wehub-resource-sync
117 lines
3.7 KiB
Python
117 lines
3.7 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 unittest
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import numpy as np
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from op_test_ipu import IPUOpTest
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import paddle
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import paddle.static
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class TestWeightSharing(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_training()
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self.set_data_feed()
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self.set_feed_attr()
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self.set_op_attrs()
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def set_atol(self):
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self.atol = 1e-6
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self.rtol = 1e-5
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self.atol_fp16 = 1e-2
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self.rtol_fp16 = 1e-3
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def set_data_feed(self):
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x = np.random.randint(0, 768, size=(128, 1)).astype(np.int32)
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self.feed_cpu = {"x": x.astype(np.int64)}
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self.feed_ipu = {
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"x": np.tile(x.astype(np.int64)[np.newaxis, :], [3, 1, 1])
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}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed_cpu.values()]
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self.feed_list = list(self.feed_cpu.keys())
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def set_op_attrs(self):
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self.attrs = {}
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@IPUOpTest.static_graph
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def build_model(self):
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x = paddle.static.data(
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name=self.feed_list[0], shape=self.feed_shape[0], dtype='int64'
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)
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with paddle.static.ipu_shard_guard(index=0, stage=0):
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y = paddle.static.nn.embedding(
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input=x,
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size=[768, 768],
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dtype='float32',
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param_attr=paddle.base.ParamAttr(name='word_embedding'),
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is_sparse=False,
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)
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with paddle.static.ipu_shard_guard(index=1, stage=1):
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z = paddle.static.nn.fc(
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x=y, size=768, weight_attr=paddle.base.ParamAttr(name="fc")
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)
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with paddle.static.ipu_shard_guard(index=0, stage=2):
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out = paddle.matmul(
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x=z,
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y=self.main_prog.global_block().var('word_embedding'),
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transpose_y=True,
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)
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self.feed_list = [x.name]
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self.fetch_list = [out.name]
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def run_model(self, run_ipu):
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self.build_model()
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if run_ipu:
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place = paddle.IPUPlace()
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else:
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(self.startup_prog)
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if run_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=2,
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is_training=self.is_training,
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enable_manual_shard=True,
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)
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ipu_strategy.set_pipelining_config(
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enable_pipelining=True, batches_per_step=3
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)
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program = paddle.static.IpuCompiledProgram(
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self.main_prog, ipu_strategy=ipu_strategy
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).compile(self.feed_list, self.fetch_list)
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else:
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program = self.main_prog
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feed = self.feed_ipu if run_ipu else self.feed_cpu
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result = exe.run(program, feed=feed, fetch_list=self.fetch_list)
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return result[0]
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def test_base(self):
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res0 = self.run_model(False)
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res1 = self.run_model(True)
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np.testing.assert_allclose(
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res0.flatten(), res1[0].flatten(), rtol=1e-05, atol=self.atol
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)
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if __name__ == "__main__":
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unittest.main()
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