feat(router): make router input validation optional (#164)

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OlivierDehaene 2023-04-09 20:22:27 +02:00 committed by GitHub
parent 7dec65a244
commit 9987960062
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27 changed files with 1127 additions and 866 deletions

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version = "0.9.4" version = "0.9.4"
@ -2557,12 +2619,6 @@ dependencies = [
"try-lock", "try-lock",
] ]
[[package]]
name = "wasi"
version = "0.10.0+wasi-snapshot-preview1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1a143597ca7c7793eff794def352d41792a93c481eb1042423ff7ff72ba2c31f"
[[package]] [[package]]
name = "wasi" name = "wasi"
version = "0.11.0+wasi-snapshot-preview1" version = "0.11.0+wasi-snapshot-preview1"
@ -2779,23 +2835,50 @@ dependencies = [
[[package]] [[package]]
name = "zip" name = "zip"
version = "0.5.13" version = "0.6.4"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "93ab48844d61251bb3835145c521d88aa4031d7139e8485990f60ca911fa0815" checksum = "0445d0fbc924bb93539b4316c11afb121ea39296f99a3c4c9edad09e3658cdef"
dependencies = [ dependencies = [
"aes",
"byteorder", "byteorder",
"bzip2", "bzip2",
"constant_time_eq",
"crc32fast", "crc32fast",
"crossbeam-utils",
"flate2", "flate2",
"thiserror", "hmac",
"pbkdf2",
"sha1",
"time", "time",
"zstd",
] ]
[[package]] [[package]]
name = "zip-extensions" name = "zstd"
version = "0.6.1" version = "0.11.2+zstd.1.5.2"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a64c3c977bc3434ce2d4bcea8ad3c644672de0f2c402b72b9171ca80a8885d14" checksum = "20cc960326ece64f010d2d2107537f26dc589a6573a316bd5b1dba685fa5fde4"
dependencies = [ dependencies = [
"zip", "zstd-safe",
]
[[package]]
name = "zstd-safe"
version = "5.0.2+zstd.1.5.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "1d2a5585e04f9eea4b2a3d1eca508c4dee9592a89ef6f450c11719da0726f4db"
dependencies = [
"libc",
"zstd-sys",
]
[[package]]
name = "zstd-sys"
version = "2.0.8+zstd.1.5.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5556e6ee25d32df2586c098bbfa278803692a20d0ab9565e049480d52707ec8c"
dependencies = [
"cc",
"libc",
"pkg-config",
] ]

View File

@ -27,7 +27,7 @@ serde = {version = "1.0.142", features = ["derive"]}
serde_json = "1.0" serde_json = "1.0"
text-generation-client = { path = "../router/client" } text-generation-client = { path = "../router/client" }
thiserror = "1.0.38" thiserror = "1.0.38"
tokenizers = "0.13.2" tokenizers = "0.13.3"
tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] } tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
tui = {package = "ratatui", version = "0.20", default-features = false, features = ["crossterm"]} tui = {package = "ratatui", version = "0.20", default-features = false, features = ["crossterm"]}
tracing = "0.1.37" tracing = "0.1.37"

View File

@ -75,7 +75,7 @@ async fn generate_runs(
// Warmups on batch size // Warmups on batch size
for _ in 0..warmups { for _ in 0..warmups {
let (_, decode_batch) = let (_, decode_batch) =
prefill(sequence.clone(), b, decode_length, &mut client).await?; prefill(sequence.clone(), sequence_length, b, decode_length, &mut client).await?;
let _ = decode(decode_batch, &mut client).await?; let _ = decode(decode_batch, &mut client).await?;
// Send warmup message // Send warmup message
run_sender.send(Ok(Message::Warmup)).await.unwrap_or(()); run_sender.send(Ok(Message::Warmup)).await.unwrap_or(());
@ -83,7 +83,7 @@ async fn generate_runs(
for _ in 0..n_runs { for _ in 0..n_runs {
let (prefill, decode_batch) = let (prefill, decode_batch) =
prefill(sequence.clone(), b, decode_length, &mut client).await?; prefill(sequence.clone(), sequence_length, b, decode_length, &mut client).await?;
// Send prefill message // Send prefill message
run_sender run_sender
.send(Ok(Message::Prefill(prefill))) .send(Ok(Message::Prefill(prefill)))
@ -110,6 +110,7 @@ async fn generate_runs(
// Run a prefill step // Run a prefill step
async fn prefill( async fn prefill(
sequence: String, sequence: String,
sequence_length: u32,
batch_size: u32, batch_size: u32,
decode_length: u32, decode_length: u32,
client: &mut ShardedClient, client: &mut ShardedClient,
@ -119,6 +120,7 @@ async fn prefill(
.map(|id| Request { .map(|id| Request {
id: id.into(), id: id.into(),
inputs: sequence.clone(), inputs: sequence.clone(),
truncate: sequence_length,
parameters: Some(NextTokenChooserParameters { parameters: Some(NextTokenChooserParameters {
temperature: 1.0, temperature: 1.0,
top_k: 0, top_k: 0,

View File

@ -63,10 +63,12 @@ message Request {
uint64 id = 1; uint64 id = 1;
/// The generation context /// The generation context
string inputs = 2; string inputs = 2;
/// Context truncation
uint32 truncate = 3;
/// Next Token Chooser Parameters /// Next Token Chooser Parameters
NextTokenChooserParameters parameters = 3; NextTokenChooserParameters parameters = 4;
/// Stopping Criteria Parameters /// Stopping Criteria Parameters
StoppingCriteriaParameters stopping_parameters = 4; StoppingCriteriaParameters stopping_parameters = 5;
} }
message Batch { message Batch {

View File

@ -18,21 +18,20 @@ axum = { version = "0.6.4", features = ["json"] }
axum-tracing-opentelemetry = "0.9.0" axum-tracing-opentelemetry = "0.9.0"
text-generation-client = { path = "client" } text-generation-client = { path = "client" }
clap = { version = "4.1.4", features = ["derive", "env"] } clap = { version = "4.1.4", features = ["derive", "env"] }
flume = "0.10.14"
futures = "0.3.26" futures = "0.3.26"
metrics = "0.20.1" metrics = "0.20.1"
metrics-exporter-prometheus = { version = "0.11.0", features = [] } metrics-exporter-prometheus = { version = "0.11.0", features = [] }
nohash-hasher = "0.2.0" nohash-hasher = "0.2.0"
opentelemetry = { version = "0.18.0", features = ["rt-tokio"] } opentelemetry = { version = "0.18.0", features = ["rt-tokio"] }
opentelemetry-otlp = "0.11.0" opentelemetry-otlp = "0.11.0"
parking_lot = "0.12.1"
rand = "0.8.5" rand = "0.8.5"
reqwest = { version = "0.11.14", features = [] } reqwest = { version = "0.11.14", features = [] }
serde = "1.0.152" serde = "1.0.152"
serde_json = "1.0.93" serde_json = "1.0.93"
thiserror = "1.0.38" thiserror = "1.0.38"
tokenizers = "0.13.2" tokenizers = "0.13.3"
tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] } tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
tokio-stream = "0.1.11"
tower-http = { version = "0.3.5", features = ["cors"] } tower-http = { version = "0.3.5", features = ["cors"] }
tracing = "0.1.37" tracing = "0.1.37"
tracing-opentelemetry = "0.18.0" tracing-opentelemetry = "0.18.0"

View File

@ -2,17 +2,17 @@
use crate::validation::{Validation, ValidationError}; use crate::validation::{Validation, ValidationError};
use crate::{Entry, Queue, Token}; use crate::{Entry, Queue, Token};
use crate::{GenerateRequest, PrefillToken}; use crate::{GenerateRequest, PrefillToken};
use flume::r#async::RecvStream;
use futures::future::try_join_all; use futures::future::try_join_all;
use futures::stream::StreamExt;
use nohash_hasher::IntMap; use nohash_hasher::IntMap;
use std::sync::Arc; use std::sync::Arc;
use text_generation_client::{ use text_generation_client::{
Batch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient, Batch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
}; };
use thiserror::Error; use thiserror::Error;
use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError}; use tokio::sync::{Notify, Semaphore, TryAcquireError};
use tokio::time::Instant; use tokio::time::Instant;
use tokio_stream::wrappers::UnboundedReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info_span, instrument, Instrument, Span}; use tracing::{info_span, instrument, Instrument, Span};
/// Inference struct /// Inference struct
@ -73,7 +73,7 @@ impl Infer {
pub(crate) async fn generate_stream( pub(crate) async fn generate_stream(
&self, &self,
request: GenerateRequest, request: GenerateRequest,
) -> Result<UnboundedReceiverStream<Result<InferStreamResponse, InferError>>, InferError> { ) -> Result<RecvStream<Result<InferStreamResponse, InferError>>, InferError> {
// Limit concurrent requests by acquiring a permit from the semaphore // Limit concurrent requests by acquiring a permit from the semaphore
// This permit will live as long as Entry // This permit will live as long as Entry
let permit = self let permit = self
@ -87,10 +87,14 @@ impl Infer {
})?; })?;
// Validate request // Validate request
let valid_request = self.validation.validate(request).await?; let valid_request = self.validation.validate(request).await.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
err
})?;
// MPSC channel to communicate with the background batching task // MPSC channel to communicate with the background batching task
let (response_tx, response_rx) = mpsc::unbounded_channel(); let (response_tx, response_rx) = flume::unbounded();
// Append the request to the queue // Append the request to the queue
self.queue.append(Entry { self.queue.append(Entry {
@ -108,7 +112,7 @@ impl Infer {
self.shared.batching_task.notify_one(); self.shared.batching_task.notify_one();
// Return stream // Return stream
Ok(UnboundedReceiverStream::new(response_rx)) Ok(response_rx.into_stream())
} }
/// Add a new request to the queue and return a InferResponse /// Add a new request to the queue and return a InferResponse

View File

@ -37,7 +37,7 @@ struct Args {
max_waiting_tokens: usize, max_waiting_tokens: usize,
#[clap(default_value = "3000", long, short, env)] #[clap(default_value = "3000", long, short, env)]
port: u16, port: u16,
#[clap(default_value = "/tmp/text-generation-0", long, env)] #[clap(default_value = "/tmp/text-generation-server-0", long, env)]
master_shard_uds_path: String, master_shard_uds_path: String,
#[clap(default_value = "bigscience/bloom", long, env)] #[clap(default_value = "bigscience/bloom", long, env)]
tokenizer_name: String, tokenizer_name: String,
@ -94,11 +94,11 @@ fn main() -> Result<(), std::io::Error> {
if local_path.exists() && local_path.is_dir() && local_path.join("tokenizer.json").exists() if local_path.exists() && local_path.is_dir() && local_path.join("tokenizer.json").exists()
{ {
// Load local tokenizer // Load local tokenizer
Tokenizer::from_file(local_path.join("tokenizer.json")).unwrap() Tokenizer::from_file(local_path.join("tokenizer.json")).ok()
} else { } else {
// Download and instantiate tokenizer // Download and instantiate tokenizer
// We need to download it outside of the Tokio runtime // We need to download it outside of the Tokio runtime
Tokenizer::from_pretrained(tokenizer_name.clone(), None).unwrap() Tokenizer::from_pretrained(tokenizer_name.clone(), None).ok()
}; };
// Launch Tokio runtime // Launch Tokio runtime
@ -109,6 +109,13 @@ fn main() -> Result<(), std::io::Error> {
.block_on(async { .block_on(async {
init_logging(otlp_endpoint, json_output); init_logging(otlp_endpoint, json_output);
if tokenizer.is_none() {
tracing::warn!(
"Could not find a fast tokenizer implementation for {tokenizer_name}"
);
tracing::warn!("Rust input length validation and truncation is disabled");
}
// Get pipeline tag // Get pipeline tag
let model_info = reqwest::get(format!( let model_info = reqwest::get(format!(
"https://huggingface.co/api/models/{tokenizer_name}" "https://huggingface.co/api/models/{tokenizer_name}"

View File

@ -4,8 +4,7 @@ use crate::validation::ValidGenerateRequest;
use nohash_hasher::{BuildNoHashHasher, IntMap}; use nohash_hasher::{BuildNoHashHasher, IntMap};
use std::cmp::min; use std::cmp::min;
use text_generation_client::{Batch, Request}; use text_generation_client::{Batch, Request};
use tokio::sync::mpsc::{UnboundedReceiver, UnboundedSender}; use tokio::sync::{oneshot, OwnedSemaphorePermit};
use tokio::sync::{mpsc, oneshot, OwnedSemaphorePermit};
use tokio::time::Instant; use tokio::time::Instant;
use tracing::{info_span, instrument, Span}; use tracing::{info_span, instrument, Span};
@ -15,7 +14,7 @@ pub(crate) struct Entry {
/// Request /// Request
pub request: ValidGenerateRequest, pub request: ValidGenerateRequest,
/// Response sender to communicate between the Infer struct and the batching_task /// Response sender to communicate between the Infer struct and the batching_task
pub response_tx: UnboundedSender<Result<InferStreamResponse, InferError>>, pub response_tx: flume::Sender<Result<InferStreamResponse, InferError>>,
/// Span that will live as long as entry /// Span that will live as long as entry
pub span: Span, pub span: Span,
/// Temporary span used as a guard when logging inference, wait times... /// Temporary span used as a guard when logging inference, wait times...
@ -32,13 +31,13 @@ pub(crate) struct Entry {
#[derive(Debug, Clone)] #[derive(Debug, Clone)]
pub(crate) struct Queue { pub(crate) struct Queue {
/// Channel to communicate with the background queue task /// Channel to communicate with the background queue task
queue_sender: UnboundedSender<QueueCommand>, queue_sender: flume::Sender<QueueCommand>,
} }
impl Queue { impl Queue {
pub(crate) fn new() -> Self { pub(crate) fn new() -> Self {
// Create channel // Create channel
let (queue_sender, queue_receiver) = mpsc::unbounded_channel(); let (queue_sender, queue_receiver) = flume::unbounded();
// Launch background queue task // Launch background queue task
tokio::spawn(queue_task(queue_receiver)); tokio::spawn(queue_task(queue_receiver));
@ -82,10 +81,10 @@ impl Queue {
} }
// Background task responsible of the queue state // Background task responsible of the queue state
async fn queue_task(mut receiver: UnboundedReceiver<QueueCommand>) { async fn queue_task(receiver: flume::Receiver<QueueCommand>) {
let mut state = State::new(); let mut state = State::new();
while let Some(cmd) = receiver.recv().await { while let Ok(cmd) = receiver.recv_async().await {
match cmd { match cmd {
QueueCommand::Append(entry, span) => span.in_scope(|| state.append(entry)), QueueCommand::Append(entry, span) => span.in_scope(|| state.append(entry)),
QueueCommand::NextBatch { QueueCommand::NextBatch {
@ -174,6 +173,7 @@ impl State {
batch_requests.push(Request { batch_requests.push(Request {
id, id,
inputs: entry.request.inputs.clone(), inputs: entry.request.inputs.clone(),
truncate: entry.request.truncate,
parameters: Some(entry.request.parameters.clone()), parameters: Some(entry.request.parameters.clone()),
stopping_parameters: Some(entry.request.stopping_parameters.clone()), stopping_parameters: Some(entry.request.stopping_parameters.clone()),
}); });
@ -215,17 +215,18 @@ mod tests {
use super::*; use super::*;
use std::sync::Arc; use std::sync::Arc;
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters}; use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
use tokio::sync::{mpsc, Semaphore}; use tokio::sync::Semaphore;
use tracing::info_span; use tracing::info_span;
fn default_entry() -> Entry { fn default_entry() -> Entry {
let semaphore = Arc::new(Semaphore::new(1)); let semaphore = Arc::new(Semaphore::new(1));
let (response_tx, _) = mpsc::unbounded_channel(); let (response_tx, _) = flume::unbounded();
let permit = semaphore.try_acquire_owned().unwrap(); let permit = semaphore.try_acquire_owned().unwrap();
Entry { Entry {
request: ValidGenerateRequest { request: ValidGenerateRequest {
inputs: "".to_string(), inputs: "".to_string(),
truncate: 0,
parameters: NextTokenChooserParameters { parameters: NextTokenChooserParameters {
temperature: 0.0, temperature: 0.0,
top_k: 0, top_k: 0,

View File

@ -13,6 +13,7 @@ use axum::response::{IntoResponse, Response};
use axum::routing::{get, post}; use axum::routing::{get, post};
use axum::{http, Json, Router}; use axum::{http, Json, Router};
use axum_tracing_opentelemetry::opentelemetry_tracing_layer; use axum_tracing_opentelemetry::opentelemetry_tracing_layer;
use futures::stream::StreamExt;
use futures::Stream; use futures::Stream;
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle}; use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
use std::convert::Infallible; use std::convert::Infallible;
@ -21,7 +22,6 @@ use text_generation_client::ShardedClient;
use tokenizers::Tokenizer; use tokenizers::Tokenizer;
use tokio::signal; use tokio::signal;
use tokio::time::Instant; use tokio::time::Instant;
use tokio_stream::StreamExt;
use tower_http::cors::{AllowOrigin, CorsLayer}; use tower_http::cors::{AllowOrigin, CorsLayer};
use tracing::{info_span, instrument, Instrument}; use tracing::{info_span, instrument, Instrument};
use utoipa::OpenApi; use utoipa::OpenApi;
@ -87,21 +87,21 @@ async fn health(infer: Extension<Infer>) -> Result<(), (StatusCode, Json<ErrorRe
/// Generate tokens /// Generate tokens
#[utoipa::path( #[utoipa::path(
post, post,
tag = "Text Generation Inference", tag = "Text Generation Inference",
path = "/generate", path = "/generate",
request_body = GenerateRequest, request_body = GenerateRequest,
responses( responses(
(status = 200, description = "Generated Text", body = GenerateResponse), (status = 200, description = "Generated Text", body = GenerateResponse),
(status = 424, description = "Generation Error", body = ErrorResponse, (status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})), example = json ! ({"error": "Request failed during generation"})),
(status = 429, description = "Model is overloaded", body = ErrorResponse, (status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"})), example = json ! ({"error": "Model is overloaded"})),
(status = 422, description = "Input validation error", body = ErrorResponse, (status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"})), example = json ! ({"error": "Input validation error"})),
(status = 500, description = "Incomplete generation", body = ErrorResponse, (status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"})), example = json ! ({"error": "Incomplete generation"})),
) )
)] )]
#[instrument( #[instrument(
skip(infer), skip(infer),
@ -264,26 +264,26 @@ async fn generate(
/// Generate a stream of token using Server-Sent Events /// Generate a stream of token using Server-Sent Events
#[utoipa::path( #[utoipa::path(
post, post,
tag = "Text Generation Inference", tag = "Text Generation Inference",
path = "/generate_stream", path = "/generate_stream",
request_body = GenerateRequest, request_body = GenerateRequest,
responses( responses(
(status = 200, description = "Generated Text", body = StreamResponse, (status = 200, description = "Generated Text", body = StreamResponse,
content_type = "text/event-stream"), content_type = "text/event-stream"),
(status = 424, description = "Generation Error", body = ErrorResponse, (status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"}), example = json ! ({"error": "Request failed during generation"}),
content_type = "text/event-stream"), content_type = "text/event-stream"),
(status = 429, description = "Model is overloaded", body = ErrorResponse, (status = 429, description = "Model is overloaded", body = ErrorResponse,
example = json ! ({"error": "Model is overloaded"}), example = json ! ({"error": "Model is overloaded"}),
content_type = "text/event-stream"), content_type = "text/event-stream"),
(status = 422, description = "Input validation error", body = ErrorResponse, (status = 422, description = "Input validation error", body = ErrorResponse,
example = json ! ({"error": "Input validation error"}), example = json ! ({"error": "Input validation error"}),
content_type = "text/event-stream"), content_type = "text/event-stream"),
(status = 500, description = "Incomplete generation", body = ErrorResponse, (status = 500, description = "Incomplete generation", body = ErrorResponse,
example = json ! ({"error": "Incomplete generation"}), example = json ! ({"error": "Incomplete generation"}),
content_type = "text/event-stream"), content_type = "text/event-stream"),
) )
)] )]
#[instrument( #[instrument(
skip(infer), skip(infer),
@ -447,10 +447,10 @@ async fn generate_stream(
/// Prometheus metrics scrape endpoint /// Prometheus metrics scrape endpoint
#[utoipa::path( #[utoipa::path(
get, get,
tag = "Text Generation Inference", tag = "Text Generation Inference",
path = "/metrics", path = "/metrics",
responses((status = 200, description = "Prometheus Metrics", body = String)) responses((status = 200, description = "Prometheus Metrics", body = String))
)] )]
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String { async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
prom_handle.render() prom_handle.render()
@ -468,7 +468,7 @@ pub async fn run(
max_batch_size: usize, max_batch_size: usize,
max_waiting_tokens: usize, max_waiting_tokens: usize,
client: ShardedClient, client: ShardedClient,
tokenizer: Tokenizer, tokenizer: Option<Tokenizer>,
validation_workers: usize, validation_workers: usize,
addr: SocketAddr, addr: SocketAddr,
allow_origin: Option<AllowOrigin>, allow_origin: Option<AllowOrigin>,
@ -476,36 +476,36 @@ pub async fn run(
// OpenAPI documentation // OpenAPI documentation
#[derive(OpenApi)] #[derive(OpenApi)]
#[openapi( #[openapi(
paths( paths(
generate, generate,
generate_stream, generate_stream,
metrics, metrics,
), ),
components( components(
schemas( schemas(
GenerateRequest, GenerateRequest,
GenerateParameters, GenerateParameters,
PrefillToken, PrefillToken,
Token, Token,
GenerateResponse, GenerateResponse,
BestOfSequence, BestOfSequence,
Details, Details,
FinishReason, FinishReason,
StreamResponse, StreamResponse,
StreamDetails, StreamDetails,
ErrorResponse, ErrorResponse,
) )
), ),
tags( tags(
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API") (name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
), ),
info( info(
title = "Text Generation Inference", title = "Text Generation Inference",
license( license(
name = "Apache 2.0", name = "Apache 2.0",
url = "https://www.apache.org/licenses/LICENSE-2.0" url = "https://www.apache.org/licenses/LICENSE-2.0"
) )
) )
)] )]
struct ApiDoc; struct ApiDoc;

View File

@ -1,50 +1,129 @@
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput}; use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
/// Payload validation logic /// Payload validation logic
use crate::{GenerateParameters, GenerateRequest}; use crate::{GenerateParameters, GenerateRequest};
use rand::rngs::ThreadRng; use rand::{thread_rng, Rng};
use rand::Rng;
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters}; use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
use thiserror::Error; use thiserror::Error;
use tokenizers::tokenizer::Tokenizer; use tokenizers::tokenizer::Tokenizer;
use tokenizers::TruncationDirection; use tokenizers::TruncationDirection;
use tokio::sync::{mpsc, oneshot}; use tokio::sync::oneshot;
use tracing::{instrument, Span}; use tracing::{instrument, Span};
/// Validation /// Validation
#[derive(Debug, Clone)] #[derive(Debug, Clone)]
pub struct Validation { pub struct Validation {
/// maximum value for the best_of parameter /// Validation parameters
#[allow(dead_code)]
max_best_of: usize, max_best_of: usize,
/// Channel to communicate with the background validation task max_stop_sequences: usize,
sender: mpsc::UnboundedSender<ValidationRequest>, max_input_length: usize,
max_total_tokens: usize,
/// Channel to communicate with the background tokenization task
sender: Option<flume::Sender<TokenizerRequest>>,
} }
impl Validation { impl Validation {
pub(crate) fn new( pub(crate) fn new(
workers: usize, workers: usize,
tokenizer: Tokenizer, tokenizer: Option<Tokenizer>,
max_best_of: usize, max_best_of: usize,
max_stop_sequences: usize, max_stop_sequences: usize,
max_input_length: usize, max_input_length: usize,
max_total_tokens: usize, max_total_tokens: usize,
) -> Self { ) -> Self {
// Create channel if max_input_length >= max_total_tokens {
let (validation_sender, validation_receiver) = mpsc::unbounded_channel(); panic!("`max_input_length` must be < `max_total_tokens`");
}
// Launch background validation task // If we have a fast tokenizer
tokio::spawn(validation_task( let sender = if let Some(tokenizer) = tokenizer {
workers, // Create channel
tokenizer, let (validation_sender, validation_receiver) = flume::unbounded();
max_stop_sequences,
max_input_length, // Create workers
max_total_tokens, for _ in 0..workers {
validation_receiver, let tokenizer_clone = tokenizer.clone();
)); let receiver_clone = validation_receiver.clone();
// Spawn worker
tokio::task::spawn_blocking(move || {
tokenizer_worker(tokenizer_clone, receiver_clone)
});
}
Some(validation_sender)
} else {
None
};
Self { Self {
max_best_of, max_best_of,
sender: validation_sender, sender,
max_stop_sequences,
max_input_length,
max_total_tokens,
}
}
#[instrument(skip_all)]
async fn validate_input(
&self,
inputs: String,
truncate: Option<usize>,
max_new_tokens: u32,
) -> Result<String, ValidationError> {
// If we have a fast tokenizer
if let Some(sender) = &self.sender {
// Create response channel
let (response_sender, response_receiver) = oneshot::channel();
// Send request to the background validation task
// Unwrap is safe here
sender
.send(((inputs, truncate), response_sender, Span::current()))
.unwrap();
// Await on response channel
// Unwrap is safe here
let (inputs, input_length) = response_receiver.await.unwrap()?;
// Get total tokens
let total_tokens = input_length + max_new_tokens as usize;
// Validate MaxTotalTokens
if total_tokens > self.max_total_tokens {
return Err(ValidationError::MaxTotalTokens(
self.max_total_tokens,
input_length,
max_new_tokens,
));
}
// Validate InputLength
if input_length > self.max_input_length {
return Err(ValidationError::InputLength(
self.max_input_length,
input_length,
));
}
metrics::histogram!("tgi_request_input_length", input_length as f64);
Ok(inputs)
}
// Return inputs without validation
else {
// In this case, we don't know the real length in tokens of the inputs
// However, the inputs will be truncated by the python servers
// We make sure that truncate + max_new_tokens <= self.max_total_tokens
// Validate MaxNewTokens
if (truncate.unwrap_or(self.max_input_length) as u32 + max_new_tokens)
> self.max_total_tokens as u32
{
return Err(ValidationError::MaxNewTokens(
self.max_total_tokens - self.max_input_length,
max_new_tokens,
));
}
Ok(inputs)
} }
} }
@ -54,16 +133,139 @@ impl Validation {
&self, &self,
request: GenerateRequest, request: GenerateRequest,
) -> Result<ValidGenerateRequest, ValidationError> { ) -> Result<ValidGenerateRequest, ValidationError> {
// Create response channel let GenerateParameters {
let (sender, receiver) = oneshot::channel(); best_of,
// Send request to the background validation task temperature,
// Unwrap is safe here repetition_penalty,
self.sender top_k,
.send((request, sender, Span::current())) top_p,
.unwrap(); typical_p,
// Await on response channel do_sample,
// Unwrap is safe here max_new_tokens,
receiver.await.unwrap() stop: stop_sequences,
truncate,
seed,
watermark,
..
} = request.parameters;
// sampling must be true when best_of > 1
let best_of = best_of.unwrap_or(1);
let sampling = do_sample
|| temperature.is_some()
|| top_k.is_some()
|| top_p.is_some()
|| typical_p.is_some();
if best_of > 1 && !sampling {
return Err(BestOfSampling);
}
let temperature = temperature.unwrap_or(1.0);
if temperature <= 0.0 {
return Err(ValidationError::Temperature);
}
let repetition_penalty = repetition_penalty.unwrap_or(1.0);
if repetition_penalty <= 0.0 {
return Err(ValidationError::RepetitionPenalty);
}
// Different because the proto default value is not a valid value
// for the user
let top_p = top_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TopP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let typical_p = typical_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TypicalP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let top_k: u32 = top_k
.map(|value| {
if value <= 0 {
return Err(ValidationError::TopK);
}
Ok(value as u32)
})
.unwrap_or(Ok(0))?;
if max_new_tokens == 0 {
return Err(ValidationError::NegativeMaxNewTokens);
}
if stop_sequences.len() > self.max_stop_sequences {
return Err(ValidationError::StopSequence(
self.max_stop_sequences,
stop_sequences.len(),
));
}
// If seed is None, assign a random one
let seed = match seed {
None => thread_rng().gen(),
Some(seed) => {
if best_of > 1 {
return Err(BestOfSeed);
}
seed
}
};
// Check if inputs is empty
if request.inputs.is_empty() {
return Err(EmptyInput);
}
// Check if truncate is strictly positive and less than max_input_length
let truncate = truncate
.map(|value| {
if value == 0 || value > self.max_input_length {
return Err(ValidationError::Truncate(self.max_input_length, value));
}
Ok(Some(value))
})
.unwrap_or(Ok(None))?;
// Validate inputs
let inputs = self
.validate_input(request.inputs, truncate, max_new_tokens)
.await?;
let parameters = NextTokenChooserParameters {
temperature,
repetition_penalty,
top_k,
top_p,
typical_p,
do_sample,
seed,
watermark,
};
let stopping_parameters = StoppingCriteriaParameters {
max_new_tokens,
stop_sequences,
ignore_eos_token: false,
};
metrics::histogram!("tgi_request_max_new_tokens", max_new_tokens as f64);
Ok(ValidGenerateRequest {
inputs,
truncate: truncate.unwrap_or(self.max_input_length) as u32,
parameters,
stopping_parameters,
})
} }
/// Validate the best_of parameter /// Validate the best_of parameter
@ -81,262 +283,57 @@ impl Validation {
} }
} }
/// Validation task /// Start tokenization workers
/// Load balance the validation requests between multiple validation workers fn tokenizer_worker(tokenizer: Tokenizer, receiver: flume::Receiver<TokenizerRequest>) {
async fn validation_task(
workers: usize,
tokenizer: Tokenizer,
max_stop_sequences: usize,
max_input_length: usize,
max_total_tokens: usize,
mut receiver: mpsc::UnboundedReceiver<ValidationRequest>,
) {
let mut workers_senders = Vec::with_capacity(workers);
// Create workers
for _ in 0..workers {
let tokenizer_clone: Tokenizer = tokenizer.clone().into();
// 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_stop_sequences,
max_input_length,
max_total_tokens,
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: Tokenizer,
max_stop_sequences: usize,
max_input_length: usize,
max_total_tokens: usize,
mut receiver: mpsc::Receiver<ValidationRequest>,
) {
// Seed rng
let mut rng = rand::thread_rng();
// Loop over requests // Loop over requests
while let Some((request, response_tx, parent_span)) = receiver.blocking_recv() { while let Ok(((inputs, truncate), response_tx, parent_span)) = receiver.recv() {
parent_span.in_scope(|| { parent_span.in_scope(|| {
response_tx response_tx
.send( .send(prepare_input(inputs, truncate, &tokenizer))
validate(
request,
&tokenizer,
max_stop_sequences,
max_input_length,
max_total_tokens,
&mut rng,
)
.map_err(|err| {
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
tracing::error!("{err}");
err
}),
)
.unwrap_or(()) .unwrap_or(())
}) })
} }
} }
fn validate( /// Get input length and optionally truncate it
request: GenerateRequest, fn prepare_input(
inputs: String,
truncate: Option<usize>,
tokenizer: &Tokenizer, tokenizer: &Tokenizer,
max_stop_sequences: usize, ) -> Result<(String, usize), ValidationError> {
max_input_length: usize,
max_total_tokens: usize,
rng: &mut ThreadRng,
) -> Result<ValidGenerateRequest, ValidationError> {
let GenerateParameters {
best_of,
temperature,
repetition_penalty,
top_k,
top_p,
typical_p,
do_sample,
max_new_tokens,
stop: stop_sequences,
truncate,
seed,
watermark,
..
} = request.parameters;
// sampling must be true when best_of > 1
let best_of = best_of.unwrap_or(1);
let sampling = do_sample
|| temperature.is_some()
|| top_k.is_some()
|| top_p.is_some()
|| typical_p.is_some();
if best_of > 1 && !sampling {
return Err(BestOfSampling);
}
let temperature = temperature.unwrap_or(1.0);
if temperature <= 0.0 {
return Err(ValidationError::Temperature);
}
let repetition_penalty = repetition_penalty.unwrap_or(1.0);
if repetition_penalty <= 0.0 {
return Err(ValidationError::RepetitionPenalty);
}
// Different because the proto default value is not a valid value
// for the user
let top_p = top_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TopP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let typical_p = typical_p
.map(|value| {
if value <= 0.0 || value >= 1.0 {
return Err(ValidationError::TypicalP);
}
Ok(value)
})
.unwrap_or(Ok(1.0))?;
let top_k: u32 = top_k
.map(|value| {
if value <= 0 {
return Err(ValidationError::TopK);
}
Ok(value as u32)
})
.unwrap_or(Ok(0))?;
if max_new_tokens == 0 {
return Err(ValidationError::MaxNewTokens);
}
if stop_sequences.len() > max_stop_sequences {
return Err(ValidationError::StopSequence(
max_stop_sequences,
stop_sequences.len(),
));
}
// If seed is None, assign a random one
let seed = match seed {
None => rng.gen(),
Some(seed) => {
if best_of > 1 {
return Err(BestOfSeed);
}
seed
}
};
// Check if inputs is empty
if request.inputs.is_empty() {
return Err(EmptyInput);
}
// Check if truncate is strictly positive and less than max_input_length
let truncate = truncate
.map(|value| {
if value == 0 || value > max_input_length {
return Err(ValidationError::Truncate(max_input_length, value));
}
Ok(Some(value))
})
.unwrap_or(Ok(None))?;
// Get the number of tokens in the input // Get the number of tokens in the input
let mut encoding = tokenizer let mut encoding = tokenizer
.encode(request.inputs.clone(), true) .encode(inputs.clone(), true)
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?; .map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
let (inputs, input_length) = if let Some(truncate) = truncate { // Optionally truncate
// truncate encoding and decode new inputs let (inputs, input_length) = match truncate {
encoding.truncate(truncate, 0, TruncationDirection::Left); // Truncate is some and > encoding length
let inputs = tokenizer Some(truncate) if truncate > encoding.len() => {
.decode(Vec::from(encoding.get_ids()), false) // truncate encoding and decode new inputs
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?; encoding.truncate(truncate, 0, TruncationDirection::Left);
(inputs, encoding.len()) let inputs = tokenizer
} else { .decode(Vec::from(encoding.get_ids()), false)
(request.inputs, encoding.len()) .map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
(inputs, encoding.len())
}
// Nothing to do
_ => (inputs, encoding.len()),
}; };
if input_length > max_input_length { Ok((inputs, input_length))
return Err(ValidationError::InputLength(max_input_length, input_length));
}
let total_tokens = input_length + max_new_tokens as usize;
if total_tokens > max_total_tokens {
return Err(ValidationError::MaxTotalTokens(
max_total_tokens,
input_length,
max_new_tokens,
));
}
// Return ValidGenerateRequest
let parameters = NextTokenChooserParameters {
temperature,
repetition_penalty,
top_k,
top_p,
typical_p,
do_sample,
seed,
watermark,
};
let stopping_parameters = StoppingCriteriaParameters {
max_new_tokens,
stop_sequences,
ignore_eos_token: false,
};
metrics::histogram!("tgi_request_input_length", input_length as f64);
metrics::histogram!("tgi_request_max_new_tokens", max_new_tokens as f64);
Ok(ValidGenerateRequest {
inputs,
parameters,
stopping_parameters,
})
} }
type ValidationRequest = ( type TokenizerRequest = (
GenerateRequest, (String, Option<usize>),
oneshot::Sender<Result<ValidGenerateRequest, ValidationError>>, oneshot::Sender<Result<(String, usize), ValidationError>>,
Span, Span,
); );
#[derive(Debug)] #[derive(Debug)]
pub(crate) struct ValidGenerateRequest { pub(crate) struct ValidGenerateRequest {
pub inputs: String, pub inputs: String,
pub truncate: u32,
pub parameters: NextTokenChooserParameters, pub parameters: NextTokenChooserParameters,
pub stopping_parameters: StoppingCriteriaParameters, pub stopping_parameters: StoppingCriteriaParameters,
} }
@ -366,7 +363,9 @@ pub enum ValidationError {
#[error("`typical_p` must be > 0.0 and < 1.0")] #[error("`typical_p` must be > 0.0 and < 1.0")]
TypicalP, TypicalP,
#[error("`max_new_tokens` must be strictly positive")] #[error("`max_new_tokens` must be strictly positive")]
MaxNewTokens, NegativeMaxNewTokens,
#[error("`max_new_tokens` must be <= {0}. Given: {1}")]
MaxNewTokens(usize, u32),
#[error("`inputs` tokens + `max_new_tokens` must be <= {0}. Given: {1} `inputs` tokens and {2} `max_new_tokens`")] #[error("`inputs` tokens + `max_new_tokens` must be <= {0}. Given: {1} `inputs` tokens and {2} `max_new_tokens`")]
MaxTotalTokens(usize, usize, u32), MaxTotalTokens(usize, usize, u32),
#[error("`inputs` must have less than {0} tokens. Given: {1}")] #[error("`inputs` must have less than {0} tokens. Given: {1}")]

View File

@ -24,6 +24,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request( return generate_pb2.Request(
id=0, id=0,
inputs="Test", inputs="Test",
truncate=100,
parameters=default_pb_parameters, parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters, stopping_parameters=default_pb_stop_parameters,
) )

View File

@ -25,6 +25,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request( return generate_pb2.Request(
id=0, id=0,
inputs="Test", inputs="Test",
truncate=100,
parameters=default_pb_parameters, parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters, stopping_parameters=default_pb_stop_parameters,
) )

View File

@ -15,6 +15,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request( return generate_pb2.Request(
id=0, id=0,
inputs="def", inputs="def",
truncate=100,
parameters=default_pb_parameters, parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters, stopping_parameters=default_pb_stop_parameters,
) )
@ -30,6 +31,7 @@ def default_fim_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request( return generate_pb2.Request(
id=0, id=0,
inputs="<fim-prefix>def<fim-suffix>world<fim-middle>", inputs="<fim-prefix>def<fim-suffix>world<fim-middle>",
truncate=100,
parameters=default_pb_parameters, parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters, stopping_parameters=default_pb_stop_parameters,
) )

View File

@ -28,6 +28,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request( return generate_pb2.Request(
id=0, id=0,
inputs="Test", inputs="Test",
truncate=100,
parameters=default_pb_parameters, parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters, stopping_parameters=default_pb_stop_parameters,
) )

View File

@ -68,7 +68,7 @@ class BLOOMSharded(BLOOM):
dtype = torch.float32 dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
config = AutoConfig.from_pretrained( config = AutoConfig.from_pretrained(

View File

@ -66,6 +66,7 @@ class CausalLMBatch(Batch):
stopping_criterias = [] stopping_criterias = []
# Parse batch # Parse batch
max_truncation = 0
padding_right_offset = 0 padding_right_offset = 0
for r in pb.requests: for r in pb.requests:
inputs.append(r.inputs) inputs.append(r.inputs)
@ -74,6 +75,7 @@ class CausalLMBatch(Batch):
r.stopping_parameters, tokenizer r.stopping_parameters, tokenizer
) )
stopping_criterias.append(stopping_criteria) stopping_criterias.append(stopping_criteria)
max_truncation = max(max_truncation, r.truncate)
padding_right_offset = max( padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens padding_right_offset, stopping_criteria.max_new_tokens
) )
@ -83,6 +85,8 @@ class CausalLMBatch(Batch):
return_tensors="pt", return_tensors="pt",
padding=True, padding=True,
return_token_type_ids=False, return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device) ).to(device)
input_lengths = tokenized_inputs["attention_mask"].sum(1) input_lengths = tokenized_inputs["attention_mask"].sum(1)

View File

@ -38,7 +38,7 @@ from flash_attn.layers.rotary import RotaryEmbedding
class FastLayerNorm(nn.LayerNorm): class FastLayerNorm(nn.LayerNorm):
def forward(self, hidden_states, residual=None): def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 6144: if hidden_states.shape[-1] > 8192:
if residual is not None: if residual is not None:
hidden_states += residual hidden_states += residual
residual = hidden_states residual = hidden_states
@ -624,13 +624,16 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel): class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
def __init__(self, config): def __init__(self, config, process_group=None):
super().__init__(config) super().__init__(config)
if config.tp_parallel: self.process_group = process_group
process_group = torch.distributed.distributed_c10d._get_default_group() if self.process_group is not None:
self.world_size = self.process_group.size()
self.rank = self.process_group.rank()
else: else:
process_group = None self.world_size = 1
self.rank = 0
self.gpt_neox = FlashGPTNeoXModel(config, process_group) self.gpt_neox = FlashGPTNeoXModel(config, process_group)
@ -668,4 +671,13 @@ class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
hidden_states, present = self.gpt_neox( hidden_states, present = self.gpt_neox(
input_ids, position_ids, cu_seqlens, max_s, past_key_values input_ids, position_ids, cu_seqlens, max_s, past_key_values
) )
return self.embed_out(hidden_states), present logits = self.embed_out(hidden_states)
if self.gpt_neox.tp_embeddings:
# Logits are sharded, so we need to gather them
world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
torch.distributed.all_gather(world_logits, logits, group=self.process_group)
world_logits = torch.cat(world_logits, dim=1)
return world_logits, present
return logits, present

View File

@ -11,7 +11,7 @@ import dropout_layer_norm
class FastLayerNorm(nn.LayerNorm): class FastLayerNorm(nn.LayerNorm):
def forward(self, hidden_states, residual=None): def forward(self, hidden_states, residual=None):
if hidden_states.shape[-1] > 6144: if hidden_states.shape[-1] > 8192:
if residual is not None: if residual is not None:
hidden_states += residual hidden_states += residual
residual = hidden_states residual = hidden_states

View File

@ -78,7 +78,9 @@ class FlashCausalLMBatch(Batch):
# Parse batch # Parse batch
for r in pb.requests: for r in pb.requests:
tokenized_input = tokenizer(r.inputs)["input_ids"] tokenized_input = tokenizer(
r.inputs, truncation=True, max_length=r.truncate
)["input_ids"]
input_length = len(tokenized_input) input_length = len(tokenized_input)
max_seqlen = max(max_seqlen, input_length) max_seqlen = max(max_seqlen, input_length)
input_lengths.append(input_length) input_lengths.append(input_length)
@ -208,7 +210,7 @@ class FlashCausalLM(Model):
raise NotImplementedError("FlashCausalLM does not support quantization") raise NotImplementedError("FlashCausalLM does not support quantization")
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
self.model = ( self.model = (
model_cls.from_pretrained( model_cls.from_pretrained(

View File

@ -45,18 +45,19 @@ class FlashNeoXSharded(FlashNeoX):
raise NotImplementedError("FlashNeoX does not support quantization") raise NotImplementedError("FlashNeoX does not support quantization")
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
config = AutoConfig.from_pretrained( config = AutoConfig.from_pretrained(
model_id, revision=revision, tp_parallel=True model_id,
revision=revision,
) )
torch.distributed.barrier(group=self.process_group) torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors") filenames = weight_files(model_id, revision=revision, extension=".safetensors")
with init_empty_weights(): with init_empty_weights():
model = FlashGPTNeoXForCausalLM(config) model = FlashGPTNeoXForCausalLM(config, self.process_group)
torch.distributed.barrier(group=self.process_group) torch.distributed.barrier(group=self.process_group)
self.load_weights( self.load_weights(
@ -147,32 +148,3 @@ class FlashNeoXSharded(FlashNeoX):
module._parameters[param_name] = tensor module._parameters[param_name] = tensor
else: else:
module._buffers[param_name] = tensor module._buffers[param_name] = tensor
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlens: torch.Tensor,
max_s: int,
past_key_values: Optional = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self.model.gpt_neox.tp_embeddings:
logits, present = self.model.forward(
input_ids=input_ids,
position_ids=position_ids,
cu_seqlens=cu_seqlens,
max_s=max_s,
past_key_values=past_key_values,
)
# Logits are sharded, so we need to gather them
world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
torch.distributed.all_gather(world_logits, logits, group=self.process_group)
world_logits = torch.cat(world_logits, dim=1)
return world_logits, present
# While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
else:
return super(FlashNeoXSharded, self).forward(
input_ids, position_ids, cu_seqlens, max_s, past_key_values
)

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@ -33,7 +33,7 @@ class FlashSantacoder(FlashCausalLM):
raise NotImplementedError("FlashSantacoder does not support quantization") raise NotImplementedError("FlashSantacoder does not support quantization")
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
config = AutoConfig.from_pretrained( config = AutoConfig.from_pretrained(
@ -56,6 +56,8 @@ class FlashSantacoder(FlashCausalLM):
self.load_weights( self.load_weights(
model, model,
filenames, filenames,
device,
dtype,
) )
self.model = model.eval().to(device).to(dtype) self.model = model.eval().to(device).to(dtype)
@ -68,10 +70,14 @@ class FlashSantacoder(FlashCausalLM):
def load_weights( def load_weights(
model: FlashSantacoderForCausalLM, model: FlashSantacoderForCausalLM,
filenames: List[Path], filenames: List[Path],
device: torch.device,
dtype: torch.dtype,
): ):
for filename in filenames: for filename in filenames:
state_dict = torch.load(filename, map_location="cpu") state_dict = torch.load(filename, map_location="cpu")
for key, value in state_dict.items(): for key, value in state_dict.items():
value = value.to(device).to(dtype)
layer_name = ".".join(key.split(".")[:4]) layer_name = ".".join(key.split(".")[:4])
# Fused qkv # Fused qkv
@ -141,6 +147,8 @@ class FlashSantacoder(FlashCausalLM):
else: else:
module._buffers[param_name] = value module._buffers[param_name] = value
del value
torch.cuda.empty_cache() torch.cuda.empty_cache()
model.post_load_weights() model.post_load_weights()

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@ -96,7 +96,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
input_lengths = [] input_lengths = []
# Parse batch # Parse batch
max_sequence_length = 0 max_truncation = 0
padding_right_offset = 0 padding_right_offset = 0
for r in pb.requests: for r in pb.requests:
# Add escape_custom_split_sequence to the CausalLMBatch logic # Add escape_custom_split_sequence to the CausalLMBatch logic
@ -107,7 +107,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
r.stopping_parameters, tokenizer r.stopping_parameters, tokenizer
) )
stopping_criterias.append(stopping_criteria) stopping_criterias.append(stopping_criteria)
max_sequence_length = max(max_sequence_length, r.input_length) max_truncation = max(max_truncation, r.truncate)
padding_right_offset = max( padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens padding_right_offset, stopping_criteria.max_new_tokens
) )
@ -118,14 +118,20 @@ class GalacticaCausalLMBatch(CausalLMBatch):
return_tensors="pt", return_tensors="pt",
padding=True, padding=True,
return_token_type_ids=False, return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device) ).to(device)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"] input_ids = tokenized_inputs["input_ids"]
# Allocate maximum attention_mask # Allocate maximum attention_mask
attention_mask = input_ids.new_zeros( attention_mask = input_ids.new_zeros(
(pb.size, max_sequence_length + padding_right_offset) (pb.size, max_input_length + padding_right_offset)
) )
# Copy tokenizer attention_mask into fully allocated attention_mask # Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_sequence_length] = tokenized_inputs["attention_mask"] attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1 position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1) position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
@ -143,7 +149,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
next_token_choosers=next_token_choosers, next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias, stopping_criterias=stopping_criterias,
size=pb.size, size=pb.size,
max_sequence_length=max_sequence_length, max_input_length=max_input_length,
padding_right_offset=padding_right_offset, padding_right_offset=padding_right_offset,
) )
@ -188,7 +194,7 @@ class GalacticaSharded(Galactica):
dtype = torch.float32 dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
config = AutoConfig.from_pretrained( config = AutoConfig.from_pretrained(

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@ -44,7 +44,7 @@ class GPTNeoxSharded(CausalLM):
dtype = torch.float32 dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token = tokenizer.eos_token

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@ -26,7 +26,7 @@ class SantaCoder(CausalLM):
dtype = torch.float32 dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
tokenizer.add_special_tokens( tokenizer.add_special_tokens(
{ {

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@ -73,6 +73,7 @@ class Seq2SeqLMBatch(Batch):
decoder_input_lengths = [] decoder_input_lengths = []
# Parse batch # Parse batch
max_truncation = 0
padding_right_offset = 0 padding_right_offset = 0
for r in pb.requests: for r in pb.requests:
inputs.append(r.inputs) inputs.append(r.inputs)
@ -84,6 +85,7 @@ class Seq2SeqLMBatch(Batch):
r.stopping_parameters, tokenizer r.stopping_parameters, tokenizer
) )
stopping_criterias.append(stopping_criteria) stopping_criterias.append(stopping_criteria)
max_truncation = max(max_truncation, r.truncate)
padding_right_offset = max( padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens padding_right_offset, stopping_criteria.max_new_tokens
) )
@ -94,6 +96,8 @@ class Seq2SeqLMBatch(Batch):
return_tensors="pt", return_tensors="pt",
padding=True, padding=True,
return_token_type_ids=False, return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device) ).to(device)
input_lengths = tokenized_inputs["attention_mask"].sum(1) input_lengths = tokenized_inputs["attention_mask"].sum(1)

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@ -44,7 +44,7 @@ class T5Sharded(Seq2SeqLM):
dtype = torch.float32 dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained( tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left" model_id, revision=revision, padding_side="left", truncation_side="left"
) )
config = AutoConfig.from_pretrained( config = AutoConfig.from_pretrained(