异构并行训练
概述
异构并行训练方法是通过分析图上算子内存占用和计算密集度,将内存消耗巨大或适合CPU逻辑处理的算子切分到CPU子图,将内存消耗较小计算密集型算子切分到硬件加速器子图,框架协同不同子图进行网络训练,使得处于不同硬件且无依赖关系的子图能够并行进行执行的过程。
计算流程
MindSpore异构并行训练典型的计算流程如下图所示:
用户设置网络执行的后端
[ ]:
import mindspore as ms
ms.set_context(device_target="GPU")
用户设置特定算子执行后端
[1]:
from mindspore import ops
prim = ops.Add()
prim.set_device("CPU")
框架根据计算图算子标志进行切图
框架调度不同后端执行子图
当前典型使用异构并行计算的场景有:优化器异构、Embedding异构、PS异构。
优化器异构
在盘古或GPT3大模型训练过程中,优化器状态占用了大量内存,进而限制了可训练的模型规模。使用优化器异构,将优化器指定到CPU上执行,可以极大扩展可训练模型规模:
如图所示,将Adam算子配置到CPU执行同时指定加速器进行FP16计算,可以将参数内存占用降低到原始的1/3。
配置优化器算子到CPU执行
初始化FP16的权重参数以及FP32的优化器状态变量
将输入优化器的梯度转为FP16(如果本来就是FP16梯度,可忽略这步)
权重和梯度转为FP32参与优化器运算
更新后的FP32权重赋值给FP16的权重
优化器异构代码样例如下:
[2]:
import numpy as np
import mindspore as ms
import mindspore.ops as ops
from mindspore.common.initializer import initializer
from mindspore.nn import Optimizer
_adam_opt = ops.MultitypeFuncGraph("adam_opt")
host_assign = ops.Assign()
host_assign.set_device("CPU")
host_cast = ops.Cast()
host_cast.set_device("CPU")
device_cast = ops.Cast()
@_adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
"Tensor", "Bool", "Bool")
def _update_run_kernel(opt, beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flags, optim_filter):
"""
Update parameters by AdamWeightDecay op.
"""
success = True
if optim_filter:
param32 = host_cast(param, ms.float32)
gradient = device_cast(gradient, ms.float32)
if decay_flags:
next_param = opt(param32, m, v, lr, beta1, beta2, eps, weight_decay, gradient)
else:
next_param = opt(param32, m, v, lr, beta1, beta2, eps, 0.0, gradient)
ret = host_assign(param, host_cast(ops.depend(param32, next_param), ops.dtype(param)))
return ops.depend(success, ret)
return success
class AdamWeightDecayOp(Optimizer):
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0):
super(AdamWeightDecayOp, self).__init__(learning_rate, params, weight_decay)
self.beta1 = ms.Tensor(np.array([beta1]).astype(np.float32))
self.beta2 = ms.Tensor(np.array([beta2]).astype(np.float32))
self.eps = ms.Tensor(np.array([eps]).astype(np.float32))
self.moments1 = self.clone_param32(prefix="adam_m", init='zeros')
self.moments2 = self.clone_param32(prefix="adam_v", init='zeros')
self.opt = ops.AdamWeightDecay()
self.hyper_map = ops.HyperMap()
self.opt.set_device("CPU")
def construct(self, gradients):
"""AdamWeightDecayOp"""
lr = self.get_lr()
if self.is_group:
if self.is_group_lr:
optim_result = self.map_reverse(ops.partial(_adam_opt, self.opt, self.beta1, self.beta2, self.eps),
lr, self.weight_decay, self.parameters, self.moments1, self.moments2,
gradients, self.decay_flags, self.optim_filter)
else:
optim_result = self.map_reverse(ops.partial(_adam_opt, self.opt, self.beta1, self.beta2, self.eps, lr),
self.weight_decay, self.parameters, self.moments1, self.moments2,
gradients, self.decay_flags, self.optim_filter)
else:
optim_result = self.map_reverse(ops.partial(_adam_opt, self.opt, self.beta1, self.beta2, self.eps, lr,
self.weight_decay), self.parameters, self.moments1, self.moments2,
gradients, self.decay_flags, self.optim_filter)
return optim_result
def clone_param32(self, prefix, init=None):
new = []
for old_param in self.parameters:
param_init = init
if init is None:
param_init = old_param.init
new_state = old_param.clone()
new_state.set_dtype(ms.float32)
new_state.set_data(initializer(param_init, shape=old_param.shape, dtype=ms.float32))
new_state.name = prefix + '.' + new_state.name
new.append(new_state)
return ms.ParameterTuple(new)
步骤4、5也可以直接融合到优化器算子中做进一步优化,完整的优化器异构训练流程可以参考: https://gitee.com/mindspore/models/tree/r2.0.0-alpha/official/nlp/Pangu_alpha
Embedding异构
在一些需要查Embedding大表的网络中,Embedding表往往有上百G的规模,受加速器内存大小限制,无法直接将整表加载到加速器上执行。通过将与权重表相连的算子放到CPU上执行,避免加速器由于内存限制而无法训练网络的问题。
配置EmbeddingLookup算子到CPU执行
[3]:
ops.EmbeddingLookup().set_device('CPU')
配置EmbeddingLookup相关优化器到CPU执行
[4]:
use_locking = False
use_nesterov = False
ops.FusedSparseLazyAdam(use_locking, use_nesterov).set_device("CPU")
EmbeddingLookup算子设置代码样例如下:
[5]:
import mindspore.nn as nn
import mindspore.ops as ops
import mindspore as ms
from mindspore.common.initializer import initializer
class EmbeddingLookup(nn.Cell):
def __init__(self, vocab_size, embedding_size, param_init='normal',
target='CPU', sparse=True):
"""Initialize EmbeddingLookup."""
super(EmbeddingLookup, self).__init__()
validator.check_value_type('sparse', sparse, [bool], self.cls_name)
self.vocab_size = validator.check_positive_int(vocab_size, 'vocab_size')
self.target = target
self.sparse = sparse
if target not in ('CPU', 'DEVICE'):
raise ValueError('Attr \'target\' of \'EmbeddingLookup\' Op passed '
+ str(target) + ', should be one of values in \'CPU\', \'DEVICE\'.')
if not sparse and target == 'CPU':
raise ValueError('When target is CPU, embedding_lookup must be sparse.')
if sparse:
self.gatherv2 = ops.SparseGatherV2()
else:
self.gatherv2 = ops.Gather()
self.embeddinglookup = ops.EmbeddingLookup().set_device('CPU')
self.embedding_size = validator.check_positive_int(embedding_size, 'embedding_size')
self.embedding_table = ms.Parameter(initializer(param_init, [self.vocab_size, self.embedding_size]),
name='embedding_table')
def construct(self, indices):
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, indices, 0)
else:
out = self.gatherv2(self.embedding_table, indices, 0)
return out
当前nn目录下的EmbeddingLookup、FTRL、LazyAdam等算子已经封装好异构接口,用户只需设置target属性为CPU或DEVICE即可切换执行后端。
整体调用流程可以参考:https://gitee.com/mindspore/models/tree/r2.0.0-alpha/official/recommend/Wide_and_Deep
PS异构
在EmbeddingTable达到T级别,单机内存无法放下时,使用Parameter Server,通过异构的Pull/Push算子进行权重的拉取和更新。
Parameter Server封装异构流程,用户只需配置参数使用PS即可,具体配置流程请参考Parameter Server训练流程。
此外,wide&deep网络中也有使用PS的流程,可参考:https://gitee.com/mindspore/models/tree/r2.0.0-alpha/official/recommend/Wide_and_Deep
约束
当前需要用户指定算子执行的后端,不支持根据网络进行自动化配置。