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https://www.hiascend.com/document/detail/zh/mindcluster/70rc1/clustersched/dlug/mxdlug_007.html

有如下几类 configmap

  • cmDevice: ns, kube-system; cmName, mindx-dl-deviceinfo-{NodeName}; which is reported by device-plugin
  • cmNode: ns, mindx-dl; cmName, mindx-dl-nodeinfo-{NodeName}; which is reported by nodeD
  • cmPingMesh: ns, cluster-system; cmName, pingmesh-config;
  • cmSuperPodDevice: ns, cluster-system; cmName, super-pod-{SuperPodId}; clusterD 维护
    • 特别的 {RAS_NET_ROOT_PATH}/cluster/super-pod-{SuperPodId}/super-pod-{SuperPodId}.json; clusterD 维护
  • cmPubicFault: mc-consumer-publicfault=true label;

其中 cmDevice configmap mindx-dl-deviceinfo-{NodeName}, 由 device-plugin 上报, 包括如下信息

  • DeviceInfoCfg
  • SwitchInfoCfg

cmPubicFault configmap, 包括如下信息

  • PublicFault

pingmesh-config 的格式为 global pingmesh 任务的配置或者是指定 superpodid 的任务配置

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{
    "activate": "on",
    "task_interval": 5
}

node annotation 中包括如下信息

  • product-serial-number
  • superPodID
  • baseDeviceInfos
  • serverType
  • serverIndex

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THP

https://alexandrnikitin.github.io/blog/transparent-hugepages-measuring-the-performance-impact/

增加 page 大小, 从而减少 TLB 大小; 由于 walk TLB 开销较大, 所以是个优化

THP 会让 os 申请连续的内存空间大小, 但如果申请不到, 则 os 会开始 compact, reclaim or page out other pages;

That process is expensive and could cause latency spikes (up to seconds)

cat /proc/buddyinfo

Each column represents the number of pages of a certain order which are
available. In this case, there are 0 chunks of 2^0PAGE_SIZE available in
ZONE_DMA, 4 chunks of 2^1PAGE_SIZE in ZONE_DMA, 101 chunks of 2^4*PAGE_SIZE
available in ZONE_NORMAL, etc…

https://andorian.blogspot.com/2014/03/making-sense-of-procbuddyinfo.html

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https://wangcong.net/article/FPandBP.html

pathways

https://blog.research.google/2022/04/pathways-language-model-palm-scaling-to.html

a single model that could generalize across domains and tasks while being highly efficient. An important milestone toward realizing this vision was to develop the new Pathways system to orchestrate distributed computation for accelerators.

few-shot

TPU v4 Pods

Pipelining is typically used with DCN

word to vector, This vector represents the word’s meaning and context within the given language

embedding layer, lookup table

Positional encoding

https://medium.com/@tech-gumptions/transformer-architecture-simplified-3fb501d461c8

This means that the output of a layer is added to the initial input, allowing the model to learn to only make small changes to the input

The decoder’s job is to produce the English sentence based on both the original French sentence and the bits of the English sentence it has generated so far.

Input Embedding: Just as with the Encoder, the input to the Decoder (which is the target sequence during training) is first embedded into continuous vectors.

It’s important to note that this masking is only applied during training. During inference, the decoder can attend to all words in the target sequence, including future words.

To summarize, the Decoder in the Transformer architecture processes its input through self-attention, cross-attention with the Encoder’s output, and position-wise Feed-Forward networks, repeatedly for each stacked block, culminating in a final output sequence after the softmax operation.

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https://github.com/NVIDIA/go-nvml

The nvml.h file is a direct copy of nvml.h from the NVIDIA driver. Since the NVML API is guaranteed to be backwards compatible, we should strive to keep this always up to date with the latest.

https://github.com/xlab/c-for-go.git

golang cgo

https://www.rectcircle.cn/posts/go-static-compile-and-cgo

https://chai2010.cn/advanced-go-programming-book/ch2-cgo/ch2-05-internal.html

poc env, windows11 + wsl2 ubuntu 18.04

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~/projects/go-nvml ❯ nvidia-smi
Sun Dec 17 18:57:57 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.04              Driver Version: 546.17       CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3070 Ti     On  | 00000000:06:00.0  On |                  N/A |
|  0%   33C    P8              13W / 290W |   1139MiB /  8192MiB |      1%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A        23      G   /Xwayland                                 N/A      |
+---------------------------------------------------------------------------------------+

test code

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package main

import (
	"fmt"
	"log"
	"os"

	"github.com/NVIDIA/go-nvml/pkg/nvml"
)

func getNvidiaDeviceCount() {
	ret := nvml.Init()
	if ret != nvml.SUCCESS {
		log.Fatalf("Unable to initialize NVML: %v", nvml.ErrorString(ret))
	}
	count, ret := nvml.DeviceGetCount()
	if ret != nvml.SUCCESS {
		log.Fatalf("Unable to get device count: %v", nvml.ErrorString(ret))
	}
	fmt.Printf("%d\n", count)
}

func main() {
	args := os.Args
	if len(args) < 2 {
		fmt.Println("hello")
	} else {
		getNvidiaDeviceCount()
	}
}

build commands

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export CGO_LDFLAGS="-Wl,-z,now"

go build main.go
./main
./main: symbol lookup error: ./main: undefined symbol: nvmlGpuInstanceGetComputeInstanceProfileInfoV

./main fake
./main: symbol lookup error: ./main: undefined symbol: nvmlGpuInstanceGetComputeInstanceProfileInfoV

# now to lazy
export CGO_LDFLAGS="-Wl,-z,lazy"
go build main.go
./main
hello

./main fake
1

go clean --cache && rm -rf main
go build -work -x main.go

go build -x

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cd /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml
TERM='dumb' CGO_LDFLAGS='"-Wl,-z,lazy" "-Wl,--unresolved-symbols=ignore-in-object-files" "-Wl,--unresolved-symbols=ignore-in-object-files"' /root/tools/go/pkg/tool/linux_amd64/cgo -objdir $WORK/b002/ -importpath github.com/NVIDIA/go-nvml/pkg/nvml -- -I $WORK/b002/ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 ./cgo_helpers.go ./const.go ./init.go ./nvml.go
cd $WORK/b002
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_x001.o -c _cgo_export.c
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_x002.o -c cgo_helpers.cgo2.c
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_x003.o -c const.cgo2.c
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_x004.o -c init.cgo2.c
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_x005.o -c nvml.cgo2.c
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -I ./ -g -O2 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -DNVML_NO_UNVERSIONED_FUNC_DEFS=1 -o ./_cgo_main.o -c _cgo_main.c
cd /root/projects/go-nvml
TERM='dumb' gcc -I /root/go/pkg/mod/github.com/!n!v!i!d!i!a/go-nvml@v0.12.0-1/pkg/nvml -fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=$WORK/b002=/tmp/go-build -gno-record-gcc-switches -o $WORK/b002/_cgo_.o $WORK/b002/_cgo_main.o $WORK/b002/_x001.o $WORK/b002/_x002.o $WORK/b002/_x003.o $WORK/b002/_x004.o $WORK/b002/_x005.o -Wl,-z,lazy -Wl,--unresolved-symbols=ignore-in-object-files -Wl,--unresolved-symbols=ignore-in-object-files
TERM='dumb' /root/tools/go/pkg/tool/linux_amd64/cgo -dynpackage nvml -dynimport $WORK/b002/_cgo_.o -dynout $WORK/b002/_cgo_import.go
  1. /tmp/go-build2475505462/b002/nvml.cgo1.go
  2. /tmp/go-build2475505462/b002/nvml.cgo2.c
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CGO_NO_SANITIZE_THREAD
void
_cgo_c813f6172e91_Cfunc_nvmlGpuInstanceGetComputeInstanceProfileInfoV(void *v)
{
        struct {
                nvmlGpuInstance_t p0;
                unsigned int p1;
                unsigned int p2;
                nvmlComputeInstanceProfileInfo_v2_t* p3;
                nvmlReturn_t r;
                char __pad28[4];
        } __attribute__((__packed__, __gcc_struct__)) *_cgo_a = v;
        char *_cgo_stktop = _cgo_topofstack();
        __typeof__(_cgo_a->r) _cgo_r;
        _cgo_tsan_acquire();
        _cgo_r = nvmlGpuInstanceGetComputeInstanceProfileInfoV(_cgo_a->p0, _cgo_a->p1, _cgo_a->p2, _cgo_a->p3);
        _cgo_tsan_release();
        _cgo_a = (void*)((char*)_cgo_a + (_cgo_topofstack() - _cgo_stktop));
        _cgo_a->r = _cgo_r;
        _cgo_msan_write(&_cgo_a->r, sizeof(_cgo_a->r));
}

ChatGPT 3.5

-Wl,-z,lazy, -Wl,-z,now

-Wl,-z,lazy: The -Wl,-z,lazy flag in the gcc command is a linker option used to instruct the linker to utilize lazy binding for dynamic libraries during the linking process.
When a program uses shared libraries (dynamic libraries), such as .so files in Linux, the linking process involves resolving symbols (functions or global variables) from these libraries. Lazy binding delays the resolution of these symbols until they are actually referenced during the program’s execution, rather than resolving all symbols at startup.
Lazy binding delays the resolution of these symbols until they are actually referenced during the program’s execution, rather than resolving all symbols at startup.

-Wl,-z,now: When you compile a program using gcc with the -Wl,-z,now flag, it influences how the dynamic linker behaves at runtime, particularly when the program is executed and loaded into memory. This flag impacts the linking stage, ensuring that symbols from shared libraries are resolved and bound immediately during the linking phase.
During the binary’s execution, when shared libraries are loaded, immediate binding might help in reducing the overhead associated with symbol resolution at runtime because the symbols are already resolved and bound during the linking process.
In summary, the -Wl,-z,now flag influences the behavior of the linker while creating the binary, affecting how symbol resolution occurs when the binary is loaded and executed, potentially impacting the startup performance by pre-resolving symbols.

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近期的一些杂项

infra decouple from kind of internal production system

重点并不是 infra 如何帮忙自动恢复, 只是略有提到; 重点还是训练的调参

famous uncorrectable ECC error

we just restart the run

try to make run stable (数学上的稳定)

FP16

Lost GPU
CUDA errors
Job hanging
NCCL error
Job Slowdown

High DRAM correctable errors etc.
blob storage issues

when we are training these models, we kind of just stare at tensorboard all day

in general the mixture of hardware issues, training like numerical converting issues

~30days change the hyperparameter to try to get through

56days, 53 - 54 restarts, OPT-175B survived 143K steps

Andrej Karpathy

LLM

LAMA-2-70B

fp16, 2bytes, 70B

2 * 70B = 140B bytes = 140 * 1,000,000,000 bytes = 140,000,000,000 bytes = 140 gigabytes (bytes, kbytes, mbytes, gbytes)

140GB

tokenize

encoder, 将字符串转换为整数编码
decoder, 将整数编码转为字符串

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Terms

  • SXM: Server PCI Express Module, a high bandwidth socket solution for connecting Nvidia Compute Accelerators to a system
  • NVL: NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia. Unlike PCI Express, a device can consist of multiple NVLinks, and devices use mesh networking to communicate instead of a central hub.
  • PCIe: PCI Express (Peripheral Component Interconnect Express), officially abbreviated as PCIe or PCI-e,[1] is a high-speed serial computer expansion bus standard

from Wikipedia

H800 vs H100

  1. https://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core-gpu-datasheet

  2. NVIDIA H100 Tensor Core GPU

  3. H800 没找到 NVIDIA 官网 Specification, 只能从代理商和一些B站UP主看到的数据

H800 SXM H100 SXM
FP64 1 teraFLOPS 34 teraFLOPS
FP64 Tensor Core 1 teraFLOPS 67 teraFLOPS
FP32 67 teraFLOPS 67 teraFLOPS
TF32 Tensor Core 989 teraFLOPS 989 teraFLOPS
BFLOAT16 Tensor Core 1,979 teraFLOPS 1,979 teraFLOPS
FP16 Tensor Core 1,979 teraFLOPS 1,979 teraFLOPS
FP8 Tensor Core 3,958 teraFLOPS 3,958 teraFLOPS
INT8 Tensor Core 3,958 TOPS 3,958 TOPS
GPU memory 80GB 80GB
GPU memory bandwidth 3.35TB/s 3.35TB/s
Interconnect NVLink 400GB/s PCIe Gen5: 128GB/s NVLink 900GB/s PCIe Gen5: 128GB/s
  • H800 FP64 算力限制

Driver

https://resources.nvidia.com/en-us-tensor-core/gtc22-whitepaper-hopper

https://www.nvidia.com/content/dam/en-zz/Solutions/gtcs22/data-center/h100/PB-11133-001_v01.pdf

Software Specifications

Specification Description
Driver support Linux: R520 or later

CUDA

https://docs.nvidia.com/datacenter/tesla/drivers/index.html#cuda-arch-matrix

Architecture CUDA Capabilities First CUDA Toolkit Support
Hopper 9.0 CUDA 11.8
CUDA 12.0

TensorFlow

https://www.tensorflow.org/install/source#tested_build_configurations

Version Python version Compiler Build tools cuDNN CUDA
tensorflow-2.15.0 3.9-3.11 Clang 16.0.0 Bazel 6.1.0 8.8 12.2
tensorflow-2.14.0 3.9-3.11 Clang 16.0.0 Bazel 6.1.0 8.7 11.8
tensorflow-2.13.0 3.8-3.11 Clang 16.0.0 Bazel 5.3.0 8.6 11.8
tensorflow-2.12.0 3.8-3.11 GCC 9.3.1 Bazel 5.3.0 8.6 11.8
tensorflow-2.11.0 3.7-3.10 GCC 9.3.1 Bazel 5.3.0 8.1 11.2
tensorflow-2.6.0 3.6-3.9 GCC 7.3.1 Bazel 3.7.2 8.1 11.2

candidates on H800

  • >= tensorflow-2.12.0

docker images

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docker pull tensorflow/tensorflow:2.14.0-gpu
docker pull tensorflow/tensorflow:2.13.0-gpu
docker pull tensorflow/tensorflow:2.12.0-gpu

PyTorch

https://pytorch.org/get-started/previous-versions/

Version CUDA
v1.13.1 11.6, 11.7
v2.0.0 11.7, 11.8
v2.0.1 11.7, 11.8
v2.1.0 11.8, 12.1
v2.1.1 11.8, 12.1

candidates on H800

  • >= v2.0.0, with cuda 11.8 support

docker images

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docker pull pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
docker pull pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime

docker pull pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
docker pull pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

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from modelarts.session import Session
from modelarts.estimatorV2 import Estimator
from modelarts.train_params import OutputData
from modelarts.train_params import InputData

session = Session(access_key='XXX',secret_key='YYY', project_id='ZZZ', region_name='cn-north-4')

# list job
# job_list = Estimator.get_job_list(session=session, offset=0, limit=10, sort_by="create_time", order="desc")
# print(job_list)

# create a basic training job
estimator = Estimator(session=session,
                      job_description='This is a basic training job',
                      user_image_url="deep-learning-demo/mpi:3.0.0-cuda10.2", # main container 的容器镜像地址
                      user_command="echo hello-world",  # main container 的启动命令
                      outputs=[OutputData(obs_path="obs://zs-modelarts/pytorch/model/", name="model", local_path="/model", access_method="env")],
                      log_url="obs://zs-modelarts/pytorch/log/", # 训练作业日志转存 obs 路径
                      train_instance_type="modelarts.p3.large.public.free", # 公共资源池
                      train_instance_count=1 # 训练作业节点个数
                      )

job_instance = estimator.fit(job_name="job-0")

# get job id in job_instance
print(job_instance.job_id)

# view the training job log
# estimator = Estimator(session=session, job_id="2bfc13b6-782e-45ad-ae90-476dfa97591a")
# info = estimator.get_job_log()
# print(info)

# view the training job metrics
# estimator = Estimator(session=session, job_id="2bfc13b6-782e-45ad-ae90-476dfa97591a")
# info = estimator.get_job_metrics()
# print(info)

# delete the training job metrics
# Estimator.delete_job_by_id(session=session, job_id="2bfc13b6-782e-45ad-ae90-476dfa97591a")

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个人理解记录

所谓无损, 也就是不丢包; 通过 global pause, pfc, dcqcn 等不断演进的流控/拥塞控制协议, 来保障在丢包之前控制源头降速, 避免丢包

ifconfig vs ethtool

https://enterprise-support.nvidia.com/s/article/ibdev2netdev

ibdev2netdev

执行上述命令可查询得到 it maps the adapter port to the net device

对于 infiniband 类型的 link layer, 一般来说上述命令得到的是 ib0 设备, 即 IPoIB 虚拟网卡; 对于 ethernet 类型的 link layer，一般来说上述命令得到的是 ens[xxx] 网卡设备

另外注意到 ifconfig ens[xxx] 中显示的 Tx 与 Rx, 实际上与 ethtool -S ens[xxx] 中的如下值一致

https://enterprise-support.nvidia.com/s/article/understanding-mlx5-ethtool-counters

  • rx_bytes: Representor only: bytes received, that were handled by the hypervisor. supported from kernel 4.18
  • tx_bytes: Representor only: bytes transmitted, that were handled by the hypervisor. supported from kernel 4.18

经过实际测试，在使用 rdma 网卡通信时，上述两值并没有明显的计数增加，而观察到 ethtool counters rx_bytes_phy / tx_bytes_phy 才有与实际流量相当的计数增加。所以可能早期（或者内核？） ifconfig 中获取到的数值，仅是网卡的其中某个计数器，而那个计数器又并不能代表真正的实际情况，所以可能 ifconfig 中的数值会是个误导。我们应使用 ethtool -S ens[xxx] 查看 rdma 网卡的统计信息。

rx_bytes_phy, ConnectX-3 naming : rx_bytes
例如在 cx3 网卡时，当前主机安装的 ifconfig，取的的确就是“正确”的；而在 cx4/5/6 网卡，rx_bytes 的物理意义发生了变化，变为了记录 Representor only: bytes received, that were handled by the hypervisor. supported from kernel 4.18

交换机端口常用查询命令

https://support.huawei.com/enterprise/zh/doc/EDOC1100153180/e4418444

https://www.infoq.cn/article/o3rnxl2trb1gxemmxdoj

egress/ingress port

查看是否出现丢包

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display interface 100GE1/0/1
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Input:                                                                      
  Unicast:            11657620879,   Multicast:                     695     
  Broadcast:                    0,   Jumbo:                           0     
  Discard:                      0,   Frames:                          0     
  Pause:                        0                                           

  Total Error:                  0                                           
  CRC:                          0,   Giants:                          0     
  Jabbers:                      0,   Fragments:                       0     
  Runts:                        0,   DropEvents:                      0     
  Alignments:                   0,   Symbols:                         0     
  Ignoreds:                     0                                           

Output:                                                                     
  Unicast:              536390526,   Multicast:                     695     
  Broadcast:                    0,   Jumbo:                           0     
  Discard:                      0,   Buffers Purged:                  0     
  Pause:                 18913700

上行方向丢包 Input

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display qos buffer ingress-statistics interface 100GE1/0/1

查看入方向统计值

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Interface                   Dropped        Drop Rate   Drop Time                
                     (Packets/Bytes)        (pps/bps)                           
----------------------------------------------------------------                
100GE1/0/1                         0                0          -                
                                   0                0                           
----------------------------------------------------------------

下行出现丢包 Output

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display qos queue statistics interface 100GE1/0/1

查看队列统计情况

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----------------------------------------------------------------------------------------------                                           
    4         0                   6             0                   0             0          -                                                                 
      100000000                1092             0                   0             0                                                                            
----------------------------------------------------------------------------------------------

查看接口出方向队列的缓存使用情况

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display qos buffer egress-usage interface 100GE1/0/1

可以查看无损队列

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Egress Buffer Usage (KBytes) on single queue: (Current/Total)                   
*: Dynamic threshold                                                            
------------------------------------------------------------                    
Interface       Queue   Type        Guaranteed        Shared                    

------------------------------------------------------------                    
100GE1/0/1          0   Lossy              0/1          0/5*                    
                    1   Lossy              0/1          0/5*                    
                    2   Lossy              0/1          0/5*                    
                    3   Lossless           0/1       0/10156                    
                    4   Lossy              0/1          0/5*                    
                    5   Lossy              0/1          0/5*                    
                    6   Lossy              0/1          0/5*                    
                    7   Lossy              0/1          0/5*                    
------------------------------------------------------------                    
Lossless Service Pool (cells):  0/0                                             
Lossy    Service Pool (cells):  0/151136                                        
------------------------------------------------------------