forked from mindspore-Ecosystem/mindspore
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
# Copyright 2023 Huawei Technologies Co., Ltd
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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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# ============================================================================
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'''test overflow'''
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import pytest
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import numpy as np
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from mindspore import Tensor, Parameter, nn, ops
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import mindspore.amp as amp
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import mindspore as ms
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class Net(nn.Cell):
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def __init__(self, in_features, out_features):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.full([in_features, out_features], 2, np.float16)),
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name='weight')
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self.matmul = ops.MatMul()
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def construct(self, x):
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output = self.matmul(x, self.weight)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_functional_amp_overflow(mode):
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"""
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Feature: mindspore.amp.overflow
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Description: test amp overflow
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Expectation: Success.
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"""
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ms.set_context(mode=mode)
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size, in_features, out_features = 1, 2, 2
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net = Net(in_features, out_features)
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loss_fn = nn.MSELoss()
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def forward_fn(data, label):
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logits = net(data)
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loss = loss_fn(logits, label)
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return loss, logits
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grad_fn = ops.value_and_grad(forward_fn, grad_position=None, weights=net.trainable_params())
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@ms.jit
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def train_step(data, label):
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(loss, _), grads = grad_fn(data, label)
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is_finite = amp.all_finite(grads)
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return loss, is_finite
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shape = (size, in_features)
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inputs = [
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Tensor(np.full(shape, -np.inf, np.float16)),
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Tensor(np.full(shape, 0, np.float16)),
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Tensor(np.full(shape, 40000, np.float16)),
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Tensor(np.full(shape, 10, np.float16)),
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Tensor(np.full(shape, np.inf, np.float16)),
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]
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label = Tensor(np.full([out_features,], 0, np.float16))
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datasets = list(zip(inputs, [label for _ in range(len(inputs))]))
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expect_results = [False, True, False, True, False]
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outputs = []
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for data, label in datasets:
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_, is_finite = train_step(data, label)
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outputs.append(is_finite.asnumpy().tolist())
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assert outputs == expect_results
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