Added Horovod distributed backend

.circleci/config.yml

@@ -10,6 +10,7 @@ references:
     run:
       name: Install Dependences
       command: |
+        sudo apt-get update && sudo apt-get install -y cmake
         pip install "$TORCH_VERSION"
         pip install -r requirements.txt -q
         sudo pip install pytest pytest-cov pytest-flake8 -q

.drone.yml

@@ -6,24 +6,33 @@ name: torch-GPU
 
 steps:
 - name: testing
-  image: pytorch/pytorch:1.4-cuda10.1-cudnn7-runtime
+  image: pytorch/pytorch:1.4-cuda10.1-cudnn7-devel
 
   environment:
     SLURM_LOCALID: 0
     CODECOV_TOKEN:
       from_secret: codecov_token
+    HOROVOD_GPU_ALLREDUCE: NCCL
+    HOROVOD_GPU_BROADCAST: NCCL
+    HOROVOD_WITH_PYTORCH: 1
+    HOROVOD_WITHOUT_TENSORFLOW: 1
+    HOROVOD_WITHOUT_MXNET: 1
+    HOROVOD_WITH_GLOO: 1
+    HOROVOD_WITHOUT_MPI: 1
 
   #volumes:
   #  # Mount pip cache from host
   #  - name: pip_cache
   #    path: /opt/conda/lib/python3.7/site-packages
 
   commands:
+    - export PATH="$PATH:/root/.local/bin"
     - python --version
     - pip install pip -U
     - pip --version
     - nvidia-smi
-    - bash ./tests/install_AMP.sh
+#    - bash ./tests/install_AMP.sh
+    - apt-get update && apt-get install -y cmake
     - pip install -r requirements.txt --user -q
     - pip install coverage pytest pytest-cov pytest-flake8 codecov -q
     - pip install -r ./tests/requirements.txt --user -q

.github/workflows/ci-testing.yml

@@ -41,9 +41,15 @@ jobs:
       if: runner.os == 'macOS'
       run: |
         brew install libomp  # https://github.com/pytorch/pytorch/issues/20030
+        brew install openmpi  # Horovod on macOS requires OpenMPI, Gloo not currently supported
 
-    # TODO: remove after https://github.com/pytorch/pytorch/issues/32186 is resolved
     - name: Setup Windows
+      if: runner.os == 'windows'
+      run: |
+        python -c "lines = [line for line in open('requirements-extra.txt').readlines() if not line.startswith('horovod')] ; open('requirements-extra.txt', 'w').writelines(lines)"
+
+    # TODO: remove after https://github.com/pytorch/pytorch/issues/32186 is resolved
+    - name: Setup Windows on Latest
       if: runner.os == 'windows' && matrix.requires == 'latest'
       run: |
         python -c "req = open('requirements.txt').read().replace('torch>=1.1', 'torch<1.5') ; open('requirements.txt', 'w').write(req)"
@@ -75,11 +81,12 @@ jobs:
       run: |
         # python -m pip install --upgrade --user pip
         pip install -r requirements.txt -U -f https://download.pytorch.org/whl/torch_stable.html -q
-        pip install -r ./tests/requirements.txt -q
+        HOROVOD_BUILD_ARCH_FLAGS="-mfma" pip install -r ./tests/requirements.txt -q
         # pip install tox coverage
         python --version
         pip --version
         pip list
+      shell: bash
 
     - name: Tests
       # env:

CHANGELOG.md

@@ -24,6 +24,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `ddp_cpu` backend for testing ddp without GPUs ([#1158](https://github.com/PyTorchLightning/pytorch-lightning/pull/1158))
 
+- Added [Horovod](http://horovod.ai) support as a distributed backend `Trainer(distributed_backend='horovod')` ([#1529](https://github.com/PyTorchLightning/pytorch-lightning/pull/1529))
 
 ### Changed
 

docs/source/multi_gpu.rst

@@ -76,6 +76,7 @@ Lightning allows multiple ways of training
 - Data Parallel (`distributed_backend='dp'`) (multiple-gpus, 1 machine)
 - DistributedDataParallel (`distributed_backend='ddp'`) (multiple-gpus across many machines).
 - DistributedDataParallel2 (`distributed_backend='ddp2'`) (dp in a machine, ddp across machines).
+- Horovod (`distributed_backend='horovod'`) (multi-machine, multi-gpu, configured at runtime)
 - TPUs (`num_tpu_cores=8|x`) (tpu or TPU pod)
 
 Data Parallel (dp)
@@ -136,6 +137,43 @@ In  this case, we can use ddp2 which behaves like dp in a machine and ddp across
     # train on 32 GPUs (4 nodes)
     trainer = pl.Trainer(gpus=8, distributed_backend='ddp2', num_nodes=4)
 
+Horovod
+^^^^^^^
+`Horovod <http://horovod.ai>`_ allows the same training script to be used for single-GPU,
+multi-GPU, and multi-node training.
+
+Like Distributed Data Parallel, every process in Horovod operates on a single GPU with a fixed
+subset of the data.  Gradients are averaged across all GPUs in parallel during the backward pass,
+then synchronously applied before beginning the next step.
+
+The number of worker processes is configured by a driver application (`horovodrun` or `mpirun`). In
+the training script, Horovod will detect the number of workers from the environment, and automatically
+scale the learning rate to compensate for the increased total batch size.
+
+Horovod can be configured in the training script to run with any number of GPUs / processes as follows:
+
+.. code-block:: python
+
+    # train Horovod on GPU (number of GPUs / machines provided on command-line)
+    trainer = pl.Trainer(distributed_backend='horovod', gpus=1)
+
+    # train Horovod on CPU (number of processes / machines provided on command-line)
+    trainer = pl.Trainer(distributed_backend='horovod')
+
+When starting the training job, the driver application will then be used to specify the total
+number of worker processes:
+
+.. code-block:: bash
+
+    # run training with 4 GPUs on a single machine
+    horovodrun -np 4 python train.py
+
+    # run training with 8 GPUs on two machines (4 GPUs each)
+    horovodrun -np 8 -H hostname1:4,hostname2:4 python train.py
+
+See the official `Horovod documentation <https://horovod.readthedocs.io/en/stable>`_ for details
+on installation and performance tuning.
+
 DP/DDP2 caveats
 ^^^^^^^^^^^^^^^
 In DP and DDP2 each GPU within a machine sees a portion of a batch.

pytorch_lightning/trainer/data_loading.py

@@ -26,6 +26,13 @@
 else:
     XLA_AVAILABLE = True
 
+try:
+    import horovod.torch as hvd
+except ImportError:
+    HOROVOD_AVAILABLE = False
+else:
+    HOROVOD_AVAILABLE = True
+
 
 def _has_len(dataloader: DataLoader) -> bool:
     """ Checks if a given Dataloader has __len__ method implemented i.e. if
@@ -47,6 +54,7 @@ class TrainerDataLoadingMixin(ABC):
     proc_rank: int
     use_ddp: bool
     use_ddp2: bool
+    use_horovod: bool
     shown_warnings: ...
     val_check_interval: float
     use_tpu: bool
@@ -89,7 +97,7 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
         # don't do anything if it's not a dataloader
         if not isinstance(dataloader, DataLoader):
             return dataloader
-        need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_tpu)
+        need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)
         if self.replace_sampler_ddp and need_dist_sampler:
 
             skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']
@@ -104,6 +112,10 @@ def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
                     num_replicas=xm.xrt_world_size(),
                     rank=xm.get_ordinal(),
                 )
+            elif self.use_horovod:
+                sampler = DistributedSampler(dataloader.dataset,
+                                             num_replicas=hvd.size(),
+                                             rank=hvd.rank())
             else:
                 world_size = {
                     'ddp': self.num_nodes * self.num_processes,
@@ -254,6 +266,10 @@ def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:
             # all processes wait until data download has happened
             torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')
 
+        elif self.use_horovod:
+            # all processes wait until data download has happened
+            hvd.join()
+
         return dataloader
 
     def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float,

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -131,6 +131,13 @@ def train_fx(trial_hparams, cluster_manager, _):
 else:
     APEX_AVAILABLE = True
 
+try:
+    import horovod.torch as hvd
+except ImportError:
+    HOROVOD_AVAILABLE = False
+else:
+    HOROVOD_AVAILABLE = True
+
 
 class TrainerDDPMixin(ABC):
 
@@ -178,10 +185,14 @@ def set_distributed_mode(self, distributed_backend):
         self.use_dp = False
         self.use_ddp = False
         self.use_ddp2 = False
+        self.use_horovod = False
         self.single_gpu = False
 
         if distributed_backend is None:
-            if self.num_gpus == 0:
+            if self.has_horovodrun():
+                self.check_horovod()
+                self.use_horovod = True
+            elif self.num_gpus == 0:
                 if self.num_nodes > 1 or self.num_processes > 1:
                     self.use_ddp = True  # ddp_cpu
             elif self.num_gpus == 1:
@@ -219,6 +230,9 @@ def set_distributed_mode(self, distributed_backend):
             self.use_ddp = True
             self.data_parallel_device_ids = None
             self.on_gpu = False
+        elif distributed_backend == 'horovod':
+            self.check_horovod()
+            self.use_horovod = True
 
         # throw error to force user ddp or ddp2 choice
         if self.num_nodes > 1 and not (self.use_ddp2 or self.use_ddp):
@@ -402,3 +416,22 @@ def resolve_root_node_address(self, root_node):
             root_node = name + number
 
         return root_node
+
+    def check_horovod(self):
+        """Raises a `MisconfigurationException` if the Trainer is not configured correctly for Horovod."""
+        if not HOROVOD_AVAILABLE:
+            raise MisconfigurationException(
+                'Requested `distributed_backend="horovod"`, but Horovod is not installed.'
+                'Install with \n $HOROVOD_WITH_PYTORCH=1 pip install horovod[pytorch]'
+            )
+
+        if self.num_gpus > 1 or self.num_nodes > 1:
+            raise MisconfigurationException(
+                'Horovod does not support setting num_nodes / num_gpus explicitly. Use '
+                'horovodrun / mpirun to configure the number of processes.'
+            )
+
+    @staticmethod
+    def has_horovodrun():
+        """Returns True if running with `horovodrun` using Gloo or OpenMPI."""
+        return 'OMPI_COMM_WORLD_RANK' in os.environ or 'HOROVOD_RANK' in os.environ

pytorch_lightning/trainer/distrib_parts.py

@@ -337,13 +337,16 @@
 
 """
 
+from contextlib import ExitStack
 import os
 from abc import ABC, abstractmethod
 import time
 import random
 import torch
+from typing import Union
 
 from pytorch_lightning import _logger as log
+from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.overrides.data_parallel import (
     LightningDistributedDataParallel,
     LightningDataParallel,
@@ -365,6 +368,13 @@
 else:
     XLA_AVAILABLE = True
 
+try:
+    import horovod.torch as hvd
+except ImportError:
+    HOROVOD_AVAILABLE = False
+else:
+    HOROVOD_AVAILABLE = True
+
 
 class TrainerDPMixin(ABC):
 
@@ -385,6 +395,7 @@ class TrainerDPMixin(ABC):
     tpu_global_core_rank: int
     use_tpu: bool
     data_parallel_device_ids: ...
+    logger: Union[LightningLoggerBase, bool]
 
     @property
     @abstractmethod
@@ -540,6 +551,64 @@ def dp_train(self, model):
 
         self.run_pretrain_routine(model)
 
+    def horovod_train(self, model):
+        # Horovod: initialize library
+        hvd.init()
+
+        if torch.cuda.is_available() and self.on_gpu:
+            # Horovod: pin GPU to local rank
+            torch.cuda.set_device(hvd.local_rank())
+            model.cuda(hvd.local_rank())
+
+        # Only show progress bar from the first worker
+        self.progress_bar_refresh_rate = self.progress_bar_refresh_rate if hvd.rank() == 0 else 0
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
+
+        # Horovod: scale the learning rate by the number of workers to account for
+        # increased total batch size
+        for optimizer in self.optimizers:
+            for param_group in optimizer.param_groups:
+                param_group['lr'] *= hvd.size()
+
+        if self.use_amp:
+            # An example
+            model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
+            self.optimizers = optimizers
+
+        # Horovod: broadcast parameters & optimizer state to ensure consistent initialization
+        hvd.broadcast_parameters(model.state_dict(), root_rank=0)
+        for optimizer in self.optimizers:
+            hvd.broadcast_optimizer_state(optimizer, root_rank=0)
+
+        def filter_named_parameters(model, optimizer):
+            opt_params = set([p for group in optimizer.param_groups for p in group.get('params', [])])
+            return [(name, p) for name, p in model.named_parameters() if p in opt_params]
+
+        # Horovod: wrap optimizers to perform gradient aggregation via allreduce
+        self.optimizers = [
+            hvd.DistributedOptimizer(optimizer, named_parameters=filter_named_parameters(model, optimizer))
+            for optimizer in self.optimizers
+        ]
+
+        # Update logger rank info from Horovod to avoid race conditions from  different ranks
+        # creating directories / writing files in the same locations.
+        self.proc_rank = hvd.rank()
+        set_proc_rank(self.proc_rank)
+        if self.logger:
+            self.logger.rank = self.proc_rank
+        if model.logger:
+            model.logger.rank = self.proc_rank
+
+        with ExitStack() as stack:
+            for optimizer in self.optimizers:
+                # Synchronization will be performed explicitly following backward()
+                stack.enter_context(optimizer.skip_synchronize())
+
+            self.run_pretrain_routine(model)
+
 
 def normalize_parse_gpu_string_input(s):
     if isinstance(s, str):

pytorch_lightning/trainer/evaluation_loop.py

@@ -145,6 +145,13 @@
 else:
     XLA_AVAILABLE = True
 
+try:
+    import horovod.torch as hvd
+except ImportError:
+    HOROVOD_AVAILABLE = False
+else:
+    HOROVOD_AVAILABLE = True
+
 
 class TrainerEvaluationLoopMixin(ABC):
 
@@ -153,9 +160,11 @@ class TrainerEvaluationLoopMixin(ABC):
     test_progress_bar: ...
     val_progress_bar: ...
     main_progress_bar: ...
+    on_gpu: bool
     use_ddp: bool
     use_dp: bool
     use_ddp2: bool
+    use_horovod: bool
     single_gpu: bool
     data_parallel_device_ids: ...
     model: LightningModule
@@ -429,6 +438,11 @@ def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode:
             output = model(*args)
             return output
 
+        # Horovod
+        if self.use_horovod and self.on_gpu:
+            batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())
+            args[0] = batch
+
         # single GPU data transfer
         if self.single_gpu:
             # for single GPU put inputs on gpu manually