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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
from functools import reduce
import operator
import torch
from deepspeed.accelerator import get_accelerator
from megatron import get_args
from megatron.core import mpu
def _communicate(tensor_send_next, tensor_send_prev, recv_prev, recv_next,
use_ring_exchange=False):
"""Communicate tensors between stages. Used as helper method in other
communication methods that are used in megatron/schedules.py.
Takes the following arguments:
tensor_send_next: tensor to send to next rank (no tensor sent if
set to None).
tensor_send_prev: tensor to send to prev rank (no tensor sent if
set to None).
recv_prev: boolean for whether tensor should be received from
previous rank.
recv_next: boolean for whether tensor should be received from
next rank.
use_ring_exchange: boolean for whether torch.distributed.ring_exchange()
API should be used.
Returns:
(tensor_recv_prev, tensor_recv_next)
"""
args = get_args()
# Create placeholder tensors for receive in forward and backward directions
# if needed.
tensor_recv_prev = None
tensor_recv_next = None
tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size)
if args.scatter_gather_tensors_in_pipeline:
tensor_chunk_shape = reduce(operator.mul, tensor_shape, 1) // \
mpu.get_tensor_model_parallel_world_size()
else:
tensor_chunk_shape = tensor_shape
dtype = args.params_dtype
if args.fp32_residual_connection:
dtype = torch.float
if recv_prev:
tensor_recv_prev = torch.empty(tensor_chunk_shape,
requires_grad=True,
device=get_accelerator().current_device_name(),
dtype=dtype)
if recv_next:
tensor_recv_next = torch.empty(tensor_chunk_shape,
requires_grad=True,
device=get_accelerator().current_device_name(),
dtype=dtype)
# Split tensor into smaller chunks if using scatter-gather optimization.
if args.scatter_gather_tensors_in_pipeline:
if tensor_send_next is not None:
tensor_send_next = mpu.split_tensor_into_1d_equal_chunks(tensor_send_next)
if tensor_send_prev is not None:
tensor_send_prev = mpu.split_tensor_into_1d_equal_chunks(tensor_send_prev)
# Send tensors in both the forward and backward directions as appropriate.
if use_ring_exchange:
torch.distributed.ring_exchange(tensor_send_prev=tensor_send_prev,
tensor_recv_prev=tensor_recv_prev,
tensor_send_next=tensor_send_next,
tensor_recv_next=tensor_recv_next,
group=mpu.get_pipeline_model_parallel_group())
else:
ops = []
if tensor_send_prev is not None:
send_prev_op = torch.distributed.P2POp(
torch.distributed.isend, tensor_send_prev,
mpu.get_pipeline_model_parallel_prev_rank())
ops.append(send_prev_op)
if tensor_recv_prev is not None:
recv_prev_op = torch.distributed.P2POp(
torch.distributed.irecv, tensor_recv_prev,
mpu.get_pipeline_model_parallel_prev_rank())
ops.append(recv_prev_op)
if tensor_send_next is not None:
send_next_op = torch.distributed.P2POp(
torch.distributed.isend, tensor_send_next,
mpu.get_pipeline_model_parallel_next_rank())
ops.append(send_next_op)
if tensor_recv_next is not None:
recv_next_op = torch.distributed.P2POp(
torch.distributed.irecv, tensor_recv_next,
mpu.get_pipeline_model_parallel_next_rank())
ops.append(recv_next_op)
if len(ops) > 0:
reqs = torch.distributed.batch_isend_irecv(ops)
for req in reqs:
req.wait()
# To protect against race condition when using batch_isend_irecv().
get_accelerator().synchronize()
# If using scatter-gather optimization, gather smaller chunks.
if args.scatter_gather_tensors_in_pipeline:
if recv_prev:
tensor_recv_prev = mpu.gather_split_1d_tensor(
tensor_recv_prev).view(tensor_shape).requires_grad_()
if recv_next:
tensor_recv_next = mpu.gather_split_1d_tensor(
tensor_recv_next).view(tensor_shape).requires_grad_()
return tensor_recv_prev, tensor_recv_next
def recv_forward(timers=None):
"""Receive tensor from previous rank in pipeline (forward receive)."""
if mpu.is_pipeline_first_stage():
input_tensor = None
else:
if timers is not None:
timers('forward-recv').start()
input_tensor, _ = _communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_prev=True,
recv_next=False)
if timers is not None:
timers('forward-recv').stop()
return input_tensor
def recv_backward(timers=None):
"""Receive tensor from next rank in pipeline (backward receive)."""
if mpu.is_pipeline_last_stage():
output_tensor_grad = None
else:
if timers is not None:
timers('backward-recv').start()
_, output_tensor_grad = _communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_prev=False,
recv_next=True)
if timers is not None:
timers('backward-recv').stop()
return output_tensor_grad
def send_forward(output_tensor, timers=None):
"""Send tensor to next rank in pipeline (forward send)."""
if not mpu.is_pipeline_last_stage():
if timers is not None:
timers('forward-send').start()
_communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=False,
recv_next=False)
if timers is not None:
timers('forward-send').stop()
def send_backward(input_tensor_grad, timers=None):
"""Send tensor to previous rank in pipeline (backward send)."""
if not mpu.is_pipeline_first_stage():
if timers is not None:
timers('backward-send').start()
_communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=False,
recv_next=False)
if timers is not None:
timers('backward-send').stop()
def send_forward_recv_backward(output_tensor, timers=None):
"""Batched send and recv with next rank in pipeline."""
if mpu.is_pipeline_last_stage():
output_tensor_grad = None
else:
if timers is not None:
timers('forward-send-backward-recv').start()
_, output_tensor_grad = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=False,
recv_next=True)
if timers is not None:
timers('forward-send-backward-recv').stop()
return output_tensor_grad
def send_backward_recv_forward(input_tensor_grad, timers=None):
"""Batched send and recv with previous rank in pipeline."""
if mpu.is_pipeline_first_stage():
input_tensor = None
else:
if timers is not None:
timers('backward-send-forward-recv').start()
input_tensor, _ = _communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=True,
recv_next=False)
if timers is not None:
timers('backward-send-forward-recv').stop()
return input_tensor
def send_forward_recv_forward(output_tensor, recv_prev, timers=None):
"""Batched recv from previous rank and send to next rank in pipeline."""
if timers is not None:
timers('forward-send-forward-recv').start()
input_tensor, _ = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_prev=recv_prev,
recv_next=False)
if timers is not None:
timers('forward-send-forward-recv').stop()
return input_tensor
def send_backward_recv_backward(input_tensor_grad, recv_next, timers=None):
"""Batched recv from next rank and send to previous rank in pipeline."""
if timers is not None:
timers('backward-send-backward-recv').start()
_, output_tensor_grad = _communicate(
tensor_send_next=None,
tensor_send_prev=input_tensor_grad,
recv_prev=False,
recv_next=recv_next)
if timers is not None:
timers('backward-send-backward-recv').stop()
return output_tensor_grad
def send_forward_backward_recv_forward_backward(
output_tensor, input_tensor_grad, recv_prev,
recv_next, timers=None):
"""Batched send and recv with previous and next ranks in pipeline."""
if timers is not None:
timers('forward-backward-send-forward-backward-recv').start()
input_tensor, output_tensor_grad = _communicate(
tensor_send_next=output_tensor,
tensor_send_prev=input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next)
if timers is not None:
timers('forward-backward-send-forward-backward-recv').stop()
return input_tensor, output_tensor_grad
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