# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle import paddle.fluid as fluid import contextlib import math import sys import numpy import unittest import os import copy import numpy as np import tempfile from paddle.static.amp import decorate paddle.enable_static() def resnet_cifar10(input, depth=32): def conv_bn_layer( input, ch_out, filter_size, stride, padding, act='relu', bias_attr=False ): tmp = paddle.static.nn.conv2d( input=input, filter_size=filter_size, num_filters=ch_out, stride=stride, padding=padding, act=None, bias_attr=bias_attr, ) return paddle.static.nn.batch_norm(input=tmp, act=act) def shortcut(input, ch_in, ch_out, stride): if ch_in != ch_out: return conv_bn_layer(input, ch_out, 1, stride, 0, None) else: return input def basicblock(input, ch_in, ch_out, stride): tmp = conv_bn_layer(input, ch_out, 3, stride, 1) tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True) short = shortcut(input, ch_in, ch_out, stride) return paddle.nn.functional.relu(paddle.add(x=tmp, y=short)) def layer_warp(block_func, input, ch_in, ch_out, count, stride): tmp = block_func(input, ch_in, ch_out, stride) for i in range(1, count): tmp = block_func(tmp, ch_out, ch_out, 1) return tmp assert (depth - 2) % 6 == 0 n = (depth - 2) // 6 conv1 = conv_bn_layer( input=input, ch_out=16, filter_size=3, stride=1, padding=1 ) res1 = layer_warp(basicblock, conv1, 16, 16, n, 1) res2 = layer_warp(basicblock, res1, 16, 32, n, 2) res3 = layer_warp(basicblock, res2, 32, 64, n, 2) pool = paddle.nn.functional.avg_pool2d(x=res3, kernel_size=8, stride=1) return pool def vgg16_bn_drop(input): def conv_block(input, num_filter, groups, dropouts): return fluid.nets.img_conv_group( input=input, pool_size=2, pool_stride=2, conv_num_filter=[num_filter] * groups, conv_filter_size=3, conv_act='relu', conv_with_batchnorm=True, conv_batchnorm_drop_rate=dropouts, pool_type='max', ) conv1 = conv_block(input, 64, 2, [0.3, 0]) conv2 = conv_block(conv1, 128, 2, [0.4, 0]) conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0]) conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0]) conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0]) drop = paddle.nn.functional.dropout(x=conv5, p=0.5) fc1 = fluid.layers.fc(input=drop, size=4096, act=None) bn = paddle.static.nn.batch_norm(input=fc1, act='relu') drop2 = paddle.nn.functional.dropout(x=bn, p=0.5) fc2 = fluid.layers.fc(input=drop2, size=4096, act=None) return fc2 def train(net_type, use_cuda, save_dirname, is_local): classdim = 10 data_shape = [3, 32, 32] train_program = fluid.Program() startup_prog = fluid.Program() train_program.random_seed = 123 startup_prog.random_seed = 456 with fluid.program_guard(train_program, startup_prog): images = fluid.layers.data( name='pixel', shape=data_shape, dtype='float32' ) label = fluid.layers.data(name='label', shape=[1], dtype='int64') if net_type == "vgg": print("train vgg net") net = vgg16_bn_drop(images) elif net_type == "resnet": print("train resnet") net = resnet_cifar10(images, 32) else: raise ValueError("%s network is not supported" % net_type) logits = fluid.layers.fc(input=net, size=classdim, act="softmax") cost, predict = paddle.nn.functional.softmax_with_cross_entropy( logits, label, return_softmax=True ) avg_cost = paddle.mean(cost) acc = paddle.static.accuracy(input=predict, label=label) # Test program test_program = train_program.clone(for_test=True) optimizer = fluid.optimizer.Lamb(learning_rate=0.001) amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists( custom_black_varnames={"loss", "conv2d_0.w_0"} ) mp_optimizer = decorate( optimizer=optimizer, amp_lists=amp_lists, init_loss_scaling=8.0, use_dynamic_loss_scaling=True, ) mp_optimizer.minimize(avg_cost) loss_scaling = mp_optimizer.get_loss_scaling() scaled_loss = mp_optimizer.get_scaled_loss() BATCH_SIZE = 128 PASS_NUM = 1 # no shuffle for unit test train_reader = paddle.batch( paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE ) test_reader = paddle.batch( paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE ) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) feeder = fluid.DataFeeder(place=place, feed_list=[images, label]) def train_loop(main_program): exe.run(startup_prog) loss = 0.0 for pass_id in range(PASS_NUM): for batch_id, data in enumerate(train_reader()): np_scaled_loss, loss = exe.run( main_program, feed=feeder.feed(data), fetch_list=[scaled_loss, avg_cost], ) print( 'PassID {0:1}, BatchID {1:04}, train loss {2:2.4}, scaled train closs {3:2.4}'.format( pass_id, batch_id + 1, float(loss), float(np_scaled_loss), ) ) if (batch_id % 10) == 0: acc_list = [] avg_loss_list = [] for tid, test_data in enumerate(test_reader()): loss_t, acc_t = exe.run( program=test_program, feed=feeder.feed(test_data), fetch_list=[avg_cost, acc], ) if math.isnan(float(loss_t)): sys.exit("got NaN loss, training failed.") acc_list.append(float(acc_t)) avg_loss_list.append(float(loss_t)) break # Use 1 segment for speeding up CI acc_value = numpy.array(acc_list).mean() avg_loss_value = numpy.array(avg_loss_list).mean() print( 'PassID {0:1}, BatchID {1:04}, test loss {2:2.2}, acc {3:2.2}'.format( pass_id, batch_id + 1, float(avg_loss_value), float(acc_value), ) ) if acc_value > 0.08: # Low threshold for speeding up CI fluid.io.save_inference_model( save_dirname, ["pixel"], [predict], exe, main_program=train_program, clip_extra=True, ) return if is_local: train_loop(train_program) else: port = os.getenv("PADDLE_PSERVER_PORT", "6174") pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip... eplist = [] for ip in pserver_ips.split(","): eplist.append(':'.join([ip, port])) pserver_endpoints = ",".join(eplist) # ip:port,ip:port... trainers = int(os.getenv("PADDLE_TRAINERS")) current_endpoint = os.getenv("POD_IP") + ":" + port trainer_id = int(os.getenv("PADDLE_TRAINER_ID")) training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER") t = fluid.DistributeTranspiler() t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers) if training_role == "PSERVER": pserver_prog = t.get_pserver_program(current_endpoint) pserver_startup = t.get_startup_program( current_endpoint, pserver_prog ) exe.run(pserver_startup) exe.run(pserver_prog) elif training_role == "TRAINER": train_loop(t.get_trainer_program()) def infer(use_cuda, save_dirname=None): if save_dirname is None: return place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # Use fluid.io.load_inference_model to obtain the inference program desc, # the feed_target_names (the names of variables that will be fed # data using feed operators), and the fetch_targets (variables that # we want to obtain data from using fetch operators). [ inference_program, feed_target_names, fetch_targets, ] = fluid.io.load_inference_model(save_dirname, exe) # The input's dimension of conv should be 4-D or 5-D. # Use normilized image pixels as input data, which should be in the range [0, 1.0]. batch_size = 1 tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32") # Construct feed as a dictionary of {feed_target_name: feed_target_data} # and results will contain a list of data corresponding to fetch_targets. results = exe.run( inference_program, feed={feed_target_names[0]: tensor_img}, fetch_list=fetch_targets, ) print("infer results: ", results[0]) fluid.io.save_inference_model( save_dirname, feed_target_names, fetch_targets, exe, inference_program, clip_extra=True, ) class TestImageClassification(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def main(self, net_type, use_cuda, is_local=True): if use_cuda and not fluid.core.is_compiled_with_cuda(): return # Directory for saving the trained model save_dirname = os.path.join( self.temp_dir.name, "image_classification_" + net_type + ".inference.model", ) train(net_type, use_cuda, save_dirname, is_local) # infer(use_cuda, save_dirname) def test_amp_lists(self): white_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists() 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_1(self): white_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 1. w={'exp}, b=None white_list.add('exp') black_list.remove('exp') amp_lists = fluid.contrib.mixed_precision.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( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 2. w={'tanh'}, b=None white_list.add('tanh') gray_list.remove('tanh') amp_lists = fluid.contrib.mixed_precision.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( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 3. w={'lstm'}, b=None white_list.add('lstm') amp_lists = fluid.contrib.mixed_precision.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( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 4. w=None, b={'conv2d'} white_list.remove('conv2d') black_list.add('conv2d') amp_lists = fluid.contrib.mixed_precision.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( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 5. w=None, b={'tanh'} black_list.add('tanh') gray_list.remove('tanh') amp_lists = fluid.contrib.mixed_precision.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( fluid.contrib.mixed_precision.fp16_lists.white_list ) black_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.black_list ) gray_list = copy.copy( fluid.contrib.mixed_precision.fp16_lists.gray_list ) # 6. w=None, b={'lstm'} black_list.add('lstm') amp_lists = fluid.contrib.mixed_precision.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, fluid.contrib.mixed_precision.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 = fluid.Program() startup_prog = fluid.Program() scope = fluid.core.Scope() with fluid.scope_guard(scope): with fluid.program_guard(prog, startup_prog): yield class TestAmpWithNonIterableDataLoader(unittest.TestCase): def decorate_with_data_loader(self): main_prog = paddle.static.Program() start_prog = paddle.static.Program() with paddle.static.program_guard(main_prog, start_prog): with paddle.fluid.unique_name.guard(): image = fluid.layers.data( name='image', shape=[3, 224, 224], dtype='float32' ) label = fluid.layers.data( name='label', shape=[1], dtype='int64' ) py_reader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=4, iterable=False, use_double_buffer=False, ) net = vgg16_bn_drop(image) logits = fluid.layers.fc(input=net, size=10, act="softmax") cost, predict = paddle.nn.functional.softmax_with_cross_entropy( logits, label, return_softmax=True ) avg_cost = paddle.mean(cost) optimizer = fluid.optimizer.Lamb(learning_rate=0.001) amp_lists = ( fluid.contrib.mixed_precision.AutoMixedPrecisionLists( custom_black_varnames={"loss", "conv2d_0.w_0"} ) ) mp_optimizer = decorate( optimizer=optimizer, amp_lists=amp_lists, init_loss_scaling=8.0, use_dynamic_loss_scaling=True, ) mp_optimizer.minimize(avg_cost) def test_non_iterable_dataloader(self): self.decorate_with_data_loader() if __name__ == '__main__': unittest.main()