wehub-resource-sync
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
# Copyright (c) 2019 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 os
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import tempfile
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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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class TestNanInfDirCheckResult(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def generate_inputs(self, shape, low=0, high=1, dtype="float32"):
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data = np.random.random(size=shape).astype(dtype)
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x = (data * (high - low) + low) * np.random.randint(
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low=0, high=2, size=shape
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).astype(dtype)
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return x
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def get_reference_num_nan_inf(self, x):
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out = np.log(x)
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num_nan = np.sum(np.isnan(out))
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num_inf = np.sum(np.isinf(out))
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print(f"-- [reference] num_nan={num_nan}, num_inf={num_inf}")
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return num_nan, num_inf
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def get_num_nan_inf(self, x_np, use_cuda=True, output_dir=None):
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if use_cuda:
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paddle.device.set_device("gpu:0")
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else:
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paddle.device.set_device("cpu")
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x = paddle.to_tensor(x_np)
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x = x * 0.5
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out = paddle.log(x)
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if use_cuda:
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paddle.device.cuda.synchronize()
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self.assertEqual(
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os.path.exists(output_dir) and os.path.isdir(output_dir), True
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)
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num_nan = 0
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num_inf = 0
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prefix = "worker_gpu" if use_cuda else "worker_cpu"
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for filename in os.listdir(output_dir):
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if filename.startswith(prefix):
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filepath = os.path.join(output_dir, filename)
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print(f"-- Parse {filepath}")
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with open(filepath, "rb") as fp:
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for e in fp:
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err_str_list = (
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str(e)
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.replace("(", " ")
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.replace(")", " ")
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.replace(",", " ")
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.split(" ")
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)
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for err_str in err_str_list:
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if "num_nan" in err_str:
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num_nan = int(err_str.split("=")[1])
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elif "num_inf" in err_str:
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num_inf = int(err_str.split("=")[1])
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print(
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f"-- [paddle] use_cuda={use_cuda}, num_nan={num_nan}, num_inf={num_inf}"
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)
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return num_nan, num_inf
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def check_num_nan_inf(self, x_np, use_cuda, subdir):
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output_dir = self.temp_dir.name + "/" + subdir
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print(f"-- output_dir: {output_dir}")
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checker_config = paddle.amp.debugging.TensorCheckerConfig(
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enable=True,
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debug_mode=paddle.amp.debugging.DebugMode.CHECK_ALL,
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output_dir=output_dir,
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)
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paddle.amp.debugging.enable_tensor_checker(checker_config)
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num_nan_np, num_inf_np = self.get_reference_num_nan_inf(x_np)
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num_nan, num_inf = self.get_num_nan_inf(
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x_np,
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use_cuda,
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output_dir,
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)
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self.assertEqual(num_nan, num_nan_np)
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self.assertEqual(num_inf, num_inf_np)
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paddle.amp.debugging.disable_tensor_checker()
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def test_num_nan_inf(self):
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shape = [32, 32]
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x_np = self.generate_inputs(shape, -10, 10)
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self.check_num_nan_inf(
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x_np, use_cuda=False, subdir="check_nan_inf_dir_cpu"
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)
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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self.check_num_nan_inf(
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x_np, use_cuda=True, subdir="check_nan_inf_dir_gpu"
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)
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if __name__ == '__main__':
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unittest.main()
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