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在模型、資料集和 Spaces 上進行協作

透過加速推理獲得更快的示例

切換文件主題

開始使用

簡介

本食譜旨在向您展示如何正確地對 TGI 進行基準測試。有關更多背景細節和解釋，請先檢視這篇熱門部落格。

設定

請確保您有一個安裝了 TGI 的環境；docker 是一個很好的選擇。這裡的命令可以輕鬆地複製/貼上到終端中，這可能會更方便。不必非要使用 Jupyter。如果您只是想測試一下，可以複製並使用 derek-thomas/tgi-benchmark-space。

TGI 啟動器

>>> !text-generation-launcher --version

text-generation-launcher 2.2.1-dev0

下面我們可以看到 TGI 的不同設定。請務必仔細閱讀它們，並決定哪些設定對您的用例最重要。

以下是一些最重要的設定

--model-id
--quantize 量化可以節省記憶體，但並不總是能提高速度
--max-input-tokens 這允許 TGI 最佳化預填充操作
--max-total-tokens 結合上述引數，TGI 現在知道了最大輸入和輸出詞元數
--max-batch-size 這讓 TGI 知道它一次可以處理多少個請求。

最後 3 個引數共同提供了必要的限制，以便為您的用例進行最佳化。透過儘可能恰當地設定這些引數，您可以發現很多效能提升。

>>> !text-generation-launcher -h

Text Generation Launcher

[1m[4mUsage:[0m [1mtext-generation-launcher[0m [OPTIONS]

[1m[4mOptions:[0m
      [1m--model-id[0m 
          The name of the model to load. Can be a MODEL_ID as listed on  like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers [env: MODEL_ID=] [default: bigscience/bloom-560m]
      [1m--revision[0m 
          The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id or a branch like `refs/pr/2` [env: REVISION=]
      [1m--validation-workers[0m 
          The number of tokenizer workers used for payload validation and truncation inside the router [env: VALIDATION_WORKERS=] [default: 2]
      [1m--sharded[0m 
          Whether to shard the model across multiple GPUs By default text-generation-inference will use all available GPUs to run the model. Setting it to `false` deactivates `num_shard` [env: SHARDED=] [possible values: true, false]
      [1m--num-shard[0m 
          The number of shards to use if you don't want to use all GPUs on a given machine. You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2` and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance [env: NUM_SHARD=]
      [1m--quantize[0m 
          Whether you want the model to be quantized [env: QUANTIZE=] [possible values: awq, eetq, exl2, gptq, marlin, bitsandbytes, bitsandbytes-nf4, bitsandbytes-fp4, fp8]
      [1m--speculate[0m 
          The number of input_ids to speculate on If using a medusa model, the heads will be picked up automatically Other wise, it will use n-gram speculation which is relatively free in terms of compute, but the speedup heavily depends on the task [env: SPECULATE=]
      [1m--dtype[0m 
          The dtype to be forced upon the model. This option cannot be used with `--quantize` [env: DTYPE=] [possible values: float16, bfloat16]
      [1m--trust-remote-code[0m
          Whether you want to execute hub modelling code. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision [env: TRUST_REMOTE_CODE=]
      [1m--max-concurrent-requests[0m 
          The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle backpressure correctly [env: MAX_CONCURRENT_REQUESTS=] [default: 128]
      [1m--max-best-of[0m 
          This is the maximum allowed value for clients to set `best_of`. Best of makes `n` generations at the same time, and return the best in terms of overall log probability over the entire generated sequence [env: MAX_BEST_OF=] [default: 2]
      [1m--max-stop-sequences[0m 
          This is the maximum allowed value for clients to set `stop_sequences`. Stop sequences are used to allow the model to stop on more than just the EOS token, and enable more complex "prompting" where users can preprompt the model in a specific way and define their "own" stop token aligned with their prompt [env: MAX_STOP_SEQUENCES=] [default: 4]
      [1m--max-top-n-tokens[0m 
          This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens` is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking [env: MAX_TOP_N_TOKENS=] [default: 5]
      [1m--max-input-tokens[0m 
          This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle. Default to min(max_position_embeddings - 1, 4095) [env: MAX_INPUT_TOKENS=]
      [1m--max-input-length[0m 
          Legacy version of [`Args::max_input_tokens`] [env: MAX_INPUT_LENGTH=]
      [1m--max-total-tokens[0m 
          This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be. Default to min(max_position_embeddings, 4096) [env: MAX_TOTAL_TOKENS=]
      [1m--waiting-served-ratio[0m 
          This represents the ratio of waiting queries vs running queries where you want to start considering pausing the running queries to include the waiting ones into the same batch. `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's only 10 queries left in the current batch we check if we can fit those 12 waiting queries into the batching strategy, and if yes, then batching happens delaying the 10 running queries by a `prefill` run [env: WAITING_SERVED_RATIO=] [default: 0.3]
      [1m--max-batch-prefill-tokens[0m 
          Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent. Default to `max_input_tokens + 50` to give a bit of room [env: MAX_BATCH_PREFILL_TOKENS=]
      [1m--max-batch-total-tokens[0m 
          **IMPORTANT** This is one critical control to allow maximum usage of the available hardware [env: MAX_BATCH_TOTAL_TOKENS=]
      [1m--max-waiting-tokens[0m 
          This setting defines how many tokens can be passed before forcing the waiting queries to be put on the batch (if the size of the batch allows for it). New queries require 1 `prefill` forward, which is different from `decode` and therefore you need to pause the running batch in order to run `prefill` to create the correct values for the waiting queries to be able to join the batch [env: MAX_WAITING_TOKENS=] [default: 20]
      [1m--max-batch-size[0m 
          Enforce a maximum number of requests per batch Specific flag for hardware targets that do not support unpadded inference [env: MAX_BATCH_SIZE=]
      [1m--cuda-graphs[0m 
          Specify the batch sizes to compute cuda graphs for. Use "0" to disable. Default = "1,2,4,8,16,32" [env: CUDA_GRAPHS=]
      [1m--hostname[0m 
          The IP address to listen on [env: HOSTNAME=r-derek-thomas-tgi-benchmark-space-geij6846-b385a-lont4] [default: 0.0.0.0]
  [1m-p[0m, [1m--port[0m 
          The port to listen on [env: PORT=80] [default: 3000]
      [1m--shard-uds-path[0m 
          The name of the socket for gRPC communication between the webserver and the shards [env: SHARD_UDS_PATH=] [default: /tmp/text-generation-server]
      [1m--master-addr[0m 
          The address the master shard will listen on. (setting used by torch distributed) [env: MASTER_ADDR=] [default: localhost]
      [1m--master-port[0m 
          The address the master port will listen on. (setting used by torch distributed) [env: MASTER_PORT=] [default: 29500]
      [1m--huggingface-hub-cache[0m 
          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance [env: HUGGINGFACE_HUB_CACHE=]
      [1m--weights-cache-override[0m 
          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance [env: WEIGHTS_CACHE_OVERRIDE=]
      [1m--disable-custom-kernels[0m
          For some models (like bloom), text-generation-inference implemented custom cuda kernels to speed up inference. Those kernels were only tested on A100. Use this flag to disable them if you're running on different hardware and encounter issues [env: DISABLE_CUSTOM_KERNELS=]
      [1m--cuda-memory-fraction[0m 
          Limit the CUDA available memory. The allowed value equals the total visible memory multiplied by cuda-memory-fraction [env: CUDA_MEMORY_FRACTION=] [default: 1.0]
      [1m--rope-scaling[0m 
          Rope scaling will only be used for RoPE models and allow rescaling the position rotary to accomodate for larger prompts [env: ROPE_SCALING=] [possible values: linear, dynamic]
      [1m--rope-factor[0m 
          Rope scaling will only be used for RoPE models See `rope_scaling` [env: ROPE_FACTOR=]
      [1m--json-output[0m
          Outputs the logs in JSON format (useful for telemetry) [env: JSON_OUTPUT=]
      [1m--otlp-endpoint[0m 
          [env: OTLP_ENDPOINT=]
      [1m--otlp-service-name[0m 
          [env: OTLP_SERVICE_NAME=] [default: text-generation-inference.router]
      [1m--cors-allow-origin[0m 
          [env: CORS_ALLOW_ORIGIN=]
      [1m--api-key[0m 
          [env: API_KEY=]
      [1m--watermark-gamma[0m 
          [env: WATERMARK_GAMMA=]
      [1m--watermark-delta[0m 
          [env: WATERMARK_DELTA=]
      [1m--ngrok[0m
          Enable ngrok tunneling [env: NGROK=]
      [1m--ngrok-authtoken[0m 
          ngrok authentication token [env: NGROK_AUTHTOKEN=]
      [1m--ngrok-edge[0m 
          ngrok edge [env: NGROK_EDGE=]
      [1m--tokenizer-config-path[0m 
          The path to the tokenizer config file. This path is used to load the tokenizer configuration which may include a `chat_template`. If not provided, the default config will be used from the model hub [env: TOKENIZER_CONFIG_PATH=]
      [1m--disable-grammar-support[0m
          Disable outlines grammar constrained generation. This is a feature that allows you to generate text that follows a specific grammar [env: DISABLE_GRAMMAR_SUPPORT=]
  [1m-e[0m, [1m--env[0m
          Display a lot of information about your runtime environment
      [1m--max-client-batch-size[0m 
          Control the maximum number of inputs that a client can send in a single request [env: MAX_CLIENT_BATCH_SIZE=] [default: 4]
      [1m--lora-adapters[0m 
          Lora Adapters a list of adapter ids i.e. `repo/adapter1,repo/adapter2` to load during startup that will be available to callers via the `adapter_id` field in a request [env: LORA_ADAPTERS=]
      [1m--usage-stats[0m 
          Control if anonymous usage stats are collected. Options are "on", "off" and "no-stack" Defaul is on [env: USAGE_STATS=] [default: on] [possible values: on, off, no-stack]
  [1m-h[0m, [1m--help[0m
          Print help (see more with '--help')
  [1m-V[0m, [1m--version[0m
          Print version

我們可以直接從食譜中啟動，因為我們不需要命令是互動式的。

在本食譜中，我們將只使用預設值，因為目的是瞭解基準測試工具。

如果您在 Space 上執行，這些引數被更改了，因為我們不希望與 Spaces 伺服器衝突

--hostname
--port

您可以根據您的需求隨意更改或刪除它們。

>>> !RUST_BACKTRACE=1 \
>>> text-generation-launcher \
>>> --model-id astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit \
>>> --quantize gptq \
>>> --hostname 0.0.0.0 \
>>> --port 1337

[2m2024-08-16T12:07:56.411768Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Args &#123;
    model_id: "astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit",
    revision: None,
    validation_workers: 2,
    sharded: None,
    num_shard: None,
    quantize: Some(
        Gptq,
    ),
    speculate: None,
    dtype: None,
    trust_remote_code: false,
    max_concurrent_requests: 128,
    max_best_of: 2,
    max_stop_sequences: 4,
    max_top_n_tokens: 5,
    max_input_tokens: None,
    max_input_length: None,
    max_total_tokens: None,
    waiting_served_ratio: 0.3,
    max_batch_prefill_tokens: None,
    max_batch_total_tokens: None,
    max_waiting_tokens: 20,
    max_batch_size: None,
    cuda_graphs: None,
    hostname: "0.0.0.0",
    port: 1337,
    shard_uds_path: "/tmp/text-generation-server",
    master_addr: "localhost",
    master_port: 29500,
    huggingface_hub_cache: None,
    weights_cache_override: None,
    disable_custom_kernels: false,
    cuda_memory_fraction: 1.0,
    rope_scaling: None,
    rope_factor: None,
    json_output: false,
    otlp_endpoint: None,
    otlp_service_name: "text-generation-inference.router",
    cors_allow_origin: [],
    api_key: None,
    watermark_gamma: None,
    watermark_delta: None,
    ngrok: false,
    ngrok_authtoken: None,
    ngrok_edge: None,
    tokenizer_config_path: None,
    disable_grammar_support: false,
    env: false,
    max_client_batch_size: 4,
    lora_adapters: None,
    usage_stats: On,
}
[2m2024-08-16T12:07:56.411941Z[0m [32m INFO[0m [2mhf_hub[0m[2m:[0m Token file not found "/data/token"    
[2Kconfig.json [00:00:00] [████████████████████████] 1021 B/1021 B 50.70 KiB/s (0s)[2m2024-08-16T12:07:56.458451Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Model supports up to 8192 but tgi will now set its default to 4096 instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens=8242 --max-total-tokens=8192 --max-input-tokens=8191`.
[2m2024-08-16T12:07:56.458473Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_input_tokens` to 4095
[2m2024-08-16T12:07:56.458480Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_total_tokens` to 4096
[2m2024-08-16T12:07:56.458487Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_batch_prefill_tokens` to 4145
[2m2024-08-16T12:07:56.458494Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Using default cuda graphs [1, 2, 4, 8, 16, 32]
[2m2024-08-16T12:07:56.458606Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Starting check and download process for astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit
[2m2024-08-16T12:07:59.750101Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Download file: model.safetensors
^C
[2m2024-08-16T12:08:09.101893Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Terminating download
[2m2024-08-16T12:08:09.102368Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Waiting for download to gracefully shutdown

TGI 基準測試

現在，讓我們學習如何啟動基準測試工具！

這裡我們可以看到 TGI 基準測試的不同設定。

以下是一些更重要的 TGI 基準測試設定

--tokenizer-name 這是必需的，以便工具知道使用哪個分詞器
--batch-size 這對於負載測試很重要。我們應該使用足夠多的值來觀察吞吐量和延遲的變化。請注意，在基準測試工具的上下文中，batch-size 是虛擬使用者的數量。
--sequence-length AKA 輸入詞元數，匹配您的用例需求非常重要
--decode-length AKA 輸出詞元數，匹配您的用例需求非常重要
--runs 預設為 10

💡 提示： 在探索時使用較小的 --runs 值，但在最終確定時使用較大的值以獲得更精確的統計資料

>>> !text-generation-benchmark -h

Text Generation Benchmarking tool

[1m[4mUsage:[0m [1mtext-generation-benchmark[0m [OPTIONS] [1m--tokenizer-name[0m 

[1m[4mOptions:[0m
  [1m-t[0m, [1m--tokenizer-name[0m 
          The name of the tokenizer (as in model_id on the huggingface hub, or local path) [env: TOKENIZER_NAME=]
      [1m--revision[0m 
          The revision to use for the tokenizer if on the hub [env: REVISION=] [default: main]
  [1m-b[0m, [1m--batch-size[0m 
          The various batch sizes to benchmark for, the idea is to get enough batching to start seeing increased latency, this usually means you're moving from memory bound (usual as BS=1) to compute bound, and this is a sweet spot for the maximum batch size for the model under test
  [1m-s[0m, [1m--sequence-length[0m 
          This is the initial prompt sent to the text-generation-server length in token. Longer prompt will slow down the benchmark. Usually the latency grows somewhat linearly with this for the prefill step [env: SEQUENCE_LENGTH=] [default: 10]
  [1m-d[0m, [1m--decode-length[0m 
          This is how many tokens will be generated by the server and averaged out to give the `decode` latency. This is the *critical* number you want to optimize for LLM spend most of their time doing decoding [env: DECODE_LENGTH=] [default: 8]
  [1m-r[0m, [1m--runs[0m 
          How many runs should we average from [env: RUNS=] [default: 10]
  [1m-w[0m, [1m--warmups[0m 
          Number of warmup cycles [env: WARMUPS=] [default: 1]
  [1m-m[0m, [1m--master-shard-uds-path[0m 
          The location of the grpc socket. This benchmark tool bypasses the router completely and directly talks to the gRPC processes [env: MASTER_SHARD_UDS_PATH=] [default: /tmp/text-generation-server-0]
      [1m--temperature[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TEMPERATURE=]
      [1m--top-k[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_K=]
      [1m--top-p[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_P=]
      [1m--typical-p[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TYPICAL_P=]
      [1m--repetition-penalty[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: REPETITION_PENALTY=]
      [1m--frequency-penalty[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: FREQUENCY_PENALTY=]
      [1m--watermark[0m
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: WATERMARK=]
      [1m--do-sample[0m
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: DO_SAMPLE=]
      [1m--top-n-tokens[0m 
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_N_TOKENS=]
  [1m-h[0m, [1m--help[0m
          Print help (see more with '--help')
  [1m-V[0m, [1m--version[0m
          Print version

這是一個示例命令。請注意，我重複添加了感興趣的批處理大小，以確保它們都被基準測試工具使用。我還在根據估計的使用者活動考慮哪些批處理大小是重要的。

⚠️ 警告： 請注意，TGI 基準測試工具設計用於在終端中工作，而不是在 Jupyter Notebook 中。這意味著您需要將命令複製/貼上到 Jupyter 終端選項卡中。我把它放在這裡是為了方便。

!text-generation-benchmark \
--tokenizer-name astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit \
--sequence-length 70 \
--decode-length 50 \
--batch-size 1 \
--batch-size 2 \
--batch-size 4 \
--batch-size 8 \
--batch-size 16 \
--batch-size 32 \
--batch-size 64 \
--batch-size 128

< > 在 GitHub 上更新

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