hf_text-generation-inference/launcher/src/main.rs

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feat(server): GPTQ quantization (step1) (#277) Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
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use clap::{Parser, ValueEnum};
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
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use std::io::{BufRead, BufReader, Read};
use std::path::Path;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::mpsc::TryRecvError;
use std::sync::Arc;
use std::sync::{mpsc, Mutex};
use std::thread;
use std::thread::sleep;
use std::time::{Duration, Instant};
use std::{fs, io};
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use subprocess::{ExitStatus, Popen, PopenConfig, PopenError, Redirection};
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mod env_runtime;
feat(server): GPTQ quantization (step1) (#277) Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
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#[derive(Clone, Copy, Debug, ValueEnum)]
enum Quantization {
Bitsandbytes,
Gptq,
}
impl std::fmt::Display for Quantization {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
// To keep in track with `server`.
match self {
Quantization::Bitsandbytes => {
write!(f, "bitsandbytes")
}
Quantization::Gptq => {
write!(f, "gptq")
}
}
}
}
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/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
/// The name of the model to load.
/// Can be a MODEL_ID as listed on <https://hf.co/models> 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
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#[clap(default_value = "bigscience/bloom-560m", long, env)]
model_id: String,
/// 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`.
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#[clap(long, env)]
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revision: Option<String>,
/// 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`.
#[clap(long, env)]
sharded: Option<bool>,
/// 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.
#[clap(long, env)]
num_shard: Option<usize>,
/// Whether you want the model to be quantized. This will use `bitsandbytes` for
feat(server): GPTQ quantization (step1) (#277) Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
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/// quantization on the fly, or `gptq`.
#[clap(long, env, value_enum)]
quantize: Option<Quantization>,
/// 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.
#[clap(long, env, value_enum)]
trust_remote_code: bool,
/// 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.
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#[clap(default_value = "128", long, env)]
max_concurrent_requests: usize,
/// 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
#[clap(default_value = "2", long, env)]
max_best_of: usize,
/// 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.
#[clap(default_value = "4", long, env)]
max_stop_sequences: usize,
/// 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.
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#[clap(default_value = "1000", long, env)]
max_input_length: usize,
/// 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.
#[clap(default_value = "1512", long, env)]
max_total_tokens: usize,
/// 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.
///
/// This setting is only applied if there is room in the batch
/// as defined by `max_batch_total_tokens`.
#[clap(default_value = "1.2", long, env)]
waiting_served_ratio: f32,
/// 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.
#[clap(default_value = "4096", long, env)]
max_batch_prefill_tokens: u32,
/// **IMPORTANT** This is one critical control to allow maximum usage
/// of the available hardware.
///
/// This represents the total amount of potential tokens within a batch.
/// When using padding (not recommended) this would be equivalent of
/// `batch_size` * `max_total_tokens`.
///
/// However in the non-padded (flash attention) version this can be much finer.
///
/// For `max_batch_total_tokens=1000`, you could fit `10` queries of `total_tokens=100`
/// or a single query of `1000` tokens.
///
/// Overall this number should be the largest possible amount that fits the
/// remaining memory (after the model is loaded). Since the actual memory overhead
/// depends on other parameters like if you're using quantization, flash attention
/// or the model implementation, text-generation-inference cannot infer this number
/// automatically.
#[clap(default_value = "16000", long, env)]
max_batch_total_tokens: u32,
/// 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.
///
/// With a value too small, queries will always "steal" the compute to run `prefill`
/// and running queries will be delayed by a lot.
///
/// With a value too big, waiting queries could wait for a very long time
/// before being allowed a slot in the running batch. If your server is busy
/// that means that requests that could run in ~2s on an empty server could
/// end up running in ~20s because the query had to wait for 18s.
///
/// This number is expressed in number of tokens to make it a bit more
/// "model" agnostic, but what should really matter is the overall latency
/// for end users.
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#[clap(default_value = "20", long, env)]
max_waiting_tokens: usize,
/// The port to listen on.
#[clap(default_value = "3000", long, short, env)]
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port: u16,
/// The name of the socket for gRPC communication between the webserver
/// and the shards.
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#[clap(default_value = "/tmp/text-generation-server", long, env)]
shard_uds_path: String,
/// The address the master shard will listen on. (setting used by torch distributed)
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#[clap(default_value = "localhost", long, env)]
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master_addr: String,
/// The address the master port will listen on. (setting used by torch distributed)
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#[clap(default_value = "29500", long, env)]
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master_port: usize,
/// The location of the huggingface hub cache.
/// Used to override the location if you want to provide a mounted disk for instance
#[clap(long, env)]
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huggingface_hub_cache: Option<String>,
/// The location of the huggingface hub cache.
/// Used to override the location if you want to provide a mounted disk for instance
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#[clap(long, env)]
weights_cache_override: Option<String>,
/// 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.
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#[clap(long, env)]
disable_custom_kernels: bool,
/// Outputs the logs in JSON format (useful for telemetry)
#[clap(long, env)]
json_output: bool,
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#[clap(long, env)]
otlp_endpoint: Option<String>,
#[clap(long, env)]
cors_allow_origin: Vec<String>,
#[clap(long, env)]
watermark_gamma: Option<f32>,
#[clap(long, env)]
watermark_delta: Option<f32>,
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/// Enable ngrok tunneling
#[clap(long, env)]
ngrok: bool,
/// ngrok authentication token
#[clap(long, env)]
ngrok_authtoken: Option<String>,
/// ngrok domain name where the axum webserver will be available at
#[clap(long, env)]
ngrok_domain: Option<String>,
/// ngrok basic auth username
#[clap(long, env)]
ngrok_username: Option<String>,
/// ngrok basic auth password
#[clap(long, env)]
ngrok_password: Option<String>,
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/// Display a lot of information about your runtime environment
#[clap(long, short, action)]
env: bool,
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}
#[derive(Debug)]
enum ShardStatus {
Ready,
Failed((usize, String)),
}
#[allow(clippy::too_many_arguments)]
fn shard_manager(
model_id: String,
revision: Option<String>,
feat(server): GPTQ quantization (step1) (#277) Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
2023-05-12 06:46:41 -06:00
quantize: Option<Quantization>,
trust_remote_code: bool,
uds_path: String,
rank: usize,
world_size: usize,
master_addr: String,
master_port: usize,
huggingface_hub_cache: Option<String>,
weights_cache_override: Option<String>,
disable_custom_kernels: bool,
watermark_gamma: Option<f32>,
watermark_delta: Option<f32>,
otlp_endpoint: Option<String>,
status_sender: mpsc::Sender<ShardStatus>,
shutdown: Arc<Mutex<bool>>,
_shutdown_sender: mpsc::Sender<()>,
) {
// Get UDS path
let uds_string = format!("{uds_path}-{rank}");
let uds = Path::new(&uds_string);
// Clean previous runs
fs::remove_file(uds).unwrap_or_default();
// Process args
let mut shard_argv = vec![
"text-generation-server".to_string(),
"serve".to_string(),
model_id,
"--uds-path".to_string(),
uds_path,
"--logger-level".to_string(),
"INFO".to_string(),
"--json-output".to_string(),
];
// Activate trust remote code
if trust_remote_code {
shard_argv.push("--trust-remote-code".to_string());
}
// Activate tensor parallelism
if world_size > 1 {
shard_argv.push("--sharded".to_string());
}
feat(server): GPTQ quantization (step1) (#277) Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
2023-05-12 06:46:41 -06:00
if let Some(quantize) = quantize {
shard_argv.push("--quantize".to_string());
shard_argv.push(quantize.to_string())
}
// Model optional revision
if let Some(revision) = revision {
shard_argv.push("--revision".to_string());
shard_argv.push(revision)
}
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// OpenTelemetry
if let Some(otlp_endpoint) = otlp_endpoint {
shard_argv.push("--otlp-endpoint".to_string());
shard_argv.push(otlp_endpoint);
}
// Copy current process env
let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
// Use cuda allocator. It leads to less memory fragmentation
env.push((
"PYTORCH_CUDA_ALLOC_CONF".into(),
"backend:cudaMallocAsync".into(),
));
// Torch Distributed Env vars
env.push(("RANK".into(), rank.to_string().into()));
env.push(("WORLD_SIZE".into(), world_size.to_string().into()));
env.push(("MASTER_ADDR".into(), master_addr.into()));
env.push(("MASTER_PORT".into(), master_port.to_string().into()));
env.push(("NCCL_ASYNC_ERROR_HANDLING".into(), "1".into()));
// Safetensors load fast
env.push(("SAFETENSORS_FAST_GPU".into(), "1".into()));
// Enable hf transfer for insane download speeds
let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
env.push((
"HF_HUB_ENABLE_HF_TRANSFER".into(),
enable_hf_transfer.into(),
));
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
// If huggingface_hub_cache is some, pass it to the shard
// Useful when running inside a docker container
if let Some(huggingface_hub_cache) = huggingface_hub_cache {
env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
};
// If weights_cache_override is some, pass it to the shard
// Useful when running inside a HuggingFace Inference Endpoint
if let Some(weights_cache_override) = weights_cache_override {
env.push((
"WEIGHTS_CACHE_OVERRIDE".into(),
weights_cache_override.into(),
));
};
// If disable_custom_kernels is true, pass it to the shard as an env var
if disable_custom_kernels {
env.push(("DISABLE_CUSTOM_KERNELS".into(), "True".into()))
}
// Watermark Gamma
if let Some(watermark_gamma) = watermark_gamma {
env.push(("WATERMARK_GAMMA".into(), watermark_gamma.to_string().into()))
}
// Watermark Delta
if let Some(watermark_delta) = watermark_delta {
env.push(("WATERMARK_DELTA".into(), watermark_delta.to_string().into()))
}
// Start process
tracing::info!("Starting shard {rank}");
let mut p = match Popen::create(
&shard_argv,
PopenConfig {
stdout: Redirection::Pipe,
stderr: Redirection::Pipe,
// Needed for the shutdown procedure
setpgid: true,
// NCCL env vars
env: Some(env),
..Default::default()
},
) {
Ok(p) => p,
Err(err) => {
if let PopenError::IoError(ref err) = err {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-server not found in PATH");
tracing::error!("Please install it with `make install-server`")
}
}
status_sender
.send(ShardStatus::Failed((rank, err.to_string())))
.unwrap();
return;
}
};
// Redirect STDOUT to the console
let shard_stdout = p.stdout.take().unwrap();
thread::spawn(move || {
// Enter shard-manager tracing span
let stdout = BufReader::new(shard_stdout);
let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
for line in stdout.lines() {
// Parse loguru logs
if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
log.trace();
}
}
});
let mut ready = false;
let start_time = Instant::now();
let mut wait_time = Instant::now();
loop {
// Process exited
if let Some(exit_status) = p.poll() {
let mut err = String::new();
p.stderr.take().unwrap().read_to_string(&mut err).unwrap();
if let ExitStatus::Signaled(signal) = exit_status {
tracing::error!("Shard process was signaled to shutdown with signal {signal}");
}
status_sender
.send(ShardStatus::Failed((rank, err)))
.unwrap();
return;
}
// We received a shutdown signal
if *shutdown.lock().unwrap() {
p.kill().unwrap();
let _ = p.wait_timeout(Duration::from_secs(90));
tracing::info!("Shard {rank} terminated");
return;
}
// Shard is ready
if uds.exists() && !ready {
tracing::info!("Shard {rank} ready in {:?}", start_time.elapsed());
status_sender.send(ShardStatus::Ready).unwrap();
ready = true;
} else if !ready && wait_time.elapsed() > Duration::from_secs(10) {
tracing::info!("Waiting for shard {rank} to be ready...");
wait_time = Instant::now();
}
sleep(Duration::from_millis(100));
}
}
fn shutdown_shards(shutdown: Arc<Mutex<bool>>, shutdown_receiver: &mpsc::Receiver<()>) {
tracing::info!("Shutting down shards");
// Update shutdown value to true
// This will be picked up by the shard manager
{
let mut shutdown = shutdown.lock().unwrap();
*shutdown = true;
}
// Wait for shards to shutdown
// This will block till all shutdown_sender are dropped
let _ = shutdown_receiver.recv();
}
fn num_cuda_devices() -> Option<usize> {
let devices = match env::var("CUDA_VISIBLE_DEVICES") {
Ok(devices) => devices,
Err(_) => env::var("NVIDIA_VISIBLE_DEVICES").ok()?,
};
let n_devices = devices.split(',').count();
Some(n_devices)
}
#[derive(Deserialize)]
#[serde(rename_all = "UPPERCASE")]
enum PythonLogLevelEnum {
Trace,
Debug,
Info,
Success,
Warning,
Error,
Critical,
}
#[derive(Deserialize)]
struct PythonLogLevel {
name: PythonLogLevelEnum,
}
#[derive(Deserialize)]
struct PythonLogRecord {
level: PythonLogLevel,
}
#[derive(Deserialize)]
struct PythonLogMessage {
text: String,
record: PythonLogRecord,
}
impl PythonLogMessage {
fn trace(&self) {
match self.record.level.name {
PythonLogLevelEnum::Trace => tracing::trace!("{}", self.text),
PythonLogLevelEnum::Debug => tracing::debug!("{}", self.text),
PythonLogLevelEnum::Info => tracing::info!("{}", self.text),
PythonLogLevelEnum::Success => tracing::info!("{}", self.text),
PythonLogLevelEnum::Warning => tracing::warn!("{}", self.text),
PythonLogLevelEnum::Error => tracing::error!("{}", self.text),
PythonLogLevelEnum::Critical => tracing::error!("{}", self.text),
}
}
}
fn find_num_shards(sharded: Option<bool>, num_shard: Option<usize>) -> usize {
// get the number of shards given `sharded` and `num_shard`
let num_shard = match (sharded, num_shard) {
(Some(true), None) => {
// try to default to the number of available GPUs
tracing::info!("Parsing num_shard from CUDA_VISIBLE_DEVICES/NVIDIA_VISIBLE_DEVICES");
let n_devices = num_cuda_devices()
.expect("--num-shard and CUDA_VISIBLE_DEVICES/NVIDIA_VISIBLE_DEVICES are not set");
if n_devices <= 1 {
panic!("`sharded` is true but only found {n_devices} CUDA devices");
}
n_devices
}
(Some(true), Some(num_shard)) => {
// we can't have only one shard while sharded
if num_shard <= 1 {
panic!("`sharded` is true but `num_shard` <= 1");
}
num_shard
}
(Some(false), Some(num_shard)) => num_shard,
(Some(false), None) => 1,
(None, None) => num_cuda_devices().unwrap_or(1),
(None, Some(num_shard)) => num_shard,
};
if num_shard < 1 {
panic!("`num_shard` cannot be < 1");
}
num_shard
}
#[derive(Debug)]
enum LauncherError {
DownloadError,
ShardCannotStart,
ShardDisconnected,
ShardFailed,
WebserverFailed,
WebserverCannotStart,
}
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fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), LauncherError> {
let mut download_argv = vec![
"text-generation-server".to_string(),
"download-weights".to_string(),
args.model_id.to_string(),
"--extension".to_string(),
".safetensors".to_string(),
"--logger-level".to_string(),
"INFO".to_string(),
"--json-output".to_string(),
];
// Model optional revision
if let Some(revision) = &args.revision {
download_argv.push("--revision".to_string());
download_argv.push(revision.to_string())
}
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// Copy current process env
let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
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// If huggingface_hub_cache is set, pass it to the download process
// Useful when running inside a docker container
if let Some(ref huggingface_hub_cache) = args.huggingface_hub_cache {
env.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
};
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// Enable hf transfer for insane download speeds
let enable_hf_transfer = env::var("HF_HUB_ENABLE_HF_TRANSFER").unwrap_or("1".to_string());
env.push((
"HF_HUB_ENABLE_HF_TRANSFER".into(),
enable_hf_transfer.into(),
));
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
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// If args.weights_cache_override is some, pass it to the download process
// Useful when running inside a HuggingFace Inference Endpoint
if let Some(weights_cache_override) = &args.weights_cache_override {
env.push((
"WEIGHTS_CACHE_OVERRIDE".into(),
weights_cache_override.into(),
));
};
// Start process
tracing::info!("Starting download process.");
let mut download_process = match Popen::create(
&download_argv,
PopenConfig {
stdout: Redirection::Pipe,
stderr: Redirection::Pipe,
// Needed for the shutdown procedure
setpgid: true,
env: Some(env),
..Default::default()
},
) {
Ok(p) => p,
Err(err) => {
if let PopenError::IoError(ref err) = err {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-server not found in PATH");
tracing::error!("Please install it with `make install-server`")
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}
}
return Err(LauncherError::DownloadError);
}
};
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// Redirect STDOUT to the console
let download_stdout = download_process.stdout.take().unwrap();
thread::spawn(move || {
// Enter download tracing span
let stdout = BufReader::new(download_stdout);
let _span = tracing::span!(tracing::Level::INFO, "download").entered();
for line in stdout.lines() {
// Parse loguru logs
if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
log.trace();
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}
}
});
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loop {
if let Some(status) = download_process.poll() {
match status {
ExitStatus::Exited(exit_code) => {
if exit_code == 0 {
tracing::info!("Successfully downloaded weights.");
break;
} else {
let mut err = String::new();
download_process
.stderr
.take()
.unwrap()
.read_to_string(&mut err)
.unwrap();
tracing::error!("Download encountered an error: {err}");
return Err(LauncherError::DownloadError);
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}
}
ExitStatus::Signaled(signal) => {
let mut err = String::new();
download_process
.stderr
.take()
.unwrap()
.read_to_string(&mut err)
.unwrap();
tracing::error!(
"Download process was signaled to shutdown with signal {signal}: {err}"
);
return Err(LauncherError::DownloadError);
}
e => {
tracing::error!("Download process exited with an unknown status.: {e:?}");
return Err(LauncherError::DownloadError);
}
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}
}
if !running.load(Ordering::SeqCst) {
download_process.terminate().unwrap();
tracing::info!("Waiting for download process to gracefully shutdown");
download_process
.wait_timeout(Duration::from_secs(90))
.unwrap();
tracing::info!("Download process terminated");
return Ok(());
}
sleep(Duration::from_millis(100));
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}
Ok(())
}
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#[allow(clippy::too_many_arguments)]
fn spawn_shards(
num_shard: usize,
args: &Args,
shutdown: Arc<Mutex<bool>>,
shutdown_receiver: &mpsc::Receiver<()>,
shutdown_sender: mpsc::Sender<()>,
status_receiver: &mpsc::Receiver<ShardStatus>,
status_sender: mpsc::Sender<ShardStatus>,
running: Arc<AtomicBool>,
) -> Result<(), LauncherError> {
if args.trust_remote_code {
tracing::warn!(
"`trust_remote_code` is set. Trusting that model `{}` do not contain malicious code.",
args.model_id
);
if args.revision.is_none() {
tracing::warn!("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.");
}
}
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// Start shard processes
for rank in 0..num_shard {
let model_id = args.model_id.clone();
let revision = args.revision.clone();
let uds_path = args.shard_uds_path.clone();
let master_addr = args.master_addr.clone();
let huggingface_hub_cache = args.huggingface_hub_cache.clone();
let weights_cache_override = args.weights_cache_override.clone();
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let status_sender = status_sender.clone();
let shutdown = shutdown.clone();
let shutdown_sender = shutdown_sender.clone();
let otlp_endpoint = args.otlp_endpoint.clone();
let quantize = args.quantize;
let trust_remote_code = args.trust_remote_code;
let master_port = args.master_port;
let disable_custom_kernels = args.disable_custom_kernels;
let watermark_gamma = args.watermark_gamma;
let watermark_delta = args.watermark_delta;
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thread::spawn(move || {
shard_manager(
model_id,
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revision,
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quantize,
trust_remote_code,
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uds_path,
rank,
num_shard,
master_addr,
master_port,
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huggingface_hub_cache,
weights_cache_override,
disable_custom_kernels,
watermark_gamma,
watermark_delta,
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otlp_endpoint,
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status_sender,
shutdown,
shutdown_sender,
)
});
}
drop(shutdown_sender);
// Wait for shard to start
let mut shard_ready = 0;
while running.load(Ordering::SeqCst) {
match status_receiver.try_recv() {
Ok(ShardStatus::Ready) => {
shard_ready += 1;
if shard_ready == num_shard {
break;
}
}
Err(TryRecvError::Empty) => {
sleep(Duration::from_millis(100));
}
Ok(ShardStatus::Failed((rank, err))) => {
tracing::error!("Shard {} failed to start:\n{}", rank, err);
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::ShardCannotStart);
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}
Err(TryRecvError::Disconnected) => {
tracing::error!("Shard status channel disconnected");
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::ShardDisconnected);
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}
}
}
Ok(())
}
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fn spawn_webserver(
args: Args,
shutdown: Arc<Mutex<bool>>,
shutdown_receiver: &mpsc::Receiver<()>,
) -> Result<Popen, LauncherError> {
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// All shard started
// Start webserver
tracing::info!("Starting Webserver");
let mut argv = vec![
"text-generation-router".to_string(),
"--max-concurrent-requests".to_string(),
args.max_concurrent_requests.to_string(),
"--max-best-of".to_string(),
args.max_best_of.to_string(),
"--max-stop-sequences".to_string(),
args.max_stop_sequences.to_string(),
"--max-input-length".to_string(),
args.max_input_length.to_string(),
"--max-total-tokens".to_string(),
args.max_total_tokens.to_string(),
"--max-batch-prefill-tokens".to_string(),
args.max_batch_prefill_tokens.to_string(),
"--max-batch-total-tokens".to_string(),
args.max_batch_total_tokens.to_string(),
"--waiting-served-ratio".to_string(),
args.waiting_served_ratio.to_string(),
"--max-waiting-tokens".to_string(),
args.max_waiting_tokens.to_string(),
"--port".to_string(),
args.port.to_string(),
"--master-shard-uds-path".to_string(),
format!("{}-0", args.shard_uds_path),
"--tokenizer-name".to_string(),
args.model_id,
];
// Model optional revision
if let Some(ref revision) = args.revision {
argv.push("--revision".to_string());
argv.push(revision.to_string())
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}
if args.json_output {
argv.push("--json-output".to_string());
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}
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// OpenTelemetry
if let Some(otlp_endpoint) = args.otlp_endpoint {
argv.push("--otlp-endpoint".to_string());
argv.push(otlp_endpoint);
}
// CORS origins
for origin in args.cors_allow_origin.into_iter() {
argv.push("--cors-allow-origin".to_string());
argv.push(origin);
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}
// Ngrok
if args.ngrok {
let authtoken = args.ngrok_authtoken.ok_or_else(|| {
tracing::error!("`ngrok-authtoken` must be set when using ngrok tunneling");
LauncherError::WebserverCannotStart
})?;
argv.push("--ngrok".to_string());
argv.push("--ngrok-authtoken".to_string());
argv.push(authtoken);
if let Some(domain) = args.ngrok_domain {
argv.push("--ngrok-domain".to_string());
argv.push(domain);
}
if let (Some(username), Some(password)) = (args.ngrok_username, args.ngrok_password) {
argv.push("--ngrok-username".to_string());
argv.push(username);
argv.push("--ngrok-password".to_string());
argv.push(password);
}
}
// Copy current process env
let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
// Parse Inference API token
if let Ok(api_token) = env::var("HF_API_TOKEN") {
env.push(("HUGGING_FACE_HUB_TOKEN".into(), api_token.into()))
};
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let mut webserver = match Popen::create(
&argv,
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PopenConfig {
stdout: Redirection::Pipe,
stderr: Redirection::Pipe,
// Needed for the shutdown procedure
setpgid: true,
env: Some(env),
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..Default::default()
},
) {
Ok(p) => p,
Err(err) => {
tracing::error!("Failed to start webserver: {}", err);
if let PopenError::IoError(err) = err {
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if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-router not found in PATH");
tracing::error!("Please install it with `make install-router`")
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}
} else {
tracing::error!("{}", err);
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}
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::WebserverCannotStart);
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}
};
// Redirect STDOUT and STDERR to the console
let webserver_stdout = webserver.stdout.take().unwrap();
let webserver_stderr = webserver.stderr.take().unwrap();
thread::spawn(move || {
let stdout = BufReader::new(webserver_stdout);
let stderr = BufReader::new(webserver_stderr);
for line in stdout.lines() {
println!("{}", line.unwrap());
}
for line in stderr.lines() {
println!("{}", line.unwrap());
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}
});
Ok(webserver)
}
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fn main() -> Result<(), LauncherError> {
// Pattern match configuration
let args = Args::parse();
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if args.json_output {
tracing_subscriber::fmt().json().init();
} else {
tracing_subscriber::fmt().compact().init();
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}
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if args.env {
let env_runtime = env_runtime::Env::new();
tracing::info!("{}", env_runtime);
}
tracing::info!("{:?}", args);
let num_shard = find_num_shards(args.sharded, args.num_shard);
if num_shard > 1 {
tracing::info!("Sharding model on {num_shard} processes");
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}
// Signal handler
let running = Arc::new(AtomicBool::new(true));
let r = running.clone();
ctrlc::set_handler(move || {
r.store(false, Ordering::SeqCst);
})
.expect("Error setting Ctrl-C handler");
// Download and convert model weights
download_convert_model(&args, running.clone())?;
// Shared shutdown bool
let shutdown = Arc::new(Mutex::new(false));
// Shared shutdown channel
// When shutting down, the main thread will wait for all senders to be dropped
let (shutdown_sender, shutdown_receiver) = mpsc::channel();
// Shared channel to track shard status
let (status_sender, status_receiver) = mpsc::channel();
spawn_shards(
num_shard,
&args,
shutdown.clone(),
&shutdown_receiver,
shutdown_sender,
&status_receiver,
status_sender,
running.clone(),
)?;
// We might have received a termination signal
if !running.load(Ordering::SeqCst) {
shutdown_shards(shutdown, &shutdown_receiver);
return Ok(());
}
let mut webserver = spawn_webserver(args, shutdown.clone(), &shutdown_receiver)?;
// Default exit code
let mut exit_code = Ok(());
while running.load(Ordering::SeqCst) {
if let Ok(ShardStatus::Failed((rank, err))) = status_receiver.try_recv() {
tracing::error!("Shard {rank} failed:\n{err}");
exit_code = Err(LauncherError::ShardFailed);
break;
};
match webserver.poll() {
Some(_) => {
tracing::error!("Webserver Crashed");
shutdown_shards(shutdown, &shutdown_receiver);
return Err(LauncherError::WebserverFailed);
}
None => {
sleep(Duration::from_millis(100));
}
};
}
// Graceful termination
webserver.terminate().unwrap();
tracing::info!("Waiting for webserver to gracefully shutdown");
webserver.wait_timeout(Duration::from_secs(90)).unwrap();
tracing::info!("Webserver terminated");
shutdown_shards(shutdown, &shutdown_receiver);
exit_code
}