feat(router): make router input validation optional (#164)
This commit is contained in:
OlivierDehaene
GitHub
parent
7dec65a244
commit
9987960062
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@ -8,6 +8,18 @@ version = "1.0.2"
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@ -17,15 +29,6 @@ dependencies = [
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@ -105,17 +108,6 @@ dependencies = [
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@ -190,6 +182,12 @@ version = "0.21.0"
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@ -246,9 +244,9 @@ dependencies = [
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@ -264,7 +262,6 @@ dependencies = [
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@ -278,6 +275,9 @@ name = "cc"
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@ -286,18 +286,12 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@ -321,7 +315,7 @@ dependencies = [
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@ -370,6 +364,12 @@ dependencies = [
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@ -484,9 +484,9 @@ dependencies = [
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@ -494,23 +494,23 @@ dependencies = [
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[[package]]
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@ -532,26 +532,32 @@ dependencies = [
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@ -563,13 +569,14 @@ checksum = "8168378f4e5023e7218c89c891c0fd8ecdb5e5e4f18cb78f38cf245dd021e76f"
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@ -835,7 +842,7 @@ checksum = "c05aeb6a22b8f62540c194aac980f2115af067bfe15a0734d7277a768d396b31"
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[[package]]
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@ -885,15 +892,6 @@ version = "0.4.1"
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@ -909,6 +907,15 @@ version = "0.3.1"
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@ -1113,6 +1120,15 @@ version = "1.0.6"
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@ -1240,10 +1256,31 @@ checksum = "5b9d9a46eff5b4ff64b45a9e316a6d1e0bc719ef429cbec4dc630684212bfdf9"
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|
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[[package]]
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"pkg-config",
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[[package]]
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[[package]]
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"windows-sys 0.45.0",
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]
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[[package]]
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|
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[[package]]
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@ -1978,24 +2055,18 @@ dependencies = [
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"unicode-segmentation",
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[[package]]
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|
||||
checksum = "6446ced80d6c486436db5c078dde11a9f73d42b57fb273121e160b84f63d894c"
|
||||
|
||||
[[package]]
|
||||
name = "strsim"
|
||||
version = "0.10.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "73473c0e59e6d5812c5dfe2a064a6444949f089e20eec9a2e5506596494e4623"
|
||||
|
||||
[[package]]
|
||||
name = "subtle"
|
||||
version = "2.4.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "6bdef32e8150c2a081110b42772ffe7d7c9032b606bc226c8260fd97e0976601"
|
||||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "1.0.109"
|
||||
|
|
@ -2053,7 +2124,7 @@ name = "text-generation-benchmark"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"average",
|
||||
"clap 4.2.1",
|
||||
"clap",
|
||||
"crossterm",
|
||||
"float-ord",
|
||||
"ratatui",
|
||||
|
|
@ -2084,15 +2155,6 @@ dependencies = [
|
|||
"tracing-error",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "textwrap"
|
||||
version = "0.11.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d326610f408c7a4eb6f51c37c330e496b08506c9457c9d34287ecc38809fb060"
|
||||
dependencies = [
|
||||
"unicode-width",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thiserror"
|
||||
version = "1.0.40"
|
||||
|
|
@ -2125,15 +2187,20 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "time"
|
||||
version = "0.1.45"
|
||||
version = "0.3.20"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1b797afad3f312d1c66a56d11d0316f916356d11bd158fbc6ca6389ff6bf805a"
|
||||
checksum = "cd0cbfecb4d19b5ea75bb31ad904eb5b9fa13f21079c3b92017ebdf4999a5890"
|
||||
dependencies = [
|
||||
"libc",
|
||||
"wasi 0.10.0+wasi-snapshot-preview1",
|
||||
"winapi",
|
||||
"serde",
|
||||
"time-core",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "time-core"
|
||||
version = "0.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2e153e1f1acaef8acc537e68b44906d2db6436e2b35ac2c6b42640fff91f00fd"
|
||||
|
||||
[[package]]
|
||||
name = "tinyvec"
|
||||
version = "1.6.0"
|
||||
|
|
@ -2151,13 +2218,13 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
|
|||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.13.2"
|
||||
version = "0.13.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f4ff2dd291eac98dcea13e8cf7a0b28c373a90dc9210ccdab0fa9e69ee0cac69"
|
||||
checksum = "5cf49017523bf0bc01c9966f172c5f120bbb7b96cccd1708772dd42e767fb9f5"
|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"cached-path",
|
||||
"clap 2.34.0",
|
||||
"clap",
|
||||
"derive_builder",
|
||||
"dirs",
|
||||
"esaxx-rs",
|
||||
|
|
@ -2167,6 +2234,7 @@ dependencies = [
|
|||
"lazy_static",
|
||||
"log",
|
||||
"macro_rules_attribute",
|
||||
"monostate",
|
||||
"onig",
|
||||
"paste",
|
||||
"rand",
|
||||
|
|
@ -2535,12 +2603,6 @@ version = "0.2.15"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "accd4ea62f7bb7a82fe23066fb0957d48ef677f6eeb8215f372f52e48bb32426"
|
||||
|
||||
[[package]]
|
||||
name = "vec_map"
|
||||
version = "0.8.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f1bddf1187be692e79c5ffeab891132dfb0f236ed36a43c7ed39f1165ee20191"
|
||||
|
||||
[[package]]
|
||||
name = "version_check"
|
||||
version = "0.9.4"
|
||||
|
|
@ -2557,12 +2619,6 @@ dependencies = [
|
|||
"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]]
|
||||
name = "wasi"
|
||||
version = "0.11.0+wasi-snapshot-preview1"
|
||||
|
|
@ -2779,23 +2835,50 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "zip"
|
||||
version = "0.5.13"
|
||||
version = "0.6.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "93ab48844d61251bb3835145c521d88aa4031d7139e8485990f60ca911fa0815"
|
||||
checksum = "0445d0fbc924bb93539b4316c11afb121ea39296f99a3c4c9edad09e3658cdef"
|
||||
dependencies = [
|
||||
"aes",
|
||||
"byteorder",
|
||||
"bzip2",
|
||||
"constant_time_eq",
|
||||
"crc32fast",
|
||||
"crossbeam-utils",
|
||||
"flate2",
|
||||
"thiserror",
|
||||
"hmac",
|
||||
"pbkdf2",
|
||||
"sha1",
|
||||
"time",
|
||||
"zstd",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zip-extensions"
|
||||
version = "0.6.1"
|
||||
name = "zstd"
|
||||
version = "0.11.2+zstd.1.5.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "a64c3c977bc3434ce2d4bcea8ad3c644672de0f2c402b72b9171ca80a8885d14"
|
||||
checksum = "20cc960326ece64f010d2d2107537f26dc589a6573a316bd5b1dba685fa5fde4"
|
||||
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",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ serde = {version = "1.0.142", features = ["derive"]}
|
|||
serde_json = "1.0"
|
||||
text-generation-client = { path = "../router/client" }
|
||||
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"] }
|
||||
tui = {package = "ratatui", version = "0.20", default-features = false, features = ["crossterm"]}
|
||||
tracing = "0.1.37"
|
||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@ async fn generate_runs(
|
|||
// Warmups on batch size
|
||||
for _ in 0..warmups {
|
||||
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?;
|
||||
// Send warmup message
|
||||
run_sender.send(Ok(Message::Warmup)).await.unwrap_or(());
|
||||
|
|
@ -83,7 +83,7 @@ async fn generate_runs(
|
|||
|
||||
for _ in 0..n_runs {
|
||||
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
|
||||
run_sender
|
||||
.send(Ok(Message::Prefill(prefill)))
|
||||
|
|
@ -110,6 +110,7 @@ async fn generate_runs(
|
|||
// Run a prefill step
|
||||
async fn prefill(
|
||||
sequence: String,
|
||||
sequence_length: u32,
|
||||
batch_size: u32,
|
||||
decode_length: u32,
|
||||
client: &mut ShardedClient,
|
||||
|
|
@ -119,6 +120,7 @@ async fn prefill(
|
|||
.map(|id| Request {
|
||||
id: id.into(),
|
||||
inputs: sequence.clone(),
|
||||
truncate: sequence_length,
|
||||
parameters: Some(NextTokenChooserParameters {
|
||||
temperature: 1.0,
|
||||
top_k: 0,
|
||||
|
|
|
|||
|
|
@ -63,10 +63,12 @@ message Request {
|
|||
uint64 id = 1;
|
||||
/// The generation context
|
||||
string inputs = 2;
|
||||
/// Context truncation
|
||||
uint32 truncate = 3;
|
||||
/// Next Token Chooser Parameters
|
||||
NextTokenChooserParameters parameters = 3;
|
||||
NextTokenChooserParameters parameters = 4;
|
||||
/// Stopping Criteria Parameters
|
||||
StoppingCriteriaParameters stopping_parameters = 4;
|
||||
StoppingCriteriaParameters stopping_parameters = 5;
|
||||
}
|
||||
|
||||
message Batch {
|
||||
|
|
|
|||
|
|
@ -18,21 +18,20 @@ axum = { version = "0.6.4", features = ["json"] }
|
|||
axum-tracing-opentelemetry = "0.9.0"
|
||||
text-generation-client = { path = "client" }
|
||||
clap = { version = "4.1.4", features = ["derive", "env"] }
|
||||
flume = "0.10.14"
|
||||
futures = "0.3.26"
|
||||
metrics = "0.20.1"
|
||||
metrics-exporter-prometheus = { version = "0.11.0", features = [] }
|
||||
nohash-hasher = "0.2.0"
|
||||
opentelemetry = { version = "0.18.0", features = ["rt-tokio"] }
|
||||
opentelemetry-otlp = "0.11.0"
|
||||
parking_lot = "0.12.1"
|
||||
rand = "0.8.5"
|
||||
reqwest = { version = "0.11.14", features = [] }
|
||||
serde = "1.0.152"
|
||||
serde_json = "1.0.93"
|
||||
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-stream = "0.1.11"
|
||||
tower-http = { version = "0.3.5", features = ["cors"] }
|
||||
tracing = "0.1.37"
|
||||
tracing-opentelemetry = "0.18.0"
|
||||
|
|
|
|||
|
|
@ -2,17 +2,17 @@
|
|||
use crate::validation::{Validation, ValidationError};
|
||||
use crate::{Entry, Queue, Token};
|
||||
use crate::{GenerateRequest, PrefillToken};
|
||||
use flume::r#async::RecvStream;
|
||||
use futures::future::try_join_all;
|
||||
use futures::stream::StreamExt;
|
||||
use nohash_hasher::IntMap;
|
||||
use std::sync::Arc;
|
||||
use text_generation_client::{
|
||||
Batch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
|
||||
};
|
||||
use thiserror::Error;
|
||||
use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError};
|
||||
use tokio::sync::{Notify, Semaphore, TryAcquireError};
|
||||
use tokio::time::Instant;
|
||||
use tokio_stream::wrappers::UnboundedReceiverStream;
|
||||
use tokio_stream::StreamExt;
|
||||
use tracing::{info_span, instrument, Instrument, Span};
|
||||
|
||||
/// Inference struct
|
||||
|
|
@ -73,7 +73,7 @@ impl Infer {
|
|||
pub(crate) async fn generate_stream(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<UnboundedReceiverStream<Result<InferStreamResponse, InferError>>, InferError> {
|
||||
) -> Result<RecvStream<Result<InferStreamResponse, InferError>>, InferError> {
|
||||
// Limit concurrent requests by acquiring a permit from the semaphore
|
||||
// This permit will live as long as Entry
|
||||
let permit = self
|
||||
|
|
@ -87,10 +87,14 @@ impl Infer {
|
|||
})?;
|
||||
|
||||
// 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
|
||||
let (response_tx, response_rx) = mpsc::unbounded_channel();
|
||||
let (response_tx, response_rx) = flume::unbounded();
|
||||
|
||||
// Append the request to the queue
|
||||
self.queue.append(Entry {
|
||||
|
|
@ -108,7 +112,7 @@ impl Infer {
|
|||
self.shared.batching_task.notify_one();
|
||||
|
||||
// Return stream
|
||||
Ok(UnboundedReceiverStream::new(response_rx))
|
||||
Ok(response_rx.into_stream())
|
||||
}
|
||||
|
||||
/// Add a new request to the queue and return a InferResponse
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ struct Args {
|
|||
max_waiting_tokens: usize,
|
||||
#[clap(default_value = "3000", long, short, env)]
|
||||
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,
|
||||
#[clap(default_value = "bigscience/bloom", long, env)]
|
||||
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()
|
||||
{
|
||||
// Load local tokenizer
|
||||
Tokenizer::from_file(local_path.join("tokenizer.json")).unwrap()
|
||||
Tokenizer::from_file(local_path.join("tokenizer.json")).ok()
|
||||
} else {
|
||||
// Download and instantiate tokenizer
|
||||
// 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
|
||||
|
|
@ -109,6 +109,13 @@ fn main() -> Result<(), std::io::Error> {
|
|||
.block_on(async {
|
||||
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
|
||||
let model_info = reqwest::get(format!(
|
||||
"https://huggingface.co/api/models/{tokenizer_name}"
|
||||
|
|
|
|||
|
|
@ -4,8 +4,7 @@ use crate::validation::ValidGenerateRequest;
|
|||
use nohash_hasher::{BuildNoHashHasher, IntMap};
|
||||
use std::cmp::min;
|
||||
use text_generation_client::{Batch, Request};
|
||||
use tokio::sync::mpsc::{UnboundedReceiver, UnboundedSender};
|
||||
use tokio::sync::{mpsc, oneshot, OwnedSemaphorePermit};
|
||||
use tokio::sync::{oneshot, OwnedSemaphorePermit};
|
||||
use tokio::time::Instant;
|
||||
use tracing::{info_span, instrument, Span};
|
||||
|
||||
|
|
@ -15,7 +14,7 @@ pub(crate) struct Entry {
|
|||
/// Request
|
||||
pub request: ValidGenerateRequest,
|
||||
/// 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
|
||||
pub span: Span,
|
||||
/// Temporary span used as a guard when logging inference, wait times...
|
||||
|
|
@ -32,13 +31,13 @@ pub(crate) struct Entry {
|
|||
#[derive(Debug, Clone)]
|
||||
pub(crate) struct Queue {
|
||||
/// Channel to communicate with the background queue task
|
||||
queue_sender: UnboundedSender<QueueCommand>,
|
||||
queue_sender: flume::Sender<QueueCommand>,
|
||||
}
|
||||
|
||||
impl Queue {
|
||||
pub(crate) fn new() -> Self {
|
||||
// Create channel
|
||||
let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
|
||||
let (queue_sender, queue_receiver) = flume::unbounded();
|
||||
|
||||
// Launch background queue task
|
||||
tokio::spawn(queue_task(queue_receiver));
|
||||
|
|
@ -82,10 +81,10 @@ impl Queue {
|
|||
}
|
||||
|
||||
// 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();
|
||||
|
||||
while let Some(cmd) = receiver.recv().await {
|
||||
while let Ok(cmd) = receiver.recv_async().await {
|
||||
match cmd {
|
||||
QueueCommand::Append(entry, span) => span.in_scope(|| state.append(entry)),
|
||||
QueueCommand::NextBatch {
|
||||
|
|
@ -174,6 +173,7 @@ impl State {
|
|||
batch_requests.push(Request {
|
||||
id,
|
||||
inputs: entry.request.inputs.clone(),
|
||||
truncate: entry.request.truncate,
|
||||
parameters: Some(entry.request.parameters.clone()),
|
||||
stopping_parameters: Some(entry.request.stopping_parameters.clone()),
|
||||
});
|
||||
|
|
@ -215,17 +215,18 @@ mod tests {
|
|||
use super::*;
|
||||
use std::sync::Arc;
|
||||
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
|
||||
use tokio::sync::{mpsc, Semaphore};
|
||||
use tokio::sync::Semaphore;
|
||||
use tracing::info_span;
|
||||
|
||||
fn default_entry() -> Entry {
|
||||
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();
|
||||
|
||||
Entry {
|
||||
request: ValidGenerateRequest {
|
||||
inputs: "".to_string(),
|
||||
truncate: 0,
|
||||
parameters: NextTokenChooserParameters {
|
||||
temperature: 0.0,
|
||||
top_k: 0,
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ use axum::response::{IntoResponse, Response};
|
|||
use axum::routing::{get, post};
|
||||
use axum::{http, Json, Router};
|
||||
use axum_tracing_opentelemetry::opentelemetry_tracing_layer;
|
||||
use futures::stream::StreamExt;
|
||||
use futures::Stream;
|
||||
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
|
||||
use std::convert::Infallible;
|
||||
|
|
@ -21,7 +22,6 @@ use text_generation_client::ShardedClient;
|
|||
use tokenizers::Tokenizer;
|
||||
use tokio::signal;
|
||||
use tokio::time::Instant;
|
||||
use tokio_stream::StreamExt;
|
||||
use tower_http::cors::{AllowOrigin, CorsLayer};
|
||||
use tracing::{info_span, instrument, Instrument};
|
||||
use utoipa::OpenApi;
|
||||
|
|
@ -87,21 +87,21 @@ async fn health(infer: Extension<Infer>) -> Result<(), (StatusCode, Json<ErrorRe
|
|||
|
||||
/// Generate tokens
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/generate",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = GenerateResponse),
|
||||
(status = 424, description = "Generation Error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Request failed during generation"})),
|
||||
(status = 429, description = "Model is overloaded", body = ErrorResponse,
|
||||
example = json ! ({"error": "Model is overloaded"})),
|
||||
(status = 422, description = "Input validation error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Input validation error"})),
|
||||
(status = 500, description = "Incomplete generation", body = ErrorResponse,
|
||||
example = json ! ({"error": "Incomplete generation"})),
|
||||
)
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/generate",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = GenerateResponse),
|
||||
(status = 424, description = "Generation Error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Request failed during generation"})),
|
||||
(status = 429, description = "Model is overloaded", body = ErrorResponse,
|
||||
example = json ! ({"error": "Model is overloaded"})),
|
||||
(status = 422, description = "Input validation error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Input validation error"})),
|
||||
(status = 500, description = "Incomplete generation", body = ErrorResponse,
|
||||
example = json ! ({"error": "Incomplete generation"})),
|
||||
)
|
||||
)]
|
||||
#[instrument(
|
||||
skip(infer),
|
||||
|
|
@ -264,26 +264,26 @@ async fn generate(
|
|||
|
||||
/// Generate a stream of token using Server-Sent Events
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/generate_stream",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = StreamResponse,
|
||||
content_type = "text/event-stream"),
|
||||
(status = 424, description = "Generation Error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Request failed during generation"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 429, description = "Model is overloaded", body = ErrorResponse,
|
||||
example = json ! ({"error": "Model is overloaded"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 422, description = "Input validation error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Input validation error"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 500, description = "Incomplete generation", body = ErrorResponse,
|
||||
example = json ! ({"error": "Incomplete generation"}),
|
||||
content_type = "text/event-stream"),
|
||||
)
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/generate_stream",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = StreamResponse,
|
||||
content_type = "text/event-stream"),
|
||||
(status = 424, description = "Generation Error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Request failed during generation"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 429, description = "Model is overloaded", body = ErrorResponse,
|
||||
example = json ! ({"error": "Model is overloaded"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 422, description = "Input validation error", body = ErrorResponse,
|
||||
example = json ! ({"error": "Input validation error"}),
|
||||
content_type = "text/event-stream"),
|
||||
(status = 500, description = "Incomplete generation", body = ErrorResponse,
|
||||
example = json ! ({"error": "Incomplete generation"}),
|
||||
content_type = "text/event-stream"),
|
||||
)
|
||||
)]
|
||||
#[instrument(
|
||||
skip(infer),
|
||||
|
|
@ -447,10 +447,10 @@ async fn generate_stream(
|
|||
|
||||
/// Prometheus metrics scrape endpoint
|
||||
#[utoipa::path(
|
||||
get,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/metrics",
|
||||
responses((status = 200, description = "Prometheus Metrics", body = String))
|
||||
get,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/metrics",
|
||||
responses((status = 200, description = "Prometheus Metrics", body = String))
|
||||
)]
|
||||
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
|
||||
prom_handle.render()
|
||||
|
|
@ -468,7 +468,7 @@ pub async fn run(
|
|||
max_batch_size: usize,
|
||||
max_waiting_tokens: usize,
|
||||
client: ShardedClient,
|
||||
tokenizer: Tokenizer,
|
||||
tokenizer: Option<Tokenizer>,
|
||||
validation_workers: usize,
|
||||
addr: SocketAddr,
|
||||
allow_origin: Option<AllowOrigin>,
|
||||
|
|
@ -476,36 +476,36 @@ pub async fn run(
|
|||
// OpenAPI documentation
|
||||
#[derive(OpenApi)]
|
||||
#[openapi(
|
||||
paths(
|
||||
generate,
|
||||
generate_stream,
|
||||
metrics,
|
||||
),
|
||||
components(
|
||||
schemas(
|
||||
GenerateRequest,
|
||||
GenerateParameters,
|
||||
PrefillToken,
|
||||
Token,
|
||||
GenerateResponse,
|
||||
BestOfSequence,
|
||||
Details,
|
||||
FinishReason,
|
||||
StreamResponse,
|
||||
StreamDetails,
|
||||
ErrorResponse,
|
||||
)
|
||||
),
|
||||
tags(
|
||||
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
|
||||
),
|
||||
info(
|
||||
title = "Text Generation Inference",
|
||||
license(
|
||||
name = "Apache 2.0",
|
||||
url = "https://www.apache.org/licenses/LICENSE-2.0"
|
||||
)
|
||||
)
|
||||
paths(
|
||||
generate,
|
||||
generate_stream,
|
||||
metrics,
|
||||
),
|
||||
components(
|
||||
schemas(
|
||||
GenerateRequest,
|
||||
GenerateParameters,
|
||||
PrefillToken,
|
||||
Token,
|
||||
GenerateResponse,
|
||||
BestOfSequence,
|
||||
Details,
|
||||
FinishReason,
|
||||
StreamResponse,
|
||||
StreamDetails,
|
||||
ErrorResponse,
|
||||
)
|
||||
),
|
||||
tags(
|
||||
(name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
|
||||
),
|
||||
info(
|
||||
title = "Text Generation Inference",
|
||||
license(
|
||||
name = "Apache 2.0",
|
||||
url = "https://www.apache.org/licenses/LICENSE-2.0"
|
||||
)
|
||||
)
|
||||
)]
|
||||
struct ApiDoc;
|
||||
|
||||
|
|
|
|||
|
|
@ -1,50 +1,129 @@
|
|||
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
|
||||
/// Payload validation logic
|
||||
use crate::{GenerateParameters, GenerateRequest};
|
||||
use rand::rngs::ThreadRng;
|
||||
use rand::Rng;
|
||||
use rand::{thread_rng, Rng};
|
||||
use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParameters};
|
||||
use thiserror::Error;
|
||||
use tokenizers::tokenizer::Tokenizer;
|
||||
use tokenizers::TruncationDirection;
|
||||
use tokio::sync::{mpsc, oneshot};
|
||||
use tokio::sync::oneshot;
|
||||
use tracing::{instrument, Span};
|
||||
|
||||
/// Validation
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Validation {
|
||||
/// maximum value for the best_of parameter
|
||||
#[allow(dead_code)]
|
||||
/// Validation parameters
|
||||
max_best_of: usize,
|
||||
/// Channel to communicate with the background validation task
|
||||
sender: mpsc::UnboundedSender<ValidationRequest>,
|
||||
max_stop_sequences: usize,
|
||||
max_input_length: usize,
|
||||
max_total_tokens: usize,
|
||||
/// Channel to communicate with the background tokenization task
|
||||
sender: Option<flume::Sender<TokenizerRequest>>,
|
||||
}
|
||||
|
||||
impl Validation {
|
||||
pub(crate) fn new(
|
||||
workers: usize,
|
||||
tokenizer: Tokenizer,
|
||||
tokenizer: Option<Tokenizer>,
|
||||
max_best_of: usize,
|
||||
max_stop_sequences: usize,
|
||||
max_input_length: usize,
|
||||
max_total_tokens: usize,
|
||||
) -> Self {
|
||||
// Create channel
|
||||
let (validation_sender, validation_receiver) = mpsc::unbounded_channel();
|
||||
if max_input_length >= max_total_tokens {
|
||||
panic!("`max_input_length` must be < `max_total_tokens`");
|
||||
}
|
||||
|
||||
// Launch background validation task
|
||||
tokio::spawn(validation_task(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_stop_sequences,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
validation_receiver,
|
||||
));
|
||||
// If we have a fast tokenizer
|
||||
let sender = if let Some(tokenizer) = tokenizer {
|
||||
// Create channel
|
||||
let (validation_sender, validation_receiver) = flume::unbounded();
|
||||
|
||||
// Create workers
|
||||
for _ in 0..workers {
|
||||
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 {
|
||||
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,
|
||||
request: GenerateRequest,
|
||||
) -> Result<ValidGenerateRequest, 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, Span::current()))
|
||||
.unwrap();
|
||||
// Await on response channel
|
||||
// Unwrap is safe here
|
||||
receiver.await.unwrap()
|
||||
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::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
|
||||
|
|
@ -81,262 +283,57 @@ impl Validation {
|
|||
}
|
||||
}
|
||||
|
||||
/// Validation task
|
||||
/// Load balance the validation requests between multiple validation workers
|
||||
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();
|
||||
|
||||
/// Start tokenization workers
|
||||
fn tokenizer_worker(tokenizer: Tokenizer, receiver: flume::Receiver<TokenizerRequest>) {
|
||||
// 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(|| {
|
||||
response_tx
|
||||
.send(
|
||||
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
|
||||
}),
|
||||
)
|
||||
.send(prepare_input(inputs, truncate, &tokenizer))
|
||||
.unwrap_or(())
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
fn validate(
|
||||
request: GenerateRequest,
|
||||
/// Get input length and optionally truncate it
|
||||
fn prepare_input(
|
||||
inputs: String,
|
||||
truncate: Option<usize>,
|
||||
tokenizer: &Tokenizer,
|
||||
max_stop_sequences: usize,
|
||||
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))?;
|
||||
|
||||
) -> Result<(String, usize), ValidationError> {
|
||||
// Get the number of tokens in the input
|
||||
let mut encoding = tokenizer
|
||||
.encode(request.inputs.clone(), true)
|
||||
.encode(inputs.clone(), true)
|
||||
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
|
||||
|
||||
let (inputs, input_length) = if let Some(truncate) = truncate {
|
||||
// truncate encoding and decode new inputs
|
||||
encoding.truncate(truncate, 0, TruncationDirection::Left);
|
||||
let inputs = tokenizer
|
||||
.decode(Vec::from(encoding.get_ids()), false)
|
||||
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
|
||||
(inputs, encoding.len())
|
||||
} else {
|
||||
(request.inputs, encoding.len())
|
||||
// Optionally truncate
|
||||
let (inputs, input_length) = match truncate {
|
||||
// Truncate is some and > encoding length
|
||||
Some(truncate) if truncate > encoding.len() => {
|
||||
// truncate encoding and decode new inputs
|
||||
encoding.truncate(truncate, 0, TruncationDirection::Left);
|
||||
let inputs = tokenizer
|
||||
.decode(Vec::from(encoding.get_ids()), false)
|
||||
.map_err(|err| ValidationError::Tokenizer(err.to_string()))?;
|
||||
(inputs, encoding.len())
|
||||
}
|
||||
// Nothing to do
|
||||
_ => (inputs, encoding.len()),
|
||||
};
|
||||
|
||||
if input_length > max_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,
|
||||
})
|
||||
Ok((inputs, input_length))
|
||||
}
|
||||
|
||||
type ValidationRequest = (
|
||||
GenerateRequest,
|
||||
oneshot::Sender<Result<ValidGenerateRequest, ValidationError>>,
|
||||
type TokenizerRequest = (
|
||||
(String, Option<usize>),
|
||||
oneshot::Sender<Result<(String, usize), ValidationError>>,
|
||||
Span,
|
||||
);
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) struct ValidGenerateRequest {
|
||||
pub inputs: String,
|
||||
pub truncate: u32,
|
||||
pub parameters: NextTokenChooserParameters,
|
||||
pub stopping_parameters: StoppingCriteriaParameters,
|
||||
}
|
||||
|
|
@ -366,7 +363,9 @@ pub enum ValidationError {
|
|||
#[error("`typical_p` must be > 0.0 and < 1.0")]
|
||||
TypicalP,
|
||||
#[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`")]
|
||||
MaxTotalTokens(usize, usize, u32),
|
||||
#[error("`inputs` must have less than {0} tokens. Given: {1}")]
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
|
|||
return generate_pb2.Request(
|
||||
id=0,
|
||||
inputs="Test",
|
||||
truncate=100,
|
||||
parameters=default_pb_parameters,
|
||||
stopping_parameters=default_pb_stop_parameters,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
|
|||
return generate_pb2.Request(
|
||||
id=0,
|
||||
inputs="Test",
|
||||
truncate=100,
|
||||
parameters=default_pb_parameters,
|
||||
stopping_parameters=default_pb_stop_parameters,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
|
|||
return generate_pb2.Request(
|
||||
id=0,
|
||||
inputs="def",
|
||||
truncate=100,
|
||||
parameters=default_pb_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(
|
||||
id=0,
|
||||
inputs="<fim-prefix>def<fim-suffix>world<fim-middle>",
|
||||
truncate=100,
|
||||
parameters=default_pb_parameters,
|
||||
stopping_parameters=default_pb_stop_parameters,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
|
|||
return generate_pb2.Request(
|
||||
id=0,
|
||||
inputs="Test",
|
||||
truncate=100,
|
||||
parameters=default_pb_parameters,
|
||||
stopping_parameters=default_pb_stop_parameters,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -68,7 +68,7 @@ class BLOOMSharded(BLOOM):
|
|||
dtype = torch.float32
|
||||
|
||||
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(
|
||||
|
|
|
|||
|
|
@ -66,6 +66,7 @@ class CausalLMBatch(Batch):
|
|||
stopping_criterias = []
|
||||
|
||||
# Parse batch
|
||||
max_truncation = 0
|
||||
padding_right_offset = 0
|
||||
for r in pb.requests:
|
||||
inputs.append(r.inputs)
|
||||
|
|
@ -74,6 +75,7 @@ class CausalLMBatch(Batch):
|
|||
r.stopping_parameters, tokenizer
|
||||
)
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
max_truncation = max(max_truncation, r.truncate)
|
||||
padding_right_offset = max(
|
||||
padding_right_offset, stopping_criteria.max_new_tokens
|
||||
)
|
||||
|
|
@ -83,6 +85,8 @@ class CausalLMBatch(Batch):
|
|||
return_tensors="pt",
|
||||
padding=True,
|
||||
return_token_type_ids=False,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
).to(device)
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ from flash_attn.layers.rotary import RotaryEmbedding
|
|||
|
||||
class FastLayerNorm(nn.LayerNorm):
|
||||
def forward(self, hidden_states, residual=None):
|
||||
if hidden_states.shape[-1] > 6144:
|
||||
if hidden_states.shape[-1] > 8192:
|
||||
if residual is not None:
|
||||
hidden_states += residual
|
||||
residual = hidden_states
|
||||
|
|
@ -624,13 +624,16 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
|
|||
|
||||
|
||||
class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
def __init__(self, config, process_group=None):
|
||||
super().__init__(config)
|
||||
|
||||
if config.tp_parallel:
|
||||
process_group = torch.distributed.distributed_c10d._get_default_group()
|
||||
self.process_group = process_group
|
||||
if self.process_group is not None:
|
||||
self.world_size = self.process_group.size()
|
||||
self.rank = self.process_group.rank()
|
||||
else:
|
||||
process_group = None
|
||||
self.world_size = 1
|
||||
self.rank = 0
|
||||
|
||||
self.gpt_neox = FlashGPTNeoXModel(config, process_group)
|
||||
|
||||
|
|
@ -668,4 +671,13 @@ class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
|
|||
hidden_states, present = self.gpt_neox(
|
||||
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
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import dropout_layer_norm
|
|||
|
||||
class FastLayerNorm(nn.LayerNorm):
|
||||
def forward(self, hidden_states, residual=None):
|
||||
if hidden_states.shape[-1] > 6144:
|
||||
if hidden_states.shape[-1] > 8192:
|
||||
if residual is not None:
|
||||
hidden_states += residual
|
||||
residual = hidden_states
|
||||
|
|
|
|||
|
|
@ -78,7 +78,9 @@ class FlashCausalLMBatch(Batch):
|
|||
|
||||
# Parse batch
|
||||
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)
|
||||
max_seqlen = max(max_seqlen, input_length)
|
||||
input_lengths.append(input_length)
|
||||
|
|
@ -208,7 +210,7 @@ class FlashCausalLM(Model):
|
|||
raise NotImplementedError("FlashCausalLM does not support quantization")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id, revision=revision, padding_side="left"
|
||||
model_id, revision=revision, padding_side="left", truncation_side="left"
|
||||
)
|
||||
self.model = (
|
||||
model_cls.from_pretrained(
|
||||
|
|
|
|||
|
|
@ -45,18 +45,19 @@ class FlashNeoXSharded(FlashNeoX):
|
|||
raise NotImplementedError("FlashNeoX does not support quantization")
|
||||
|
||||
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(
|
||||
model_id, revision=revision, tp_parallel=True
|
||||
model_id,
|
||||
revision=revision,
|
||||
)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
|
||||
with init_empty_weights():
|
||||
model = FlashGPTNeoXForCausalLM(config)
|
||||
model = FlashGPTNeoXForCausalLM(config, self.process_group)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
self.load_weights(
|
||||
|
|
@ -147,32 +148,3 @@ class FlashNeoXSharded(FlashNeoX):
|
|||
module._parameters[param_name] = tensor
|
||||
else:
|
||||
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
|
||||
)
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ class FlashSantacoder(FlashCausalLM):
|
|||
raise NotImplementedError("FlashSantacoder does not support quantization")
|
||||
|
||||
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(
|
||||
|
|
@ -56,6 +56,8 @@ class FlashSantacoder(FlashCausalLM):
|
|||
self.load_weights(
|
||||
model,
|
||||
filenames,
|
||||
device,
|
||||
dtype,
|
||||
)
|
||||
self.model = model.eval().to(device).to(dtype)
|
||||
|
||||
|
|
@ -68,10 +70,14 @@ class FlashSantacoder(FlashCausalLM):
|
|||
def load_weights(
|
||||
model: FlashSantacoderForCausalLM,
|
||||
filenames: List[Path],
|
||||
device: torch.device,
|
||||
dtype: torch.dtype,
|
||||
):
|
||||
for filename in filenames:
|
||||
state_dict = torch.load(filename, map_location="cpu")
|
||||
for key, value in state_dict.items():
|
||||
value = value.to(device).to(dtype)
|
||||
|
||||
layer_name = ".".join(key.split(".")[:4])
|
||||
|
||||
# Fused qkv
|
||||
|
|
@ -141,6 +147,8 @@ class FlashSantacoder(FlashCausalLM):
|
|||
else:
|
||||
module._buffers[param_name] = value
|
||||
|
||||
del value
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
model.post_load_weights()
|
||||
|
||||
|
|
|
|||
|
|
@ -96,7 +96,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
input_lengths = []
|
||||
|
||||
# Parse batch
|
||||
max_sequence_length = 0
|
||||
max_truncation = 0
|
||||
padding_right_offset = 0
|
||||
for r in pb.requests:
|
||||
# Add escape_custom_split_sequence to the CausalLMBatch logic
|
||||
|
|
@ -107,7 +107,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
r.stopping_parameters, tokenizer
|
||||
)
|
||||
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, stopping_criteria.max_new_tokens
|
||||
)
|
||||
|
|
@ -118,14 +118,20 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
return_tensors="pt",
|
||||
padding=True,
|
||||
return_token_type_ids=False,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
).to(device)
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
max_input_length = input_lengths.max()
|
||||
|
||||
input_ids = tokenized_inputs["input_ids"]
|
||||
# Allocate maximum attention_mask
|
||||
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
|
||||
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.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
|
||||
|
|
@ -143,7 +149,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
size=pb.size,
|
||||
max_sequence_length=max_sequence_length,
|
||||
max_input_length=max_input_length,
|
||||
padding_right_offset=padding_right_offset,
|
||||
)
|
||||
|
||||
|
|
@ -188,7 +194,7 @@ class GalacticaSharded(Galactica):
|
|||
dtype = torch.float32
|
||||
|
||||
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(
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ class GPTNeoxSharded(CausalLM):
|
|||
dtype = torch.float32
|
||||
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class SantaCoder(CausalLM):
|
|||
dtype = torch.float32
|
||||
|
||||
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(
|
||||
{
|
||||
|
|
|
|||
|
|
@ -73,6 +73,7 @@ class Seq2SeqLMBatch(Batch):
|
|||
decoder_input_lengths = []
|
||||
|
||||
# Parse batch
|
||||
max_truncation = 0
|
||||
padding_right_offset = 0
|
||||
for r in pb.requests:
|
||||
inputs.append(r.inputs)
|
||||
|
|
@ -84,6 +85,7 @@ class Seq2SeqLMBatch(Batch):
|
|||
r.stopping_parameters, tokenizer
|
||||
)
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
max_truncation = max(max_truncation, r.truncate)
|
||||
padding_right_offset = max(
|
||||
padding_right_offset, stopping_criteria.max_new_tokens
|
||||
)
|
||||
|
|
@ -94,6 +96,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
return_tensors="pt",
|
||||
padding=True,
|
||||
return_token_type_ids=False,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
).to(device)
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ class T5Sharded(Seq2SeqLM):
|
|||
dtype = torch.float32
|
||||
|
||||
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(
|
||||
|
|
|
|||
Loading…
Reference in New Issue