wehub-resource-sync
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import numpy as np
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import paddle
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from paddle import nn
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# NOTE(Pan Zhaowu): Using legacy linear to fulfill the hard-coded op_count in test_nan_inf.py,
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# which summon this script individually, with horrible design.
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paddle.set_flags({"FLAGS_use_legacy_linear": True})
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# os.environ["GLOG_vmodule"] = "nan_inf_utils_detail=10"
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paddle.seed(0)
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np.random.seed(0)
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class TestLayer(nn.Layer):
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def __init__(self):
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super().__init__()
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w_1_np = np.random.random([32, 400]).astype("float32")
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self.linear1 = nn.Linear(
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in_features=32,
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out_features=400,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Assign(w_1_np)
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),
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)
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w_2_np = np.random.random([400, 10]).astype("float32")
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self.linear2 = nn.Linear(
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in_features=400,
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out_features=10,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Assign(w_2_np)
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),
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)
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def forward(self, x):
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out = self.linear1(x)
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out = nn.functional.sigmoid(out)
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out = self.linear2(out)
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mask = paddle.randint(low=0, high=2, shape=out.shape).astype("float32")
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out = paddle.divide(out, mask)
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out = nn.functional.softmax(out)
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return out
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def check_main(use_cuda, use_amp=False):
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paddle.set_device('gpu' if use_cuda else 'cpu')
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model = TestLayer()
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sgd = paddle.optimizer.SGD(
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learning_rate=0.05, parameters=model.parameters()
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)
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if use_cuda and use_amp:
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scaler = paddle.amp.GradScaler()
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x_np = 10000 * np.random.random([128, 32]).astype("float32")
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x = paddle.to_tensor(x_np)
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if use_cuda and use_amp:
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with paddle.amp.auto_cast(enable=True, dtype="float16", level="O1"):
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out = model(x)
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loss = paddle.mean(out)
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.minimize(sgd, scaled)
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else:
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out = model(x)
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loss = paddle.mean(out)
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loss.backward()
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sgd.step()
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sgd.clear_grad()
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def run_check(args):
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paddle.set_flags(
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{
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"FLAGS_check_nan_inf": 1,
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"FLAGS_check_nan_inf_level": args.check_nan_inf_level,
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}
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)
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use_cuda = args.use_cuda and paddle.is_compiled_with_cuda()
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if args.check_nan_inf_level == 0:
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if use_cuda:
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try:
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check_main(use_cuda=True, use_amp=args.use_amp)
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raise AssertionError
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except Exception as e:
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print(e)
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print(type(e))
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# Note. Enforce in cuda kernel may not catch in paddle, and
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# Exception type will be RuntimeError
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assert type(e) == OSError or type(e) == RuntimeError
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else:
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try:
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check_main(use_cuda=False, use_amp=False)
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raise AssertionError
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except Exception as e:
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print(e)
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print(type(e))
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assert type(e) == RuntimeError
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else:
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check_main(use_cuda=use_cuda, use_amp=args.use_amp)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--use_cuda', action='store_true', default=False)
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parser.add_argument('--use_amp', action='store_true', default=False)
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parser.add_argument('--check_nan_inf_level', type=int, default=0)
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args = parser.parse_args()
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run_check(args)
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