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# 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 threading import Event, Lock, Thread
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
import torch
from torch import nn
from torch.distributed import ProcessGroup, rpc
from torch.distributed.distributed_c10d import _get_global_rank
from fairscale.nn.model_parallel.initialize import get_pipeline_parallel_group
from .async_pipe import AsyncPipe
from .types import EVENT_LOOP_QUEUE, PipeMessage, TensorOrTensors
DEFAULT_MAX_SOURCE_POSITIONS = 1024
DEFAULT_MAX_TARGET_POSITIONS = 1024
PipeModel: AsyncPipe
PipeResult: TensorOrTensors
SizeOrSizes = Union[torch.Size, List[torch.Size]]
DtypeOrDtypes = Union[torch.dtype, List[torch.dtype]]
def set_device_based_on_group(group: ProcessGroup) -> None:
# torch.cuda.set_device(group.rank() % torch.cuda.device_count())
torch.cuda.set_device(torch.distributed.get_rank() % torch.cuda.device_count())
def get_shapes(tensor: TensorOrTensors) -> SizeOrSizes:
if isinstance(tensor, torch.Tensor):
return tensor.shape
else:
return [t.shape for t in tensor]
def get_dtype(tensor: TensorOrTensors) -> DtypeOrDtypes:
if isinstance(tensor, torch.Tensor):
return tensor.dtype
else:
return [t.dtype for t in tensor]
def get_global_ranks_from_group(group: ProcessGroup) -> List[int]:
return [_get_global_rank(group, r) for r in range(group.size())]
class PipeBackRedirect(torch.autograd.Function):
@staticmethod
# type: ignore
def forward(ctx, inputs, dest, event, message, transport, futures):
ctx.dest = dest
ctx.event = event
ctx.message = message
ctx.transport = transport
ctx.futures = futures
return inputs
@staticmethod
# type: ignore
def backward(ctx, *grad):
ctx.message.tensors = tuple(grad)
ctx.transport.send_message(ctx.message, sync=False, skip_header=True)
ctx.event.set()
# torch.futures.wait_all(ctx.futures)
return (None, None, None, None, None, None)
def callback_with_model(callback: Callable[[Any, AsyncPipe], None], ctx: Any) -> None:
try:
group = get_pipeline_parallel_group() # FIXME(tom) handle dynamic group
set_device_based_on_group(group)
with PipeModel.lock:
callback(ctx, PipeModel)
except Exception as e:
print(f"callback_with_model got {e}")
class PipeRPCWrapper(nn.Module):
"""A wrapper for Pipe to control the entire pipeline from a single process.
Typical usecase would have rank 0 construct `PipeRPCWrapper` and run the
training loop as normal, and all other ranks would call
`torch.distributed.rpc.shutdown()`
To run code on each worker, e.g. to run the optimizer, use `foreach_worker`
"""
def __init__(self, *args: Any, **kwargs: Any):
super().__init__()
self.group = cast(ProcessGroup, kwargs.get("group")) or get_pipeline_parallel_group()
assert self.group.rank() == 0
self.lock = Lock()
if True:
assert (
self.group == get_pipeline_parallel_group()
), "Can't pickle groups, so group must be `get_pipeline_parallel_group()`"
kwargs["group"] = None
else:
kwargs["group"] = self.group
kwargs["input_device"] = torch.device("cuda", torch.cuda.current_device())
self.model = AsyncPipe(*args, **kwargs)
self.worker_map = kwargs["worker_map"]
self._foreach_worker(self._register_remote_model, args=(args, kwargs))
self.model.cuda()
def _get_rpc_name(self, rank: int) -> str:
return self.worker_map[_get_global_rank(self.group, rank)]
def _foreach_worker(self, callback: Callable, args: Any = None) -> None:
futures = [rpc.rpc_async(self._get_rpc_name(rank), callback, args=args) for rank in range(1, self.group.size())]
futures = [f.wait() for f in futures]
def foreach_worker(
self, callback: Callable[[Any, AsyncPipe], None], ctx: Any = None, *, include_self: bool = False
) -> None:
"""Call `callback` on each worker with the `ctx` and model local to that
worker. e.g.
def register_optimizer(ctx, model):
args, kwargs = ctx
model.optimizer = torch.optim.SGD(model.parameters(), *args, **kwargs)
pipe_model = PipeRPCWrapper( ... )
pipe_model.foreach_worker(
register_optimizer,
([], {"lr" : 0.01, "momentum" : 0.9})
)
"""
self._foreach_worker(callback_with_model, args=(callback, ctx))
if include_self:
with self.model.lock:
callback(ctx, self.model)
def forward(self, tensor: TensorOrTensors) -> TensorOrTensors: # type: ignore
shape = get_shapes(tensor)
dtype = get_dtype(tensor)
if isinstance(tensor, torch.Tensor):
num_tensors = 1
else:
num_tensors = len(tensor)
futures = [
rpc.rpc_async(self._get_rpc_name(rank), self._model_forward, args=(self.model.training, shape, dtype))
for rank in range(1, self.group.size())
]
if self.model.final_stage:
return self.model(tensor)
else:
event = Event()
t = Thread(target=self._model_forward_first_stage, args=(tensor, event))
t.start()
shape, dtype = futures.pop().wait()
dest_rank = self.group.size() - 1
dest = self._get_rpc_name(dest_rank)
dest_global_rank = _get_global_rank(self.group, dest_rank)
src_global_rank = torch.distributed.get_rank()
queue = EVENT_LOOP_QUEUE
activations = PipeMessage(dest_global_rank, src_global_rank, queue_name=queue, tensor_count=num_tensors)
grads = PipeMessage(src_global_rank, dest_global_rank, queue_name=queue, tensor_count=num_tensors)
back_fut = rpc.rpc_async(
dest, self._send_result_and_do_backwards, args=(self.model.training, activations, grads)
)
futures.append(back_fut)
result = self._recv_result(self.model, shape, dtype, activations)
if isinstance(result, torch.Tensor):
result.requires_grad_()
else:
for r in result:
r.requires_grad_()
assert self.model.pipeline
return PipeBackRedirect.apply(
result, dest_global_rank, event, grads, self.model.pipeline.transport, futures
)
@property
def final_stage(self) -> bool:
return self.model.final_stage
@staticmethod
def _recv_result(
model: AsyncPipe, shapes: SizeOrSizes, dtypes: DtypeOrDtypes, message: PipeMessage
) -> TensorOrTensors:
group = get_pipeline_parallel_group()
set_device_based_on_group(group)
assert model.pipeline
transport = model.pipeline.transport
if isinstance(shapes, torch.Size):
message.tensor_shapes = [cast(torch.Size, shapes)]
message.tensor_dtypes = [cast(torch.dtype, dtypes)]
message = transport.recv_message_tensors(message)
return message.tensors[0]
else:
message.tensor_shapes = cast(List[torch.Size], shapes)
message.tensor_dtypes = cast(List[torch.dtype], dtypes)
message = transport.recv_message_tensors(message)
return message.tensors
@staticmethod
def _send_result_and_do_backwards(training: bool, message: PipeMessage, grads_message: PipeMessage) -> None:
group = get_pipeline_parallel_group()
set_device_based_on_group(group)
result = PipeResult
model = PipeModel
if isinstance(result, torch.Tensor):
result = tuple([result])
message.tensors = tuple(result)
assert model.pipeline
transport = model.pipeline.transport
transport.send_message(message, sync=False, skip_header=True)
if training:
grads_message.tensor_shapes = [r.shape for r in result]
grads_message.tensor_dtypes = [r.dtype for r in result]
grads_message = transport.recv_message_tensors(grads_message)
with model.lock:
torch.autograd.backward(result, grads_message.tensors, retain_graph=True)
@staticmethod
def _register_remote_model(args: List[Any], kwargs: Dict[str, Any]) -> None:
group = get_pipeline_parallel_group() # FIXME(tom) handle dynamic group
set_device_based_on_group(group)
kwargs["group"] = group
kwargs["input_device"] = torch.device("cuda", torch.cuda.current_device())
model = AsyncPipe(*args, **kwargs)
model.cuda()
global PipeModel
PipeModel = model
@staticmethod
def _model_forward(
training: bool, shape: torch.Size, dtype: torch.dtype
) -> Optional[Tuple[SizeOrSizes, DtypeOrDtypes]]:
try:
if isinstance(shape, torch.Size):
tensor = torch.empty(shape, dtype=dtype)
else:
tensor = tuple([torch.empty(s, dtype=d) for s, d in zip(shape, dtype)])
model = PipeModel
assert model.group
set_device_based_on_group(model.group)
model.train(training)
result = model(tensor)
if model.final_stage:
global PipeResult
PipeResult = result
return (get_shapes(result), get_dtype(result))
return None
except Exception as e:
print(f"_model_forward got {e}")
raise e
def _model_forward_first_stage(self, tensor: TensorOrTensors, event: Event) -> None:
try:
assert self.model.group
set_device_based_on_group(self.model.group)
self.model(tensor, event=event)
except Exception as e:
print(f"_model_forward got {e}")
raise e
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