wehub-resource-sync
655 lines
22 KiB
Python
655 lines
22 KiB
Python
# Copyright (c) 2019 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 copy
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import math
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import os
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import sys
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import tempfile
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import unittest
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import numpy
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# TODO: remove sys.path.append
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sys.path.append("../legacy_test")
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import nets
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import paddle
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from paddle import base
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from paddle.framework import in_pir_mode
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from paddle.nn import Layer
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from paddle.static.amp import decorate
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paddle.enable_static()
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def img_conv_group_pir(
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input,
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in_channels,
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out_channels,
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conv_num_filter,
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kernel_size,
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pool_size,
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pool_stride=1,
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pool_padding=0,
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pool_type='max',
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global_pooling=False,
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conv_with_batchnorm=False,
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conv_batchnorm_drop_rate=0.0,
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conv_stride=1,
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conv_padding=1,
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conv_filter_size=3,
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conv_dilation=1,
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conv_groups=1,
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param_attr=None,
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bias_attr=None,
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conv_act=None,
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use_cudnn=True,
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):
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tmp = input
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assert isinstance(conv_num_filter, (list, tuple))
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def __extend_list__(obj):
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if not hasattr(obj, '__len__'):
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return [obj] * len(conv_num_filter)
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else:
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assert len(obj) == len(conv_num_filter)
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return obj
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conv_padding = __extend_list__(conv_padding)
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conv_filter_size = __extend_list__(conv_filter_size)
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param_attr = __extend_list__(param_attr)
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conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
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conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)
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for i in range(len(conv_num_filter)):
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local_conv_act = conv_act
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if conv_with_batchnorm[i]:
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local_conv_act = None
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conv = paddle.nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size,
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stride=conv_stride,
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padding=conv_padding[i],
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dilation=conv_dilation,
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groups=conv_groups,
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bias_attr=bias_attr,
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)
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conv_out = conv(input)
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if conv_with_batchnorm[i]:
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batch_norm = paddle.nn.BatchNorm(in_channels, act=conv_act)
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tmp = batch_norm(tmp)
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drop_rate = conv_batchnorm_drop_rate[i]
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if abs(drop_rate) > 1e-5:
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tmp = paddle.nn.functional.dropout(x=tmp, p=drop_rate)
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if pool_type == 'max':
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pool_out = paddle.nn.functional.max_pool2d(
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x=tmp,
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kernel_size=pool_size,
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stride=pool_stride,
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)
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else:
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pool_out = paddle.nn.functional.avg_pool2d(
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x=tmp,
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kernel_size=pool_size,
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stride=pool_stride,
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)
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return pool_out
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def resnet_cifar10(input, depth=32):
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def conv_bn_layer(
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input,
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ch_out,
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filter_size,
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stride,
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padding,
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act='relu',
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bias_attr=False,
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):
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if in_pir_mode():
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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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else:
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tmp = paddle.static.nn.conv2d(
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input=input,
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filter_size=filter_size,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=None,
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bias_attr=bias_attr,
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)
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return paddle.static.nn.batch_norm(input=tmp, act=act)
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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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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 vgg16_bn_drop(input):
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def conv_block(input, num_filter, groups, dropouts):
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if in_pir_mode():
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return img_conv_group_pir(
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input,
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in_channels=3,
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out_channels=num_filter,
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conv_num_filter=[num_filter] * groups,
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kernel_size=3,
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pool_size=2,
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pool_stride=2,
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pool_padding=0,
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pool_type='max',
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conv_act='relu',
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conv_with_batchnorm=True,
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)
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else:
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return nets.img_conv_group(
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input=input,
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pool_size=2,
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pool_stride=2,
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conv_num_filter=[num_filter] * groups,
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conv_filter_size=3,
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conv_act='relu',
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conv_with_batchnorm=True,
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conv_batchnorm_drop_rate=dropouts,
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pool_type='max',
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)
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conv1 = conv_block(input, 64, 2, [0.3, 0])
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conv2 = conv_block(conv1, 128, 2, [0.4, 0])
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conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
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conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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drop = paddle.nn.functional.dropout(x=conv5, p=0.5)
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fc1 = paddle.static.nn.fc(x=drop, size=4096, activation=None)
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if in_pir_mode():
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batch_norm = paddle.nn.BatchNorm(4096)
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bn = batch_norm(fc1)
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else:
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bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
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drop2 = paddle.nn.functional.dropout(x=bn, p=0.5)
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fc2 = paddle.static.nn.fc(x=drop2, size=4096, activation=None)
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return fc2
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def train(net_type, use_cuda, save_dirname, is_local):
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classdim = 10
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data_shape = [3, 32, 32]
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train_program = paddle.static.Program()
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startup_prog = paddle.static.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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if net_type == "vgg":
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print("train vgg net")
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net = vgg16_bn_drop(images)
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elif net_type == "resnet":
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print("train resnet")
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net = resnet_cifar10(images, 32)
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else:
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raise ValueError(f"{net_type} network is not supported")
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optimizer = paddle.optimizer.Lamb(learning_rate=0.001)
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if in_pir_mode():
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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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(
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cost,
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predict,
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) = paddle.nn.functional.softmax_with_cross_entropy(
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logits, label, return_softmax=True
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)
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return cost, predict
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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=8.0, use_dynamic_loss_scaling=True
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)
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with paddle.amp.auto_cast(
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enable=True,
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level='O2',
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dtype='float16',
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custom_black_list={'transpose2', 'concat'},
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use_promote=True,
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):
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cost, predict = model(net)
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avg_cost = paddle.mean(cost)
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acc = paddle.static.accuracy(input=predict, label=label)
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# Test program
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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 = []
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fetch_list.append(value_map.look_up(avg_cost))
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fetch_list.append(value_map.look_up(acc))
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scaled = scaler.scale(avg_cost)
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scaler.minimize(optimizer, scaled, startup_program=startup_prog)
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loss_scaling = optimizer.get_loss_scaling()
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scaled_loss = optimizer.get_scaled_loss()
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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, predict = paddle.nn.functional.softmax_with_cross_entropy(
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logits, label, return_softmax=True
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)
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avg_cost = paddle.mean(cost)
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acc = paddle.static.accuracy(input=predict, label=label)
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# Test program
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test_program = train_program.clone(for_test=True)
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fetch_list = [avg_cost, acc]
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amp_lists = paddle.static.amp.AutoMixedPrecisionLists(
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custom_black_varnames={"loss", "conv2d_0.w_0"}
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)
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mp_optimizer = decorate(
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optimizer=optimizer,
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amp_lists=amp_lists,
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init_loss_scaling=8.0,
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use_dynamic_loss_scaling=True,
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)
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mp_optimizer.minimize(avg_cost)
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loss_scaling = mp_optimizer.get_loss_scaling()
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scaled_loss = mp_optimizer.get_scaled_loss()
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BATCH_SIZE = 128
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PASS_NUM = 1
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# no shuffle for unit test
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train_reader = paddle.batch(
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paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE
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)
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test_reader = paddle.batch(
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paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE
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)
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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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(main_program):
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exe.run(startup_prog)
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loss = 0.0
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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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np_scaled_loss, loss = exe.run(
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main_program,
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feed=feeder.feed(data),
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fetch_list=[scaled_loss, avg_cost],
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)
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print(
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f'PassID {pass_id:1}, BatchID {batch_id + 1:04}, train loss {float(numpy.asarray(loss).item()):2.4}, scaled train loss {float(numpy.asarray(np_scaled_loss).item()):2.4}'
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)
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if (batch_id % 10) == 0:
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acc_list = []
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avg_loss_list = []
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for tid, test_data in enumerate(test_reader()):
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loss_t, acc_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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loss_t = float(numpy.asarray(loss_t).item())
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acc_t = float(numpy.asarray(acc_t).item())
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if math.isnan(loss_t):
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sys.exit("got NaN loss, training failed.")
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acc_list.append(acc_t)
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avg_loss_list.append(loss_t)
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break # Use 1 segment for speeding up CI
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acc_value = numpy.array(acc_list).mean()
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avg_loss_value = numpy.array(avg_loss_list).mean()
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print(
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f'PassID {pass_id:1}, BatchID {batch_id + 1:04}, test loss {float(avg_loss_value):2.2}, acc {float(acc_value):2.2}'
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)
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if acc_value > 0.08: # Low threshold for speeding up CI
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paddle.static.io.save_inference_model(
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save_dirname,
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images,
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[predict],
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exe,
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program=train_program,
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clip_extra=True,
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)
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return
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if is_local:
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train_loop(train_program)
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else:
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port = os.getenv("PADDLE_PSERVER_PORT", "6174")
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pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
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eplist = []
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for ip in pserver_ips.split(","):
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eplist.append(':'.join([ip, port]))
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pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
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trainers = int(os.getenv("PADDLE_TRAINERS"))
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current_endpoint = os.getenv("POD_IP") + ":" + port
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trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
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training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
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t = paddle.distributed.transpiler.DistributeTranspiler()
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t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
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if training_role == "PSERVER":
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pserver_prog = t.get_pserver_program(current_endpoint)
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pserver_startup = t.get_startup_program(
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current_endpoint, pserver_prog
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)
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exe.run(pserver_startup)
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exe.run(pserver_prog)
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elif training_role == "TRAINER":
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train_loop(t.get_trainer_program())
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def infer(use_cuda, save_dirname=None):
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if save_dirname is None:
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return
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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inference_scope = base.core.Scope()
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with base.scope_guard(inference_scope):
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# Use paddle.static.io.load_inference_model to obtain the inference program desc,
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# the feed_target_names (the names of variables that will be fed
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# data using feed operators), and the fetch_targets (variables that
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# we want to obtain data from using fetch operators).
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.io.load_inference_model(save_dirname, exe)
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# The input's dimension of conv should be 4-D or 5-D.
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# Use normalized image pixels as input data, which should be in the range [0, 1.0].
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batch_size = 1
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tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
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# Construct feed as a dictionary of {feed_target_name: feed_target_data}
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# and results will contain a list of data corresponding to fetch_targets.
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results = exe.run(
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inference_program,
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feed={feed_target_names[0]: tensor_img},
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fetch_list=fetch_targets,
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)
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print("infer results: ", results[0])
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paddle.static.save_inference_model(
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save_dirname,
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feed_target_names,
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fetch_targets,
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exe,
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program=inference_program,
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clip_extra=True,
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)
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class TestImageClassification(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def main(self, net_type, use_cuda, is_local=True):
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if use_cuda and not base.core.is_compiled_with_cuda():
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return
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# Directory for saving the trained model
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save_dirname = os.path.join(
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self.temp_dir.name,
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"image_classification_" + net_type + ".inference.model",
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)
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train(net_type, use_cuda, save_dirname, is_local)
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# infer(use_cuda, save_dirname)
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def test_amp_lists(self):
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white_list = (
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copy.copy(paddle.static.amp.fp16_lists.white_list)
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| paddle.static.amp.fp16_lists._only_supported_fp16_list
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)
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black_list = copy.copy(
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paddle.static.amp.fp16_lists.black_list
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| paddle.static.amp.fp16_lists._extra_black_list
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)
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gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
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amp_lists = paddle.static.amp.AutoMixedPrecisionLists()
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self.assertEqual(amp_lists.white_list, white_list)
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self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_1(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 1. w={'exp}, b=None
|
|
white_list.add('exp')
|
|
black_list.remove('exp')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists({'exp'})
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_2(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 2. w={'tanh'}, b=None
|
|
white_list.add('tanh')
|
|
gray_list.remove('tanh')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists({'tanh'})
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_3(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 3. w={'lstm'}, b=None
|
|
white_list.add('lstm')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists({'lstm'})
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_4(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 4. w=None, b={'conv2d'}
|
|
white_list.remove('conv2d')
|
|
black_list.add('conv2d')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists(
|
|
custom_black_list={'conv2d'}
|
|
)
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_5(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 5. w=None, b={'tanh'}
|
|
black_list.add('tanh')
|
|
gray_list.remove('tanh')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists(
|
|
custom_black_list={'tanh'}
|
|
)
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_6(self):
|
|
white_list = (
|
|
copy.copy(paddle.static.amp.fp16_lists.white_list)
|
|
| paddle.static.amp.fp16_lists._only_supported_fp16_list
|
|
)
|
|
black_list = copy.copy(
|
|
paddle.static.amp.fp16_lists.black_list
|
|
| paddle.static.amp.fp16_lists._extra_black_list
|
|
)
|
|
gray_list = copy.copy(paddle.static.amp.fp16_lists.gray_list)
|
|
|
|
# 6. w=None, b={'lstm'}
|
|
black_list.add('lstm')
|
|
|
|
amp_lists = paddle.static.amp.AutoMixedPrecisionLists(
|
|
custom_black_list={'lstm'}
|
|
)
|
|
self.assertEqual(amp_lists.white_list, white_list)
|
|
self.assertEqual(amp_lists.black_list, black_list)
|
|
self.assertEqual(amp_lists.gray_list, gray_list)
|
|
|
|
def test_amp_lists_7(self):
|
|
# 7. w={'lstm'} b={'lstm'}
|
|
# raise ValueError
|
|
self.assertRaises(
|
|
ValueError,
|
|
paddle.static.amp.AutoMixedPrecisionLists,
|
|
{'lstm'},
|
|
{'lstm'},
|
|
)
|
|
|
|
def test_vgg_cuda(self):
|
|
with self.scope_prog_guard():
|
|
self.main('vgg', use_cuda=True)
|
|
|
|
def test_resnet_cuda(self):
|
|
with self.scope_prog_guard():
|
|
self.main('resnet', use_cuda=True)
|
|
|
|
@contextlib.contextmanager
|
|
def scope_prog_guard(self):
|
|
prog = base.Program()
|
|
startup_prog = base.Program()
|
|
scope = base.core.Scope()
|
|
with (
|
|
base.scope_guard(scope),
|
|
base.program_guard(prog, startup_prog),
|
|
):
|
|
yield
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|