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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 abc import ABC
from abc import abstractmethod
import math
import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from megatron import get_args
from megatron import mpu
from .module import MegatronModule
class MemoryBuffer:
def __init__(self, numel, numel_padded, dtype):
self.numel = numel
self.numel_padded = numel_padded
self.dtype = dtype
self.data = torch.zeros(self.numel_padded,
dtype=self.dtype,
device=torch.cuda.current_device(),
requires_grad=False)
def zero(self):
"""Reset the buffer to zero."""
self.data.zero_()
def get(self, shape, start_index):
"""Return a tensor with the input `shape` as a view into the
1-D data starting at `start_index`."""
end_index = start_index + shape.numel()
assert end_index <= self.numel, \
'requested tensor is out of the buffer range.'
buffer_tensor = self.data[start_index:end_index]
buffer_tensor = buffer_tensor.view(shape)
return buffer_tensor
class DistributedDataParallelBase(MegatronModule, ABC):
"""Abstract class for DDP."""
def __init__(self, module):
super(DistributedDataParallelBase, self).__init__()
# Keep a pointer to the model.
self.module = module
@abstractmethod
def allreduce_gradients(self):
pass
def forward(self, *inputs, **kwargs):
return self.module(*inputs, **kwargs)
def state_dict(self, destination=None, prefix='', keep_vars=False):
return self.module.state_dict(destination, prefix, keep_vars)
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
keep_vars=False):
return self.module.state_dict_for_save_checkpoint(destination, prefix,
keep_vars)
def load_state_dict(self, state_dict, strict=True):
self.module.load_state_dict(state_dict, strict=strict)
class DistributedDataParallel(DistributedDataParallelBase):
"""DDP with contiguous buffers options to storre and accumulate gradients.
This class:
- has the potential to reduce memory fragmentation.
- provides the option to do the gradient accumulation
in a type other than the params type (for example fp32)
Arguments:
module: input model.
accumulate_allreduce_grads_in_fp32: if true do the gradient accumulation
and the gradient all-reduce all in in float32. If this option is
true, we require `use_contiguous_buffers` to be true too.
use_contiguous_buffers: if true, use a contiguous buffer to store the
gradients.
"""
def __init__(self, module,
accumulate_allreduce_grads_in_fp32,
use_contiguous_buffers):
super(DistributedDataParallel, self).__init__(module)
self.accumulate_allreduce_grads_in_fp32 \
= accumulate_allreduce_grads_in_fp32
self.use_contiguous_buffers = use_contiguous_buffers
# If we are using fp32-accumulate-allreduce explicitly
# this means we need main grads in a continous buffer.
if self.accumulate_allreduce_grads_in_fp32:
assert self.use_contiguous_buffers
# ===================================
# Rest of this part applies only to
# the case we use continuous buffers.
# ===================================
self._grad_buffers = None
self._grad_buffer_param_index_map = None
if self.use_contiguous_buffers:
self._grad_buffers = {}
self._grad_buffer_param_index_map = {}
data_parallel_world_size = mpu.get_data_parallel_world_size()
# Simple function to define buffer type.
def _get_buffer_type(param):
return torch.float if \
self.accumulate_allreduce_grads_in_fp32 else param.dtype
# First calculate total number of elements per type.
type_num_elements = {}
for param in self.module.parameters():
if param.requires_grad:
dtype = _get_buffer_type(param)
type_num_elements[dtype] = type_num_elements.get(dtype, 0) \
+ param.data.nelement()
# Allocate the buffer.
for dtype, num_elements in type_num_elements.items():
# If using distributed optimizer, pad memory buffer to be
# multiple of data_parallel_world_size. (This padding is done
# due to a constraint with the reduce_scatter op, which requires
# all tensors have equal size. See: optimizer.py.)
num_elements_padded = data_parallel_world_size * \
int(math.ceil(num_elements / data_parallel_world_size))
# Allocate grad buffer.
self._grad_buffers[dtype] = MemoryBuffer(num_elements,
num_elements_padded,
dtype)
# Assume the back prop order is reverse the params order,
# store the start index for the gradients.
for param in self.module.parameters():
if param.requires_grad:
dtype = _get_buffer_type(param)
type_num_elements[dtype] -= param.data.nelement()
param.main_grad = self._grad_buffers[dtype].get(
param.data.shape, type_num_elements[dtype])
if dtype not in self._grad_buffer_param_index_map:
self._grad_buffer_param_index_map[dtype] = {}
self._grad_buffer_param_index_map[dtype][param] = (
type_num_elements[dtype],
type_num_elements[dtype] + param.data.nelement(),
)
# Backward hook.
# Accumalation function for the gradients. We need
# to store them so they don't go out of scope.
self.grad_accs = []
# Loop over all the parameters in the model.
for param in self.module.parameters():
if param.requires_grad:
# Expand so we get access to grad_fn.
param_tmp = param.expand_as(param)
# Get the gradient accumulator functtion.
grad_acc = param_tmp.grad_fn.next_functions[0][0]
grad_acc.register_hook(self._make_param_hook(param))
self.grad_accs.append(grad_acc)
def _make_param_hook(self, param):
"""Create the all-reduce hook for backprop."""
# Hook used for back-prop.
def param_hook(*unused):
# Add the gradient to the buffer.
if param.grad is not None:
# The gradient function of linear layers is fused with GEMMs
param.main_grad.add_(param.grad.data)
# Now we can deallocate grad memory.
param.grad = None
return param_hook
def zero_grad_buffer(self):
"""Set the grad buffer data to zero. Needs to be called at the
begining of each iteration."""
assert self._grad_buffers is not None, 'buffers are not initialized.'
for _, buffer_ in self._grad_buffers.items():
buffer_.zero()
def broadcast_params(self):
for param in self.module.parameters():
torch.distributed.broadcast(param.data,
src=mpu.get_data_parallel_src_rank(),
group=mpu.get_data_parallel_group())
def allreduce_gradients(self):
"""Reduce gradients across data parallel ranks."""
# If we have buffers, simply reduce the data in the buffer.
if self._grad_buffers is not None:
for _, buffer_ in self._grad_buffers.items():
buffer_.data /= mpu.get_data_parallel_world_size()
torch.distributed.all_reduce(
buffer_.data, group=mpu.get_data_parallel_group())
else:
# Otherwise, bucketize and all-reduce
buckets = {}
# Pack the buckets.
for param in self.module.parameters():
if param.requires_grad and param.grad is not None:
tp = param.data.type()
if tp not in buckets:
buckets[tp] = []
buckets[tp].append(param)
param.main_grad = param.grad
# For each bucket, all-reduce and copy all-reduced grads.
for tp in buckets:
bucket = buckets[tp]
grads = [param.grad.data for param in bucket]
coalesced = _flatten_dense_tensors(grads)
coalesced /= mpu.get_data_parallel_world_size()
torch.distributed.all_reduce(
coalesced, group=mpu.get_data_parallel_group())
for buf, synced in zip(grads, _unflatten_dense_tensors(
coalesced, grads)):
buf.copy_(synced)
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