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6e22427929
Summary: Travis CI is complaining about test_load_model_from_checkpoints in recent PRs. E: AssertionError: 'trainer:1/task/GivenTensorInt64Fill:0, a C++ native class of type nullptr (uninitialized).' != array([103]) See for example https://travis-ci.org/caffe2/caffe2/jobs/265665119 Reason unkown yet. First disable this then try to fix it Reviewed By: Yangqing Differential Revision: D5655068 fbshipit-source-id: 10949339ec92b0a4c2f0e59246040f1b0510be12
234 lines
9.1 KiB
Python
234 lines
9.1 KiB
Python
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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from caffe2.python.schema import Struct, ConstRecord
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from caffe2.python import core, workspace
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from caffe2.python.session import LocalSession
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from caffe2.python.dataset import Dataset
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from caffe2.python.pipeline import pipe
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from caffe2.python.checkpoint import (
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CheckpointManager, MultiNodeCheckpointManager, Job, JobRunner,
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UploadTaskGroupBuilder)
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from caffe2.python.net_builder import ops
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from caffe2.python.task import Node, Task, TaskGroup, WorkspaceType
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from caffe2.python.test_util import TestCase
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from caffe2.python.dataio import ReaderWithLimit
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import numpy as np
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import os
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import shutil
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import tempfile
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import unittest
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def build_pipeline(node_id):
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with Node('trainer:%d' % node_id):
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with Job.current().init_group, Task():
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data_arr = Struct(('val', np.array(list(range(10)))))
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data = ConstRecord(ops, data_arr)
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ds = Dataset(data, name='dataset:%d' % node_id)
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full_reader = ds.reader(ops)
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total = ops.Const([100])
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def inc_total(rec):
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ops.Add([total, rec.val()], [total])
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epoch_reader = ReaderWithLimit(full_reader, num_iter=3)
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pipe(epoch_reader, processor=inc_total)
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Job.current().add_stop_signal(epoch_reader.data_finished())
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return [total]
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EXPECTED_TOTALS = [103, 115, 136, 145]
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def local_copy_op(src, dest):
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def copy_op(inputs, outputs):
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shutil.copyfile(src, dest)
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return copy_op
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class UploadToLocalFile(UploadTaskGroupBuilder):
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def __init__(self, dest_dir):
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self.dest_dir = dest_dir
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def build(self, epoch, checkpoint_manager):
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with TaskGroup(WorkspaceType.GLOBAL) as upload_task_group:
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for node, manager in checkpoint_manager._node_managers:
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with Node(str(node)), Task():
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src_path = manager._db_name(epoch)
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dest_path = os.path.join(self.dest_dir, str(node))
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ops.Python((local_copy_op,
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[src_path, dest_path], {}))([], [])
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return upload_task_group
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class TestCheckpoint(TestCase):
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def run_with(self, builder):
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with Job() as job:
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outputs = build_pipeline(node_id=0)
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output_fetcher = Task(step=core.Net('empty'), outputs=outputs)
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def fetch_total(session):
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session.run(output_fetcher)
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return output_fetcher.outputs()[0].fetch()
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session, checkpoint = builder()
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compiled_job = job.compile(LocalSession)
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num_epochs = JobRunner(compiled_job, checkpoint)(session)
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self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
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self.assertEquals(fetch_total(session), EXPECTED_TOTALS[-1])
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for initial_epoch in range(1, num_epochs + 1):
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session, checkpoint = builder()
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JobRunner(
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compiled_job,
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checkpoint, resume_from_epoch=initial_epoch)(session)
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self.assertEquals(fetch_total(session), EXPECTED_TOTALS[-1])
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for epoch in range(1, num_epochs + 1):
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session.run(checkpoint.load(epoch))
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self.assertEquals(fetch_total(session), EXPECTED_TOTALS[epoch - 1])
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def test_single_checkpoint(self):
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# test single node
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with tempfile.NamedTemporaryFile() as tmp:
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def builder():
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ws = workspace.C.Workspace()
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session = LocalSession(ws)
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checkpoint = CheckpointManager(tmp.name, 'minidb')
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return session, checkpoint
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self.run_with(builder)
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# test multi-node
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try:
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tmpdir = tempfile.mkdtemp()
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def builder():
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ws = workspace.C.Workspace()
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session = LocalSession(ws)
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checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
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return session, checkpoint
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self.run_with(builder)
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finally:
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shutil.rmtree(tmpdir)
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# Note(wyiming): we are yet to find out why Travis gives out like:
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# E: AssertionError: 'trainer:1/task/GivenTensorInt64Fill:0, a C++ native class of type nullptr (uninitialized).' != array([103])
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# See for example https://travis-ci.org/caffe2/caffe2/jobs/265665119
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# As a result, we will check if this is travis, and if yes, disable it.
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@unittest.skipIf(os.environ.get("TRAVIS"), "DPMTest has a known issue with Travis.")
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def test_load_model_from_checkpoints(self):
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try:
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tmpdir = tempfile.mkdtemp()
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for node_id in range(3):
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ws = workspace.C.Workspace()
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session = LocalSession(ws)
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checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
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with Job() as job:
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build_pipeline(node_id)
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compiled_job = job.compile(LocalSession)
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job_runner = JobRunner(compiled_job, checkpoint)
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num_epochs = job_runner(session)
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self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
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# There are 12 global blobs after finishing up the job runner.
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# (only blobs on init_group are checkpointed)
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self.assertEquals(len(ws.blobs), 12)
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ws = workspace.C.Workspace()
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session = LocalSession(ws)
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self.assertEquals(len(ws.blobs), 0)
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model_blob_names = ['trainer:1/task/GivenTensorInt64Fill:0',
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'trainer:2/task/GivenTensorInt64Fill:0']
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checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
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with Job() as job:
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for node_id in range(3):
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build_pipeline(node_id)
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compiled_job = job.compile(LocalSession)
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job_runner = JobRunner(compiled_job, checkpoint)
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job_runner.load_blobs_from_checkpoints(blob_names=model_blob_names,
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epoch=1, session=session)
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# Check that we can successfully load from checkpoints of epochs
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# 1 to 4, but not epoch 5.
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for epoch in range(1, 5):
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self.assertTrue(
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job_runner.load_blobs_from_checkpoints(
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blob_names=model_blob_names, epoch=epoch,
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session=session))
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# Check that all the model blobs are loaded.
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for blob_name in model_blob_names:
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self.assertTrue(ws.has_blob(blob_name))
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self.assertEquals(ws.fetch_blob(blob_name),
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np.array([EXPECTED_TOTALS[epoch - 1]]))
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self.assertFalse(
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job_runner.load_blobs_from_checkpoints(
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blob_names=model_blob_names, epoch=5, session=session))
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finally:
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shutil.rmtree(tmpdir)
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def test_get_ckpt_db_name(self):
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try:
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tmpdir = tempfile.mkdtemp()
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num_nodes = 3
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checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
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with Job() as job:
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for node_id in range(num_nodes):
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build_pipeline(node_id)
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compiled_job = job.compile(LocalSession)
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checkpoint.init(compiled_job.nodes_to_checkpoint())
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for node_id in range(num_nodes):
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epoch = 5
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node_name = 'trainer:%d' % node_id
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expected_db_name = tmpdir + '/' + node_name + '.000005'
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self.assertEquals(
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checkpoint.get_ckpt_db_name(node_name, epoch),
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expected_db_name)
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finally:
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shutil.rmtree(tmpdir)
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def test_upload_checkpoint(self):
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try:
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tmpdir = tempfile.mkdtemp()
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upload_dir = os.path.join(tmpdir, "upload")
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os.mkdir(upload_dir)
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num_nodes = 3
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# The uploaded files do not exist yet.
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for node_id in range(num_nodes):
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node_name = 'trainer:%d' % node_id
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upload_path = os.path.join(upload_dir, node_name)
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self.assertFalse(os.path.exists(upload_path))
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# Create and run the job runner.
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for node_id in range(3):
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ws = workspace.C.Workspace()
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session = LocalSession(ws)
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checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
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with Job() as job:
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build_pipeline(node_id)
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compiled_job = job.compile(LocalSession)
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local_upload_builder = UploadToLocalFile(upload_dir)
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job_runner = JobRunner(
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compiled_job, checkpoint,
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upload_task_group_builder=local_upload_builder)
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num_epochs = job_runner(session)
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self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
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# The uploaded files should exist now.
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for node_id in range(num_nodes):
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node_name = 'trainer:%d' % node_id
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upload_path = os.path.join(upload_dir, node_name)
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self.assertTrue(os.path.exists(upload_path))
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finally:
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shutil.rmtree(tmpdir)
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