mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
2caca70a37
Summary: (Work in progress). This diff will allow shifting of activations to other GPUs, in case the model does not fit into memory. To see the API, check the code in data_parallel_model_test, which tests shifting two activations from 0 and 1 to gpu 4, and from gpu 2 and 3 to gpu 5. I will need to further test on ResNets, and probablly add copy operations to handle device change points. Reviewed By: asaadaldien Differential Revision: D5591674 fbshipit-source-id: eb12d23651a56d64fa4db91090c6474218705270
1117 lines
42 KiB
Python
1117 lines
42 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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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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##############################################################################
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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.utils import viewkeys
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from multiprocessing import Process, Queue
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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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from hypothesis import assume, given
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import hypothesis.strategies as st
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from caffe2.proto import caffe2_pb2
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from caffe2.python import brew, core, cnn, data_parallel_model, dyndep, \
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model_helper, optimizer, rnn_cell, workspace, data_parallel_model_utils
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from caffe2.python.test_util import TestCase
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dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops")
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class TemporaryDirectory:
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def __enter__(self):
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self.tmpdir = tempfile.mkdtemp()
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return self.tmpdir
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def __exit__(self, type, value, traceback):
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shutil.rmtree(self.tmpdir)
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# Note(jiayq): we are yet to find out why Travis gives out an error in gloo
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# like:
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# RuntimeError: [enforce fail at /home/travis/build/caffe2/caffe2/third_party/gloo/gloo/transport/tcp/device.cc:113] ifa != nullptr. Unable to find interface for: [127.0.1.1]
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# See for example https://travis-ci.org/caffe2/caffe2/jobs/262433866
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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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class DataParallelModelTest(TestCase):
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def run_model(self, devices, gpu):
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'''
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Helper function for test_equiv
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'''
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def input_builder_fun(model):
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return None
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def model_build_fun(model, loss_scale):
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fc = model.FC("data", "fc", 16, 1,
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("ConstantFill", {}), ("ConstantFill", {}))
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fc_fl = model.FlattenToVec(fc, "fc_fl")
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sigm = model.Sigmoid(fc_fl, "sigm")
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sq = model.SquaredL2Distance([sigm, "label"], "sq")
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loss = model.AveragedLoss(sq, "loss")
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loss = model.Scale(loss, scale=loss_scale)
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# For testing explicit sync
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model.param_init_net.UniformFill([], ["sync_num"], shape=[1])
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return [loss]
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def add_optimizer(model):
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return optimizer.build_sgd(
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model,
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0.1,
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policy="fixed",
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max_gradient_norm=5.0,
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allow_lr_injection=True,
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)
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workspace.ResetWorkspace()
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model = cnn.CNNModelHelper(
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order="NHWC",
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name="test{}".format(devices),
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)
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data_parallel_model.Parallelize(
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model,
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input_builder_fun=input_builder_fun,
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forward_pass_builder_fun=model_build_fun,
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optimizer_builder_fun=add_optimizer,
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devices=devices,
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cpu_device=not gpu,
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shared_model=not gpu,
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)
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data_parallel_model.AddBlobSync(model, ["sync_num"])
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# Light test for LR names
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lr_names = data_parallel_model.GetLearningRateBlobNames(model)
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self.assertGreater(len(lr_names), 0)
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np.random.seed(2603)
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# Each run has same input, independent of number of gpus
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batch_size = 64
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for i in range(0, 10):
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full_data = np.random.rand(batch_size, 16)
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full_labels = np.round(full_data[:, 0])
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batch_per_device = batch_size // len(devices)
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for (j, g) in enumerate(devices):
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st = j * batch_per_device
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en = st + batch_per_device
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data = full_data[st:en, :].astype(np.float32)
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labels = full_labels[st:en].astype(np.float32)
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with core.DeviceScope(core.DeviceOption(model._device_type, g)):
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workspace.FeedBlob(
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"{}_{}/data".format(model._device_prefix, g), data
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)
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workspace.FeedBlob(
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"{}_{}/label".format(model._device_prefix, g), labels
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)
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if i == 0:
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workspace.RunNetOnce(model.param_init_net)
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workspace.CreateNet(model.net)
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workspace.FeedBlob(
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model._device_prefix + "_0/sync_num",
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np.array([i * 2]).astype(np.float32),
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device_option=core.DeviceOption(model._device_type, 0))
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workspace.RunNet(model.net.Proto().name)
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# Test AddBlobSync
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for j in model._devices:
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sync = workspace.FetchBlob(
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model._device_prefix + "_{}/sync_num".format(j))[0]
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self.assertTrue(abs(sync - i * 2) < 0.01)
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return workspace.FetchBlob("{}_0/fc_w".format(model._device_prefix))
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def run_test_locally(self, fn, device_option=None, **kwargs):
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# Queue for assertion errors on subprocesses
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queue = Queue()
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# Capture any exception thrown by the subprocess
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def run_fn(*args, **kwargs):
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try:
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if device_option is None:
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fn(*args, **kwargs)
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workspace.ResetWorkspace()
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else:
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with core.DeviceScope(device_option):
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fn(*args, **kwargs)
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workspace.ResetWorkspace()
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except Exception as ex:
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queue.put(ex)
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# Start N processes in the background
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procs = []
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for i in range(kwargs['comm_size']):
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kwargs['comm_rank'] = i
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proc = Process(
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target=run_fn,
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kwargs=kwargs)
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proc.start()
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procs.append(proc)
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# Test complete, join background processes
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while len(procs) > 0:
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proc = procs.pop(0)
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while proc.is_alive():
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proc.join(1)
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# Raise exception if we find any.
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# Note that the following is executed ALSO after
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# the last process was joined, so if ANY exception
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# was raised, it will be re-raised here.
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if not queue.empty():
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raise queue.get()
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def test_equiv(self):
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'''
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Test that the model produces exactly same results given
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total batchsize, independent of number of GPUs.
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'''
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for gpu in [True, False]:
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if gpu and (not workspace.has_gpu_support or
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workspace.NumCudaDevices() < 2):
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continue
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result_2gpus = self.run_model([0, 1], gpu=gpu)
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result_1gpus = self.run_model([0], gpu=gpu)
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self.assertTrue(np.allclose(result_1gpus, result_2gpus))
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if not gpu or workspace.NumCudaDevices() >= 4:
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result_4gpus = self.run_model(list(range(4)), gpu=gpu)
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self.assertTrue(np.allclose(result_1gpus, result_4gpus))
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if not gpu or workspace.NumCudaDevices() >= 8:
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result_8gpus = self.run_model(list(range(8)), gpu=gpu)
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self.assertTrue(np.allclose(result_1gpus, result_8gpus))
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if not gpu or workspace.NumCudaDevices() >= 16:
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result_16gpus = self.run_model(list(range(16)), gpu=gpu)
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self.assertTrue(np.allclose(result_1gpus, result_16gpus))
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def test_checkpoint_params(self):
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def add_input_ops(model):
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pass
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def add_model_ops(model, loss_scale):
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model.NHWC2NCHW("data", "data_nchw")
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model.Conv("data_nchw", 'conv1', 3, 64,
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weight_init=("MSRAFill", {}), kernel=7,
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stride=2, pad=3, no_bias=0)
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model.SpatialBN('conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False)
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model.Relu('conv1_spatbn_relu', 'conv1_spatbn_relu')
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model.MaxPool('conv1_spatbn_relu', 'pool1', kernel=3, stride=2)
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model.FC('pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=100)
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model.Sigmoid('fc', 'fc_sigm')
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model.Softmax('fc_sigm', 'softmax')
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model.LabelCrossEntropy(['softmax', 'label'], 'xent')
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loss = model.AveragedLoss('xent', 'loss')
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# Add a duplicate param init to ensure it does not cause issues
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model.param_init_net.ConstantFill(
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[], ["fc_w"], shape=((64 * 56 * 56), 1000)
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)
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return [loss]
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def add_optimizer(model):
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optimizer.build_sgd(model, 0.1, policy="fixed", momentum=0.9)
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model = cnn.CNNModelHelper(
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order="NHWC",
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name="test",
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)
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data_parallel_model.Parallelize_CPU(
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model,
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input_builder_fun=add_input_ops,
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forward_pass_builder_fun=add_model_ops,
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optimizer_builder_fun=add_optimizer,
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devices=[1, 2, 3],
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)
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# Only gpu_1 params should be returned (gpu_1 is the first gpu)
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checkpoint_params = data_parallel_model.GetCheckpointParams(model)
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for p in model.GetParams("cpu_1/"):
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self.assertTrue(p in checkpoint_params)
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self.assertTrue(p + "_momentum" in checkpoint_params)
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for p in model.GetParams("cpu_2/"):
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self.assertFalse(p in checkpoint_params)
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self.assertTrue(
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core.BlobReference("cpu_1/fc_w_momentum") in checkpoint_params)
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for c in model.GetComputedParams("cpu_1/"):
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self.assertTrue(c in checkpoint_params)
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for c in model.GetComputedParams("cpu_2/"):
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self.assertFalse(c in checkpoint_params)
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self.assertFalse(core.BlobReference("cpu_1/data") in checkpoint_params)
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self.assertTrue(core.BlobReference("optimizer_iteration") in checkpoint_params)
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def test_net_conversion_and_append_net(self):
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other = model_helper.ModelHelper()
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fc1 = brew.fc(other, "data", "other_fc1", dim_in=3*227*227, dim_out=10)
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fc2 = brew.fc(other, fc1, "other_fc2", dim_in=10, dim_out=10)
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brew.fc(other, fc2, "other_fc3", dim_in=10, dim_out=10)
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def add_input_ops(model):
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model.net.UniformFill([], ["data"], shape=[4, 227, 227, 3])
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model.net.UniformFill([], ["label"], shape=[4])
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def add_model_ops(model, loss_scale):
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model.NHWC2NCHW("data", "data_nchw")
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model.Conv("data_nchw", 'conv1', 3, 64,
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weight_init=("MSRAFill", {}), kernel=7,
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stride=2, pad=3, no_bias=0)
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model.SpatialBN('conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False)
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model.Relu('conv1_spatbn_relu', 'conv1_spatbn_relu')
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model.MaxPool('conv1_spatbn_relu', 'pool1', kernel=3, stride=2)
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model.FC('pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=10)
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# Append the net and param_init_net of the other model
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appendnet = data_parallel_model.ConvertNetForDevice(other.net)
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model.net.AppendNet(appendnet)
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model.param_init_net.AppendNet(
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data_parallel_model.ConvertNetForDevice(other.param_init_net))
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model.Sigmoid('fc', 'fc_sigm')
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model.Softmax('fc_sigm', 'softmax')
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loss = model.AveragedLoss('softmax', 'loss')
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return [loss]
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def add_optimizer(model):
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optimizer.build_sgd(model, 0.1, policy="fixed", momentum=0.9)
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model = cnn.CNNModelHelper(
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order="NCHW",
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name="test",
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)
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data_parallel_model.Parallelize_CPU(
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model,
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input_builder_fun=add_input_ops,
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forward_pass_builder_fun=add_model_ops,
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optimizer_builder_fun=add_optimizer,
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devices=range(4)
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)
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# Just create and run net and confirm no exception is thrown
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workspace.RunNetOnce(model.param_init_net)
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workspace.CreateNet(model.net)
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workspace.RunNet(model.net)
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def test_synchronization_barrier(self):
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def run(comm_rank, comm_size, tmpdir):
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def add_input_ops(model):
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pass
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def add_model_ops(model, loss_scale):
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return []
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def add_optimizer(model):
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pass
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store_handler = "store_handler"
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"FileStoreHandlerCreate",
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[],
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[store_handler],
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path=tmpdir))
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rendezvous = dict(
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kv_handler=store_handler,
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shard_id=comm_rank,
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num_shards=comm_size,
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engine='GLOO',
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)
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model = cnn.CNNModelHelper(
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order="NHWC",
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name="test",
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)
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data_parallel_model.Parallelize_CPU(
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model,
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input_builder_fun=add_input_ops,
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forward_pass_builder_fun=add_model_ops,
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optimizer_builder_fun=add_optimizer,
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devices=[1, 2, 3],
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rendezvous=rendezvous
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)
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data_parallel_model.RunInitNet(model)
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for _ in range(2):
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data_parallel_model.Synchronize(model)
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with TemporaryDirectory() as tmpdir:
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self.run_test_locally(
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run,
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comm_size=2,
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device_option=None,
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tmpdir=tmpdir)
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def test_device_scope_check(self):
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with self.assertRaises(AssertionError):
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with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, 0)):
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data_parallel_model.Parallelize_GPU(None, None, None)
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class RecurrentNetworkParallelTest(TestCase):
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def run_model(self, devices, gpu):
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'''
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Helper function for test_equiv
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'''
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def input_builder_fun(model):
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return None
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def model_build_fun(model, loss_scale):
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workspace.FeedBlob(
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core.ScopedBlobReference("seq_lengths"),
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np.array([self.T] * self.batch_per_device, dtype=np.int32)
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)
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model.param_init_net.ConstantFill(
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[],
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"hidden_init",
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value=0.0,
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shape=[1, self.batch_per_device, self.hidden_dim]
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)
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model.param_init_net.ConstantFill(
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[],
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"cell_init",
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value=0.0,
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shape=[1, self.batch_per_device, self.hidden_dim]
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)
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output, _last_hidden, _, _last_state, = rnn_cell.LSTM(
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model=model,
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input_blob="data",
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seq_lengths="seq_lengths",
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initial_states=("hidden_init", "cell_init"),
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dim_in=self.input_dim,
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dim_out=self.hidden_dim,
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scope="partest",
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)
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# A silly loss function
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loss = model.AveragedLoss(
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model.Sub([output, "target"], "dist"),
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"loss",
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)
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loss = model.Scale(loss, "loss_scaled", scale=loss_scale)
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return [loss]
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def param_update_fun(model):
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ITER = model.Iter("ITER")
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LR = model.net.LearningRate(
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[ITER],
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"LR",
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base_lr=(-0.1),
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policy="fixed",
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)
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ONE = model.param_init_net.ConstantFill(
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[], "ONE", shape=[1], value=1.0,
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)
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for param in model.GetParams():
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param_grad = model.param_to_grad[param]
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model.WeightedSum([param, ONE, param_grad, LR], param)
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assert len(model.GetParams()) == len(model.params) // len(model._devices)
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workspace.ResetWorkspace()
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model = cnn.CNNModelHelper(
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name="recurrent_test{}".format(devices),
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)
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self.T = 8
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self.batch_size = 64
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self.input_dim = 8
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self.hidden_dim = 31
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self.batch_per_device = self.batch_size // len(devices)
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data_parallel_model.Parallelize(
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model,
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input_builder_fun=input_builder_fun,
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forward_pass_builder_fun=model_build_fun,
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param_update_builder_fun=param_update_fun,
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devices=devices,
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optimize_gradient_memory=True,
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cpu_device=not gpu,
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)
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# Change all initialization to be ConstantFills so that
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# the everything is deterministic
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for op in model.param_init_net.Proto().op:
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if op.type.endswith('Fill'):
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op.type = 'ConstantFill'
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# Each run has same input, independent of number of gpus
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np.random.seed(20150210)
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for i in range(0, 10):
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full_data = np.random.rand(self.T, self.batch_size, self.input_dim)
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full_target = np.random.rand(
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self.T, self.batch_size, self.hidden_dim
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)
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for (j, g) in enumerate(devices):
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st = j * self.batch_per_device
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en = st + self.batch_per_device
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data = full_data[:, st:en, :].astype(np.float32)
|
|
targets = full_target[:, st:en, :].astype(np.float32)
|
|
with core.DeviceScope(core.DeviceOption(model._device_type, g)):
|
|
workspace.FeedBlob(
|
|
"{}_{}/data".format(model._device_prefix, g), data
|
|
)
|
|
workspace.FeedBlob(
|
|
"{}_{}/target".format(model._device_prefix, g), targets
|
|
)
|
|
|
|
if i == 0:
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
workspace.CreateNet(model.net)
|
|
|
|
workspace.RunNet(model.net.Proto().name)
|
|
|
|
return workspace.FetchBlob("{}_0/partest/i2h_w".format(model._device_prefix))
|
|
|
|
def test_equiv_recurrent(self):
|
|
'''
|
|
Test that the model produces exactly same results given
|
|
total batchsize, independent of number of GPUs/CPUs.
|
|
'''
|
|
for gpu in [True, False]:
|
|
if gpu and not workspace.has_gpu_support:
|
|
continue
|
|
result_2gpus = self.run_model([0, 1], gpu)
|
|
result_1gpus = self.run_model([0], gpu)
|
|
|
|
self.assertTrue(np.allclose(result_1gpus, result_2gpus))
|
|
|
|
if not gpu or workspace.NumCudaDevices() >= 4:
|
|
result_4gpus = self.run_model(list(range(4)), gpu)
|
|
self.assertTrue(np.allclose(result_1gpus, result_4gpus))
|
|
|
|
if not gpu or workspace.NumCudaDevices() >= 8:
|
|
result_8gpus = self.run_model(list(range(8)), gpu)
|
|
self.assertTrue(np.allclose(result_1gpus, result_8gpus))
|
|
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
|
|
@unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.")
|
|
class SparseDataParallelModelTest(TestCase):
|
|
|
|
'''
|
|
Create and run the model. We try with both storing indices for gather
|
|
on CPU and on GPU
|
|
'''
|
|
def run_model(self, V, gpu_devices, cpu_indices):
|
|
|
|
def input_builder_fun(model):
|
|
return None
|
|
|
|
def model_build_fun(model, loss_scale):
|
|
if cpu_indices:
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
gathered_cpu = model.net.Gather(
|
|
[self.vecs, 'indices'], 'gathered_cpu')
|
|
|
|
gathered = model.CopyCPUToGPU(gathered_cpu, "gathered")
|
|
else:
|
|
gpu_vecs = model.param_init_net.CopyCPUToGPU(
|
|
self.vecs, "gpuvecs",
|
|
)
|
|
model.params.append(gpu_vecs)
|
|
gathered = model.net.Gather([gpu_vecs, 'indices'], 'gathered')
|
|
flattened = model.Flatten(gathered, "flattened")
|
|
fc = model.FC(flattened, "fc", 16 * 16, 1,
|
|
("ConstantFill", {}), ("ConstantFill", {}))
|
|
fc_fl = model.FlattenToVec(fc, "fc_fl")
|
|
sigm = model.Sigmoid(fc_fl, "sigm")
|
|
sq = model.SquaredL2Distance([sigm, "label"], "sq")
|
|
loss = model.AveragedLoss(sq, "loss")
|
|
loss = model.Scale(loss, scale=loss_scale)
|
|
return [loss]
|
|
|
|
def param_update_fun(model):
|
|
ONE = model.param_init_net.ConstantFill(
|
|
[], "ONE", shape=[1], value=1.0,
|
|
)
|
|
LR = model.CopyCPUToGPU(self.LR, "LR")
|
|
for param in model.GetParams():
|
|
param_grad = model.param_to_grad[param]
|
|
if not isinstance(param_grad, core.GradientSlice):
|
|
model.WeightedSum([param, ONE, param_grad, LR], param)
|
|
else:
|
|
param_momentum = model.param_init_net.ConstantFill(
|
|
[param],
|
|
param + '_momentum',
|
|
value=0.0,
|
|
)
|
|
model.net.SparseMomentumSGDUpdate(
|
|
[
|
|
param_grad.values,
|
|
param_momentum,
|
|
LR,
|
|
param,
|
|
param_grad.indices,
|
|
],
|
|
[
|
|
param_grad.values, param_momentum, param
|
|
],
|
|
momentum=0.1,
|
|
nesterov=0,
|
|
)
|
|
|
|
workspace.ResetWorkspace()
|
|
model = cnn.CNNModelHelper(
|
|
order="NHWC",
|
|
name="sparse_test{}".format(gpu_devices),
|
|
)
|
|
|
|
with core.NameScope("cpu"):
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
self.ITER = model.Iter("ITER")
|
|
self.LR = model.net.LearningRate(
|
|
[self.ITER],
|
|
"LR",
|
|
base_lr=(-0.1),
|
|
policy="fixed",
|
|
)
|
|
self.vecs = model.param_init_net.UniformFill(
|
|
[], "vecs", shape=[V, 16])
|
|
if cpu_indices:
|
|
model.params.append(self.vecs)
|
|
self.ONE_CPU = model.param_init_net.ConstantFill(
|
|
[], "ONE_CPU", shape=[1], value=1.0,
|
|
)
|
|
|
|
data_parallel_model.Parallelize_GPU(
|
|
model,
|
|
input_builder_fun=input_builder_fun,
|
|
forward_pass_builder_fun=model_build_fun,
|
|
param_update_builder_fun=param_update_fun,
|
|
devices=gpu_devices,
|
|
)
|
|
|
|
# Update the vecs
|
|
if cpu_indices:
|
|
with core.NameScope("cpu"):
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
for param in model.GetParams():
|
|
param_grad = model.param_to_grad[param]
|
|
model.ScatterWeightedSum([param, self.ONE_CPU,
|
|
param_grad.indices,
|
|
param_grad.values,
|
|
self.LR],
|
|
self.vecs)
|
|
else:
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, 0)):
|
|
model.CopyGPUToCPU("gpu_0/gpuvecs", self.vecs)
|
|
|
|
np.random.seed(2603)
|
|
|
|
# Each run has same input, independent of number of gpus
|
|
batch_size = 64
|
|
for i in range(0, 10):
|
|
full_indices = np.random.permutation(V)[:batch_size * 16].reshape(
|
|
batch_size, 16
|
|
)
|
|
full_labels = full_indices[:, 0] % 2
|
|
batch_per_device = batch_size // len(gpu_devices)
|
|
|
|
for (j, g) in enumerate(gpu_devices):
|
|
st = j * batch_per_device
|
|
en = st + batch_per_device
|
|
indices = full_indices[st:en, :].astype(np.int32)
|
|
labels = full_labels[st:en].astype(np.float32)
|
|
|
|
device_for_indices = core.DeviceOption(caffe2_pb2.CPU)
|
|
if not cpu_indices:
|
|
device_for_indices = core.DeviceOption(caffe2_pb2.CUDA, g)
|
|
|
|
with core.DeviceScope(device_for_indices):
|
|
workspace.FeedBlob("gpu_{}/indices".format(g), indices)
|
|
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, g)):
|
|
workspace.FeedBlob("gpu_{}/label".format(g), labels)
|
|
|
|
if i == 0:
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
# Force vecs to be same on all runs
|
|
orig_vecs = np.random.rand(V, 16).astype(np.float32)
|
|
workspace.FeedBlob(
|
|
self.vecs,
|
|
orig_vecs
|
|
)
|
|
if not cpu_indices:
|
|
for g in gpu_devices:
|
|
workspace.FeedBlob(
|
|
"gpu_{}/gpuvecs".format(g),
|
|
orig_vecs,
|
|
device_option=core.DeviceOption(caffe2_pb2.CUDA, g),
|
|
)
|
|
workspace.CreateNet(model.net)
|
|
|
|
workspace.RunNet(model.net.Proto().name)
|
|
if len(gpu_devices) == 2:
|
|
if not cpu_indices:
|
|
idx = workspace.FetchBlob("gpu_0/indices")
|
|
idx = list(idx.flatten())
|
|
n = len(idx)
|
|
nu = len(set(idx))
|
|
assert n == nu, "We cannot have duplicate indices"
|
|
|
|
# Sanity check to see the vecs were updated
|
|
self.assertFalse(
|
|
np.allclose(workspace.FetchBlob(self.vecs), orig_vecs))
|
|
return [workspace.FetchBlob(self.vecs if cpu_indices else "gpu_0/gpuvecs"),
|
|
workspace.FetchBlob("gpu_0/fc_w")]
|
|
|
|
def _test_equiv_sparse(self, cpu_indices):
|
|
'''
|
|
Test that the model produces exactly same results given
|
|
total batchsize, independent of number of GPUs.
|
|
'''
|
|
V = 10000
|
|
result_2gpus = self.run_model(V, [0, 1], cpu_indices)
|
|
result_1gpus = self.run_model(V, [0], cpu_indices)
|
|
|
|
self.assertTrue(np.allclose(result_1gpus[0], result_2gpus[0]))
|
|
self.assertTrue(np.allclose(result_1gpus[1], result_2gpus[1]))
|
|
|
|
if workspace.NumCudaDevices() >= 4:
|
|
result_4gpus = self.run_model(V, list(range(4)), cpu_indices)
|
|
self.assertTrue(np.allclose(result_1gpus[0], result_4gpus[0]))
|
|
self.assertTrue(np.allclose(result_1gpus[1], result_4gpus[1]))
|
|
|
|
if workspace.NumCudaDevices() >= 8:
|
|
result_8gpus = self.run_model(V, list(range(8)), cpu_indices)
|
|
self.assertTrue(np.allclose(result_1gpus[0], result_8gpus[0]))
|
|
self.assertTrue(np.allclose(result_1gpus[1], result_8gpus[1]))
|
|
|
|
def test_equiv_sparse(self):
|
|
self._test_equiv_sparse(True)
|
|
self._test_equiv_sparse(False)
|
|
|
|
|
|
@unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.")
|
|
class ParallelizeBMUFTest(TestCase):
|
|
|
|
def _run_model(self, gpu_devices):
|
|
'''
|
|
Helper function for test_equiv
|
|
'''
|
|
def input_builder_fun(model):
|
|
return None
|
|
|
|
def _model_build_fun(self, model, loss_scale):
|
|
fc = model.FC(
|
|
"data", "fc", 16, 1, ("ConstantFill", {}), ("ConstantFill", {})
|
|
)
|
|
fc_fl = model.FlattenToVec(fc, "fc_fl")
|
|
sigm = model.Sigmoid(fc_fl, "sigm")
|
|
sq = model.SquaredL2Distance([sigm, "label"], "sq")
|
|
loss = model.AveragedLoss(sq, "loss")
|
|
loss = model.Scale(loss, scale=loss_scale)
|
|
|
|
return [loss]
|
|
|
|
def _param_update_fun(self, model):
|
|
ITER = model.Iter("ITER")
|
|
LR = model.net.LearningRate(
|
|
[ITER],
|
|
"LR",
|
|
base_lr=(-0.1),
|
|
policy="fixed",
|
|
)
|
|
ONE = model.param_init_net.ConstantFill(
|
|
[], "ONE", shape=[1], value=1.0,
|
|
)
|
|
for param in model.GetParams():
|
|
grad = model.param_to_grad[param]
|
|
model.WeightedSum([param, ONE, grad, LR], param)
|
|
|
|
def _generate_data(self, devices, device_type, device_prefix):
|
|
np.random.seed(26)
|
|
# Each run has same input, independent of number of gpus
|
|
batch_size = 64
|
|
for _ in range(0, 10):
|
|
full_data = np.random.rand(batch_size, 16)
|
|
full_labels = np.round(full_data[:, 0])
|
|
batch_per_device = batch_size // len(devices)
|
|
|
|
for (j, g) in enumerate(devices):
|
|
st = j * batch_per_device
|
|
en = st + batch_per_device
|
|
data = full_data[st:en, :].astype(np.float32)
|
|
labels = full_labels[st:en].astype(np.float32)
|
|
with core.DeviceScope(core.DeviceOption(device_type, g)):
|
|
workspace.FeedBlob("{}_{}/data".format(device_prefix, g), data)
|
|
workspace.FeedBlob("{}_{}/label".format(device_prefix, g), labels)
|
|
|
|
@given(
|
|
cpu_device=st.booleans()
|
|
)
|
|
def test_parallelize_bmuf(self, cpu_device):
|
|
assume(cpu_device or workspace.has_gpu_support)
|
|
|
|
workspace.ResetWorkspace()
|
|
|
|
model = cnn.CNNModelHelper(
|
|
order="NHWC",
|
|
name="test"
|
|
)
|
|
devices = [0, 1]
|
|
|
|
def input_builder_fun(model):
|
|
return None
|
|
|
|
if not cpu_device:
|
|
device_type = caffe2_pb2.CUDA
|
|
device_prefix = "gpu"
|
|
else:
|
|
device_type = caffe2_pb2.CPU
|
|
device_prefix = "cpu"
|
|
self._generate_data(devices, device_type, device_prefix)
|
|
|
|
data_parallel_model.Parallelize_BMUF(
|
|
model,
|
|
input_builder_fun,
|
|
self._model_build_fun,
|
|
self._param_update_fun,
|
|
devices=devices,
|
|
cpu_device=cpu_device
|
|
)
|
|
|
|
data_parallel_model.RunInitNet(model)
|
|
|
|
# Check initial momentum params are zeros
|
|
self.assertEqual(
|
|
list(viewkeys(model._device_grouped_blobs)), ['fc_w', 'fc_b']
|
|
)
|
|
self.assertEqual(workspace.FetchBlob('{}_0/fc_b_v'.format(device_prefix)), 0)
|
|
np.testing.assert_equal(
|
|
workspace.FetchBlob('{}_0/fc_w_v'.format(device_prefix)),
|
|
np.zeros(16).astype(np.float32).reshape(1, 16)
|
|
)
|
|
|
|
# Run the algorithm for one iteration to have non-zero params.
|
|
data_parallel_model.RunNet(model, 1)
|
|
|
|
# Save iteration momentum and post local update params
|
|
v_b_ = workspace.FetchBlob('{}_0/fc_b_v'.format(device_prefix))
|
|
v_w_ = workspace.FetchBlob('{}_0/fc_w_v'.format(device_prefix))
|
|
|
|
workspace.RunNetOnce(model.net)
|
|
|
|
b_0_ = workspace.FetchBlob('{}_0/fc_b'.format(device_prefix))
|
|
w_0_ = workspace.FetchBlob('{}_0/fc_w'.format(device_prefix))
|
|
b_1_ = workspace.FetchBlob('{}_1/fc_b'.format(device_prefix))
|
|
w_1_ = workspace.FetchBlob('{}_1/fc_w'.format(device_prefix))
|
|
|
|
# Compute block gradients.
|
|
b_g_ = workspace.FetchBlob('{}_0/fc_b_g'.format(device_prefix))
|
|
w_g_ = workspace.FetchBlob('{}_0/fc_w_g'.format(device_prefix))
|
|
workspace.RunNetOnce(model._global_model_param_updates_net)
|
|
|
|
g_b = (b_0_ + b_1_) / 2 - b_g_
|
|
g_w = (w_0_ + w_1_) / 2 - w_g_
|
|
v_b = workspace.FetchBlob('{}_0/fc_b_v'.format(device_prefix))
|
|
v_w = workspace.FetchBlob('{}_0/fc_w_v'.format(device_prefix))
|
|
|
|
w_g = workspace.FetchBlob('{}_0/fc_w_g'.format(device_prefix))
|
|
b_g = workspace.FetchBlob('{}_0/fc_b_g'.format(device_prefix))
|
|
w_0 = workspace.FetchBlob('{}_0/fc_w'.format(device_prefix))
|
|
b_0 = workspace.FetchBlob('{}_0/fc_b'.format(device_prefix))
|
|
w_1 = workspace.FetchBlob('{}_1/fc_w'.format(device_prefix))
|
|
b_1 = workspace.FetchBlob('{}_1/fc_b'.format(device_prefix))
|
|
|
|
# Check momentum update step
|
|
np.testing.assert_equal(v_b, 0.5 * v_b_ + g_b)
|
|
np.testing.assert_equal(v_w, 0.5 * v_w_ + g_w)
|
|
|
|
np.testing.assert_equal(w_g, w_0)
|
|
np.testing.assert_equal(w_g, w_1)
|
|
np.testing.assert_equal(b_g, b_0)
|
|
np.testing.assert_equal(b_g, b_1)
|
|
|
|
# Check params update step
|
|
np.testing.assert_equal(w_0, w_g_ + v_w)
|
|
np.testing.assert_equal(b_0, b_g_ + v_b)
|
|
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
|
|
@unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.")
|
|
class SparseDataParallelModelTestWithSharedIndices(TestCase):
|
|
|
|
'''
|
|
Create and run the model. We try with both storing indices for gather
|
|
on CPU and on GPU
|
|
'''
|
|
def run_model(self, V, gpu_devices):
|
|
|
|
def input_builder_fun(model):
|
|
return None
|
|
|
|
def model_build_fun(model, loss_scale):
|
|
gpu_vecs_gathered = []
|
|
gpu_vecs = []
|
|
for num, vec in enumerate(self.vecs):
|
|
gpu_vec = model.param_init_net.CopyCPUToGPU(
|
|
vec, 'gpuvec_{}'.format(num),
|
|
)
|
|
if num != 2:
|
|
model.params.append(gpu_vec)
|
|
gpu_vecs.append(gpu_vec)
|
|
for num, gpu_vec in enumerate(gpu_vecs):
|
|
gpu_vec_gathered = model.net.Gather(
|
|
[gpu_vec, 'indices'],
|
|
['gpu_vec_gathered_{}'.format(num)]
|
|
)
|
|
gpu_vecs_gathered.append(gpu_vec_gathered)
|
|
|
|
assert len(gpu_vecs_gathered) == 3
|
|
|
|
fc = model.net.FC(
|
|
[
|
|
gpu_vecs_gathered[2],
|
|
gpu_vecs_gathered[0],
|
|
gpu_vecs_gathered[1],
|
|
],
|
|
['fc'],
|
|
)
|
|
_, loss = model.net.SoftmaxWithLoss(
|
|
[fc, 'label'],
|
|
['ce_loss', 'avg_loss'],
|
|
only_loss=True,
|
|
)
|
|
loss = model.Scale(loss, scale=loss_scale)
|
|
model.net.Print(loss, [], limit=10)
|
|
return [loss]
|
|
|
|
def param_update_fun(model):
|
|
ONE = model.param_init_net.ConstantFill(
|
|
[], "ONE", shape=[1], value=1.0,
|
|
)
|
|
LR = model.CopyCPUToGPU(self.LR, "LR")
|
|
for param in model.GetParams():
|
|
param_grad = model.param_to_grad[param]
|
|
if not isinstance(param_grad, core.GradientSlice):
|
|
model.WeightedSum([param, ONE, param_grad, LR], param)
|
|
else:
|
|
model.net.ScatterWeightedSum(
|
|
[
|
|
param,
|
|
ONE,
|
|
param_grad.indices,
|
|
param_grad.values,
|
|
ONE,
|
|
],
|
|
param,
|
|
)
|
|
|
|
workspace.ResetWorkspace()
|
|
model = cnn.CNNModelHelper(
|
|
order="NHWC",
|
|
name="sparse_test{}".format(gpu_devices),
|
|
)
|
|
batch_size = 32
|
|
batch_per_device = batch_size // len(gpu_devices)
|
|
|
|
with core.NameScope("cpu"):
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
|
|
self.ITER = model.Iter("ITER")
|
|
self.LR = model.net.LearningRate(
|
|
[self.ITER],
|
|
"LR",
|
|
base_lr=(-0.1),
|
|
policy="fixed",
|
|
)
|
|
'''
|
|
self.vecs consists of 3 big blobs on which we call Gather:
|
|
1) FC weights, shape=(V, 16)
|
|
2) FC bias, shape=(V)
|
|
3) FC input, shape=(batch_per_device, 16)
|
|
'''
|
|
self.vecs = [
|
|
model.param_init_net.UniformFill(
|
|
[], "vec_{}".format(num), shape=[V, 16])
|
|
for num in range(2)
|
|
]
|
|
self.vecs.append(
|
|
model.param_init_net.UniformFill(
|
|
[],
|
|
"vec_2", shape=[batch_per_device, 16]
|
|
)
|
|
)
|
|
self.ONE_CPU = model.param_init_net.ConstantFill(
|
|
[], "ONE_CPU", shape=[1], value=1.0,
|
|
)
|
|
|
|
data_parallel_model.Parallelize_GPU(
|
|
model,
|
|
input_builder_fun=input_builder_fun,
|
|
forward_pass_builder_fun=model_build_fun,
|
|
param_update_builder_fun=param_update_fun,
|
|
devices=gpu_devices,
|
|
)
|
|
|
|
# Update the vecs
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, 0)):
|
|
for num, vec in enumerate(self.vecs[:-1]):
|
|
model.CopyGPUToCPU("gpu_0/gpuvec_{}".format(num), vec)
|
|
|
|
# Each run has same input, independent of number of gpus
|
|
for i in range(0, 10):
|
|
np.random.seed(2603)
|
|
full_indices = np.random.permutation(V)[:batch_size].reshape(
|
|
batch_size
|
|
)
|
|
full_labels = full_indices[:] % batch_per_device
|
|
|
|
for (j, g) in enumerate(gpu_devices):
|
|
st = j * batch_per_device
|
|
en = st + batch_per_device
|
|
indices = full_indices[st:en].astype(np.int32)
|
|
labels = full_labels[st:en].astype(np.int32)
|
|
|
|
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CUDA, g)):
|
|
workspace.FeedBlob("gpu_{}/indices".format(g), indices)
|
|
workspace.FeedBlob("gpu_{}/label".format(g), labels)
|
|
|
|
if i == 0:
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
# Force vecs to be same on all runs
|
|
orig_vecs = [
|
|
np.random.rand(V, 16).astype(np.float32),
|
|
np.random.rand(V).astype(np.float32),
|
|
np.random.rand(V, 16).astype(np.float32),
|
|
]
|
|
for vec, orig_vec in zip(self.vecs, orig_vecs):
|
|
workspace.FeedBlob(
|
|
vec,
|
|
orig_vec
|
|
)
|
|
for g in gpu_devices:
|
|
for num, orig_vec in enumerate(orig_vecs):
|
|
workspace.FeedBlob(
|
|
"gpu_{}/gpuvec_{}".format(g, num),
|
|
orig_vec,
|
|
device_option=core.DeviceOption(
|
|
caffe2_pb2.CUDA, g),
|
|
)
|
|
workspace.CreateNet(model.net)
|
|
|
|
workspace.RunNet(model.net.Proto().name)
|
|
|
|
idx = workspace.FetchBlob('gpu_0/indices')
|
|
grad_slices = [
|
|
workspace.FetchBlob(
|
|
'gpu_{}/gpu_vec_gathered_{}_grad'.format(g, num))
|
|
for g in gpu_devices for num in range(2)
|
|
]
|
|
for grad_slice in grad_slices:
|
|
# print (len(idx), len(grad_slice))
|
|
assert len(idx) == len(grad_slice), (
|
|
'Number of indices {} is not same as number of gradient '
|
|
'slices {}. This might lead to illegal memory access'.format(
|
|
len(idx), len(grad_slice)
|
|
)
|
|
)
|
|
|
|
def test_sparse_shared_indices_gpu(self):
|
|
'''
|
|
Test that the model has same number of indices and gradient rows
|
|
given total batchsize, independent of number of GPUs.
|
|
'''
|
|
V = 10000
|
|
self.run_model(V, [0, 1])
|
|
self.run_model(V, [0])
|
|
|
|
if workspace.NumCudaDevices() >= 4:
|
|
self.run_model(V, list(range(4)))
|
|
|
|
if workspace.NumCudaDevices() >= 8:
|
|
self.run_model(V, list(range(8)))
|
|
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
|
|
class DeviceShiftTest(TestCase):
|
|
|
|
def create_model(self):
|
|
def input_builder_fun(model):
|
|
model.param_init_net.UniformFill([], ["data"], shape=[32, 8])
|
|
|
|
def model_build_fun(model, loss_scale):
|
|
fc1 = brew.fc(model, "data", "fc1", dim_in=8, dim_out=8)
|
|
fc2 = brew.fc(model, fc1, "fc2", dim_in=8, dim_out=8)
|
|
fc3 = brew.fc(model, fc2, "fc3", dim_in=8, dim_out=8)
|
|
fc4 = brew.fc(model, fc3, "fc4", dim_in=8, dim_out=8)
|
|
fc5 = brew.fc(model, fc4, "fc5", dim_in=8, dim_out=8)
|
|
loss = model.net.SumElements([fc5], ["loss"])
|
|
return [loss]
|
|
|
|
def add_optimizer(model):
|
|
return optimizer.build_sgd(model, 0.1, policy="fixed")
|
|
|
|
model = model_helper.ModelHelper()
|
|
data_parallel_model.Parallelize(
|
|
model,
|
|
input_builder_fun=input_builder_fun,
|
|
forward_pass_builder_fun=model_build_fun,
|
|
optimizer_builder_fun=add_optimizer,
|
|
devices=[0, 1, 2, 3],
|
|
)
|
|
return model
|
|
|
|
def test_activation_blobs(self):
|
|
model = self.create_model()
|
|
activations = data_parallel_model_utils.GetActivationBlobs(model)
|
|
self.assertEqual(activations, ["fc1", "fc2", "fc3", "fc4", "fc5", "loss"])
|
|
|
|
def test_shift_gpu(self):
|
|
model = self.create_model()
|
|
data_parallel_model_utils.ShiftActivationDevices(
|
|
model,
|
|
activations=["fc4", "fc5"],
|
|
shifts={0: 4, 1: 4, 2: 5, 3: 5},
|
|
)
|
|
for op in model.param_init_net.Proto().op:
|
|
for outp in op.output:
|
|
prefix = outp.split("/")[0]
|
|
if outp.split("/")[-1] in set(['fc4_w', 'fc5_w', 'fc4_b', 'fc5_b']):
|
|
if prefix == 'gpu_0' or prefix == 'gpu_1':
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 4)
|
|
else:
|
|
self.assertEqual(op.device_option.cuda_gpu_id, 5)
|
|
if outp.split("/")[-1] in set(['fc1_w', 'fc2_w', 'fc3_b', 'fc3_w']):
|
|
gpu_id = int(prefix.split("_")[-1])
|
|
self.assertEqual(gpu_id, op.device_option.cuda_gpu_id)
|
|
|
|
# Test that we can run the net
|
|
if workspace.NumCudaDevices() >= 6:
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
workspace.CreateNet(model.net)
|
|
workspace.RunNet(model.net.Proto().name)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|