diff --git a/tests/core/metrics/test_accuracy_torch.py b/tests/core/metrics/test_accuracy_torch.py index b62200db..ab81cefc 100644 --- a/tests/core/metrics/test_accuracy_torch.py +++ b/tests/core/metrics/test_accuracy_torch.py @@ -15,6 +15,7 @@ from sklearn.metrics import accuracy_score as sklearn_accuracy from fastNLP.core.dataset import DataSet from fastNLP.core.metrics.accuracy import Accuracy from fastNLP.core.metrics.metric import Metric +from .utils import find_free_network_port, setup_ddp, _assert_allclose set_start_method("spawn", force=True) @@ -23,42 +24,6 @@ NUM_PROCESSES = 2 pool = None -def setup_ddp(rank: int, world_size: int, master_port: int) -> None: - """Setup ddp environment.""" - - os.environ["MASTER_ADDR"] = "localhost" - os.environ["MASTER_PORT"] = str(master_port) - print(torch.cuda.device_count()) - if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"): - torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size) - - -def find_free_network_port() -> int: - """Finds a free port on localhost. - - It is useful in single-node training when we don't want to connect to a real master node but have to set the - `MASTER_PORT` environment variable. - """ - s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - s.bind(("", 0)) - s.listen(1) - port = s.getsockname()[1] - s.close() - return port - - -def _assert_allclose(my_result: Union[float, np.ndarray], sklearn_result: Union[float, np.ndarray], - atol: float = 1e-8) -> None: - """ - 测试对比结果，这里不用非得是必须数组且维度对应，一些其他情况例如 np.allclose(np.array([[1e10, ], ]), 1e10+1) 也是 True - :param my_result: 可以不限设备等 - :param sklearn_result: - :param atol: - :return: - """ - assert np.allclose(a=my_result, b=sklearn_result, atol=atol) - - def _test(local_rank: int, world_size: int, device: torch.device, diff --git a/tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py b/tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py index c9174e41..268adbd3 100644 --- a/tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py +++ b/tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py @@ -1,8 +1,32 @@ +from functools import partial +import copy + import pytest import torch import numpy as np +from torch.multiprocessing import Pool, set_start_method from fastNLP.core.metrics import ClassifyFPreRecMetric +from fastNLP.core.dataset import DataSet +from .utils import find_free_network_port, setup_ddp + +set_start_method("spawn", force=True) + + +def _test(local_rank: int, world_size: int, device: torch.device, + dataset: DataSet, metric_class, metric_kwargs, metric_result): + metric = metric_class(**metric_kwargs) + # dataset 也类似（每个进程有自己的一个） + dataset = copy.deepcopy(dataset) + metric.to(device) + # 把数据拆到每个 GPU 上，有点模仿 DistributedSampler 的感觉，但这里数据单位是一个 batch（即每个 i 取了一个 batch 到自己的 GPU 上） + for i in range(local_rank, len(dataset), world_size): + pred, tg = dataset[i]['pred'].to(device), dataset[i]['tg'].to(device) + metric.update(pred, tg) + + my_result = metric.get_metric() + for keys in ['f', 'pre', 'rec']: + np.allclose(my_result[keys], metric_result[keys], atol=0.000001) class TestClassfiyFPreRecMetric: @@ -86,3 +110,68 @@ class TestClassfiyFPreRecMetric: tmp_d = {"p": "precision", "r": "recall", "f": "f1-score"} gk = tmp_d[keys[0]] np.allclose(result_dict[keys], ground_truth[gl][gk], atol=0.000001) + + @pytest.mark.parametrize("f_type, f1_score,recall,pre", + [('macro', 0.1882051282051282, 0.1619047619047619, 0.23928571428571427), + ('micro', 0.21875, 0.21875, 0.21875)]) + def test_case_2(self, f_type, f1_score, recall, pre): + dataset = DataSet({ + 'pred': [torch.tensor([[-0.4375, -0.1779, -1.0985, -1.1592, 0.4910], + [1.3410, 0.2889, -0.8667, -1.8580, 0.3029], + [0.7459, -1.1957, 0.3231, 0.0308, -0.1847], + [1.1439, -0.0057, 0.8203, 0.0312, -1.0051], + [-0.4870, 0.3215, -0.8290, 0.9221, 0.4683], + [0.9078, 1.0674, -0.5629, 0.3895, 0.8917], + [-0.7743, -0.4041, -0.9026, 0.2112, 1.0892], + [1.8232, -1.4188, -2.5615, -2.4187, 0.5907], + [-1.0592, 0.4164, -0.1192, 1.4238, -0.9258], + [-1.1137, 0.5773, 2.5778, 0.5398, -0.3323], + [-0.3868, -0.5165, 0.2286, -1.3876, 0.5561], + [-0.3304, 1.3619, -1.5744, 0.4902, -0.7661], + [1.8387, 0.5234, 0.4269, 1.3748, -1.2793], + [0.6692, 0.2571, 1.2425, -0.5894, -0.0184], + [0.4165, 0.4084, -0.1280, 1.4489, -2.3058], + [-0.5826, -0.5469, 1.5898, -0.2786, -0.9882]]), + torch.tensor([ + [-1.5548, -2.2891, 0.2983, -1.2145, -0.1947], + [-0.7222, 2.3543, -0.5801, -0.0640, -1.5614], + [-1.4978, 1.9297, -1.3652, -0.2358, 2.5566], + [0.1561, -0.0316, 0.9331, 1.0363, 2.3949], + [0.2650, -0.8459, 1.3221, 0.1321, -1.1900], + [0.0664, -1.2353, -0.5242, -1.4491, 1.3300], + [-0.2744, 0.0941, 0.7157, 0.1404, 1.2046], + [0.9341, -0.6652, 1.4512, 0.9608, -0.3623], + [-1.1641, 0.0873, 0.1163, -0.2068, -0.7002], + [1.4775, -2.0025, -0.5634, -0.1589, 0.0247], + [1.0151, 1.0304, -0.1042, -0.6955, -0.0629], + [-0.3119, -0.4558, 0.7757, 0.0758, -1.6297], + [1.0654, 0.0313, -0.7716, 0.1194, 0.6913], + [-0.8088, -0.6648, -0.5018, -0.0230, -0.8207], + [-0.7753, -0.3508, 1.6163, 0.7158, 1.5207], + [0.8692, 0.7718, -0.6734, 0.6515, 0.0641] + ])], + 'tg': [ + torch.LongTensor([0, 2, 4, 1, 4, 0, 1, 3, 3, 3, 1, 3, 4, 4, 3, 4]), + torch.LongTensor([0, 2, 4, 4, 3, 4, 4, 3, 0, 3, 0, 0, 0, 1, 3, 1]) + ] + }) + metric_kwargs = { + 'f_type': f_type, + 'num_class': 5, + 'only_gross': False, + 'aggregate_when_get_metric': True + } + ground_truth = {'f': f1_score, 'pre': pre, 'rec': recall} + + NUM_PROCESSES = 2 + pool = Pool(processes=NUM_PROCESSES) + master_port = find_free_network_port() + pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)]) + + pool.starmap(partial(_test, dataset=dataset, + metric_class=ClassifyFPreRecMetric, + metric_kwargs=metric_kwargs, + metric_result=ground_truth), + [(rank, NUM_PROCESSES, torch.device(f'cuda:{rank+4}')) for rank in range(NUM_PROCESSES)]) + pool.close() + pool.join() diff --git a/tests/core/metrics/test_span_f1_rec_acc_torch.py b/tests/core/metrics/test_span_f1_rec_acc_torch.py index bc711a54..f0a420d9 100644 --- a/tests/core/metrics/test_span_f1_rec_acc_torch.py +++ b/tests/core/metrics/test_span_f1_rec_acc_torch.py @@ -14,6 +14,7 @@ from torch.multiprocessing import Pool, set_start_method from fastNLP.core.vocabulary import Vocabulary from fastNLP.core.metrics import SpanFPreRecMetric from fastNLP.core.dataset import DataSet +from .utils import find_free_network_port, setup_ddp set_start_method("spawn", force=True) @@ -41,40 +42,6 @@ NUM_PROCESSES = 2 pool = None -def setup_ddp(rank: int, world_size: int, master_port: int) -> None: - """Setup ddp environment.""" - - os.environ["MASTER_ADDR"] = "localhost" - os.environ["MASTER_PORT"] = str(master_port) - if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"): - torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size) - - -def find_free_network_port() -> int: - """Finds a free port on localhost. - - It is useful in single-node training when we don't want to connect to a real master node but have to set the - `MASTER_PORT` environment variable. - """ - s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - s.bind(("", 0)) - s.listen(1) - port = s.getsockname()[1] - s.close() - return port - - -# @pytest.fixture(scope='class', autouse=True) -# def pre_process(): -# global pool -# pool = Pool(processes=NUM_PROCESSES) -# master_port = find_free_network_port() -# pool.starmap(setup_ddp, [(rank, NUM_PROCESSES, master_port) for rank in range(NUM_PROCESSES)]) -# yield -# pool.close() -# pool.join() - - def _test(local_rank: int, world_size: int, device: torch.device, diff --git a/tests/core/metrics/utils.py b/tests/core/metrics/utils.py new file mode 100644 index 00000000..10157438 --- /dev/null +++ b/tests/core/metrics/utils.py @@ -0,0 +1,42 @@ +import os, sys +import socket +from typing import Union + +import torch +from torch import distributed +import numpy as np + + +def setup_ddp(rank: int, world_size: int, master_port: int) -> None: + """Setup ddp environment.""" + + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(master_port) + if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"): + torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size) + + +def find_free_network_port() -> int: + """Finds a free port on localhost. + + It is useful in single-node training when we don't want to connect to a real master node but have to set the + `MASTER_PORT` environment variable. + """ + s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + s.bind(("", 0)) + s.listen(1) + port = s.getsockname()[1] + s.close() + return port + + +def _assert_allclose(my_result: Union[float, np.ndarray], sklearn_result: Union[float, np.ndarray], + atol: float = 1e-8) -> None: + """ + 测试对比结果，这里不用非得是必须数组且维度对应，一些其他情况例如 np.allclose(np.array([[1e10, ], ]), 1e10+1) 也是 True + :param my_result: 可以不限设备等 + :param sklearn_result: + :param atol: + :return: + """ + assert np.allclose(a=my_result, b=sklearn_result, atol=atol)