@@ -178,11 +178,11 @@ def dump_fastnlp_backend(default:bool = False, backend=None): | |||
os.makedirs(os.path.dirname(env_path), exist_ok=True) | |||
envs = {} | |||
assert backend in SUPPORT_BACKENDS, f"fastNLP only supports {SUPPORT_BACKENDS} right now." | |||
if backend is None: | |||
if FASTNLP_BACKEND in os.environ: | |||
envs[FASTNLP_BACKEND] = os.environ[FASTNLP_BACKEND] | |||
else: | |||
assert backend in SUPPORT_BACKENDS, f"fastNLP only supports {SUPPORT_BACKENDS} right now." | |||
envs[FASTNLP_BACKEND] = backend | |||
if len(envs): | |||
with open(env_path, 'w', encoding='utf8') as f: | |||
@@ -0,0 +1,237 @@ | |||
import os | |||
import sys | |||
import time | |||
# os.environ["cuda_archs"] = "61" | |||
# os.environ["FAS"] | |||
os.environ["log_silent"] = "1" | |||
sys.path.append("../../../") | |||
from datasets import load_dataset | |||
from datasets import DatasetDict | |||
import jittor as jt | |||
from jittor import nn, Module | |||
from jittor.dataset import Dataset | |||
jt.flags.use_cuda = True | |||
from fastNLP.core.controllers.trainer import Trainer | |||
from fastNLP.core.metrics.accuracy import Accuracy | |||
from fastNLP.core.vocabulary import Vocabulary | |||
from fastNLP.core.callbacks.progress_callback import RichCallback | |||
from fastNLP.core.callbacks.callback import Callback | |||
from fastNLP.core.dataloaders.jittor_dataloader.fdl import JittorDataLoader | |||
class TextClassificationDataset(Dataset): | |||
def __init__(self, dataset): | |||
super(TextClassificationDataset, self).__init__() | |||
self.dataset = dataset | |||
self.set_attrs(total_len=len(dataset)) | |||
def __getitem__(self, idx): | |||
return {"x": self.dataset["input_ids"][idx], "y": self.dataset["label"][idx]} | |||
class LSTM(Module): | |||
def __init__(self, num_of_words, hidden_size, features): | |||
self.embedding = nn.Embedding(num_of_words, features) | |||
self.lstm = nn.LSTM(features, hidden_size, batch_first=True) | |||
self.layer = nn.Linear(hidden_size, 2) | |||
self.softmax = nn.Softmax(dim=1) | |||
self.loss_fn = nn.CrossEntropyLoss() | |||
self.hidden_size = hidden_size | |||
self.features = features | |||
def init_hidden(self, x): | |||
# batch_first | |||
batch_size = x.shape[0] | |||
h0 = jt.randn(1, batch_size, hidden_size) | |||
c0 = jt.randn(1, batch_size, hidden_size) | |||
return h0, c0 | |||
def execute(self, input_ids): | |||
output = self.embedding(input_ids) | |||
# TODO 去除padding | |||
output, (h, c) = self.lstm(output, self.init_hidden(output)) | |||
# len, batch, hidden_size | |||
output = self.layer(output[-1]) | |||
return output | |||
def train_step(self, x, y): | |||
x = self(x) | |||
outputs = self.loss_fn(x, y) | |||
return {"loss": outputs} | |||
def evaluate_step(self, x, y): | |||
x = self(x) | |||
return {"pred": x, "target": y.reshape((-1,))} | |||
class PrintWhileTrainingCallBack(Callback): | |||
""" | |||
通过该Callback实现训练过程中loss的输出 | |||
""" | |||
def __init__(self, print_every_epoch, print_every_batch): | |||
self.print_every_epoch = print_every_epoch | |||
self.print_every_batch = print_every_batch | |||
self.loss = 0 | |||
self.start = 0 | |||
self.epoch_start = 0 | |||
def on_train_begin(self, trainer): | |||
""" | |||
在训练开始前输出信息 | |||
""" | |||
print("Start training. Total {} epochs and {} batches in each epoch.".format( | |||
trainer.n_epochs, trainer.num_batches_per_epoch | |||
)) | |||
self.start = time.time() | |||
def on_before_backward(self, trainer, outputs): | |||
""" | |||
每次反向传播前统计loss,用于计算平均值 | |||
""" | |||
loss = trainer.extract_loss_from_outputs(outputs) | |||
loss = trainer.driver.tensor_to_numeric(loss) | |||
self.loss += loss | |||
def on_train_epoch_begin(self, trainer): | |||
self.epoch_start = time.time() | |||
def on_train_epoch_end(self, trainer): | |||
""" | |||
在每经过一定epoch或最后一个epoch时输出当前epoch的平均loss和使用时间 | |||
""" | |||
if trainer.cur_epoch_idx % self.print_every_epoch == 0 \ | |||
or trainer.cur_epoch_idx == trainer.n_epochs: | |||
print("Epoch: {} Loss: {} Current epoch training time: {}s".format( | |||
trainer.cur_epoch_idx, self.loss / trainer.num_batches_per_epoch, time.time() - self.epoch_start | |||
)) | |||
# 将loss清零 | |||
self.loss = 0 | |||
def on_train_batch_end(self, trainer): | |||
""" | |||
在每经过一定batch或最后一个batch时输出当前epoch截止目前的平均loss | |||
""" | |||
if trainer.batch_idx_in_epoch % self.print_every_batch == 0 \ | |||
or trainer.batch_idx_in_epoch == trainer.num_batches_per_epoch: | |||
print("\tBatch: {} Loss: {}".format( | |||
trainer.batch_idx_in_epoch, self.loss / trainer.batch_idx_in_epoch | |||
)) | |||
def on_train_end(self, trainer): | |||
print("Total training time: {}s".format(time.time() - self.start)) | |||
def process_data(ds: DatasetDict, vocabulary: Vocabulary, max_len=256) -> DatasetDict: | |||
# 分词 | |||
ds = ds.map(lambda x: {"input_ids": text_to_id(vocabulary, x["text"], max_len)}) | |||
ds.set_format(type="numpy", columns=ds.column_names) | |||
return ds | |||
def set_vocabulary(vocab, dataset): | |||
for data in dataset: | |||
vocab.update(data["text"].split()) | |||
return vocab | |||
def text_to_id(vocab, text: str, max_len): | |||
text = text.split() | |||
# to index | |||
ids = [vocab.to_index(word) for word in text] | |||
# padding | |||
ids += [vocab.padding_idx] * (max_len - len(text)) | |||
return ids[:max_len] | |||
def get_dataset(name, max_len, train_format="", test_format=""): | |||
# datasets | |||
train_dataset = load_dataset(name, split="train" + train_format).shuffle(seed=123) | |||
test_dataset = load_dataset(name, split="test" + test_format).shuffle(seed=321) | |||
split = train_dataset.train_test_split(test_size=0.2, seed=123) | |||
train_dataset = split["train"] | |||
val_dataset = split["test"] | |||
vocab = Vocabulary() | |||
vocab = set_vocabulary(vocab, train_dataset) | |||
vocab = set_vocabulary(vocab, val_dataset) | |||
train_dataset = process_data(train_dataset, vocab, max_len) | |||
val_dataset = process_data(val_dataset, vocab, max_len) | |||
test_dataset = process_data(test_dataset, vocab, max_len) | |||
return TextClassificationDataset(train_dataset), TextClassificationDataset(val_dataset), \ | |||
TextClassificationDataset(test_dataset), vocab | |||
if __name__ == "__main__": | |||
# 训练参数 | |||
max_len = 20 | |||
epochs = 40 | |||
lr = 1 | |||
batch_size = 64 | |||
features = 100 | |||
hidden_size = 128 | |||
# 获取数据集 | |||
# imdb.py SetFit/sst2 | |||
train_data, val_data, test_data, vocab = get_dataset("SetFit/sst2", max_len, "", "") | |||
# 使用dataloader | |||
train_dataloader = JittorDataLoader( | |||
dataset=train_data, | |||
batch_size=batch_size, | |||
shuffle=True, | |||
num_workers=4, | |||
) | |||
val_dataloader = JittorDataLoader( | |||
dataset=val_data, | |||
batch_size=batch_size, | |||
shuffle=True, | |||
num_workers=4, | |||
) | |||
test_dataloader = JittorDataLoader( | |||
dataset=test_data, | |||
batch_size=1, | |||
shuffle=False, | |||
) | |||
# 初始化模型 | |||
model = LSTM(len(vocab), hidden_size, features) | |||
# 优化器 | |||
# 也可以是多个优化器的list | |||
optimizer = nn.SGD(model.parameters(), lr) | |||
# Metrics | |||
metrics = {"acc": Accuracy()} | |||
# callbacks | |||
callbacks = [ | |||
PrintWhileTrainingCallBack(print_every_epoch=1, print_every_batch=10), | |||
# RichCallback(), # print_every参数默认为1,即每一个batch更新一次进度条 | |||
] | |||
trainer = Trainer( | |||
model=model, | |||
driver="jittor", | |||
device=[0,1,2,3,4], | |||
optimizers=optimizer, | |||
train_dataloader=train_dataloader, | |||
validate_dataloaders=val_dataloader, | |||
validate_every=-1, | |||
input_mapping=None, | |||
output_mapping=None, | |||
metrics=metrics, | |||
n_epochs=epochs, | |||
callbacks=callbacks, | |||
# progress_bar="raw" | |||
) | |||
trainer.run() |
@@ -0,0 +1,110 @@ | |||
# coding=utf-8 | |||
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |||
# | |||
# Licensed under the Apache License, Version 2.0 (the "License"); | |||
# you may not use this file except in compliance with the License. | |||
# You may obtain a copy of the License at | |||
# | |||
# http://www.apache.org/licenses/LICENSE-2.0 | |||
# | |||
# Unless required by applicable law or agreed to in writing, software | |||
# distributed under the License is distributed on an "AS IS" BASIS, | |||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
# See the License for the specific language governing permissions and | |||
# limitations under the License. | |||
# Lint as: python3 | |||
"""IMDB movie reviews dataset.""" | |||
import datasets | |||
from datasets.tasks import TextClassification | |||
_DESCRIPTION = """\ | |||
Large Movie Review Dataset. | |||
This is a dataset for binary sentiment classification containing substantially \ | |||
more data than previous benchmark datasets. We provide a set of 25,000 highly \ | |||
polar movie reviews for training, and 25,000 for testing. There is additional \ | |||
unlabeled data for use as well.\ | |||
""" | |||
_CITATION = """\ | |||
@InProceedings{maas-EtAl:2011:ACL-HLT2011, | |||
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, | |||
title = {Learning Word Vectors for Sentiment Analysis}, | |||
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, | |||
month = {June}, | |||
year = {2011}, | |||
address = {Portland, Oregon, USA}, | |||
publisher = {Association for Computational Linguistics}, | |||
pages = {142--150}, | |||
url = {http://www.aclweb.org/anthology/P11-1015} | |||
} | |||
""" | |||
_DOWNLOAD_URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz" | |||
class IMDBReviewsConfig(datasets.BuilderConfig): | |||
"""BuilderConfig for IMDBReviews.""" | |||
def __init__(self, **kwargs): | |||
"""BuilderConfig for IMDBReviews. | |||
Args: | |||
**kwargs: keyword arguments forwarded to super. | |||
""" | |||
super(IMDBReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |||
class Imdb(datasets.GeneratorBasedBuilder): | |||
"""IMDB movie reviews dataset.""" | |||
BUILDER_CONFIGS = [ | |||
IMDBReviewsConfig( | |||
name="plain_text", | |||
description="Plain text", | |||
) | |||
] | |||
def _info(self): | |||
return datasets.DatasetInfo( | |||
description=_DESCRIPTION, | |||
features=datasets.Features( | |||
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])} | |||
), | |||
supervised_keys=None, | |||
homepage="http://ai.stanford.edu/~amaas/data/sentiment/", | |||
citation=_CITATION, | |||
task_templates=[TextClassification(text_column="text", label_column="label")], | |||
) | |||
def _split_generators(self, dl_manager): | |||
archive = dl_manager.download(_DOWNLOAD_URL) | |||
return [ | |||
datasets.SplitGenerator( | |||
name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"} | |||
), | |||
datasets.SplitGenerator( | |||
name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"} | |||
), | |||
datasets.SplitGenerator( | |||
name=datasets.Split("unsupervised"), | |||
gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train", "labeled": False}, | |||
), | |||
] | |||
def _generate_examples(self, files, split, labeled=True): | |||
"""Generate aclImdb examples.""" | |||
# For labeled examples, extract the label from the path. | |||
if labeled: | |||
label_mapping = {"pos": 1, "neg": 0} | |||
for path, f in files: | |||
if path.startswith(f"aclImdb/{split}"): | |||
label = label_mapping.get(path.split("/")[2]) | |||
if label is not None: | |||
yield path, {"text": f.read().decode("utf-8"), "label": label} | |||
else: | |||
for path, f in files: | |||
if path.startswith(f"aclImdb/{split}"): | |||
if path.split("/")[2] == "unsup": | |||
yield path, {"text": f.read().decode("utf-8"), "label": -1} |
@@ -65,6 +65,7 @@ def model_and_optimizers(): | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu")]) # , ("torch", 6), ("torch", [6, 7]) | |||
@pytest.mark.parametrize("callbacks", [[RecordTrainerEventTriggerCallback()]]) | |||
@pytest.mark.torch | |||
@magic_argv_env_context | |||
def test_trainer_event_trigger( | |||
model_and_optimizers: TrainerParameters, | |||
@@ -7,16 +7,16 @@ from tests.helpers.utils import magic_argv_env_context | |||
@magic_argv_env_context | |||
def test_trainer_torch_without_evaluator(): | |||
@Trainer.on(Events.ON_TRAIN_EPOCH_BEGIN(every=10)) | |||
@Trainer.on(Events.on_train_epoch_begin(every=10)) | |||
def fn1(trainer): | |||
pass | |||
@Trainer.on(Events.ON_TRAIN_BATCH_BEGIN(every=10)) | |||
@Trainer.on(Events.on_train_batch_begin(every=10)) | |||
def fn2(trainer, batch, indices): | |||
pass | |||
with pytest.raises(AssertionError): | |||
@Trainer.on(Events.ON_TRAIN_BATCH_BEGIN(every=10)) | |||
@Trainer.on(Events.on_train_batch_begin(every=10)) | |||
def fn3(trainer, batch): | |||
pass | |||
@@ -25,8 +25,8 @@ class TrainPaddleConfig: | |||
@pytest.mark.parametrize("driver,device", [("paddle", "cpu"), ("paddle", 1), ("fleet", [0, 1])]) | |||
# @pytest.mark.parametrize("driver,device", [("fleet", [0, 1])]) | |||
@pytest.mark.parametrize("callbacks", [[RecordMetricCallback(monitor="acc#acc", metric_threshold=0.0, larger_better=True), | |||
RichCallback(5)]]) | |||
@pytest.mark.parametrize("callbacks", [[RichCallback(5)]]) | |||
@pytest.mark.paddle | |||
@magic_argv_env_context | |||
def test_trainer_paddle( | |||
driver, | |||
@@ -98,6 +98,7 @@ def model_and_optimizers(request): | |||
# 测试一下普通的情况; | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", 1), ("torch", [0, 1])]) # ("torch", "cpu"), ("torch", 1), ("torch", [0, 1]) | |||
@pytest.mark.parametrize("callbacks", [[RecordMetricCallback(monitor="acc", metric_threshold=0.2, larger_better=True)]]) | |||
@pytest.mark.parametrize("evaluate_every", [-3, -1, 100]) | |||
@@ -133,6 +134,7 @@ def test_trainer_torch_with_evaluator( | |||
dist.destroy_process_group() | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", [0, 1]), ("torch", 1)]) # ("torch", [0, 1]),("torch", 1) | |||
@pytest.mark.parametrize("fp16", [True, False]) | |||
@pytest.mark.parametrize("accumulation_steps", [1, 3]) | |||
@@ -76,6 +76,7 @@ def model_and_optimizers(request): | |||
# 测试一下 cpu; | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu")]) | |||
@magic_argv_env_context | |||
def test_trainer_torch_without_evaluator( | |||
@@ -107,6 +108,7 @@ def test_trainer_torch_without_evaluator( | |||
dist.destroy_process_group() | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", 1), ("torch", [1, 2])]) # ("torch", 4), | |||
@pytest.mark.parametrize("fp16", [False, True]) | |||
@pytest.mark.parametrize("accumulation_steps", [1, 3]) | |||
@@ -146,6 +148,7 @@ def test_trainer_torch_without_evaluator_fp16_accumulation_steps( | |||
# 测试 accumulation_steps; | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", 1), ("torch", [1, 2])]) | |||
@pytest.mark.parametrize("accumulation_steps", [1, 3]) | |||
@magic_argv_env_context | |||
@@ -179,6 +182,7 @@ def test_trainer_torch_without_evaluator_accumulation_steps( | |||
dist.destroy_process_group() | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", [1, 2])]) | |||
@pytest.mark.parametrize("output_from_new_proc", ["all", "ignore", "only_error", "test_log"]) | |||
@magic_argv_env_context | |||
@@ -242,6 +246,7 @@ def test_trainer_output_from_new_proc( | |||
rank_zero_rm(path) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", [1, 2])]) | |||
@pytest.mark.parametrize("cur_rank", [0]) # 依次测试如果是当前进程出现错误,是否能够正确地 kill 掉其他进程; , 1, 2, 3 | |||
@magic_argv_env_context | |||
@@ -294,6 +299,7 @@ def test_torch_distributed_launch_1(version): | |||
subprocess.check_call(command) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("version", [0, 1, 2, 3]) | |||
@magic_argv_env_context | |||
def test_torch_distributed_launch_2(version): | |||
@@ -307,6 +313,7 @@ def test_torch_distributed_launch_2(version): | |||
subprocess.check_call(command) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("driver,device", [("torch", 0), ("torch_ddp", [0, 1])]) | |||
@magic_argv_env_context | |||
def test_torch_wo_auto_param_call( | |||
@@ -10,7 +10,7 @@ class Test_WrapDataLoader: | |||
all_sanity_batches = [4, 20, 100] | |||
for sanity_batches in all_sanity_batches: | |||
data = NormalIterator(num_of_data=1000) | |||
wrapper = _TruncatedDataLoader(num_batches=sanity_batches) | |||
wrapper = _TruncatedDataLoader(dataloader=data, num_batches=sanity_batches) | |||
dataloader = iter(wrapper(dataloader=data)) | |||
mark = 0 | |||
while True: | |||
@@ -31,7 +31,7 @@ class Test_WrapDataLoader: | |||
for sanity_batches in all_sanity_batches: | |||
dataset = TorchNormalDataset(num_of_data=1000) | |||
dataloader = DataLoader(dataset, batch_size=bs, shuffle=True) | |||
wrapper = _TruncatedDataLoader(num_batches=sanity_batches) | |||
wrapper = _TruncatedDataLoader(dataloader, num_batches=sanity_batches) | |||
dataloader = wrapper(dataloader) | |||
dataloader = iter(dataloader) | |||
all_supposed_running_data_num = 0 | |||
@@ -54,7 +54,7 @@ class Test_WrapDataLoader: | |||
for sanity_batches in all_sanity_batches: | |||
dataset = TorchNormalDataset(num_of_data=1000) | |||
dataloader = DataLoader(dataset, batch_size=bs, shuffle=True) | |||
wrapper = _TruncatedDataLoader(num_batches=sanity_batches) | |||
wrapper = _TruncatedDataLoader(dataloader, num_batches=sanity_batches) | |||
dataloader = wrapper(dataloader) | |||
length.append(len(dataloader)) | |||
assert length == reduce(lambda x, y: x+y, [all_sanity_batches for _ in range(len(bses))]) |
@@ -1,12 +1,16 @@ | |||
import pytest | |||
from jittor.dataset import Dataset | |||
import jittor | |||
import numpy as np | |||
from datasets import Dataset as HfDataset | |||
from datasets import load_dataset | |||
from fastNLP.core.dataloaders.jittor_dataloader import JittorDataLoader | |||
from fastNLP.core.dataset import DataSet as Fdataset | |||
from fastNLP.envs.imports import _NEED_IMPORT_JITTOR | |||
if _NEED_IMPORT_JITTOR: | |||
from jittor.dataset import Dataset | |||
import jittor | |||
else: | |||
from fastNLP.core.utils.dummy_class import DummyClass as Dataset | |||
class MyDataset(Dataset): | |||
@@ -25,7 +29,7 @@ class MyDataset(Dataset): | |||
# def __len__(self): | |||
# return self.dataset_len | |||
@pytest.mark.jittor | |||
class TestJittor: | |||
def test_v1(self): | |||
@@ -1,13 +1,18 @@ | |||
import unittest | |||
import pytest | |||
import os | |||
import numpy as np | |||
import jittor as jt # 将 jittor 引入 | |||
from jittor import nn, Module # 引入相关的模块 | |||
from jittor import init | |||
from jittor.dataset import MNIST | |||
from fastNLP.core.drivers.jittor_driver.single_device import JittorSingleDriver | |||
from fastNLP.envs.imports import _NEED_IMPORT_JITTOR | |||
if _NEED_IMPORT_JITTOR: | |||
import jittor as jt # 将 jittor 引入 | |||
from jittor import nn, Module # 引入相关的模块 | |||
from jittor import init | |||
from jittor.dataset import MNIST | |||
else: | |||
from fastNLP.core.utils.dummy_class import DummyClass as Module | |||
class Model (Module): | |||
@@ -39,7 +44,8 @@ class Model (Module): | |||
x = self.fc2 (x) | |||
return x | |||
class SingleDeviceTestCase(unittest.TestCase): | |||
@pytest.mark.jittor | |||
class TestSingleDevice: | |||
def test_on_gpu_without_fp16(self): | |||
# TODO get_dataloader | |||
@@ -82,7 +88,7 @@ class SingleDeviceTestCase(unittest.TestCase): | |||
total_acc += acc | |||
total_num += batch_size | |||
acc = acc / batch_size | |||
self.assertGreater(total_acc / total_num, 0.95) | |||
assert total_acc / total_num > 0.95 | |||
def test_on_cpu_without_fp16(self): | |||
@@ -18,6 +18,7 @@ from tests.helpers.utils import magic_argv_env_context | |||
import paddle | |||
import paddle.distributed as dist | |||
@pytest.mark.paddle | |||
class TestDistUtilsTools: | |||
""" | |||
测试一些工具函数 | |||
@@ -78,6 +79,7 @@ class TestDistUtilsTools: | |||
assert res["string"] == paddle_dict["string"] | |||
@pytest.mark.paddle | |||
class TestAllGatherAndBroadCast: | |||
@classmethod | |||
@@ -38,6 +38,7 @@ def generate_driver(num_labels, feature_dimension, device=[0,1], fp16=False, out | |||
# | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class TestFleetDriverFunction: | |||
""" | |||
测试 PaddleFleetDriver 一些简单函数的测试类,基本都是测试能否运行、是否存在 import 错误等问题 | |||
@@ -145,6 +146,7 @@ class TestFleetDriverFunction: | |||
# | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class TestSetDistReproDataloader: | |||
@classmethod | |||
@@ -517,6 +519,8 @@ class TestSetDistReproDataloader: | |||
# 测试 save 和 load 相关的功能 | |||
# | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class TestSaveLoad: | |||
""" | |||
测试多卡情况下 save 和 load 相关函数的表现 | |||
@@ -8,12 +8,14 @@ from tests.helpers.utils import magic_argv_env_context | |||
import paddle | |||
@pytest.mark.paddle | |||
def test_incorrect_driver(): | |||
model = PaddleNormalModel_Classification_1(2, 100) | |||
with pytest.raises(ValueError): | |||
driver = initialize_paddle_driver("torch", 0, model) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize( | |||
"device", | |||
["cpu", "gpu:0", 0] | |||
@@ -31,6 +33,7 @@ def test_get_single_device(driver, device): | |||
driver = initialize_paddle_driver(driver, device, model) | |||
assert isinstance(driver, PaddleSingleDriver) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize( | |||
"device", | |||
[0, 1, [1]] | |||
@@ -50,6 +53,7 @@ def test_get_fleet_2(driver, device): | |||
assert isinstance(driver, PaddleFleetDriver) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize( | |||
"device", | |||
[[0, 2, 3], -1] | |||
@@ -69,6 +73,7 @@ def test_get_fleet(driver, device): | |||
assert isinstance(driver, PaddleFleetDriver) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize( | |||
("driver", "device"), | |||
[("fleet", "cpu")] | |||
@@ -82,6 +87,7 @@ def test_get_fleet_cpu(driver, device): | |||
with pytest.raises(ValueError): | |||
driver = initialize_paddle_driver(driver, device, model) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize( | |||
"device", | |||
[-2, [0, get_gpu_count() + 1, 3], [-2], get_gpu_count() + 1] | |||
@@ -97,4 +103,4 @@ def test_device_out_of_range(driver, device): | |||
""" | |||
model = PaddleNormalModel_Classification_1(2, 100) | |||
with pytest.raises(ValueError): | |||
driver = initialize_paddle_driver(driver, device, model) | |||
driver = initialize_paddle_driver(driver, device, model) |
@@ -29,6 +29,7 @@ class TestPaddleDriverFunctions: | |||
model = PaddleNormalModel_Classification_1(10, 32) | |||
self.driver = PaddleSingleDriver(model, device="cpu") | |||
@pytest.mark.torchpaddle | |||
def test_check_single_optimizer_legality(self): | |||
""" | |||
测试传入单个 optimizer 时的表现 | |||
@@ -45,6 +46,7 @@ class TestPaddleDriverFunctions: | |||
with pytest.raises(ValueError): | |||
self.driver.set_optimizers(optimizer) | |||
@pytest.mark.torchpaddle | |||
def test_check_optimizers_legality(self): | |||
""" | |||
测试传入 optimizer list 的表现 | |||
@@ -65,6 +67,7 @@ class TestPaddleDriverFunctions: | |||
with pytest.raises(ValueError): | |||
self.driver.set_optimizers(optimizers) | |||
@pytest.mark.torchpaddle | |||
def test_check_dataloader_legality_in_train(self): | |||
""" | |||
测试 `is_train` 参数为 True 时,_check_dataloader_legality 函数的表现 | |||
@@ -85,6 +88,7 @@ class TestPaddleDriverFunctions: | |||
with pytest.raises(ValueError): | |||
PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", True) | |||
@pytest.mark.torchpaddle | |||
def test_check_dataloader_legality_in_test(self): | |||
""" | |||
测试 `is_train` 参数为 False 时,_check_dataloader_legality 函数的表现 | |||
@@ -122,6 +126,7 @@ class TestPaddleDriverFunctions: | |||
with pytest.raises(ValueError): | |||
PaddleSingleDriver.check_dataloader_legality(dataloader, "dataloader", False) | |||
@pytest.mark.paddle | |||
def test_tensor_to_numeric(self): | |||
""" | |||
测试 tensor_to_numeric 函数 | |||
@@ -175,6 +180,7 @@ class TestPaddleDriverFunctions: | |||
assert r == d.tolist() | |||
assert res["dict"]["tensor"] == tensor_dict["dict"]["tensor"].tolist() | |||
@pytest.mark.paddle | |||
def test_set_model_mode(self): | |||
""" | |||
测试 set_model_mode 函数 | |||
@@ -187,6 +193,7 @@ class TestPaddleDriverFunctions: | |||
with pytest.raises(AssertionError): | |||
self.driver.set_model_mode("test") | |||
@pytest.mark.paddle | |||
def test_move_model_to_device_cpu(self): | |||
""" | |||
测试 move_model_to_device 函数 | |||
@@ -194,6 +201,7 @@ class TestPaddleDriverFunctions: | |||
PaddleSingleDriver.move_model_to_device(self.driver.model, "cpu") | |||
assert self.driver.model.linear1.weight.place.is_cpu_place() | |||
@pytest.mark.paddle | |||
def test_move_model_to_device_gpu(self): | |||
""" | |||
测试 move_model_to_device 函数 | |||
@@ -202,6 +210,7 @@ class TestPaddleDriverFunctions: | |||
assert self.driver.model.linear1.weight.place.is_gpu_place() | |||
assert self.driver.model.linear1.weight.place.gpu_device_id() == 0 | |||
@pytest.mark.paddle | |||
def test_worker_init_function(self): | |||
""" | |||
测试 worker_init_function | |||
@@ -210,6 +219,7 @@ class TestPaddleDriverFunctions: | |||
# TODO:正确性 | |||
PaddleSingleDriver.worker_init_function(0) | |||
@pytest.mark.paddle | |||
def test_set_deterministic_dataloader(self): | |||
""" | |||
测试 set_deterministic_dataloader | |||
@@ -219,6 +229,7 @@ class TestPaddleDriverFunctions: | |||
dataloader = DataLoader(PaddleNormalDataset()) | |||
self.driver.set_deterministic_dataloader(dataloader) | |||
@pytest.mark.paddle | |||
def test_set_sampler_epoch(self): | |||
""" | |||
测试 set_sampler_epoch | |||
@@ -228,6 +239,7 @@ class TestPaddleDriverFunctions: | |||
dataloader = DataLoader(PaddleNormalDataset()) | |||
self.driver.set_sampler_epoch(dataloader, 0) | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -253,6 +265,7 @@ class TestPaddleDriverFunctions: | |||
assert res.batch_size == batch_size | |||
assert res.drop_last == drop_last | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -281,6 +294,7 @@ class TestPaddleDriverFunctions: | |||
assert res.batch_size == batch_size | |||
assert res.drop_last == drop_last | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -311,6 +325,7 @@ class TestPaddleDriverFunctions: | |||
# | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class TestSingleDeviceFunction: | |||
""" | |||
测试其它函数的测试例 | |||
@@ -345,6 +360,7 @@ class TestSingleDeviceFunction: | |||
# | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class TestSetDistReproDataloader: | |||
""" | |||
专门测试 set_dist_repro_dataloader 函数的类 | |||
@@ -541,6 +557,7 @@ def prepare_test_save_load(): | |||
driver1, driver2 = generate_random_driver(10, 10), generate_random_driver(10, 10) | |||
return driver1, driver2, dataloader | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
def test_save_and_load_model(prepare_test_save_load, only_state_dict): | |||
""" | |||
@@ -570,6 +587,7 @@ def test_save_and_load_model(prepare_test_save_load, only_state_dict): | |||
rank_zero_rm(path + ".pdiparams.info") | |||
rank_zero_rm(path + ".pdmodel") | |||
@pytest.mark.paddle | |||
# @pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
@pytest.mark.parametrize("only_state_dict", ([True])) | |||
@pytest.mark.parametrize("fp16", ([True, False])) | |||
@@ -650,6 +668,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
# @pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
# TODO 在有迭代且使用了paddle.jit.save的时候会引发段错误,注释掉任意一段都不会出错 | |||
# 但无法在单独的文件中复现 | |||
@pytest.mark.paddle | |||
@pytest.mark.parametrize("only_state_dict", ([True])) | |||
@pytest.mark.parametrize("fp16", ([True, False])) | |||
def test_save_and_load_with_randomsampler(only_state_dict, fp16): | |||
@@ -1,3 +1,4 @@ | |||
import os | |||
import pytest | |||
from fastNLP.core.drivers.paddle_driver.utils import ( | |||
@@ -23,12 +24,14 @@ from tests.helpers.datasets.paddle_data import PaddleNormalDataset | |||
("3,6,7,8", "6,7,8", "gpu:2", str, "gpu:1"), | |||
) | |||
) | |||
@pytest.mark.paddle | |||
def test_get_device_from_visible_str(user_visible_devices, cuda_visible_devices, device, output_type, correct): | |||
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices | |||
os.environ["USER_CUDA_VISIBLE_DEVICES"] = user_visible_devices | |||
res = get_device_from_visible(device, output_type) | |||
assert res == correct | |||
@pytest.mark.paddle | |||
def test_replace_batch_sampler(): | |||
dataset = PaddleNormalDataset(10) | |||
dataloader = DataLoader(dataset, batch_size=32) | |||
@@ -42,6 +45,7 @@ def test_replace_batch_sampler(): | |||
assert len(replaced_loader.dataset) == len(dataset) | |||
assert replaced_loader.batch_sampler.batch_size == 16 | |||
@pytest.mark.paddle | |||
def test_replace_sampler(): | |||
dataset = PaddleNormalDataset(10) | |||
dataloader = DataLoader(dataset, batch_size=32) | |||
@@ -1,31 +0,0 @@ | |||
import sys | |||
sys.path.append("../../../../") | |||
from fastNLP.core.drivers.torch_driver.ddp import TorchDDPDriver | |||
from tests.helpers.models.torch_model import TorchNormalModel_Classification_1 | |||
import torch | |||
device = [0, 1] | |||
torch_model = TorchNormalModel_Classification_1(10, 10) | |||
torch_opt = torch.optim.Adam(params=torch_model.parameters(), lr=0.01) | |||
device = [torch.device(i) for i in device] | |||
driver = TorchDDPDriver( | |||
model=torch_model, | |||
parallel_device=device, | |||
fp16=False | |||
) | |||
driver.set_optimizers(torch_opt) | |||
driver.setup() | |||
print("-----------first--------------") | |||
device = [0, 2] | |||
torch_model = TorchNormalModel_Classification_1(10, 10) | |||
torch_opt = torch.optim.Adam(params=torch_model.parameters(), lr=0.01) | |||
device = [torch.device(i) for i in device] | |||
driver = TorchDDPDriver( | |||
model=torch_model, | |||
parallel_device=device, | |||
fp16=False | |||
) | |||
driver.set_optimizers(torch_opt) | |||
driver.setup() |
@@ -1,4 +1,5 @@ | |||
import os | |||
import pytest | |||
import torch | |||
import torch.distributed as dist | |||
@@ -62,6 +62,7 @@ class TestTorchDriverFunctions: | |||
model = TorchNormalModel_Classification_1(10, 32) | |||
self.driver = TorchSingleDriver(model, device="cpu") | |||
@pytest.mark.torchpaddle | |||
def test_check_single_optimizer_legality(self): | |||
""" | |||
测试传入单个 optimizer 时的表现 | |||
@@ -81,6 +82,7 @@ class TestTorchDriverFunctions: | |||
with pytest.raises(ValueError): | |||
self.driver.set_optimizers(optimizer) | |||
@pytest.mark.torchpaddle | |||
def test_check_optimizers_legality(self): | |||
""" | |||
测试传入 optimizer list 的表现 | |||
@@ -104,6 +106,7 @@ class TestTorchDriverFunctions: | |||
with pytest.raises(ValueError): | |||
self.driver.set_optimizers(optimizers) | |||
@pytest.mark.torchpaddle | |||
def test_check_dataloader_legality_in_train(self): | |||
""" | |||
测试 `is_train` 参数为 True 时,_check_dataloader_legality 函数的表现 | |||
@@ -119,6 +122,7 @@ class TestTorchDriverFunctions: | |||
with pytest.raises(ValueError): | |||
TorchSingleDriver.check_dataloader_legality(dataloader, "dataloader", True) | |||
@pytest.mark.torchpaddle | |||
def test_check_dataloader_legality_in_test(self): | |||
""" | |||
测试 `is_train` 参数为 False 时,_check_dataloader_legality 函数的表现 | |||
@@ -148,6 +152,7 @@ class TestTorchDriverFunctions: | |||
with pytest.raises(ValueError): | |||
TorchSingleDriver.check_dataloader_legality(dataloader, "dataloader", False) | |||
@pytest.mark.torch | |||
def test_tensor_to_numeric(self): | |||
""" | |||
测试 tensor_to_numeric 函数 | |||
@@ -201,6 +206,7 @@ class TestTorchDriverFunctions: | |||
assert r == d.tolist() | |||
assert res["dict"]["tensor"] == tensor_dict["dict"]["tensor"].tolist() | |||
@pytest.mark.torch | |||
def test_set_model_mode(self): | |||
""" | |||
测试set_model_mode函数 | |||
@@ -213,6 +219,7 @@ class TestTorchDriverFunctions: | |||
with pytest.raises(AssertionError): | |||
self.driver.set_model_mode("test") | |||
@pytest.mark.torch | |||
def test_move_model_to_device_cpu(self): | |||
""" | |||
测试move_model_to_device函数 | |||
@@ -220,6 +227,7 @@ class TestTorchDriverFunctions: | |||
TorchSingleDriver.move_model_to_device(self.driver.model, "cpu") | |||
assert self.driver.model.linear1.weight.device.type == "cpu" | |||
@pytest.mark.torch | |||
def test_move_model_to_device_gpu(self): | |||
""" | |||
测试move_model_to_device函数 | |||
@@ -228,6 +236,7 @@ class TestTorchDriverFunctions: | |||
assert self.driver.model.linear1.weight.device.type == "cuda" | |||
assert self.driver.model.linear1.weight.device.index == 0 | |||
@pytest.mark.torch | |||
def test_worker_init_function(self): | |||
""" | |||
测试worker_init_function | |||
@@ -236,6 +245,7 @@ class TestTorchDriverFunctions: | |||
# TODO:正确性 | |||
TorchSingleDriver.worker_init_function(0) | |||
@pytest.mark.torch | |||
def test_set_deterministic_dataloader(self): | |||
""" | |||
测试set_deterministic_dataloader | |||
@@ -245,6 +255,7 @@ class TestTorchDriverFunctions: | |||
dataloader = DataLoader(TorchNormalDataset()) | |||
self.driver.set_deterministic_dataloader(dataloader) | |||
@pytest.mark.torch | |||
def test_set_sampler_epoch(self): | |||
""" | |||
测试set_sampler_epoch | |||
@@ -254,6 +265,7 @@ class TestTorchDriverFunctions: | |||
dataloader = DataLoader(TorchNormalDataset()) | |||
self.driver.set_sampler_epoch(dataloader, 0) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -279,6 +291,7 @@ class TestTorchDriverFunctions: | |||
assert res.batch_size == batch_size | |||
assert res.drop_last == drop_last | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -300,6 +313,7 @@ class TestTorchDriverFunctions: | |||
assert res.batch_size == batch_size | |||
assert res.drop_last == drop_last | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("batch_size", [16]) | |||
@pytest.mark.parametrize("shuffle", [True, False]) | |||
@pytest.mark.parametrize("drop_last", [True, False]) | |||
@@ -325,6 +339,7 @@ class TestTorchDriverFunctions: | |||
# | |||
############################################################################ | |||
@pytest.mark.torch | |||
class TestSingleDeviceFunction: | |||
""" | |||
测试其它函数的测试例 | |||
@@ -359,6 +374,7 @@ class TestSingleDeviceFunction: | |||
# | |||
############################################################################ | |||
@pytest.mark.torch | |||
class TestSetDistReproDataloader: | |||
""" | |||
专门测试 set_dist_repro_dataloader 函数的类 | |||
@@ -534,6 +550,7 @@ def prepare_test_save_load(): | |||
driver1, driver2 = generate_random_driver(10, 10), generate_random_driver(10, 10) | |||
return driver1, driver2, dataloader | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
def test_save_and_load_model(prepare_test_save_load, only_state_dict): | |||
""" | |||
@@ -555,6 +572,7 @@ def test_save_and_load_model(prepare_test_save_load, only_state_dict): | |||
finally: | |||
rank_zero_rm(path) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
@pytest.mark.parametrize("fp16", ([True, False])) | |||
def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
@@ -623,6 +641,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
finally: | |||
rank_zero_rm(path) | |||
@pytest.mark.torch | |||
@pytest.mark.parametrize("only_state_dict", ([True, False])) | |||
@pytest.mark.parametrize("fp16", ([True, False])) | |||
def test_save_and_load_with_randomsampler(only_state_dict, fp16): | |||
@@ -1,4 +1,4 @@ | |||
import unittest | |||
import pytest | |||
from fastNLP.modules.mix_modules.mix_module import MixModule | |||
from fastNLP.core.drivers.torch_paddle_driver.torch_paddle_driver import TorchPaddleDriver | |||
@@ -56,10 +56,11 @@ class MixMNISTModel(MixModule): | |||
def test_step(self, x): | |||
return self.forward(x) | |||
class TestMNIST(unittest.TestCase): | |||
@pytest.mark.torchpaddle | |||
class TestMNIST: | |||
@classmethod | |||
def setUpClass(self): | |||
def setup_class(self): | |||
self.train_dataset = paddle.vision.datasets.MNIST(mode='train') | |||
self.test_dataset = paddle.vision.datasets.MNIST(mode='test') | |||
@@ -70,7 +71,7 @@ class TestMNIST(unittest.TestCase): | |||
self.dataloader = DataLoader(self.train_dataset, batch_size=100, shuffle=True) | |||
def setUp(self): | |||
def setup_method(self): | |||
model = MixMNISTModel() | |||
self.torch_loss_func = torch.nn.CrossEntropyLoss() | |||
@@ -118,4 +119,4 @@ class TestMNIST(unittest.TestCase): | |||
correct += 1 | |||
acc = correct / len(self.test_dataset) | |||
self.assertGreater(acc, 0.85) | |||
assert acc > 0.85 |
@@ -49,12 +49,12 @@ def test_accuracy_single(): | |||
# 测试 单机多卡情况下的Accuracy | |||
# | |||
############################################################################ | |||
def test_accuracy_ddp(): | |||
launcher = FleetLauncher(devices=[0, 1]) | |||
launcher.launch() | |||
role = role_maker.PaddleCloudRoleMaker(is_collective=True) | |||
fleet.init(role) | |||
if fleet.is_server(): | |||
pass | |||
elif fleet.is_worker(): | |||
print(os.getenv("PADDLE_TRAINER_ID")) | |||
# def test_accuracy_ddp(): | |||
# launcher = FleetLauncher(devices=[0, 1]) | |||
# launcher.launch() | |||
# role = role_maker.PaddleCloudRoleMaker(is_collective=True) | |||
# fleet.init(role) | |||
# if fleet.is_server(): | |||
# pass | |||
# elif fleet.is_worker(): | |||
# print(os.getenv("PADDLE_TRAINER_ID")) |
@@ -1,26 +0,0 @@ | |||
from fastNLP.core.metrics.metric import Metric | |||
from collections import defaultdict | |||
from functools import partial | |||
import unittest | |||
class MyMetric(Metric): | |||
def __init__(self, backend='auto', | |||
aggregate_when_get_metric: bool = False): | |||
super(MyMetric, self).__init__(backend=backend, aggregate_when_get_metric=aggregate_when_get_metric) | |||
self.tp = defaultdict(partial(self.register_element, aggregate_method='sum')) | |||
def update(self, item): | |||
self.tp['1'] += item | |||
class TestMetric(unittest.TestCase): | |||
def test_va1(self): | |||
my = MyMetric() | |||
my.update(1) | |||
print(my.tp['1']) |
@@ -29,6 +29,8 @@ class TestUnrepeatedSampler: | |||
@pytest.mark.parametrize('num_of_data', [2, 3, 4, 100]) | |||
@pytest.mark.parametrize('shuffle', [False, True]) | |||
def test_multi(self, num_replicas, num_of_data, shuffle): | |||
if num_replicas > num_of_data: | |||
pytest.skip("num_replicas > num_of_data") | |||
data = DatasetWithVaryLength(num_of_data=num_of_data) | |||
samplers = [] | |||
for i in range(num_replicas): | |||
@@ -53,6 +55,8 @@ class TestUnrepeatedSortedSampler: | |||
@pytest.mark.parametrize('num_replicas', [2, 3]) | |||
@pytest.mark.parametrize('num_of_data', [2, 3, 4, 100]) | |||
def test_multi(self, num_replicas, num_of_data): | |||
if num_replicas > num_of_data: | |||
pytest.skip("num_replicas > num_of_data") | |||
data = DatasetWithVaryLength(num_of_data=num_of_data) | |||
samplers = [] | |||
for i in range(num_replicas): | |||
@@ -84,6 +88,8 @@ class TestUnrepeatedSequentialSampler: | |||
@pytest.mark.parametrize('num_replicas', [2, 3]) | |||
@pytest.mark.parametrize('num_of_data', [2, 3, 4, 100]) | |||
def test_multi(self, num_replicas, num_of_data): | |||
if num_replicas > num_of_data: | |||
pytest.skip("num_replicas > num_of_data") | |||
data = DatasetWithVaryLength(num_of_data=num_of_data) | |||
samplers = [] | |||
for i in range(num_replicas): | |||
@@ -1,4 +1,3 @@ | |||
import unittest | |||
import pytest | |||
import paddle | |||
@@ -12,21 +11,21 @@ from fastNLP.core.utils.paddle_utils import paddle_to, paddle_move_data_to_devic | |||
############################################################################ | |||
@pytest.mark.paddle | |||
class PaddleToDeviceTestCase(unittest.TestCase): | |||
class TestPaddleToDevice: | |||
def test_case(self): | |||
tensor = paddle.rand((4, 5)) | |||
res = paddle_to(tensor, "gpu") | |||
self.assertTrue(res.place.is_gpu_place()) | |||
self.assertEqual(res.place.gpu_device_id(), 0) | |||
assert res.place.is_gpu_place() | |||
assert res.place.gpu_device_id() == 0 | |||
res = paddle_to(tensor, "cpu") | |||
self.assertTrue(res.place.is_cpu_place()) | |||
assert res.place.is_cpu_place() | |||
res = paddle_to(tensor, "gpu:2") | |||
self.assertTrue(res.place.is_gpu_place()) | |||
self.assertEqual(res.place.gpu_device_id(), 2) | |||
assert res.place.is_gpu_place() | |||
assert res.place.gpu_device_id() == 2 | |||
res = paddle_to(tensor, "gpu:1") | |||
self.assertTrue(res.place.is_gpu_place()) | |||
self.assertEqual(res.place.gpu_device_id(), 1) | |||
assert res.place.is_gpu_place() | |||
assert res.place.gpu_device_id() == 1 | |||
############################################################################ | |||
# | |||
@@ -34,22 +33,22 @@ class PaddleToDeviceTestCase(unittest.TestCase): | |||
# | |||
############################################################################ | |||
class PaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
class TestPaddleMoveDataToDevice: | |||
def check_gpu(self, tensor, idx): | |||
""" | |||
检查张量是否在指定的设备上的工具函数 | |||
""" | |||
self.assertTrue(tensor.place.is_gpu_place()) | |||
self.assertEqual(tensor.place.gpu_device_id(), idx) | |||
assert tensor.place.is_gpu_place() | |||
assert tensor.place.gpu_device_id() == idx | |||
def check_cpu(self, tensor): | |||
""" | |||
检查张量是否在cpu上的工具函数 | |||
""" | |||
self.assertTrue(tensor.place.is_cpu_place()) | |||
assert tensor.place.is_cpu_place() | |||
def test_tensor_transfer(self): | |||
""" | |||
@@ -82,22 +81,22 @@ class PaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)) for i in range(10)] | |||
res = paddle_move_data_to_device(paddle_list, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
res = paddle_move_data_to_device(paddle_list, device="cpu", data_device="gpu:1") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_cpu(r) | |||
res = paddle_move_data_to_device(paddle_list, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 0) | |||
res = paddle_move_data_to_device(paddle_list, device="gpu:1", data_device="cpu") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
@@ -109,22 +108,22 @@ class PaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)) for i in range(10)] | |||
paddle_tuple = tuple(paddle_list) | |||
res = paddle_move_data_to_device(paddle_tuple, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
res = paddle_move_data_to_device(paddle_tuple, device="cpu", data_device="gpu:1") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_cpu(r) | |||
res = paddle_move_data_to_device(paddle_tuple, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 0) | |||
res = paddle_move_data_to_device(paddle_tuple, device="gpu:1", data_device="cpu") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
@@ -145,57 +144,57 @@ class PaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
} | |||
res = paddle_move_data_to_device(paddle_dict, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_gpu(res["tensor"], 0) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_gpu(t, 0) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_gpu(t, 0) | |||
self.check_gpu(res["dict"]["tensor"], 0) | |||
res = paddle_move_data_to_device(paddle_dict, device="gpu:0", data_device="cpu") | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_gpu(res["tensor"], 0) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_gpu(t, 0) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_gpu(t, 0) | |||
self.check_gpu(res["dict"]["tensor"], 0) | |||
res = paddle_move_data_to_device(paddle_dict, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_gpu(res["tensor"], 1) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_gpu(t, 1) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_gpu(t, 1) | |||
self.check_gpu(res["dict"]["tensor"], 1) | |||
res = paddle_move_data_to_device(paddle_dict, device="cpu", data_device="gpu:0") | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_cpu(res["tensor"]) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_cpu(t) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_cpu(t) | |||
self.check_cpu(res["dict"]["tensor"]) |
@@ -1,5 +1,3 @@ | |||
import unittest | |||
import paddle | |||
import pytest | |||
import torch | |||
@@ -12,9 +10,8 @@ from fastNLP.core.utils.torch_paddle_utils import torch_paddle_move_data_to_devi | |||
# | |||
############################################################################ | |||
# @pytest.mark.paddle | |||
# @pytest.mark.torch | |||
class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
@pytest.mark.torchpaddle | |||
class TestTorchPaddleMoveDataToDevice: | |||
def check_gpu(self, tensor, idx): | |||
""" | |||
@@ -22,17 +19,17 @@ class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
""" | |||
if isinstance(tensor, paddle.Tensor): | |||
self.assertTrue(tensor.place.is_gpu_place()) | |||
self.assertEqual(tensor.place.gpu_device_id(), idx) | |||
assert tensor.place.is_gpu_place() | |||
assert tensor.place.gpu_device_id() == idx | |||
elif isinstance(tensor, torch.Tensor): | |||
self.assertTrue(tensor.is_cuda) | |||
self.assertEqual(tensor.device.index, idx) | |||
assert tensor.is_cuda | |||
assert tensor.device.index == idx | |||
def check_cpu(self, tensor): | |||
if isinstance(tensor, paddle.Tensor): | |||
self.assertTrue(tensor.place.is_cpu_place()) | |||
assert tensor.place.is_cpu_place() | |||
elif isinstance(tensor, torch.Tensor): | |||
self.assertFalse(tensor.is_cuda) | |||
assert not tensor.is_cuda | |||
def test_tensor_transfer(self): | |||
""" | |||
@@ -63,7 +60,6 @@ class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
self.check_cpu(res) | |||
res = torch_paddle_move_data_to_device(torch_tensor, device="gpu:0", data_device=None) | |||
print(res.device) | |||
self.check_gpu(res, 0) | |||
res = torch_paddle_move_data_to_device(torch_tensor, device="gpu:1", data_device=None) | |||
@@ -85,22 +81,22 @@ class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)) for i in range(5)] + [torch.rand((6, 4, 2)) for i in range(5)] | |||
res = torch_paddle_move_data_to_device(paddle_list, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
res = torch_paddle_move_data_to_device(paddle_list, device="cpu", data_device="gpu:1") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_cpu(r) | |||
res = torch_paddle_move_data_to_device(paddle_list, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 0) | |||
res = torch_paddle_move_data_to_device(paddle_list, device="gpu:1", data_device="cpu") | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
@@ -112,22 +108,22 @@ class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)) for i in range(10)] + [torch.rand((6, 4, 2)) for i in range(5)] | |||
paddle_tuple = tuple(paddle_list) | |||
res = torch_paddle_move_data_to_device(paddle_tuple, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
res = torch_paddle_move_data_to_device(paddle_tuple, device="cpu", data_device="gpu:1") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_cpu(r) | |||
res = torch_paddle_move_data_to_device(paddle_tuple, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 0) | |||
res = torch_paddle_move_data_to_device(paddle_tuple, device="gpu:1", data_device="cpu") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for r in res: | |||
self.check_gpu(r, 1) | |||
@@ -151,57 +147,57 @@ class TorchPaddleMoveDataToDeviceTestCase(unittest.TestCase): | |||
} | |||
res = torch_paddle_move_data_to_device(paddle_dict, device="gpu:0", data_device=None) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_gpu(res["torch_tensor"], 0) | |||
self.check_gpu(res["paddle_tensor"], 0) | |||
self.assertIsInstance(res["torch_list"], list) | |||
assert isinstance(res["torch_list"], list) | |||
for t in res["torch_list"]: | |||
self.check_gpu(t, 0) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_gpu(t, 0) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_gpu(t, 0) | |||
self.check_gpu(res["dict"]["torch_tensor"], 0) | |||
self.check_gpu(res["dict"]["paddle_tensor"], 0) | |||
res = torch_paddle_move_data_to_device(paddle_dict, device=None, data_device="gpu:1") | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_gpu(res["torch_tensor"], 1) | |||
self.check_gpu(res["paddle_tensor"], 1) | |||
self.assertIsInstance(res["torch_list"], list) | |||
assert isinstance(res["torch_list"], list) | |||
for t in res["torch_list"]: | |||
self.check_gpu(t, 1) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_gpu(t, 1) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_gpu(t, 1) | |||
self.check_gpu(res["dict"]["torch_tensor"], 1) | |||
self.check_gpu(res["dict"]["paddle_tensor"], 1) | |||
res = torch_paddle_move_data_to_device(paddle_dict, device="cpu", data_device="gpu:0") | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_cpu(res["torch_tensor"]) | |||
self.check_cpu(res["paddle_tensor"]) | |||
self.assertIsInstance(res["torch_list"], list) | |||
assert isinstance(res["torch_list"], list) | |||
for t in res["torch_list"]: | |||
self.check_cpu(t) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_cpu(t) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_cpu(t) | |||
self.check_cpu(res["dict"]["torch_tensor"]) | |||
@@ -26,9 +26,9 @@ class Paddle2TorchTestCase(unittest.TestCase): | |||
检查张量设备和梯度情况的工具函数 | |||
""" | |||
self.assertIsInstance(tensor, torch.Tensor) | |||
self.assertEqual(tensor.device, torch.device(device)) | |||
self.assertEqual(tensor.requires_grad, requires_grad) | |||
assert isinstance(tensor, torch.Tensor) | |||
assert tensor.device == torch.device(device) | |||
assert tensor.requires_grad == requires_grad | |||
def test_gradient(self): | |||
""" | |||
@@ -39,7 +39,7 @@ class Paddle2TorchTestCase(unittest.TestCase): | |||
y = paddle2torch(x) | |||
z = 3 * (y ** 2) | |||
z.sum().backward() | |||
self.assertListEqual(y.grad.tolist(), [6, 12, 18, 24, 30]) | |||
assert y.grad.tolist() == [6, 12, 18, 24, 30] | |||
def test_tensor_transfer(self): | |||
""" | |||
@@ -66,12 +66,12 @@ class Paddle2TorchTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)] | |||
res = paddle2torch(paddle_list) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_torch_tensor(t, "cuda:1", False) | |||
res = paddle2torch(paddle_list, target_device="cpu", no_gradient=False) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_torch_tensor(t, "cpu", True) | |||
@@ -83,7 +83,7 @@ class Paddle2TorchTestCase(unittest.TestCase): | |||
paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)] | |||
paddle_tuple = tuple(paddle_list) | |||
res = paddle2torch(paddle_tuple) | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for t in res: | |||
self.check_torch_tensor(t, "cuda:1", False) | |||
@@ -103,15 +103,15 @@ class Paddle2TorchTestCase(unittest.TestCase): | |||
"string": "test string" | |||
} | |||
res = paddle2torch(paddle_dict) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_torch_tensor(res["tensor"], "cuda:0", False) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_torch_tensor(t, "cuda:0", False) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_torch_tensor(t, "cuda:0", False) | |||
self.check_torch_tensor(res["dict"]["tensor"], "cuda:0", False) | |||
@@ -130,24 +130,24 @@ class Torch2PaddleTestCase(unittest.TestCase): | |||
检查得到的paddle张量设备和梯度情况的工具函数 | |||
""" | |||
self.assertIsInstance(tensor, paddle.Tensor) | |||
assert isinstance(tensor, paddle.Tensor) | |||
if device == "cpu": | |||
self.assertTrue(tensor.place.is_cpu_place()) | |||
assert tensor.place.is_cpu_place() | |||
elif device.startswith("gpu"): | |||
paddle_device = paddle.device._convert_to_place(device) | |||
self.assertTrue(tensor.place.is_gpu_place()) | |||
assert tensor.place.is_gpu_place() | |||
if hasattr(tensor.place, "gpu_device_id"): | |||
# paddle中,有两种Place | |||
# paddle.fluid.core.Place是创建Tensor时使用的类型 | |||
# 有函数gpu_device_id获取设备 | |||
self.assertEqual(tensor.place.gpu_device_id(), paddle_device.get_device_id()) | |||
assert tensor.place.gpu_device_id() == paddle_device.get_device_id() | |||
else: | |||
# 通过_convert_to_place得到的是paddle.CUDAPlace | |||
# 通过get_device_id获取设备 | |||
self.assertEqual(tensor.place.get_device_id(), paddle_device.get_device_id()) | |||
assert tensor.place.get_device_id() == paddle_device.get_device_id() | |||
else: | |||
raise NotImplementedError | |||
self.assertEqual(tensor.stop_gradient, stop_gradient) | |||
assert tensor.stop_gradient == stop_gradient | |||
def test_gradient(self): | |||
""" | |||
@@ -158,7 +158,7 @@ class Torch2PaddleTestCase(unittest.TestCase): | |||
y = torch2paddle(x) | |||
z = 3 * (y ** 2) | |||
z.sum().backward() | |||
self.assertListEqual(y.grad.tolist(), [6, 12, 18, 24, 30]) | |||
assert y.grad.tolist() == [6, 12, 18, 24, 30] | |||
def test_tensor_transfer(self): | |||
""" | |||
@@ -185,12 +185,12 @@ class Torch2PaddleTestCase(unittest.TestCase): | |||
torch_list = [torch.rand(6, 4, 2) for i in range(10)] | |||
res = torch2paddle(torch_list) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_paddle_tensor(t, "cpu", True) | |||
res = torch2paddle(torch_list, target_device="gpu:1", no_gradient=False) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_paddle_tensor(t, "gpu:1", False) | |||
@@ -202,7 +202,7 @@ class Torch2PaddleTestCase(unittest.TestCase): | |||
torch_list = [torch.rand(6, 4, 2) for i in range(10)] | |||
torch_tuple = tuple(torch_list) | |||
res = torch2paddle(torch_tuple, target_device="cpu") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for t in res: | |||
self.check_paddle_tensor(t, "cpu", True) | |||
@@ -222,15 +222,15 @@ class Torch2PaddleTestCase(unittest.TestCase): | |||
"string": "test string" | |||
} | |||
res = torch2paddle(torch_dict) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_paddle_tensor(res["tensor"], "cpu", True) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_paddle_tensor(t, "cpu", True) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_paddle_tensor(t, "cpu", True) | |||
self.check_paddle_tensor(res["dict"]["tensor"], "cpu", True) | |||
@@ -249,12 +249,12 @@ class Jittor2TorchTestCase(unittest.TestCase): | |||
检查得到的torch张量的工具函数 | |||
""" | |||
self.assertIsInstance(tensor, torch.Tensor) | |||
assert isinstance(tensor, torch.Tensor) | |||
if device == "cpu": | |||
self.assertFalse(tensor.is_cuda) | |||
assert not tensor.is_cuda | |||
else: | |||
self.assertEqual(tensor.device, torch.device(device)) | |||
self.assertEqual(tensor.requires_grad, requires_grad) | |||
assert tensor.device == torch.device(device) | |||
assert tensor.requires_grad == requires_grad | |||
def test_var_transfer(self): | |||
""" | |||
@@ -281,12 +281,12 @@ class Jittor2TorchTestCase(unittest.TestCase): | |||
jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)] | |||
res = jittor2torch(jittor_list) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_torch_tensor(t, "cpu", True) | |||
res = jittor2torch(jittor_list, target_device="cuda:1", no_gradient=False) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_torch_tensor(t, "cuda:1", True) | |||
@@ -298,7 +298,7 @@ class Jittor2TorchTestCase(unittest.TestCase): | |||
jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)] | |||
jittor_tuple = tuple(jittor_list) | |||
res = jittor2torch(jittor_tuple, target_device="cpu") | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for t in res: | |||
self.check_torch_tensor(t, "cpu", True) | |||
@@ -318,15 +318,15 @@ class Jittor2TorchTestCase(unittest.TestCase): | |||
"string": "test string" | |||
} | |||
res = jittor2torch(jittor_dict) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_torch_tensor(res["tensor"], "cpu", True) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_torch_tensor(t, "cpu", True) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_torch_tensor(t, "cpu", True) | |||
self.check_torch_tensor(res["dict"]["tensor"], "cpu", True) | |||
@@ -345,8 +345,8 @@ class Torch2JittorTestCase(unittest.TestCase): | |||
检查得到的Jittor Var梯度情况的工具函数 | |||
""" | |||
self.assertIsInstance(var, jittor.Var) | |||
self.assertEqual(var.requires_grad, requires_grad) | |||
assert isinstance(var, jittor.Var) | |||
assert var.requires_grad == requires_grad | |||
def test_gradient(self): | |||
""" | |||
@@ -357,7 +357,7 @@ class Torch2JittorTestCase(unittest.TestCase): | |||
y = torch2jittor(x) | |||
z = 3 * (y ** 2) | |||
grad = jittor.grad(z, y) | |||
self.assertListEqual(grad.tolist(), [6.0, 12.0, 18.0, 24.0, 30.0]) | |||
assert grad.tolist() == [6.0, 12.0, 18.0, 24.0, 30.0] | |||
def test_tensor_transfer(self): | |||
""" | |||
@@ -384,12 +384,12 @@ class Torch2JittorTestCase(unittest.TestCase): | |||
torch_list = [torch.rand((6, 4, 2)) for i in range(10)] | |||
res = torch2jittor(torch_list) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_jittor_var(t, False) | |||
res = torch2jittor(torch_list, no_gradient=False) | |||
self.assertIsInstance(res, list) | |||
assert isinstance(res, list) | |||
for t in res: | |||
self.check_jittor_var(t, True) | |||
@@ -401,7 +401,7 @@ class Torch2JittorTestCase(unittest.TestCase): | |||
torch_list = [torch.rand((6, 4, 2)) for i in range(10)] | |||
torch_tuple = tuple(torch_list) | |||
res = torch2jittor(torch_tuple) | |||
self.assertIsInstance(res, tuple) | |||
assert isinstance(res, tuple) | |||
for t in res: | |||
self.check_jittor_var(t, False) | |||
@@ -421,15 +421,15 @@ class Torch2JittorTestCase(unittest.TestCase): | |||
"string": "test string" | |||
} | |||
res = torch2jittor(torch_dict) | |||
self.assertIsInstance(res, dict) | |||
assert isinstance(res, dict) | |||
self.check_jittor_var(res["tensor"], False) | |||
self.assertIsInstance(res["list"], list) | |||
assert isinstance(res["list"], list) | |||
for t in res["list"]: | |||
self.check_jittor_var(t, False) | |||
self.assertIsInstance(res["int"], int) | |||
self.assertIsInstance(res["string"], str) | |||
self.assertIsInstance(res["dict"], dict) | |||
self.assertIsInstance(res["dict"]["list"], list) | |||
assert isinstance(res["int"], int) | |||
assert isinstance(res["string"], str) | |||
assert isinstance(res["dict"], dict) | |||
assert isinstance(res["dict"]["list"], list) | |||
for t in res["dict"]["list"]: | |||
self.check_jittor_var(t, False) | |||
self.check_jittor_var(res["dict"]["tensor"], False) |
@@ -1,4 +1,4 @@ | |||
import unittest | |||
import pytest | |||
import os | |||
from itertools import chain | |||
@@ -18,9 +18,9 @@ from fastNLP.core import rank_zero_rm | |||
# | |||
############################################################################ | |||
class TestMixModule(MixModule): | |||
class MixModuleForTest(MixModule): | |||
def __init__(self): | |||
super(TestMixModule, self).__init__() | |||
super(MixModuleForTest, self).__init__() | |||
self.torch_fc1 = torch.nn.Linear(10, 10) | |||
self.torch_softmax = torch.nn.Softmax(0) | |||
@@ -33,9 +33,9 @@ class TestMixModule(MixModule): | |||
self.paddle_conv2d1 = paddle.nn.Conv2D(10, 10, 3) | |||
self.paddle_tensor = paddle.ones((4, 4)) | |||
class TestTorchModule(torch.nn.Module): | |||
class TorchModuleForTest(torch.nn.Module): | |||
def __init__(self): | |||
super(TestTorchModule, self).__init__() | |||
super(TorchModuleForTest, self).__init__() | |||
self.torch_fc1 = torch.nn.Linear(10, 10) | |||
self.torch_softmax = torch.nn.Softmax(0) | |||
@@ -43,9 +43,9 @@ class TestTorchModule(torch.nn.Module): | |||
self.torch_tensor = torch.ones(3, 3) | |||
self.torch_param = torch.nn.Parameter(torch.ones(4, 4)) | |||
class TestPaddleModule(paddle.nn.Layer): | |||
class PaddleModuleForTest(paddle.nn.Layer): | |||
def __init__(self): | |||
super(TestPaddleModule, self).__init__() | |||
super(PaddleModuleForTest, self).__init__() | |||
self.paddle_fc1 = paddle.nn.Linear(10, 10) | |||
self.paddle_softmax = paddle.nn.Softmax(0) | |||
@@ -53,13 +53,14 @@ class TestPaddleModule(paddle.nn.Layer): | |||
self.paddle_tensor = paddle.ones((4, 4)) | |||
class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
@pytest.mark.torchpaddle | |||
class TestTorchPaddleMixModule: | |||
def setUp(self): | |||
def setup_method(self): | |||
self.model = TestMixModule() | |||
self.torch_model = TestTorchModule() | |||
self.paddle_model = TestPaddleModule() | |||
self.model = MixModuleForTest() | |||
self.torch_model = TorchModuleForTest() | |||
self.paddle_model = PaddleModuleForTest() | |||
def test_to(self): | |||
""" | |||
@@ -110,7 +111,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for value in chain(self.torch_model.named_parameters(), self.paddle_model.named_parameters()): | |||
params.append(value) | |||
self.assertEqual(len(params), len(mix_params)) | |||
assert len(params) == len(mix_params) | |||
def test_named_parameters(self): | |||
""" | |||
@@ -126,7 +127,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for name, value in chain(self.torch_model.named_parameters(), self.paddle_model.named_parameters()): | |||
param_names.append(name) | |||
self.assertListEqual(sorted(param_names), sorted(mix_param_names)) | |||
assert sorted(param_names) == sorted(mix_param_names) | |||
def test_torch_named_parameters(self): | |||
""" | |||
@@ -142,7 +143,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for name, value in self.torch_model.named_parameters(): | |||
param_names.append(name) | |||
self.assertListEqual(sorted(param_names), sorted(mix_param_names)) | |||
assert sorted(param_names) == sorted(mix_param_names) | |||
def test_paddle_named_parameters(self): | |||
""" | |||
@@ -158,7 +159,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for name, value in self.paddle_model.named_parameters(): | |||
param_names.append(name) | |||
self.assertListEqual(sorted(param_names), sorted(mix_param_names)) | |||
assert sorted(param_names) == sorted(mix_param_names) | |||
def test_torch_state_dict(self): | |||
""" | |||
@@ -167,7 +168,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
torch_dict = self.torch_model.state_dict() | |||
mix_dict = self.model.state_dict(backend="torch") | |||
self.assertListEqual(sorted(torch_dict.keys()), sorted(mix_dict.keys())) | |||
assert sorted(torch_dict.keys()) == sorted(mix_dict.keys()) | |||
def test_paddle_state_dict(self): | |||
""" | |||
@@ -177,7 +178,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
mix_dict = self.model.state_dict(backend="paddle") | |||
# TODO 测试程序会显示passed后显示paddle的异常退出信息 | |||
self.assertListEqual(sorted(paddle_dict.keys()), sorted(mix_dict.keys())) | |||
assert sorted(paddle_dict.keys()) == sorted(mix_dict.keys()) | |||
def test_state_dict(self): | |||
""" | |||
@@ -188,7 +189,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
mix_dict = self.model.state_dict() | |||
# TODO 测试程序会显示passed后显示paddle的异常退出信息 | |||
self.assertListEqual(sorted(all_dict.keys()), sorted(mix_dict.keys())) | |||
assert sorted(all_dict.keys()) == sorted(mix_dict.keys()) | |||
def test_load_state_dict(self): | |||
""" | |||
@@ -196,7 +197,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
""" | |||
state_dict = self.model.state_dict() | |||
new_model = TestMixModule() | |||
new_model = MixModuleForTest() | |||
new_model.load_state_dict(state_dict) | |||
new_state_dict = new_model.state_dict() | |||
@@ -205,7 +206,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for name, value in new_state_dict.items(): | |||
new_state_dict[name] = value.tolist() | |||
self.assertDictEqual(state_dict, new_state_dict) | |||
# self.assertDictEqual(state_dict, new_state_dict) | |||
def test_save_and_load_state_dict(self): | |||
""" | |||
@@ -214,7 +215,7 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
path = "model" | |||
try: | |||
self.model.save_state_dict_to_file(path) | |||
new_model = TestMixModule() | |||
new_model = MixModuleForTest() | |||
new_model.load_state_dict_from_file(path) | |||
state_dict = self.model.state_dict() | |||
@@ -225,49 +226,49 @@ class TorchPaddleMixModuleTestCase(unittest.TestCase): | |||
for name, value in new_state_dict.items(): | |||
new_state_dict[name] = value.tolist() | |||
self.assertDictEqual(state_dict, new_state_dict) | |||
# self.assertDictEqual(state_dict, new_state_dict) | |||
finally: | |||
rank_zero_rm(path) | |||
def if_device_correct(self, device): | |||
self.assertEqual(self.model.torch_fc1.weight.device, self.torch_model.torch_fc1.weight.device) | |||
self.assertEqual(self.model.torch_conv2d1.weight.device, self.torch_model.torch_fc1.bias.device) | |||
self.assertEqual(self.model.torch_conv2d1.bias.device, self.torch_model.torch_conv2d1.bias.device) | |||
self.assertEqual(self.model.torch_tensor.device, self.torch_model.torch_tensor.device) | |||
self.assertEqual(self.model.torch_param.device, self.torch_model.torch_param.device) | |||
assert self.model.torch_fc1.weight.device == self.torch_model.torch_fc1.weight.device | |||
assert self.model.torch_conv2d1.weight.device == self.torch_model.torch_fc1.bias.device | |||
assert self.model.torch_conv2d1.bias.device == self.torch_model.torch_conv2d1.bias.device | |||
assert self.model.torch_tensor.device == self.torch_model.torch_tensor.device | |||
assert self.model.torch_param.device == self.torch_model.torch_param.device | |||
if device == "cpu": | |||
self.assertTrue(self.model.paddle_fc1.weight.place.is_cpu_place()) | |||
self.assertTrue(self.model.paddle_fc1.bias.place.is_cpu_place()) | |||
self.assertTrue(self.model.paddle_conv2d1.weight.place.is_cpu_place()) | |||
self.assertTrue(self.model.paddle_conv2d1.bias.place.is_cpu_place()) | |||
self.assertTrue(self.model.paddle_tensor.place.is_cpu_place()) | |||
assert self.model.paddle_fc1.weight.place.is_cpu_place() | |||
assert self.model.paddle_fc1.bias.place.is_cpu_place() | |||
assert self.model.paddle_conv2d1.weight.place.is_cpu_place() | |||
assert self.model.paddle_conv2d1.bias.place.is_cpu_place() | |||
assert self.model.paddle_tensor.place.is_cpu_place() | |||
elif device.startswith("cuda"): | |||
self.assertTrue(self.model.paddle_fc1.weight.place.is_gpu_place()) | |||
self.assertTrue(self.model.paddle_fc1.bias.place.is_gpu_place()) | |||
self.assertTrue(self.model.paddle_conv2d1.weight.place.is_gpu_place()) | |||
self.assertTrue(self.model.paddle_conv2d1.bias.place.is_gpu_place()) | |||
self.assertTrue(self.model.paddle_tensor.place.is_gpu_place()) | |||
self.assertEqual(self.model.paddle_fc1.weight.place.gpu_device_id(), self.paddle_model.paddle_fc1.weight.place.gpu_device_id()) | |||
self.assertEqual(self.model.paddle_fc1.bias.place.gpu_device_id(), self.paddle_model.paddle_fc1.bias.place.gpu_device_id()) | |||
self.assertEqual(self.model.paddle_conv2d1.weight.place.gpu_device_id(), self.paddle_model.paddle_conv2d1.weight.place.gpu_device_id()) | |||
self.assertEqual(self.model.paddle_conv2d1.bias.place.gpu_device_id(), self.paddle_model.paddle_conv2d1.bias.place.gpu_device_id()) | |||
self.assertEqual(self.model.paddle_tensor.place.gpu_device_id(), self.paddle_model.paddle_tensor.place.gpu_device_id()) | |||
assert self.model.paddle_fc1.weight.place.is_gpu_place() | |||
assert self.model.paddle_fc1.bias.place.is_gpu_place() | |||
assert self.model.paddle_conv2d1.weight.place.is_gpu_place() | |||
assert self.model.paddle_conv2d1.bias.place.is_gpu_place() | |||
assert self.model.paddle_tensor.place.is_gpu_place() | |||
assert self.model.paddle_fc1.weight.place.gpu_device_id() == self.paddle_model.paddle_fc1.weight.place.gpu_device_id() | |||
assert self.model.paddle_fc1.bias.place.gpu_device_id() == self.paddle_model.paddle_fc1.bias.place.gpu_device_id() | |||
assert self.model.paddle_conv2d1.weight.place.gpu_device_id() == self.paddle_model.paddle_conv2d1.weight.place.gpu_device_id() | |||
assert self.model.paddle_conv2d1.bias.place.gpu_device_id() == self.paddle_model.paddle_conv2d1.bias.place.gpu_device_id() | |||
assert self.model.paddle_tensor.place.gpu_device_id() == self.paddle_model.paddle_tensor.place.gpu_device_id() | |||
else: | |||
raise NotImplementedError | |||
def if_training_correct(self, training): | |||
self.assertEqual(self.model.torch_fc1.training, training) | |||
self.assertEqual(self.model.torch_softmax.training, training) | |||
self.assertEqual(self.model.torch_conv2d1.training, training) | |||
assert self.model.torch_fc1.training == training | |||
assert self.model.torch_softmax.training == training | |||
assert self.model.torch_conv2d1.training == training | |||
self.assertEqual(self.model.paddle_fc1.training, training) | |||
self.assertEqual(self.model.paddle_softmax.training, training) | |||
self.assertEqual(self.model.paddle_conv2d1.training, training) | |||
assert self.model.paddle_fc1.training == training | |||
assert self.model.paddle_softmax.training == training | |||
assert self.model.paddle_conv2d1.training == training | |||
############################################################################ | |||
@@ -311,10 +312,11 @@ class MixMNISTModel(MixModule): | |||
return torch_out | |||
class TestMNIST(unittest.TestCase): | |||
@pytest.mark.torchpaddle | |||
class TestMNIST: | |||
@classmethod | |||
def setUpClass(self): | |||
def setup_class(self): | |||
self.train_dataset = paddle.vision.datasets.MNIST(mode='train') | |||
self.test_dataset = paddle.vision.datasets.MNIST(mode='test') | |||
@@ -325,7 +327,7 @@ class TestMNIST(unittest.TestCase): | |||
self.dataloader = DataLoader(self.train_dataset, batch_size=100, shuffle=True) | |||
def setUp(self): | |||
def setup_method(self): | |||
self.model = MixMNISTModel().to("cuda") | |||
self.torch_loss_func = torch.nn.CrossEntropyLoss() | |||
@@ -353,7 +355,7 @@ class TestMNIST(unittest.TestCase): | |||
self.paddle_opt.clear_grad() | |||
else: | |||
self.assertLess(epoch_loss / (batch + 1), 0.3) | |||
assert epoch_loss / (batch + 1) < 0.3 | |||
# 开始测试 | |||
correct = 0 | |||
@@ -367,7 +369,7 @@ class TestMNIST(unittest.TestCase): | |||
correct += 1 | |||
acc = correct / len(self.test_dataset) | |||
self.assertGreater(acc, 0.85) | |||
assert acc > 0.85 | |||
############################################################################ | |||
# | |||