| |
| |
| |
| |
| from multiprocessing import Process, Queue |
| import numpy as np |
| import os |
| import shutil |
| import tempfile |
| import unittest |
| import time |
| from unittest.mock import Mock |
| from hypothesis import assume, given, settings |
| import hypothesis.strategies as st |
| |
| from caffe2.proto import caffe2_pb2 |
| from caffe2.python import brew, core, cnn, data_parallel_model, dyndep, \ |
| model_helper, optimizer, rnn_cell, workspace |
| from caffe2.python.test_util import TestCase |
| |
| |
| dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops") |
| |
| |
| class TemporaryDirectory: |
| def __enter__(self): |
| self.tmpdir = tempfile.mkdtemp() |
| return self.tmpdir |
| |
| def __exit__(self, type, value, traceback): |
| shutil.rmtree(self.tmpdir) |
| |
| # Note(jiayq): we are yet to find out why Travis gives out an error in gloo |
| # like: |
| # 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] |
| # See for example https://travis-ci.org/caffe2/caffe2/jobs/262433866 |
| # As a result, we will check if this is travis, and if yes, disable it. |
| @unittest.skipIf(os.environ.get("TRAVIS"), "DPMTest has a known issue with Travis.") |
| class DataParallelModelTest(TestCase): |
| |
| def run_model(self, devices, gpu): |
| ''' |
| Helper function for test_equiv |
| ''' |
| def input_builder_fun(model): |
| return None |
| |
| 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 add_optimizer(model): |
| return optimizer.build_sgd( |
| model, |
| 0.1, |
| policy="fixed", |
| max_gradient_norm=5.0, |
| allow_lr_injection=True, |
| ) |
| |
| workspace.ResetWorkspace() |
| model = cnn.CNNModelHelper( |
| order="NHWC", |
| name="test{}".format(devices), |
| ) |
| data_parallel_model.Parallelize( |
| model, |
| input_builder_fun=input_builder_fun, |
| forward_pass_builder_fun=model_build_fun, |
| optimizer_builder_fun=add_optimizer, |
| devices=devices, |
| cpu_device=not gpu, |
| shared_model=not gpu, |
| combine_spatial_bn=not gpu, |
| ) |
| data_parallel_model.AddBlobSync(model, ["sync_num"]) |
| |
| # Light test for LR names |
| lr_names = data_parallel_model.GetLearningRateBlobNames(model) |
| self.assertGreater(len(lr_names), 0) |
| |
| np.random.seed(2603) |
| |
| # Each run has same input, independent of number of gpus |
| batch_size = 64 |
| for i 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(model._device_type, g)): |
| workspace.FeedBlob( |
| "{}_{}/data".format(model._device_prefix, g), data |
| ) |
| workspace.FeedBlob( |
| "{}_{}/label".format(model._device_prefix, g), labels |
| ) |
| |
| if i == 0: |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.CreateNet(model.net) |
| |
| workspace.FeedBlob( |
| model._device_prefix + "_0/sync_num", |
| np.array([i * 2]).astype(np.float32), |
| device_option=core.DeviceOption(model._device_type, 0)) |
| workspace.RunNet(model.net.Proto().name) |
| |
| # Test AddBlobSync |
| for j in model._devices: |
| sync = workspace.FetchBlob( |
| model._device_prefix + "_{}/sync_num".format(j))[0] |
| self.assertTrue(abs(sync - i * 2) < 0.01) |
| |
| return workspace.FetchBlob("{}_0/fc_w".format(model._device_prefix)) |
| |
| def run_test_locally(self, fn, device_option=None, **kwargs): |
| # Queue for assertion errors on subprocesses |
| queue = Queue() |
| |
| # Capture any exception thrown by the subprocess |
| def run_fn(*args, **kwargs): |
| try: |
| if device_option is None: |
| fn(*args, **kwargs) |
| workspace.ResetWorkspace() |
| else: |
| with core.DeviceScope(device_option): |
| fn(*args, **kwargs) |
| workspace.ResetWorkspace() |
| except Exception as ex: |
| queue.put(ex) |
| |
| # Start N processes in the background |
| procs = [] |
| for i in range(kwargs['comm_size']): |
| kwargs['comm_rank'] = i |
| proc = Process( |
| target=run_fn, |
| kwargs=kwargs) |
| proc.start() |
| procs.append(proc) |
| |
| # Test complete, join background processes |
| while len(procs) > 0: |
| proc = procs.pop(0) |
| while proc.is_alive(): |
| proc.join(1) |
| |
| # Raise exception if we find any. |
| # Note that the following is executed ALSO after |
| # the last process was joined, so if ANY exception |
| # was raised, it will be re-raised here. |
| if not queue.empty(): |
| raise queue.get() |
| |
| def test_equiv(self): |
| ''' |
| Test that the model produces exactly same results given |
| total batchsize, independent of number of GPUs. |
| ''' |
| for gpu in [True, False]: |
| if gpu and (not workspace.has_gpu_support or |
| workspace.NumCudaDevices() < 2): |
| continue |
| result_2gpus = self.run_model([0, 1], gpu=gpu) |
| result_1gpus = self.run_model([0], gpu=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=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=gpu) |
| self.assertTrue(np.allclose(result_1gpus, result_8gpus)) |
| |
| if not gpu or workspace.NumCudaDevices() >= 16: |
| result_16gpus = self.run_model(list(range(16)), gpu=gpu) |
| self.assertTrue(np.allclose(result_1gpus, result_16gpus)) |
| |
| def test_checkpoint_params(self): |
| def add_input_ops(model): |
| pass |
| |
| def add_model_ops(model, loss_scale): |
| model.NHWC2NCHW("data", "data_nchw") |
| model.Conv("data_nchw", 'conv1', 3, 64, |
| weight_init=("MSRAFill", {}), kernel=7, |
| stride=2, pad=3, no_bias=0) |
| model.SpatialBN('conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False) |
| model.Relu('conv1_spatbn_relu', 'conv1_spatbn_relu') |
| model.MaxPool('conv1_spatbn_relu', 'pool1', kernel=3, stride=2) |
| model.FC('pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=100) |
| model.Sigmoid('fc', 'fc_sigm') |
| model.Softmax('fc_sigm', 'softmax') |
| model.LabelCrossEntropy(['softmax', 'label'], 'xent') |
| loss = model.AveragedLoss('xent', 'loss') |
| |
| # Add a duplicate param init to ensure it does not cause issues |
| model.param_init_net.ConstantFill( |
| [], ["fc_w"], shape=((64 * 56 * 56), 1000) |
| ) |
| return [loss] |
| |
| def add_optimizer(model): |
| optimizer.build_sgd(model, 0.1, policy="fixed", momentum=0.9) |
| |
| model = cnn.CNNModelHelper( |
| order="NHWC", |
| name="test", |
| ) |
| data_parallel_model.Parallelize_CPU( |
| model, |
| input_builder_fun=add_input_ops, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=[1, 2, 3], |
| ) |
| |
| # Only gpu_1 params should be returned (gpu_1 is the first gpu) |
| checkpoint_params = data_parallel_model.GetCheckpointParams(model) |
| for p in model.GetParams("cpu_1/"): |
| self.assertTrue(p in checkpoint_params) |
| self.assertTrue(p + "_momentum" in checkpoint_params) |
| for p in model.GetParams("cpu_2/"): |
| self.assertFalse(p in checkpoint_params) |
| self.assertTrue( |
| core.BlobReference("cpu_1/fc_w_momentum") in checkpoint_params) |
| for c in model.GetComputedParams("cpu_1/"): |
| self.assertTrue(c in checkpoint_params) |
| for c in model.GetComputedParams("cpu_2/"): |
| self.assertFalse(c in checkpoint_params) |
| self.assertFalse(core.BlobReference("cpu_1/data") in checkpoint_params) |
| self.assertTrue(core.BlobReference("optimizer_iteration") in checkpoint_params) |
| |
| def test_net_conversion_and_append_net(self): |
| other = model_helper.ModelHelper() |
| fc1 = brew.fc(other, "data", "other_fc1", dim_in=3*227*227, dim_out=10) |
| fc2 = brew.fc(other, fc1, "other_fc2", dim_in=10, dim_out=10) |
| brew.fc(other, fc2, "other_fc3", dim_in=10, dim_out=10) |
| |
| def add_input_ops(model): |
| model.net.UniformFill([], ["data"], shape=[4, 227, 227, 3]) |
| model.net.UniformFill([], ["label"], shape=[4]) |
| |
| def add_model_ops(model, loss_scale): |
| model.NHWC2NCHW("data", "data_nchw") |
| model.Conv("data_nchw", 'conv1', 3, 64, |
| weight_init=("MSRAFill", {}), kernel=7, |
| stride=2, pad=3, no_bias=0) |
| model.SpatialBN('conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False) |
| model.Relu('conv1_spatbn_relu', 'conv1_spatbn_relu') |
| model.MaxPool('conv1_spatbn_relu', 'pool1', kernel=3, stride=2) |
| model.FC('pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=10) |
| |
| # Append the net and param_init_net of the other model |
| appendnet = data_parallel_model.ConvertNetForDevice(other.net) |
| model.net.AppendNet(appendnet) |
| |
| model.param_init_net.AppendNet( |
| data_parallel_model.ConvertNetForDevice(other.param_init_net)) |
| |
| model.Sigmoid('fc', 'fc_sigm') |
| model.Softmax('fc_sigm', 'softmax') |
| loss = model.AveragedLoss('softmax', 'loss') |
| return [loss] |
| |
| def add_optimizer(model): |
| optimizer.build_sgd(model, 0.1, policy="fixed", momentum=0.9) |
| |
| model = cnn.CNNModelHelper( |
| order="NCHW", |
| name="test", |
| ) |
| data_parallel_model.Parallelize_CPU( |
| model, |
| input_builder_fun=add_input_ops, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=range(4) |
| ) |
| |
| # Just create and run net and confirm no exception is thrown |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.CreateNet(model.net) |
| workspace.RunNet(model.net) |
| |
| @unittest.skip("Test fails on GPU/RE") |
| def test_synchronization_barrier(self): |
| def run(comm_rank, comm_size, tmpdir): |
| def add_input_ops(model): |
| pass |
| |
| def add_model_ops(model, loss_scale): |
| return [] |
| |
| def add_optimizer(model): |
| pass |
| |
| store_handler = "store_handler" |
| workspace.RunOperatorOnce( |
| core.CreateOperator( |
| "FileStoreHandlerCreate", |
| [], |
| [store_handler], |
| path=tmpdir)) |
| rendezvous = dict( |
| kv_handler=store_handler, |
| shard_id=comm_rank, |
| num_shards=comm_size, |
| engine='GLOO', |
| ) |
| |
| model = cnn.CNNModelHelper( |
| order="NHWC", |
| name="test", |
| ) |
| data_parallel_model.Parallelize_CPU( |
| model, |
| input_builder_fun=add_input_ops, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=[1, 2, 3], |
| rendezvous=rendezvous |
| ) |
| data_parallel_model.RunInitNet(model) |
| |
| for _ in range(2): |
| data_parallel_model.Synchronize(model) |
| |
| with TemporaryDirectory() as tmpdir: |
| self.run_test_locally( |
| run, |
| comm_size=2, |
| device_option=None, |
| tmpdir=tmpdir) |
| |
| @unittest.skip("Test fails on GPU/RE") |
| def test_pre_train_synchronization_barrier(self): |
| def run(comm_rank, comm_size, tmpdir): |
| def add_input_ops(model): |
| pass |
| |
| def add_model_ops(model, loss_scale): |
| return [] |
| |
| def add_optimizer(model): |
| pass |
| |
| workspace.ResetWorkspace() |
| store_handler = "store_handler" |
| workspace.RunOperatorOnce( |
| core.CreateOperator( |
| "FileStoreHandlerCreate", |
| [], |
| [store_handler], |
| path=tmpdir)) |
| rendezvous = dict( |
| kv_handler=store_handler, |
| shard_id=comm_rank, |
| num_shards=comm_size, |
| engine='GLOO', |
| ) |
| |
| model = cnn.CNNModelHelper( |
| order="NHWC", |
| name="test", |
| ) |
| # Set network timeout to 2 seconds, and add a 3 seconds |
| # sleep for 1 host. Make sure there is no timeout on the |
| # second RunNet. |
| data_parallel_model._DEFAULT_TIMEOUT_SEC = 2 |
| data_parallel_model.Parallelize_CPU( |
| model, |
| input_builder_fun=add_input_ops, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=[1, 2, 3], |
| rendezvous=rendezvous, |
| barrier_net_timeout_sec=5 |
| ) |
| data_parallel_model.RunInitNet(model) |
| data_parallel_model.RunNet(model, 2) |
| if comm_rank == 0: |
| time.sleep(data_parallel_model._DEFAULT_TIMEOUT_SEC) |
| data_parallel_model.RunNet(model, 2) |
| |
| with TemporaryDirectory() as tmpdir: |
| self.run_test_locally( |
| run, |
| comm_size=2, |
| device_option=None, |
| tmpdir=tmpdir) |
| |
| def test_device_scope_check(self): |
| with self.assertRaises(AssertionError): |
| with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)): |
| data_parallel_model.Parallelize_GPU(None, None, None) |
| |
| def test_net_transformer_function(self): |
| devices = [1, 2, 3] |
| |
| def add_input_ops(model): |
| model.param_init_net.UniformFill([], ["data"], shape=[32, 8]) |
| |
| def add_optimizer(model): |
| optimizer.build_sgd(model, 0.1) |
| |
| def add_model_ops(model, loss_scale): |
| fc1 = brew.fc(model, "data", "fc1", dim_in=8, dim_out=8) |
| return [fc1] |
| |
| kwargs = { |
| 'input_builder_fun': add_input_ops, |
| 'forward_pass_builder_fun': add_model_ops, |
| 'devices': devices, |
| } |
| |
| # assert that the transformer is called for both train and test cases |
| transform = Mock() |
| kwargs['net_transformer_fun'] = transform |
| model = model_helper.ModelHelper(name="r", init_params=False) |
| data_parallel_model.Parallelize_CPU(model, **kwargs) |
| self.assertTrue(transform.called) |
| self.assertEqual(transform.call_count, 1) |
| |
| transform = Mock() |
| kwargs['net_transformer_fun'] = transform |
| kwargs['optimizer_builder_fun'] = add_optimizer |
| model = model_helper.ModelHelper(name="r", init_params=True) |
| data_parallel_model.Parallelize_CPU(model, **kwargs) |
| self.assertTrue(transform.called) |
| self.assertEqual(transform.call_count, 1) |
| |
| @given(seed=st.integers(0, 65535), batch_size=st.integers(1, 20)) |
| @settings(deadline=2000) |
| def test_multi_device_bn_op_level_cpu(self, seed, batch_size): |
| self._bn_check_op_level("cpu", seed, batch_size) |
| |
| @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.") |
| @unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.") |
| @given(seed=st.integers(0, 65535), batch_size=st.integers(1, 20)) |
| @settings(deadline=2000) |
| def test_multi_device_bn_op_level_gpu(self, seed, batch_size): |
| self._bn_check_op_level("gpu", seed, batch_size) |
| |
| def _bn_check_op_level(self, device_type, seed, batch_size): |
| ''' |
| Test multi device batch normalization at the operation level. This is |
| done by checking the outputs of batch normalization and its gradient |
| operator. We compare values produced with our manually calculated |
| batch normalization values and gradients. |
| ''' |
| devices = [0, 1] |
| epsilon = 1e-3 |
| tolerance = 1e-3 |
| |
| def _test_forward_pass(x, devices, device_type, scale, bias, epsilon): |
| x_concat = np.concatenate(x) |
| mean = np.mean(x_concat, axis=0) |
| var = np.var(x_concat, axis=0) |
| for device in devices: |
| x_i = x[device] |
| x_hat = (x_i - mean) / (np.sqrt(var + epsilon)) |
| expected_out = scale * x_hat + bias |
| spatial_out = workspace.FetchBlob( |
| "{}_{}/bn_out".format(device_type, device)) |
| rel_error = np.linalg.norm(spatial_out - expected_out) \ |
| / np.linalg.norm(expected_out) |
| self.assertTrue(rel_error < 0.005) |
| |
| def _test_backward_pass(x, devices, device_type, scale, tolerance): |
| dBias_arr = [] |
| dY_arr = [] |
| dGamma_arr = [] |
| num_devices = len(devices) |
| mean = np.array(workspace.FetchBlob( |
| "{}_0/bn_out_sm".format(device_type)), dtype=np.float32) |
| inv_var = np.array(workspace.FetchBlob( |
| "{}_0/bn_out_siv".format(device_type)), dtype=np.float32) |
| |
| # dBias |
| # Sum dBias values over all devices to find the average gradient |
| for device in devices: |
| dY_blob = workspace.FetchBlob( |
| "{}_{}/bn_out_grad".format(device_type, device)) |
| dY = np.array(dY_blob, dtype=np.float32) |
| dY_arr.append(dY) |
| dBias_arr.append(np.array(np.sum(dY, axis=0), dtype=np.float32)) |
| dBias = np.sum(dBias_arr, dtype=np.float32) |
| dBias_avg = dBias / num_devices |
| for device in devices: |
| dBiasActual = np.sum(workspace.FetchBlob("{}_{}/bn_out_b_grad" |
| .format(device_type, device)), dtype=np.float32) |
| self.assertTrue(np.isclose([dBiasActual], [dBias], atol=tolerance)) |
| |
| # dGamma |
| # Sum dGamma values over all devices to find the average gradient |
| for device in devices: |
| dGamma = np.sum((x[device] - mean) * inv_var * dY_arr[device], |
| axis=0, dtype=np.float32) |
| dGamma_arr.append(dGamma) |
| dGamma = np.sum(dGamma_arr, axis=0, dtype=np.float32) |
| dGamma_avg = dGamma / num_devices |
| for device in devices: |
| dGammaActual = workspace.FetchBlob( |
| "{}_{}/bn_out_s_grad".format(device_type, device)) |
| self.assertTrue(np.isclose([dGamma], [dGammaActual], atol=tolerance)) |
| |
| # dX |
| scale_inv_var = scale * inv_var / batch_size |
| for device in devices: |
| dX = scale_inv_var * (dY_arr[device] * batch_size - dBias_avg |
| - (x[device] - mean) * dGamma_avg * inv_var) |
| dX_actual = workspace.FetchBlob( |
| "{}_{}/tanh_grad".format(device_type, device)) |
| self.assertTrue(np.isclose([dX], [dX_actual], atol=tolerance).all()) |
| |
| def add_input_ops(model): |
| for device in devices: |
| data = np.random.rand(batch_size, 1, 1, 1).astype(np.float32) |
| workspace.FeedBlob("{}_{}/data".format(device_type, device), data) |
| |
| def add_model_ops(model, loss_scale): |
| if device_type == "gpu": |
| model.CopyCPUToGPU("data", "device_data") |
| model.Tanh("device_data", "tanh") |
| else: |
| model.Tanh("data", "tanh") |
| model.SpatialBN("tanh", "bn_out", 1, epsilon=epsilon, is_test=False) |
| model.Sqr("bn_out", "sqr") |
| loss = model.SumElements("sqr", "loss") |
| return [loss] |
| |
| def add_optimizer(model): |
| return optimizer.build_sgd(model, 0.1) |
| |
| np.random.seed(seed) |
| workspace.ResetWorkspace() |
| model = cnn.CNNModelHelper( |
| order="NCHW", |
| name="test" |
| ) |
| data_parallel_model.Parallelize( |
| model, |
| input_builder_fun=add_input_ops, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=devices, |
| cpu_device=device_type == "cpu", |
| shared_model=False, |
| combine_spatial_bn=True, |
| ) |
| |
| workspace.RunNetOnce(model.param_init_net) |
| scale = workspace.FetchBlob("{}_0/bn_out_s".format(device_type)) |
| bias = workspace.FetchBlob("{}_0/bn_out_b".format(device_type)) |
| workspace.RunNetOnce(model.net) |
| |
| x = [] |
| for device in devices: |
| x_blob = workspace.FetchBlob("{}_{}/tanh".format(device_type, device)) |
| x_i = np.array(x_blob, dtype=np.float32) |
| x.append(x_i) |
| |
| _test_forward_pass(x, devices, device_type, scale, bias, epsilon) |
| _test_backward_pass(x, devices, device_type, scale, tolerance) |
| |
| @given(seed=st.integers(0, 65535), batch_size=st.integers(1, 20)) |
| @settings(deadline=2000) |
| def test_multi_device_bn_net_lvl_cpu(self, seed, batch_size): |
| if batch_size % 2 == 1: |
| batch_size += 1 |
| self._test_multi_device_bn_net_lvl("cpu", seed, batch_size) |
| |
| @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.") |
| @unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.") |
| @given(seed=st.integers(0, 65535), batch_size=st.integers(1, 20)) |
| @settings(deadline=2000) |
| def test_multi_device_bn_net_lvl_gpu(self, seed, batch_size): |
| if batch_size % 2 == 1: |
| batch_size += 1 |
| self._test_multi_device_bn_net_lvl("gpu", seed, batch_size) |
| |
| def _test_multi_device_bn_net_lvl(self, device_type, seed, batch_size): |
| ''' |
| Test multi device batch normalization at the net level. This is done |
| by verifying that the final batch normalization outputs and the |
| gradient outputs from multiple devices are the same as those produced |
| from a single device |
| ''' |
| |
| # Verify that the gradients calculated over multiple devices are the |
| # same as the gradients calculated over one device. These values should |
| # be equivalent because combine_spatial_bn sums values over all devices |
| def _verify_bn_outputs( |
| devices, |
| device_type, |
| tolerance, |
| single_device_bn_out, |
| two_device_bn_out_vals, |
| single_device_grads, |
| two_device_grads, |
| ): |
| two_device_bn_out = np.concatenate(two_device_bn_out_vals) |
| self.assertTrue(np.isclose( |
| [single_device_bn_out], [two_device_bn_out], atol=tolerance).all()) |
| |
| # Scalar and Bias gradients should be the same across devices |
| gradient_names = ["bn_out_s_grad", "bn_out_b_grad"] |
| for name in gradient_names: |
| expected_grad = single_device_grads[name] |
| for device in devices: |
| actual_grad = two_device_grads[device][name] |
| self.assertTrue( |
| np.isclose([actual_grad], [expected_grad], atol=tolerance)) |
| |
| # Expected tanh_grad should be the combined tanh_grad vectors |
| # across the devices |
| first_grad = two_device_grads[0]["tanh_grad"] |
| second_grad = two_device_grads[1]["tanh_grad"] |
| actual_grad = np.concatenate([first_grad, second_grad]) |
| expected_grad = single_device_grads["tanh_grad"] |
| rel_error = np.linalg.norm(actual_grad - expected_grad) \ |
| / np.linalg.norm(expected_grad) |
| self.assertTrue(rel_error < 1e-3) |
| |
| def _create_model(multiple_devices): |
| def add_input_ops_no_combine(model): |
| workspace.FeedBlob("{}_0/data".format(device_type), data) |
| |
| def add_input_ops_combine(model): |
| half = int(batch_size / 2) |
| workspace.FeedBlob("{}_0/data".format(device_type), data[:half]) |
| workspace.FeedBlob("{}_1/data".format(device_type), data[half:]) |
| |
| def add_model_ops(model, loss_scale): |
| if device_type == "gpu": |
| model.CopyCPUToGPU("data", "device_data") |
| model.Tanh("device_data", "tanh") |
| else: |
| model.Tanh("data", "tanh") |
| model.SpatialBN("tanh", "bn_out", 1, epsilon=epsilon, is_test=False) |
| model.Sqr("bn_out", "sqr") |
| loss = model.SumElements("sqr", "loss") |
| return [loss] |
| |
| def add_optimizer(model): |
| return optimizer.build_sgd(model, 0.1) |
| |
| if multiple_devices: |
| input_fun = add_input_ops_combine |
| devices = [0, 1] |
| combine_spatial_bn = True |
| else: |
| input_fun = add_input_ops_no_combine |
| devices = [0] |
| combine_spatial_bn = False |
| model = cnn.CNNModelHelper( |
| order="NCHW", |
| name="test" |
| ) |
| data_parallel_model.Parallelize( |
| model, |
| input_builder_fun=input_fun, |
| forward_pass_builder_fun=add_model_ops, |
| optimizer_builder_fun=add_optimizer, |
| devices=devices, |
| cpu_device=device_type == "cpu", |
| shared_model=False, |
| combine_spatial_bn=combine_spatial_bn, |
| ) |
| return model |
| |
| devices = [0, 1] |
| epsilon = 1e-3 |
| tolerance = 1e-3 |
| # We are generating random data |
| np.random.seed(seed) |
| data = np.random.rand(batch_size, 1, 1, 1).astype(np.float32) |
| data = np.reshape(data, (batch_size, 1, 1, 1)) |
| |
| # Get values calculated without combine_spatial_bn |
| workspace.ResetWorkspace() |
| model_no_combine = _create_model(multiple_devices=False) |
| workspace.RunNetOnce(model_no_combine.param_init_net) |
| workspace.RunNetOnce(model_no_combine.net) |
| single_device_bn_out = workspace.FetchBlob("{}_0/bn_out".format(device_type)) |
| single_device_grads = {} |
| single_device_grads["bn_out_s_grad"] = workspace.FetchBlob( |
| "{}_0/bn_out_s_grad".format(device_type)) |
| single_device_grads["bn_out_b_grad"] = workspace.FetchBlob( |
| "{}_0/bn_out_b_grad".format(device_type)) |
| single_device_grads["tanh_grad"] = workspace.FetchBlob( |
| "{}_0/tanh_grad".format(device_type)) |
| |
| # Get values calculated over multiple devices with combine_spatial_bn true |
| workspace.ResetWorkspace() |
| model_combine = _create_model(multiple_devices=True) |
| workspace.RunNetOnce(model_combine.param_init_net) |
| workspace.RunNetOnce(model_combine.net) |
| two_device_bn_out_vals = [] |
| two_device_grads = {} |
| for device in devices: |
| bn_out_blob = "{}_{}/bn_out".format(device_type, device) |
| two_device_bn_out_vals.append(workspace.FetchBlob(bn_out_blob)) |
| two_device_grads[device] = {} |
| two_device_grads[device]["bn_out_s_grad"] = workspace.FetchBlob( |
| "{}_{}/bn_out_s_grad".format(device_type, device)) |
| two_device_grads[device]["bn_out_b_grad"] = workspace.FetchBlob( |
| "{}_{}/bn_out_b_grad".format(device_type, device)) |
| two_device_grads[device]["tanh_grad"] = workspace.FetchBlob( |
| "{}_{}/tanh_grad".format(device_type, device)) |
| |
| # Check to see if the combined values are equivalent |
| _verify_bn_outputs( |
| devices, |
| device_type, |
| tolerance, |
| single_device_bn_out, |
| two_device_bn_out_vals, |
| single_device_grads, |
| two_device_grads |
| ) |
| |
| class RecurrentNetworkParallelTest(TestCase): |
| |
| def run_model(self, devices, gpu): |
| |
| ''' |
| Helper function for test_equiv |
| ''' |
| def input_builder_fun(model): |
| return None |
| |
| def model_build_fun(model, loss_scale): |
| workspace.FeedBlob( |
| core.ScopedBlobReference("seq_lengths"), |
| np.array([self.T] * self.batch_per_device, dtype=np.int32) |
| ) |
| model.param_init_net.ConstantFill( |
| [], |
| "hidden_init", |
| value=0.0, |
| shape=[1, self.batch_per_device, self.hidden_dim] |
| ) |
| model.param_init_net.ConstantFill( |
| [], |
| "cell_init", |
| value=0.0, |
| shape=[1, self.batch_per_device, self.hidden_dim] |
| ) |
| |
| output, _last_hidden, _, _last_state, = rnn_cell.LSTM( |
| model=model, |
| input_blob="data", |
| seq_lengths="seq_lengths", |
| initial_states=("hidden_init", "cell_init"), |
| dim_in=self.input_dim, |
| dim_out=self.hidden_dim, |
| scope="partest", |
| ) |
| |
| # A silly loss function |
| loss = model.AveragedLoss( |
| model.Sub([output, "target"], "dist"), |
| "loss", |
| ) |
| loss = model.Scale(loss, "loss_scaled", scale=loss_scale) |
| return [loss] |
| |
| 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(): |
| param_grad = model.param_to_grad[param] |
| model.WeightedSum([param, ONE, param_grad, LR], param) |
| |
| assert len(model.GetParams()) == len(model.params) // len(model._devices) |
| |
| workspace.ResetWorkspace() |
| model = cnn.CNNModelHelper( |
| name="recurrent_test{}".format(devices), |
| ) |
| |
| self.T = 8 |
| self.batch_size = 64 |
| self.input_dim = 8 |
| self.hidden_dim = 31 |
| self.batch_per_device = self.batch_size // len(devices) |
| |
| data_parallel_model.Parallelize( |
| model, |
| input_builder_fun=input_builder_fun, |
| forward_pass_builder_fun=model_build_fun, |
| param_update_builder_fun=param_update_fun, |
| devices=devices, |
| optimize_gradient_memory=True, |
| cpu_device=not gpu, |
| ) |
| |
| # Change all initialization to be ConstantFills so that |
| # the everything is deterministic |
| for op in model.param_init_net.Proto().op: |
| if op.type.endswith('Fill'): |
| op.type = 'ConstantFill' |
| |
| # Each run has same input, independent of number of gpus |
| np.random.seed(20150210) |
| for i in range(0, 10): |
| full_data = np.random.rand(self.T, self.batch_size, self.input_dim) |
| full_target = np.random.rand( |
| self.T, self.batch_size, self.hidden_dim |
| ) |
| |
| for (j, g) in enumerate(devices): |
| st = j * self.batch_per_device |
| en = st + self.batch_per_device |
| 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)) |
| |
| @unittest.skip("Test is flaky: https://github.com/pytorch/pytorch/issues/10322") |
| 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(workspace.GpuDeviceType, 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(workspace.GpuDeviceType, g) |
| |
| with core.DeviceScope(device_for_indices): |
| workspace.FeedBlob("gpu_{}/indices".format(g), indices) |
| |
| with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 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(workspace.GpuDeviceType, 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(not workspace.has_gpu_support, "No gpu support.") |
| @unittest.skipIf(workspace.NumGpuDevices() < 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() |
| ) |
| @settings(deadline=2000) |
| def test_parallelize_bmuf(self, cpu_device): |
| assume(cpu_device or workspace.has_gpu_support or workspace.has_hip_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 = workspace.GpuDeviceType |
| 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(model._device_grouped_blobs.keys()), ['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.NumGpuDevices() < 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(workspace.GpuDeviceType, 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(workspace.GpuDeviceType, 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( |
| workspace.GpuDeviceType, 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.NumGpuDevices() >= 4: |
| self.run_model(V, list(range(4))) |
| |
| if workspace.NumGpuDevices() >= 8: |
| self.run_model(V, list(range(8))) |
| |
| |
| if __name__ == "__main__": |
| import unittest |
| unittest.main() |
