wehub-resource-sync
442 lines
14 KiB
Python
442 lines
14 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 TestBase(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_attrs()
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self.set_data_feed()
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def set_training(self):
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self.is_training = False
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self.epoch = 10
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np_image}
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self.feed_ipu = {"image": np_image}
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def _test_base(self, run_ipu=True):
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scope = paddle.static.Scope()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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paddle.seed(self.SEED)
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bs = self.ipu_bs if run_ipu else self.cpu_bs
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with paddle.static.scope_guard(scope):
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with paddle.static.program_guard(main_prog, startup_prog):
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image = paddle.static.data(
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name='image', shape=[bs, 3, 10, 10], dtype='float32'
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)
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with paddle.static.ipu_shard_guard(index=0):
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conv1 = paddle.nn.Conv2D(
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in_channels=image.shape[1],
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out_channels=3,
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kernel_size=3,
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bias_attr=False,
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)(image)
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with paddle.static.ipu_shard_guard(index=1):
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conv2 = paddle.nn.Conv2D(
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in_channels=conv1.shape[1],
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out_channels=3,
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kernel_size=3,
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bias_attr=False,
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)(conv1)
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# should consider influence of bs
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loss = paddle.mean(conv2)
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if self.is_training:
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if self.optimizer == 'sgd':
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opt = paddle.optimizer.SGD(learning_rate=1e-2)
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elif self.optimizer == 'adam':
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opt = paddle.optimizer.Adam(learning_rate=1e-2)
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elif self.optimizer == 'lamb':
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opt = paddle.optimizer.Lamb(learning_rate=1e-2)
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else:
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raise Exception('optimizer must be sgd, adam or lamb')
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opt.minimize(loss)
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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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executor = paddle.static.Executor(place)
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executor.run(startup_prog)
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if run_ipu:
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feed_list = [image.name]
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fetch_list = [loss.name]
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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 * self.ipu_options['replicated_graph_count'],
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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_options(self.ipu_options)
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program = paddle.static.IpuCompiledProgram(
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main_prog, ipu_strategy=ipu_strategy
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).compile(feed_list, fetch_list)
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else:
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program = main_prog
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feed = self.feed_ipu if run_ipu else self.feed_cpu
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epoch = self.epoch
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if not run_ipu:
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epoch *= self.ipu_options['replicated_graph_count']
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epoch *= self.ipu_options['batches_per_step']
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epoch *= self.ipu_options['accumulation_factor']
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epoch = epoch / (self.cpu_bs / self.ipu_bs)
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result = []
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for i in range(int(epoch)):
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loss_res = executor.run(program, feed=feed, fetch_list=[loss])
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result.append(loss_res)
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return np.array(result).flatten()
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def test(self):
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cpu_outputs = self._test_base(False)
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ipu_outputs = self._test_base(True)
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np.testing.assert_allclose(
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cpu_outputs, ipu_outputs, rtol=1e-05, atol=self.atol
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)
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class TestReplicaInference(TestBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np_image}
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self.feed_ipu = {
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"image": np.tile(
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np_image, [self.ipu_options['replicated_graph_count'], 1, 1, 1]
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)
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}
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class TestReplicaCollectiveInference(TestBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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"accumulate_outer_fragment": {0: []},
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"replicated_collectives_settings": {
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"prepare_schedule_for_merging_collectives": True,
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"merge_all_reduce_collectives": True,
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},
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np_image}
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self.feed_ipu = {
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"image": np.tile(
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np_image, [self.ipu_options['replicated_graph_count'], 1, 1, 1]
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)
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}
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class TestPipelineInference(TestBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 2,
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"enable_pipelining": True,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np_image}
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self.feed_ipu = {
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"image": np.tile(
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np_image, [self.ipu_options['batches_per_step'], 1, 1, 1]
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)
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}
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class TestTrainBase(TestBase):
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def set_training(self):
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self.is_training = True
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self.epoch = 10
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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self.optimizer = 'sgd'
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class TestReplicaTrain(TestTrainBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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}
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self.cpu_bs = 2
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self.ipu_bs = 1
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self.optimizer = 'sgd'
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np.tile(np_image, [self.cpu_bs, 1, 1, 1])}
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self.feed_ipu = {
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"image": np.tile(
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np_image, [self.ipu_options['replicated_graph_count'], 1, 1, 1]
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)
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}
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def test(self):
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cpu_outputs = self._test_base(False)
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ipu_outputs = self._test_base(True)[::2]
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np.testing.assert_allclose(
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cpu_outputs, ipu_outputs, rtol=1e-05, atol=self.atol
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)
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class TestReplicaCollectiveTrain(TestTrainBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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"accumulate_outer_fragment": {0: []},
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"replicated_collectives_settings": {
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"prepare_schedule_for_merging_collectives": True,
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"merge_all_reduce_collectives": True,
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},
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}
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self.cpu_bs = 2
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self.ipu_bs = 1
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self.optimizer = 'sgd'
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np.tile(np_image, [self.cpu_bs, 1, 1, 1])}
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self.feed_ipu = {
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"image": np.tile(
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np_image, [self.ipu_options['replicated_graph_count'], 1, 1, 1]
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)
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}
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def test(self):
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cpu_outputs = self._test_base(False)
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ipu_outputs = self._test_base(True)[::2]
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np.testing.assert_allclose(
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cpu_outputs, ipu_outputs, rtol=1e-05, atol=self.atol
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)
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class TestPipelineTrain(TestTrainBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 3,
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"enable_pipelining": True,
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"enable_gradient_accumulation": True,
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"accumulation_factor": 3,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 3
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self.ipu_bs = 1
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self.optimizer = 'sgd'
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def set_data_feed(self):
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np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
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self.feed_cpu = {"image": np.tile(np_image, [self.cpu_bs, 1, 1, 1])}
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bps_acc = (
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self.ipu_options['batches_per_step']
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* self.ipu_options['accumulation_factor']
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)
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self.feed_ipu = {"image": np.tile(np_image, [bps_acc, 1, 1, 1])}
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def test(self):
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cpu_outputs = self._test_base(False)
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ipu_outputs = self._test_base(True)[::3]
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np.testing.assert_allclose(
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cpu_outputs, ipu_outputs, rtol=1e-05, atol=self.atol
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)
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class TestAdamTrain(TestTrainBase):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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self.optimizer = 'adam'
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class TestAdamReplicaTrain(TestReplicaTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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}
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self.cpu_bs = 2
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self.ipu_bs = 1
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self.optimizer = 'adam'
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class TestAdamPipelineTrain(TestPipelineTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 3,
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"enable_pipelining": True,
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"enable_gradient_accumulation": True,
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"accumulation_factor": 3,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 3
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self.ipu_bs = 1
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self.optimizer = 'adam'
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class TestAdamRecomputationTrain(TestPipelineTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 3,
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"enable_pipelining": True,
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"enable_gradient_accumulation": True,
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"accumulation_factor": 3,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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"auto_recomputation": 3,
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}
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self.cpu_bs = 3
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self.ipu_bs = 1
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self.optimizer = 'adam'
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class TestLambTrain(TestAdamTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 1
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self.ipu_bs = 1
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self.optimizer = 'lamb'
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class TestLambReplicaTrain(TestAdamReplicaTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 1,
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"enable_pipelining": False,
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"enable_gradient_accumulation": False,
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"accumulation_factor": 1,
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"enable_replicated_graphs": True,
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"replicated_graph_count": 2,
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}
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self.cpu_bs = 2
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self.ipu_bs = 1
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self.optimizer = 'lamb'
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class TestLambPipelineTrain(TestAdamPipelineTrain):
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def set_attrs(self):
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self.ipu_options = {
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"batches_per_step": 3,
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"enable_pipelining": True,
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"enable_gradient_accumulation": True,
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"accumulation_factor": 3,
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"enable_replicated_graphs": False,
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"replicated_graph_count": 1,
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}
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self.cpu_bs = 3
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self.ipu_bs = 1
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self.optimizer = 'lamb'
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if __name__ == "__main__":
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unittest.main()
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