[ADD] support Distributed Data Parallel

oslo/torch/nn/parallel/data_parallel/__init__.py

@@ -1 +1,6 @@
+from oslo.torch.nn.parallel.data_parallel.data_parallel import (
+    DistributedDataParallel,
+)
 from oslo.torch.nn.parallel.data_parallel.zero import *
+
+__ALL__ = ["DistributedDataParallel", "ZeroRedundancyOptimizer"]

oslo/torch/nn/parallel/data_parallel/_reducer.py

@@ -0,0 +1,116 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import functools
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+from torch import Tensor
+from torch.distributed import ProcessGroup
+
+
+class Bucket:
+    def __init__(
+        self, size: int, dtype: torch.dtype, device: torch.device, group: ProcessGroup
+    ):
+        self.buffer = torch.zeros(size, dtype=dtype, device=device)
+        self.group = group
+        self.offset = 0
+        self.callbacks: List[Callable] = []
+
+    def flush(self) -> None:
+        """Flush content of the bucket."""
+        if self.offset == 0:
+            assert len(self.callbacks) == 0
+            return
+        # reduce-scatter bucket
+        dist.all_reduce(self.buffer[: self.offset], group=self.group)
+
+        # execute post-reduction callbacks
+        for callback_fn in self.callbacks:
+            callback_fn()
+        # reuse input bucket but allocate a fresh output shard
+        self.offset = 0
+        self.callbacks.clear()
+        self.buffer = torch.zeros_like(self.buffer)
+
+    def alloc(self) -> None:
+
+        if self.buffer.storage().size() == 0:
+            self.buffer.storage().resize_(self.buffer.numel())
+
+    def free(self) -> None:
+
+        assert self.offset == 0 and self.callbacks == [], "Incorrect call of teardown"
+        self.buffer.storage().resize_(0)
+
+    def append(self, tensor: Tensor, callback_fn: Callable):
+        tensor_size = tensor.numel()
+        offset = self.offset
+        self.buffer[offset : offset + tensor_size].copy_(tensor.flatten())
+        self.offset += tensor_size
+
+        # callback will be given the reduced result
+        if callback_fn is not None:
+            result_view = self.buffer[offset : offset + tensor_size].view(tensor.shape)
+            self.callbacks.append(functools.partial(callback_fn, result_view))
+
+    @property
+    def avail_size(self) -> int:
+        return self.buffer.size(0) - self.offset
+
+
+class Reducer:
+    def __init__(self, bucket_size_mb: int = 25):
+        self.bucket_size_mb = bucket_size_mb
+        self.buckets: Dict[Tuple[torch.dtype, torch.device, ProcessGroup], Bucket] = {}
+
+    @torch.no_grad()
+    def all_reduce_async(
+        self,
+        tensor: Tensor,
+        group: ProcessGroup,
+        callback_fn: Optional[Callable] = None,
+    ) -> None:
+        bucket_size = self._get_bucket_size(tensor.element_size())
+
+        if tensor.numel() >= bucket_size:
+            dist.all_reduce(tensor, group=group)
+            if callback_fn is not None:
+                callback_fn(tensor)
+            return
+
+        bucket = self._get_bucket(tensor, group)
+        if tensor.numel() > bucket.avail_size:
+            # not enough space remaining in bucket, flush it now
+            bucket.flush()
+        bucket.append(tensor, callback_fn)
+
+    @torch.no_grad()
+    def flush(self) -> None:
+        for bucket in self.buckets.values():
+            bucket.flush()
+
+    @torch.no_grad()
+    def free(self) -> None:
+        for bucket in self.buckets.values():
+            bucket.free()
+
+    @functools.lru_cache()
+    def _get_bucket_size(self, element_size: int) -> int:
+        if self.bucket_size_mb <= 0:  # Values <= 0 disable bucketing.
+            return 0
+        MB = 1024 * 1024
+        bucket_size = self.bucket_size_mb * MB / element_size
+        return int(bucket_size)
+
+    def _get_bucket(self, tensor: Tensor, group: ProcessGroup) -> Bucket:
+        key = (tensor.dtype, tensor.device, group)
+        if key not in self.buckets:
+            bucket_size = self._get_bucket_size(tensor.element_size())
+            self.buckets[key] = Bucket(bucket_size, tensor.dtype, tensor.device, group)
+        self.buckets[key].alloc()
+        return self.buckets[key]

oslo/torch/nn/parallel/data_parallel/_utils.py

@@ -0,0 +1,24 @@
+from typing import Iterable
+
+import torch
+
+
+def is_ddp_ignored(p):
+    return getattr(p, "_ddp_to_ignore", False)
+
+
+def set_params_to_ignore(params_to_ignore: Iterable[torch.Tensor]) -> None:
+    """Sets parameters to be ignored by DDP.
+    This method must be called before initializing DistributedDataParallel.
+    Example:
+        >>> params_to_ignore = []
+        >>> for p in module.parameters():
+        >>>     if should_ignore(p):
+        >>>         params_to_ignore.append(p)
+        >>>         set_params_to_ignore(params_to_ignore)
+        >>> module = DistributedDataParallel(module)
+    Args:
+        params_to_ignore (Iterable[torch.Tensor]): A list of parameters to be ignored.
+    """
+    for p in params_to_ignore:
+        p._ddp_to_ignore = True

oslo/torch/nn/parallel/data_parallel/data_parallel.py

@@ -0,0 +1,179 @@
+import copy
+from functools import partial
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.distributed as dist
+
+try:
+    from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX
+except ImportError:
+    _EXTRA_STATE_KEY_SUFFIX = "_extra_state"
+
+from oslo.torch.distributed.parallel_context import ParallelContext
+from oslo.torch.distributed.parallel_mode import ParallelMode
+from oslo.torch.nn.parallel.utils import (
+    get_parallel_context,
+    add_wrapper,
+    OsloParallelWrapper,
+)
+from oslo.torch.nn.parallel.data_parallel._reducer import Reducer
+from oslo.torch.nn.parallel.data_parallel._utils import is_ddp_ignored
+
+
+def free_storage(data: torch.Tensor) -> None:
+    """Free underlying storage of a Tensor."""
+    if data.storage().size() > 0:
+        # Since we're modifying the Tensor's Storage directly, make sure the Tensor
+        # is the sole occupant of the Storage.
+        assert data.storage_offset() == 0
+        data.storage().resize_(0)
+
+
+class BackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, module, inputs):
+        ctx.module = module
+        return inputs
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        ctx.module._pre_backward()
+        # Enqueue a callback to flush the reducer.
+        # This callback is triggered after all gradients' calculation is completed.
+        Variable._execution_engine.queue_callback(ctx.module._post_backward)
+        return (None,) + grad_outputs
+
+
+def DistributedDataParallel(
+    module: nn.Module,
+    parallel_context: ParallelContext,
+    bucket_cap_mb: int = 25,
+    rebuild_bucket: bool = True,
+):
+    ddp = _DistributedDataParallel(
+        module=module,
+        parallel_context=parallel_context,
+        bucket_cap_mb=bucket_cap_mb,
+        rebuild_bucket=rebuild_bucket,
+    )
+
+    add_wrapper(
+        module,
+        mode=ParallelMode.DATA,
+        wrapper=ddp,
+        parallel_context=parallel_context,
+    )
+    return module
+
+
+class _DistributedDataParallel(OsloParallelWrapper):
+    """Distributed data parallel wrapper for Oslo.
+    Example:
+        >>> from oslo.torch.nn.parallel import DistributedDataParallel as DDP
+        >>> model = torch.nn.Linear(20, 1)
+        >>> model = DDP(model, parallel_context)
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+        >>> olso.ready(model, parallel_context)
+        >>> model.zero_grad()
+        >>> logits = model(x)
+        >>> loss = criterion(logits, labels)
+        >>> loss.backward()
+        >>> optimizer.step()
+    Args:
+        module (nn.Module): PyTorch module object
+        parallel_context (ParallelContext): process group object
+    """
+
+    def __init__(
+        self,
+        module: torch.nn.Module,
+        parallel_context: ParallelContext = None,
+        bucket_cap_mb: int = 25,
+        rebuild_bucket: bool = True,
+    ) -> None:
+        super().__init__(parallelism_priority=99)
+        self.module = module
+
+        self.comm_stream: torch.cuda.Stream = torch.cuda.Stream()
+        assert parallel_context
+        self.parallel_context = get_parallel_context(module, parallel_context)
+        self.dp_world_size = self.parallel_context.get_world_size(ParallelMode.DATA)
+
+        self.reducer = Reducer(bucket_cap_mb)
+        self.rebuild_bucket = rebuild_bucket
+
+        for p in module.parameters():
+            if is_ddp_ignored(p):
+                continue
+            if p.requires_grad:
+                p.register_hook(partial(self.grad_handle, p))
+
+    def parallelize(self):
+        self._forward = copy.copy(self.module.forward)
+        self.module.zero_grad = self.zero_grad
+
+    def forward(self, *args, **kwargs):
+        return BackwardFunction.apply(self, self._forward(*args, **kwargs))
+
+    def _pre_backward(self):
+        pass
+
+    def _post_backward(self):
+        with torch.cuda.stream(self.comm_stream):
+            self.reducer.flush()
+        torch.cuda.current_stream().wait_stream(self.comm_stream)
+        if self.rebuild_bucket:
+            self.reducer.free()
+        for p in self.module.parameters():
+            if is_ddp_ignored(p):
+                continue
+            if p.grad.device.type != "cpu":
+                p.grad = p._saved_grad
+
+    def grad_handle(self, p, grad):
+        if grad.device.type != "cpu":
+            empty_grad = torch.empty_like(grad)
+            free_storage(empty_grad)
+            if self.dp_world_size > 1:
+                grad = grad / self.dp_world_size
+                self.comm_stream.wait_stream(torch.cuda.current_stream())
+                with torch.cuda.stream(self.comm_stream):
+                    self.reducer.all_reduce_async(
+                        grad,
+                        group=self.parallel_context.get_group(ParallelMode.DATA),
+                        callback_fn=partial(self._save_grad, p),
+                    )
+                grad.record_stream(self.comm_stream)
+            else:
+                _DistributedDataParallel._save_grad(p, grad)
+
+            return empty_grad
+
+        else:
+            # You must model.to('cpu') after oslo.ready() to use cpu.
+            dist.all_reduce(
+                grad, group=self.parallel_context.get_cpu_group(ParallelMode.DATA)
+            )
+            return grad
+
+    @staticmethod
+    def _save_grad(p, grad):
+        if hasattr(p, "_saved_grad"):
+            p._saved_grad.add_(grad)
+        else:
+            p._saved_grad = grad
+
+    def zero_grad(self, set_to_none: bool = False) -> None:
+        super().zero_grad(set_to_none=True)
+        for p in self.module.parameters():
+            if getattr(p, "_saved_grad", None) is not None:
+                if set_to_none:
+                    p._saved_grad = None
+                else:
+                    if p._saved_grad.grad_fn is not None:
+                        p._saved_grad.detach_()
+                    else:
+                        p._saved_grad.requires_grad_(False)
+                    p._saved_grad.zero_()

oslo/torch/nn/parallel/data_parallel/zero/__init__.py

@@ -2,4 +2,4 @@
     ZeroRedundancyOptimizer,
 )
 
-__all__ = ["ZeroRedundancyOptimizer"]
+__ALL__ = ["ZeroRedundancyOptimizer"]

oslo/torch/nn/parallel/data_parallel/zero/sharded_optim/__init__.py

@@ -2,4 +2,4 @@
     ZeroRedundancyOptimizer,
 )
 
-__all__ = ["ZeroRedundancyOptimizer"]
+__ALL__ = ["ZeroRedundancyOptimizer"]

oslo/torch/nn/parallel/data_parallel/zero/sharded_optim/bookkeeping/__init__.py

@@ -3,4 +3,4 @@
 from .parameter_store import ParameterStore
 from .tensor_store import TensorBucket
 
-_all__ = ["BucketStore", "GradientStore", "ParameterStore", "TensorBucket"]
+_ALL__ = ["BucketStore", "GradientStore", "ParameterStore", "TensorBucket"]