wehub-resource-sync
135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
# Copyright (c) 2022 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 tempfile
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import unittest
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import numpy as np
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from op_test_ipu import IPUD2STest
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import paddle
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class SimpleLayer(paddle.nn.Layer):
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def __init__(self, use_ipu=False):
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super().__init__()
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self.use_ipu = use_ipu
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self.conv = paddle.nn.Conv2D(
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in_channels=3, out_channels=1, kernel_size=2, stride=1
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)
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def forward(self, x, target=None):
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x = self.conv(x)
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x = paddle.flatten(x, 1, -1)
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if target is not None:
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x = paddle.nn.functional.softmax(x)
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loss = paddle.nn.functional.cross_entropy(
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x, target, reduction='none', use_softmax=False
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)
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if self.use_ipu:
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loss = paddle.incubate.identity_loss(loss, 1)
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else:
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loss = paddle.mean(loss)
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return x, loss
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return x
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class TestBase(IPUD2STest):
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def setUp(self):
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super().setUp()
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self.save_path = tempfile.TemporaryDirectory()
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def tearDown(self):
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super().tearDown()
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self.save_path.cleanup()
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def _test(self, use_ipu=False):
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paddle.seed(self.SEED)
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np.random.seed(self.SEED)
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model = SimpleLayer(use_ipu)
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specs = [
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paddle.static.InputSpec(
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name="x", shape=[32, 3, 10, 10], dtype="float32"
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),
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paddle.static.InputSpec(name="target", shape=[32], dtype="int64"),
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]
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model = paddle.jit.to_static(model, input_spec=specs, full_graph=True)
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optim = paddle.optimizer.Adam(
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learning_rate=0.01, parameters=model.parameters()
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)
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data = paddle.uniform((32, 3, 10, 10), dtype='float32')
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label = paddle.randint(0, 10, shape=[32], dtype='int64')
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model_path = '{}/model_state_dict_{}.pdparams'.format(
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self.save_path, 'ipu' if use_ipu else 'cpu'
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)
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optim_path = '{}/optim_state_dict_{}.pdopt'.format(
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self.save_path, 'ipu' if use_ipu else 'cpu'
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)
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if use_ipu:
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paddle.set_device('ipu')
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ipu_strategy = paddle.static.IpuStrategy()
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ipu_strategy.set_graph_config(
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num_ipus=1,
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is_training=True,
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micro_batch_size=1,
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enable_manual_shard=False,
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)
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ipu_strategy.set_precision_config(enable_fp16=True)
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ipu_strategy.set_optimizer(optim)
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data = data.astype(np.float16)
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epochs = 100
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result = []
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for _ in range(epochs):
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# ipu only needs call model() to do forward/backward/grad_update
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pred, loss = model(data, label)
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if not use_ipu:
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loss.backward()
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optim.step()
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optim.clear_grad()
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result.append(loss)
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if use_ipu:
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paddle.base.core.IpuBackend.get_instance().weights_to_host()
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paddle.save(model.state_dict(), model_path)
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paddle.save(optim.state_dict(), optim_path)
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model.set_state_dict(paddle.load(model_path))
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optim.set_state_dict(paddle.load(optim_path))
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for _ in range(epochs):
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# ipu only needs call model() to do forward/backward/grad_update
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pred, loss = model(data, label)
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if not use_ipu:
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loss.backward()
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optim.step()
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optim.clear_grad()
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result.append(loss)
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if use_ipu:
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ipu_strategy.release_patch()
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return np.array(result)
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def test_training(self):
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cpu_loss = self._test(False).flatten()
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ipu_loss = self._test(True).flatten()
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np.testing.assert_allclose(ipu_loss, cpu_loss, rtol=1e-05, atol=0.01)
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if __name__ == "__main__":
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unittest.main()
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