import fcntl import multiprocessing import os import sys import copy import time import unittest from functools import wraps, reduce from contextlib import contextmanager import torch import torch.cuda import torch.nn as nn import torch.distributed as dist import torch.optim as optim import torch.nn.functional as F from torch.autograd import Variable from common import TestCase BACKEND = os.environ['BACKEND'] TEMP_DIR = os.environ['TEMP_DIR'] INIT_METHOD = os.getenv('INIT_METHOD', 'env://') MASTER_PORT = '29500' MASTER_ADDR = '127.0.0.1' DEFAULT_TIMEOUT = 15 CUSTOMIZED_TIMEOUT = {'test_DistributedDataParallel': 25} def get_timeout(test_id): test_name = test_id.split('.')[-1] if test_name in CUSTOMIZED_TIMEOUT: return CUSTOMIZED_TIMEOUT[test_name] else: return DEFAULT_TIMEOUT if not dist.is_available(): print('Distributed not available, skipping tests') sys.exit(0) SKIP_IF_NO_CUDA_EXIT_CODE = 75 SKIP_IF_NO_GPU_EXIT_CODE = 76 SKIP_IF_SMALL_WORLDSIZE_EXIT_CODE = 77 def skip_if_no_cuda_distributed(func): func.skip_if_no_cuda_distributed = True @wraps(func) def wrapper(*args, **kwargs): if not torch.cuda.is_available(): sys.exit(SKIP_IF_NO_CUDA_EXIT_CODE) return func(*args, **kwargs) return wrapper def skip_if_no_gpu(func): """ Nccl multigpu tests requires at least 2 GPUS. Skip if this is not met""" func.skip_if_no_gpu = True @wraps(func) def wrapper(*args, **kwargs): if not torch.cuda.is_available(): sys.exit(SKIP_IF_NO_CUDA_EXIT_CODE) if torch.cuda.device_count() < int(os.environ['WORLD_SIZE']): sys.exit(SKIP_IF_NO_GPU_EXIT_CODE) return func(*args, **kwargs) return wrapper def skip_if_small_worldsize(func): func.skip_if_small_worldsize = True @wraps(func) def wrapper(*args, **kwargs): if (os.environ['BACKEND'] != "mpi") and int(os.environ['WORLD_SIZE']) <= 2: sys.exit(SKIP_IF_SMALL_WORLDSIZE_EXIT_CODE) return func(*args, **kwargs) return wrapper def apply_hack_for_nccl(): # This is a hack for a known NCCL issue using multiprocess # in conjunction with multiple threads to manage different GPUs which # may cause ncclCommInitRank to fail. # http://docs.nvidia.com/deeplearning/sdk/nccl-release-notes/rel_2.1.4.html#rel_2.1.4 # It slows down the performance of collective operations. # Without this setting NCCL might throw unhandled error. os.environ['NCCL_MAX_NRINGS'] = '1' @contextmanager def _lock(): lockfile = os.path.join(TEMP_DIR, 'lockfile') with open(lockfile, 'w') as lf: try: fcntl.flock(lf.fileno(), fcntl.LOCK_EX) yield finally: fcntl.flock(lf.fileno(), fcntl.LOCK_UN) lf.close() def _build_tensor(size, value=None): if value is None: value = size return torch.FloatTensor(size, size, size).fill_(value) class Barrier(object): barrier_id = 0 @classmethod def init(cls): cls.barrier_id = 0 barrier_dir = os.path.join(TEMP_DIR, 'barrier') for f_name in os.listdir(barrier_dir): os.unlink(os.path.join(barrier_dir, f_name)) @classmethod def sync(cls, timeout=5): cls.barrier_id += 1 barrier_dir = os.path.join(TEMP_DIR, 'barrier') pid = str(os.getpid()) barrier_file = os.path.join(barrier_dir, pid) with _lock(): with open(barrier_file, 'w') as f: f.write(str(cls.barrier_id)) start_time = time.time() while True: arrived = 0 with _lock(): for f_name in os.listdir(barrier_dir): with open(os.path.join(barrier_dir, f_name), 'r') as f: data = f.read() if int(data) >= cls.barrier_id: arrived += 1 if arrived == dist.get_world_size(): break if time.time() - start_time > timeout: raise RuntimeError("barrier timeout") time.sleep(0.1) class _DistTestBase(object): def _barrier(self, *args, **kwargs): Barrier.sync(*args, **kwargs) def _init_group_test(self): group = [1, 2] group_id = dist.new_group(group) rank = dist.get_rank() if rank not in group: return ([], None, rank) return (group, group_id, rank) def _init_global_test(self): group = [i for i in range(0, dist.get_world_size())] group_id = dist.group.WORLD rank = dist.get_rank() return (group, group_id, rank) # HELPER FOR MULTIGPU TESTS def _init_multigpu_helper(self): """Multigpu tests are designed to simulate the multi nodes with multi GPUs on each node. Nccl backend requires equal #GPUs in each process. On a single node, all visible GPUs are evenly divided to subsets, each process only uses a subset. """ nGPUs = torch.cuda.device_count() world_size = dist.get_world_size() visible_devices = range(nGPUs) if BACKEND == 'nccl': apply_hack_for_nccl() nGPUs_per_process = nGPUs // world_size rank_to_GPU = {i: list(visible_devices[i * nGPUs_per_process: (i + 1) * nGPUs_per_process]) for i in range(world_size)} return rank_to_GPU # GET RANK def test_get_rank(self): test_dir = os.path.join(TEMP_DIR, 'test_dir') pid = str(os.getpid()) num_processes = dist.get_world_size() with open(os.path.join(test_dir, pid), 'w') as f: f.write(str(dist.get_rank())) self._barrier() all_ranks = set() for f_name in os.listdir(test_dir): with open(os.path.join(test_dir, f_name), 'r') as f: all_ranks.add(int(f.read())) self.assertEqual(len(all_ranks), num_processes) self._barrier() if dist.get_rank() == 0: for f_name in os.listdir(test_dir): os.unlink(os.path.join(test_dir, f_name)) self._barrier() # SEND RECV @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support send/recv") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support send/recv") def test_send_recv(self): rank = dist.get_rank() tensor = _build_tensor(rank + 1) for dest in range(0, dist.get_world_size()): if dest == rank: continue dist.send(tensor, dest) for src in range(0, dist.get_world_size()): if src == rank: continue tensor = _build_tensor(src + 1, value=-1) expected_tensor = _build_tensor(src + 1) dist.recv(tensor, src) self.assertEqual(tensor, expected_tensor) self._barrier() # SEND RECV ANY SOURCE @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support send/recv from any source") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support send/recv from any source") def test_send_recv_any_source(self): rank = dist.get_rank() tensor = _build_tensor(10, rank) for dest in range(0, dist.get_world_size()): if dest == rank: continue dist.send(tensor, dest) recv_ranks = set() for src in range(0, dist.get_world_size()): if src == rank: continue tensor = _build_tensor(10, value=-1) sender = dist.recv(tensor) self.assertTrue(tensor.eq(sender).all()) recv_ranks.add(sender) self.assertEqual(len(recv_ranks), dist.get_world_size() - 1) self._barrier() # ISEND @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support isend") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support isend") def test_isend(self): rank = dist.get_rank() world_size = dist.get_world_size() if rank == 0: requests = [ dist.isend(_build_tensor(dest, 10), dest) for dest in range(1, world_size) ] for request in requests: request.wait() self.assertTrue(request.is_completed()) else: tensor = _build_tensor(rank, -1) dist.recv(tensor, 0) self.assertEqual(tensor, _build_tensor(rank, 10)) self._barrier() # IRECV @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support irecv") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support irecv") def test_irecv(self): rank = dist.get_rank() world_size = dist.get_world_size() if rank == 0: expected_tensors = [_build_tensor(src, -1) for src in range(1, world_size)] requests = [ dist.irecv(expected_tensors[src - 1], src) for src in range(1, world_size) ] for src in range(1, world_size): requests[src - 1].wait() self.assertTrue(requests[src - 1].is_completed()) self.assertEqual(expected_tensors[src - 1], _build_tensor(src, 10)) else: tensor = _build_tensor(rank, 10) dist.send(tensor, 0) self._barrier() # BROADCAST def _test_broadcast_helper(self, group, group_id, rank, cuda=False, rank_to_GPU=None): for ttype, value, requires_cuda in [ ('torch.FloatTensor', -1e-10, False), ('torch.DoubleTensor', -1e-100, False), ('torch.HalfTensor', -0.1, True), ('torch.CharTensor', -2, False), ('torch.ByteTensor', 129, False), ('torch.IntTensor', -1e5, False), ('torch.LongTensor', -1e15, False), ]: if requires_cuda and not cuda: continue for src in group: expected_tensor = _build_tensor(src + 1, value).type(ttype) if cuda: expected_tensor = expected_tensor.cuda(rank_to_GPU[rank][0]) if rank == src: dist.broadcast(expected_tensor, src, group_id) else: tensor = _build_tensor(src + 1, -1).type(ttype) if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) dist.broadcast(tensor, src, group_id) self.assertEqual(tensor.size(), expected_tensor.size()) self.assertEqual(tensor.ne(expected_tensor).max(), 0) self._barrier() @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_broadcast(self): group, group_id, rank = self._init_global_test() self._test_broadcast_helper(group, group_id, rank) @unittest.skipIf(BACKEND != 'gloo' and BACKEND != 'nccl', "Only Gloo and Nccl backend supports CUDA allReduce") @skip_if_no_cuda_distributed @skip_if_no_gpu def test_broadcast_cuda(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_broadcast_helper(group, group_id, rank, True, rank_to_GPU) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_broadcast_group(self): group, group_id, rank = self._init_group_test() self._test_broadcast_helper(group, group_id, rank) # REDUCE def _test_reduce_helper(self, group, group_id, rank, op, master_value, worker_value, expected_value, cuda=False, rank_to_GPU=None): for src in group: if rank == src: tensor = _build_tensor(src + 1).fill_(master_value) if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) dist.reduce(tensor, src, op, group_id) self.assertEqual(tensor, _build_tensor(src + 1, expected_value)) else: tensor = _build_tensor(src + 1).fill_(worker_value) if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) dist.reduce(tensor, src, op, group_id) self._barrier() @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_reduce_sum(self): group, group_id, rank = self._init_global_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + (10 * (len(group) - 1)) ) @unittest.skipIf(BACKEND != 'nccl', "Only Nccl supports CUDA reduce") @skip_if_no_cuda_distributed @skip_if_no_gpu def test_reduce_sum_cuda(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + 10 * (len(group) - 1), True, rank_to_GPU) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_reduce_product(self): group, group_id, rank = self._init_global_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.PRODUCT, 2, 10, reduce((lambda x, y: x * y), [10] * (len(group) - 1), 2) ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_reduce_min(self): group, group_id, rank = self._init_global_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.MIN, 1010, 1, 1 ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_reduce_max(self): group, group_id, rank = self._init_global_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.MAX, -1, 10, 10 ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_reduce_group_sum(self): group, group_id, rank = self._init_group_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + (10 * (len(group) - 1)) ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_reduce_group_product(self): group, group_id, rank = self._init_group_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.PRODUCT, 2, 10, reduce((lambda x, y: x * y), [10] * (len(group) - 1), 2) ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_reduce_group_min(self): group, group_id, rank = self._init_group_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.MIN, 1010, 1, 1 ) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support reduce") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_reduce_group_max(self): group, group_id, rank = self._init_group_test() self._test_reduce_helper( group, group_id, rank, dist.reduce_op.MAX, -1, 10, 10 ) # ALL REDUCE def _test_all_reduce_helper(self, group, group_id, rank, op, master_value, worker_value, expected_value, cuda=False, rank_to_GPU=None): for src in group: if rank == src: tensor = _build_tensor(src + 1).fill_(master_value) if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) dist.all_reduce(tensor, op, group_id) self.assertEqual(tensor, _build_tensor(src + 1, expected_value)) else: tensor = _build_tensor(src + 1).fill_(worker_value) if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) dist.all_reduce(tensor, op, group_id) self.assertEqual(tensor, _build_tensor(src + 1, expected_value)) self._barrier() @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_all_reduce_sum(self): group, group_id, rank = self._init_global_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + (10 * (len(group) - 1)) ) @unittest.skipIf(BACKEND != 'gloo' and BACKEND != 'nccl', "Only Gloo & Nccl backend support CUDA allReduce") @skip_if_no_cuda_distributed @skip_if_no_gpu def test_all_reduce_sum_cuda(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + (10 * (len(group) - 1)), True, rank_to_GPU ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_all_reduce_product(self): group, group_id, rank = self._init_global_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.PRODUCT, 2, 10, reduce((lambda x, y: x * y), [10] * (len(group) - 1), 2) ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_all_reduce_min(self): group, group_id, rank = self._init_global_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.MIN, 1010, 1, 1 ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_all_reduce_max(self): group, group_id, rank = self._init_global_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.MAX, -1, 10, 10 ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_all_reduce_group_sum(self): group, group_id, rank = self._init_group_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.SUM, 2, 10, 2 + (10 * (len(group) - 1)) ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_all_reduce_group_product(self): group, group_id, rank = self._init_group_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.PRODUCT, 2, 10, reduce((lambda x, y: x * y), [10] * (len(group) - 1), 2) ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_all_reduce_group_min(self): group, group_id, rank = self._init_group_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.MIN, 1010, 1, 1 ) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_all_reduce_group_max(self): group, group_id, rank = self._init_group_test() self._test_all_reduce_helper( group, group_id, rank, dist.reduce_op.MAX, -1, 10, 10 ) # SCATTER def _test_scatter_helper(self, group, group_id, rank): for dest in group: tensor = _build_tensor(dest + 1, -1) expected_tensor = _build_tensor(dest + 1, rank) tensors = [_build_tensor(dest + 1, i) for i in group] if rank == dest else [] dist.scatter(tensor, src=dest, scatter_list=tensors, group=group_id) self.assertEqual(tensor, expected_tensor) self._barrier() @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support scatter") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support scatter") def test_scatter(self): group, group_id, rank = self._init_global_test() self._test_scatter_helper(group, group_id, rank) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support scatter") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support scatter") @skip_if_small_worldsize def test_scatter_group(self): group, group_id, rank = self._init_group_test() self._test_scatter_helper(group, group_id, rank) # GATHER def _test_gather_helper(self, group, group_id, rank): for dest in group: tensor = _build_tensor(dest + 1, rank) tensors = [_build_tensor(dest + 1, -1) for i in group] if rank == dest else [] dist.gather(tensor, dst=dest, gather_list=tensors, group=group_id) if rank == dest: expected_tensors = [_build_tensor(dest + 1, i) for i in group] for t1, t2 in zip(tensors, expected_tensors): self.assertEqual(t1, t2) self._barrier() @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support gather") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_gather(self): group, group_id, rank = self._init_global_test() self._test_gather_helper(group, group_id, rank) @unittest.skipIf(BACKEND == 'gloo', "Gloo does not support gather") @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_gather_group(self): group, group_id, rank = self._init_group_test() self._test_gather_helper(group, group_id, rank) # ALL GATHER def _test_all_gather_helper(self, group, group_id, rank, cuda=False, rank_to_GPU=None): for dest in group: tensor = _build_tensor(dest + 1, rank) tensors = [_build_tensor(dest + 1, -1) for i in group] if cuda: tensor = tensor.cuda(rank_to_GPU[rank][0]) tensors = [t.cuda(rank_to_GPU[rank][0]) for t in tensors] dist.all_gather(tensors, tensor, group_id) expected_tensors = [_build_tensor(dest + 1, i) for i in group] for t1, t2 in zip(tensors, expected_tensors): self.assertEqual(t1, t2) self._barrier() @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_all_gather(self): group, group_id, rank = self._init_global_test() self._test_all_gather_helper(group, group_id, rank) @unittest.skipIf(BACKEND != 'nccl', "Only Nccl supports CUDA all gather") @skip_if_no_cuda_distributed @skip_if_no_gpu def test_all_gather_cuda(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_all_gather_helper(group, group_id, rank, True, rank_to_GPU) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_all_gather_group(self): group, group_id, rank = self._init_group_test() self._test_all_gather_helper(group, group_id, rank) # BARRIER def _test_barrier_helper(self, group, group_id, rank): WAIT_TIME = 0.3 # seconds for dest in group: expected_time = torch.DoubleTensor(1).fill_(0.0) if dest == rank: expected_time.fill_(time.time() + WAIT_TIME) dist.broadcast(expected_time, dest, group_id) time.sleep(WAIT_TIME + 0.1) # sleep a little bit longer dist.barrier(group_id) else: dist.broadcast(expected_time, dest, group_id) dist.barrier(group_id) self.assertGreaterEqual(time.time(), expected_time[0]) self._barrier() @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support CPU tensors") def test_barrier(self): group, group_id, rank = self._init_global_test() self._test_barrier_helper(group, group_id, rank) @unittest.skipIf(BACKEND == 'nccl', "Nccl does not support newGroup") @skip_if_small_worldsize def test_barrier_group(self): group, group_id, rank = self._init_group_test() self._test_barrier_helper(group, group_id, rank) def _test_broadcast_multigpu_helper(self, group, group_id, rank, rank_to_GPU): for src in group: expected_tensor = _build_tensor(src + 1) tensors = [_build_tensor(src + 1, -1).cuda(device=i) for i in rank_to_GPU[rank]] if rank == src: tensors[0] = expected_tensor.cuda( device=rank_to_GPU[rank][0]) dist.broadcast_multigpu(tensors, src, group_id) for tensor in tensors: self.assertEqual(tensor, expected_tensor) self._barrier() @unittest.skipIf(BACKEND != 'nccl', "Only Nccl backend supports broadcast multigpu") @skip_if_no_gpu def test_broadcast_multigpu(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_broadcast_multigpu_helper(group, group_id, rank, rank_to_GPU) def _test_all_reduce_multigpu_helper(self, group, group_id, rank, rank_to_GPU, op, master_value, worker_value, expected_value): for src in group: if rank == src: tensors = [_build_tensor(src + 1, master_value).cuda(device=i) for i in rank_to_GPU[rank]] else: tensors = [_build_tensor(src + 1, worker_value).cuda(device=i) for i in rank_to_GPU[rank]] dist.all_reduce_multigpu(tensors, op, group_id) expected_tensor = _build_tensor(src + 1, expected_value) for tensor in tensors: self.assertEqual(tensor, expected_tensor) self._barrier() @unittest.skipIf(BACKEND != 'nccl', "Only Nccl backend supports allreduce multigpu") @skip_if_no_gpu def test_all_reduce_multigpu(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_all_reduce_multigpu_helper( group, group_id, rank, rank_to_GPU, dist.reduce_op.SUM, 2, 10, (2 + 10 * (len(group) - 1)) * len(rank_to_GPU[0])) def _test_reduce_multigpu_helper(self, group, group_id, rank, rank_to_GPU, op, master_value, worker_value, expected_value): for src in group: if rank == src: tensors = [_build_tensor(src + 1, master_value).cuda(device=i) for i in rank_to_GPU[rank]] dist.reduce_multigpu(tensors, src, op, group_id) expected_tensor = _build_tensor(src + 1, expected_value) self.assertEqual(tensors[0], expected_tensor) else: tensors = [_build_tensor(src + 1, worker_value).cuda(device=i) for i in rank_to_GPU[rank]] dist.reduce_multigpu(tensors, src, op, group_id) self._barrier() @unittest.skipIf(BACKEND != 'nccl', "Only Nccl backend supports reduce multigpu") @skip_if_no_gpu def test_reduce_multigpu(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_reduce_multigpu_helper( group, group_id, rank, rank_to_GPU, dist.reduce_op.SUM, 2, 10, (2 + 10 * (len(group) - 1)) * len(rank_to_GPU[0])) def _test_all_gather_multigpu_helper(self, group, group_id, rank, rank_to_GPU): for dest in group: tensors = [_build_tensor(dest + 1).cuda(device=i) for i in rank_to_GPU[rank]] # construct expected output along with # a place holder to receive all gather results output_tensors = [] expected_output = [] output_per_gpu = [_build_tensor(dest + 1, -1)] * len(rank_to_GPU[0]) * len(group) expected_per_gpu = [_build_tensor(dest + 1)] * len(rank_to_GPU[0]) * len(group) for gpu in rank_to_GPU[rank]: output_tensors.append([t.cuda(device=gpu) for t in output_per_gpu]) expected_output.append([t.cuda(device=gpu) for t in expected_per_gpu]) dist.all_gather_multigpu(output_tensors, tensors, group_id) self.assertEqual(output_tensors, expected_output) self._barrier() @unittest.skipIf(BACKEND != 'nccl', "Only Nccl backend supports allgather multigpu") @skip_if_no_gpu def test_all_gather_multigpu(self): group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() self._test_all_gather_multigpu_helper(group, group_id, rank, rank_to_GPU) def _create_Net(self): class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(2, 10, bias=False) self.fc2 = nn.Linear(10, 50, bias=False) self.fc3 = nn.Linear(50, 4, bias=False) self.relu = nn.ReLU() def forward(self, x): x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) x = self.fc3(x) return F.softmax(x, dim=1) return Net() def _model_step(self, model): for param in model.parameters(): param.data += param.grad param.grad = None def _prepare_dummy_data(self, local_bs): # global_bs for DDP should be divisible by WORLD_SIZE global_bs = int(WORLD_SIZE) * local_bs input_cpu = torch.randn(global_bs, 2) target = torch.randn(global_bs, 4) loss = nn.MSELoss() return global_bs, input_cpu, target, loss # END TO END TEST FOR DISTRIBUTEDDATAPARALLEL def _test_DDP_helper(self, model, input_var, target, loss): model.train() output = model(input_var) l = loss(output, target) l.backward() def _assert_equal_param(self, param_gpu, param_DDP): self.assertEqual(len(param_gpu), len(param_DDP)) for p_gpu, p_DDP in zip(param_gpu, param_DDP): self.assertEqual(p_gpu, p_DDP) def _test_DDP_2iter(self, model_base, model_DDP, input, target, loss, local_bs, rank, batch_size): for i in range(2): # single cpu/gpu training self._test_DDP_helper(model_base, input, target, loss) # DDP training, DDP scatters subsets of input_cpu to nodes/GPUs self._test_DDP_helper(model_DDP, input[rank * local_bs: (rank + 1) * local_bs], target[rank * local_bs: (rank + 1) * local_bs], loss) # Update weights and run a second iteration to shake out errors self._model_step(model_base) self._model_step(model_DDP) self._assert_equal_param(list(model_base.parameters()), list(model_DDP.module.parameters())) # Shuffle the input so that DDP input is different input = input[torch.randperm(batch_size)] @unittest.skipIf(BACKEND != 'nccl' and BACKEND != 'gloo', "Only Nccl & Gloo backend support DistributedDataParallel") @skip_if_no_cuda_distributed @skip_if_no_gpu def test_DistributedDataParallel(self): # Run a simple end to end DDP model, use result of single node model # as baseline group, group_id, rank = self._init_global_test() rank_to_GPU = self._init_multigpu_helper() # cpu training setup model = self._create_Net() # single gpu training setup model_gpu = copy.deepcopy(model) gpu_subset = list(rank_to_GPU[rank]) model_gpu.cuda(gpu_subset[0]) # DDP training setup model_DDP = copy.deepcopy(model) model_DDP.cuda(gpu_subset[0]) model_DDP = nn.parallel.DistributedDataParallel(model_DDP, device_ids=gpu_subset) # dummy data initialization local_bs = len(gpu_subset) global_bs, input_cpu, target, loss = self._prepare_dummy_data(local_bs) # check two model parameters over 2 iterations self._test_DDP_2iter(model_gpu, model_DDP, input_cpu.cuda(gpu_subset[0]), target.cuda(gpu_subset[0]), loss, local_bs, rank, global_bs) self._barrier() @unittest.skipIf(BACKEND == 'nccl', "nccl does not support DistributedDataParallelCPU") def test_DistributedDataParallelCPU(self): # Run a simple end to end DDP-CPU model, use result of single node # model as baseline group, group_id, rank = self._init_global_test() # cpu training setup model_base = self._create_Net() # DDP-CPU training setup model_DDP = copy.deepcopy(model_base) model_DDP = nn.parallel.DistributedDataParallelCPU(model_DDP) # dummy data initialization local_bs = 2 global_bs, input_cpu, target, loss = self._prepare_dummy_data(local_bs) # check two model parameters over 2 iterations self._test_DDP_2iter(model_base, model_DDP, input_cpu, target, loss, local_bs, rank, global_bs) self._barrier() if BACKEND == 'tcp' or BACKEND == 'gloo' or BACKEND == 'nccl': WORLD_SIZE = os.environ['WORLD_SIZE'] class TestDistBackend(TestCase, _DistTestBase): MANAGER_PROCESS_RANK = -1 @staticmethod def manager_join(fn): @wraps(fn) def wrapper(self): if self.rank == self.MANAGER_PROCESS_RANK: self._join_and_reduce(fn) else: fn(self) return wrapper @classmethod def setUpClass(cls): os.environ['MASTER_ADDR'] = MASTER_ADDR os.environ['MASTER_PORT'] = MASTER_PORT os.environ['WORLD_SIZE'] = WORLD_SIZE for attr in dir(cls): if attr.startswith('test'): fn = getattr(cls, attr) setattr(cls, attr, cls.manager_join(fn)) def setUp(self): self.processes = [] self.rank = self.MANAGER_PROCESS_RANK Barrier.init() for rank in range(int(WORLD_SIZE)): self.processes.append(self._spawn_process(rank)) def tearDown(self): for p in self.processes: p.terminate() def _spawn_process(self, rank): os.environ['RANK'] = str(rank) name = 'process ' + str(rank) process = multiprocessing.Process(target=self._run, name=name, args=(rank,)) process.start() return process def _run(self, rank): self.rank = rank try: dist.init_process_group(init_method=INIT_METHOD, backend=BACKEND, world_size=int(WORLD_SIZE)) except RuntimeError as e: if 'recompile' in e.args[0]: sys.exit(0) raise # self.id() == e.g. '__main__.TestDistributed.test_get_rank' # We're retreiving a corresponding test and executing it. getattr(self, self.id().split(".")[2])() sys.exit(0) def _join_and_reduce(self, fn): skip_ok = getattr(fn, "skip_if_no_cuda_distributed", False) \ or getattr(fn, "skip_if_no_gpu", False) \ or getattr(fn, "skip_if_small_worldsize", False) self.JOIN_TIMEOUT = get_timeout(self.id()) for p in self.processes: p.join(self.JOIN_TIMEOUT) if not skip_ok: self.assertEqual(p.exitcode, 0) if skip_ok: first_process = self.processes[0] # do this first so we don't give an error message about # mismatched exit codes if the first isn't valid assert first_process.exitcode == 0 \ or first_process.exitcode == SKIP_IF_NO_CUDA_EXIT_CODE \ or first_process.exitcode == SKIP_IF_NO_GPU_EXIT_CODE \ or first_process.exitcode == SKIP_IF_SMALL_WORLDSIZE_EXIT_CODE for p in self.processes: self.assertEqual(p.exitcode, first_process.exitcode) if first_process.exitcode == SKIP_IF_NO_CUDA_EXIT_CODE: raise unittest.SkipTest("cuda is not available") if first_process.exitcode == SKIP_IF_NO_GPU_EXIT_CODE: raise unittest.SkipTest("One unique gpu per process is not available") if first_process.exitcode == SKIP_IF_SMALL_WORLDSIZE_EXIT_CODE: raise unittest.SkipTest("worldsize is too small to run group tests") elif BACKEND == 'mpi': WORLD_SIZE = os.environ['WORLD_SIZE'] dist.init_process_group(init_method=INIT_METHOD, backend='mpi') class TestMPI(TestCase, _DistTestBase): pass if __name__ == '__main__': unittest.main()