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

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use clap::Parser;
use serde::Deserialize;
use std::env;
use std::ffi::OsString;
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};
use subprocess::{ExitStatus, Popen, PopenConfig, PopenError, Redirection};
mod env_runtime;
/// 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
#[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`.
#[clap(long, env)]
revision: Option<String>,
/// Wether to shard or not 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_DEVICE=0,1 text-generation-launcher... --num_shard 2`
/// and `CUDA_VISIBLE_DEVICE=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>,
/// Wether you want the model to be quantized or not. This will use bitsandbytes for
/// quantization on the fly.
#[clap(long, env)]
quantize: 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.
#[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.
#[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,
/// The maximum allowed batch size during dynamic batching.
/// Using `max_batch_total_tokens` should be favored in general
/// as it's a finer way to control RAM usage.
#[clap(long, env)]
max_batch_size: Option<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,
/// **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.
///
/// So you don't have to control that finely
/// `max_batch_size` or `max_total_tokens`. In fact you could mostly relax them if you
/// want maximum flexibility. However, for your users if they are asking for the full amount of
/// total tokens, they are likely to wait for a very long time to get a spot
/// in the batch (since they are going to be alone) so setting `max_batch_size`
/// and `max_total_tokens` can still be useful to prevent those long waiting times.
///
/// 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 = "32000", 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.
#[clap(default_value = "20", long, env)]
max_waiting_tokens: usize,
#[clap(default_value = "3000", long, short, env)]
/// The port to listen on.
port: u16,
/// The name of the socket for gRPC communication between the webserver
/// and the shards.
#[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)
#[clap(default_value = "localhost", long, env)]
master_addr: String,
/// The address the master port will listen on. (setting used by torch distributed)
#[clap(default_value = "29500", long, env)]
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)]
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
#[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.
#[clap(long, env)]
disable_custom_kernels: bool,
/// Outputs the logs in JSON format (useful for telemetry)
#[clap(long, env)]
json_output: bool,
#[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>,
/// Display a lot of information about your runtime environment
#[clap(long, short, action)]
env: bool,
}
#[derive(Debug)]
enum ShardStatus {
Ready,
Failed((usize, String)),
}
#[allow(clippy::too_many_arguments)]
fn shard_manager(
model_id: String,
revision: Option<String>,
quantize: 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 tensor parallelism
if world_size > 1 {
shard_argv.push("--sharded".to_string());
}
if quantize {
shard_argv.push("--quantize".to_string())
}
// Model optional revision
if let Some(revision) = revision {
shard_argv.push("--revision".to_string());
shard_argv.push(revision)
}
// 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();
// 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 p.poll().is_some() {
let mut err = String::new();
p.stderr.take().unwrap().read_to_string(&mut err).unwrap();
status_sender
.send(ShardStatus::Failed((rank, err)))
.unwrap();
return;
}
// We received a shutdown signal
if *shutdown.lock().unwrap() {
p.terminate().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> {
if let Ok(cuda_visible_devices) = env::var("CUDA_VISIBLE_DEVICES") {
let n_devices = cuda_visible_devices.split(',').count();
return Some(n_devices);
}
None
}
#[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");
let n_devices =
num_cuda_devices().expect("--num-shard and CUDA_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,
}
fn download_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())
}
// Copy current process env
let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
// If huggingface_hub_cache is set, pass it to the shard
// 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()));
};
// 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()))
};
// 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`")
}
}
return Err(LauncherError::DownloadError);
}
};
// 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();
}
}
});
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);
}
}
_ => {
tracing::error!("Download process exited with an unknown status.");
return Err(LauncherError::DownloadError);
}
}
}
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));
}
Ok(())
}
#[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> {
// 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();
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 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;
thread::spawn(move || {
shard_manager(
model_id,
revision,
quantize,
uds_path,
rank,
num_shard,
master_addr,
master_port,
huggingface_hub_cache,
weights_cache_override,
disable_custom_kernels,
watermark_gamma,
watermark_delta,
otlp_endpoint,
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);
}
Err(TryRecvError::Disconnected) => {
tracing::error!("Shard status channel disconnected");
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::ShardDisconnected);
}
}
}
Ok(())
}
fn spawn_webserver(
args: Args,
shutdown: Arc<Mutex<bool>>,
shutdown_receiver: &mpsc::Receiver<()>,
) -> Result<Popen, LauncherError> {
// 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(),
"--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,
];
// Deprecate max_batch_size
if let Some(max_batch_size) = args.max_batch_size {
argv.push("--max-batch-size".to_string());
argv.push(max_batch_size.to_string())
} else {
argv.push("--max-batch-total-tokens".to_string());
argv.push(args.max_batch_total_tokens.to_string())
}
// Model optional revision
if let Some(ref revision) = args.revision {
argv.push("--revision".to_string());
argv.push(revision.to_string())
}
if args.json_output {
argv.push("--json-output".to_string());
}
// 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);
}
// 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()))
};
let mut webserver = match Popen::create(
&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) => {
tracing::error!("Failed to start webserver: {}", err);
if let PopenError::IoError(err) = err {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("text-generation-router not found in PATH");
tracing::error!("Please install it with `make install-router`")
}
} else {
tracing::error!("{}", err);
}
shutdown_shards(shutdown, shutdown_receiver);
return Err(LauncherError::WebserverCannotStart);
}
};
// 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());
}
});
Ok(webserver)
}
fn main() -> Result<(), LauncherError> {
// Pattern match configuration
let args = Args::parse();
if args.json_output {
tracing_subscriber::fmt().json().init();
} else {
tracing_subscriber::fmt().compact().init();
}
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");
}
// 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");
// Check if model_id is a local model
let local_path = Path::new(&args.model_id);
let is_local_model = local_path.exists() && local_path.is_dir();
// Download weights for sharded models
if !is_local_model && args.weights_cache_override.is_none() && num_shard > 1 {
download_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
}
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