mirror of
https://github.com/pytorch/pytorch.git
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aa88c2c0b6
Summary: Assign `has_gpu_support = has_cuda_support or has_hip_support` and make according changes in python tests. Pull Request resolved: https://github.com/pytorch/pytorch/pull/16748 Differential Revision: D13983132 Pulled By: bddppq fbshipit-source-id: ca496fd8c6ae3549b736bebd3ace7fa20a6dad7f
289 lines
9.6 KiB
Python
289 lines
9.6 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 multiprocessing import Process, Manager
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import numpy as np
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import unittest
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import tempfile
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import shutil
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import logging
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from hypothesis import given
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import hypothesis.strategies as st
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log = logging.getLogger("parallelize_bmuf_distributed_test")
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log.setLevel(logging.INFO)
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def bmuf_process(filestore_dir, process_id, shared_results,
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cpu_device=False, nesterov=False):
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# We need to import caffe2 in every process to initialize CUDA independently.
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from caffe2.python import core, cnn, data_parallel_model, dyndep, workspace
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from caffe2.proto import caffe2_pb2
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dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops")
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if not cpu_device:
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if not workspace.has_gpu_support:
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log.info('No GPU support test is Ignored.')
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return
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if workspace.NumGpuDevices() < 4:
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log.info('Not enough GPU support, test IGNORED')
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return
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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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if not cpu_device:
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device_type = workspace.GpuDeviceType
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device_prefix = "gpu"
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else:
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device_type = caffe2_pb2.CPU
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device_prefix = "cpu"
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devices = [0, 1] if process_id == 0 else [2, 3]
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def _model_build_fun(model, loss_scale):
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fc = model.FC(
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"data", "fc", 16, 1, ("ConstantFill", {}), ("ConstantFill", {})
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)
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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 _input_builder_fun(model):
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return None
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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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grad = model.param_to_grad[param]
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model.WeightedSum([param, ONE, grad, LR], param)
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def _generate_data(devices, process_id, device_type, device_prefix):
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np.random.seed(26 + process_id * 10)
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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 _ 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(device_type, g)):
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workspace.FeedBlob("{}_{}/data".format(device_prefix, g), data)
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workspace.FeedBlob("{}_{}/label".format(device_prefix, g), labels)
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_generate_data(devices, process_id, device_type, device_prefix)
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"FileStoreHandlerCreate", [], ["store_handler"],
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path=filestore_dir
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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=process_id,
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num_shards=2,
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engine="GLOO",
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exit_nets=None
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)
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data_parallel_model.Parallelize_BMUF(
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model,
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_input_builder_fun,
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_model_build_fun,
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_param_update_fun,
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devices=devices,
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rendezvous=rendezvous,
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nesterov=nesterov,
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add_blobs_to_sync=["sync_num"],
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cpu_device=cpu_device
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)
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data_parallel_model.RunInitNet(model)
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def _device_pid(device, pid):
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if pid == 1:
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return device + 2
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return device
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np.testing.assert_equal(
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workspace.FetchBlob("{}_{}/fc_w_v".format(
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device_prefix, _device_pid(0, process_id))),
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np.zeros(16).astype(np.float32).reshape(1, 16)
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)
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# Run the algorithm for one iteration to have non-zero params.
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data_parallel_model.RunNet(model, 1)
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# Save iteration momentum and post local update params
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results = {}
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v_b_ = workspace.FetchBlob(
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"{}_{}/fc_b_v".format(device_prefix, _device_pid(0, process_id)))
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v_w_ = workspace.FetchBlob(
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"{}_{}/fc_w_v".format(device_prefix, _device_pid(0, process_id)))
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results['v_b_'] = v_b_
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results['v_w_'] = v_w_
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workspace.RunNetOnce(model.net)
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b_0_ = workspace.FetchBlob(
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"{}_{}/fc_b".format(device_prefix, _device_pid(0, process_id)))
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w_0_ = workspace.FetchBlob(
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"{}_{}/fc_w".format(device_prefix, _device_pid(0, process_id)))
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b_1_ = workspace.FetchBlob(
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"{}_{}/fc_b".format(device_prefix, _device_pid(1, process_id)))
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w_1_ = workspace.FetchBlob(
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"{}_{}/fc_w".format(device_prefix, _device_pid(1, process_id)))
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results['b_0_'] = b_0_
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results['w_0_'] = w_0_
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results['b_1_'] = b_1_
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results['w_1_'] = w_1_
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# Test sync
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if process_id == 0:
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workspace.FeedBlob(
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device_prefix + "_0/sync_num",
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np.array([2603]).astype(np.float32),
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device_option=core.DeviceOption(device_type, 0))
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# Compute block gradients.
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b_g_ = workspace.FetchBlob(
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"{}_{}/fc_b_g".format(device_prefix, _device_pid(0, process_id)))
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w_g_ = workspace.FetchBlob(
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"{}_{}/fc_w_g".format(device_prefix, _device_pid(0, process_id)))
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results['b_g_'] = b_g_
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results['w_g_'] = w_g_
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workspace.RunNetOnce(model._global_model_param_updates_net)
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# g_b = (b_0_ + b_1_) / 2 - b_g_
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# g_w = (w_0_ + w_1_) / 2 - w_g_
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v_b = workspace.FetchBlob(
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"{}_{}/fc_b_v".format(device_prefix, _device_pid(0, process_id)))
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v_w = workspace.FetchBlob(
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"{}_{}/fc_w_v".format(device_prefix, _device_pid(0, process_id)))
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w_g = workspace.FetchBlob(
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"{}_{}/fc_w_g".format(device_prefix, _device_pid(0, process_id)))
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b_g = workspace.FetchBlob(
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"{}_{}/fc_b_g".format(device_prefix, _device_pid(0, process_id)))
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w_0 = workspace.FetchBlob(
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"{}_{}/fc_w".format(device_prefix, _device_pid(0, process_id)))
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b_0 = workspace.FetchBlob(
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"{}_{}/fc_b".format(device_prefix, _device_pid(0, process_id)))
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w_1 = workspace.FetchBlob(
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"{}_{}/fc_w".format(device_prefix, _device_pid(1, process_id)))
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b_1 = workspace.FetchBlob(
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"{}_{}/fc_b".format(device_prefix, _device_pid(1, process_id)))
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results['v_b'] = v_b
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results['v_w'] = v_w
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results['w_g'] = w_g
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results['b_g'] = b_g
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results['w_0'] = w_0
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results['b_0'] = b_0
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results['w_1'] = w_1
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results['b_1'] = b_1
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# Test add_blobs_to_sync
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for j in devices:
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sync = workspace.FetchBlob(
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device_prefix + "_{}/sync_num".format(j))[0]
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results['sync_{}'.format(j)] = sync
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shared_results[process_id] = results
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class DistributedTest(unittest.TestCase):
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@given(
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cpu_device=st.booleans(),
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nesterov=st.booleans()
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)
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def test_bmuf_distributed(self, cpu_device, nesterov):
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self._test_bmuf_distributed(cpu_device=cpu_device, nesterov=nesterov)
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def _test_bmuf_distributed(self, cpu_device=False, nesterov=False):
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processes = []
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filestore_dir = tempfile.mkdtemp()
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results = Manager().dict()
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for idx in range(0, 2):
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process = Process(
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target=bmuf_process,
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args=(filestore_dir, idx, results, cpu_device, nesterov)
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)
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processes.append(process)
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process.start()
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while len(processes) > 0:
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process = processes.pop()
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process.join()
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shutil.rmtree(filestore_dir)
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if len(results) == 0:
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return
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w_0 = results[0]['w_0']
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w_1 = results[0]['w_1']
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b_0 = results[0]['b_0']
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b_1 = results[0]['b_1']
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# Check parameters are in sync.
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np.testing.assert_equal(w_0, w_1)
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np.testing.assert_equal(w_0, results[1]['w_0'])
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np.testing.assert_equal(w_0, results[1]['w_1'])
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np.testing.assert_equal(b_0, b_1)
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np.testing.assert_equal(b_0, results[1]['b_0'])
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np.testing.assert_equal(b_0, results[1]['b_1'])
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w_g_ = results[0]['w_g_']
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b_g_ = results[0]['b_g_']
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g_b = (results[0]['b_0_'] + results[1]['b_0_'] + results[0]['b_1_'] +
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results[1]['b_1_']) / 4 - b_g_
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g_w = (results[0]['w_0_'] + results[1]['w_0_'] + results[0]['w_1_'] +
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results[1]['w_1_']) / 4 - w_g_
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v_b_ = results[0]['v_b_']
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v_b = results[0]['v_b']
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v_w_ = results[0]['v_w_']
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v_w = results[0]['v_w']
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for pid in results.keys():
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for k in results[pid].keys():
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if k.startswith("sync_num"):
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self.assertEqual(2603, results[pid][k])
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# Check block gradients are correct.
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np.testing.assert_almost_equal(v_b, 0.75 * v_b_ + g_b)
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np.testing.assert_almost_equal(v_w, 0.75 * v_w_ + g_w)
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# Check params update step
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if nesterov:
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np.testing.assert_equal(w_0, w_g_ + v_w - 0.75 * (v_w - v_w_))
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np.testing.assert_equal(b_0, b_g_ + v_b - 0.75 * (v_b - v_b_))
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else:
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np.testing.assert_equal(w_0, w_g_ + v_w)
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np.testing.assert_equal(b_0, b_g_ + v_b)
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