chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2020 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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import paddle
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from paddle import base, nn
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from paddle.framework import in_pir_mode
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def get_value_by_name(name, ops):
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for op in ops:
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if op.name() == "builtin.parameter" or op.name() == "pd_op.data":
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value = op.result(0)
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if value.name == name:
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return value
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class TestModelAverage(unittest.TestCase):
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def test_model_average_static(self):
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paddle.enable_static()
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place = base.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = base.Executor(place)
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train_program = paddle.static.Program()
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startup = paddle.static.Program()
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test_program = paddle.static.Program()
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with (
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paddle.static.program_guard(train_program, startup),
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base.unique_name.guard(),
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):
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data = paddle.static.data(
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name='X', shape=[None, 1], dtype='float32'
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)
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hidden = paddle.nn.Linear(
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in_features=data.shape[1], out_features=10
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)(data)
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loss = paddle.mean(hidden)
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test_program = train_program.clone()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.2, momentum=0.1
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)
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optimizer.minimize(loss)
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# build ModelAverage optimizer
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model_average = paddle.incubate.optimizer.ModelAverage(
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0.15, min_average_window=2, max_average_window=10
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)
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exe.run(startup)
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params_list = [
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'linear_0.b_0',
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'linear_0.b_0_sum_1_0',
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'linear_0.b_0_sum_2_0',
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'linear_0.b_0_sum_3_0',
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'linear_0.b_0_num_accumulates_0',
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'linear_0.b_0_old_num_accumulates_0',
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'linear_0.b_0_num_updates_0',
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]
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if in_pir_mode():
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ops = train_program.global_block().ops
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fetch_list = [
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get_value_by_name(param, ops) for param in params_list
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]
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else:
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fetch_list = params_list
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for i in range(10):
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x = np.random.random(size=(10, 1)).astype('float32')
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(
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latest_b,
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sum_1,
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sum_2,
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sum_3,
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num_accumulates,
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old_num_accumulates,
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num_updates,
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) = exe.run(
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program=train_program,
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feed={'X': x},
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fetch_list=fetch_list,
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)
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self.assertTrue(
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np.equal(sum_1, np.zeros(shape=[10], dtype='float32')).all()
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)
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self.assertTrue(
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np.equal(sum_2, np.zeros(shape=[10], dtype='float32')).all()
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)
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self.assertTrue(
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np.equal(num_accumulates, np.array([0], dtype='int64')).all()
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)
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self.assertTrue(
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np.equal(old_num_accumulates, np.array([2], dtype='int64')).all()
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)
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self.assertTrue(
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np.equal(num_updates, np.array([10], dtype='int64')).all()
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)
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average_b = (sum_1 + sum_2 + sum_3) / (
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num_accumulates + old_num_accumulates
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).astype('float32')
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if in_pir_mode():
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ops = test_program.global_block().ops
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fetch_list = [
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ops[-1].result(0),
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get_value_by_name("linear_0.b_0", ops),
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]
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else:
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fetch_list = [loss.name, 'linear_0.b_0']
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# apply ModelAverage
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with model_average.apply(exe):
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x = np.random.random(size=(10, 1)).astype('float32')
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outs, b = exe.run(
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program=test_program,
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feed={'X': x},
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fetch_list=fetch_list,
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)
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self.assertAlmostEqual(np.mean(average_b), np.mean(b))
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x = np.random.random(size=(10, 1)).astype('float32')
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outs, b = exe.run(
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program=test_program,
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feed={'X': x},
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fetch_list=fetch_list,
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)
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self.assertAlmostEqual(np.mean(latest_b), np.mean(b))
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def test_model_average_dygraph(self):
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BATCH_SIZE = 16
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BATCH_NUM = 4
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EPOCH_NUM = 4
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IMAGE_SIZE = 784
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CLASS_NUM = 10
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# define a random dataset
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([IMAGE_SIZE]).astype('float32')
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label = np.random.randint(0, CLASS_NUM - 1, (1,)).astype(
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'int64'
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)
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return image, label
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def __len__(self):
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return self.num_samples
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class LinearNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
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self.bias = self._linear.bias
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@paddle.jit.to_static
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def forward(self, x):
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return self._linear(x)
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def train(layer, loader, loss_fn, opt, model_average):
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for epoch_id in range(EPOCH_NUM):
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for batch_id, (image, label) in enumerate(loader()):
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out = layer(image)
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loss = loss_fn(out, label)
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loss.backward()
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opt.step()
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model_average.step()
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opt.clear_grad()
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model_average.clear_grad()
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# print("Train Epoch {} batch {}: loss = {}, bias = {}".format(
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# epoch_id, batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
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sum_1 = model_average._get_accumulator('sum_1', layer.bias)
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sum_2 = model_average._get_accumulator('sum_2', layer.bias)
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sum_3 = model_average._get_accumulator('sum_3', layer.bias)
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num_accumulates = model_average._get_accumulator(
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'num_accumulates', layer.bias
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)
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old_num_accumulates = model_average._get_accumulator(
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'old_num_accumulates', layer.bias
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)
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num_updates = model_average._get_accumulator(
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'num_updates', layer.bias
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)
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return (
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(
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(sum_1 + sum_2 + sum_3)
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/ (num_accumulates + old_num_accumulates).astype('float32')
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)
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.astype(sum_1.dtype)
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.numpy()
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)
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def evaluate(layer, loader, loss_fn, check_param):
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for batch_id, (image, label) in enumerate(loader()):
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out = layer(image)
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loss = loss_fn(out, label)
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loss.backward()
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self.assertAlmostEqual(
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np.mean(layer.bias.numpy()),
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np.mean(check_param),
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delta=5e-3,
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)
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# print("Evaluate batch {}: loss = {}, bias = {}".format(
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# batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
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# create network
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layer = LinearNet()
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loss_fn = nn.CrossEntropyLoss()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.2, momentum=0.1, parameters=layer.parameters()
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)
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# build ModelAverage optimizer
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model_average = paddle.incubate.optimizer.ModelAverage(
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0.15,
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parameters=layer.parameters(),
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min_average_window=2,
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max_average_window=10,
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)
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# create data loader
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dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
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loader = paddle.io.DataLoader(
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dataset,
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batch_size=BATCH_SIZE,
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shuffle=True,
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drop_last=True,
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num_workers=2,
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)
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eval_loader = paddle.io.DataLoader(
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dataset,
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batch_size=BATCH_SIZE,
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shuffle=True,
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drop_last=True,
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num_workers=1,
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)
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# train
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check_param = train(layer, loader, loss_fn, optimizer, model_average)
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# print(check_param)
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with model_average.apply(need_restore=False):
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evaluate(layer, eval_loader, loss_fn, check_param)
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check_param = (
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model_average._get_accumulator('restore', layer.bias)
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).numpy()
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# print(check_param)
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# print("\nEvaluate With Restored Parameters")
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model_average.restore()
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evaluate(layer, eval_loader, loss_fn, check_param)
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if __name__ == "__main__":
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unittest.main()
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