wehub-resource-sync
290 lines
8.2 KiB
Python
290 lines
8.2 KiB
Python
# Copyright (c) 2023 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 os
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import unittest
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import numpy as np
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from dist_pass_test_base import DistPassTestBase
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import paddle
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from paddle import ParamAttr, nn
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from paddle.distributed import fleet
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from paddle.distributed.passes import PassManager, new_pass
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from paddle.nn import BatchNorm, Conv2D
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from paddle.nn.initializer import Constant, KaimingNormal
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paddle.enable_static()
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np.random.seed(12345)
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paddle.seed(12345)
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def skip_unit_test():
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return (
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not paddle.is_compiled_with_cuda()
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or paddle.device.cuda.get_device_capability()[0] < 8
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or paddle.get_cudnn_version() < 8900
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)
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skip_msg = (
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"only support with cuda and CUDNN 8.9 or later,"
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" and only Ampere or later devices are supported"
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)
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class ConvBNLayer(nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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lr_mult=1.0,
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data_format="NCHW",
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bn_weight_decay=True,
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):
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super().__init__()
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self.act = act
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self.conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(
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learning_rate=lr_mult, initializer=KaimingNormal()
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),
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bias_attr=False,
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data_format=data_format,
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)
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self.bn = BatchNorm(
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num_filters,
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param_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=(
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None if bn_weight_decay else paddle.regularizer.L2Decay(0.0)
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),
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initializer=Constant(1.0),
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),
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bias_attr=ParamAttr(
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learning_rate=lr_mult,
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regularizer=(
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None if bn_weight_decay else paddle.regularizer.L2Decay(0.0)
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),
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initializer=Constant(0.0),
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),
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data_layout=data_format,
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)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.act:
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x = self.relu(x)
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return x
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class BottleneckBlock(nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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lr_mult=1.0,
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data_format="NCHW",
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bn_weight_decay=True,
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):
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super().__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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act="relu",
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lr_mult=lr_mult,
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data_format=data_format,
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bn_weight_decay=bn_weight_decay,
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)
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act="relu",
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lr_mult=lr_mult,
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data_format=data_format,
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bn_weight_decay=bn_weight_decay,
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)
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None,
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lr_mult=lr_mult,
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data_format=data_format,
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bn_weight_decay=bn_weight_decay,
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)
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 4,
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filter_size=1,
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stride=stride,
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lr_mult=lr_mult,
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data_format=data_format,
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bn_weight_decay=bn_weight_decay,
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)
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self.relu = nn.ReLU()
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self.shortcut = shortcut
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def forward(self, x):
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identity = x
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x = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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if self.shortcut:
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short = identity
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else:
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short = self.short(identity)
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x = paddle.add(x=x, y=short)
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x = self.relu(x)
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return x
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class ResUnitNet(nn.Layer):
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def __init__(self, shortcut):
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super().__init__()
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self.shortcut = shortcut
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self.conv1 = ConvBNLayer(
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num_channels=3,
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num_filters=64,
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filter_size=3,
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act='relu',
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data_format='NHWC',
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)
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self.block = BottleneckBlock(
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num_channels=64,
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num_filters=16,
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stride=1,
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shortcut=self.shortcut,
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lr_mult=1.0,
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data_format="NHWC",
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bn_weight_decay=True,
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)
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self.conv2 = ConvBNLayer(
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num_channels=64,
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num_filters=64,
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filter_size=3,
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act='relu',
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data_format='NHWC',
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)
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def forward(self, x):
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out = self.conv1(x)
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out = self.block(out)
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out = self.conv2(out)
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out = paddle.flatten(out, 1)
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return out
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@unittest.skipIf(skip_unit_test(), skip_msg)
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class TestFuseResUnitPass(DistPassTestBase):
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def init(self):
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self.atol = 1e-2
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self.rtol = 1e-2
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paddle.set_flags({'FLAGS_conv_workspace_size_limit': 1000})
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self.init_attr()
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def init_attr(self):
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self.shortcut = True
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def get_model(self, place, batch_size=32, image_shape=[224, 224, 3]):
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image = paddle.static.data(
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shape=[batch_size, *image_shape], dtype='float32', name='image'
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)
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model = ResUnitNet(self.shortcut)
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pred_out = model(image)
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loss = paddle.mean(pred_out)
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optimizer = paddle.optimizer.Adam(learning_rate=1e-3)
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dist_strategy = fleet.DistributedStrategy()
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dist_strategy.fuse_all_reduce_ops = False
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dist_strategy.without_graph_optimization = True
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dist_strategy.amp = True
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dist_strategy.amp_configs = {
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"init_loss_scaling": 128.0,
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"use_dynamic_loss_scaling": True,
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}
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build_strategy = paddle.static.BuildStrategy()
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settings = {
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"fuse_bn_act_ops": False,
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"fuse_bn_add_act_ops": False,
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"enable_inplace": False,
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}
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for k, v in settings.items():
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setattr(build_strategy, k, v)
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dist_strategy.build_strategy = build_strategy
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fleet.init(is_collective=True, strategy=dist_strategy)
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optimizer = fleet.distributed_optimizer(optimizer)
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optimizer.minimize(loss)
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rank = paddle.distributed.get_rank()
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def reader():
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seed = int(os.environ.get("SEED", 0))
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np.random.seed(seed + rank)
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for _ in range(10):
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image_np = np.random.random(size=image.shape).astype('float32')
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yield (image_np,)
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main_program = paddle.static.default_main_program()
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startup_program = paddle.static.default_startup_program()
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return main_program, startup_program, [image], [loss], reader
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def apply_passes(self, main_prog, startup_prog):
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pass_manager = PassManager([new_pass("fuse_resunit")])
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pass_manager.apply([main_prog], [startup_prog])
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print(pass_manager.names)
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op_type = []
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for op in main_prog.global_block().ops:
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op_type.append(op.type)
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self.assertTrue("fused_scale_bias_add_relu" in op_type)
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self.assertTrue("fused_scale_bias_relu_conv_bn" in op_type)
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self.assertTrue("fused_dconv_drelu_dbn" in op_type)
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def test_fuse_resunit(self):
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self.check_main()
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@unittest.skipIf(skip_unit_test(), skip_msg)
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class TestFuseResUnitPassDual(TestFuseResUnitPass):
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def init_attr(self):
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self.shortcut = False
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if __name__ == "__main__":
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unittest.main()
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