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v0.1.0

This commit is contained in:
Olivier Dehaene 2022-10-18 15:19:03 +02:00 committed by OlivierDehaene
parent 92c1ecd008
commit f16f2f5ae1
36 changed files with 1556 additions and 677 deletions
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2
.dockerignore
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@ -1,2 +1,2 @@
aml
router/target
target

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.gitignore vendored
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.idea
.idea
target

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router/Cargo.lock → Cargo.lock generated
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@ -1872,6 +1940,7 @@ dependencies = [
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11
Cargo.toml Normal file
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@ -0,0 +1,11 @@
[workspace]
members = [
"router",
"router/client",
"launcher"
]
[profile.release]
debug = 1
incremental = true
lto = "off"

36
Dockerfile
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@ -1,4 +1,4 @@
FROM rust:1.64 as builder
FROM rust:1.64 as router-builder
WORKDIR /usr/src
@ -9,7 +9,17 @@ WORKDIR /usr/src/router
RUN cargo install --path .
FROM nvidia/cuda:11.6.1-devel-ubuntu18.04
FROM rust:1.64 as launcher-builder
WORKDIR /usr/src
COPY launcher launcher
WORKDIR /usr/src/launcher
RUN cargo install --path .
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
ENV LANG=C.UTF-8 \
LC_ALL=C.UTF-8 \
@ -34,17 +44,15 @@ RUN cd ~ && \
bash ./Miniconda3-latest-Linux-x86_64.sh -bf -p /opt/miniconda && \
conda create -n text-generation python=3.9 -y
WORKDIR /usr/src
COPY server/Makefile server/Makefile
# Install specific version of torch
RUN /opt/miniconda/envs/text-generation/bin/pip install torch --extra-index-url https://download.pytorch.org/whl/cu116 --no-cache-dir
RUN cd server && make install-torch
# Install specific version of transformers
RUN wget https://github.com/huggingface/transformers/archive/46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip && \
unzip 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip && \
rm 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip && \
cd transformers-46d37bece7d3ffdef97b1ee4a3170c0a0627d921 && \
/opt/miniconda/envs/text-generation/bin/python setup.py install
WORKDIR /usr/src
RUN cd server && make install-transformers
# Install server
COPY server server
@ -52,9 +60,7 @@ RUN cd server && \
/opt/miniconda/envs/text-generation/bin/pip install . --no-cache-dir
# Install router
COPY --from=builder /usr/local/cargo/bin/text-generation-router /usr/local/bin/text-generation-router
COPY --from=router-builder /usr/local/cargo/bin/text-generation-router /usr/local/bin/text-generation-router
COPY --from=launcher-builder /usr/local/cargo/bin/text-generation-launcher /usr/local/bin/text-generation-launcher
COPY run.sh .
RUN chmod +x run.sh
CMD ["./run.sh"]
CMD text-generation-launcher --model-name $MODEL_NAME --num-shard $NUM_GPUS --shard-directory $MODEL_BASE_PATH

19
Makefile Normal file
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@ -0,0 +1,19 @@
install-server:
cd server && make pip-install
install-router:
cd router && cargo install --path .
install-launcher:
cd launcher && cargo install --path .
install:
make install-server
make install-router
make install-launcher
run-bloom-560m:
text-generation-launcher --model-name bigscience/bloom-560m --shard-directory /tmp/models --num-shard 2
run-bloom:
text-generation-launcher --model-name bigscience/bloom --shard-directory /tmp/models --num-shard 8

55
README.md
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@ -1,50 +1,51 @@
# Text Generation Inference
# LLM Text Generation Inference
A Rust and gRPC server for text generation inference.
<div align="center">
## Load Tests
![architecture](assets/architecture.jpg)
</div>
A Rust and gRPC server for large language models text generation inference.
## Load Tests for BLOOM
See `k6/load_test.js`
We send the default examples with a 1 second delay between each request.
We send the default examples with a 1 second delay between requests.
Stages:
- Ramp up to 50 concurrent requests per second in 1min
- Ramp up from 50 to 100 concurrent requests per second in 2min
- Ramp down to 0 concurrent requests per second in 1min
- Ramp up to 50 vus in 1min
- Ramp up from 50 to 100 vus in 2min
- Ramp down to 0 vus in 1min
| | avg | min | med | max | p(90) | p(95) | RPS |
|------------------------|-----------|-----------|-----------|------------|-----------|-----------|----------|
| Original code | 8.9s | 1s | 9.12s | 16.69s | 13.7s | 14.26s | 5.9 |
| ISO with original code | 8.88s | 959.53ms | 8.89s | 17.08s | 13.34s | 14.12s | 5.94 |
| New batching logic | **5.44s** | **1.27s** | **5.28s** | **13.12s** | **7.78s** | **8.92s** | **9.08** |
| | avg | min | med | max | p(90) | p(95) | RPS |
|--------------------------------------------------------------|-----------|--------------|-----------|------------|-----------|-----------|----------|
| [Original code](https://github.com/huggingface/transformers_bloom_parallel) | 8.9s | 1s | 9.12s | 16.69s | 13.7s | 14.26s | 5.9 |
| ISO with original code | 8.88s | **959.53ms** | 8.89s | 17.08s | 13.34s | 14.12s | 5.94 |
| New batching logic | **5.44s** | 1.27s | **5.28s** | **13.12s** | **7.78s** | **8.92s** | **9.08** |
## Install
```shell
cd server
pip install .
make install
```
```
cd router
cargo build --release
```
## Run
## Run
```shell
python server/bloom_inference/main.py bigscience/bloom --num-gpus 8 --shard-directory /dev/shm/models
make run-bloom-560m
```
## Test
```shell
./router/target/release/router
curl 127.0.0.1:3000/generate \
-X POST \
-d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
-H 'Content-Type: application/json'
```
## TODO:
- [ ] Add docstrings + comments everywhere as the codebase is fairly complicated
- [ ] Add tests
- [ ] Add shutdown logic in router and server
- [ ] Improve multi-processing logic in server
- [ ] Improve past key layer indexing?
- [ ] Add tests for the `server/model` logic

2
aml/deployment.yaml
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@ -8,7 +8,7 @@ environment_variables:
MODEL_NAME: bigscience/bloom
NUM_GPUS: 8
environment:
image: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation:0.3
image: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation-inference:0.2
inference_config:
liveness_route:
port: 3000

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13
launcher/Cargo.toml Normal file
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@ -0,0 +1,13 @@
[package]
name = "text-generation-launcher"
version = "0.1.0"
edition = "2021"
authors = ["Olivier Dehaene"]
description = "Text Generation Launcher"
[dependencies]
clap = { version = "4.0.15", features = ["derive", "env"] }
ctrlc = "3.2.3"
subprocess = "0.2.9"
tracing = "0.1.37"
tracing-subscriber = "0.3.16"

358
launcher/src/main.rs Normal file
View File

@ -0,0 +1,358 @@
use clap::Parser;
use std::io::{BufRead, BufReader, Read};
use std::path::Path;
use std::process::ExitCode;
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::{Popen, PopenConfig, PopenError, Redirection};
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
#[clap(default_value = "bigscience/bloom-560m", long, env)]
model_name: String,
#[clap(long, env)]
num_shard: Option<usize>,
#[clap(long, env)]
shard_directory: Option<String>,
#[clap(default_value = "128", long, env)]
max_concurrent_requests: usize,
#[clap(default_value = "1000", long, env)]
max_input_length: usize,
#[clap(default_value = "32", long, env)]
max_batch_size: usize,
#[clap(default_value = "5", long, env)]
max_waiting_time: u64,
#[clap(default_value = "3000", long, short, env)]
port: u16,
#[clap(default_value = "/tmp/text-generation-server", long, env)]
shard_uds_path: String,
#[clap(default_value = "localhost", long, env)]
master_addr: String,
#[clap(default_value = "29500", long, env)]
master_port: usize,
}
fn main() -> ExitCode {
tracing_subscriber::fmt::init();
// Pattern match configuration
let Args {
model_name,
num_shard,
shard_directory,
max_concurrent_requests,
max_input_length,
max_batch_size,
max_waiting_time,
port,
shard_uds_path,
master_addr,
master_port,
} = Args::parse();
// By default we only have one master shard
let num_shard = num_shard.unwrap_or(1);
// 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");
// 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();
// Start shard processes
for rank in 0..num_shard {
let model_name = model_name.clone();
let uds_path = shard_uds_path.clone();
let shard_directory = shard_directory.clone();
let master_addr = master_addr.clone();
let status_sender = status_sender.clone();
let shutdown = shutdown.clone();
let shutdown_sender = shutdown_sender.clone();
thread::spawn(move || {
shard_manager(
model_name,
uds_path,
shard_directory,
rank,
num_shard,
master_addr,
master_port,
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 ExitCode::FAILURE;
}
Err(TryRecvError::Disconnected) => {
tracing::error!("Shard status channel disconnected");
shutdown_shards(shutdown, &shutdown_receiver);
return ExitCode::FAILURE;
}
}
}
// We might have received a termination signal
if !running.load(Ordering::SeqCst) {
shutdown_shards(shutdown, &shutdown_receiver);
return ExitCode::SUCCESS;
}
// All shard started
// Start webserver
tracing::info!("Starting Webserver");
let mut webserver = match Popen::create(
&[
"text-generation-router",
"--max-concurrent-requests",
&max_concurrent_requests.to_string(),
"--max-input-length",
&max_input_length.to_string(),
"--max-batch-size",
&max_batch_size.to_string(),
"--max-waiting-time",
&max_waiting_time.to_string(),
"--port",
&port.to_string(),
"--master-shard-uds-path",
&format!("{}-0", shard_uds_path),
"--tokenizer-name",
&model_name,
],
PopenConfig {
stdout: Redirection::Pipe,
stderr: Redirection::Pipe,
// Needed for the shutdown procedure
setpgid: true,
..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`")
}
}
shutdown_shards(shutdown, &shutdown_receiver);
return ExitCode::FAILURE;
}
};
// 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());
}
});
// Default exit code
let mut exit_code = ExitCode::SUCCESS;
while running.load(Ordering::SeqCst) {
if let Ok(ShardStatus::Failed((rank, err))) = status_receiver.try_recv() {
tracing::error!("Shard {} failed:\n{}", rank, err);
exit_code = ExitCode::FAILURE;
break;
};
match webserver.poll() {
Some(_) => {
tracing::error!("Webserver Crashed");
shutdown_shards(shutdown, &shutdown_receiver);
return ExitCode::FAILURE;
}
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
}
#[derive(Debug)]
enum ShardStatus {
Ready,
Failed((usize, String)),
}
#[allow(clippy::too_many_arguments)]
fn shard_manager(
model_name: String,
uds_path: String,
shard_directory: Option<String>,
rank: usize,
world_size: usize,
master_addr: String,
master_port: usize,
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![
"bloom-inference-server".to_string(),
"serve".to_string(),
model_name,
"--uds-path".to_string(),
uds_path,
];
if world_size > 1 {
shard_argv.push("--sharded".to_string());
}
if let Some(shard_directory) = shard_directory {
shard_argv.push("--shard-directory".to_string());
shard_argv.push(shard_directory);
}
// 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(vec![
("RANK".parse().unwrap(), rank.to_string().parse().unwrap()),
(
"WORLD_SIZE".parse().unwrap(),
world_size.to_string().parse().unwrap(),
),
("MASTER_ADDR".parse().unwrap(), master_addr.parse().unwrap()),
(
"MASTER_PORT".parse().unwrap(),
master_port.to_string().parse().unwrap(),
),
]),
..Default::default()
},
) {
Ok(p) => p,
Err(err) => {
if let PopenError::IoError(ref err) = err {
if err.kind() == io::ErrorKind::NotFound {
tracing::error!("bloom-inference-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;
}
};
let mut ready = false;
let start_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 {} terminated", rank);
return;
}
// Shard is ready
if uds.exists() && !ready {
tracing::info!("Shard {} ready in {:?}", rank, start_time.elapsed());
status_sender.send(ShardStatus::Ready).unwrap();
ready = true;
} else if !ready {
tracing::info!("Waiting for shard {} to be ready...", rank);
}
sleep(Duration::from_secs(5));
}
}
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();
}

28
proto/generate.proto
View File

@ -11,10 +11,6 @@ service TextGenerationService {
rpc Generate (GenerateRequest) returns (GenerateResponse);
/// Generate tokens for a list of cached batches
rpc GenerateWithCache (GenerateWithCacheRequest) returns (GenerateWithCacheResponse);
/// Generate tokens until the text of at least one request of the batch is generated
rpc GenerateUntilFinished (GenerateUntilFinishedRequest) returns (GenerateUntilFinishedResponse);
/// Generate tokens until the text of at least one request of the cached batches i finished
rpc GenerateUntilFinishedWithCache (GenerateUntilFinishedWithCacheRequest) returns (GenerateUntilFinishedWithCacheResponse);
}
/// Empty request
@ -92,27 +88,3 @@ message GenerateWithCacheResponse {
/// Next batch (cached)
optional Batch batch = 2;
}
message GenerateUntilFinishedRequest {
/// Batch
Batch batch = 1;
}
message GenerateUntilFinishedResponse {
/// Finished requests
repeated GeneratedText generated_texts = 1;
/// Next batch (cached)
optional Batch batch = 2;
}
message GenerateUntilFinishedWithCacheRequest {
/// Cached batches
repeated Batch batches = 1;
}
message GenerateUntilFinishedWithCacheResponse {
/// Finished requests
repeated GeneratedText generated_texts = 1;
/// Next batch (cached)
optional Batch batch = 2;
}

1
router/.gitignore vendored
View File

@ -1 +0,0 @@
/target

11
router/Cargo.toml
View File

@ -22,16 +22,7 @@ serde = "1.0.145"
serde_json = "1.0.85"
thiserror = "1.0.37"
tokenizers = "0.13.0"
tokio = { version = "1.21.1", features = ["rt-multi-thread", "parking_lot", "sync"] }
tokio = { version = "1.21.1", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
tracing = "0.1.36"
tracing-subscriber = "0.3.15"
[workspace]
members = [
"client",
]
[profile.release]
debug = 1
incremental = true
lto = "off"

2
router/client/Cargo.toml
View File

@ -5,8 +5,6 @@ edition = "2021"
[dependencies]
futures = "^0.3"
#grpc-error-details = { path = "../../grpc-error-details" }
#grpc-metadata = { path = "../../grpc-metadata" }
prost = "^0.9"
thiserror = "^1.0"
tokio = { version = "^1.21", features = ["sync"] }

44
router/client/src/client.rs
View File

@ -1,10 +1,11 @@
/// Single shard Client
use crate::pb::generate::v1::text_generation_service_client::TextGenerationServiceClient;
use crate::pb::generate::v1::*;
use crate::Result;
use tonic::transport::{Channel, Uri};
use tracing::*;
/// BLOOM Inference gRPC client
/// Text Generation Inference gRPC client
#[derive(Clone)]
pub struct Client {
stub: TextGenerationServiceClient<Channel>,
@ -34,6 +35,7 @@ impl Client {
})
}
/// Returns a list of uris or unix sockets of all shards
#[instrument(skip(self))]
pub async fn service_discovery(&mut self) -> Result<Vec<String>> {
let request = tonic::Request::new(ServiceDiscoveryRequest {});
@ -46,6 +48,7 @@ impl Client {
.into_inner()
.urls
.into_iter()
// Remove unix socket prefix
.map(|url| match url.strip_prefix("unix://") {
None => url,
Some(stripped_url) => stripped_url.to_string(),
@ -54,6 +57,7 @@ impl Client {
Ok(urls)
}
/// Clear the past generations cache
#[instrument(skip(self))]
pub async fn clear_cache(&mut self) -> Result<()> {
let request = tonic::Request::new(ClearCacheRequest {});
@ -64,6 +68,10 @@ impl Client {
Ok(())
}
/// Generate one token for each request in the given batch
///
/// Returns a list of generated texts of request that met their stopping criteria
/// and the next cached batch
#[instrument(skip(self))]
pub async fn generate(&mut self, batch: Batch) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
let request = tonic::Request::new(GenerateRequest { batch: Some(batch) });
@ -76,6 +84,10 @@ impl Client {
Ok((response.generated_texts, response.batch))
}
/// Generate one token for each request in the given cached batch
///
/// Returns a list of generated texts of request that met their stopping criteria
/// and the next cached batch
#[instrument(skip(self))]
pub async fn generate_with_cache(
&mut self,
@ -90,34 +102,4 @@ impl Client {
.into_inner();
Ok((response.generated_texts, response.batch))
}
#[instrument(skip(self))]
pub async fn generate_until_finished(
&mut self,
batch: Batch,
) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
let request = tonic::Request::new(GenerateUntilFinishedRequest { batch: Some(batch) });
let response = self
.stub
.generate_until_finished(request)
.instrument(info_span!("generate_until_finished"))
.await?
.into_inner();
Ok((response.generated_texts, response.batch))
}
#[instrument(skip(self))]
pub async fn generate_until_finished_with_cache(
&mut self,
batches: Vec<Batch>,
) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
let request = tonic::Request::new(GenerateUntilFinishedWithCacheRequest { batches });
let response = self
.stub
.generate_until_finished_with_cache(request)
.instrument(info_span!("generate_until_finished_with_cache"))
.await?
.into_inner();
Ok((response.generated_texts, response.batch))
}
}

7
router/client/src/lib.rs
View File

@ -1,6 +1,7 @@
//! BLOOM Inference gRPC client library
//! Text Generation gRPC client library
mod client;
#[allow(clippy::derive_partial_eq_without_eq)]
mod pb;
mod sharded_client;
@ -8,7 +9,7 @@ pub use client::Client;
pub use pb::generate::v1::{Batch, GeneratedText, LogitsWarperParameters, Request};
pub use sharded_client::ShardedClient;
use thiserror::Error;
pub use tonic::transport;
use tonic::transport;
use tonic::Status;
#[derive(Error, Debug, Clone)]
@ -21,7 +22,7 @@ pub enum ClientError {
impl From<Status> for ClientError {
fn from(err: Status) -> Self {
Self::Generation(err.to_string())
Self::Generation(err.message().to_string())
}
}

96
router/client/src/sharded_client.rs
View File

@ -1,9 +1,11 @@
/// Multi shard Client
use crate::Result;
use crate::{Batch, Client, GeneratedText};
use futures::future::join_all;
use tokio::sync::{broadcast, mpsc};
use tonic::transport::Uri;
/// List of all available commands that can be sent through the command channel
#[derive(Clone, Debug)]
enum Command {
Generate(
@ -14,36 +16,32 @@ enum Command {
Vec<Batch>,
mpsc::Sender<Result<(Vec<GeneratedText>, Option<Batch>)>>,
),
GenerateUntilFinished(
Batch,
mpsc::Sender<Result<(Vec<GeneratedText>, Option<Batch>)>>,
),
GenerateUntilFinishedWithCache(
Vec<Batch>,
mpsc::Sender<Result<(Vec<GeneratedText>, Option<Batch>)>>,
),
ClearCache(mpsc::Sender<Result<()>>),
}
/// Tokio task that handles the communication with a single shard
///
/// We subscribe on a broadcast channel to receive commands that will be sent by
/// the ShardedClient.
///
/// Each command is fan out to all shards.
///
/// The result of the command is sent back to the ShardedClient through a mpsc channel (multi
/// producer = the shards, single consumer = the ShardedClient).
async fn client_task(mut client: Client, mut request_subscriber: broadcast::Receiver<Command>) {
while let Ok(message) = request_subscriber.recv().await {
match message {
Command::Generate(batch, response_tx) => {
let result = client.generate(batch).await;
// We can unwrap_or(()) here because the only error that can happen is if the
// receiver is dropped, which means that the ShardedClient already received a
// response from another shard
response_tx.try_send(result).unwrap_or(());
}
Command::GenerateWithCache(batches, response_tx) => {
let result = client.generate_with_cache(batches).await;
response_tx.try_send(result).unwrap_or(());
}
Command::GenerateUntilFinished(batch, response_tx) => {
let result = client.generate_until_finished(batch).await;
response_tx.try_send(result).unwrap_or(());
}
Command::GenerateUntilFinishedWithCache(batches, response_tx) => {
let result = client.generate_until_finished_with_cache(batches).await;
response_tx.try_send(result).unwrap_or(());
}
Command::ClearCache(response_tx) => {
let result = client.clear_cache().await;
response_tx.try_send(result).unwrap_or(());
@ -52,30 +50,42 @@ async fn client_task(mut client: Client, mut request_subscriber: broadcast::Rece
}
}
/// Text Generation Inference gRPC multi client
pub struct ShardedClient {
_clients: Vec<Client>,
request_tx: broadcast::Sender<Command>,
}
impl ShardedClient {
fn new(mut clients: Vec<Client>) -> Self {
fn new(clients: Vec<Client>) -> Self {
// The broadcast channel to communicate with the shards
// We use a capacity of one as the shards are not asynchronous and can only process one
// command at a time
let (request_tx, _) = broadcast::channel(1);
for client in clients.drain(..) {
// Spawn client tasks
for client in clients.iter() {
let request_subscriber = request_tx.subscribe();
tokio::spawn(client_task(client, request_subscriber));
tokio::spawn(client_task(client.clone(), request_subscriber));
}
Self { request_tx }
Self {
_clients: clients,
request_tx,
}
}
/// Create a new ShardedClient from a master client. The master client will communicate with
/// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
async fn from_master_client(mut master_client: Client) -> Result<Self> {
// Get all uris/unix sockets from the master client
let uris = master_client.service_discovery().await.unwrap();
let futures = uris.into_iter().map(|path| Client::connect_uds(path));
let futures = uris.into_iter().map(Client::connect_uds);
let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
Ok(Self::new(clients?))
}
/// Returns a client connected to the given url
/// Returns a client connected to the given uri
pub async fn connect(uri: Uri) -> Result<Self> {
let master_client = Client::connect(uri).await?;
Self::from_master_client(master_client).await
@ -87,51 +97,43 @@ impl ShardedClient {
Self::from_master_client(master_client).await
}
/// Generate one token for each request in the given batch
///
/// Returns a list of generated texts of request that met their stopping criteria
/// and the next cached batch
pub async fn generate(&self, batch: Batch) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
// Create a channel to receive the response from the shards
// We will only ever receive one message on this channel
let (response_tx, mut response_rx) = mpsc::channel(1);
self.request_tx
.send(Command::Generate(batch, response_tx))
.unwrap();
// As soon as we receive one response, we can return as all shards will return the same
response_rx.recv().await.unwrap()
}
/// Generate one token for each request in the given cached batch
///
/// Returns a list of generated texts of request that met their stopping criteria
/// and the next cached batch
pub async fn generate_with_cache(
&self,
batches: Vec<Batch>,
) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
// Create a channel to receive the response from the shards
// We will only ever receive one message on this channel
let (response_tx, mut response_rx) = mpsc::channel(1);
self.request_tx
.send(Command::GenerateWithCache(batches, response_tx))
.unwrap();
// As soon as we receive one response, we can return as all shards will return the same
response_rx.recv().await.unwrap()
}
pub async fn generate_until_finished(
&self,
batch: Batch,
) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
let (response_tx, mut response_rx) = mpsc::channel(1);
self.request_tx
.send(Command::GenerateUntilFinished(batch, response_tx))
.unwrap();
response_rx.recv().await.unwrap()
}
pub async fn generate_until_finished_with_cache(
&self,
batches: Vec<Batch>,
) -> Result<(Vec<GeneratedText>, Option<Batch>)> {
let (response_tx, mut response_rx) = mpsc::channel(1);
self.request_tx
.send(Command::GenerateUntilFinishedWithCache(
batches,
response_tx,
))
.unwrap();
response_rx.recv().await.unwrap()
}
/// Clear the past generations cache
pub async fn clear_cache(&self) -> Result<()> {
// Create a channel to receive the response from the shards
// We will only ever receive one message on this channel
let (response_tx, mut response_rx) = mpsc::channel(1);
self.request_tx
.send(Command::ClearCache(response_tx))

167
router/src/batcher.rs
View File

@ -1,129 +1,158 @@
use crate::server::GenerateRequest;
/// Batching and inference logic
use crate::GenerateRequest;
use crate::{Db, Entry};
use axum::http::StatusCode;
use bloom_inference_client::{Batch, ClientError, GeneratedText, ShardedClient};
use std::future::Future;
use std::sync::Arc;
use std::time::Duration;
use thiserror::Error;
use tokio::sync::{oneshot, Notify};
use tokio::time::Instant;
use tracing::instrument;
const MAX_LENGTH: usize = 128;
#[derive(Debug, Error)]
pub enum InferError {
#[error("Request failed during generation: {0}")]
GenerationError(String),
#[error("Model is overloaded")]
Overloaded,
}
impl From<InferError> for (StatusCode, String) {
fn from(err: InferError) -> Self {
match err {
InferError::GenerationError(_) => (StatusCode::INTERNAL_SERVER_ERROR, err.to_string()),
InferError::Overloaded => (StatusCode::TOO_MANY_REQUESTS, err.to_string()),
}
}
}
/// Batcher
#[derive(Clone)]
pub struct Batcher {
/// Request database
db: Db,
/// Shared state
shared: Arc<Shared>,
}
/// Batcher shared state
struct Shared {
/// Batching background Tokio task notifier
batching_task: Notify,
}
impl Batcher {
pub(crate) fn new(client: ShardedClient, max_batch_size: usize) -> Self {
pub(crate) fn new(
client: ShardedClient,
max_batch_size: usize,
max_waiting_time: Duration,
) -> Self {
// Batcher shared state
let db = Db::new();
let shared = Arc::new(Shared {
batching_task: Notify::new(),
});
tokio::spawn(batching_task(max_batch_size, client, db.clone(), shared.clone()));
// Spawn batching background task that contains all the inference logic
tokio::spawn(batching_task(
max_batch_size,
max_waiting_time,
client,
db.clone(),
shared.clone(),
));
Self { db, shared }
}
/// Add a new request to the database and return a future that will generate the text
pub(crate) async fn infer(
&self,
input_length: usize,
request: GenerateRequest,
) -> Result<String, InferError> {
if self.db.len() > MAX_LENGTH {
return Err(InferError::Overloaded);
}
let (request_tx, request_rx) = oneshot::channel();
// One shot channel to communicate with the background batching task
let (response_tx, response_rx) = oneshot::channel();
// Try to append the request to the database
self.db.append(Entry {
request,
response_tx: request_tx,
response_tx,
input_length,
time: Instant::now(),
});
// Notify the background task that we have a new entry in the database that needs
// to be batched
self.shared.batching_task.notify_waiters();
match request_rx.await.unwrap() {
// Await on the response from the background task
// We can safely unwrap as the background task will never drop the sender
match response_rx.await.unwrap() {
Ok(output) => Ok(output),
Err(err) => Err(InferError::GenerationError(err.to_string())),
}
}
}
async fn batching_task(max_batch_size: usize,
client: ShardedClient,
db: Db,
shared: Arc<Shared>) {
/// Batching logic
/// Will be launched in a background Tokio task
///
/// Batches requests and sends them to the inference server
#[instrument(skip(client, db, shared))]
async fn batching_task(
max_batch_size: usize,
max_waiting_time: Duration,
client: ShardedClient,
db: Db,
shared: Arc<Shared>,
) {
// Minimum batch size after which we try to add more requests
let limit_min_batch_size = (max_batch_size / 2) as u32;
// Infinite loop
loop {
// Wait for a notification from the Batcher struct
shared.batching_task.notified().await;
if let Some(batch) = db.next_batch(max_batch_size) {
let request_ids = batch.requests.iter().map(|req| req.id).collect();
let mut cached_batch = match batch.size {
size if size > limit_min_batch_size => {
wrap_future(client.generate_until_finished(batch), request_ids, &db).await
}
_ => wrap_future(client.generate(batch), request_ids, &db).await,
};
// Get the next batch from the DB
// This batch might be smaller than the maximum batch size if there are not enough requests
// waiting in the DB
if let Some((request_ids, batch)) = db.next_batch(None, max_batch_size, None) {
let mut cached_batch = wrap_future(client.generate(batch), request_ids, &db).await;
// We loop until we do not receive any cached batch from the inference server (== until
// all requests have met their stopping criteria)
while let Some(batch) = cached_batch {
let mut current_batch_size = batch.size;
// Get current batch info
let batch_size = batch.size;
let mut request_ids: Vec<u64> = batch.requests.iter().map(|req| req.id).collect();
let mut batches = vec![batch];
if current_batch_size <= limit_min_batch_size {
if let Some(new_batch) = db.next_batch_minimum_size(limit_min_batch_size as usize, max_batch_size) {
let new_batch_request_ids =
new_batch.requests.iter().map(|req| req.id).collect();
// If the current batch is too small, we try to add more requests to it
if batch_size <= limit_min_batch_size {
// Get the next batch from the DB that meet our minimum size criteria
if let Some((new_request_ids, new_batch)) =
db.next_batch(Some(limit_min_batch_size as usize), max_batch_size, None)
{
// Generate one token for this new batch to have the attention past in cache
let new_cached_batch =
wrap_future(client.generate(new_batch), new_batch_request_ids, &db)
.await;
wrap_future(client.generate(new_batch), new_request_ids, &db).await;
// Extend current batch with the new batch
if let Some(new_cached_batch) = new_cached_batch {
request_ids.extend(new_cached_batch.requests.iter().map(|req| req.id));
batches.push(new_cached_batch);
}
}
// If we don't have enough requests to meet the minimum size criteria, we
// try to get the next batch from the DB that have been waiting over
// the max_waiting_time
else if let Some((new_request_ids, new_batch)) =
db.next_batch(None, max_batch_size, Some(max_waiting_time))
{
let new_cached_batch =
wrap_future(client.generate(new_batch), new_request_ids, &db).await;
// Extend current batch with the new batch
if let Some(new_cached_batch) = new_cached_batch {
current_batch_size += new_cached_batch.size;
request_ids.extend(new_cached_batch.requests.iter().map(|req| req.id));
batches.push(new_cached_batch);
}
}
}
cached_batch = match current_batch_size {
size if size > limit_min_batch_size => {
wrap_future(
client.generate_until_finished_with_cache(batches),
request_ids,
&db,
)
.await
}
_ => wrap_future(client.generate_with_cache(batches), request_ids, &db).await,
};
cached_batch =
wrap_future(client.generate_with_cache(batches), request_ids, &db).await;
}
}
}
}
/// Wrap a future inside a match statement to handle errors and send the response to the Batcher
async fn wrap_future(
future: impl Future<Output = Result<(Vec<GeneratedText>, Option<Batch>), ClientError>>,
request_ids: Vec<u64>,
@ -134,6 +163,7 @@ async fn wrap_future(
send_generated(generated_texts, db);
next_batch
}
// If we have an error, we discard the whole batch
Err(err) => {
send_error(err, request_ids, db);
None
@ -141,16 +171,20 @@ async fn wrap_future(
}
}
/// Send errors to the Batcher for all `request_ids`
fn send_error(error: ClientError, request_ids: Vec<u64>, db: &Db) {
request_ids.into_iter().for_each(|id| {
// We can `expect` here as the request id should always be in the DB
let entry = db.remove(&id).expect("ID not found in db. This is a bug.");
// unwrap_or is valid here as we don't care if the receiver is gone.
entry.response_tx.send(Err(error.clone())).unwrap_or(());
});
}
/// Send `generated_text` to the Batcher for all `finished`
fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
finished.into_iter().for_each(|output| {
// We can `expect` here as the request id should always be in the DB
let entry = db
.remove(&output.request.unwrap().id)
.expect("ID not found in db. This is a bug.");
@ -158,3 +192,18 @@ fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
entry.response_tx.send(Ok(output.output)).unwrap_or(());
});
}
#[derive(Debug, Error)]
pub enum InferError {
#[error("Request failed during generation: {0}")]
GenerationError(String),
}
/// Convert to Axum supported format
impl From<InferError> for (StatusCode, String) {
fn from(err: InferError) -> Self {
match err {
InferError::GenerationError(_) => (StatusCode::INTERNAL_SERVER_ERROR, err.to_string()),
}
}
}

287
router/src/db.rs
View File

@ -1,16 +1,173 @@
/// This code is massively inspired by Tokio mini-redis
use crate::server::{GenerateParameters, GenerateRequest};
use crate::{GenerateParameters, GenerateRequest};
use bloom_inference_client::{Batch, ClientError, LogitsWarperParameters, Request};
use parking_lot::RwLock;
use parking_lot::Mutex;
use std::collections::BTreeMap;
use std::sync::Arc;
use std::time::Duration;
use tokio::sync::oneshot::Sender;
use tokio::time::Instant;
/// Database entry
#[derive(Debug)]
pub(crate) struct Entry {
/// Request
pub request: GenerateRequest,
/// Response sender to communicate between the Batcher and the batching_task
pub response_tx: Sender<Result<String, ClientError>>,
/// Number of tokens in the input
pub input_length: usize,
/// Instant when this entry was created
pub time: Instant,
}
/// Request Database
#[derive(Debug, Clone)]
pub(crate) struct Db {
pub shared: Arc<Shared>,
}
/// Shared state
#[derive(Debug)]
pub struct Shared {
state: Mutex<State>,
}
/// Database State
#[derive(Debug)]
struct State {
/// Database entries organized in a BTreeMap to be able to iterate over them in order
entries: BTreeMap<u64, Entry>,
/// Id of the next entry
next_id: u64,
/// Id of the next batch
next_batch_id: u64,
/// Start ID of the next batch. Used to iterate inside the entries BTreeMap
next_batch_start_id: u64,
}
impl State {
/// Get the next requests
fn next_requests(
&self,
max_size: usize,
min_waiting_time: Option<Duration>,
) -> Option<(Vec<u64>, Vec<Request>)> {
// Iterates for max_size over the BTreemap starting from next_batch_start_id
let mut requests = Vec::new();
let mut ids = Vec::new();
for (id, entry) in self
.entries
// Start from next_batch_start_id
.range(self.next_batch_start_id..)
// Take max_size
.take(max_size)
{
if let Some(min_waiting_time) = min_waiting_time {
// Only take entries that waited for at least min_waiting_time
if entry.time.elapsed() < min_waiting_time {
// Since entries are ordered, we already know that all following entries won't
// satisfy the condition
break;
}
}
requests.push(Request {
id: *id,
inputs: entry.request.inputs.clone(),
input_length: entry.input_length as u32,
parameters: Some(LogitsWarperParameters::from(
entry.request.parameters.clone(),
)),
max_new_tokens: entry.request.parameters.max_new_tokens,
});
ids.push(*id);
}
if requests.is_empty() {
None
} else {
Some((ids, requests))
}
}
}
impl Db {
pub(crate) fn new() -> Self {
// Shared state
let shared = Arc::new(Shared {
state: Mutex::new(State {
entries: BTreeMap::new(),
next_id: 0,
next_batch_id: 0,
next_batch_start_id: 0,
}),
});
Self { shared }
}
/// Append an entry to the database
pub(crate) fn append(&self, entry: Entry) {
// Acquire lock
let mut state = self.shared.state.lock();
// Insert entry
let id = state.next_id;
state.next_id += 1;
state.entries.insert(id, entry);
}
/// Remove an entry from the database if it exists
pub(crate) fn remove(&self, id: &u64) -> Option<Entry> {
let mut state = self.shared.state.lock();
state.entries.remove(id)
}
// Get the next batch
pub(crate) fn next_batch(
&self,
min_size: Option<usize>,
max_size: usize,
min_waiting_time: Option<Duration>,
) -> Option<(Vec<u64>, Batch)> {
// Acquire lock
let mut state = self.shared.state.lock();
// Get requests from the database
if let Some((ids, requests)) = state.next_requests(max_size, min_waiting_time) {
if let Some(min_size) = min_size {
// If min_size is set, only return a batch if there are enough requests
if requests.len() < min_size {
return None;
}
}
// Batch size
let size = requests.len();
// Longest input length for all requests in batch size
// Used for padding inside the inference server
let max_sequence_length = requests.iter().map(|r| r.input_length).max().unwrap();
let batch = Batch {
id: state.next_batch_id,
requests,
size: size as u32,
max_sequence_length,
};
// Update next_batch_start_id to the last id in the batch + 1
state.next_batch_start_id = ids.last().unwrap() + 1;
// Increment batch id
state.next_batch_id += 1;
return Some((ids, batch));
}
None
}
}
impl From<GenerateParameters> for LogitsWarperParameters {
@ -23,129 +180,3 @@ impl From<GenerateParameters> for LogitsWarperParameters {
}
}
}
#[derive(Debug, Clone)]
pub(crate) struct Db {
pub shared: Arc<Shared>,
}
#[derive(Debug)]
pub struct Shared {
state: RwLock<State>,
}
#[derive(Debug)]
struct State {
entries: BTreeMap<u64, Entry>,
/// Identifier to use for the next expiration. Each expiration is associated
/// with a unique identifier. See above for why.
next_id: u64,
next_batch_id: u64,
/// Current batch id
next_batch_start_id: u64,
}
impl Db {
pub(crate) fn new() -> Self {
let shared = Arc::new(Shared {
state: RwLock::new(State {
entries: BTreeMap::new(),
next_id: 0,
next_batch_id: 0,
next_batch_start_id: 0,
}),
});
Self { shared }
}
pub(crate) fn append(&self, entry: Entry) {
let mut state = self.shared.state.write();
let id = state.next_id;
state.next_id += 1;
state.entries.insert(id, entry);
}
pub(crate) fn remove(&self, id: &u64) -> Option<Entry> {
let mut state = self.shared.state.write();
state.entries.remove(id)
}
pub(crate) fn len(&self) -> usize {
let state = self.shared.state.read();
state.entries.len()
}
fn next_requests(&self, max_size: usize) -> Option<(u64, Vec<Request>)> {
let state = self.shared.state.read();
let requests: Vec<Request> = state
.entries
.range(state.next_batch_start_id..)
.take(max_size)
.map(|(id, entry)| Request {
id: *id,
inputs: entry.request.inputs.clone(),
input_length: entry.input_length as u32,
parameters: Some(LogitsWarperParameters::from(
entry.request.parameters.clone(),
)),
max_new_tokens: entry.request.parameters.max_new_tokens,
})
.collect();
if requests.is_empty() {
None
} else {
let last_id = requests.last().unwrap().id;
Some((last_id, requests))
}
}
pub(crate) fn next_batch(&self, max_size: usize) -> Option<Batch> {
if let Some((last_id, requests)) = self.next_requests(max_size) {
let mut state = self.shared.state.write();
let size = requests.len();
let max_sequence_length = requests.iter().map(|r| r.input_length).max().unwrap();
let batch = Batch {
id: state.next_batch_id,
requests,
size: size as u32,
max_sequence_length,
};
state.next_batch_start_id = last_id + 1;
state.next_batch_id += 1;
return Some(batch);
}
None
}
pub(crate) fn next_batch_minimum_size(
&self,
min_size: usize,
max_size: usize,
) -> Option<Batch> {
if let Some((last_id, requests)) = self.next_requests(max_size) {
if requests.len() >= min_size {
let mut state = self.shared.state.write();
let size = requests.len();
let max_sequence_length = requests.iter().map(|r| r.input_length).max().unwrap();
let batch = Batch {
id: state.next_batch_id,
requests,
size: size as u32,
max_sequence_length,
};
state.next_batch_start_id = last_id + 1;
state.next_batch_id += 1;
return Some(batch);
}
}
None
}
}

64
router/src/lib.rs
View File

@ -1,8 +1,68 @@
/// Text Generation Inference Webserver
mod batcher;
mod db;
mod validation;
pub mod server;
mod validation;
use db::{Db, Entry};
use batcher::Batcher;
use db::{Db, Entry};
use serde::{Deserialize, Serialize};
use validation::Validation;
#[derive(Clone, Debug, Deserialize)]
pub(crate) struct GenerateParameters {
#[serde(default = "default_temperature")]
pub temperature: f32,
#[serde(default = "default_top_k")]
pub top_k: i32,
#[serde(default = "default_top_p")]
pub top_p: f32,
#[serde(default = "default_do_sample")]
pub do_sample: bool,
#[serde(default = "default_max_new_tokens")]
pub max_new_tokens: u32,
}
fn default_temperature() -> f32 {
1.0
}
fn default_top_k() -> i32 {
0
}
fn default_top_p() -> f32 {
1.0
}
fn default_do_sample() -> bool {
false
}
fn default_max_new_tokens() -> u32 {
20
}
fn default_parameters() -> GenerateParameters {
GenerateParameters {
temperature: default_temperature(),
top_k: default_top_k(),
top_p: default_top_p(),
do_sample: default_do_sample(),
max_new_tokens: default_max_new_tokens(),
}
}
#[derive(Clone, Debug, Deserialize)]
pub(crate) struct GenerateRequest {
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
}
#[derive(Serialize)]
pub(crate) struct GeneratedText {
pub generated_text: String,
}
pub(crate) type GenerateResponse = Vec<GeneratedText>;

52
router/src/main.rs
View File

@ -1,37 +1,61 @@
/// Text Generation Inference webserver entrypoint
use bloom_inference_client::ShardedClient;
use clap::Parser;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::time::Duration;
use text_generation_router::server;
use tokenizers::Tokenizer;
use clap::Parser;
/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
#[clap(default_value = "32", long, short, env)]
#[clap(default_value = "128", long, env)]
max_concurrent_requests: usize,
#[clap(default_value = "1000", long, env)]
max_input_length: usize,
#[clap(default_value = "32", long, env)]
max_batch_size: usize,
#[clap(default_value = "5", long, env)]
max_waiting_time: u64,
#[clap(default_value = "3000", long, short, env)]
port: u16,
#[clap(default_value = "/tmp/bloom-inference-0", long, env)]
shard_uds_path: String,
master_shard_uds_path: String,
#[clap(default_value = "bigscience/bloom", long, env)]
tokenizer_name: String,
#[clap(default_value = "2", long, env)]
validation_workers: usize,
}
fn main() -> Result<(), std::io::Error> {
// Get args
let args = Args::parse();
// Pattern match configuration
// Pattern match configuration
let Args {
max_concurrent_requests,
max_input_length,
max_batch_size,
max_waiting_time,
port,
shard_uds_path,
master_shard_uds_path,
tokenizer_name,
validation_workers,
} = args;
if validation_workers == 1 {
panic!("validation_workers must be > 0");
}
let max_waiting_time = Duration::from_secs(max_waiting_time);
// Download and instantiate tokenizer
// This will only be used to validate payloads
//
// We need to download it outside of the Tokio runtime
let tokenizer = Tokenizer::from_pretrained(tokenizer_name, None).unwrap();
// Launch Tokio runtime
tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()
@ -39,18 +63,32 @@ fn main() -> Result<(), std::io::Error> {
.block_on(async {
tracing_subscriber::fmt::init();
let sharded_client = ShardedClient::connect_uds(shard_uds_path)
// Instantiate sharded client from the master unix socket
let sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
.await
.expect("Could not connect to server");
// Clear the cache; useful if the webserver rebooted
sharded_client
.clear_cache()
.await
.expect("Unable to clear cache");
tracing::info!("Connected");
// Binds on localhost
let addr = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(0, 0, 0, 0)), port);
server::run(max_batch_size, sharded_client, tokenizer, addr).await;
// Run server
server::run(
max_concurrent_requests,
max_input_length,
max_batch_size,
max_waiting_time,
sharded_client,
tokenizer,
validation_workers,
addr,
)
.await;
Ok(())
})
}

164
router/src/server.rs
View File

@ -1,68 +1,44 @@
use crate::{Batcher, Validation};
use crate::{
Batcher, GenerateParameters, GenerateRequest, GenerateResponse, GeneratedText, Validation,
};
use axum::extract::Extension;
use axum::http::StatusCode;
use axum::routing::{get, post};
use axum::{Json, Router};
use bloom_inference_client::ShardedClient;
use serde::Deserialize;
use std::net::SocketAddr;
use std::sync::Arc;
use std::time::Duration;
use tokenizers::Tokenizer;
use tokio::signal;
use tokio::sync::Semaphore;
use tokio::time::Instant;
use tracing::instrument;
#[derive(Clone, Debug, Deserialize)]
pub(crate) struct GenerateParameters {
#[serde(default = "default_temperature")]
pub temperature: f32,
#[serde(default = "default_top_k")]
pub top_k: i32,
#[serde(default = "default_top_p")]
pub top_p: f32,
#[serde(default = "default_do_sample")]
pub do_sample: bool,
#[serde(default = "default_max_new_tokens")]
pub max_new_tokens: u32,
}
fn default_temperature() -> f32 {
1.0
}
fn default_top_k() -> i32 {
0
}
fn default_top_p() -> f32 {
1.0
}
fn default_do_sample() -> bool {
false
}
fn default_max_new_tokens() -> u32 {
20
}
fn default_parameters() -> GenerateParameters {
GenerateParameters {
temperature: default_temperature(),
top_k: default_top_k(),
top_p: default_top_p(),
do_sample: default_do_sample(),
max_new_tokens: default_max_new_tokens(),
}
}
#[derive(Clone, Debug, Deserialize)]
pub(crate) struct GenerateRequest {
pub inputs: String,
#[serde(default = "default_parameters")]
pub parameters: GenerateParameters,
// Server shared state
#[derive(Clone)]
struct ServerState {
validation: Validation,
batcher: Batcher,
limit_concurrent_requests: Arc<Semaphore>,
}
/// Health check method
#[instrument(skip(state), fields(time, time_per_token))]
async fn liveness(state: Extension<ServerState>) -> Result<(), (StatusCode, String)> {
async fn health(state: Extension<ServerState>) -> Result<(), (StatusCode, String)> {
// TODO: while this is the best health check we can do, it is a bit on the heavy side and might
// be a bit too slow for a health check.
// What we should do instead if check if the gRPC channels are still healthy.
// Limit concurrent requests by acquiring a permit from the semaphore
let _permit = state.limit_concurrent_requests.try_acquire().map_err(|_| {
(
StatusCode::TOO_MANY_REQUESTS,
"Model is overloaded".to_string(),
)
})?;
// Send a small inference request
state
.batcher
.infer(
@ -82,23 +58,35 @@ async fn liveness(state: Extension<ServerState>) -> Result<(), (StatusCode, Stri
Ok(())
}
/// Generate method
#[instrument(skip(state), fields(time, time_per_token))]
async fn generate(
state: Extension<ServerState>,
req: Json<GenerateRequest>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
) -> Result<Json<GenerateResponse>, (StatusCode, String)> {
let start = Instant::now();
// Limit concurrent requests by acquiring a permit from the semaphore
let _permit = state.limit_concurrent_requests.try_acquire().map_err(|_| {
(
StatusCode::TOO_MANY_REQUESTS,
"Model is overloaded".to_string(),
)
})?;
// Validate request
let (input_length, validated_request) = state
.validation
// FIXME: can't we get rid of the cloning here??
.validate(GenerateRequest {
inputs: req.inputs.clone(),
parameters: req.parameters.clone(),
})
.await?;
// Inference
let generated_text = state.batcher.infer(input_length, validated_request).await?;
// Tracing metadata
tracing::Span::current().record("time", format!("{:?}", start.elapsed()));
tracing::Span::current().record(
"time_per_token",
@ -106,31 +94,71 @@ async fn generate(
);
tracing::info!("response: {}", generated_text);
Ok(Json(serde_json::json!({
"generated_text": generated_text,
})))
// Send response
let response = vec![GeneratedText { generated_text }];
Ok(Json(response))
}
#[derive(Clone)]
struct ServerState {
validation: Validation,
batcher: Batcher,
}
pub async fn run(max_batch_size: usize, client: ShardedClient, tokenizer: Tokenizer, addr: SocketAddr) {
let batcher = Batcher::new(client, max_batch_size);
let validation = Validation::new(tokenizer);
let shared_state = ServerState { validation, batcher };
/// Serving method
#[allow(clippy::too_many_arguments)]
pub async fn run(
max_concurrent_requests: usize,
max_input_length: usize,
max_batch_size: usize,
max_waiting_time: Duration,
client: ShardedClient,
tokenizer: Tokenizer,
validation_workers: usize,
addr: SocketAddr,
) {
// Create state
let batcher = Batcher::new(client, max_batch_size, max_waiting_time);
let validation = Validation::new(validation_workers, tokenizer, max_input_length);
let shared_state = ServerState {
validation,
batcher,
limit_concurrent_requests: Arc::new(Semaphore::new(max_concurrent_requests)),
};
// Create router
let app = Router::new()
.route("/generate", post(generate))
.layer(Extension(shared_state.clone()))
.route("/health", get(liveness))
.route("/health", get(health))
.layer(Extension(shared_state.clone()));
// Run server
axum::Server::bind(&addr)
.serve(app.into_make_service())
// Wait until all requests are finished to shut down
.with_graceful_shutdown(shutdown_signal())
.await
.unwrap();
}
/// Shutdown signal handler
async fn shutdown_signal() {
let ctrl_c = async {
signal::ctrl_c()
.await
.expect("failed to install Ctrl+C handler");
};
#[cfg(unix)]
let terminate = async {
signal::unix::signal(signal::unix::SignalKind::terminate())
.expect("failed to install signal handler")
.recv()
.await;
};
#[cfg(not(unix))]
let terminate = std::future::pending::<()>();
tokio::select! {
_ = ctrl_c => {},
_ = terminate => {},
}
tracing::info!("signal received, starting graceful shutdown");
}

131
router/src/validation.rs
View File

@ -1,62 +1,105 @@
use crate::server::GenerateRequest;
/// Payload validation logic
use crate::GenerateRequest;
use axum::http::StatusCode;
use thiserror::Error;
use tokenizers::tokenizer::Tokenizer;
use tokenizers::{
DecoderWrapper, ModelWrapper, NormalizerWrapper, PostProcessorWrapper, PreTokenizerWrapper,
TokenizerImpl,
};
use tokio::sync::{mpsc, oneshot};
#[derive(Error, Debug)]
pub enum ValidationError {
#[error("Temperature must be strictly positive")]
Temperature,
#[error("Top p must be <= 0.0 or > 1.0")]
TopP,
#[error("Top k must be strictly positive")]
TopK,
#[error("Max New Tokens must be < 512")]
MaxNewTokens,
#[error("Inputs must have less than 1000 tokens. Given: {0}")]
InputLength(usize),
}
impl From<ValidationError> for (StatusCode, String) {
fn from(err: ValidationError) -> Self {
(StatusCode::BAD_REQUEST, err.to_string())
}
}
type ValidationRequest = (
GenerateRequest,
oneshot::Sender<Result<(usize, GenerateRequest), ValidationError>>,
);
/// Validation
#[derive(Debug, Clone)]
pub struct Validation {
/// Channel to communicate with the background validation task
sender: mpsc::Sender<ValidationRequest>,
}
impl Validation {
pub(crate) fn new(tokenizer: Tokenizer) -> Self {
pub(crate) fn new(workers: usize, tokenizer: Tokenizer, max_input_length: usize) -> Self {
// Crate channel
let (validation_sender, validation_receiver) = mpsc::channel(128);
tokio::spawn(validation_task(tokenizer, validation_receiver));
// Launch background validation task
tokio::spawn(validation_task(
workers,
tokenizer,
max_input_length,
validation_receiver,
));
Self {
sender: validation_sender,
}
}
/// Validate a payload and get the number of tokens in the input
pub(crate) async fn validate(
&self,
request: GenerateRequest,
) -> Result<(usize, GenerateRequest), ValidationError> {
// Create response channel
let (sender, receiver) = oneshot::channel();
// Send request to the background validation task
// Unwrap is safe here
self.sender.send((request, sender)).await.unwrap();
// Await on response channel
// Unwrap is safe here
receiver.await.unwrap()
}
}
async fn validation_task(tokenizer: Tokenizer, mut receiver: mpsc::Receiver<ValidationRequest>) {
while let Some((request, response_tx)) = receiver.recv().await {
/// Validation task
/// Load balance the validation requests between multiple validation workers
async fn validation_task(
workers: usize,
tokenizer: Tokenizer,
max_input_length: usize,
mut receiver: mpsc::Receiver<ValidationRequest>,
) {
let mut workers_senders = Vec::with_capacity(workers);
// Create workers
for _ in 0..workers {
let tokenizer_clone = tokenizer.clone();
// Create channel to communicate with worker
let (worker_sender, worker_receiver) = mpsc::channel(workers);
workers_senders.push(worker_sender);
// Spawn worker
tokio::task::spawn_blocking(move || {
validation_worker(tokenizer_clone, max_input_length, worker_receiver)
});
}
loop {
// Load balance requests between workers
for sender in workers_senders.iter() {
if let Some(validation_request) = receiver.recv().await {
sender.send(validation_request).await.unwrap();
} else {
return;
}
}
}
}
/// Check the parameters inside the payload and get the number of tokens inside the input using
/// the tokenizer
fn validation_worker(
tokenizer: TokenizerImpl<
ModelWrapper,
NormalizerWrapper,
PreTokenizerWrapper,
PostProcessorWrapper,
DecoderWrapper,
>,
max_input_length: usize,
mut receiver: mpsc::Receiver<ValidationRequest>,
) {
// Loop over requests
while let Some((request, response_tx)) = receiver.blocking_recv() {
if request.parameters.temperature < 0.0 {
response_tx
.send(Err(ValidationError::Temperature))
@ -78,10 +121,11 @@ async fn validation_task(tokenizer: Tokenizer, mut receiver: mpsc::Receiver<Vali
continue;
}
// Get the number of tokens in the input
let inputs = tokenizer.encode(request.inputs.clone(), false).unwrap();
let input_length = inputs.len();
if input_length > 1000 {
if input_length > max_input_length {
response_tx
.send(Err(ValidationError::InputLength(input_length)))
.unwrap_or(());
@ -91,3 +135,28 @@ async fn validation_task(tokenizer: Tokenizer, mut receiver: mpsc::Receiver<Vali
response_tx.send(Ok((input_length, request))).unwrap_or(());
}
}
type ValidationRequest = (
GenerateRequest,
oneshot::Sender<Result<(usize, GenerateRequest), ValidationError>>,
);
#[derive(Error, Debug)]
pub enum ValidationError {
#[error("Temperature must be strictly positive")]
Temperature,
#[error("Top p must be <= 0.0 or > 1.0")]
TopP,
#[error("Top k must be strictly positive")]
TopK,
#[error("Max New Tokens must be < 512")]
MaxNewTokens,
#[error("Inputs must have less than 1000 tokens. Given: {0}")]
InputLength(usize),
}
impl From<ValidationError> for (StatusCode, String) {
fn from(err: ValidationError) -> Self {
(StatusCode::BAD_REQUEST, err.to_string())
}
}

30
run.sh
View File

@ -1,30 +0,0 @@
#!/usr/bin/env bash
server_cmd="bloom-inference-server launcher $MODEL_NAME --num-gpus $NUM_GPUS --shard-directory $MODEL_BASE_PATH"
# Run in background
$server_cmd 2>&1 > /dev/null &
# Check if server is running by checking if the unix socket is created
FILE=/tmp/bloom-inference-0
while :
do
if test -S "$FILE"; then
echo "Text Generation Python gRPC server started"
break
else
echo "Waiting for Text Generation Python gRPC server to start"
sleep 5
fi
done
sleep 1
# Run in background
text-generation-router &
# Wait for any process to exit
wait -n
# Exit with status of process that exited first
exit $?

0
router/rust-toolchain.toml → rust-toolchain.toml
View File

155
server/.gitignore vendored Normal file
View File

@ -0,0 +1,155 @@
# Byte-compiled / optimized / DLL files
__pycache__/
bloom_inference/__pycache__/
bloom_inference/pb/__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/

21
server/Makefile
View File

@ -4,17 +4,28 @@ gen-server:
find bloom_inference/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
touch bloom_inference/pb/__init__.py
unit-tests:
python -m pytest --cov=bloom_inference tests
install-transformers:
# Install specific version of transformers
rm transformers || true
wget https://github.com/huggingface/transformers/archive/46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
unzip 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
rm 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
mv transformers-46d37bece7d3ffdef97b1ee4a3170c0a0627d921 transformers
cd transformers && python setup.py install
unit-tests-reporting:
python -m pytest --junitxml=report.xml --cov=bloom_inference tests
install-torch:
# Install specific version of torch
pip install torch --extra-index-url https://download.pytorch.org/whl/cu116 --no-cache-dir
pip-install:
pip install grpcio-tools
make gen-server
make install-torch
make install-transformers
pip install .
install:
poetry install
make gen-server
make gen-server
make install-torch
make install-transformers

54
server/bloom_inference/cli.py
View File

@ -1,41 +1,51 @@
import os
import typer
from pathlib import Path
from torch.distributed.launcher import launch_agent, LaunchConfig
from typing import Optional
from bloom_inference import server
from bloom_inference import prepare_weights, server
app = typer.Typer()
@app.command()
def launcher(
model_name: str,
num_gpus: int = 1,
shard_directory: Optional[Path] = None,
def serve(
model_name: str,
sharded: bool = False,
shard_directory: Optional[Path] = None,
uds_path: Path = "/tmp/bloom-inference",
):
if num_gpus == 1:
serve(model_name, False, shard_directory)
if sharded:
assert (
shard_directory is not None
), "shard_directory must be set when sharded is True"
assert (
os.getenv("RANK", None) is not None
), "RANK must be set when sharded is True"
assert (
os.getenv("WORLD_SIZE", None) is not None
), "WORLD_SIZE must be set when sharded is True"
assert (
os.getenv("MASTER_ADDR", None) is not None
), "MASTER_ADDR must be set when sharded is True"
assert (
os.getenv("MASTER_PORT", None) is not None
), "MASTER_PORT must be set when sharded is True"
else:
config = LaunchConfig(
min_nodes=1,
max_nodes=1,
nproc_per_node=num_gpus,
rdzv_backend="c10d",
max_restarts=0,
)
launch_agent(config, server.serve, [model_name, True, shard_directory])
server.serve(model_name, sharded, uds_path, shard_directory)
@app.command()
def serve(
model_name: str,
sharded: bool = False,
shard_directory: Optional[Path] = None,
def prepare_weights(
model_name: str,
shard_directory: Path,
cache_directory: Path,
num_shard: int = 1,
):
server.serve(model_name, sharded, shard_directory)
prepare_weights.prepare_weights(
model_name, cache_directory, shard_directory, num_shard
)
if __name__ == "__main__":

36
server/bloom_inference/model.py
View File

@ -24,6 +24,7 @@ torch.manual_seed(0)
class Batch:
batch_id: int
requests: List[generate_pb2.Request]
all_input_lengths: List[int]
input_ids: Dict[str, torch.Tensor]
all_input_ids: List[torch.Tensor]
next_token_choosers: List[NextTokenChooser]
@ -46,12 +47,12 @@ class Batch:
inputs = []
next_token_choosers = []
stopping_criterias = []
input_lengths = []
all_input_lengths = []
# Parse batch
for r in pb.requests:
inputs.append(r.inputs)
input_lengths.append(r.input_length)
all_input_lengths.append(r.input_length)
next_token_choosers.append(
NextTokenChooser(
temperature=r.parameters.temperature,
@ -63,17 +64,12 @@ class Batch:
stopping_criterias.append(StoppingCriteria(max_new_tokens=r.max_new_tokens))
input_ids = tokenizer(inputs, return_tensors="pt", padding=True).to(device)
# Remove padding from all_input_ids
all_input_ids = [
input_ids.squeeze(0)[-length:].unsqueeze(-1)
for length, input_ids in zip(
input_lengths, input_ids["input_ids"].split(1, dim=0)
)
]
all_input_ids = input_ids["input_ids"].unsqueeze(-1)
return cls(
batch_id=pb.id,
requests=pb.requests,
all_input_lengths=all_input_lengths,
input_ids=input_ids,
all_input_ids=all_input_ids,
next_token_choosers=next_token_choosers,
@ -91,6 +87,7 @@ class Batch:
# Batch attributes
input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
requests = []
all_input_lengths = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
@ -100,6 +97,7 @@ class Batch:
start_index = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
all_input_lengths.extend(batch.all_input_lengths)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
@ -198,6 +196,7 @@ class Batch:
return cls(
batch_id=batches[0].batch_id,
requests=requests,
all_input_lengths=all_input_lengths,
input_ids=input_ids,
all_input_ids=all_input_ids,
next_token_choosers=next_token_choosers,
@ -227,7 +226,10 @@ class BLOOM:
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self.model = (
AutoModelForCausalLM.from_pretrained(model_name).eval().to(self.device).to(dtype)
AutoModelForCausalLM.from_pretrained(model_name)
.eval()
.to(self.device)
.to(dtype)
)
self.num_heads = self.model.base_model.num_heads
@ -253,6 +255,7 @@ class BLOOM:
# New input_ids for next forward
next_batch_input_ids = []
next_batch_all_input_ids = []
next_all_input_lengths = []
next_batch_size = 0
next_batch_max_sequence_length = 0
@ -263,6 +266,7 @@ class BLOOM:
# Zipped iterator
iterator = zip(
batch.requests,
batch.all_input_lengths,
outputs.logits,
batch.next_token_choosers,
batch.stopping_criterias,
@ -272,6 +276,7 @@ class BLOOM:
# For each member of the batch
for i, (
request,
input_length,
logits,
next_token_chooser,
stopping_criteria,
@ -302,8 +307,10 @@ class BLOOM:
next_batch_input_ids.append(next_token)
next_batch_all_input_ids.append(all_tokens)
next_batch_size += 1
new_input_length = input_length + 1
next_all_input_lengths.append(new_input_length)
next_batch_max_sequence_length = max(
next_batch_max_sequence_length, len(all_tokens)
next_batch_max_sequence_length, new_input_length
)
# We finished all generations in the batch; there is no next batch
@ -350,6 +357,7 @@ class BLOOM:
next_batch = Batch(
batch_id=batch.batch_id,
requests=next_batch_requests,
all_input_lengths=next_all_input_lengths,
input_ids=next_batch_input_ids,
all_input_ids=next_batch_all_input_ids,
next_token_choosers=next_batch_next_token_choosers,
@ -378,7 +386,10 @@ class BLOOMSharded(BLOOM):
if self.master:
# TODO @thomasw21 do some caching
shard_state_dict_paths = prepare_weights(
model_name, shard_directory / "cache", shard_directory, tp_world_size=self.world_size
model_name,
shard_directory / "cache",
shard_directory,
tp_world_size=self.world_size,
)
shard_state_dict_paths = [
str(path.absolute()) for path in shard_state_dict_paths
@ -443,6 +454,7 @@ class BLOOMSharded(BLOOM):
use_cache=True,
)
# Logits are sharded, so we need to gather them
logits_shard = outputs.logits[:, -1, :].contiguous()
batch_size, vocab_shard_size = logits_shard.shape

2
server/bloom_inference/pb/.gitignore vendored
View File

@ -1,2 +1,2 @@
*.py
*.py-e
*.py-e

97
server/bloom_inference/prepare_weights.py
View File

@ -14,15 +14,15 @@ from huggingface_hub.file_download import _request_wrapper, hf_raise_for_status
def match_suffix(text, suffix):
return text[-len(suffix):] == suffix
return text[-len(suffix) :] == suffix
def http_get(
url: str,
temp_file: BinaryIO,
*,
timeout=10.0,
max_retries=0,
url: str,
temp_file: BinaryIO,
*,
timeout=10.0,
max_retries=0,
):
"""
Download a remote file. Do not gobble up errors, and will return errors tailored to the Hugging Face Hub.
@ -54,7 +54,9 @@ def cache_download_url(url: str, root_dir: Path):
return filename
def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world_size: int):
def prepare_weights(
model_name: str, cache_path: Path, save_path: Path, tp_world_size: int
):
save_paths = [
save_path / f"{model_name}_tp-rank-{tp_rank}-of-{tp_world_size}.pty"
for tp_rank in range(tp_world_size)
@ -68,6 +70,7 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
if model_name == "bigscience/bloom-560m":
url = hf_hub_url(model_name, filename="pytorch_model.bin")
cache_download_url(url, cache_path)
elif model_name == "bigscience/bloom":
url = hf_hub_url(model_name, filename="pytorch_model.bin.index.json")
index_path = cache_download_url(url, cache_path)
@ -75,10 +78,14 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
index = json.load(f)
# Get unique file names
weight_files = list(set([filename for filename in index["weight_map"].values()]))
weight_files = list(
set([filename for filename in index["weight_map"].values()])
)
urls = [hf_hub_url(model_name, filename=filename) for filename in weight_files]
Parallel(n_jobs=5)(delayed(cache_download_url)(url, cache_path) for url in tqdm(urls))
Parallel(n_jobs=5)(
delayed(cache_download_url)(url, cache_path) for url in tqdm(urls)
)
else:
raise ValueError(f"Unknown model name: {model_name}")
@ -91,14 +98,14 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
for state_name in keys:
state = state_dict[state_name]
if any(
match_suffix(state_name, candidate)
for candidate in [
"self_attention.query_key_value.weight",
"self_attention.query_key_value.bias",
"mlp.dense_h_to_4h.weight",
"mlp.dense_h_to_4h.bias",
"word_embeddings.weight",
]
match_suffix(state_name, candidate)
for candidate in [
"self_attention.query_key_value.weight",
"self_attention.query_key_value.bias",
"mlp.dense_h_to_4h.weight",
"mlp.dense_h_to_4h.bias",
"word_embeddings.weight",
]
):
output_size = state.shape[0]
assert output_size % tp_world_size == 0
@ -107,7 +114,9 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
assert len(sharded_weights) == tp_world_size
for tp_rank, shard in enumerate(sharded_weights):
shards_state_dicts[tp_rank]["transformer." + state_name] = shard.detach().clone()
shards_state_dicts[tp_rank][
"transformer." + state_name
] = shard.detach().clone()
elif match_suffix(state_name, "lm_head.weight"):
output_size = state.shape[0]
@ -120,11 +129,11 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
shards_state_dicts[tp_rank][state_name] = shard.detach().clone()
elif any(
match_suffix(state_name, candidate)
for candidate in [
"self_attention.dense.weight",
"mlp.dense_4h_to_h.weight",
]
match_suffix(state_name, candidate)
for candidate in [
"self_attention.dense.weight",
"mlp.dense_4h_to_h.weight",
]
):
input_size = state.shape[1]
assert input_size % tp_world_size == 0
@ -132,23 +141,31 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
sharded_weights = torch.split(state, block_size, dim=1)
assert len(sharded_weights) == tp_world_size
for tp_rank, shard in enumerate(sharded_weights):
shards_state_dicts[tp_rank]["transformer." + state_name] = shard.detach().clone()
shards_state_dicts[tp_rank][
"transformer." + state_name
] = shard.detach().clone()
elif any(
match_suffix(state_name, candidate)
for candidate in [
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
]
match_suffix(state_name, candidate)
for candidate in [
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
]
):
shards_state_dicts[0]["transformer." + state_name] = state.detach().clone()
shards_state_dicts[0][
"transformer." + state_name
] = state.detach().clone()
for tp_rank in range(1, tp_world_size):
shards_state_dicts[tp_rank]["transformer." + state_name] = torch.zeros_like(state)
shards_state_dicts[tp_rank][
"transformer." + state_name
] = torch.zeros_like(state)
else:
# We duplicate parameters across tp ranks
for tp_rank in range(tp_world_size):
shards_state_dicts[tp_rank]["transformer." + state_name] = state.detach().clone()
shards_state_dicts[tp_rank][
"transformer." + state_name
] = state.detach().clone()
del state_dict[state_name] # delete key from state_dict
del state # delete tensor
@ -156,7 +173,7 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
# we save state_dict
for tp_rank, (save_path, shard_state_dict) in enumerate(
zip(save_paths, shards_state_dicts)
zip(save_paths, shards_state_dicts)
):
save_paths.append(save_path)
save_path.parent.mkdir(parents=True, exist_ok=True)
@ -166,17 +183,3 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
torch.save(shard_state_dict, save_path)
return save_paths
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--model-name", required=True, type=str)
parser.add_argument("--cache-path", required=True, type=str)
parser.add_argument("--save-path", required=True, type=str)
parser.add_argument("--world-size", required=True, type=int)
args = parser.parse_args()
prepare_weights(args.model_name, Path(args.cache_path), Path(args.save_path), args.world_size)

67
server/bloom_inference/server.py
View File

@ -64,70 +64,31 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
batch=next_batch.to_pb() if next_batch else None,
)
async def GenerateUntilFinished(self, request, context):
batch = Batch.from_pb(request.batch, self.model.tokenizer, self.model.device)
generated_texts = []
while not generated_texts:
generated_texts, next_batch = self.model.generate_token(batch)
batch = next_batch
self.cache.set(next_batch)
return generate_pb2.GenerateUntilFinishedResponse(
generated_texts=[
generated_text.to_pb() for generated_text in generated_texts
],
batch=next_batch.to_pb() if next_batch else None,
)
async def GenerateUntilFinishedWithCache(self, request, context):
if len(request.batches) == 0:
raise ValueError("Must provide at least one batch")
batches = []
for batch_pb in request.batches:
batch = self.cache.pop(batch_pb.id)
if batch is None:
raise ValueError(f"Batch ID {batch_pb.id} not found in cache.")
batches.append(batch)
if len(batches) > 1:
batch = Batch.concatenate(batches)
else:
batch = batches[0]
generated_texts = []
while not generated_texts:
generated_texts, next_batch = self.model.generate_token(batch)
batch = next_batch
self.cache.set(next_batch)
return generate_pb2.GenerateUntilFinishedWithCacheResponse(
generated_texts=[
generated_text.to_pb() for generated_text in generated_texts
],
batch=next_batch.to_pb() if next_batch else None,
)
def serve(model_name, sharded, shard_directory):
def serve(
model_name: str,
sharded: bool,
uds_path: Path,
shard_directory: Optional[Path] = None,
):
async def serve_inner(
model_name: str,
sharded: bool = False,
shard_directory: Optional[Path] = None,
):
unix_socket_template = "unix:///tmp/bloom-inference-{}"
unix_socket_template = "unix://{}-{}"
if sharded:
if shard_directory is None:
raise ValueError("shard_directory must be set when sharded is True")
model = BLOOMSharded(model_name, shard_directory)
server_urls = [
unix_socket_template.format(rank) for rank in range(model.world_size)
unix_socket_template.format(uds_path, rank)
for rank in range(model.world_size)
]
local_url = unix_socket_template.format(model.rank)
local_url = server_urls[model.rank]
else:
model = BLOOM(model_name)
local_url = unix_socket_template.format(0)
local_url = unix_socket_template.format(uds_path, 0)
server_urls = [local_url]
server = aio.server()
@ -142,6 +103,10 @@ def serve(model_name, sharded, shard_directory):
server.add_insecure_port(local_url)
await server.start()
print("Server started at {}".format(local_url))
await server.wait_for_termination()
try:
await server.wait_for_termination()
except KeyboardInterrupt:
print("Signal received. Shutting down")
await server.stop(0)
asyncio.run(serve_inner(model_name, sharded, shard_directory))

1
server/bloom_inference/utils.py
View File

@ -82,7 +82,6 @@ def initialize_torch_distributed():
world_size=world_size,
rank=rank,
timeout=timedelta(seconds=60),
init_method="tcp://localhost:6000",
)
return torch.distributed.distributed_c10d._get_default_group(), rank, world_size

2
server/poetry.lock generated
View File

@ -205,7 +205,7 @@ python-versions = ">=3.7"
[metadata]
lock-version = "1.1"
python-versions = "^3.9"
content-hash = "f3dc5b2420183f2e7e9257e372489409d7bd26d1dcc535fc2558ebca50c988c2"
content-hash = "a4eef5f52e8d046aa883082c865b0865047f611a3240b18250487d4b6e831496"
[metadata.files]
accelerate = [

1
server/pyproject.toml
View File

@ -11,7 +11,6 @@ bloom-inference-server = 'bloom_inference.cli:app'
python = "^3.9"
protobuf = "^4.21.7"
grpcio = "^1.49.1"
torch = "^1.12.1"
typer = "^0.6.1"
grpcio-reflection = "^1.49.1"
accelerate = "^0.12.0"