异构并行训练

Ascend GPU 设计 模型运行

在线运行下载Notebook下载样例代码查看源文件

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:

heterogeneous-heter

  1. 用户设置网络执行的后端

[ ]:
from mindspore import context
context.set_context(device_target="GPU")
  1. 用户设置特定算子执行后端

[1]:
from mindspore import ops

prim = ops.Add()

prim.add_prim_attr("primitive_target", "CPU")
  1. 框架根据计算图算子标志进行切图

  2. 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:

heterogeneous-heter-opt

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。

  1. 配置优化器算子到CPU执行

  2. 初始化FP16的权重参数以及FP32的优化器状态变量

  3. 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)

  4. 权重和梯度转为FP32参与优化器运算

  5. 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
from mindspore import dtype as mstype
import mindspore.ops as ops
from mindspore.common.initializer import initializer
from mindspore import Tensor
from mindspore import ParameterTuple
from mindspore.nn import Optimizer
_adam_opt = ops.MultitypeFuncGraph("adam_opt")
host_assign = ops.Assign()
host_assign.add_prim_attr("primitive_target", "CPU")
host_cast = ops.Cast()
host_cast.add_prim_attr("primitive_target", "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, mstype.float32)
        gradient = device_cast(gradient, mstype.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 = Tensor(np.array([beta1]).astype(np.float32))
        self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
        self.eps = 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.add_prim_attr("primitive_target", "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(mstype.float32)
            new_state.set_data(initializer(param_init, shape=old_param.shape, dtype=mstype.float32))
            new_state.name = prefix + '.' + new_state.name
            new.append(new_state)
        return 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/r1.6/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。

heterogeneous-heter-embed

  1. 配置EmbeddingLookup算子到CPU执行

[3]:
ops.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU')
  1. 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).add_prim_attr("primitive_target", "CPU")

EmbeddingLookup算子设置代码样例如下:

[5]:
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Parameter
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().add_prim_attr('primitive_target', 'CPU')
        self.embedding_size = validator.check_positive_int(embedding_size, 'embedding_size')
        self.embedding_table = 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/r1.6/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。

heterogeneous-heter-ps

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/r1.6/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。