wehub-resource-sync
317 lines
11 KiB
Python
317 lines
11 KiB
Python
# 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 contextlib
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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
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from paddle.io import Dataset
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from paddle.nn import Layer
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paddle.enable_static()
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class RandomDataset(Dataset):
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def __init__(self, num_samples, seed=123):
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super().__init__()
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np.random.seed(seed)
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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([3, 32, 32]).astype('float32')
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label = np.random.randint(0, 9, (1,)).astype('int64')
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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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def reader_decorator(reader):
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def __reader__():
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for i in range(len(reader)):
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yield reader[i]
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return __reader__
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def resnet_cifar10(input, depth=32):
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def conv_bn_layer(
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input, ch_out, filter_size, stride, padding, act='relu', bias_attr=False
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):
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conv = paddle.nn.Conv2D(
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in_channels=input.shape[1],
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out_channels=ch_out,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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bias_attr=bias_attr,
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)
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tmp = conv(input)
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bn = paddle.nn.BatchNorm(tmp.shape[1], act=act)
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return bn(tmp)
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def shortcut(input, ch_in, ch_out, stride):
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if ch_in != ch_out:
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return conv_bn_layer(input, ch_out, 1, stride, 0, None)
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else:
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return input
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def basicblock(input, ch_in, ch_out, stride):
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tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
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tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
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short = shortcut(input, ch_in, ch_out, stride)
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return paddle.nn.functional.relu(paddle.add(x=tmp, y=short))
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def layer_warp(block_func, input, ch_in, ch_out, count, stride):
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tmp = block_func(input, ch_in, ch_out, stride)
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for i in range(1, count):
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tmp = block_func(tmp, ch_out, ch_out, 1)
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return tmp
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assert (depth - 2) % 6 == 0
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n = (depth - 2) // 6
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conv1 = conv_bn_layer(
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input=input, ch_out=16, filter_size=3, stride=1, padding=1
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)
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if paddle.framework.in_pir_mode():
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with paddle.amp.auto_cast(level='O2'):
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res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
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res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
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res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
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else:
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with paddle.static.amp.fp16_guard():
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res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
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res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
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res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
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pool = paddle.nn.functional.avg_pool2d(x=res3, kernel_size=8, stride=1)
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return pool
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def train(use_pure_fp16=True, use_nesterov=False, optimizer=""):
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classdim = 10
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data_shape = [3, 32, 32]
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PASS_NUM = 1
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train_program = base.Program()
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startup_prog = base.Program()
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paddle.seed(123)
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with base.program_guard(train_program, startup_prog):
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images = paddle.static.data(
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name='pixel', shape=[-1, *data_shape], dtype='float32'
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)
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label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64')
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net = resnet_cifar10(images)
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if optimizer == "Adam":
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optimizer = paddle.optimizer.AdamW(
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learning_rate=0.001,
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epsilon=1e-8,
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weight_decay=0.0,
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multi_precision=True,
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)
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elif optimizer == "Lars":
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optimizer = paddle.incubate.optimizer.LarsMomentumOptimizer(
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learning_rate=0.001, momentum=0.9, multi_precision=use_pure_fp16
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)
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else:
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.001,
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momentum=0.9,
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use_nesterov=use_nesterov,
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weight_decay=paddle.regularizer.L2Decay(1e-4),
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multi_precision=use_pure_fp16,
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)
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if paddle.framework.in_pir_mode() and use_pure_fp16:
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class layer(Layer):
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def __init__(self, classdim, act):
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super().__init__()
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self.classdim = classdim
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self.act = act
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def forward(self, x):
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logits = paddle.static.nn.fc(
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x=x, size=self.classdim, activation=self.act
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)
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cost = paddle.nn.functional.softmax_with_cross_entropy(
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logits, label, return_softmax=False
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)
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return cost
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model = layer(classdim, "softmax")
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model, optimizer = paddle.amp.decorate(
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models=model,
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optimizers=optimizer,
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level="O2",
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dtype="float16",
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)
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scaler = paddle.amp.GradScaler(
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init_loss_scaling=128.0, use_dynamic_loss_scaling=True
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)
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with paddle.amp.auto_cast(
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enable=True, level="O2", dtype="float16", use_promote=True
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):
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cost = model(net)
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sum_cost = paddle.sum(cost)
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value_map = paddle.pir.IrMapping()
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test_program = train_program.clone(value_map)
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fetch_list = [value_map.look_up(sum_cost)]
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scaled = scaler.scale(sum_cost)
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scaler.minimize(optimizer, scaled, startup_program=startup_prog)
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else:
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logits = paddle.static.nn.fc(
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x=net, size=classdim, activation="softmax"
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)
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cost = paddle.nn.functional.softmax_with_cross_entropy(
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logits, label, return_softmax=False
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)
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sum_cost = paddle.sum(cost)
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# Test program
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if paddle.framework.in_pir_mode():
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value_map = paddle.pir.IrMapping()
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test_program = train_program.clone(value_map)
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fetch_list = [value_map.look_up(sum_cost)]
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else:
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test_program = train_program.clone(for_test=True)
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fetch_list = [sum_cost]
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if use_pure_fp16:
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optimizer = paddle.static.amp.decorate(
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optimizer,
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init_loss_scaling=128.0,
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use_dynamic_loss_scaling=True,
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use_pure_fp16=True,
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)
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optimizer.minimize(sum_cost)
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train_reader = paddle.batch(
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reader_decorator(RandomDataset(16 * 5, seed=123)),
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batch_size=16,
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drop_last=True,
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)
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test_reader = paddle.batch(
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reader_decorator(RandomDataset(4 * 5, seed=456)),
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batch_size=4,
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drop_last=True,
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)
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place = base.CUDAPlace(0)
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exe = base.Executor(place)
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feeder = base.DataFeeder(place=place, feed_list=[images, label])
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def train_loop():
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exe.run(startup_prog)
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if use_pure_fp16 and not paddle.framework.in_pir_mode():
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optimizer.amp_init(
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place, test_program=test_program, use_fp16_test=True
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)
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train_loss_list = []
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test_loss_list = []
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for pass_id in range(PASS_NUM):
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for batch_id, data in enumerate(train_reader()):
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(loss,) = exe.run(
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train_program, feed=feeder.feed(data), fetch_list=[sum_cost]
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)
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loss_v = float(loss) if isinstance(loss, np.ndarray) else loss
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print(
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f'PassID {pass_id:1}, Train Batch ID {batch_id + 1:04}, train loss {float(loss_v):2.4}'
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)
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train_loss_list.append(float(loss_v))
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for tid, test_data in enumerate(test_reader()):
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(loss_t,) = exe.run(
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program=test_program,
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feed=feeder.feed(test_data),
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fetch_list=fetch_list,
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)
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test_loss_list.append(float(loss_t))
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print(
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f'PassID {pass_id:1}, Test Batch ID {tid + 1:04}, test loss {float(loss_t):2.4}'
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)
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return train_loss_list, test_loss_list
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return train_loop()
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class TestImageMultiPrecision(unittest.TestCase):
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def test_resnet_pure_fp16(self):
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if not base.core.is_compiled_with_cuda():
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return
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def do_test(use_nesterov=False, optimizer=""):
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if optimizer == "Adam":
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suffix = "use Adam"
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elif optimizer == "Lars":
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suffix = "use Lars"
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else:
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suffix = "with Nesterov" if use_nesterov else "without Nesterov"
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with self.scope_prog_guard():
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print(f"-----------------FP16 Train {suffix}-----------------")
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train_loss_fp16, test_loss_fp16 = train(
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use_pure_fp16=True,
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use_nesterov=use_nesterov,
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optimizer=optimizer,
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)
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with self.scope_prog_guard():
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print(f"-----------------FP32 Train {suffix}-----------------")
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train_loss_fp32, test_loss_fp32 = train(
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use_pure_fp16=False,
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use_nesterov=use_nesterov,
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optimizer=optimizer,
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)
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np.testing.assert_allclose(
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np.array(train_loss_fp16),
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np.array(train_loss_fp32),
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rtol=0.01,
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atol=1e-05,
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equal_nan=True,
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err_msg='Failed to train in pure FP16.',
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)
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np.testing.assert_allclose(
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np.array(test_loss_fp16),
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np.array(test_loss_fp32),
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rtol=0.01,
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atol=1e-05,
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equal_nan=True,
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err_msg='Failed to test in pure FP16.',
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)
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do_test(use_nesterov=False)
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do_test(use_nesterov=True)
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do_test(optimizer="Adam")
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do_test(optimizer="Lars")
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@contextlib.contextmanager
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def scope_prog_guard(self):
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prog = base.Program()
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startup_prog = base.Program()
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scope = base.core.Scope()
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with (
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base.scope_guard(scope),
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base.program_guard(prog, startup_prog),
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):
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yield
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if __name__ == '__main__':
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unittest.main()
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