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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import queue
from dataclasses import dataclass
from typing import Optional
import torch
from inference.infra.distributed import parallel_state as mpu
@dataclass
class TensorAndHandler:
tensor: torch.Tensor
handler: torch.distributed.Work
class PPScheduler:
def __init__(self):
"""Initialize an instance of the PPScheduler class"""
self.device: torch.device = torch.device(f"cuda:{torch.cuda.current_device()}")
self.recv_queue: queue.Queue = queue.Queue()
def isend_next(self, tensor: torch.Tensor) -> torch.distributed.Work:
"""Asynchronously send a tensor to the next pipeline and return the send handle.
Args:
tensor (torch.Tensor): The tensor to be sent.
Returns:
torch.distributed.Work: The handle for the send operation.
"""
handle = torch.distributed.isend(
tensor.contiguous(), dst=mpu.get_pipeline_model_parallel_next_rank(), group=mpu.get_pp_group()
)
return handle
def irecv_prev(self, buffer: torch.Tensor) -> torch.distributed.Work:
"""Asynchronously receive a tensor from the previous pipeline and return the receive handle.
Args:
buffer (torch.Tensor): The buffer tensor for receiving data.
Returns:
torch.distributed.Work: The handle for the receive operation.
"""
handle = torch.distributed.irecv(buffer, src=mpu.get_pipeline_model_parallel_prev_rank(), group=mpu.get_pp_group())
return handle
def recv_prev_data(self, shape: torch.Size, dtype: torch.dtype) -> torch.Tensor:
"""Receive data from the previous pipeline and return the received tensor.
Args:
shape (torch.Size): The shape of the tensor to receive.
dtype (torch.dtype): The data type of the tensor to receive.
Returns:
torch.Tensor: The received tensor.
"""
recv_tensor = torch.empty(shape, dtype=dtype, device=self.device)
self.irecv_prev(recv_tensor).wait()
return recv_tensor
def queue_irecv_prev(self, shape: torch.Size, dtype: torch.dtype) -> None:
"""Put the asynchronously received tensor and handle into the receive queue.
Args:
shape (torch.Size): The shape of the tensor to receive.
dtype (torch.dtype): The data type of the tensor to receive.
"""
recv_tensor = torch.empty(shape, dtype=dtype, device=self.device)
handle = self.irecv_prev(recv_tensor)
self.recv_queue.put(TensorAndHandler(tensor=recv_tensor, handler=handle))
def queue_irecv_prev_data(self) -> torch.Tensor:
"""Get a tensor from the receive queue and wait for the receive operation to complete.
Returns:
torch.Tensor: The received tensor obtained from the queue.
"""
tensor_and_handler = self.recv_queue.get()
tensor_and_handler.handler.wait()
return tensor_and_handler.tensor
_PP_SCHEDULER: Optional[PPScheduler] = None
def init_pp_scheduler():
"""Initialize the PPScheduler instance.
Raises:
AssertionError: If the PPScheduler is already initialized.
"""
global _PP_SCHEDULER
assert _PP_SCHEDULER is None, "pipeline model parallel group is already initialized"
_PP_SCHEDULER = PPScheduler()
def pp_scheduler() -> PPScheduler:
"""Get the current PPScheduler instance.
Returns:
PPScheduler: The current PPScheduler instance.
Raises:
AssertionError: If the PPScheduler has not been initialized.
"""
assert _PP_SCHEDULER is not None, "pipeline model parallel group is not initialized"
return _PP_SCHEDULER
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