:
            for i in range(num_gpus):
                tmp_res_files.append(tmp_res_file + str(i))
        else:
            tmp_res_files = [tmp_res_file]
        self.addCleanup(self.clean_tmp, [tmp_run_file] + tmp_res_files)

        tmp_env = copy.deepcopy(os.environ)
        tmp_env['PYTHONPATH'] = ':'.join((tmp_env.get('PYTHONPATH', ''), script_dir)).lstrip(':')
        # avoid distributed test hang
        tmp_env['NCCL_P2P_DISABLE'] = '1'
        script_params = '--save_all_ranks=%s --save_file=%s' % (save_all_ranks, tmp_res_file)
        script_cmd = '%s %s %s' % (dist_start_cmd, tmp_run_file, script_params)
        print('script command: %s' % script_cmd)
        res = subprocess.call(script_cmd, shell=True, env=tmp_env)

        script_res = []
        for res_file in tmp_res_files:
            with open(res_file, 'rb') as f:
                script_res.append(pickle.load(f))
        if not save_all_ranks:
            script_res = script_res[0]

        if assert_callback:
            assert_callback(script_res)

        self.assertEqual(res, 0, msg='The test function ``{}`` in ``{}`` run failed!'.format(func_name, script_name))

        return script_res

    def start(self, func, num_gpus, assert_callback=None, save_all_ranks=False, *args, **kwargs):
        from .torch_utils import _find_free_port
        ip = socket.gethostbyname(socket.gethostname())
        if 'dist_start_cmd' in kwargs:
            dist_start_cmd = kwargs.pop('dist_start_cmd')
        else:
            dist_start_cmd = '%s -m torch.distributed.launch --nproc_per_node=%d ' \
                             '--master_addr=\'%s\' --master_port=%s' % (sys.executable, num_gpus, ip, _find_free_port())

        return self._start(
            dist_start_cmd=dist_start_cmd,
            func=func,
            num_gpus=num_gpus,
            assert_callback=assert_callback,
            save_all_ranks=save_all_ranks,
            *args,
            **kwargs)

    def clean_tmp(self, tmp_file_list):
        for file in tmp_file_list:
            if os.path.exists(file):
                if os.path.isdir(file):
                    shutil.rmtree(file)
                else:
                    os.remove(file)