/usr/local/lib64/python3.6/site-packages/caffe2/python
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/checkpoint_test.py (13405B)
from caffe2.python.schema import Struct, ConstRecord
from caffe2.python import core, workspace, model_helper
from caffe2.python.session import LocalSession
from caffe2.python.dataset import Dataset
from caffe2.python.pipeline import pipe
from caffe2.python.checkpoint import (
CheckpointManager, MultiNodeCheckpointManager, Job, JobRunner, epoch_limiter,
UploadTaskGroupBuilder, db_name)
from caffe2.python.net_builder import ops
from caffe2.python.task import Node, Task, TaskGroup, WorkspaceType, Cluster
from caffe2.python.test_util import TestCase
from caffe2.python.dataio import ReaderWithLimit
import numpy as np
import os
import shutil
import tempfile
def build_pipeline(node_id):
with Node('trainer_%d' % node_id):
with Job.current().init_group, Task():
data_arr = Struct(('val', np.array(list(range(10)))))
data = ConstRecord(ops, data_arr)
ds = Dataset(data, name='dataset:%d' % node_id)
full_reader = ds.reader(ops)
total = ops.Const([100])
def inc_total(rec):
ops.Add([total, rec.val()], [total])
epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
pipe(epoch_reader, processor=inc_total)
Job.current().add_stop_condition(epoch_reader.data_finished())
return [total]
EXPECTED_TOTALS = [103, 115, 136, 145]
def local_copy_op(src, dest):
def copy_op(inputs, outputs):
shutil.copyfile(src, dest)
return copy_op
class UploadToLocalFile(UploadTaskGroupBuilder):
def __init__(self, dest_dir):
self.dest_dir = dest_dir
def build(self, epoch, checkpoint_manager):
with TaskGroup(WorkspaceType.GLOBAL) as upload_task_group:
for node, manager in checkpoint_manager._node_managers:
with Node(str(node)), Task():
src_path = db_name(epoch, manager._node_name, manager._db_prefix)
dest_path = os.path.join(self.dest_dir, str(node))
ops.Python((local_copy_op,
[src_path, dest_path], {}))([], [])
return upload_task_group
class TestCheckpoint(TestCase):
def run_with(self, builder):
with Cluster():
with Job() as job:
outputs = build_pipeline(node_id=0)
output_fetcher = Task(step=core.Net('empty'), outputs=outputs)
def fetch_total(session):
session.run(output_fetcher)
return output_fetcher.outputs()[0].fetch()
session, checkpoint = builder()
job.compile(LocalSession)
num_epochs = JobRunner(job, checkpoint).train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
self.assertEquals(fetch_total(session), EXPECTED_TOTALS[-1])
for initial_epoch in range(1, num_epochs + 1):
session, checkpoint = builder()
JobRunner(
job,
checkpoint, resume_from_epoch=initial_epoch
).train(session)
self.assertEquals(fetch_total(session), EXPECTED_TOTALS[-1])
for epoch in range(1, num_epochs + 1):
session.run(checkpoint.load(epoch))
self.assertEquals(fetch_total(session),
EXPECTED_TOTALS[epoch - 1])
def test_single_checkpoint(self):
# test single node
try:
tmpdir = tempfile.mkdtemp()
def builder():
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = CheckpointManager(tmpdir, 'temp_node', 'minidb')
return session, checkpoint
self.run_with(builder)
finally:
shutil.rmtree(tmpdir)
# test multi-node
try:
tmpdir = tempfile.mkdtemp()
def builder():
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
return session, checkpoint
self.run_with(builder)
finally:
shutil.rmtree(tmpdir)
def test_ckpt_name_and_load_model_from_ckpts(self):
try:
num_nodes = 3
tmpdir = tempfile.mkdtemp()
# First, check if the checkpoint name generation mechanism is
# correct.
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
checkpoint.init(job.nodes_to_checkpoint())
for node_id in range(num_nodes):
epoch = 5
node_name = 'trainer_%d' % node_id
expected_db_name = tmpdir + '/' + node_name + '.5'
self.assertEquals(
checkpoint.get_ckpt_db_name(node_name, epoch),
expected_db_name)
shutil.rmtree(tmpdir)
# Next, check mechanism to load model from checkpoints.
tmpdir = tempfile.mkdtemp()
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# There are 17 global blobs after finishing up the job runner.
# (only blobs on init_group are checkpointed)
self.assertEquals(len(ws.blobs), 17)
ws = workspace.C.Workspace()
session = LocalSession(ws)
self.assertEquals(len(ws.blobs), 0)
model_blob_names = ['trainer_1/task_2/GivenTensorInt64Fill:0',
'trainer_2/task_2/GivenTensorInt64Fill:0']
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=1, session=session)
# Check that we can successfully load from checkpoints of epochs
# 1 to 4, but not epoch 5.
for epoch in range(1, 5):
self.assertTrue(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=epoch,
session=session))
# Check that all the model blobs are loaded.
for blob_name in model_blob_names:
self.assertTrue(ws.has_blob(blob_name))
self.assertEquals(
ws.fetch_blob(blob_name),
np.array([EXPECTED_TOTALS[epoch - 1]]))
self.assertFalse(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=5, session=session))
finally:
shutil.rmtree(tmpdir)
def test_upload_checkpoint(self):
try:
tmpdir = tempfile.mkdtemp()
upload_dir = os.path.join(tmpdir, "upload")
os.mkdir(upload_dir)
num_nodes = 3
# The uploaded files do not exist yet.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertFalse(os.path.exists(upload_path))
# Create and run the job runner.
for node_id in range(3):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
local_upload_builder = UploadToLocalFile(upload_dir)
job_runner = JobRunner(
job, checkpoint,
upload_task_group_builder=local_upload_builder)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# The uploaded files should exist now.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertTrue(os.path.exists(upload_path))
finally:
shutil.rmtree(tmpdir)
def test_ckpt_save_failure(self):
num_nodes = 3
# The goal of this test is to ensure that the job runs
# successfully even if saving a checkpoint fails.
# Hence tmpdir is a non existent directory to emulate a failure
# while saving checkpoints
tmpdir = "/tmp/path_does_not_exist/"
# Check the saving checkpoint failure does not cause job failure
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
# make sure all epochs are executed even though saving the checkpoint failed
# Saving checkpoint failure should not cause job failure
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
def test_download_group_simple(self):
"""
A simple test that ensures we have download task group
executed between epoch_group and exit_group.
"""
model = model_helper.ModelHelper(name="test_model")
download_net = core.Net("download_net")
for name in ["input1", "input2", "output", "download_result"]:
model.param_init_net.ConstantFill([],
[name],
shape=[8, ],
value=1.0,
run_once=0)
model.net.Add(["input1", "input2"], ["output"])
download_net.Copy(["output"], ["download_result"])
# All blob values are initialized as 1.0, after download_net executed
# we expect to see download result is the same as training result.
with Job() as job:
with Node("trainer:0"):
with job.init_group:
Task(step=model.param_init_net)
with job.epoch_group:
with Task():
with ops.loop(1):
ops.net(model.net)
with job.download_group:
Task(step=download_net)
epoch_limiter(job, 1)
ws = workspace.C.Workspace()
session = LocalSession(ws)
job_runner = JobRunner(job)
job_runner.train(session)
expected_result = np.full(8, 2.0).astype(np.float32)
self.assertTrue(np.array_equal(expected_result,
ws.fetch_blob("output")))
self.assertTrue(np.array_equal(expected_result,
ws.fetch_blob("download_result")))
def test_reuse_checkpoint_manager(self):
"""
A simple test that ensures we can reuse a MultiNodeCheckpointManager
object.
"""
try:
tmpdir = tempfile.mkdtemp()
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Job() as job:
outputs = build_pipeline(node_id=0)
output_fetcher = Task(step=core.Net('empty'), outputs=outputs)
job.compile(LocalSession)
def fetch_total(session):
session.run(output_fetcher)
return output_fetcher.outputs()[0].fetch()
num_epochs = JobRunner(job, checkpoint).train(session)
for initial_epoch in range(1, num_epochs + 1):
JobRunner(
job,
checkpoint,
resume_from_epoch=initial_epoch
).train(session)
self.assertEquals(fetch_total(session), EXPECTED_TOTALS[-1])
finally:
shutil.rmtree(tmpdir)