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[npu] add update_loss_scaling npu min value (#35270)
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python/paddle/fluid/tests/unittests/npu/test_update_loss_scaling_min_op_npu.py
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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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. | ||
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| import unittest | ||
| import numpy as np | ||
| import sys | ||
| import os | ||
| sys.path.append("..") | ||
| from op_test import OpTest | ||
| import paddle | ||
| import paddle.fluid as fluid | ||
| import paddle.fluid.contrib.mixed_precision.amp_nn as amp_nn | ||
| from test_update_loss_scaling_op_npu import TestUpdateLossScalingOpBad | ||
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| paddle.enable_static() | ||
| SEED = 2021 | ||
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| class TestUpdateLossScalingOpMinLossScalingBad(TestUpdateLossScalingOpBad): | ||
| def setUp(self): | ||
| self.set_npu() | ||
| self.op_type = "update_loss_scaling" | ||
| self.place = paddle.NPUPlace(0) | ||
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| self.init() | ||
| fluid.core.globals()['FLAGS_min_loss_scaling'] = 1639 | ||
| found_inf = np.array([True], dtype=np.bool) | ||
| x = np.random.random((1024, 1024)).astype(self.dtype) | ||
| i = np.random.randint(0, 1024, 1) | ||
| j = np.random.randint(0, 1024, 1) | ||
| x[i[0]][j[0]] = np.inf | ||
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| self.inputs = { | ||
| 'X': [('x0', x)], | ||
| 'FoundInfinite': found_inf, | ||
| 'PrevLossScaling': self.prev_loss_scaling, | ||
| 'InGoodSteps': self.num_good_steps, | ||
| 'InBadSteps': self.num_bad_steps | ||
| } | ||
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| self.outputs = { | ||
| 'Out': [('out0', np.zeros_like(x))], | ||
| 'LossScaling': np.array([1639.0]).astype(self.dtype), | ||
| 'OutGoodSteps': self.zero_steps, | ||
| 'OutBadSteps': self.zero_steps | ||
| } | ||
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| def init(self): | ||
| self.incr_ratio = 2.0 | ||
| self.decr_ratio = 0.8 | ||
| self.dtype = np.float32 | ||
| self.prev_loss_scaling = np.array([2048]).astype(self.dtype) | ||
| self.num_good_steps = np.array([999], dtype=np.int32) | ||
| self.num_bad_steps = np.array([1], dtype=np.int32) | ||
| self.zero_steps = np.array([0], dtype=np.int32) | ||
| self.attrs = { | ||
| 'incr_every_n_steps': 1000, | ||
| 'decr_every_n_nan_or_inf': 2, | ||
| 'incr_ratio': self.incr_ratio, | ||
| 'decr_ratio': self.decr_ratio, | ||
| } | ||
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| if __name__ == '__main__': | ||
| unittest.main() |