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Merge branch 'dev0.8.0' of github.com:fastnlp/fastNLP into dev0.8.0

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x54-729 2022-04-12 08:48:16 +00:00
commit e08ae33bfd
4 changed files with 133 additions and 70 deletions
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37
tests/core/metrics/test_accuracy_torch.py
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@ -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,

89
tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py
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@ -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()

35
tests/core/metrics/test_span_f1_rec_acc_torch.py
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@ -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,

42
tests/core/metrics/utils.py Normal file
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@ -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)