| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| import unittest |
| from caffe2.proto import caffe2_pb2 |
| from caffe2.python import core, workspace, data_parallel_model, cnn, recurrent |
| from caffe2.python.test_util import TestCase |
| |
| |
| @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.") |
| @unittest.skipIf(workspace.NumCudaDevices() < 2, "Need at least 2 GPUs.") |
| class GPUDataParallelModelTest(TestCase): |
| |
| def run_model(self, gpu_devices): |
| ''' |
| 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) |
| 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(): |
| grad = model.param_to_grad[param] |
| model.WeightedSum([param, ONE, grad, LR], param) |
| |
| workspace.ResetWorkspace() |
| model = cnn.CNNModelHelper( |
| order="NHWC", |
| name="test{}".format(gpu_devices), |
| ) |
| 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, |
| ) |
| |
| 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(gpu_devices) |
| |
| for (j, g) in enumerate(gpu_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(caffe2_pb2.CUDA, g)): |
| workspace.FeedBlob("gpu_{}/data".format(g), data) |
| workspace.FeedBlob("gpu_{}/label".format(g), labels) |
| |
| if i == 0: |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.CreateNet(model.net) |
| |
| print(i, workspace.FetchBlob("gpu_0/fc_w").flatten()[:5]) |
| workspace.RunNet(model.net.Proto().name) |
| |
| return workspace.FetchBlob("gpu_0/fc_w") |
| |
| def test_equiv(self): |
| ''' |
| Test that the model produces exactly same results given |
| total batchsize, independent of number of GPUs. |
| ''' |
| result_2gpus = self.run_model([0, 1]) |
| result_1gpus = self.run_model([0]) |
| |
| self.assertTrue(np.allclose(result_1gpus, result_2gpus)) |
| |
| if workspace.NumCudaDevices() >= 4: |
| result_4gpus = self.run_model(range(4)) |
| self.assertTrue(np.allclose(result_1gpus, result_4gpus)) |
| |
| if workspace.NumCudaDevices() >= 8: |
| result_8gpus = self.run_model(range(8)) |
| 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 RecurrentNetworkParallelTest(TestCase): |
| |
| def run_model(self, gpu_devices): |
| |
| ''' |
| 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, = recurrent.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(gpu_devices), |
| ) |
| |
| self.T = 8 |
| self.batch_size = 64 |
| self.input_dim = 8 |
| self.hidden_dim = 31 |
| self.batch_per_device = self.batch_size // len(gpu_devices) |
| |
| 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, |
| ) |
| |
| # 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(gpu_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(caffe2_pb2.CUDA, g)): |
| workspace.FeedBlob("gpu_{}/data".format(g), data) |
| workspace.FeedBlob("gpu_{}/target".format(g), targets) |
| |
| if i == 0: |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.CreateNet(model.net) |
| |
| workspace.RunNet(model.net.Proto().name) |
| |
| return workspace.FetchBlob("gpu_0/partest/i2h_w") |
| |
| def test_equiv_recurrent(self): |
| ''' |
| Test that the model produces exactly same results given |
| total batchsize, independent of number of GPUs. |
| ''' |
| result_2gpus = self.run_model([0, 1]) |
| result_1gpus = self.run_model([0]) |
| |
| print("result 1", result_1gpus.flatten()[:5]) |
| print("result 2", result_2gpus.flatten()[:5]) |
| |
| self.assertTrue(np.allclose(result_1gpus, result_2gpus)) |
| |
| if workspace.NumCudaDevices() >= 4: |
| result_4gpus = self.run_model(range(4)) |
| self.assertTrue(np.allclose(result_1gpus, result_4gpus)) |
| |
| if workspace.NumCudaDevices() >= 8: |
| result_8gpus = self.run_model(range(8)) |
| 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): |
| |
| def run_model(self, V, gpu_devices): |
| |
| ''' |
| Helper function for test_equiv |
| ''' |
| def input_builder_fun(model): |
| return None |
| |
| def model_build_fun(model, loss_scale): |
| with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)): |
| gathered_cpu = model.net.Gather( |
| [self.vecs, 'indices'], 'gathered_cpu') |
| gathered = model.CopyCPUToGPU(gathered_cpu, "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] |
| assert not isinstance(param_grad, core.GradientSlice) |
| model.WeightedSum([param, ONE, param_grad, LR], param) |
| |
| 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]) |
| model.params.append(self.vecs) |
| |
| 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 |
| ONE_CPU = model.param_init_net.ConstantFill( |
| [], "ONE_CPU", shape=[1], value=1.0, |
| ) |
| |
| 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, ONE_CPU, |
| param_grad.indices, |
| param_grad.values, |
| self.LR], |
| 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.rand(batch_size, 16) * V).astype(np.int32) |
| 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) |
| |
| with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)): |
| 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 |
| ) |
| workspace.CreateNet(model.net) |
| |
| workspace.RunNet(model.net.Proto().name) |
| |
| # Sanity check to see the vecs were updated |
| self.assertFalse( |
| np.allclose(workspace.FetchBlob(self.vecs), orig_vecs)) |
| return [workspace.FetchBlob(self.vecs), |
| workspace.FetchBlob("gpu_0/fc_w")] |
| |
| def test_equiv_sparse(self): |
| ''' |
| 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]) |
| result_1gpus = self.run_model(V, [0]) |
| |
| 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, range(4)) |
| 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, range(8)) |
| self.assertTrue(np.allclose(result_1gpus[0], result_8gpus[0])) |
| self.assertTrue(np.allclose(result_1gpus[1], result_8gpus[1])) |
