from __future__ import absolute_import from __future__ import division from __future__ import print_function from multiprocessing import Process, Manager import numpy as np import unittest import tempfile import shutil import logging from hypothesis import given import hypothesis.strategies as st log = logging.getLogger("parallelize_bmuf_distributed_test") log.setLevel(logging.INFO) def bmuf_process(filestore_dir, process_id, shared_results, cpu_device=False, nesterov=False): # We need to import caffe2 in every process to initialize CUDA independently. from caffe2.python import core, cnn, data_parallel_model, dyndep, workspace from caffe2.proto import caffe2_pb2 dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops") if not cpu_device: if not workspace.has_gpu_support and not workspace.has_hip_support: log.info('No GPU support test is Ignored.') return if workspace.NumGpuDevices() < 4: log.info('Not enough GPU support, test IGNORED') return model = cnn.CNNModelHelper( order="NHWC", name="test" ) if not cpu_device: device_type = workspace.GpuDeviceType device_prefix = "gpu" else: device_type = caffe2_pb2.CPU device_prefix = "cpu" devices = [0, 1] if process_id == 0 else [2, 3] def _model_build_fun(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) # For testing explicit sync model.param_init_net.UniformFill([], ["sync_num"], shape=[1]) return [loss] def _input_builder_fun(model): return None def _param_update_fun(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(devices, process_id, device_type, device_prefix): np.random.seed(26 + process_id * 10) # 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) _generate_data(devices, process_id, device_type, device_prefix) workspace.RunOperatorOnce( core.CreateOperator( "FileStoreHandlerCreate", [], ["store_handler"], path=filestore_dir ) ) rendezvous = dict( kv_handler="store_handler", shard_id=process_id, num_shards=2, engine="GLOO", exit_nets=None ) data_parallel_model.Parallelize_BMUF( model, _input_builder_fun, _model_build_fun, _param_update_fun, devices=devices, rendezvous=rendezvous, nesterov=nesterov, add_blobs_to_sync=["sync_num"], cpu_device=cpu_device ) data_parallel_model.RunInitNet(model) def _device_pid(device, pid): if pid == 1: return device + 2 return device np.testing.assert_equal( workspace.FetchBlob("{}_{}/fc_w_v".format( device_prefix, _device_pid(0, process_id))), 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 results = {} v_b_ = workspace.FetchBlob( "{}_{}/fc_b_v".format(device_prefix, _device_pid(0, process_id))) v_w_ = workspace.FetchBlob( "{}_{}/fc_w_v".format(device_prefix, _device_pid(0, process_id))) results['v_b_'] = v_b_ results['v_w_'] = v_w_ workspace.RunNetOnce(model.net) b_0_ = workspace.FetchBlob( "{}_{}/fc_b".format(device_prefix, _device_pid(0, process_id))) w_0_ = workspace.FetchBlob( "{}_{}/fc_w".format(device_prefix, _device_pid(0, process_id))) b_1_ = workspace.FetchBlob( "{}_{}/fc_b".format(device_prefix, _device_pid(1, process_id))) w_1_ = workspace.FetchBlob( "{}_{}/fc_w".format(device_prefix, _device_pid(1, process_id))) results['b_0_'] = b_0_ results['w_0_'] = w_0_ results['b_1_'] = b_1_ results['w_1_'] = w_1_ # Test sync if process_id == 0: workspace.FeedBlob( device_prefix + "_0/sync_num", np.array([2603]).astype(np.float32), device_option=core.DeviceOption(device_type, 0)) # Compute block gradients. b_g_ = workspace.FetchBlob( "{}_{}/fc_b_g".format(device_prefix, _device_pid(0, process_id))) w_g_ = workspace.FetchBlob( "{}_{}/fc_w_g".format(device_prefix, _device_pid(0, process_id))) results['b_g_'] = b_g_ results['w_g_'] = w_g_ 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( "{}_{}/fc_b_v".format(device_prefix, _device_pid(0, process_id))) v_w = workspace.FetchBlob( "{}_{}/fc_w_v".format(device_prefix, _device_pid(0, process_id))) w_g = workspace.FetchBlob( "{}_{}/fc_w_g".format(device_prefix, _device_pid(0, process_id))) b_g = workspace.FetchBlob( "{}_{}/fc_b_g".format(device_prefix, _device_pid(0, process_id))) w_0 = workspace.FetchBlob( "{}_{}/fc_w".format(device_prefix, _device_pid(0, process_id))) b_0 = workspace.FetchBlob( "{}_{}/fc_b".format(device_prefix, _device_pid(0, process_id))) w_1 = workspace.FetchBlob( "{}_{}/fc_w".format(device_prefix, _device_pid(1, process_id))) b_1 = workspace.FetchBlob( "{}_{}/fc_b".format(device_prefix, _device_pid(1, process_id))) results['v_b'] = v_b results['v_w'] = v_w results['w_g'] = w_g results['b_g'] = b_g results['w_0'] = w_0 results['b_0'] = b_0 results['w_1'] = w_1 results['b_1'] = b_1 # Test add_blobs_to_sync for j in devices: sync = workspace.FetchBlob( device_prefix + "_{}/sync_num".format(j))[0] results['sync_{}'.format(j)] = sync shared_results[process_id] = results class DistributedTest(unittest.TestCase): @given( cpu_device=st.booleans(), nesterov=st.booleans() ) def test_bmuf_distributed(self, cpu_device, nesterov): self._test_bmuf_distributed(cpu_device=cpu_device, nesterov=nesterov) def _test_bmuf_distributed(self, cpu_device=False, nesterov=False): processes = [] filestore_dir = tempfile.mkdtemp() results = Manager().dict() for idx in range(0, 2): process = Process( target=bmuf_process, args=(filestore_dir, idx, results, cpu_device, nesterov) ) processes.append(process) process.start() while len(processes) > 0: process = processes.pop() process.join() shutil.rmtree(filestore_dir) if len(results) == 0: return w_0 = results[0]['w_0'] w_1 = results[0]['w_1'] b_0 = results[0]['b_0'] b_1 = results[0]['b_1'] # Check parameters are in sync. np.testing.assert_equal(w_0, w_1) np.testing.assert_equal(w_0, results[1]['w_0']) np.testing.assert_equal(w_0, results[1]['w_1']) np.testing.assert_equal(b_0, b_1) np.testing.assert_equal(b_0, results[1]['b_0']) np.testing.assert_equal(b_0, results[1]['b_1']) w_g_ = results[0]['w_g_'] b_g_ = results[0]['b_g_'] g_b = (results[0]['b_0_'] + results[1]['b_0_'] + results[0]['b_1_'] + results[1]['b_1_']) / 4 - b_g_ g_w = (results[0]['w_0_'] + results[1]['w_0_'] + results[0]['w_1_'] + results[1]['w_1_']) / 4 - w_g_ v_b_ = results[0]['v_b_'] v_b = results[0]['v_b'] v_w_ = results[0]['v_w_'] v_w = results[0]['v_w'] for pid in results.keys(): for k in results[pid].keys(): if k.startswith("sync_num"): self.assertEqual(2603, results[pid][k]) # Check block gradients are correct. np.testing.assert_almost_equal(v_b, 0.75 * v_b_ + g_b) np.testing.assert_almost_equal(v_w, 0.75 * v_w_ + g_w) # Check params update step if nesterov: np.testing.assert_equal(w_0, w_g_ + v_w - 0.75 * (v_w - v_w_)) np.testing.assert_equal(b_0, b_g_ + v_b - 0.75 * (v_b - v_b_)) else: np.testing.assert_equal(w_0, w_g_ + v_w) np.testing.assert_equal(b_0, b_g_ + v_b)