All gatherwith grads

CHANGELOG.md

@@ -6,6 +6,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## Unreleased
 
+### Added
+
+- Added `all_gather` method to `LightningModule` which allows gradient based tensor synchronizations for use-cases such as negative sampling. ([#5012](https://github.com/PyTorchLightning/pytorch-lightning/pull/5012))
+
 ### Fixed
 
 - Fixed `LoggerConnector` to have logged metrics on root device in DP ([#4138](https://github.com/PyTorchLightning/pytorch-lightning/pull/4138))

pytorch_lightning/accelerators/accelerator.py

@@ -172,6 +172,20 @@ def sync_tensor(self,
         """
         raise NotImplementedError()
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        raise NotImplementedError()
+
     def optimizer_state(self, optimizer: Optimizer) -> dict:
         """
         Returns state of an optimizer. Allows for syncing/collating optimizer state from processes in custom

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -25,7 +25,7 @@
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
 from pytorch_lightning.utilities import HYDRA_AVAILABLE, AMPType
-from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
+from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available, all_gather_ddp_if_available
 
 if HYDRA_AVAILABLE:
     from hydra.core.hydra_config import HydraConfig
@@ -234,6 +234,20 @@ def sync_tensor(self,
                     reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
         return sync_ddp_if_available(tensor, group, reduce_op)
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
+
     def get_reference_model(self, model) -> LightningModule:
         return self.ddp_plugin.get_model_from_plugin(model)
 

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -29,6 +29,7 @@
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
 from pytorch_lightning.utilities import HYDRA_AVAILABLE, AMPType
+from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available
 from pytorch_lightning.utilities.distributed import find_free_network_port, rank_zero_only, sync_ddp_if_available
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.seed import seed_everything
@@ -333,6 +334,20 @@ def sync_tensor(self,
         """
         return sync_ddp_if_available(tensor, group, reduce_op)
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
+
     def get_reference_model(self, model) -> LightningModule:
         return self.ddp_plugin.get_model_from_plugin(model)
 

pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py

@@ -31,6 +31,7 @@
     rank_zero_only,
     rank_zero_warn,
     sync_ddp_if_available,
+    all_gather_ddp_if_available,
 )
 
 if HYDRA_AVAILABLE:
@@ -261,6 +262,20 @@ def sync_tensor(self,
                     reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
         return sync_ddp_if_available(tensor, group, reduce_op)
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
+
     def get_reference_model(self, model) -> LightningModule:
         return self.ddp_plugin.get_model_from_plugin(model)
 

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -25,7 +25,7 @@
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
 from pytorch_lightning.utilities import HYDRA_AVAILABLE, AMPType
-from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
+from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available, all_gather_ddp_if_available
 
 if HYDRA_AVAILABLE:
     from hydra.core.hydra_config import HydraConfig
@@ -225,6 +225,20 @@ def sync_tensor(self,
                     reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
         return sync_ddp_if_available(tensor, group, reduce_op)
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
+
     def get_reference_model(self, model) -> LightningModule:
         return self.ddp_plugin.get_model_from_plugin(model)
 

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -34,6 +34,7 @@
     rank_zero_only,
     rank_zero_warn,
     sync_ddp_if_available,
+    all_gather_ddp_if_available,
 )
 from pytorch_lightning.utilities.seed import seed_everything
 
@@ -293,6 +294,20 @@ def sync_tensor(self,
                     reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
         return sync_ddp_if_available(tensor, group, reduce_op)
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        """
+        Function to gather a tensor from several distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)
+
     def get_reference_model(self, model) -> LightningModule:
         return self.ddp_plugin.get_model_from_plugin(model)
 

pytorch_lightning/core/lightning.py

@@ -365,6 +365,24 @@ def __auto_choose_log_on_epoch(self, on_epoch):
 
         return on_epoch
 
+    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
+        r"""
+        Allows users to call ``self.all_gather()`` from the LightningModule, thus making
+        the ```all_gather``` operation accelerator agnostic.
+
+        ```all_gather``` is a function provided by accelerators to gather a tensor from several
+        distributed processes
+
+        Args:
+            tensor: tensor of shape (batch, ...)
+            group: the process group to gather results from. Defaults to all processes (world)
+            sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+        Return:
+            A tensor of shape (world_size, batch, ...)
+        """
+        return self.trainer.accelerator_backend.all_gather(tensor, group=group, sync_grads=sync_grads)
+
     def forward(self, *args, **kwargs):
         r"""
         Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define

pytorch_lightning/utilities/__init__.py

@@ -21,6 +21,7 @@
 
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities.distributed import AllGatherGrad
 from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict, is_picklable
 from pytorch_lightning.utilities.xla_device_utils import XLA_AVAILABLE, XLADeviceUtils
 

pytorch_lightning/utilities/distributed.py

@@ -22,10 +22,14 @@
 
 if torch.distributed.is_available():
     from torch.distributed import ReduceOp
+    from torch.distributed import group
 else:
     class ReduceOp:
         SUM = None
 
+    class group:
+        WORLD = None
+
 
 def rank_zero_only(fn):
 
@@ -155,3 +159,54 @@ def sync_ddp(
         result = result / torch.distributed.get_world_size(group)
 
     return result
+
+
+class AllGatherGrad(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, tensor, group=group.WORLD):
+        ctx.group = group
+
+        gathered_tensor = [
+            torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())
+        ]
+
+        torch.distributed.all_gather(gathered_tensor, tensor, group=group)
+        gathered_tensor = torch.stack(gathered_tensor, dim=0)
+
+        return gathered_tensor
+
+    @staticmethod
+    def backward(ctx, *grad_output):
+        grad_output = torch.cat(grad_output)
+
+        torch.distributed.all_reduce(
+            grad_output,
+            op=torch.distributed.ReduceOp.SUM,
+            async_op=False,
+            group=ctx.group
+        )
+
+        return grad_output[torch.distributed.get_rank()]
+
+
+def all_gather_ddp_if_available(
+    tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False
+) -> torch.Tensor:
+    """
+    Function to gather a tensor from several distributed processes
+
+    Args:
+        tensor: tensor of shape (batch, ...)
+        group: the process group to gather results from. Defaults to all processes (world)
+        sync_grads: flag that allows users to synchronize gradients for all_gather op
+
+    Return:
+        A tensor of shape (world_size, batch, ...)
+    """
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        if sync_grads:
+            return AllGatherGrad.apply(tensor, group)
+        else:
+            with torch.no_grad:
+                return AllGatherGrad.apply(tensor, group)
+    return tensor

tests/utilities/test_all_gather_grad.py

@@ -0,0 +1,44 @@
+import os
+import pytest
+import sys
+import torch
+import torch.nn as nn
+
+from pytorch_lightning.utilities import AllGatherGrad
+
+
+def setup_ddp(rank, world_size):
+    """ Setup ddp enviroment """
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "8088"
+
+    if torch.distributed.is_available() and sys.platform not in ("win32", "cygwin"):
+        torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
+
+
+def _test_all_gather_ddp(rank, world_size):
+    setup_ddp(rank, world_size)
+
+    tensor1 = torch.ones(8, requires_grad=True)
+    tensor2 = torch.ones((8, 16, 32), requires_grad=True)
+
+    tensor1_gathered = AllGatherGrad.apply(tensor1)
+    tensor2_gathered = AllGatherGrad.apply(tensor2)
+
+    tensor1_gathered = tensor1_gathered * rank
+    tensor2_gathered = tensor2_gathered * rank
+
+    tensor1_gathered.sum().backward()
+    tensor2_gathered.sum().backward()
+
+    grad1 = torch.zeros_like(tensor1.grad).fill_(torch.arange(world_size).sum().float())
+    grad2 = torch.zeros_like(tensor2.grad).fill_(torch.arange(world_size).sum().float())
+
+    assert torch.allclose(grad1, tensor1.grad)
+    assert torch.allclose(grad2, tensor2.grad)
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
+def test_all_gather_ddp():
+    world_size = 3
+    torch.multiprocessing.spawn(_test_all_gather_ddp, args=(world_size,), nprocs=world_size)