[feat] Gossip/SlowMo (#378)

Add SlowMo Distributed Data Parallel for clusters with slow interconnects
Co-authored-by: Vinayak Tantia <tantia.vinayak1@gmail.com>

.gitignore

@@ -23,6 +23,7 @@ test-results/
 # Environments
 .env
 .venv
+.vscode
 env/
 venv/
 ENV/

CHANGELOG.md

@@ -14,6 +14,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
           and gradient memory to be sharded despite being needed from different layers due to
           weight sharing. [#836]
 - [MEVO]: a custom layer to help big vocab trainings. Experimental. Docs is still TBD. [#840]
+- SlowMoDistributedDataParallel[feature][experimental] - This is a distributed training wrapper which should be useful on clusters with slow network interconnects (eg Ethernet). This improves on performance as compared to Distributed Data Parallel in such clusters. [#378]
 
 ## [0.4.1] - 2021-09-17
 ### Fixed

docs/source/api/experimental/nn/slowmo_ddp.rst

+SlowMo Distributed Data Parallel
+================================
+
+.. autoclass:: fairscale.experimental.nn.data_parallel.SlowMoDistributedDataParallel
+    :members:
+    :undoc-members:
+    :exclude-members: eval, forward, load_state_dict, state_dict, train, training

docs/source/api/index.rst

@@ -12,3 +12,4 @@ API Reference
    nn/fsdp
    nn/checkpoint/checkpoint_activations
    experimental/nn/offload_model
+   experimental/nn/slowmo_ddp

docs/source/conf.py

@@ -92,6 +92,19 @@ master_doc = "index"
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = True
 
+# List of custom sections allowed. It is especially useful when the argument
+# list is very long for a constructor or function. This helps split the
+# arguments into different sections, helping us to understand the arguments
+# better.
+napoleon_custom_sections = [
+    ("SlowMo Parameters", "params_style"),
+    ("LocalSGD Parameters", "params_style"),
+    ("SGP Parameters", "params_style"),
+    ("Debugging Parameters", "params_style"),
+    ("Parameters for Advanced Users", "params_style"),
+]
+
+
 # -- Options for HTML output -------------------------------------------------
 
 

docs/source/deep_dive/slowmo_ddp.rst

+SlowMo Distributed Data Parallel
+================================
+
+Training neural networks in a distributed data-parallel manner results in non-linear scaling (slowdown) due to the time spent on communication
+between the different nodes (as well as, to a lesser extent though, synchronization between the different nodes). So, a distributed training run
+with 8 nodes is not 8x faster than a run with 1 node as we would expect it to be.
+
+SlowMo Distributed Data Parallel aims to solve this by replacing the typical exact allreduce between gradients with an approximate
+averaging of parameters. This approximate averaging reduces both the time spent on communication as well as the synchronization between different
+nodes.  It uses one of the following two algorithms (configurable) as a base algorithm for this purpose:
+
+* Local SGD (papers `#1 <https://arxiv.org/abs/1602.05629>`_ and `#2 <https://arxiv.org/abs/1705.09056>`_). This algorithm does an allreduce of the parameters every few iterations.
+
+* `Stochastic Gradient Push <https://arxiv.org/abs/1811.10792>`_ (SGP). This algorithm involves one-to-one communications between nodes.
+
+These base algorithms (LocalSGD and SGP), when used only by themselves, result in reduced model quality (measured as accuracy in a classification
+setting). The `SlowMo <https://arxiv.org/abs/1910.00643>`_ algorithm alleviates this issue by doing a slow momentum step, typically, every 48 iterations.
+
+The training process with SlowMo looks as follows:
+
+1. Compute the forward pass.
+
+2. Compute the backward pass.
+
+3. During the backward pass, using a backward hook, on each node, the gradients are synchronized using allreduce across the different GPUs on
+   that node.
+
+4. Perform the ``optimizer.step()`` to update parameters on each node with the gradients of that node.
+
+5. Approximately average the parameters using a base algorithm - one of LocalSGD or SGP (both are described above).
+
+6. Perform the slow momentum update step once every ``slowmo_frequency`` (typically 48) iterations. In this step, the parameters on different
+   nodes are (exactly) averaged, followed by a ``slowmo_optimizer.step()``. Note that this ``slowmo_optimizer`` is different from the original optimizer,
+   and it is done in a `Zero-1 <./oss_sdp_fsdp.html>`_ like manner to save memory.
+
+Best practices for using ``SlowMoDistributedDataParallel``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. SlowMo will be useful in deep learning workloads which run on more than 2 nodes in clusters with a slow interconnect, eg Ethernet.
+
+2. SlowMo should be useful in your workload if the following condition holds:
+
+   :math:`\textrm{time_taken_for_all_reduce_of_gradients} \times (1 - \frac{1}{\textrm{localsgd_frequency}} ) > \textrm{time_taken_for_backward_pass}`
+
+   Notes:
+
+   * In case you are using SGP as the base algorithm, the value of ``localsgd_frequency`` can be plugged in as 2.
+
+   * The formula above is a simplified version of:
+     :math:`\textrm{time_taken_for_all_reduce_of_gradients} > \textrm{time_taken_for_backward_pass} + \frac{\textrm{time_taken_for_all_reduce_of_gradients}}{\textrm{localsgd_frequency}}`
+     The left and right hand sides denote the total backward duration (combining the computation of gradients in the backward pass and the
+     communication cost) for DDP and SlowMo DDP, respectively. Since DDP overlaps the computation of gradients with their communication, it is
+     bottlenecked by the latter.  In contrast, there is an extra ``time_taken_for_backward_pass`` on the right hand side because we do not
+     overlap the backward pass with communication in the current implementation of SlowMo.
+
+   * In clusters with slower interconnect, ``time_taken_for_all_reduce_of_gradients`` will go up, leading to SlowMo being more useful. ``localsgd_frequency``
+     is also an important factor here. More details on varying that to affect performance are in tip 2 of
+     `Performance tips for SlowMoDistributedDataParallel`_.
+
+3. ``slowmo_momentum`` will need to be tuned for obtaining good model quality. A grid search across {0.0, 0.1, 0.2, 0.4, 0.6} should be good enough
+   for tuning. This ``slowmo_momentum`` value holds consistent across multiple runs with similar settings.  When the number of nodes used is increased,
+   however, a higher value of ``slow_momentum`` should be needed. More details about this can be found in the
+   `documentation <../api/experimental/nn/slowmo_ddp.html>`_.
+
+4. Adding SlowMo to existing Distributed Data Parallel code involves two steps, which can be found in the `tutorial <../tutorials/slowmo_ddp.html>`_.
+
+Performance tips for ``SlowMoDistributedDataParallel``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. ``nprocs_per_node`` should be set to the number of GPUs on a node (this number should be the same on each node). This allows the API
+   to exploit the fast interconnect between different GPUs on a node.
+
+2. Increasing the ``localsgd_frequency`` results in an increase in speed. However, it comes with a tradeoff of reducing the model quality.
+   We recommend keeping the ``localsgd_frequency`` at 3.
+
+3. ``slowmo_memory_efficient`` should typically be used (this is the default behavior). It reduces memory usage by sharding the additional
+   slow momentum optimizer's parameters in a `Zero-1`_ like manner.
+
+4. A call to ``model.zero_grad(set_to_none=True)`` should be made after ``optimizer.step()`` in order to save memory for the
+   ``model.perform_slowmo()`` step. More details about this can be found in the
+   `documentation for perform_slowmo() <../api/experimental/nn/slowmo_ddp.html#:~:text=net.perform_slowmo(optimizer)-,perform_slowmo,-(optimizer%3A%20torch.optim>`_.

docs/source/index.rst

@@ -42,6 +42,7 @@ modules and easy to use APIs.
    deep_dive/adascale
    deep_dive/pipeline_parallelism
    deep_dive/activation_checkpointing
+   deep_dive/slowmo_ddp
 
 |
 |
@@ -56,6 +57,7 @@ modules and easy to use APIs.
    tutorials/adascale
    tutorials/pipe
    tutorials/layer_memory_tracking
+   tutorials/slowmo_ddp
 
 |
 |

docs/source/tutorials/slowmo_ddp.rst

+Efficient Data Parallel Training with SlowMo Distributed Data Parallel
+======================================================================
+
+SlowMo Distributed Data Parallel reduces the communication between different
+nodes while performing data parallel training. It is mainly useful for use on
+clusters with low interconnect speeds between different nodes. When using
+SlowMo, the models on the different nodes are no longer kept in sync after each
+iteration, which leads to the optimization dynamics being affected. The end
+result is close to the results of Distributed Data Parallel, but is not exactly
+the same.
+
+If you have code that is setup to use Distributed Data Parallel, using SlowMo Distributed Data Parallel
+is simply replacing the DDP call with a call to
+``fairscale.experimental.nn.data_parallel.SlowMoDistributedDataParallel``, and adding a
+``model.perform_slowmo(optimizer)`` call after ``optimizer.step()`` -- preceded by
+``model.zero_grad(set_to_none=True)`` in order to reduce peak memory usage.
+The different points at which ``use_slowmo`` is used below help demonstrate these changes:
+
+.. code-block:: python
+
+
+    import torch
+    from fairscale.experimental.nn.data_parallel import SlowMoDistributedDataParallel as SlowMoDDP
+
+
+    def train(
+        rank: int,
+        world_size: int,
+        epochs: int,
+        use_slowmo: bool):
+
+        # process group init
+        dist_init(rank, world_size)
+
+        # Problem statement
+        model = MyAwesomeModel().to(rank)
+        if use_slowmo:
+            # Wrap the model into SlowMoDDP
+            model = SlowMoDDP(model, slowmo_momentum=0.5, nprocs_per_node=8)
+        else:
+            model = DDP(model, device_ids=[rank])
+
+        dataloader = MySuperFastDataloader()
+        loss_ln = MyVeryRelevantLoss()
+        optimizer = MyAmazingOptimizer()
+
+        # Any relevant training loop, with a line at the very end specific to SlowMoDDP, e.g.:
+        model.train()
+        for e in range(epochs):
+            for (data, target) in dataloader:
+                data, target = data.to(rank), target.to(rank)
+                # Train
+                outputs = model(data)
+                loss = loss_fn(outputs, target)
+                loss.backward()
+                optimizer.step()
+                model.zero_grad(set_to_none=use_slowmo)  # free memory for the perform_slowmo() call below
+                if use_slowmo:
+                    model.perform_slowmo(optimizer)
+
+In the example above, when using SlowMoDDP, we are reducing the total communication between
+nodes by 3 times as the default ``localsgd_frequency`` is set to 3.
+SlowMoDDP takes in ``slowmo_momentum`` as a parameter. This parameter may need to be tuned
+depending on your use case. It also takes in ``nproces_per_node`` which should be typically set
+to the number of GPUs on a node. Please look at the
+`documentation <../api/experimental/nn/slowmo_ddp.html>`_
+for more details on these parameters as well as other advanced settings of the SlowMo algorithm.

fairscale/experimental/nn/data_parallel/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .gossip import SlowMoBaseAlgorithm, SlowMoDistributedDataParallel  # noqa

fairscale/experimental/nn/data_parallel/gossip/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .distributed import SlowMoBaseAlgorithm, SlowMoDistributedDataParallel
+from .gossiper import PushPull, PushSum
+from .graph_manager import (
+    DynamicBipartiteExponentialGraph,
+    DynamicBipartiteLinearGraph,
+    DynamicDirectedExponentialGraph,
+    DynamicDirectedLinearGraph,
+    GraphManager,
+    NPeerDynamicDirectedExponentialGraph,
+    RingGraph,
+)
+from .mixing_manager import MixingManager, UniformMixing
+from .utils import communicate
+from .utils.cuda_metering import CudaEventRecorder

fairscale/experimental/nn/data_parallel/gossip/distributed.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Distributed Gossip Wrapper
+
+:description: Multi-Threaded Gossip Model Wrapper; designed for efficient
+              multi-peer training.
+"""
+
+from enum import Enum
+import functools
+import logging
+import os
+import sys
+import threading
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
+
+import torch
+from torch.autograd import Variable
+import torch.distributed as dist
+from torch.nn.modules import Module
+
+from .gossiper import Gossiper, PushPull, PushSum
+from .graph_manager import GraphManager
+from .graph_manager import NPeerDynamicDirectedExponentialGraph as NPDDEGraph
+from .mixing_manager import MixingManager, UniformMixing
+from .utils import (
+    MultiProcessAdapter,
+    communicate,
+    create_process_group,
+    flatten_tensors,
+    group_by_dtype,
+    make_logger,
+    unflatten_tensors,
+)
+from .utils.cuda_metering import EventRecorder, create_event_recorder
+
+HEARTBEAT_TIMEOUT = 300  # maximum time to wait for message (seconds)
+BROADCAST_BUCKET_SIZE = 10 * 1024 * 1024
+
+
+class SlowMoBaseAlgorithm(str, Enum):
+    LOCALSGD = "localsgd"
+    SGP = "sgp"
+
+
+class SlowMoDistributedDataParallel(Module):
+    """Wraps an arbitrary :class:`nn.Module <torch.nn.Module>` module and allows
+    it to be run on multiple GPUs (distributed) in a data parallel setting.
+
+    This container parallelizes the application of the given module by
+    splitting the input across the specified devices by chunking in the batch
+    dimension. The module is replicated on each machine and each device, and
+    each such replica handles a portion of the input. After the optimizer update,
+    it synchronizes the parameters on the different nodes using SlowMo
+    (https://arxiv.org/abs/1910.00643).
+
+    Please make sure to read the documentation for slowmo_memory_efficient parameter as
+    it contains a non-trivial trick in order to optimize our implementation.
+
+    Please refer to the documentation of ``torch.nn.parallel.DistributedDataParallel``
+    for other useful tips for using this container.
+
+    Parameters:
+        module (Module):
+            module to be parallelized
+        nprocs_per_node (int):
+            Number of processes per node (one per GPU). This needs to be specified for optimal accuracy and speed.
+            Syncing across GPUs in a node is extremely fast, which we utilize for performance optimization
+        broadcast_buffers (bool):
+            Flag that enables syncing (broadcasting) buffers (example - batchnorm buffers) of the module at beginning
+            of the ``forward`` function. Setting it to False would result in better performance due to less
+            communication on the network but might result in a reduced accuracy (default: ``True``)
+        slowmo_base_algorithm (SlowMoBaseAlgorithm):
+            The base algorithm to be used for approximately averaging the different parameters across nodes.  The base
+            algorithm is responsible for increasing the efficiency of this module. The base algorithm, combined with
+            SlowMo, results in significant speedups without accuracy loss. Either Stochastic Gradient Push
+            (SlowMoBaseAlgorithm.SGP) (https://arxiv.org/abs/1811.10792) or LocalSGD (SlowMoBaseAlgorithm.LOCALSGD)
+            (https://arxiv.org/abs/1808.07217) can be used here (default: SlowMoBaseAlgorithm.LOCALSGD)
+    SlowMo Parameters:
+        slowmo_momentum (float):
+            This specifies the value of slowmo momentum to be used (read https://arxiv.org/abs/1910.00643 for more
+            details). This parameter might need to be tuned and the optimal value varies according to the use case and
+            the number of nodes being run on. The optimal value typically increases with the number of nodes. On
+            training transfomers on the WMT 16 En-De dataset, we have found the optimal values to be 0 for less than 4
+            nodes, 0.2 for 4 nodes, 0.5 for 8 nodes and 0.6 for 16 nodes (default: 0.5)
+        slowmo_memory_efficient (bool):
+            If enabled, use a memory efficient implementation of SlowMo. The basic implementation of SlowMo occupies
+            extra memory equal to double the memory occupied by the model parameters. The memory efficient
+            implementation shards that memory across a certain number of shards which is specified as a parameter
+            below.
+            In addition, slowmo_memory_efficient leads to extra communication with throughput equivalent to an
+            allreduce, and performs an allreduce as a side-effect. In order to optimize the implementation, we skip
+            the typical allreduce when slowmo_base_algorithm is localsgd and the localsgd step and slowmo step occur
+            on the same iteration. Also, we skip the gossip step when slowmo_base_algorithm is sgp. We can skip these
+            because the memory-efficient slowmo step does an allreduce as a side effect. Due to this skipping, when
+            slowmo_base_algorithm is localsgd, we recommend setting slowmo_frequency to be a multiple of
+            localsgd_frequency.
+            We recommend setting this parameter to True when slowmo_base_algorithm is localsgd. In case of sgp, there
+            is a tradeoff between extra memory usage which is double the memory occupied by the parameters, and extra
+            time spent which is half the time taken up by an allreduce every slowmo_frequency iterations and we
+            suggest setting it to False (default: True)
+        slowmo_frequency (int):
+            This specifies how often (number of iterations) slow momentum is to be performed. We recommend keeping
+            slowmo_frequency as a multiple of localsgd_frequency. Please look at the documentation of
+            slowmo_memory_efficient for the reasoning (default: 48)
+        slowmo_lr (float):
+            This specifies the value of slowmo learning rate to be used (read https://arxiv.org/abs/1910.00643 for
+            more details). We do not recommend changing this (default: 1.0)
+        slowmo_num_shards (int):
+            The number of shards between which slow momentum parameters are distributed. This is only used when
+            memory_efficient is set to True.
+            The number of shards should scale with the number of parameters in the model. Increasing the number of
+            shards decreases the memory used per node for storing the slow momentum parameters. However, if the shard
+            size per node is too small, it results in a communication overhead (default: 32)
+    LocalSGD Parameters:
+        localsgd_frequency (int):
+            LocalSGD typically averages the parameters once every few iterations. This parameter specifices the
+            frequency of averaging.  We recommend keeping slowmo_frequency as a multiple of localsgd_frequency. Please
+            look at the documentation of slowmo_memory_efficient for the reasoning (default: 3)
+    SGP Parameters:
+        graph (Optional[GraphManager):
+            Graph to be used for gossip communication. This is used to specify the interaction graph between the
+            different nodes (default: None)
+        mixing (Optional[MixingManager]):
+            Mixing manager to be used for gossip communication. This is used to specify weights given to outgoing and
+            incoming messages (default: None)
+        push_sum (bool):
+            Whether to use PushSum or PushPull gossip (default: True)
+        overlap (bool):
+            Whether to use the overlap form of SGP. This feature is currently disabled until further testing is done
+            for its use (default: False)
+        synch_freq (int):
+            How often (number of iterations) to synchronize for overlap SGP. A value of 0 means to synchronize overlap
+            SGP every iteration (default: 0)
+        use_streams (bool):
+            Whether to use CUDA streams to speed up SGP overlap (default: True)
+        slowmo_sgp_average_params (bool):
+            Whether to completely average the parameters when slowmo is done instead of a partial averaging that
+            happens every iteration (default: False)
+    Debugging Parameters:
+        verbose (bool):
+            Prints various logs which are useful for debugging (default: False)
+        profile_mode (bool):
+            Prints the time taken by different parts of the code, which can help in finding bottlenecks (default: False)
+    Parameters for Advanced Users:
+        process_rank (Optional[int]):
+            Rank of the current process in the process group (default: None)
+        process_world_size (Optional[int]):
+            Size of the process group (default: None)
+        global_group (Optional[torch.distributed.ProcessGroup]):
+            Global process group initialized by init_process_group (default: None)
+        master_group (Optional[torch.distributed.ProcessGroup]):
+            Process group which only contains the master GPUs of each node (default: None)
+        local_node_group (Optional[torch.distributed.ProcessGroup]):
+            Process group which only contains the GPUs local to the current node (default: None)
+        comm_device: (Optional[torch.device]):
+            The torch.device on which torch tensors are to be placed before communication (default: None)
+
+    Example:
+        >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
+        >>> net = fairscale.data_parallel.SlowMoDistributedDataParallel(model, nprocs_per_node=8)
+        >>> loss = criterion(net(inputs), targets)
+        >>> loss.backward()
+        >>> optimizer.step()
+        >>> net.perform_slowmo(optimizer)
+    """
+
+    def __init__(
+        self,
+        module: torch.nn.Module,
+        nprocs_per_node: int,
+        broadcast_buffers: bool = True,
+        slowmo_base_algorithm: SlowMoBaseAlgorithm = SlowMoBaseAlgorithm.LOCALSGD,
+        # SlowMo Args
+        slowmo_momentum: float = 0.5,
+        slowmo_memory_efficient: bool = True,
+        slowmo_frequency: int = 48,
+        slowmo_lr: float = 1.0,
+        slowmo_num_shards: int = 32,
+        # LocalSGD Args
+        localsgd_frequency: int = 3,
+        # SGP Args
+        graph: Optional[GraphManager] = None,
+        mixing: Optional[MixingManager] = None,
+        push_sum: bool = True,
+        overlap: bool = False,
+        synch_freq: int = 0,
+        use_streams: bool = True,
+        slowmo_sgp_average_params: bool = False,
+        # Debugging Args
+        verbose: bool = False,
+        profile_mode: bool = False,
+        # Args for advanced users (these are automatically handled otherwise)
+        process_rank: Optional[int] = None,
+        process_world_size: Optional[int] = None,
+        global_group: Optional[torch.distributed.ProcessGroup] = None,
+        master_group: Optional[torch.distributed.ProcessGroup] = None,
+        local_node_group: Optional[torch.distributed.ProcessGroup] = None,
+        comm_device: Optional[torch.device] = None,
+    ) -> None:
+        super(SlowMoDistributedDataParallel, self).__init__()
+
+        # NCCL_BLOCKING_WAIT causes issues with using multiple process groups
+        assert os.environ.get("NCCL_BLOCKING_WAIT", "0") == "0"
+
+        assert nprocs_per_node >= 1
+        self.nprocs_per_node = nprocs_per_node
+
+        if process_world_size is None or process_rank is None:
+            assert dist.is_initialized()
+            process_rank = dist.get_rank()
+            process_world_size = dist.get_world_size()
+        assert process_world_size is not None and process_rank is not None
+        self.process_rank = process_rank
+        self.process_world_size = process_world_size
+
+        self._initialize_logger(verbose, self.process_rank)
+
+        # The logical prefix in the following variables denotes the variable value if nprocs_per_node processes
+        # were treated as one process and then the following variables were calculated for the resulting process
+        # group. This is how they are being treated for optimization purposes because intra-node communication is
+        # very efficient with NVLink.
+        logical_rank, logical_world_size = self._maybe_create_process_groups(
+            self.process_rank, self.process_world_size, nprocs_per_node, global_group, master_group, local_node_group
+        )
+        self.logical_rank = logical_rank
+        self.logical_world_size = logical_world_size
+
+        self.module = module
+        self.broadcast_buffers = broadcast_buffers
+        first_param_dtype = next(self.module.parameters()).dtype
+
+        # prepare local intra-node all-reduce objects
+        self.broadcast_bucket_size = BROADCAST_BUCKET_SIZE  # bytes
+        self.module_buffers = list(self.module.buffers())
+
+        # choose communication device based on backend
+        if comm_device is None:
+            cpu_comm = dist.get_backend() == "gloo"
+            comm_device = torch.device("cpu") if cpu_comm else torch.device("cuda")
+        self._cpu_comm = comm_device.type == "cpu"
+
+        # distributed backend config
+        self.dist_config = {
+            "verbose": verbose,
+            "comm_device": comm_device,
+            "logical_rank": logical_rank,
+            "process_rank": self.process_rank,
+            "logical_world_size": logical_world_size,
+            "cpu_comm": self._cpu_comm,
+        }
+        self.profile_mode = profile_mode
+        self.num_updates = 0
+        self.portion_start: Optional[int] = None
+
+        # slowmo being set to False is equivalent to slowmo_lr being set to 1 and slowmo_momentum being set to 0
+        # This condition is ensuring the values are safe to use even when slowmo is disabled
+        self.slowmo = slowmo_lr != 1 or slowmo_momentum != 0
+
+        self.slowmo_lr = slowmo_lr if self.slowmo else 1
+        self.slowmo_momentum = slowmo_momentum if self.slowmo else 0
+
+        self.slowmo_frequency = slowmo_frequency
+        self.slowmo_sgp_average_params = slowmo_sgp_average_params
+
+        self.localsgd = slowmo_base_algorithm == SlowMoBaseAlgorithm.LOCALSGD
+        self.sgp = slowmo_base_algorithm == SlowMoBaseAlgorithm.SGP
+
+        self.localsgd_frequency = localsgd_frequency
+        self.ef1: Optional[List[torch.Tensor]] = None
+        self.global_momentum_buffers_initialized = False
+
+        if self.master_group is None:
+            assert self.localsgd or self.sgp
+            self.localsgd = self.sgp = False
+            self.logger.warning("Disabling LocalSGD and SGP since a local allreduce will suffice")
+
+        if self.slowmo and not self.localsgd and not self.sgp:
+            self.logger.warning("SlowMo is being used without LocalSGD and SGP")
+
+        self.slowmo_memory_efficient = slowmo_memory_efficient
+        self.slowmo_num_shards = min(self.process_world_size, slowmo_num_shards) if self.slowmo_memory_efficient else 1
+        self.is_current_node_a_slowmo_shard = (
+            self.process_rank < self.slowmo_num_shards if self.slowmo_memory_efficient else True
+        )
+
+        self.nprocs_per_node_device = torch.tensor([self.nprocs_per_node], device=comm_device, dtype=first_param_dtype)
+
+        if self.sgp:
+            self._sgp_init(
+                module=module,
+                first_param_dtype=first_param_dtype,
+                logical_rank=logical_rank,
+                logical_world_size=logical_world_size,
+                comm_device=comm_device,
+                graph=graph,
+                mixing=mixing,
+                push_sum=push_sum,
+                overlap=overlap,
+                synch_freq=synch_freq,
+                use_streams=use_streams,
+                slowmo_sgp_average_params=slowmo_sgp_average_params,
+            )
+
+        # register ps/grad-reduction hooks
+        self._register_hooks()
+
+        self.logger.debug("Initialization of SlowMoDistributedDataParallel complete")
+
+    def _initialize_logger(self, verbose: bool, process_rank: int) -> None:
+        """ Initializes the logger """
+        self.logger = logging.getLogger(__name__)
+        if verbose:
+            self.logger.setLevel(logging.DEBUG)
+
+        # Only create an adapter if debug logging is enabled to avoid additional overhead
+        if self.logger.isEnabledFor(logging.DEBUG):
+            # Set custom adapter on top of logger
+            self.logger = cast(logging.Logger, MultiProcessAdapter(self.logger, {"process_num": process_rank}))
+
+    def _maybe_create_process_groups(
+        self,
+        process_rank: int,
+        process_world_size: int,
+        nprocs_per_node: int,
+        global_group: Optional[torch.distributed.ProcessGroup],
+        master_group: Optional[torch.distributed.ProcessGroup],
+        local_node_group: Optional[torch.distributed.ProcessGroup],
+    ) -> Tuple[int, int]:
+        """ Creates the process groups required for the SlowMo implementation """
+
+        self.local_rank = process_rank % self.nprocs_per_node
+        assert (
+            process_world_size % self.nprocs_per_node == 0
+        )  # total world size must be a multiple of `nprocs_per_node`
+        logical_world_size = process_world_size // self.nprocs_per_node
+        logical_rank = process_rank // self.nprocs_per_node
+
+        self._maybe_initialize_global_group(global_group, process_world_size)
+        self._maybe_initialize_local_node_group(local_node_group, process_rank, logical_world_size)
+        self._maybe_initialize_master_group(master_group, process_rank, process_world_size, nprocs_per_node)
+
+        self.logger.debug("Initialization of all process groups complete")
+        return logical_rank, logical_world_size
+
+    def _maybe_initialize_global_group(
+        self, global_group: Optional[torch.distributed.ProcessGroup], process_world_size: int
+    ) -> None:
+        if global_group is None:
+            all_processes = list(range(process_world_size))
+            self.global_group = create_process_group(all_processes)
+            self.logger.debug("Initialization of global group complete")
+        else:
+            self.global_group = global_group
+        self.logger.debug("Global group set")
+        self.process_group = self.global_group
+
+    def _maybe_initialize_master_group(
+        self,
+        master_group: Optional[torch.distributed.ProcessGroup],
+        process_rank: int,
+        process_world_size: int,
+        nprocs_per_node: int,
+    ) -> None:
+        if master_group is not None:
+            self.master_group: Optional[torch.distributed.ProcessGroup] = master_group
+            return
+
+        if self.nprocs_per_node > 1:
+            self.logger.debug("Initializing master process group")
+            master_nodes = [i for i in range(process_world_size) if i % nprocs_per_node == 0]
+            self.master_group = create_process_group(master_nodes) if len(master_nodes) > 1 else None
+            if self.master_group is not None and process_rank in master_nodes:
+                self.logger.debug("Initialization of master group complete")
+        else:
+            self.master_group = self.global_group
+
+    def _maybe_initialize_local_node_group(
+        self, local_node_group: Optional[torch.distributed.ProcessGroup], process_rank: int, logical_world_size: int
+    ) -> None:
+        if self.nprocs_per_node == 1:
+            self.local_node_group = None
+            return
+
+        if local_node_group is not None:
+            self.local_node_group = local_node_group
+            return
+
+        self.logger.debug("Initializing local process groups")
+        for node in range(logical_world_size):
+            node_processes_ranks = list(range(node * self.nprocs_per_node, (node + 1) * self.nprocs_per_node,))
+            # Process group to communicate between processes on this machine
+            new_local_group = create_process_group(node_processes_ranks)
+            if process_rank in node_processes_ranks:
+                self.local_node_group = new_local_group
+        assert self.local_node_group is not None
+        self.logger.debug("Initialization of local groups complete")
+
+    def forward(self, *inputs: Any, **kwargs: Any) -> Union[torch.Tensor, List[torch.Tensor]]:
+        """ Forward pass performed in parallel across all devices on node """
+        return self.module(*inputs, **kwargs)
+
+    def _sync_params(self) -> None:
+        """ Synchronize parameters across devices (intra-node) """
+        if self.local_node_group is None:
+            return
+
+        # intra-node parameter sync
+        params = cast(List[torch.Tensor], list(self.module.parameters()))
+        communication_op = functools.partial(
+            dist.broadcast, src=self.logical_rank * self.nprocs_per_node, group=self.local_node_group,
+        )
+        communicate(params, communication_op)
+        self.logger.debug("Intra-node param sync complete")
+
+    def _sync_buffers(self) -> None:
+        """ Synchronize buffers across nodes """
+        # module buffer sync
+        if self.broadcast_buffers and len(self.module_buffers) > 0:
+            # Synchronize buffers across processes.
+            # The process with rank 0 is considered the authoritative copy.
+            self._distributed_broadcast_coalesced(self.process_group, self.module_buffers, self.broadcast_bucket_size)
+        self.logger.debug("Intra-node buffer sync complete")
+
+    def _distributed_broadcast_coalesced(
+        self, process_group: torch.distributed.ProcessGroup, tensors: List[torch.Tensor], buffer_size: int
+    ) -> None:
+        dist._broadcast_coalesced(process_group, tensors, buffer_size)
+
+    def _create_event_recorder(self, event_name: str) -> EventRecorder:
+        """ Creates an cuda event recorder which helps in profiling """
+        return create_event_recorder(event_name, dummy=not self.profile_mode)
+
+    def _fp16_fp32_iterator(
+        self, optimizer: torch.optim.Optimizer, fp32_params: Optional[torch.Tensor]
+    ) -> Iterable[Tuple[torch.Tensor, torch.Tensor]]:
+        """ Iterator for those fp16 parameters which have a fp32 copy """
+        # Handle apex fp16 optimizer
+        if hasattr(optimizer, "_amp_stash") and hasattr(optimizer._amp_stash, "fp16_groups"):
+            for p_fp16_group, p_fp32_group in zip(
+                optimizer._amp_stash.fp16_groups, optimizer._amp_stash.fp32_from_fp16_groups,
+            ):
+                for p_fp16, p_fp32 in zip(p_fp16_group, p_fp32_group):
+                    yield p_fp16, p_fp32
+
+        # Handle fairseq fp16 optimizer
+        elif fp32_params is not None:
+            if isinstance(fp32_params, dict):
+                fp32_params_list = list(fp32_params.values())
+                assert len(fp32_params_list) == 1
+                fp32_params = fp32_params_list[0]
+
+            if isinstance(fp32_params, list):
+                for p, fp32_param in zip(self.parameters(), fp32_params):
+                    yield p.view(-1), fp32_param
+            else:
+                offset = 0
+                for p in self.parameters():
+                    yield p.view(-1), fp32_params[offset : offset + p.numel()]
+                    offset += p.numel()
+
+    def _should_perform_slowmo(self) -> bool:
+        return self.slowmo and (self.num_updates + 1) % self.slowmo_frequency == 0
+
+    def _should_perform_localsgd(self) -> bool:
+        return self.localsgd and (self.num_updates + 1) % self.localsgd_frequency == 0
+
+    def _skip_averaging_memory_efficient_slowmo(self) -> bool:
+        return self.slowmo_memory_efficient and self._should_perform_slowmo()
+
+    def _should_perform_sgp_common(self) -> bool:
+        return self.sgp and not self.overlap and not self._skip_averaging_memory_efficient_slowmo()
+
+    def _should_perform_sgp(self) -> bool:
+        return self._should_perform_sgp_common() and not self.overlap
+
+    def _should_perform_sgp_overlap(self) -> bool:
+        return self._should_perform_sgp_common() and self.overlap
+
+    def _should_use_error_feedback(self, fp16_fp32_list: List[Tuple[torch.Tensor, torch.Tensor]]) -> bool:
+        return bool(fp16_fp32_list) and (self._should_perform_sgp() or self._should_allreduce_params())
+
+    def _should_allreduce_params(self) -> bool:
+        # We do not all-reduce parameters with local SGD if a slow momentum step is
+        # performed, since this step contains a reduce operation already. Note that this
+        # also means there is no error feedback correction in that case: it is not needed
+        # since communication within the slow momentum step happens in fp32.
+        return (self.sgp and self._should_perform_slowmo() and self.slowmo_sgp_average_params) or (
+            self._should_perform_localsgd() and not self._skip_averaging_memory_efficient_slowmo()
+        )
+
+    def _maybe_pre_communicate_error_feedback(self, fp16_fp32_list: List[Tuple[torch.Tensor, torch.Tensor]]) -> None:
+        ef_rec = self._create_event_recorder("Error feedback")
+        if self._should_use_error_feedback(fp16_fp32_list):
+            with torch.no_grad():
+                for p_fp16, p_fp32 in fp16_fp32_list:
+                    if self._should_allreduce_params():
+                        # This division and multiplication with the same number is done
+                        # to ensure that we do not lose bits of information when we divide
+                        # before the all_reduce. In order to preserve these bits in an
+                        # error feedback (https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1050.5040&rep=rep1&type=pdf)
+                        # like manner, we are forcing the bits to be lost
+                        # initially, and storing the lost information in error feedback
+                        p_fp16.div_(self.logical_world_size)
+                        p_fp16.mul_(self.logical_world_size)
+                    p_fp32 -= p_fp16.float()
+
+                if self.ef1 is not None:
+                    for idx, (_, p_fp32) in enumerate(fp16_fp32_list):
+                        p_fp32 += self.ef1[idx]
+                        p_fp32.div_(2)
+        ef_rec.stop()
+        self.logger.debug("Error feedback completed")
+
+    def _maybe_post_communicate_error_feedback(self, fp16_fp32_list: List[Tuple[torch.Tensor, torch.Tensor]]) -> None:
+        ef_unroll_rec = self._create_event_recorder("Sync and error feedback unroll rec")
+        if self._should_use_error_feedback(fp16_fp32_list):
+            # Error Feedback Reversal
+            with torch.no_grad():
+                for p, p_fp32 in fp16_fp32_list:
+                    p_fp32 += p.float()
+        ef_unroll_rec.stop()
+        self.logger.debug("Error feedback unroll completed")
+
+    def _maybe_perform_sgp(self) -> None:
+        sgp_rec = self._create_event_recorder("SGP")
+        if self._should_perform_sgp():
+            if not self._should_allreduce_params():
+                self._sgp_transfer_params()
+                self._sgp_query_gossip_queue()
+                torch.cuda.synchronize()
+            self.logger.debug("SGP completed")
+        sgp_rec.stop()
+
+    def _maybe_allreduce(self) -> None:
+        localsgd_rec = self._create_event_recorder("Localsgd communication time")
+        if self._should_allreduce_params():
+            communication_op = functools.partial(dist.all_reduce, group=self.master_group)
+            params = cast(List[torch.Tensor], list(self.parameters()))
+            with torch.no_grad():
+                for p in params:
+                    p.div_(self.logical_world_size)
+            self.logger.debug("Params normalized before localsgd step")
+
+            # Commenting this out as it may cause an overhead. Can be uncommented if needed
+            # synch_rec = self._create_event_recorder("Synchronization time for localsgd")
+            # dist.barrier()
+            # synch_rec.stop()
+            # self.logger.debug("Barrier completed before localsgd step")
+
+            communicate(params, communication_op, self.logger)
+            torch.cuda.synchronize()
+            self.logger.debug("Allreduce completed")
+        localsgd_rec.stop()
+
+    def _maybe_sync_locally(self) -> None:
+        if self._should_perform_sgp() or self._should_allreduce_params():
+            self._sync_params()
+            torch.cuda.synchronize()
+
+    def _maybe_perform_slowmo(self, optimizer: torch.optim.Optimizer) -> None:
+        slowmo_rec = self._create_event_recorder("Slowmo")
+        if self._should_perform_slowmo():
+            self._global_momentum_step(optimizer)
+        slowmo_rec.stop()
+        self.logger.debug("Global momentum step completed")
+
+    def _maybe_copy_back_fp32_parameters(self, fp16_fp32_list: List[Tuple[torch.Tensor, torch.Tensor]]) -> None:
+        ef_copy_rec = self._create_event_recorder("Error feedback copy back")
+        if (
+            self._should_perform_sgp() or self._should_allreduce_params() or self._should_perform_slowmo()
+        ) and fp16_fp32_list:
+            with torch.no_grad():
+                for idx, (p_fp16, p_fp32) in enumerate(fp16_fp32_list):
+                    p_fp16.copy_(p_fp32)
+        ef_copy_rec.stop()
+        self.logger.debug("Error feedback copy-back completed")
+
+    def _maybe_sgp_overlap_pre_communicate_error_feedback(
+        self, fp16_fp32_list: List[Tuple[torch.Tensor, torch.Tensor]]
+    ) -> None:
+        if self._should_perform_sgp_overlap() and fp16_fp32_list:
+            # Initialize error feedback for SGP-overlap
+            if self.ef1 is None:
+                self.ef1 = [p_fp32.clone().detach_() for _, p_fp32 in fp16_fp32_list]
+
+            with torch.no_grad():
+                assert self.ef1 is not None
+                for ef1, (p_fp16, p_fp32) in zip(self.ef1, fp16_fp32_list):
+                    ef1.copy_(p_fp32 - p_fp16.float())
+
+    def perform_slowmo(self, optimizer: torch.optim.Optimizer, fp32_params: Optional[torch.Tensor] = None) -> None:
+        """ This is to be called after optimizer.step(). It performs the approximate averaging using
+        the base algorithm (SGP/ LocalSGD) and the slow momentum step. Since LocalSGD and the slow
+        momentum step are not performed every iteration, it only performs those when needed.
+
+        It is recommended to call ``model.zero_grad(set_to_none=True)`` just before calling this function. This 
+        is because ``model.zero_grad(set_to_none=True)`` frees up the memory occupied by the gradients, some of which 
+        may be reused by this function.
+
+        Args:
+            optimizer (torch.optim.Optimizer): The optimizer being used for training the model
+            fp32_params (Optional[torch.Tensor]): To be used when performing fp16 training. Needs to be
+                        set to the fp16 copy of the parameters (default: None)
+        """
+        # Done here in case the global momentum buffers have not been initialized by the caller.
+        # In an ideal implementation, this would be called by the caller. We do it here instead of
+        # waiting for it to happen in the global_momentum step function so that we store a copy of
+        # the version of the parameters at iteration 0 and can use them for a slow momentum step later.
+        if not self.global_momentum_buffers_initialized:
+            self._init_global_momentum_buffers(optimizer)
+
+        fp16_fp32_list = list(self._fp16_fp32_iterator(optimizer, fp32_params))
+        self.logger.debug("Created a list of fp16 and fp32 corresponding parameters")
+
+        self.logger.debug(
+            "Booleans set. Values - self._should_perform_slowmo()=%r, self._should_perform_localsgd()=%r, self._should_allreduce_params()=%r",
+            self._should_perform_slowmo(),
+            self._should_perform_localsgd(),
+            self._should_allreduce_params(),
+        )
+        self.logger.debug("Step number(0-indexed)=%d", self.num_updates)
+
+        if (
+            self.num_updates == 0
+            and fp32_params is None
+            and not hasattr(optimizer, "_amp_stash")
+            and any(p.dtype == torch.float16 for p in self.parameters())
+        ):
+            self.logger.warning("WARNING: please set fp32_params in perform_slowmo() in order to avoid accuracy loss")
+
+        self._maybe_pre_communicate_error_feedback(fp16_fp32_list)
+        self._maybe_perform_sgp()
+        self._maybe_allreduce()
+        self._maybe_sync_locally()
+        self._maybe_post_communicate_error_feedback(fp16_fp32_list)
+        self._maybe_perform_slowmo(optimizer)
+        self._maybe_copy_back_fp32_parameters(fp16_fp32_list)
+        self._maybe_sgp_overlap_pre_communicate_error_feedback(fp16_fp32_list)
+
+        self.num_updates += 1
+
+    def _init_global_momentum_buffers(self, optimizer: torch.optim.Optimizer) -> None:
+        """ Initializes the slow momentum buffers """
+        self.global_momentum_buffers_initialized = True
+
+        if not self.slowmo:
+            return
+
+        total_elements = 0
+        params_dtype = None
+        for group in optimizer.param_groups:
+            for p in group["params"]:
+                total_elements += p.numel()
+
+                # Assert that all parameters have the same device and dtype
+                if params_dtype is None:
+                    params_dtype, params_device = p.dtype, p.device
+                # Check that dtype is fp32 since slow mometum is to be performed in fp32
+                assert p.dtype == params_dtype == torch.float32
+                assert p.device == params_device
+
+        self.world_portion_length = (total_elements + self.slowmo_num_shards - 1) // self.slowmo_num_shards
+
+        if not self.is_current_node_a_slowmo_shard:
+            return
+
+        self.portion_start = self.process_rank * self.world_portion_length if self.slowmo_memory_efficient else 0
+        self.portion_end = (
+            min((self.process_rank + 1) * self.world_portion_length, total_elements)
+            if self.slowmo_memory_efficient
+            else total_elements
+        )
+
+        self.old_params = torch.empty(self.world_portion_length, dtype=params_dtype).to(params_device).detach()
+
+        # copy params to old_params to initialize old_params
+        offset = 0
+        for group in optimizer.param_groups:
+            for p in group["params"]:
+                numel = p.numel()
+
+                if offset + numel > self.portion_start and offset < self.portion_end:
+
+                    # start and end for each
+                    overall_start = max(self.portion_start, offset)
+                    overall_end = min(self.portion_end, offset + numel)
+
+                    p_start = overall_start - offset
+                    p_end = overall_end - offset
+
+                    buffer_start = overall_start - self.portion_start
+                    buffer_end = overall_end - self.portion_start
+
+                    # let's see size of p and split based on that
+                    current_p = p.view(-1)[p_start:p_end]
+                    current_p_old = self.old_params[buffer_start:buffer_end]
+
+                    current_p_old.copy_(current_p)
+
+                offset += numel
+
+        self.global_momentum_buffer = torch.zeros_like(self.old_params).detach()
+
+    def _distributed_comm(self, optimizer: torch.optim.Optimizer, mode: str) -> None:
+        """ Performs the communication needed for the efficient SlowMo implementation """
+        offset = 0
+        slowmo_comm_lists: List[List[torch.Tensor]] = [[] for _ in range(self.slowmo_num_shards)]
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                # aggregate different parts of p in required node
+                for p in group["params"]:
+                    numel = p.numel()
+
+                    # gather has a reduce operation so division by world size is needed
+                    if mode == "gather":
+                        p /= self.process_world_size
+
+                    current_start = offset
+                    while current_start < offset + numel:
+                        main_node = current_start // self.world_portion_length
+
+                        main_node_end = (main_node + 1) * self.world_portion_length
+                        current_end = min(offset + numel, main_node_end)
+
+                        p_start = current_start - offset
+                        p_end = current_end - offset
+
+                        slowmo_comm_lists[main_node].append(p.view(-1)[p_start:p_end])
+
+                        current_start = current_end
+                    offset += numel
+
+            for slowmo_rank, slowmo_comm_list in enumerate(slowmo_comm_lists):
+                if mode == "gather":
+                    communication_op = functools.partial(dist.reduce, dst=slowmo_rank)
+                elif mode == "scatter":
+                    communication_op = functools.partial(dist.broadcast, src=slowmo_rank)
+                communicate(slowmo_comm_list, communication_op)
+
+    def _global_momentum_step(self, optimizer: torch.optim.Optimizer) -> None:
+        """ Performs the slow momentum step """
+        if not self.slowmo:
+            return
+
+        if not self.global_momentum_buffers_initialized:
+            self._init_global_momentum_buffers(optimizer)
+
+        if self.slowmo_memory_efficient:
+            self._distributed_comm(optimizer, mode="gather")
+
+        if self.is_current_node_a_slowmo_shard:
+            self._perform_local_optimization(optimizer)
+
+        if self.slowmo_memory_efficient:
+            self._distributed_comm(optimizer, mode="scatter")
+
+    def _perform_local_optimization(self, optimizer: torch.optim.Optimizer) -> None:
+        """ Performs the slow momentum on the local shard """
+        assert self.portion_start is not None
+
+        with torch.no_grad():
+            offset = 0
+            for group in optimizer.param_groups:
+                # perform local slowmo for p
+                for p in group["params"]:
+                    numel = p.numel()
+
+                    if offset + numel > self.portion_start and offset < self.portion_end:
+
+                        # start and end for each
+                        overall_start = max(self.portion_start, offset)
+                        overall_end = min(self.portion_end, offset + numel)
+
+                        p_start = overall_start - offset
+                        p_end = overall_end - offset
+
+                        buffer_start = overall_start - self.portion_start
+                        buffer_end = overall_end - self.portion_start
+
+                        # let's see size of p and split based on that
+                        current_p = p.view(-1)[p_start:p_end]
+                        current_p_gmb = self.global_momentum_buffer[buffer_start:buffer_end]
+                        current_p_old = self.old_params[buffer_start:buffer_end]
+
+                        current_p_gmb.mul_(self.slowmo_momentum).sub_(current_p, alpha=1 / group["lr"]).add_(
+                            current_p_old, alpha=1 / group["lr"]
+                        )
+                        current_p_old.add_(current_p_gmb, alpha=-group["lr"] * self.slowmo_lr)  # type: ignore
+                        current_p.copy_(current_p_old)
+
+                    offset += numel
+
+    def _register_hooks(self) -> None:
+        """
+        Registers push-sum de-bias/bias hooks in pre-forward/post-backward
+        passes in all leaf modules
+        """
+        self.register_forward_pre_hook(self.__make_forward_pre_hook())
+        self.register_backward_hook(self.__make_backward_hook())
+
+    def __make_backward_hook(self) -> Callable[..., None]:
+        self.logger.debug("making backward hook")
+
+        def hook(*unused: Any) -> None:
+            # reduce gradients across devices on a single machine
+            if self.local_node_group is not None:
+                grads = []
+                for p in self.module.parameters():
+                    if not p.requires_grad or p.grad is None:
+                        continue
+                    p.grad.div_(self.nprocs_per_node)
+                    grads.append(p.grad)
+                self.logger.debug("Gradients ready for syncing")
+
+                communication_op = functools.partial(dist.all_reduce, group=self.local_node_group)
+                communicate(grads, communication_op, self.logger)
+                self.logger.debug("Gradient sync during backward pass in local_group complete")
+
+            if self.sgp:
+                # convert model back to ps-numerator
+                self._sgp_ps_numerator()
+
+                # gossip during training (not inference)
+                if self.gossip_enable and self.overlap and not self._skip_averaging_memory_efficient_slowmo():
+                    self._sgp_query_gossip_queue()
+
+        def queue_hook(*unused: Any) -> None:
+            Variable._execution_engine.queue_callback(hook)
+
+        return queue_hook
+
+    def __make_forward_pre_hook(self) -> Callable[..., None]:
+        self.logger.debug("making forward pre-hook")
+
+        def hook(*unused: Any) -> None:
+            """ Query gossip queue and de-bias during forward pass """
+            # sync buffers before the forward pass
+            self._sync_buffers()
+
+            # gossip during training (not inference)
+            if self.sgp:
+                if self.gossip_enable and self.overlap and not self._skip_averaging_memory_efficient_slowmo():
+                    self._sgp_transfer_params()
+
+                # convert model to de-biased estimate
+                self._sgp_unbias()
+
+        return hook
+
+    # SGP related functions
+
+    def _sgp_init(
+        self,
+        module: torch.nn.Module,
+        first_param_dtype: torch.dtype,
+        logical_rank: int,
+        logical_world_size: int,
+        comm_device: Optional[torch.device] = None,
+        graph: Optional[GraphManager] = None,
+        mixing: Optional[MixingManager] = None,
+        push_sum: bool = True,
+        overlap: bool = False,
+        synch_freq: int = 0,
+        use_streams: bool = True,
+        slowmo_sgp_average_params: bool = False,
+    ) -> None:
+        """ Perform initialization for Stochastic Gradient Push base algorithm """
+
+        if graph is None:
+            graph = NPDDEGraph(logical_rank, logical_world_size, self.nprocs_per_node, self.local_rank)
+
+        if mixing is None:
+            mixing = UniformMixing(graph, comm_device)
+
+        self.dist_config.update({"graph": graph, "mixing": mixing, "push_sum": push_sum})
+
+        self.overlap = overlap
+        assert not self.overlap  # currently disabled, see docstring
+
+        self.synch_freq = synch_freq
+        self.asynch = synch_freq > 0
+
+        # push-sum weight=1.0 ==> distributed averaging
+        self.ps_weight = torch.ones(1, device=comm_device, dtype=first_param_dtype)
+        self.is_sgp_ps_numerator = False
+        self.gossip_enable = True
+        self.gossiping = False
+        self.params_mixed = True
+        self.gossip_ps_factor = torch.zeros(1, device=comm_device, dtype=first_param_dtype)
+        self.gossip_ps_weight = self.ps_weight.clone()
+        self.gossip_params = []
+        self.gossip_device_buffer = []
+        for p in module.parameters():
+            cp = cast(torch.nn.Parameter, p.clone().detach_())
+            cp = cast(torch.nn.Parameter, cp.cpu().pin_memory() if self._cpu_comm else cp.cuda())
+            self.gossip_params.append(cp)
+            self.gossip_device_buffer.append(cp)
+
+        # prepare gossip process control objects
+        self.gossip_lock = threading.Lock()
+        self.gossip_flag = threading.Event()
+        self.train_flag = threading.Event()
+
+        if cast(torch.device, self.dist_config["comm_device"]).type != "cpu" and use_streams:
+            self.gossip_stream = torch.cuda.Stream()
+        else:
+            self.gossip_stream = torch.cuda.current_stream()
+
+        if self.process_rank % self.nprocs_per_node == 0:
+            self.gossip_thread = threading.Thread(
+                target=SlowMoDistributedDataParallel._sgp_gossip_target,
+                args=(
+                    self.dist_config,
+                    self.gossip_flag,
+                    self.train_flag,
+                    self.gossip_lock,
+                    self.gossip_params,
+                    self.gossip_device_buffer,
+                    self.gossip_ps_weight,
+                    self.gossip_ps_factor,
+                    self.gossip_stream,
+                ),
+            )
+            self.gossip_thread.daemon = True
+            self.gossip_thread.name = "Gossip-Thread"
+            self.gossip_thread.start()
+        else:
+            self.gossip_flag.set()
+
+        # wait for thread to complete initialization
+        self.gossip_flag.wait()
+        self.gossip_flag.clear()
+
+        # lazy mixing avoids additional bias/de-bias steps
+        self.lazy_mixing = not self.asynch and cast(MixingManager, self.dist_config["mixing"]).is_regular()
+        self.lazy_ps_factor = self.gossip_ps_factor.clone()
+        self.logger.debug("lazy mixing: %r", self.lazy_mixing)
+
+    def state_dict(self) -> Dict[str, Union[torch.Tensor, bool]]:  # type: ignore
+        state_dict = super(SlowMoDistributedDataParallel, self).state_dict()
+        if self.sgp:
+            state_dict["ps_weight"] = self.ps_weight.cpu()
+            state_dict["is_sgp_ps_numerator"] = self.is_sgp_ps_numerator  # type: ignore
+        return state_dict  # type: ignore
+
+    def load_state_dict(self, state_dict: Dict[str, Union[torch.Tensor, bool]]) -> None:  # type: ignore
+        if self.sgp:
+            assert isinstance(state_dict, dict)
+            self.ps_weight = cast(torch.Tensor, state_dict.pop("ps_weight")).to(
+                device=cast(torch.device, self.dist_config["comm_device"])
+            )
+            self.is_sgp_ps_numerator = cast(bool, state_dict.pop("is_sgp_ps_numerator"))
+
+        super(SlowMoDistributedDataParallel, self).load_state_dict(cast(Dict[str, torch.Tensor], state_dict))
+
+    def _sgp_ps_numerator(self) -> None:
+        """ Convert model params to ps-numerator """
+        if not self.is_sgp_ps_numerator:
+            if not self.lazy_mixing:
+                ps_weight = self.ps_weight
+                with torch.no_grad():
+                    for p in self.module.parameters():
+                        p.mul_(cast(torch.Tensor, ps_weight.type(p.dtype)))
+            self.is_sgp_ps_numerator = True
+
+    def _sgp_unbias(self) -> None:
+        """ Convert model params to de-biased estimate """
+        if self.is_sgp_ps_numerator:
+            if not self.lazy_mixing:
+                ps_weight = self.ps_weight
+                with torch.no_grad():
+                    for p in self.module.parameters():
+                        p.div_(cast(torch.Tensor, ps_weight.type(p.dtype)))  # type: ignore
+            self.is_sgp_ps_numerator = False
+
+    def train(self, mode: bool = True) -> "SlowMoDistributedDataParallel":
+        super(SlowMoDistributedDataParallel, self).train(mode)
+        if self.sgp:
+            self.gossip_enable = True
+        return self
+
+    def eval(self) -> "SlowMoDistributedDataParallel":
+        super(SlowMoDistributedDataParallel, self).eval()
+        if self.sgp:
+            self.gossip_enable = False
+            self._sgp_query_gossip_queue(non_blocking=self.asynch)
+        return self
+
+    def _sgp_query_gossip_queue(self, non_blocking: bool = False) -> bool:
+        """ Check gossip-queue for push-sum residuals and update model """
+        if not self.gossip_enable:
+            return False
+
+        self.logger.debug("querying gossip queue")
+
+        # no gossip happening right now so just return
+        if not self.gossiping:
+            if self.process_rank % self.nprocs_per_node == 0:
+                self.logger.warning("not gossiping right now")
+            return False
+
+        if not non_blocking and not self.gossip_flag.wait(timeout=HEARTBEAT_TIMEOUT):
+            raise RuntimeError("Gossip flag timeout")
+            sys.exit()  # HEARTBEAT monitor
+
+        # query gossip thread
+        if self.gossip_flag.is_set():
+            self.logger.debug("received gossip flag")
+
+            # atomic gossip was interrupted so try again
+            if self.gossip_ps_weight[0] == -1:
+                self.gossip_flag.clear()
+                self.params_mixed = True
+                self.gossiping = False
+                self._sgp_transfer_params(mix=False)
+                return False
+
+            self.lazy_ps_factor.copy_(self.gossip_ps_factor)
+
+            # convert model-params to ps numerators b4 adding residuals
+            self._sgp_ps_numerator()
+
+            # add residuals
+            self.ps_weight += self.gossip_ps_weight
+            if self.lazy_mixing:
+                self.ps_weight *= self.lazy_ps_factor
+            with torch.no_grad():
+                for p, r in zip(self.module.parameters(), self.gossip_device_buffer):
+                    p.add_(r)  # type: ignore
+                    if self.lazy_mixing:
+                        p.mul_(cast(torch.Tensor, self.lazy_ps_factor.type(p.dtype)))
+
+            # update flags
+            self.logger.debug("updated ps-weight %f", self.ps_weight)
+            self.logger.debug("updated model params")
+            self.gossip_flag.clear()
+            self.params_mixed = True
+            self.gossiping = False
+            return True
+
+        return False
+
+    def _sgp_transfer_params(self, mix: bool = True) -> bool:
+        """ Transfers COPY of model parameters to gossip queue """
+        if not self.gossip_enable or self.process_rank % self.nprocs_per_node != 0:
+            return False
+
+        self.logger.debug("transferring model params")
+
+        # don't transfer new params if old params haven't been mixed yet
+        if not self.params_mixed:
+            self.logger.warning("params not mixed")
+            return False
+
+        # using lazy mixing ==> mix on query not transfer
+        mix = mix and not self.lazy_mixing
+
+        # Transfer ps-numerators to gossip-process:
+        # --
+        self._sgp_ps_numerator()
+        if mix:
+            self.ps_weight *= self.gossip_ps_factor
+        self.gossip_ps_weight.copy_(self.ps_weight)
+        # --
+        # params gpu-gpu copy (fast)
+        # --
+        with torch.no_grad():
+            for p, gossip_device_buffer_elem in zip(self.module.parameters(), self.gossip_device_buffer):
+                if mix:
+                    p.mul_(cast(torch.Tensor, self.gossip_ps_factor.type(p.dtype)))
+                gossip_device_buffer_elem.copy_(p)
+        # --
+        # buffer to gossip-thread copy (potentially slow, but asynchronous)
+        # --
+        self.gossip_stream.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(self.gossip_stream):
+            for b, gp in zip(self.gossip_device_buffer, self.gossip_params):
+                gp.copy_(b, non_blocking=True)
+
+        # --
+
+        # update flags
+        self.logger.debug("transferred model params")
+        self.params_mixed = False
+        self.gossiping = True
+        self.train_flag.set()
+        return True
+
+    @staticmethod
+    def _sgp_gossip_into_receive_buffer(
+        send_buffer: List[torch.Tensor],
+        gossiper: Gossiper,
+        receive_buffer: List[torch.Tensor],
+        gossip_ps_weight: torch.Tensor,
+        gossip_lock: threading.Lock,
+        dist_config: Dict[Any, Any],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # flatten parameters before sending
+        out_msg = flatten_tensors(send_buffer)
+
+        # send and receive parameters
+        with gossip_lock:
+            in_msg, ps_weight = gossiper.mix(out_msg, gossip_ps_weight)
+            ps_factor = gossiper.mixing_weights["lo"]
+
+        # unflatten parameters
+        with torch.no_grad():
+            for r, g in zip(unflatten_tensors(in_msg, send_buffer), receive_buffer):
+                if dist_config["cpu_comm"]:
+                    g.copy_(r, non_blocking=True)
+                else:
+                    g.copy_(r)
+
+        return ps_weight, ps_factor
+
+    @staticmethod
+    def _sgp_gossip_target(
+        dist_config: Dict[Any, Any],
+        gossip_flag: threading.Event,
+        train_flag: threading.Event,
+        gossip_lock: threading.Lock,
+        gossip_params: List[torch.Tensor],
+        gossip_device_buffer: List[torch.Tensor],
+        gossip_ps_weight: torch.Tensor,
+        gossip_ps_factor: torch.Tensor,
+        gossip_stream: torch.cuda.Stream,
+    ) -> None:
+        """ Gossip thread, which performs push-sum on model params """
+        logger = make_logger(dist_config["logical_rank"], dist_config["verbose"])
+
+        gossip_params_by_dtype = group_by_dtype(gossip_params)
+        gossip_device_buffer_by_dtype = group_by_dtype(gossip_device_buffer)
+
+        gossipers = {}
+        # init gossip instance
+        gossiper_class = PushSum if dist_config["push_sum"] else PushPull
+        for dtype in gossip_params_by_dtype:
+            gossipers[dtype] = gossiper_class(
+                flatten_tensors(gossip_params_by_dtype[dtype]),
+                device=cast(torch.device, dist_config["comm_device"]),
+                graph=cast(GraphManager, dist_config["graph"]),
+                mixing=cast(MixingManager, dist_config["mixing"]),
+                rank=dist_config["process_rank"],
+                world_size=dist_config["logical_world_size"],
+                logger=logger,
+            )
+
+        dist_config["gossipers"] = gossipers
+        gossip_ps_factor.copy_(gossipers[list(gossipers)[0]].mixing_weights["lo"])
+        gossip_flag.set()
+
+        # gossip loop
+        while True:
+            train_flag.wait()
+            logger.debug("received train-flag")
+            try:
+                with torch.cuda.stream(gossip_stream):
+                    for dtype in gossip_params_by_dtype:
+                        (ps_weight, ps_factor,) = SlowMoDistributedDataParallel._sgp_gossip_into_receive_buffer(
+                            gossip_params_by_dtype[dtype],
+                            gossipers[dtype],
+                            gossip_device_buffer_by_dtype[dtype],
+                            gossip_ps_weight,
+                            gossip_lock,
+                            dist_config,
+                        )
+                    gossip_ps_weight.copy_(ps_weight)
+                    gossip_ps_factor.copy_(ps_factor)
+            except RuntimeError as e:
+                logger.warning("received runtime error {}".format(e))
+                for gossiper in gossipers.values():
+                    gossiper.clean_msg_buffers_()
+                gossip_ps_weight.fill_(-1)
+            finally:
+                # Make sure all queued operations are complete
+                gossip_stream.synchronize()
+                # give main thread go-ahead to read our gossip buffer
+                train_flag.clear()
+                gossip_flag.set()

fairscale/experimental/nn/data_parallel/gossip/gossiper.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Gossipers
+
+:description: Gossiper's are designed for multi-peer communication (i.e., send
+              and recv from multiple peers at each ieration)
+"""
+
+from enum import Enum
+import logging
+from typing import Iterator, List, Optional, Tuple, cast
+
+import torch
+import torch.distributed as dist
+
+from .graph_manager import GraphManager
+from .mixing_manager import MixingManager, UniformMixing
+
+
+class dist_backend(str, Enum):
+    UNDEFINED = "undefined"
+    TCP = "tcp"
+    MPI = "mpi"
+    GLOO = "gloo"
+    NCCL = "nccl"
+
+
+class Gossiper(object):
+    """ Generic gossip averaging object for multi-peer communication
+
+    Args:
+        msg (torch.Tensor): message used to initialize recv buffer
+        graph (GraphManager): Subclass of GraphManager
+        device: (torch.Device) device on which to initialize recv buffer
+        mixing (MixingManager): Subclass of MixingManager
+        logger (logging.Logger): Module used to log results
+        rank (int): Rank of the current process
+        world_size (int): World size of the current process
+    """
+
+    def __init__(
+        self,
+        msg: torch.Tensor,
+        graph: GraphManager,
+        device: Optional[torch.device] = None,
+        mixing: MixingManager = None,
+        logger: logging.Logger = None,
+        rank: Optional[int] = None,
+        world_size: Optional[int] = None,
+    ) -> None:
+        """
+        Initialize generic averaging class designed for multi-peer comms
+        """
+
+        self.logger = logger
+        if rank is None or world_size is None:
+            assert dist.is_initialized()
+            # for now p2p communication only supported with tcp and mpi
+            assert dist.get_backend() != dist_backend.GLOO
+            assert dist.get_backend() != dist_backend.NCCL
+            rank = dist.get_rank()
+            world_size = dist.get_world_size()
+
+        # graph topology properties
+        self.rank = rank
+        self.world_size = world_size
+        assert isinstance(graph, GraphManager)
+        self._graph_manager = graph
+        self.peers_per_itr_device = torch.tensor([self._graph_manager.peers_per_itr], device=device, dtype=msg.dtype)
+        # This might need to be made float16 later on
+        self.passive = self._graph_manager.is_passive()
+        self.refresh_peers_(rotate=False)  # sets in- and out-peers attributes
+
+        # mixing matrix
+        if mixing is None:
+            mixing = UniformMixing(self._graph_manager, device)
+        assert isinstance(mixing, MixingManager)
+        self._mixing_manager = mixing
+        self.refresh_mixing_weights_()  # sets mixing-weights attribute
+
+        # regular ==> we don't need to keep track of ps-weight explicitly
+        self.regular = self._mixing_manager.is_regular()
+
+        # msg buffers used during send/recv
+        self.device = device if device is not None else msg.device
+        self.out_msg_buffer: List[Tuple[dist.Work, torch.Tensor]] = []
+        self.in_msg_buffer = msg.clone().detach_().to(self.device)
+        self._ps_weight: torch.Tensor = torch.ones(1, dtype=msg.dtype).detach_().to(self.device)
+        # not using regular comms ==> need to communicate ps-weight
+        if not self.regular:
+            self.in_msg_buffer = torch.cat([self.in_msg_buffer, self.ps_weight])
+        if self.device.type == "cpu":
+            try:
+                self.in_msg_buffer = self.in_msg_buffer.pin_memory()
+            except Exception as e:
+                if self.logger is not None:
+                    self.logger.error(e)
+                else:
+                    raise
+
+        self.placeholder = self.in_msg_buffer.clone()
+
+    @property
+    def ps_weight(self) -> torch.Tensor:
+        return self._ps_weight
+
+    @ps_weight.setter
+    def ps_weight(self, v: torch.Tensor) -> None:
+        self._ps_weight.data[0] = v
+
+    @property
+    def peers_per_itr(self) -> int:
+        return self._graph_manager.peers_per_itr
+
+    @peers_per_itr.setter
+    def peers_per_itr(self, v: int) -> None:
+        self._graph_manager.peers_per_itr = v
+
+    def refresh_peers_(self, rotate: Optional[bool] = None) -> None:
+        """ Update in- and out-peers """
+        if rotate is None:
+            rotate = self._graph_manager.is_dynamic_graph()
+        # cannot cycle peers in a static graph
+        assert not (rotate and not self._graph_manager.is_dynamic_graph())
+        self.out_edges, self.in_edges = self._graph_manager.get_edges(rotate)
+
+    def refresh_mixing_weights_(self, residual_adjusted: bool = False) -> None:
+        """ Update mixing-matrix weights """
+        self.mixing_weights = self._mixing_manager.get_mixing_weights(residual_adjusted)
+
+    def mix_out_msg_(self, out_msg: torch.Tensor, ps_weight: torch.Tensor) -> Iterator[torch.Tensor]:
+        """ Returns a generator mixing messages on the fly """
+        self.refresh_mixing_weights_(residual_adjusted=True)
+        self.ps_weight = ps_weight
+
+        # check whether or not we need to communicate ps_weight
+        if not self.regular:
+            out_msg = torch.cat([out_msg, cast(torch.Tensor, self.ps_weight.type(out_msg.dtype))])
+
+        # check whether or not we need to create a buffer for each out-msg
+        if self._mixing_manager.is_uniform():
+            weight = self.mixing_weights["uniform"]
+            out_msg *= weight.type(out_msg.dtype)
+            for _ in self.out_edges:
+                yield out_msg
+        else:
+            for out_edge in self.out_edges:
+                weight = self.mixing_weights[out_edge.dest]
+                yield out_msg.mul(weight.type(out_msg.dtype))  # type: ignore
+
+    def clean_msg_buffers_(self) -> None:
+        """ Clean outgoing message buffer """
+        while len(self.out_msg_buffer) > 0:
+            req, msg = self.out_msg_buffer.pop()
+            req.wait()
+            msg.set_()
+
+    def parse_in_msg_buffer(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        """ Parse in-msg buffer and return msg and ps-weight separately """
+        msg = self.in_msg_buffer
+        if not self.regular:
+            return msg.narrow(0, 0, len(msg) - 1), msg[-1]
+        else:
+            return msg, self.ps_weight * self.peers_per_itr_device
+
+    def mix(self, out_msg: torch.Tensor, ps_weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """ Single gossip step """
+        raise NotImplementedError
+
+
+class PushSum(Gossiper):
+    """ 1-peer Push-Sum consensus averaging module """
+
+    def mix(self, out_msg: torch.Tensor, ps_weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """ Consensus averaging step """
+        # out_msg must be on the correct device
+        assert out_msg.device.type == self.device.type
+        if self.logger is not None:
+            self.logger.debug("in/out -peers {}/{}".format(self.in_edges, self.out_edges))
+
+        # prepare messages for gossip
+        mixed_out_msgs = self.mix_out_msg_(out_msg, ps_weight)
+
+        # non-blocking send
+        for out_edge in self.out_edges:
+            msg = next(mixed_out_msgs)
+            assert self.rank == out_edge.src
+            req = dist.broadcast(tensor=msg, src=out_edge.src, group=out_edge.process_group, async_op=True,)
+            self.out_msg_buffer.append((req, msg))
+
+        # blocking recv w/ some code optimization to avoid buffer prep overhead
+        if len(self.in_edges) == 1:
+            in_edge = self.in_edges[0]
+            dist.broadcast(tensor=self.in_msg_buffer, src=in_edge.src, group=in_edge.process_group)
+
+        # regular non-blocking recv
+        else:
+            # prepare in-msg buffer
+            self.in_msg_buffer.zero_()
+
+            for in_edge in self.in_edges:
+                dist.broadcast(
+                    tensor=self.placeholder, src=in_edge.src, group=in_edge.process_group,
+                )
+                self.in_msg_buffer.add_(self.placeholder)  # type: ignore
+
+        self.refresh_peers_()
+        self.clean_msg_buffers_()
+        return self.parse_in_msg_buffer()
+
+
+class PushPull(Gossiper):
+    """ Doubly-stochastic consensus averaging module """
+
+    def mix(self, out_msg: torch.Tensor, ps_weight: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        # out_msg must be on the correct device
+        assert out_msg.device.type == self.device.type
+        if self.logger is not None:
+            self.logger.debug("in/out -peers {}/{}".format(self.in_edges, self.out_edges))
+
+        # prepare messages for gossip
+        mixed_out_msgs = self.mix_out_msg_(out_msg, ps_weight)
+
+        # send-recv w/ some code optimization to avoid buffer prep overhead
+        if len(self.in_edges) == 1 and len(self.out_edges) == 1:
+            out_edge, in_edge = self.out_edges[0], self.in_edges[0]
+            msg = next(mixed_out_msgs)
+            if not self.passive:
+                dist.broadcast(tensor=msg, src=out_edge.src, group=out_edge.process_group)
+                dist.broadcast(
+                    tensor=self.in_msg_buffer, src=in_edge.src, group=in_edge.process_group,
+                )
+            else:
+                dist.broadcast(
+                    tensor=self.in_msg_buffer, src=in_edge.src, group=in_edge.process_group,
+                )
+                dist.broadcast(tensor=msg, src=out_edge.src, group=out_edge.process_group)
+
+        # regular send-recv
+        else:
+            # prepare in-msg buffer
+            self.in_msg_buffer.zero_()
+
+            # send-recv
+            for out_edge, in_edge in zip(self.out_edges, self.in_edges):
+                msg = next(mixed_out_msgs)
+                if not self.passive:
+                    dist.broadcast(tensor=msg, src=out_edge.src, group=out_edge.process_group)
+                    dist.broadcast(
+                        tensor=self.placeholder, src=in_edge.src, group=in_edge.process_group,
+                    )
+                else:
+                    dist.broadcast(
+                        tensor=self.placeholder, src=in_edge.src, group=in_edge.process_group,
+                    )
+                    dist.broadcast(tensor=msg, src=out_edge.src, group=out_edge.process_group)
+                self.in_msg_buffer.add_(self.placeholder)  # type: ignore
+
+        self.refresh_peers_()
+        self.clean_msg_buffers_()
+        return self.parse_in_msg_buffer()

fairscale/experimental/nn/data_parallel/gossip/graph_manager.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Graph Manager Class
+
+:description: Class provides an API for loading different peer-to-peer
+    communication topologies, and cycling through peers.
+"""
+
+from abc import ABC, abstractmethod
+from math import log as mlog
+from typing import List, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+
+
+class Edge(object):
+    def __init__(self, local_master_rank: int, dest: int, src: int, local_rank: int) -> None:
+        self.src = src
+        self.dest = dest
+        self.process_group = dist.new_group([src, dest])
+        if local_master_rank in [self.src, self.dest] and local_rank == 0:
+            initializer_tensor = torch.Tensor([1]).cuda()
+            dist.all_reduce(initializer_tensor, group=self.process_group)
+            initializer_tensor = torch.Tensor([1]).cuda().half()
+            dist.all_reduce(initializer_tensor, group=self.process_group)
+
+
+class GraphManager(ABC):
+    def __init__(
+        self, rank: int, world_size: int, nprocs_per_node: int = 1, local_rank: int = 0, peers_per_itr: int = 1
+    ) -> None:
+        assert int(peers_per_itr) >= 1
+        self.rank = rank
+        self.world_size = world_size
+        self.phone_book: List[List[Edge]] = [[] for _ in range(self.world_size)]
+        self._peers_per_itr = peers_per_itr
+        self._group_indices = list(range(peers_per_itr))
+        self.nprocs_per_node = nprocs_per_node
+        self.local_rank = local_rank
+        self._make_graph()
+
+    @property
+    def peers_per_itr(self) -> int:
+        return self._peers_per_itr
+
+    @peers_per_itr.setter
+    def peers_per_itr(self, v: int) -> None:
+        self._peers_per_itr = v
+        # set group-indices attr. --- point to out-peers in phone-book
+        self._group_indices = list(range(v))
+
+    @abstractmethod
+    def _make_graph(self) -> None:
+        """
+        Returns a nested list of peers; the outer-list is indexed by rank,
+        the inner list denotes the set of peers that 'rank' can send
+        messages to at any point in time
+        """
+        raise NotImplementedError
+
+    def _add_peers(self, rank: int, peers: List[int]) -> None:
+        for peer in peers:
+            if peer not in self.phone_book[rank]:
+                self.phone_book[rank].append(
+                    Edge(
+                        local_master_rank=(self.rank * self.nprocs_per_node),
+                        dest=(peer * self.nprocs_per_node),
+                        src=(rank * self.nprocs_per_node),
+                        local_rank=self.local_rank,
+                    )
+                )
+
+    @abstractmethod
+    def is_regular_graph(self) -> bool:
+        """ Whether each node has the same number of in-peers as out-peers """
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_bipartite_graph(self) -> bool:
+        """ Whether graph is bipartite or not """
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        """ Whether 'rank' is a passive node or not """
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_dynamic_graph(self) -> bool:
+        """ Whether the graph-type is dynamic (as opposed to static) """
+        raise NotImplementedError
+
+    def get_peers(self, rotate: bool = False) -> Tuple[List[int], List[int]]:
+        """ Returns the out and in-peers corresponding to 'self.rank' """
+        # cycle through in- and out-peers by updating group-index
+        if rotate:
+            self._rotate_group_indices()
+
+        # get out- and in-peers using new group-indices
+        out_peers, in_peers = [], []
+        for group_index in self._group_indices:
+            out_peers.append(self.phone_book[self.rank][group_index].dest)
+            for rank, peers in enumerate(self.phone_book):
+                if rank == self.rank:
+                    continue
+                if self.rank * self.nprocs_per_node == peers[group_index].dest:
+                    in_peers.append(rank)
+        return out_peers, in_peers
+
+    def get_edges(self, rotate: bool = False) -> Tuple[List[Edge], List[Edge]]:
+        """ Returns the pairwise process groups between rank and the out and
+        in-peers corresponding to 'self.rank' """
+        # cycle through in- and out-peers by updating group-index
+        if rotate:
+            self._rotate_group_indices()
+
+        # get out- and in-peers using new group-indices
+        out_edges, in_edges = [], []
+        for group_index in self._group_indices:
+            out_edges.append(self.phone_book[self.rank][group_index])
+            for rank, edges in enumerate(self.phone_book):
+                if rank == self.rank:
+                    continue
+                if self.rank * self.nprocs_per_node == edges[group_index].dest:
+                    in_edges.append(self.phone_book[rank][group_index])
+        return out_edges, in_edges
+
+    def _rotate_group_indices(self) -> None:
+        """ Incerement group indices to point to the next out-peer """
+        increment = self.peers_per_itr
+        for i, group_index in enumerate(self._group_indices):
+            self._group_indices[i] = int((group_index + increment) % len(self.phone_book[self.rank]))
+
+    def _rotate_forward(self, r: int, p: int) -> int:
+        """ Helper function returns peer that is p hops ahead of r """
+        return (r + p) % self.world_size
+
+    def _rotate_backward(self, r: int, p: int) -> int:
+        """ Helper function returns peer that is p hops behind r """
+        return (r - p) % self.world_size
+
+
+class DynamicDirectedExponentialGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            for i in range(0, int(mlog(self.world_size - 1, 2)) + 1):
+                f_peer = self._rotate_forward(rank, 2 ** i)
+                b_peer = self._rotate_backward(rank, 2 ** i)
+                self._add_peers(rank, [f_peer, b_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return False
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        return False
+
+    def is_dynamic_graph(self) -> bool:
+        return True
+
+
+class NPeerDynamicDirectedExponentialGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            for i in range(0, int(mlog(self.world_size - 1, self._peers_per_itr + 1)) + 1):
+                for j in range(1, self._peers_per_itr + 1):
+                    distance_to_neighbor = j * ((self._peers_per_itr + 1) ** i)
+                    f_peer = self._rotate_forward(rank, distance_to_neighbor)
+                    self._add_peers(rank, [f_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return False
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        return False
+
+    def is_dynamic_graph(self) -> bool:
+        return True
+
+
+class DynamicBipartiteExponentialGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            for i in range(0, int(mlog(self.world_size - 1, 2)) + 1):
+                if i == 0:
+                    f_peer = self._rotate_forward(rank, 1)
+                    b_peer = self._rotate_backward(rank, 1)
+                else:
+                    f_peer = self._rotate_forward(rank, 1 + 2 ** i)
+                    b_peer = self._rotate_backward(rank, 1 + 2 ** i)
+                # create directory for non-passive peers
+                if not self.is_passive(rank) and (self.is_passive(f_peer) and self.is_passive(b_peer)):
+                    self._add_peers(rank, [f_peer, b_peer])
+                # create directory for passive peers
+                elif self.is_passive(rank) and (not (self.is_passive(f_peer) or self.is_passive(b_peer))):
+                    self._add_peers(rank, [f_peer, b_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return True
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        rank = self.rank if rank is None else rank
+        return (rank % 2) == 0
+
+    def is_dynamic_graph(self) -> bool:
+        return True
+
+
+class DynamicDirectedLinearGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            for i in range(1, self.world_size):
+                if i % 2 == 0:
+                    continue
+                f_peer = self._rotate_forward(rank, i)
+                b_peer = self._rotate_backward(rank, i)
+                self._add_peers(rank, [f_peer, b_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return False
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        return False
+
+    def is_dynamic_graph(self) -> bool:
+        return True
+
+
+class DynamicBipartiteLinearGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            for i in range(1, self.world_size):
+                f_peer = self._rotate_forward(rank, i)
+                b_peer = self._rotate_backward(rank, i)
+                # create directory for non-passive peers
+                if not self.is_passive(rank) and (self.is_passive(f_peer) and self.is_passive(b_peer)):
+                    self._add_peers(rank, [f_peer, b_peer])
+                # create directory for passive peers
+                elif self.is_passive(rank) and (not (self.is_passive(f_peer) or self.is_passive(b_peer))):
+                    self._add_peers(rank, [f_peer, b_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return True
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        rank = self.rank if rank is None else rank
+        return (rank % 2) == 0
+
+    def is_dynamic_graph(self) -> bool:
+        return True
+
+
+class RingGraph(GraphManager):
+    def _make_graph(self) -> None:
+        for rank in range(self.world_size):
+            f_peer = self._rotate_forward(rank, 1)
+            b_peer = self._rotate_backward(rank, 1)
+            self._add_peers(rank, [f_peer, b_peer])
+
+    def is_regular_graph(self) -> bool:
+        return True
+
+    def is_bipartite_graph(self) -> bool:
+        return False
+
+    def is_passive(self, rank: Optional[int] = None) -> bool:
+        return False
+
+    def is_dynamic_graph(self) -> bool:
+        return False

fairscale/experimental/nn/data_parallel/gossip/mixing_manager.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Mixing Manager Class
+
+:description: Class provides an API for dynamically selecting mixing weights
+              for gossip
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, Optional, Union
+
+import torch
+
+from .graph_manager import GraphManager
+
+
+class MixingManager(ABC):
+    def __init__(self, graph: GraphManager, device: Optional[torch.device]) -> None:
+        self.graph_manager = graph
+        self.device = device
+
+    def is_regular(self) -> bool:
+        """
+        Whether there is bias accumulated in local entry of stationary
+        distribution of mixing matrix
+        """
+        return self.graph_manager.is_regular_graph() and self.is_uniform()
+
+    @abstractmethod
+    def is_uniform(self) -> bool:
+        """ Whether mixing weights are distributed uniformly over peers """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_mixing_weights(self, residual_adjusted: bool = True) -> Dict[Union[str, int], torch.Tensor]:
+        """ Create mixing weight dictionary using uniform allocation """
+        raise NotImplementedError
+
+
+class UniformMixing(MixingManager):
+    def get_mixing_weights(self, residual_adjusted: bool = True) -> Dict[Union[str, int], torch.Tensor]:
+        """ Create mixing weight dictionary using uniform allocation """
+        mixing_weights: Dict[Union[str, int], torch.Tensor] = {}
+        out_peers, _ = self.graph_manager.get_peers()
+
+        w = torch.tensor([1.0 / (len(out_peers) + 1.0)], device=self.device)
+        mixing_weights["lo"] = w.clone()
+        w_op = w if not residual_adjusted else w / mixing_weights["lo"]
+        mixing_weights["uniform"] = w_op.clone()
+        for op in out_peers:
+            mixing_weights[op] = w_op.clone()
+        return mixing_weights
+
+    def is_uniform(self) -> bool:
+        return True

fairscale/experimental/nn/data_parallel/gossip/utils/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .helpers import (
+    MultiProcessAdapter,
+    communicate,
+    create_process_group,
+    flatten_tensors,
+    group_by_dtype,
+    make_logger,
+    unflatten_tensors,
+)

fairscale/experimental/nn/data_parallel/gossip/utils/cuda_metering.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Benchmarking utils for timing cuda executions
+"""
+
+from collections import defaultdict, deque
+from functools import partial
+import statistics
+from typing import ClassVar, Deque, Dict, Optional
+
+import torch
+
+MAX_LEN_DEQUEUE = 10 ** 4
+deque_with_max_len_fixed = partial(deque, maxlen=MAX_LEN_DEQUEUE)
+
+
+def create_and_record_event() -> torch.cuda.Event:
+    event = torch.cuda.Event(enable_timing=True)
+    event.record()
+    return event
+
+
+class EventRecorder(object):
+    def stop(self) -> None:
+        pass
+
+
+def create_event_recorder(event_name: str, dummy: bool = False) -> EventRecorder:
+    if not dummy:
+        return CudaEventRecorder(event_name)
+    return DummyCudaEventRecorder()
+
+
+class CudaEventRecorder(EventRecorder):
+    """ Allows profiling in an easy-to-use manner. CudaEventRecorder can be used
+    in a loop. When it is used in a loop (or when an event recorder is created
+    multiple times with the same name), get_timings returns the statistics of the
+    timings since the last reset. Note: in case the number of timings is greater than
+    10,000, only the last 10,000 timings are used to calculate the statistics.
+
+    Usage:
+    >>> event_recorder1 = CudaEventRecorder('1')
+    >>> # Sequence of events whose time is to be measured
+    >>> event_recorder1.stop()
+    >>> event_recorder2 = CudaEventRecorder('2')
+    >>> # Sequence of events whose time is to be measured
+    >>> event_recorder2.stop()
+    >>> print(CudaEventRecorder.get_timings())
+
+    Args:
+        event_name (str): The name by which the cuda event is to be referred later on
+
+    """
+
+    event_recorders: ClassVar[Dict[str, Deque["CudaEventRecorder"]]] = defaultdict(deque_with_max_len_fixed)  # type: ignore
+    all_event_recorders: ClassVar[Dict[str, Deque["CudaEventRecorder"]]] = defaultdict(deque_with_max_len_fixed)  # type: ignore
+
+    def __init__(self, event_name: str) -> None:
+        self.event_name = event_name
+        self.start_event = create_and_record_event()
+        self.end_event: Optional[torch.cuda.Event] = None
+
+        # Adding it to global tracker
+        CudaEventRecorder.event_recorders[event_name].append(self)
+        CudaEventRecorder.all_event_recorders[event_name].append(self)
+
+    def stop(self) -> None:
+        self.end_event = create_and_record_event()
+
+    def find_time_elapsed(self) -> float:
+        if self.end_event is None:
+            raise Exception(f"stopEvent was not called for event with name {self.event_name}")
+
+        self.end_event.synchronize()
+        return self.start_event.elapsed_time(self.end_event)
+
+    @classmethod
+    def reset(cls) -> None:
+        cls.event_recorders = defaultdict(deque_with_max_len_fixed)  # type: ignore
+
+    @classmethod
+    def get_common_timings(cls, event_recorders: Dict[str, Deque["CudaEventRecorder"]], description: str) -> str:
+        all_timings_str = f"{description}:\n"
+
+        # Iterating over different types of events, eg., forward, backward
+        for event_name, event_recorder_list in event_recorders.items():
+            # Iterating over different occurences of an event type
+            time_taken_list = [event_recorder.find_time_elapsed() for event_recorder in event_recorder_list]
+
+            all_timings_str += ("{}: Time taken: avg: {}, std: {}, count: " "{}\n").format(
+                event_name, statistics.mean(time_taken_list), statistics.pstdev(time_taken_list), len(time_taken_list),
+            )
+
+        return all_timings_str
+
+    @classmethod
+    def get_timings(cls) -> str:
+        """ Returns the timings since last reset was called """
+        return cls.get_common_timings(cls.event_recorders, "Timings since last reset")
+
+    @classmethod
+    def get_all_timings(cls) -> str:
+        """ Returns the statistics of all the timings """
+        return cls.get_common_timings(cls.all_event_recorders, "All timings")
+
+
+class DummyCudaEventRecorder(EventRecorder):
+    pass

fairscale/experimental/nn/data_parallel/gossip/utils/helpers.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Collection of commonly used utility functions
+"""
+
+import collections
+import logging
+import sys
+from typing import Any, Dict, List, MutableMapping, Set, Tuple
+
+import torch
+import torch.distributed as dist
+
+
+def flatten_tensors(tensors: List[torch.Tensor]) -> torch.Tensor:
+    """
+    Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
+    same dense type.
+    Since inputs are dense, the resulting tensor will be a concatenated 1D
+    buffer. Element-wise operation on this buffer will be equivalent to
+    operating individually
+    Args:
+        tensors (Iterable[Tensor]): dense tensors to flatten
+    Returns:
+        A 1D buffer containing input tensors
+    """
+    if len(tensors) == 1:
+        return tensors[0].view(-1).clone()
+    flat = torch.cat([t.view(-1) for t in tensors], dim=0)
+    return flat
+
+
+def unflatten_tensors(flat: torch.Tensor, tensors: List[torch.Tensor]) -> List[torch.Tensor]:
+    """
+    View a flat buffer using the sizes of tensors. Assume that tensors are of
+    same dense type, and that flat is given by flatten_dense_tensors.
+    Args:
+        flat (Tensor): flattened dense tensors to unflatten
+        tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
+            unflatten flat
+    Returns:
+        Unflattened dense tensors with sizes same as tensors and values from
+        flat
+    """
+    outputs = []
+    offset = 0
+    for tensor in tensors:
+        numel = tensor.numel()
+        outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
+        offset += numel
+    return outputs
+
+
+def group_by_dtype(tensors: List[torch.Tensor]) -> Dict[torch.dtype, List[torch.Tensor]]:
+    """
+    Returns a dict mapping from the tensor dtype to a list containing all
+    tensors of that dtype.
+    Arg:
+        tensors (Iterable[Tensor]): list of tensors
+    """
+    tensors_by_dtype = collections.defaultdict(list)
+    for tensor in tensors:
+        tensors_by_dtype[tensor.dtype].append(tensor)
+    return tensors_by_dtype
+
+
+def communicate(tensors: List[torch.Tensor], communication_op: Any, logger: logging.Logger = None) -> None:
+    """
+    Communicate a list of tensors
+    Args:
+        tensors (Iterable[Tensor]): list of tensors
+        communication_op: a method or partial object which takes a tensor as
+            input and communicates it. It can be a partial object around
+            something like torch.distributed.all_reduce
+    """
+    tensors_by_dtype = group_by_dtype(tensors)
+    for tensors_with_same_dtype in tensors_by_dtype.values():
+        flat_tensor = flatten_tensors(tensors_with_same_dtype)
+        if logger is not None:
+            logger.debug("Flatten completed")
+        communication_op(tensor=flat_tensor)
+        if logger is not None:
+            logger.debug("Commmunication completed")
+        with torch.no_grad():
+            for f, t in zip(unflatten_tensors(flat_tensor, tensors_with_same_dtype), tensors_with_same_dtype,):
+                t.copy_(f)
+        if logger is not None:
+            logger.debug("Unflatten completed")
+
+
+HANDLER_AND_LEVEL_SET: Set[logging.Logger] = set()
+
+# TODO: deprecate this function
+def make_logger(rank: int, verbose: bool = True) -> logging.Logger:
+    """
+    Return a logger for writing to stdout
+    Args:
+        rank (int): rank of node making logger
+        verbose (bool): whether to set log-level to INFO; o.w. WARNING
+    Returns:
+        Python logger
+    """
+    logger = logging.getLogger(__name__)
+    if logger not in HANDLER_AND_LEVEL_SET:
+        # if not getattr(logger, "handler_and_level_set", None):
+        console = logging.StreamHandler(stream=sys.stdout)
+        format_str = "{}".format(rank)
+        format_str += ": %(levelname)s -- %(threadName)s -- %(message)s"
+        console.setFormatter(logging.Formatter(format_str))
+        logger.addHandler(console)  # prints to console
+        if verbose:
+            logger.setLevel(logging.DEBUG)
+        else:
+            logger.setLevel(logging.INFO)
+        HANDLER_AND_LEVEL_SET.add(logger)
+        # logger.handler_and_level_set = True
+    return logger
+
+
+def create_process_group(ranks: List[int]) -> torch.distributed.ProcessGroup:
+    """
+    Creates and intializes a new process group. Assumes init_process_group
+    has already been called
+    Arguments:
+        ranks (list<int>): ranks corresponding to the processes which should
+            belong the created process group
+    Returns:
+        New process group
+    """
+    new_group = dist.new_group(ranks=ranks)
+    init_tensor_fp32, init_tensor_fp16 = torch.zeros(1), torch.zeros(1).half()
+
+    for init_tensor in [init_tensor_fp32, init_tensor_fp16]:
+        if torch.cuda.is_available():
+            init_tensor = init_tensor.cuda()
+        if dist.get_rank() in ranks:
+            dist.all_reduce(init_tensor, group=new_group)
+        torch.cuda.synchronize()
+    return new_group
+
+
+class MultiProcessAdapter(logging.LoggerAdapter):
+    """
+    Creates an adapter to make logging for multiple processes cleaner
+    """
+
+    def process(self, msg: str, kwargs: Any) -> Tuple[str, MutableMapping[str, Any]]:
+        # use process_num from kwargs or the default given on instantiation
+        process_num = kwargs.pop("process_num", self.extra["process_num"])
+        return f"process: {process_num} {msg}", kwargs

fairscale/utils/testing.py

@@ -208,8 +208,21 @@ def get_world_sizes() -> List[int]:
     return [x for x in [1, 2, 4, 8] if x <= limit]
 
 
-def spawn_for_all_world_sizes(test_func: Callable, world_sizes: List[int] = get_world_sizes(), args: Any = []) -> None:
+def test_runner(
+    rank: int, test_func: Callable, deterministic: bool = False, *args: List[Any], **kwargs: Dict[str, Any]
+) -> None:
+    # At this point we're in a new process, torch options need to be set again
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+        torch.manual_seed(1357)
+
+    test_func(rank, *args, **kwargs)
 
+
+def spawn_for_all_world_sizes(
+    test_func: Callable, world_sizes: List[int] = get_world_sizes(), args: Any = [], deterministic: bool = False
+) -> None:
     for world_size in world_sizes:
         _, filename = tempfile.mkstemp()
         _, filename_rpc = tempfile.mkstemp()
@@ -217,7 +230,12 @@ def spawn_for_all_world_sizes(test_func: Callable, world_sizes: List[int] = get_
         try:
             # (lefaudeux) Let mp handle the process joining, join=False and handling context has
             # been unstable in the past.
-            mp.spawn(test_func, args=(world_size, filename, filename_rpc, *args), nprocs=world_size, join=True)
+            mp.spawn(
+                test_runner,
+                args=(test_func, deterministic, world_size, filename, filename_rpc, *args),
+                nprocs=world_size,
+                join=True,
+            )
         finally:
             rmf(filename)
             rmf(filename_rpc)
@@ -239,7 +257,19 @@ def worker_process(
 
     initialize_model_parallel(1, world_size, **kwargs)
 
+    # Make sure that CUDA operations are repeatable
+    context = (
+        torch.backends.cudnn.flags(benchmark=False, deterministic=True)  # type: ignore
+        if torch.cuda.is_available() and hasattr(torch.backends.cudnn, "flags")
+        else contextlib.suppress()
+    )
+
+    if torch.cuda.is_available() and not hasattr(torch.backends.cudnn, "flags"):
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
     try:
+        with context:
             func(*args)
         teardown()
     except BaseException as e:

pyproject.toml

@@ -27,4 +27,4 @@ use_parentheses = true
 skip_glob = ["build/*", "stubs/*"]
 # Don't split "import" and "from".
 force_sort_within_sections = true
-known_third_party = ["benchmark_dataset", "datasets", "golden_configs", "models", "numpy", "parameterized", "pytest", "recommonmark", "setuptools", "torch", "torchtext", "torchvision"]
+known_third_party = ["benchmark_dataset", "datasets", "golden_configs", "helpers", "models", "numpy", "parameterized", "pytest", "recommonmark", "setuptools", "torch", "torchtext", "torchvision"]

stubs/torch/backends/cudnn.pyi

@@ -5,3 +5,4 @@ def version() -> int: ...
 #END
 deterministic : bool
 benchmark: bool
+