mirror of
https://github.com/zebrajr/pytorch.git
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34eaa19d27
Summary: CPU -version of data parallel model. Great thing is that now we can run data_parallel_model_test in Sandcastle (as it does not have GPUs). Pretty simple change, really. I did not change all variable names with "gpu" in them, to reduce risk (and being a bit lazy). Can improve later. Reviewed By: wesolwsk Differential Revision: D5277350 fbshipit-source-id: 682e0c5f9f4ce94a8f5bd089905b0f8268bd2210
463 lines
16 KiB
Python
463 lines
16 KiB
Python
## @package resnet50_trainer
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# Module caffe2.python.examples.resnet50_trainer
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import argparse
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import logging
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import numpy as np
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import time
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import os
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from caffe2.python import core, workspace, experiment_util, data_parallel_model
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from caffe2.python import dyndep, optimizer
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from caffe2.python import timeout_guard, model_helper, brew
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import caffe2.python.models.resnet as resnet
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import caffe2.python.predictor.predictor_exporter as pred_exp
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import caffe2.python.predictor.predictor_py_utils as pred_utils
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from caffe2.python.predictor_constants import predictor_constants as predictor_constants
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'''
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Parallelized multi-GPU distributed trainer for Resnet 50. Can be used to train
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on imagenet data, for example.
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To run the trainer in single-machine multi-gpu mode by setting num_shards = 1.
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To run the trainer in multi-machine multi-gpu mode with M machines,
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run the same program on all machines, specifying num_shards = M, and
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shard_id = a unique integer in the set [0, M-1].
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For rendezvous (the trainer processes have to know about each other),
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you can either use a directory path that is visible to all processes
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(e.g. NFS directory), or use a Redis instance. Use the former by
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passing the `file_store_path` argument. Use the latter by passing the
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`redis_host` and `redis_port` arguments.
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'''
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logging.basicConfig()
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log = logging.getLogger("resnet50_trainer")
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log.setLevel(logging.DEBUG)
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dyndep.InitOpsLibrary('@/caffe2/caffe2/distributed:file_store_handler_ops')
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dyndep.InitOpsLibrary('@/caffe2/caffe2/distributed:redis_store_handler_ops')
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def AddImageInput(model, reader, batch_size, img_size):
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'''
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Image input operator that loads data from reader and
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applies certain transformations to the images.
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'''
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data, label = brew.image_input(
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model,
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reader, ["data", "label"],
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batch_size=batch_size,
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use_caffe_datum=True,
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mean=128.,
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std=128.,
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scale=256,
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crop=img_size,
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mirror=1
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)
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data = model.StopGradient(data, data)
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def SaveModel(args, train_model, epoch):
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prefix = "[]_{}".format(train_model._device_prefix, train_model._devices[0])
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predictor_export_meta = pred_exp.PredictorExportMeta(
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predict_net=train_model.net.Proto(),
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parameters=data_parallel_model.GetCheckpointParams(train_model),
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inputs=[prefix + "/data"],
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outputs=[prefix + "/softmax"],
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shapes={
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prefix + "/softmax": (1, args.num_labels),
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prefix + "/data": (args.num_channels, args.image_size, args.image_size)
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}
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)
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# save the train_model for the current epoch
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model_path = "%s/%s_%d.mdl" % (
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args.file_store_path,
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args.save_model_name,
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epoch,
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)
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# set db_type to be "minidb" instead of "log_file_db", which breaks
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# the serialization in save_to_db. Need to switch back to log_file_db
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# after migration
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pred_exp.save_to_db(
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db_type="minidb",
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db_destination=model_path,
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predictor_export_meta=predictor_export_meta,
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)
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def LoadModel(path, model):
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'''
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Load pretrained model from file
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'''
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log.info("Loading path: {}".format(path))
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meta_net_def = pred_exp.load_from_db(path, 'minidb')
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init_net = core.Net(pred_utils.GetNet(
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meta_net_def, predictor_constants.GLOBAL_INIT_NET_TYPE))
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predict_init_net = core.Net(pred_utils.GetNet(
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meta_net_def, predictor_constants.PREDICT_INIT_NET_TYPE))
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predict_init_net.RunAllOnGPU()
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init_net.RunAllOnGPU()
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assert workspace.RunNetOnce(predict_init_net)
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assert workspace.RunNetOnce(init_net)
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def RunEpoch(
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args,
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epoch,
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train_model,
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test_model,
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total_batch_size,
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num_shards,
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expname,
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explog,
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):
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'''
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Run one epoch of the trainer.
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TODO: add checkpointing here.
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'''
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# TODO: add loading from checkpoint
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log.info("Starting epoch {}/{}".format(epoch, args.num_epochs))
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epoch_iters = int(args.epoch_size / total_batch_size / num_shards)
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for i in range(epoch_iters):
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# This timeout is required (temporarily) since CUDA-NCCL
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# operators might deadlock when synchronizing between GPUs.
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timeout = 600.0 if i == 0 else 60.0
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with timeout_guard.CompleteInTimeOrDie(timeout):
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t1 = time.time()
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workspace.RunNet(train_model.net.Proto().name)
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t2 = time.time()
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dt = t2 - t1
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fmt = "Finished iteration {}/{} of epoch {} ({:.2f} images/sec)"
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log.info(fmt.format(i + 1, epoch_iters, epoch, total_batch_size / dt))
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prefix = "{}_{}".format(
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train_model._device_prefix,
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train_model._devices[0])
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accuracy = workspace.FetchBlob(prefix + '/accuracy')
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loss = workspace.FetchBlob(prefix + '/loss')
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train_fmt = "Training loss: {}, accuracy: {}"
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log.info(train_fmt.format(loss, accuracy))
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num_images = epoch * epoch_iters * total_batch_size
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prefix = "{}_{}".format(train_model._device_prefix, train_model._devices[0])
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accuracy = workspace.FetchBlob(prefix + '/accuracy')
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loss = workspace.FetchBlob(prefix + '/loss')
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learning_rate = workspace.FetchBlob(prefix + '/conv1_w_lr')
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test_accuracy = 0
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if (test_model is not None):
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# Run 100 iters of testing
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ntests = 0
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for _ in range(0, 100):
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workspace.RunNet(test_model.net.Proto().name)
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for g in test_model._devices:
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test_accuracy += np.asscalar(workspace.FetchBlob(
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"{}_{}".format(test_model._device_prefix, g) + '/accuracy'
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))
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ntests += 1
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test_accuracy /= ntests
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else:
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test_accuracy = (-1)
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explog.log(
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input_count=num_images,
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batch_count=(i + epoch * epoch_iters),
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additional_values={
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'accuracy': accuracy,
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'loss': loss,
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'learning_rate': learning_rate,
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'epoch': epoch,
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'test_accuracy': test_accuracy,
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}
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)
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assert loss < 40, "Exploded gradients :("
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# TODO: add checkpointing
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return epoch + 1
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def Train(args):
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# Either use specified device list or generate one
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if args.gpus is not None:
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gpus = [int(x) for x in args.gpus.split(',')]
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num_gpus = len(gpus)
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else:
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gpus = list(range(args.num_gpus))
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num_gpus = args.num_gpus
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log.info("Running on GPUs: {}".format(gpus))
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# Verify valid batch size
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total_batch_size = args.batch_size
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batch_per_device = total_batch_size // num_gpus
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assert \
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total_batch_size % num_gpus == 0, \
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"Number of GPUs must divide batch size"
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# Round down epoch size to closest multiple of batch size across machines
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global_batch_size = total_batch_size * args.num_shards
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epoch_iters = int(args.epoch_size / global_batch_size)
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args.epoch_size = epoch_iters * global_batch_size
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log.info("Using epoch size: {}".format(args.epoch_size))
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# Create ModelHelper object
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train_arg_scope = {
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'order': 'NCHW',
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'use_cudnn': True,
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'cudnn_exhaustice_search': True,
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'ws_nbytes_limit': (args.cudnn_workspace_limit_mb * 1024 * 1024),
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}
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train_model = model_helper.ModelHelper(
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name="resnet50", arg_scope=train_arg_scope
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)
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num_shards = args.num_shards
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shard_id = args.shard_id
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if num_shards > 1:
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# Create rendezvous for distributed computation
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store_handler = "store_handler"
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if args.redis_host is not None:
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# Use Redis for rendezvous if Redis host is specified
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"RedisStoreHandlerCreate", [], [store_handler],
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host=args.redis_host,
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port=args.redis_port,
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prefix=args.run_id,
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)
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)
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else:
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# Use filesystem for rendezvous otherwise
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"FileStoreHandlerCreate", [], [store_handler],
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path=args.file_store_path,
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)
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)
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rendezvous = dict(
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kv_handler=store_handler,
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shard_id=shard_id,
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num_shards=num_shards,
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engine="GLOO",
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exit_nets=None)
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else:
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rendezvous = None
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# Model building functions
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def create_resnet50_model_ops(model, loss_scale):
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[softmax, loss] = resnet.create_resnet50(
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model,
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"data",
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num_input_channels=args.num_channels,
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num_labels=args.num_labels,
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label="label",
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no_bias=True,
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)
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loss = model.Scale(loss, scale=loss_scale)
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brew.accuracy(model, [softmax, "label"], "accuracy")
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return [loss]
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def add_optimizer(model):
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stepsz = int(30 * args.epoch_size / total_batch_size / num_shards)
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optimizer.add_weight_decay(model, args.weight_decay)
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optimizer.build_sgd(
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model,
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args.base_learning_rate,
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momentum=0.9,
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nesterov=1,
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policy="step",
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stepsize=stepsz,
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gamma=0.1
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)
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# Input. Note that the reader must be shared with all GPUS.
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reader = train_model.CreateDB(
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"reader",
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db=args.train_data,
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db_type=args.db_type,
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num_shards=num_shards,
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shard_id=shard_id,
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)
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def add_image_input(model):
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AddImageInput(
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model,
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reader,
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batch_size=batch_per_device,
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img_size=args.image_size,
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)
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# Create parallelized model
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data_parallel_model.Parallelize(
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train_model,
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input_builder_fun=add_image_input,
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forward_pass_builder_fun=create_resnet50_model_ops,
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optimizer_builder_fun=add_optimizer,
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devices=gpus,
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rendezvous=rendezvous,
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optimize_gradient_memory=True,
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cpu_device=args.use_cpu,
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)
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# Add test model, if specified
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test_model = None
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if (args.test_data is not None):
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log.info("----- Create test net ----")
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test_arg_scope = {
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'order': "NCHW",
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'use_cudnn': True,
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'cudnn_exhaustive_search': True,
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}
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test_model = model_helper.ModelHelper(
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name="resnet50_test", arg_scope=test_arg_scope
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)
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test_reader = test_model.CreateDB(
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"test_reader",
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db=args.test_data,
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db_type=args.db_type,
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)
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def test_input_fn(model):
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AddImageInput(
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model,
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test_reader,
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batch_size=batch_per_device,
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img_size=args.image_size,
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)
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data_parallel_model.Parallelize(
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test_model,
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input_builder_fun=test_input_fn,
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forward_pass_builder_fun=create_resnet50_model_ops,
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param_update_builder_fun=None,
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devices=gpus,
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cpu_device=args.use_cpu,
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)
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workspace.RunNetOnce(test_model.param_init_net)
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workspace.CreateNet(test_model.net)
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workspace.RunNetOnce(train_model.param_init_net)
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workspace.CreateNet(train_model.net)
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epoch = 0
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# load the pre-trained model and reset epoch
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if args.load_model_path is not None:
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LoadModel(args.load_model_path, train_model)
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# Sync the model params
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data_parallel_model.FinalizeAfterCheckpoint(train_model)
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# reset epoch. load_model_path should end with *_X.mdl,
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# where X is the epoch number
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last_str = args.load_model_path.split('_')[-1]
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if last_str.endswith('.mdl'):
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epoch = int(last_str[:-4])
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log.info("Reset epoch to {}".format(epoch))
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else:
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log.warning("The format of load_model_path doesn't match!")
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expname = "resnet50_gpu%d_b%d_L%d_lr%.2f_v2" % (
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args.num_gpus,
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total_batch_size,
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args.num_labels,
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args.base_learning_rate,
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)
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explog = experiment_util.ModelTrainerLog(expname, args)
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# Run the training one epoch a time
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while epoch < args.num_epochs:
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epoch = RunEpoch(
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args,
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epoch,
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train_model,
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test_model,
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total_batch_size,
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num_shards,
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expname,
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explog
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)
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# Save the model for each epoch
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SaveModel(args, train_model, epoch)
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model_path = "%s/%s_" % (
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args.file_store_path,
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args.save_model_name
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)
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# remove the saved model from the previous epoch if it exists
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if os.path.isfile(model_path + str(epoch - 1) + ".mdl"):
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os.remove(model_path + str(epoch - 1) + ".mdl")
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def main():
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# TODO: use argv
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parser = argparse.ArgumentParser(
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description="Caffe2: Resnet-50 training"
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)
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parser.add_argument("--train_data", type=str, default=None,
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help="Path to training data or 'everstore_sampler'",
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required=True)
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parser.add_argument("--test_data", type=str, default=None,
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help="Path to test data")
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parser.add_argument("--db_type", type=str, default="lmdb",
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help="Database type (such as lmdb or leveldb)")
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parser.add_argument("--gpus", type=str,
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help="Comma separated list of GPU devices to use")
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parser.add_argument("--num_gpus", type=int, default=1,
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help="Number of GPU devices (instead of --gpus)")
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parser.add_argument("--num_channels", type=int, default=3,
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help="Number of color channels")
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parser.add_argument("--image_size", type=int, default=227,
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help="Input image size (to crop to)")
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parser.add_argument("--num_labels", type=int, default=1000,
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help="Number of labels")
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parser.add_argument("--batch_size", type=int, default=32,
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help="Batch size, total over all GPUs")
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parser.add_argument("--epoch_size", type=int, default=1500000,
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help="Number of images/epoch, total over all machines")
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parser.add_argument("--num_epochs", type=int, default=1000,
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help="Num epochs.")
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parser.add_argument("--base_learning_rate", type=float, default=0.1,
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help="Initial learning rate.")
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parser.add_argument("--weight_decay", type=float, default=1e-4,
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help="Weight decay (L2 regularization)")
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parser.add_argument("--cudnn_workspace_limit_mb", type=int, default=64,
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help="CuDNN workspace limit in MBs")
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parser.add_argument("--num_shards", type=int, default=1,
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help="Number of machines in distributed run")
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parser.add_argument("--shard_id", type=int, default=0,
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help="Shard id.")
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parser.add_argument("--run_id", type=str,
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help="Unique run identifier (e.g. uuid)")
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parser.add_argument("--redis_host", type=str,
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help="Host of Redis server (for rendezvous)")
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parser.add_argument("--redis_port", type=int, default=6379,
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help="Port of Redis server (for rendezvous)")
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parser.add_argument("--file_store_path", type=str, default="/tmp",
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help="Path to directory to use for rendezvous")
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parser.add_argument("--save_model_name", type=str, default="resnet50_model",
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help="Save the trained model to a given name")
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parser.add_argument("--load_model_path", type=str, default=None,
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help="Load previously saved model to continue training")
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parser.add_argument("--use_cpu", type=bool, default=False,
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help="Use CPU instead of GPU")
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args = parser.parse_args()
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Train(args)
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if __name__ == '__main__':
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workspace.GlobalInit(['caffe2', '--caffe2_log_level=2'])
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main()
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