feat: add SchedulerV3 (#1996)
- Refactor code to allow supporting multiple versions of the generate.proto at the same time - Add v3/generate.proto (ISO to generate.proto for now but allow for future changes without impacting v2 backends) - Add Schedule trait to abstract queuing and batching mechanisms that will be different in the future - Add SchedulerV2/V3 impl
This commit is contained in:
OlivierDehaene
GitHub
parent
fec0167a12
commit
757223b352
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@ -4,9 +4,9 @@ version = 3
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[[package]]
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name = "addr2line"
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version = "0.21.0"
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version = "0.22.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "8a30b2e23b9e17a9f90641c7ab1549cd9b44f296d3ccbf309d2863cfe398a0cb"
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checksum = "6e4503c46a5c0c7844e948c9a4d6acd9f50cccb4de1c48eb9e291ea17470c678"
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dependencies = [
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"gimli",
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]
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@ -350,9 +350,9 @@ dependencies = [
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[[package]]
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name = "backtrace"
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version = "0.3.71"
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version = "0.3.72"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "26b05800d2e817c8b3b4b54abd461726265fa9789ae34330622f2db9ee696f9d"
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checksum = "17c6a35df3749d2e8bb1b7b21a976d82b15548788d2735b9d82f329268f71a11"
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dependencies = [
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"addr2line",
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"cc",
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@ -1138,9 +1138,9 @@ dependencies = [
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[[package]]
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name = "gimli"
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version = "0.28.1"
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version = "0.29.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "4271d37baee1b8c7e4b708028c57d816cf9d2434acb33a549475f78c181f6253"
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checksum = "40ecd4077b5ae9fd2e9e169b102c6c330d0605168eb0e8bf79952b256dbefffd"
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[[package]]
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name = "glob"
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@ -1396,9 +1396,9 @@ dependencies = [
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[[package]]
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name = "hyper-util"
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version = "0.1.4"
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version = "0.1.5"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "3d8d52be92d09acc2e01dddb7fde3ad983fc6489c7db4837e605bc3fca4cb63e"
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checksum = "7b875924a60b96e5d7b9ae7b066540b1dd1cbd90d1828f54c92e02a283351c56"
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dependencies = [
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"bytes",
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"futures-util",
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@ -1938,11 +1938,10 @@ dependencies = [
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[[package]]
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name = "native-tls"
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version = "0.2.11"
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version = "0.2.12"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "07226173c32f2926027b63cce4bcd8076c3552846cbe7925f3aaffeac0a3b92e"
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checksum = "a8614eb2c83d59d1c8cc974dd3f920198647674a0a035e1af1fa58707e317466"
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dependencies = [
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"lazy_static",
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"libc",
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"log",
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"openssl",
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@ -2168,9 +2167,9 @@ checksum = "830b246a0e5f20af87141b25c173cd1b609bd7779a4617d6ec582abaf90870f3"
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[[package]]
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name = "object"
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version = "0.32.2"
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version = "0.35.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a6a622008b6e321afc04970976f62ee297fdbaa6f95318ca343e3eebb9648441"
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checksum = "b8ec7ab813848ba4522158d5517a6093db1ded27575b070f4177b8d12b41db5e"
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dependencies = [
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"memchr",
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]
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@ -2563,9 +2562,9 @@ dependencies = [
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[[package]]
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name = "proc-macro2"
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version = "1.0.84"
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version = "1.0.85"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "ec96c6a92621310b51366f1e28d05ef11489516e93be030060e5fc12024a49d6"
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checksum = "22244ce15aa966053a896d1accb3a6e68469b97c7f33f284b99f0d576879fc23"
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dependencies = [
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"unicode-ident",
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]
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@ -3554,6 +3553,7 @@ dependencies = [
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name = "text-generation-client"
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version = "2.0.5-dev0"
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dependencies = [
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"async-trait",
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"base64 0.22.1",
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"futures",
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"grpc-metadata",
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@ -3752,9 +3752,9 @@ dependencies = [
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[[package]]
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name = "tokio"
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version = "1.37.0"
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version = "1.38.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "1adbebffeca75fcfd058afa480fb6c0b81e165a0323f9c9d39c9697e37c46787"
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checksum = "ba4f4a02a7a80d6f274636f0aa95c7e383b912d41fe721a31f29e29698585a4a"
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dependencies = [
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"backtrace",
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"bytes",
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@ -3781,9 +3781,9 @@ dependencies = [
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[[package]]
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name = "tokio-macros"
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version = "2.2.0"
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version = "2.3.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b"
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checksum = "5f5ae998a069d4b5aba8ee9dad856af7d520c3699e6159b185c2acd48155d39a"
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dependencies = [
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"proc-macro2",
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"quote",
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@ -4733,9 +4733,9 @@ checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0"
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[[package]]
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name = "winnow"
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version = "0.6.8"
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version = "0.6.9"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "c3c52e9c97a68071b23e836c9380edae937f17b9c4667bd021973efc689f618d"
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checksum = "86c949fede1d13936a99f14fafd3e76fd642b556dd2ce96287fbe2e0151bfac6"
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dependencies = [
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"memchr",
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]
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@ -1,8 +1,9 @@
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use std::time::{Duration, Instant};
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use text_generation_client::{
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Batch, CachedBatch, Chunk, ClientError, Input, NextTokenChooserParameters, Request,
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ShardedClient, StoppingCriteriaParameters,
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use text_generation_client::v3::{
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Batch, CachedBatch, NextTokenChooserParameters, Request, ShardedClient,
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StoppingCriteriaParameters,
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};
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use text_generation_client::{Chunk, ClientError, Input};
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use tokenizers::{Tokenizer, TruncationDirection};
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use tokio::sync::{broadcast, mpsc};
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@ -8,7 +8,7 @@ use crate::app::App;
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use crate::event::Event;
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use crossterm::ExecutableCommand;
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use std::io;
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use text_generation_client::{GrammarType, NextTokenChooserParameters, ShardedClient};
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use text_generation_client::v3::{GrammarType, NextTokenChooserParameters, ShardedClient};
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use tokenizers::Tokenizer;
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use tokio::sync::{broadcast, mpsc};
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use tui::backend::CrosstermBackend;
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@ -4,7 +4,7 @@
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/// and: https://github.com/orhun/rust-tui-template
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use clap::Parser;
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use std::path::Path;
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use text_generation_client::ShardedClient;
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use text_generation_client::v3::ShardedClient;
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use tokenizers::{FromPretrainedParameters, Tokenizer};
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use tracing_subscriber::layer::SubscriberExt;
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use tracing_subscriber::util::SubscriberInitExt;
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@ -51,27 +51,6 @@ message ClearCacheRequest {
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/// Empty response
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message ClearCacheResponse {}
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message Image {
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/// Binary image data.
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bytes data = 1;
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/// Image MIME type.
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string mimetype = 2;
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}
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message InputChunk {
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oneof chunk {
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/// Plain text data
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string text = 1;
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/// Image data
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Image image = 2;
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}
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}
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message Input {
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repeated InputChunk chunks = 1;
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}
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enum GrammarType {
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GRAMMAR_TYPE_NONE = 0;
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GRAMMAR_TYPE_JSON = 1;
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@ -116,9 +95,7 @@ message StoppingCriteriaParameters {
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message Request {
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/// Request ID
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uint64 id = 1;
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/// The generation context as chunks
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Input input_chunks = 8;
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/// The generation context, stringified input_chunks
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/// The generation context
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string inputs = 2;
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/// Context truncation
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uint32 truncate = 3;
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@ -0,0 +1,259 @@
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syntax = "proto3";
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package generate.v3;
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service TextGenerationService {
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/// Model Info
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rpc Info (InfoRequest) returns (InfoResponse) {}
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/// Service discovery
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rpc ServiceDiscovery (ServiceDiscoveryRequest) returns (ServiceDiscoveryResponse) {}
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/// Empties batch cache
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rpc ClearCache (ClearCacheRequest) returns (ClearCacheResponse);
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/// Remove requests from a cached batch
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rpc FilterBatch (FilterBatchRequest) returns (FilterBatchResponse);
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/// Warmup the model and compute max cache size
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rpc Warmup (WarmupRequest) returns (WarmupResponse);
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/// Prefill batch and decode first token
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rpc Prefill (PrefillRequest) returns (PrefillResponse);
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/// Decode token for a list of prefilled batches
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rpc Decode (DecodeRequest) returns (DecodeResponse);
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/// Health check
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rpc Health (HealthRequest) returns (HealthResponse);
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}
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message HealthRequest {}
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message HealthResponse {}
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/// Empty request
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message InfoRequest {}
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message InfoResponse {
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bool requires_padding = 1;
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string dtype = 2;
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string device_type = 3;
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optional uint32 window_size = 4;
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uint32 speculate = 5;
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}
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/// Empty request
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message ServiceDiscoveryRequest {}
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message ServiceDiscoveryResponse {
|
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/// Other shards urls
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repeated string urls = 1;
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}
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message ClearCacheRequest {
|
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/// Optional batch id
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optional uint64 id = 1;
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}
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/// Empty response
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message ClearCacheResponse {}
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message Image {
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/// Binary image data.
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bytes data = 1;
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|
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/// Image MIME type.
|
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string mimetype = 2;
|
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}
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message InputChunk {
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oneof chunk {
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/// Plain text data
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string text = 1;
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/// Image data
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Image image = 2;
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}
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}
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message Input {
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repeated InputChunk chunks = 1;
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}
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enum GrammarType {
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GRAMMAR_TYPE_NONE = 0;
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GRAMMAR_TYPE_JSON = 1;
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GRAMMAR_TYPE_REGEX = 2;
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}
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message NextTokenChooserParameters {
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/// exponential scaling output probability distribution
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float temperature = 1;
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/// restricting to the k highest probability elements
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uint32 top_k = 2;
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/// restricting to top tokens summing to prob_cut_off <= prob_cut_off
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float top_p = 3;
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/// restricting to top tokens summing to prob_cut_off <= prob_cut_off
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float typical_p = 4;
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/// apply sampling on the logits
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bool do_sample = 5;
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/// random seed for sampling
|
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uint64 seed = 6;
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/// repetition penalty
|
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float repetition_penalty = 7;
|
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/// frequency penalty
|
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float frequency_penalty = 9;
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/// token watermarking using "A Watermark for Large Language Models"
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bool watermark = 8;
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/// grammar (applied if not empty)
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string grammar = 10;
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/// grammar type
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GrammarType grammar_type = 11;
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}
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message StoppingCriteriaParameters {
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/// Maximum number of generated tokens
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uint32 max_new_tokens = 1;
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/// Optional stopping sequences
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repeated string stop_sequences = 2;
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/// Ignore end of sequence token
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/// used for benchmarking
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bool ignore_eos_token = 3;
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}
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message Request {
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/// Request ID
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uint64 id = 1;
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/// The generation context as chunks
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Input input_chunks = 8;
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/// The generation context, stringified input_chunks
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string inputs = 2;
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/// Context truncation
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uint32 truncate = 3;
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/// Next Token Chooser Parameters
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NextTokenChooserParameters parameters = 4;
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/// Stopping Criteria Parameters
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StoppingCriteriaParameters stopping_parameters = 5;
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/// Return prefill logprobs
|
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bool prefill_logprobs = 6;
|
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/// Return most likely n tokens
|
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uint32 top_n_tokens = 7;
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}
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message Batch {
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/// Batch ID
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uint64 id = 1;
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/// Individual requests
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repeated Request requests = 2;
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/// Batch size (==len(requests))
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uint32 size = 3;
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/// Maximum number of tokens this batch will grow to
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uint32 max_tokens = 4;
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}
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message CachedBatch {
|
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/// Batch ID
|
||||
uint64 id = 1;
|
||||
/// Individual requests ids
|
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repeated uint64 request_ids = 2;
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/// Batch size (==len(requests))
|
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uint32 size = 3;
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/// Maximum number of tokens this batch will grow to
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uint32 max_tokens = 4;
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||||
}
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|
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enum FinishReason {
|
||||
FINISH_REASON_LENGTH = 0;
|
||||
FINISH_REASON_EOS_TOKEN = 1;
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||||
FINISH_REASON_STOP_SEQUENCE = 2;
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}
|
||||
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message GeneratedText {
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/// Output
|
||||
string text = 1;
|
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/// Number of generated tokens
|
||||
uint32 generated_tokens = 2;
|
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/// Finish reason
|
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FinishReason finish_reason = 3;
|
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/// Seed
|
||||
optional uint64 seed = 4;
|
||||
}
|
||||
|
||||
message Tokens {
|
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/// Token IDs
|
||||
repeated uint32 ids = 1;
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||||
/// Logprobs
|
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repeated float logprobs = 2;
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||||
/// tokens
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||||
repeated string texts = 3;
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||||
/// special
|
||||
repeated bool is_special = 4;
|
||||
}
|
||||
|
||||
message Generation {
|
||||
/// Request ID
|
||||
uint64 request_id = 1;
|
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/// Prefill tokens (optional)
|
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Tokens prefill_tokens = 2;
|
||||
Tokens tokens = 3;
|
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/// Complete generated text
|
||||
optional GeneratedText generated_text = 4;
|
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/// Top tokens
|
||||
repeated Tokens top_tokens = 5;
|
||||
}
|
||||
|
||||
message FilterBatchRequest {
|
||||
/// Batch ID
|
||||
uint64 batch_id = 1;
|
||||
/// Requests to keep
|
||||
repeated uint64 request_ids = 2;
|
||||
}
|
||||
|
||||
message FilterBatchResponse {
|
||||
/// Filtered Batch (cached)
|
||||
CachedBatch batch = 1;
|
||||
}
|
||||
|
||||
|
||||
message PrefillRequest {
|
||||
/// Batch
|
||||
Batch batch = 1;
|
||||
}
|
||||
|
||||
message PrefillResponse {
|
||||
/// Generation
|
||||
repeated Generation generations = 1;
|
||||
/// Next batch (cached)
|
||||
optional CachedBatch batch = 2;
|
||||
/// Forward elapsed time in nanoseconds
|
||||
uint64 forward_ns = 3;
|
||||
/// Decode elapsed time in nanoseconds
|
||||
uint64 decode_ns = 4;
|
||||
/// Total elapsed time in nanoseconds
|
||||
uint64 total_ns = 5;
|
||||
}
|
||||
|
||||
message DecodeRequest {
|
||||
/// Cached batches
|
||||
repeated CachedBatch batches = 1;
|
||||
}
|
||||
|
||||
message DecodeResponse {
|
||||
/// Decodes
|
||||
repeated Generation generations = 1;
|
||||
/// Next batch (cached)
|
||||
optional CachedBatch batch = 2;
|
||||
/// Forward elapsed time in nanoseconds
|
||||
uint64 forward_ns = 3;
|
||||
/// Decode elapsed time in nanoseconds
|
||||
uint64 decode_ns = 4;
|
||||
/// Total elapsed time in nanoseconds
|
||||
uint64 total_ns = 5;
|
||||
/// Concatenate elapsed time in nanoseconds
|
||||
optional uint64 concat_ns = 6;
|
||||
}
|
||||
|
||||
message WarmupRequest {
|
||||
/// Batch to warmup on
|
||||
Batch batch = 1;
|
||||
uint32 max_input_length = 2;
|
||||
uint32 max_prefill_tokens = 3;
|
||||
uint32 max_total_tokens = 4;
|
||||
}
|
||||
|
||||
message WarmupResponse {
|
||||
/// Maximum number of tokens supported by the model
|
||||
optional uint32 max_supported_total_tokens = 1;
|
||||
}
|
||||
|
|
@ -6,6 +6,7 @@ authors.workspace = true
|
|||
homepage.workspace = true
|
||||
|
||||
[dependencies]
|
||||
async-trait = "^0.1"
|
||||
base64 = { workspace = true }
|
||||
futures = "^0.3"
|
||||
grpc-metadata = { path = "../grpc-metadata" }
|
||||
|
|
|
|||
|
|
@ -1,19 +1,31 @@
|
|||
use std::fs;
|
||||
|
||||
fn main() -> Result<(), Box<dyn std::error::Error>> {
|
||||
println!("cargo:rerun-if-changed=../../proto/generate.proto");
|
||||
fs::create_dir("src/pb").unwrap_or(());
|
||||
println!("cargo:rerun-if-changed=../../proto/**");
|
||||
|
||||
fs::create_dir_all("src/v2/pb").unwrap_or(());
|
||||
let mut config = prost_build::Config::new();
|
||||
config.protoc_arg("--experimental_allow_proto3_optional");
|
||||
|
||||
tonic_build::configure()
|
||||
.build_client(true)
|
||||
.build_server(false)
|
||||
.out_dir("src/pb")
|
||||
.out_dir("src/v2/pb")
|
||||
.include_file("mod.rs")
|
||||
.compile_with_config(config, &["../../proto/generate.proto"], &["../../proto"])
|
||||
.unwrap_or_else(|e| panic!("protobuf compilation failed: {e}"));
|
||||
|
||||
fs::create_dir_all("src/v3/pb").unwrap_or(());
|
||||
let mut config = prost_build::Config::new();
|
||||
config.protoc_arg("--experimental_allow_proto3_optional");
|
||||
|
||||
tonic_build::configure()
|
||||
.build_client(true)
|
||||
.build_server(false)
|
||||
.out_dir("src/v3/pb")
|
||||
.include_file("mod.rs")
|
||||
.compile_with_config(config, &["../../proto/v3/generate.proto"], &["../../proto"])
|
||||
.unwrap_or_else(|e| panic!("protobuf compilation failed: {e}"));
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,25 +1,35 @@
|
|||
//! Text Generation gRPC client library
|
||||
|
||||
mod client;
|
||||
#[allow(clippy::derive_partial_eq_without_eq)]
|
||||
mod pb;
|
||||
mod sharded_client;
|
||||
pub mod v2;
|
||||
pub mod v3;
|
||||
|
||||
use async_trait::async_trait;
|
||||
use base64::{engine::general_purpose::STANDARD, Engine};
|
||||
pub use client::Client;
|
||||
pub use pb::generate::v2::input_chunk::Chunk;
|
||||
pub use pb::generate::v2::HealthResponse;
|
||||
pub use pb::generate::v2::Image;
|
||||
pub use pb::generate::v2::InfoResponse as ShardInfo;
|
||||
pub use pb::generate::v2::{
|
||||
Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType, Input, InputChunk,
|
||||
NextTokenChooserParameters, Request, StoppingCriteriaParameters, Tokens,
|
||||
};
|
||||
pub use sharded_client::ShardedClient;
|
||||
use thiserror::Error;
|
||||
use tonic::transport;
|
||||
use tonic::Status;
|
||||
|
||||
pub use v3::{Chunk, Image, Input, InputChunk};
|
||||
|
||||
#[async_trait]
|
||||
pub trait Health {
|
||||
/// Check if a generate server is healthy by asking it to allocate a tensor on device
|
||||
async fn device_health(&self) -> Result<()>;
|
||||
|
||||
/// Check if a generate server is healthy by doing a forward pass.
|
||||
/// EXPENSIVE
|
||||
async fn model_health(&self) -> Result<()>;
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub struct ShardInfo {
|
||||
pub requires_padding: bool,
|
||||
pub dtype: String,
|
||||
pub device_type: String,
|
||||
pub window_size: Option<u32>,
|
||||
pub speculate: u32,
|
||||
}
|
||||
|
||||
#[derive(Error, Debug, Clone)]
|
||||
pub enum ClientError {
|
||||
#[error("Could not connect to Text Generation server: {0}")]
|
||||
|
|
@ -46,8 +56,6 @@ impl From<transport::Error> for ClientError {
|
|||
}
|
||||
}
|
||||
|
||||
pub type Result<T> = std::result::Result<T, ClientError>;
|
||||
|
||||
// Small convenience re-wrapping of `Chunk`.
|
||||
impl From<Chunk> for InputChunk {
|
||||
fn from(chunk: Chunk) -> Self {
|
||||
|
|
@ -77,3 +85,7 @@ impl ChunksToString for Vec<InputChunk> {
|
|||
output
|
||||
}
|
||||
}
|
||||
|
||||
static WARMUP_IMAGE_BASE64 :&str = "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";
|
||||
|
||||
pub type Result<T> = std::result::Result<T, ClientError>;
|
||||
|
|
|
|||
|
|
@ -1 +0,0 @@
|
|||
*.rs
|
||||
|
|
@ -0,0 +1,258 @@
|
|||
/// Single shard Client
|
||||
use crate::v2::pb;
|
||||
use crate::{ClientError, Result};
|
||||
|
||||
use crate::WARMUP_IMAGE_BASE64;
|
||||
use grpc_metadata::InjectTelemetryContext;
|
||||
use pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
|
||||
use pb::generate::v2::*;
|
||||
use std::cmp::min;
|
||||
use std::time::Duration;
|
||||
use tonic::transport::{Channel, Uri};
|
||||
use tracing::instrument;
|
||||
|
||||
/// Text Generation Inference gRPC client
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Client {
|
||||
stub: TextGenerationServiceClient<Channel>,
|
||||
}
|
||||
|
||||
impl Client {
|
||||
/// Returns a client connected to the given url
|
||||
pub async fn connect(uri: Uri) -> Result<Self> {
|
||||
let channel = Channel::builder(uri).connect().await?;
|
||||
|
||||
Ok(Self {
|
||||
stub: TextGenerationServiceClient::new(channel),
|
||||
})
|
||||
}
|
||||
|
||||
/// Returns a client connected to the given unix socket
|
||||
pub async fn connect_uds(path: String) -> Result<Self> {
|
||||
let channel = Channel::from_shared("http://[::]:50051".to_string())
|
||||
.unwrap()
|
||||
.connect_with_connector(tower::service_fn(move |_: Uri| {
|
||||
tokio::net::UnixStream::connect(path.clone())
|
||||
}))
|
||||
.await?;
|
||||
|
||||
Ok(Self {
|
||||
stub: TextGenerationServiceClient::new(channel),
|
||||
})
|
||||
}
|
||||
|
||||
/// Returns a list of uris or unix sockets of all shards
|
||||
#[instrument(skip(self))]
|
||||
pub async fn service_discovery(&mut self) -> Result<Vec<String>> {
|
||||
let request = tonic::Request::new(ServiceDiscoveryRequest {}).inject_context();
|
||||
let response = self.stub.service_discovery(request).await.map_err(|_| {
|
||||
ClientError::Connection("Server does not support v2 interface".to_string())
|
||||
})?;
|
||||
let urls = response
|
||||
.into_inner()
|
||||
.urls
|
||||
.into_iter()
|
||||
// Remove unix socket prefix
|
||||
.map(|url| match url.strip_prefix("unix://") {
|
||||
None => url,
|
||||
Some(stripped_url) => stripped_url.to_string(),
|
||||
})
|
||||
.collect();
|
||||
Ok(urls)
|
||||
}
|
||||
|
||||
/// Get model info
|
||||
#[instrument(skip(self))]
|
||||
pub async fn info(&mut self) -> Result<InfoResponse> {
|
||||
let request = tonic::Request::new(InfoRequest {}).inject_context();
|
||||
let response = self.stub.info(request).await?.into_inner();
|
||||
Ok(response)
|
||||
}
|
||||
|
||||
/// Get model health
|
||||
#[instrument(skip(self))]
|
||||
pub async fn health(&mut self) -> Result<HealthResponse> {
|
||||
let request = tonic::Request::new(HealthRequest {}).inject_context();
|
||||
let response = self.stub.health(request).await?.into_inner();
|
||||
Ok(response)
|
||||
}
|
||||
|
||||
/// Clear the past generations cache
|
||||
#[instrument(skip(self))]
|
||||
pub async fn clear_cache(&mut self, batch_id: Option<u64>) -> Result<()> {
|
||||
let request = tonic::Request::new(ClearCacheRequest { id: batch_id }).inject_context();
|
||||
self.stub.clear_cache(request).await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Filter a cached batch
|
||||
#[instrument(skip(self))]
|
||||
pub async fn filter_batch(
|
||||
&mut self,
|
||||
batch_id: u64,
|
||||
request_ids: Vec<u64>,
|
||||
) -> Result<Option<CachedBatch>> {
|
||||
let request = tonic::Request::new(FilterBatchRequest {
|
||||
batch_id,
|
||||
request_ids,
|
||||
})
|
||||
.inject_context();
|
||||
let filtered_batch = self.stub.filter_batch(request).await?.into_inner();
|
||||
Ok(filtered_batch.batch)
|
||||
}
|
||||
|
||||
/// Warmup on a max size batch
|
||||
///
|
||||
/// Returns the maximum amount of tokens supported by the hardware
|
||||
#[instrument(skip_all)]
|
||||
pub async fn warmup(
|
||||
&mut self,
|
||||
max_input_length: u32,
|
||||
max_prefill_tokens: u32,
|
||||
max_total_tokens: u32,
|
||||
max_batch_size: Option<usize>,
|
||||
) -> Result<Option<u32>> {
|
||||
let mut n_tokens = 0;
|
||||
let mut requests = Vec::new();
|
||||
// Create requests
|
||||
while n_tokens < max_prefill_tokens {
|
||||
let truncate = min(max_input_length, max_prefill_tokens - n_tokens);
|
||||
|
||||
let mut inputs = String::new();
|
||||
inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
|
||||
if n_tokens == 0 {
|
||||
// 1 request is enough to test vision heads.
|
||||
// Sending images on other queries messes up easily with truncation.
|
||||
inputs.push_str(&format!(
|
||||
"",
|
||||
));
|
||||
}
|
||||
|
||||
requests.push(Request {
|
||||
id: 0,
|
||||
inputs,
|
||||
// We truncate the input on the server side to be sure that it has the correct size
|
||||
truncate,
|
||||
// Set sampling parameters to also take these ops into account in the max memory
|
||||
parameters: Some(NextTokenChooserParameters {
|
||||
temperature: 0.9,
|
||||
top_k: 10,
|
||||
top_p: 0.9,
|
||||
typical_p: 0.9,
|
||||
do_sample: false,
|
||||
seed: 0,
|
||||
repetition_penalty: 1.2,
|
||||
frequency_penalty: 0.1,
|
||||
watermark: true,
|
||||
grammar: String::new(),
|
||||
grammar_type: GrammarType::None as i32,
|
||||
}),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters {
|
||||
max_new_tokens: max_total_tokens - truncate,
|
||||
stop_sequences: vec![],
|
||||
ignore_eos_token: true,
|
||||
}),
|
||||
prefill_logprobs: true,
|
||||
top_n_tokens: 20,
|
||||
});
|
||||
n_tokens += max_input_length;
|
||||
|
||||
// Check max_batch_size
|
||||
if Some(requests.len()) == max_batch_size {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let batch = Batch {
|
||||
id: 0,
|
||||
size: requests.len() as u32,
|
||||
requests,
|
||||
max_tokens: 0,
|
||||
};
|
||||
|
||||
let request = tonic::Request::new(WarmupRequest {
|
||||
batch: Some(batch),
|
||||
max_input_length,
|
||||
max_prefill_tokens,
|
||||
max_total_tokens,
|
||||
})
|
||||
.inject_context();
|
||||
let response = self.stub.warmup(request).await?.into_inner();
|
||||
Ok(response.max_supported_total_tokens)
|
||||
}
|
||||
|
||||
/// Generate one token for each request in the given batch
|
||||
///
|
||||
/// Returns Generation for each request in batch
|
||||
/// and the next cached batch
|
||||
#[instrument(skip_all, fields(id = &batch.id, size = &batch.size))]
|
||||
pub async fn prefill(
|
||||
&mut self,
|
||||
batch: Batch,
|
||||
) -> Result<(Vec<Generation>, Option<CachedBatch>, PrefillTimings)> {
|
||||
let request = tonic::Request::new(PrefillRequest { batch: Some(batch) }).inject_context();
|
||||
let response = self.stub.prefill(request).await?.into_inner();
|
||||
Ok((
|
||||
response.generations,
|
||||
response.batch,
|
||||
PrefillTimings::new(response.forward_ns, response.decode_ns, response.total_ns),
|
||||
))
|
||||
}
|
||||
|
||||
/// Generate one token for each request in the given cached batches
|
||||
///
|
||||
/// Returns Generation for each request in batches
|
||||
/// and the next cached batch
|
||||
#[instrument(skip_all, fields(size = batches.iter().map(|batch|{batch.size}).sum::<u32>()))]
|
||||
pub async fn decode(
|
||||
&mut self,
|
||||
batches: Vec<CachedBatch>,
|
||||
) -> Result<(Vec<Generation>, Option<CachedBatch>, DecodeTimings)> {
|
||||
let request = tonic::Request::new(DecodeRequest { batches }).inject_context();
|
||||
let response = self.stub.decode(request).await?.into_inner();
|
||||
Ok((
|
||||
response.generations,
|
||||
response.batch,
|
||||
DecodeTimings::new(
|
||||
response.concat_ns,
|
||||
response.forward_ns,
|
||||
response.decode_ns,
|
||||
response.total_ns,
|
||||
),
|
||||
))
|
||||
}
|
||||
}
|
||||
|
||||
pub struct PrefillTimings {
|
||||
pub forward: Duration,
|
||||
pub decode: Duration,
|
||||
pub total: Duration,
|
||||
}
|
||||
|
||||
impl PrefillTimings {
|
||||
fn new(forward_ns: u64, decode_ns: u64, total_ns: u64) -> Self {
|
||||
Self {
|
||||
forward: Duration::from_nanos(forward_ns),
|
||||
decode: Duration::from_nanos(decode_ns),
|
||||
total: Duration::from_nanos(total_ns),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub struct DecodeTimings {
|
||||
pub concat: Option<Duration>,
|
||||
pub forward: Duration,
|
||||
pub decode: Duration,
|
||||
pub total: Duration,
|
||||
}
|
||||
|
||||
impl DecodeTimings {
|
||||
fn new(concat_ns: Option<u64>, forward_ns: u64, decode_ns: u64, total_ns: u64) -> Self {
|
||||
Self {
|
||||
concat: concat_ns.map(Duration::from_nanos),
|
||||
forward: Duration::from_nanos(forward_ns),
|
||||
decode: Duration::from_nanos(decode_ns),
|
||||
total: Duration::from_nanos(total_ns),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
#[allow(clippy::derive_partial_eq_without_eq)]
|
||||
mod pb;
|
||||
|
||||
mod client;
|
||||
mod sharded_client;
|
||||
|
||||
pub use client::Client;
|
||||
pub use pb::generate::v2::HealthResponse;
|
||||
pub use pb::generate::v2::{
|
||||
Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType, InfoResponse,
|
||||
NextTokenChooserParameters, Request, StoppingCriteriaParameters, Tokens,
|
||||
};
|
||||
pub use sharded_client::ShardedClient;
|
||||
|
|
@ -0,0 +1 @@
|
|||
*
|
||||
|
|
@ -1,10 +1,17 @@
|
|||
use crate::client::{DecodeTimings, PrefillTimings};
|
||||
/// Multi shard Client
|
||||
use crate::{Batch, CachedBatch, Client, Generation, HealthResponse, ShardInfo};
|
||||
use crate::{v2, Health, ShardInfo};
|
||||
use crate::{ClientError, Result};
|
||||
|
||||
use crate::v2::InfoResponse;
|
||||
use async_trait::async_trait;
|
||||
use futures::future::join_all;
|
||||
use tonic::transport::Uri;
|
||||
use tracing::instrument;
|
||||
use v2::client::{DecodeTimings, PrefillTimings};
|
||||
use v2::{
|
||||
Batch, CachedBatch, Client, Generation, GrammarType, HealthResponse,
|
||||
NextTokenChooserParameters, Request, StoppingCriteriaParameters,
|
||||
};
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
/// Text Generation Inference gRPC multi client
|
||||
|
|
@ -47,7 +54,7 @@ impl ShardedClient {
|
|||
.iter_mut()
|
||||
.map(|client| client.info())
|
||||
.collect();
|
||||
join_all(futures).await.pop().unwrap()
|
||||
join_all(futures).await.pop().unwrap().map(ShardInfo::from)
|
||||
}
|
||||
|
||||
/// GRPC health check
|
||||
|
|
@ -185,3 +192,60 @@ impl ShardedClient {
|
|||
Ok((generations, next_batch, timings))
|
||||
}
|
||||
}
|
||||
|
||||
impl From<InfoResponse> for ShardInfo {
|
||||
fn from(value: InfoResponse) -> Self {
|
||||
Self {
|
||||
requires_padding: value.requires_padding,
|
||||
dtype: value.dtype,
|
||||
device_type: value.device_type,
|
||||
window_size: value.window_size,
|
||||
speculate: value.speculate,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[async_trait]
|
||||
impl Health for ShardedClient {
|
||||
async fn device_health(&self) -> Result<()> {
|
||||
self.clone().health().await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
async fn model_health(&self) -> Result<()> {
|
||||
// Dummy batch of 1 token and 1 generated token
|
||||
let liveness_request = Request {
|
||||
id: u64::MAX,
|
||||
inputs: "liveness".to_string(),
|
||||
truncate: 10,
|
||||
prefill_logprobs: false,
|
||||
parameters: Some(NextTokenChooserParameters {
|
||||
temperature: 1.0,
|
||||
top_k: 0,
|
||||
top_p: 1.0,
|
||||
typical_p: 1.0,
|
||||
do_sample: false,
|
||||
seed: 0,
|
||||
repetition_penalty: 1.0,
|
||||
frequency_penalty: 0.0,
|
||||
watermark: false,
|
||||
grammar: String::new(),
|
||||
grammar_type: GrammarType::None as i32,
|
||||
}),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters {
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
ignore_eos_token: false,
|
||||
}),
|
||||
top_n_tokens: 0,
|
||||
};
|
||||
let batch = Batch {
|
||||
id: u64::MAX,
|
||||
requests: vec![liveness_request],
|
||||
size: 1,
|
||||
max_tokens: 2,
|
||||
};
|
||||
self.clone().prefill(batch).await?;
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
@ -1,17 +1,16 @@
|
|||
use crate::v3::{pb, Chunk};
|
||||
use crate::{ClientError, Result, WARMUP_IMAGE_BASE64};
|
||||
/// Single shard Client
|
||||
use crate::pb::generate::v2::text_generation_service_client::TextGenerationServiceClient;
|
||||
use crate::pb::generate::v2::*;
|
||||
use crate::{Chunk, Result};
|
||||
use base64::engine::general_purpose::STANDARD;
|
||||
use base64::Engine;
|
||||
use grpc_metadata::InjectTelemetryContext;
|
||||
use pb::generate::v3::text_generation_service_client::TextGenerationServiceClient;
|
||||
use pb::generate::v3::*;
|
||||
use std::cmp::min;
|
||||
use std::time::Duration;
|
||||
use tonic::transport::{Channel, Uri};
|
||||
use tracing::instrument;
|
||||
|
||||
static WARMUP_IMAGE_BASE64 :&str = "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";
|
||||
|
||||
/// Text Generation Inference gRPC client
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Client {
|
||||
|
|
@ -46,7 +45,9 @@ impl Client {
|
|||
#[instrument(skip(self))]
|
||||
pub async fn service_discovery(&mut self) -> Result<Vec<String>> {
|
||||
let request = tonic::Request::new(ServiceDiscoveryRequest {}).inject_context();
|
||||
let response = self.stub.service_discovery(request).await?;
|
||||
let response = self.stub.service_discovery(request).await.map_err(|_| {
|
||||
ClientError::Connection("Server does not support v3 interface".to_string())
|
||||
})?;
|
||||
let urls = response
|
||||
.into_inner()
|
||||
.urls
|
||||
|
|
@ -133,6 +134,7 @@ impl Client {
|
|||
|
||||
// Send stringly-typed inputs for compatibility for backends that haven't
|
||||
// been updated to support chunks.
|
||||
|
||||
let mut inputs = String::new();
|
||||
inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize));
|
||||
if n_tokens == 0 {
|
||||
|
|
@ -145,10 +147,10 @@ impl Client {
|
|||
|
||||
requests.push(Request {
|
||||
id: 0,
|
||||
inputs,
|
||||
input_chunks: Some(Input {
|
||||
chunks: input_chunks,
|
||||
}),
|
||||
inputs,
|
||||
// We truncate the input on the server side to be sure that it has the correct size
|
||||
truncate,
|
||||
// Set sampling parameters to also take these ops into account in the max memory
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
#[allow(clippy::derive_partial_eq_without_eq)]
|
||||
mod pb;
|
||||
|
||||
mod client;
|
||||
mod sharded_client;
|
||||
|
||||
pub use client::Client;
|
||||
pub use pb::generate::v3::{
|
||||
input_chunk::Chunk, Batch, CachedBatch, FinishReason, GeneratedText, Generation, GrammarType,
|
||||
HealthResponse, Image, InfoResponse, Input, InputChunk, NextTokenChooserParameters, Request,
|
||||
StoppingCriteriaParameters, Tokens,
|
||||
};
|
||||
pub use sharded_client::ShardedClient;
|
||||
|
|
@ -0,0 +1 @@
|
|||
*
|
||||
|
|
@ -0,0 +1,254 @@
|
|||
/// Multi shard Client
|
||||
use crate::{v3, Health, ShardInfo};
|
||||
use crate::{ClientError, Result};
|
||||
|
||||
use crate::v3::{Chunk, InfoResponse, Input};
|
||||
use async_trait::async_trait;
|
||||
use futures::future::join_all;
|
||||
use tonic::transport::Uri;
|
||||
use tracing::instrument;
|
||||
use v3::client::{DecodeTimings, PrefillTimings};
|
||||
use v3::{
|
||||
Batch, CachedBatch, Client, Generation, GrammarType, HealthResponse,
|
||||
NextTokenChooserParameters, Request, StoppingCriteriaParameters,
|
||||
};
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
/// Text Generation Inference gRPC multi client
|
||||
pub struct ShardedClient {
|
||||
clients: Vec<Client>,
|
||||
}
|
||||
|
||||
impl ShardedClient {
|
||||
fn new(clients: Vec<Client>) -> Self {
|
||||
Self { clients }
|
||||
}
|
||||
|
||||
/// Create a new ShardedClient from a master client. The master client will communicate with
|
||||
/// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
|
||||
async fn from_master_client(mut master_client: Client) -> Result<Self> {
|
||||
// Get all uris/unix sockets from the master client
|
||||
let uris = master_client.service_discovery().await?;
|
||||
let futures = uris.into_iter().map(Client::connect_uds);
|
||||
let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
|
||||
Ok(Self::new(clients?))
|
||||
}
|
||||
|
||||
/// Returns a client connected to the given uri
|
||||
pub async fn connect(uri: Uri) -> Result<Self> {
|
||||
let master_client = Client::connect(uri).await?;
|
||||
Self::from_master_client(master_client).await
|
||||
}
|
||||
|
||||
/// Returns a client connected to the given unix socket
|
||||
pub async fn connect_uds(path: String) -> Result<Self> {
|
||||
let master_client = Client::connect_uds(path).await?;
|
||||
Self::from_master_client(master_client).await
|
||||
}
|
||||
|
||||
/// Get the model info
|
||||
#[instrument(skip(self))]
|
||||
pub async fn info(&mut self) -> Result<ShardInfo> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| client.info())
|
||||
.collect();
|
||||
join_all(futures).await.pop().unwrap().map(ShardInfo::from)
|
||||
}
|
||||
|
||||
/// GRPC health check
|
||||
#[instrument(skip(self))]
|
||||
pub async fn health(&mut self) -> Result<HealthResponse> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| client.health())
|
||||
.collect();
|
||||
join_all(futures).await.pop().unwrap()
|
||||
}
|
||||
|
||||
/// Clear the past generations cache
|
||||
#[instrument(skip(self))]
|
||||
pub async fn clear_cache(&mut self, batch_id: Option<u64>) -> Result<()> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| client.clear_cache(batch_id))
|
||||
.collect();
|
||||
join_all(futures).await.into_iter().collect()
|
||||
}
|
||||
|
||||
/// Filter a cached batch
|
||||
#[instrument(skip(self))]
|
||||
pub async fn filter_batch(
|
||||
&mut self,
|
||||
batch_id: u64,
|
||||
request_ids: Vec<u64>,
|
||||
) -> Result<Option<CachedBatch>> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| Box::pin(client.filter_batch(batch_id, request_ids.clone())))
|
||||
.collect();
|
||||
// all shards return the same message
|
||||
join_all(futures).await.pop().unwrap()
|
||||
}
|
||||
|
||||
/// Warmup on a max size batch
|
||||
///
|
||||
/// Returns the maximum amount of tokens supported by the hardware
|
||||
#[instrument(skip(self))]
|
||||
pub async fn warmup(
|
||||
&mut self,
|
||||
max_input_length: u32,
|
||||
max_prefill_tokens: u32,
|
||||
max_total_tokens: u32,
|
||||
max_batch_size: Option<usize>,
|
||||
) -> Result<Option<u32>> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| {
|
||||
Box::pin(client.warmup(
|
||||
max_input_length,
|
||||
max_prefill_tokens,
|
||||
max_total_tokens,
|
||||
max_batch_size,
|
||||
))
|
||||
})
|
||||
.collect();
|
||||
// Take the minimum value
|
||||
let results = join_all(futures)
|
||||
.await
|
||||
.into_iter()
|
||||
.collect::<Result<Vec<Option<u32>>>>()?;
|
||||
Ok(results.into_iter().flatten().min())
|
||||
}
|
||||
|
||||
/// Generate one token for each request in the given batch
|
||||
///
|
||||
/// Returns Generation for each request in batch
|
||||
/// and the next cached batch
|
||||
#[instrument(skip_all, fields(id = & batch.id, size = & batch.size))]
|
||||
pub async fn prefill(
|
||||
&mut self,
|
||||
batch: Batch,
|
||||
) -> Result<(Vec<Generation>, Option<CachedBatch>, PrefillTimings)> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| Box::pin(client.prefill(batch.clone())))
|
||||
.collect();
|
||||
#[allow(clippy::type_complexity)]
|
||||
let results: Result<Vec<(Vec<Generation>, Option<CachedBatch>, PrefillTimings)>> =
|
||||
join_all(futures).await.into_iter().collect();
|
||||
let mut results = results?;
|
||||
|
||||
let (mut generations, next_batch, mut timings) =
|
||||
results.pop().ok_or(ClientError::EmptyResults)?;
|
||||
|
||||
// Merge generations from different model shards
|
||||
for (mut shard_generations, _, shard_timings) in results.into_iter() {
|
||||
generations.append(&mut shard_generations);
|
||||
// Return the timings of the slowest shard
|
||||
if shard_timings.total > timings.total {
|
||||
timings = shard_timings;
|
||||
}
|
||||
}
|
||||
Ok((generations, next_batch, timings))
|
||||
}
|
||||
|
||||
/// Generate one token for each request in the given cached batches
|
||||
///
|
||||
/// Returns Generation for each request in batches
|
||||
/// and the next cached batch
|
||||
#[instrument(skip_all, fields(size = batches.iter().map(| batch | {batch.size}).sum::< u32 > ()))]
|
||||
pub async fn decode(
|
||||
&mut self,
|
||||
batches: Vec<CachedBatch>,
|
||||
) -> Result<(Vec<Generation>, Option<CachedBatch>, DecodeTimings)> {
|
||||
let futures: Vec<_> = self
|
||||
.clients
|
||||
.iter_mut()
|
||||
.map(|client| Box::pin(client.decode(batches.clone())))
|
||||
.collect();
|
||||
#[allow(clippy::type_complexity)]
|
||||
let results: Result<Vec<(Vec<Generation>, Option<CachedBatch>, DecodeTimings)>> =
|
||||
join_all(futures).await.into_iter().collect();
|
||||
let mut results = results?;
|
||||
|
||||
let (mut generations, next_batch, mut timings) =
|
||||
results.pop().ok_or(ClientError::EmptyResults)?;
|
||||
|
||||
// Merge generations from different model shards
|
||||
for (mut shard_generations, _, shard_timings) in results.into_iter() {
|
||||
generations.append(&mut shard_generations);
|
||||
// Return the timings of the slowest shard
|
||||
if shard_timings.total > timings.total {
|
||||
timings = shard_timings;
|
||||
}
|
||||
}
|
||||
Ok((generations, next_batch, timings))
|
||||
}
|
||||
}
|
||||
|
||||
impl From<InfoResponse> for ShardInfo {
|
||||
fn from(value: InfoResponse) -> Self {
|
||||
Self {
|
||||
requires_padding: value.requires_padding,
|
||||
dtype: value.dtype,
|
||||
device_type: value.device_type,
|
||||
window_size: value.window_size,
|
||||
speculate: value.speculate,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[async_trait]
|
||||
impl Health for ShardedClient {
|
||||
async fn device_health(&self) -> Result<()> {
|
||||
self.clone().health().await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
async fn model_health(&self) -> Result<()> {
|
||||
// Dummy batch of 1 token and 1 generated token
|
||||
let liveness_request = Request {
|
||||
id: u64::MAX,
|
||||
inputs: "liveness".to_string(),
|
||||
input_chunks: Some(Input {
|
||||
chunks: vec![Chunk::Text("liveness".into()).into()],
|
||||
}),
|
||||
truncate: 10,
|
||||
prefill_logprobs: false,
|
||||
parameters: Some(NextTokenChooserParameters {
|
||||
temperature: 1.0,
|
||||
top_k: 0,
|
||||
top_p: 1.0,
|
||||
typical_p: 1.0,
|
||||
do_sample: false,
|
||||
seed: 0,
|
||||
repetition_penalty: 1.0,
|
||||
frequency_penalty: 0.0,
|
||||
watermark: false,
|
||||
grammar: String::new(),
|
||||
grammar_type: GrammarType::None as i32,
|
||||
}),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters {
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
ignore_eos_token: false,
|
||||
}),
|
||||
top_n_tokens: 0,
|
||||
};
|
||||
let batch = Batch {
|
||||
id: u64::MAX,
|
||||
requests: vec![liveness_request],
|
||||
size: 1,
|
||||
max_tokens: 2,
|
||||
};
|
||||
self.clone().prefill(batch).await?;
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
use std::sync::atomic::{AtomicBool, Ordering};
|
||||
use std::sync::Arc;
|
||||
use text_generation_client::{
|
||||
Batch, Input, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
|
||||
};
|
||||
use text_generation_client::{Chunk, GrammarType as ProtoGrammarType};
|
||||
|
||||
// Note: Request ids and batch ids cannot collide.
|
||||
const LIVENESS_ID: u64 = u64::MAX;
|
||||
const BATCH_ID: u64 = u64::MAX;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub(crate) struct Health {
|
||||
client: ShardedClient,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
}
|
||||
|
||||
impl Health {
|
||||
pub(crate) fn new(client: ShardedClient, generation_health: Arc<AtomicBool>) -> Self {
|
||||
Self {
|
||||
client,
|
||||
generation_health,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) async fn check(&mut self) -> bool {
|
||||
if self.generation_health.load(Ordering::SeqCst) {
|
||||
// Generation is healthy, we only check that the shards are answering gRPC calls
|
||||
self.client.health().await.is_ok()
|
||||
} else {
|
||||
// Generation is unhealthy or have not sent any generation request yet
|
||||
|
||||
// Dummy batch of 1 token and 1 generated token
|
||||
let liveness_request = Request {
|
||||
id: LIVENESS_ID,
|
||||
input_chunks: Some(Input {
|
||||
chunks: vec![Chunk::Text("liveness".into()).into()],
|
||||
}),
|
||||
inputs: "liveness".to_string(),
|
||||
truncate: 10,
|
||||
prefill_logprobs: false,
|
||||
parameters: Some(NextTokenChooserParameters {
|
||||
temperature: 1.0,
|
||||
top_k: 0,
|
||||
top_p: 1.0,
|
||||
typical_p: 1.0,
|
||||
do_sample: false,
|
||||
seed: 0,
|
||||
repetition_penalty: 1.0,
|
||||
frequency_penalty: 0.0,
|
||||
watermark: false,
|
||||
grammar: String::new(),
|
||||
grammar_type: ProtoGrammarType::None as i32,
|
||||
}),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters {
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
ignore_eos_token: false,
|
||||
}),
|
||||
top_n_tokens: 0,
|
||||
};
|
||||
let batch = Batch {
|
||||
id: BATCH_ID,
|
||||
requests: vec![liveness_request],
|
||||
size: 1,
|
||||
max_tokens: 2,
|
||||
};
|
||||
// Skips the queue
|
||||
let value = self.client.prefill(batch).await.is_ok();
|
||||
// Update generation health
|
||||
self.generation_health.store(value, Ordering::SeqCst);
|
||||
value
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
use std::sync::atomic::{AtomicBool, Ordering};
|
||||
use std::sync::Arc;
|
||||
use text_generation_client::Health;
|
||||
|
||||
#[derive(Clone)]
|
||||
pub(crate) struct HealthCheck {
|
||||
client: Arc<dyn Health + Send + Sync>,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
}
|
||||
|
||||
impl HealthCheck {
|
||||
pub(crate) fn new(
|
||||
client: Arc<dyn Health + Send + Sync>,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
) -> Self {
|
||||
Self {
|
||||
client,
|
||||
generation_health,
|
||||
}
|
||||
}
|
||||
|
||||
pub(crate) async fn check(&mut self) -> bool {
|
||||
let value = if self.generation_health.load(Ordering::SeqCst) {
|
||||
// Generation is healthy, we only check that the shards can allocate on device
|
||||
self.client.device_health().await
|
||||
} else {
|
||||
self.client.model_health().await
|
||||
}
|
||||
.is_ok();
|
||||
// Update generation health
|
||||
self.generation_health.store(value, Ordering::SeqCst);
|
||||
value
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,522 @@
|
|||
mod health;
|
||||
pub(crate) mod v2;
|
||||
pub(crate) mod v3;
|
||||
|
||||
pub(crate) use health::HealthCheck;
|
||||
|
||||
use crate::validation::{ValidGenerateRequest, Validation, ValidationError};
|
||||
use crate::{
|
||||
ChatTemplateInputs, ChatTemplateVersions, FinishReason, GenerateRequest, HubProcessorConfig,
|
||||
HubTokenizerConfig, Message, MessageChunk, PrefillToken, Text, TextMessage, Token,
|
||||
};
|
||||
use crate::{FunctionRef, FunctionsMap, GrammarType, Properties, Tool, ToolType, Tools};
|
||||
use futures::future::try_join_all;
|
||||
use minijinja::{Environment, ErrorKind, Template};
|
||||
use serde_json::{json, Map, Value};
|
||||
use std::collections::HashMap;
|
||||
use std::sync::Arc;
|
||||
use thiserror::Error;
|
||||
use tokio::sync::{OwnedSemaphorePermit, Semaphore, TryAcquireError};
|
||||
use tokio::time::Instant;
|
||||
use tokio_stream::wrappers::UnboundedReceiverStream;
|
||||
use tokio_stream::StreamExt;
|
||||
use tracing::instrument;
|
||||
|
||||
pub(crate) trait Scheduler {
|
||||
fn schedule(
|
||||
&self,
|
||||
request: ValidGenerateRequest,
|
||||
permit: OwnedSemaphorePermit,
|
||||
) -> Result<GenerateStreamResponse, InferError>;
|
||||
}
|
||||
|
||||
/// Inference struct
|
||||
#[derive(Clone)]
|
||||
pub struct Infer {
|
||||
/// Validation
|
||||
validation: Validation,
|
||||
/// Request scheduler
|
||||
scheduler: Arc<dyn Scheduler + Send + Sync>,
|
||||
/// Chat template
|
||||
chat_template: Option<ChatTemplate>,
|
||||
/// Inference limit
|
||||
limit_concurrent_requests: Arc<Semaphore>,
|
||||
}
|
||||
|
||||
impl Infer {
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub(crate) fn new(
|
||||
scheduler: Arc<dyn Scheduler + Send + Sync>,
|
||||
validation: Validation,
|
||||
max_concurrent_requests: usize,
|
||||
tokenizer_config: HubTokenizerConfig,
|
||||
processor_config: HubProcessorConfig,
|
||||
) -> Self {
|
||||
let chat_template = tokenizer_config
|
||||
.chat_template
|
||||
.or(processor_config.chat_template)
|
||||
.and_then(|t| match t {
|
||||
ChatTemplateVersions::Single(template) => Some(template),
|
||||
ChatTemplateVersions::Multiple(templates) => templates
|
||||
.into_iter()
|
||||
.find(|t| t.name == "default")
|
||||
.map(|t| t.template),
|
||||
})
|
||||
.map(|t| {
|
||||
// .strip() is not supported in minijinja
|
||||
// .capitalize() is not supported in minijinja but we can use | capitalize
|
||||
let t = t
|
||||
.replace(".strip()", " | trim")
|
||||
.replace(".capitalize()", " | capitalize");
|
||||
ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token)
|
||||
});
|
||||
|
||||
// Inference limit with a semaphore
|
||||
let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
|
||||
|
||||
Self {
|
||||
validation,
|
||||
scheduler,
|
||||
chat_template,
|
||||
limit_concurrent_requests: semaphore,
|
||||
}
|
||||
}
|
||||
|
||||
/// Add a new request to the queue and return a stream of InferStreamResponse
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn generate_stream(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<GenerateStreamResponse, InferError> {
|
||||
// Limit concurrent requests by acquiring a permit from the semaphore
|
||||
let permit = self
|
||||
.clone()
|
||||
.limit_concurrent_requests
|
||||
.try_acquire_owned()
|
||||
.map_err(|err| {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
|
||||
tracing::error!("{err}");
|
||||
err
|
||||
})?;
|
||||
|
||||
// Validate request
|
||||
let valid_request = self.validation.validate(request).await.map_err(|err| {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "validation");
|
||||
tracing::error!("{err}");
|
||||
err
|
||||
})?;
|
||||
|
||||
self.scheduler.schedule(valid_request, permit)
|
||||
}
|
||||
|
||||
/// Tokenizer the input
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn tokenize(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<Option<tokenizers::Encoding>, InferError> {
|
||||
// Tokenize request
|
||||
let inputs = request.inputs;
|
||||
let truncate = request.parameters.truncate;
|
||||
let encoding = self
|
||||
.validation
|
||||
.tokenize(inputs, truncate)
|
||||
.await
|
||||
.map_err(|err| {
|
||||
tracing::error!("Tokenization {err}");
|
||||
err
|
||||
})?;
|
||||
|
||||
// Return Encoding
|
||||
Ok(encoding.map(|(encoding, _)| encoding))
|
||||
}
|
||||
|
||||
/// Apply the chat template to the chat request
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) fn apply_chat_template(
|
||||
&self,
|
||||
messages: Vec<Message>,
|
||||
grammar_with_prompt: Option<(GrammarType, String)>,
|
||||
) -> Result<String, InferError> {
|
||||
self.chat_template
|
||||
.as_ref()
|
||||
.ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
|
||||
.apply(messages, grammar_with_prompt)
|
||||
.map_err(|e| {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "template");
|
||||
tracing::error!("{e}");
|
||||
e
|
||||
})
|
||||
}
|
||||
|
||||
/// Add a new request to the queue and return a InferResponse
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn generate(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<InferResponse, InferError> {
|
||||
let use_top_tokens = request.parameters.top_n_tokens.is_some_and(|x| x > 0);
|
||||
|
||||
// Create stream and keep semaphore permit as long as generate lives
|
||||
let (_permit, _input_length, mut stream) = self.generate_stream(request).await?;
|
||||
|
||||
// Return values
|
||||
let mut result_prefill = Vec::new();
|
||||
let mut result_tokens = Vec::new();
|
||||
let mut result_top_tokens = Vec::new();
|
||||
let mut result_generated_text = None;
|
||||
let mut result_start = None;
|
||||
let mut result_queued = None;
|
||||
|
||||
// Iterate on stream
|
||||
while let Some(response) = stream.next().await {
|
||||
match response? {
|
||||
// Add prefill tokens
|
||||
InferStreamResponse::Prefill(prefill_tokens) => {
|
||||
result_prefill = prefill_tokens;
|
||||
}
|
||||
// Push last token
|
||||
InferStreamResponse::Intermediate { token, top_tokens } => {
|
||||
result_tokens.push(token);
|
||||
result_top_tokens.push(top_tokens);
|
||||
}
|
||||
// Final message
|
||||
// Set return values
|
||||
InferStreamResponse::End {
|
||||
token,
|
||||
generated_text,
|
||||
start,
|
||||
queued,
|
||||
top_tokens,
|
||||
} => {
|
||||
result_tokens.push(token);
|
||||
result_top_tokens.push(top_tokens);
|
||||
result_generated_text = Some(generated_text);
|
||||
result_start = Some(start);
|
||||
result_queued = Some(queued)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check that we received a `InferStreamResponse::End` message
|
||||
if let (Some(generated_text), Some(queued), Some(start)) =
|
||||
(result_generated_text, result_queued, result_start)
|
||||
{
|
||||
Ok(InferResponse {
|
||||
prefill: result_prefill,
|
||||
_input_length,
|
||||
tokens: result_tokens,
|
||||
generated_text,
|
||||
queued,
|
||||
start,
|
||||
top_tokens: if use_top_tokens {
|
||||
result_top_tokens
|
||||
} else {
|
||||
Vec::new()
|
||||
},
|
||||
})
|
||||
} else {
|
||||
let err = InferError::IncompleteGeneration;
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
|
||||
tracing::error!("{err}");
|
||||
Err(err)
|
||||
}
|
||||
}
|
||||
/// Add best_of new requests to the queue and return a InferResponse of the sequence with
|
||||
/// the highest log probability per token
|
||||
#[instrument(skip(self, request))]
|
||||
pub(crate) async fn generate_best_of(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
best_of: usize,
|
||||
) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
|
||||
// validate best_of parameter separately
|
||||
let best_of = self.validation.validate_best_of(best_of)?;
|
||||
|
||||
// create multiple generate requests
|
||||
let mut infer_responses: Vec<InferResponse> =
|
||||
try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
|
||||
|
||||
// get the sequence with the highest log probability per token
|
||||
let mut max_index = 0;
|
||||
let mut max_logprob: f32 = f32::MIN;
|
||||
|
||||
for (i, response) in infer_responses.iter().enumerate() {
|
||||
// mean logprobs of the generated tokens
|
||||
let sequence_logprob = response
|
||||
.tokens
|
||||
.iter()
|
||||
.map(|token| token.logprob)
|
||||
.sum::<f32>()
|
||||
/ response.tokens.len() as f32;
|
||||
|
||||
// set best sequence
|
||||
if sequence_logprob > max_logprob {
|
||||
max_index = i;
|
||||
max_logprob = sequence_logprob;
|
||||
}
|
||||
}
|
||||
let best_response = infer_responses.remove(max_index);
|
||||
Ok((best_response, infer_responses))
|
||||
}
|
||||
}
|
||||
|
||||
/// Raise a exception (custom function) used in the chat templates
|
||||
fn raise_exception(err_text: String) -> Result<String, minijinja::Error> {
|
||||
Err(minijinja::Error::new(ErrorKind::SyntaxError, err_text))
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
struct ChatTemplate {
|
||||
template: Template<'static, 'static>,
|
||||
bos_token: Option<String>,
|
||||
eos_token: Option<String>,
|
||||
use_default_tool_template: bool,
|
||||
}
|
||||
|
||||
impl ChatTemplate {
|
||||
fn new(template: String, bos_token: Option<String>, eos_token: Option<String>) -> Self {
|
||||
let mut env = Box::new(Environment::new());
|
||||
let template_str = template.into_boxed_str();
|
||||
env.add_function("raise_exception", raise_exception);
|
||||
|
||||
// check if contains the tools variable within the template
|
||||
let use_default_tool_template =
|
||||
!template_str.as_ref().replace(' ', "").contains("{{tools}}");
|
||||
// leaking env and template_str as read-only, static resources for performance.
|
||||
let template = Box::leak(env)
|
||||
.template_from_str(Box::leak(template_str))
|
||||
.unwrap();
|
||||
|
||||
Self {
|
||||
template,
|
||||
bos_token,
|
||||
eos_token,
|
||||
use_default_tool_template,
|
||||
}
|
||||
}
|
||||
|
||||
fn apply(
|
||||
&self,
|
||||
mut messages: Vec<Message>,
|
||||
grammar_with_prompt: Option<(GrammarType, String)>,
|
||||
) -> Result<String, InferError> {
|
||||
if self.use_default_tool_template {
|
||||
if let Some(last_message) = messages.last_mut() {
|
||||
if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt {
|
||||
last_message.content.push(MessageChunk::Text(Text {
|
||||
text: format!("\n---\n{}\n{}", tool_prompt, tools),
|
||||
}));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
|
||||
|
||||
self.template
|
||||
.render(ChatTemplateInputs {
|
||||
messages,
|
||||
bos_token: self.bos_token.as_deref(),
|
||||
eos_token: self.eos_token.as_deref(),
|
||||
add_generation_prompt: true,
|
||||
tools: None,
|
||||
tools_prompt: None,
|
||||
})
|
||||
.map_err(InferError::TemplateError)
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ToolGrammar {}
|
||||
|
||||
impl ToolGrammar {
|
||||
pub fn apply(
|
||||
tools: Option<Vec<Tool>>,
|
||||
tool_choice: Option<ToolType>,
|
||||
) -> Result<Option<Tools>, InferError> {
|
||||
if let Some((req_tools, tool_choice)) = tools.zip(tool_choice) {
|
||||
// let tool_prompt = tool_prompt.unwrap_or_default();
|
||||
let tools_to_use = match tool_choice {
|
||||
ToolType::FunctionName(name) => {
|
||||
vec![req_tools
|
||||
.iter()
|
||||
.find(|tool| tool.function.name == *name)
|
||||
.unwrap_or_else(|| panic!("Tool with name {} not found", name))
|
||||
.clone()]
|
||||
}
|
||||
ToolType::OneOf => req_tools.to_owned(),
|
||||
};
|
||||
|
||||
// adds the error notification function for LLM feedback if required
|
||||
let mut text_response_properties = Map::new();
|
||||
text_response_properties.insert(
|
||||
"error".to_string(),
|
||||
serde_json::json!({
|
||||
"type": "string",
|
||||
"description": "The error or issue to notify"
|
||||
}),
|
||||
);
|
||||
text_response_properties.insert(
|
||||
"_name".to_string(),
|
||||
serde_json::json!({
|
||||
"type": "string",
|
||||
"const": "notify_error"
|
||||
}),
|
||||
);
|
||||
|
||||
let functions: HashMap<String, serde_json::Value> = tools_to_use
|
||||
.iter()
|
||||
.map(|tool| {
|
||||
let func = tool.function.clone();
|
||||
|
||||
// Clone the existing parameters, which are expected to be a JSON object
|
||||
let mut params = if let Value::Object(params) = &func.arguments {
|
||||
params.clone()
|
||||
} else {
|
||||
Map::new()
|
||||
};
|
||||
|
||||
// Insert the function's description at the top level, outside of properties
|
||||
params.insert(
|
||||
"description".to_string(),
|
||||
Value::String(func.description.clone().unwrap_or_default()),
|
||||
);
|
||||
|
||||
// Ensure 'properties' exists and is an object
|
||||
let properties = params
|
||||
.entry("properties".to_string())
|
||||
.or_insert_with(|| json!({}))
|
||||
.as_object_mut()
|
||||
.unwrap();
|
||||
|
||||
// Insert the constant for the function name inside 'properties'
|
||||
properties.insert(
|
||||
"_name".to_string(),
|
||||
json!({
|
||||
"type": "string",
|
||||
"const": func.name.clone(),
|
||||
// "description": "The name of the function"
|
||||
}),
|
||||
);
|
||||
|
||||
// Check if 'required' exists, and it is an array. If not, create an empty array.
|
||||
let required = params
|
||||
.entry("required".to_string())
|
||||
.or_insert_with(|| json!([]))
|
||||
.as_array_mut()
|
||||
.unwrap();
|
||||
|
||||
// Add 'name' to the 'required' array if it is not already present
|
||||
if !required.iter().any(|r| r == "_name") {
|
||||
required.push(json!("_name"));
|
||||
}
|
||||
|
||||
(func.name, Value::Object(params))
|
||||
})
|
||||
.chain([(
|
||||
"notify_error".to_string(),
|
||||
serde_json::json!({
|
||||
"properties": text_response_properties,
|
||||
"required": ["error", "_name"],
|
||||
"type": "object"
|
||||
}),
|
||||
)])
|
||||
.collect();
|
||||
|
||||
let tools = Tools {
|
||||
functions_map: FunctionsMap { functions },
|
||||
properties: Properties {
|
||||
function: tools_to_use
|
||||
.iter()
|
||||
.map(|tool| FunctionRef {
|
||||
ref_path: format!("#/$functions/{}", tool.function.name.clone()),
|
||||
})
|
||||
.chain(std::iter::once(FunctionRef {
|
||||
ref_path: "#/$functions/notify_error".to_string(),
|
||||
}))
|
||||
.collect(),
|
||||
},
|
||||
};
|
||||
|
||||
return Ok(Some(tools));
|
||||
}
|
||||
// Err(InferError::ToolError("No tools provided".to_string()))
|
||||
Ok(None)
|
||||
}
|
||||
}
|
||||
|
||||
/// Type alias for generation responses
|
||||
pub(crate) type GenerateStreamResponse = (
|
||||
OwnedSemaphorePermit,
|
||||
u32, // input_length
|
||||
UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
|
||||
);
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) struct GeneratedText {
|
||||
pub(crate) text: String,
|
||||
pub(crate) generated_tokens: u32,
|
||||
pub(crate) finish_reason: FinishReason,
|
||||
pub(crate) seed: Option<u64>,
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) enum InferStreamResponse {
|
||||
// Optional first message
|
||||
Prefill(Vec<PrefillToken>),
|
||||
// Intermediate messages
|
||||
Intermediate {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
},
|
||||
// Last message
|
||||
End {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
generated_text: GeneratedText,
|
||||
start: Instant,
|
||||
queued: Instant,
|
||||
},
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) struct InferResponse {
|
||||
/// input_length is the input as perceived by the rust tokenizer in the
|
||||
/// validation pathway. It is redundant with prefill.len() but prefill
|
||||
/// has data only if the user asked for it. This will always be filled.
|
||||
pub(crate) _input_length: u32,
|
||||
pub(crate) prefill: Vec<PrefillToken>,
|
||||
pub(crate) tokens: Vec<Token>,
|
||||
pub(crate) generated_text: GeneratedText,
|
||||
pub(crate) queued: Instant,
|
||||
pub(crate) start: Instant,
|
||||
pub(crate) top_tokens: Vec<Vec<Token>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Error)]
|
||||
pub enum InferError {
|
||||
#[error("Request failed during generation: {0}")]
|
||||
GenerationError(String),
|
||||
#[error("Model is overloaded")]
|
||||
Overloaded(#[from] TryAcquireError),
|
||||
#[error("Input validation error: {0}")]
|
||||
ValidationError(#[from] ValidationError),
|
||||
#[error("Incomplete generation")]
|
||||
IncompleteGeneration,
|
||||
#[error("Template error: {0}")]
|
||||
TemplateError(#[from] minijinja::Error),
|
||||
#[error("Tool error: {0}")]
|
||||
ToolError(String),
|
||||
}
|
||||
|
||||
impl InferError {
|
||||
pub(crate) fn error_type(&self) -> &str {
|
||||
match self {
|
||||
InferError::GenerationError(_) => "generation",
|
||||
InferError::Overloaded(_) => "overloaded",
|
||||
InferError::ValidationError(_) => "validation",
|
||||
InferError::IncompleteGeneration => "incomplete_generation",
|
||||
InferError::TemplateError(_) => "template_error",
|
||||
InferError::ToolError(_) => "tool_error",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mod queue;
|
||||
mod scheduler;
|
||||
|
||||
pub(crate) use scheduler::SchedulerV2;
|
||||
|
|
@ -0,0 +1,667 @@
|
|||
use crate::infer::{InferError, InferStreamResponse};
|
||||
use crate::validation::{
|
||||
ValidGenerateRequest, ValidGrammar, ValidParameters, ValidStoppingParameters,
|
||||
};
|
||||
use nohash_hasher::{BuildNoHashHasher, IntMap};
|
||||
use std::cmp::min;
|
||||
use std::collections::VecDeque;
|
||||
use text_generation_client::v2::{
|
||||
Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
|
||||
};
|
||||
use text_generation_client::ChunksToString;
|
||||
use tokio::sync::{mpsc, oneshot};
|
||||
use tokio::time::Instant;
|
||||
use tracing::{info_span, instrument, Span};
|
||||
|
||||
/// Queue entry
|
||||
#[derive(Debug)]
|
||||
pub(crate) struct Entry {
|
||||
/// Request
|
||||
pub request: ValidGenerateRequest,
|
||||
/// Response sender to communicate between the Infer struct and the batching_task
|
||||
pub response_tx: mpsc::UnboundedSender<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...
|
||||
pub temp_span: Option<Span>,
|
||||
/// Instant when this entry was queued
|
||||
pub queue_time: Instant,
|
||||
/// Instant when this entry was added to a batch
|
||||
pub batch_time: Option<Instant>,
|
||||
}
|
||||
|
||||
/// Request Queue
|
||||
#[derive(Debug, Clone)]
|
||||
pub(crate) struct Queue {
|
||||
/// Channel to communicate with the background queue task
|
||||
queue_sender: mpsc::UnboundedSender<QueueCommand>,
|
||||
}
|
||||
|
||||
impl Queue {
|
||||
pub(crate) fn new(
|
||||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
) -> Self {
|
||||
// Create channel
|
||||
let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
|
||||
|
||||
// Launch background queue task
|
||||
tokio::spawn(queue_task(
|
||||
requires_padding,
|
||||
block_size,
|
||||
window_size,
|
||||
speculate,
|
||||
queue_receiver,
|
||||
));
|
||||
|
||||
Self { queue_sender }
|
||||
}
|
||||
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) fn append(&self, entry: Entry) {
|
||||
// Send append command to the background task managing the state
|
||||
// Unwrap is safe here
|
||||
self.queue_sender
|
||||
.send(QueueCommand::Append(Box::new(entry), Span::current()))
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
// Get the next batch
|
||||
#[instrument(skip(self))]
|
||||
pub(crate) async fn next_batch(
|
||||
&self,
|
||||
min_size: Option<usize>,
|
||||
max_size: Option<usize>,
|
||||
prefill_token_budget: u32,
|
||||
token_budget: u32,
|
||||
) -> Option<NextBatch> {
|
||||
// Create response channel
|
||||
let (response_sender, response_receiver) = oneshot::channel();
|
||||
// Send next batch command to the background task managing the state
|
||||
// Unwrap is safe here
|
||||
self.queue_sender
|
||||
.send(QueueCommand::NextBatch {
|
||||
min_size,
|
||||
max_size,
|
||||
prefill_token_budget,
|
||||
token_budget,
|
||||
response_sender,
|
||||
span: Span::current(),
|
||||
})
|
||||
.unwrap();
|
||||
// Await on response channel
|
||||
// Unwrap is safe here
|
||||
response_receiver.await.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
// Background task responsible of the queue state
|
||||
async fn queue_task(
|
||||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
|
||||
) {
|
||||
let mut state = State::new(requires_padding, block_size, window_size, speculate);
|
||||
|
||||
while let Some(cmd) = receiver.recv().await {
|
||||
match cmd {
|
||||
QueueCommand::Append(entry, span) => {
|
||||
span.in_scope(|| state.append(*entry));
|
||||
metrics::increment_gauge!("tgi_queue_size", 1.0);
|
||||
}
|
||||
QueueCommand::NextBatch {
|
||||
min_size,
|
||||
max_size,
|
||||
prefill_token_budget,
|
||||
token_budget,
|
||||
response_sender,
|
||||
span,
|
||||
} => span.in_scope(|| {
|
||||
let next_batch =
|
||||
state.next_batch(min_size, max_size, prefill_token_budget, token_budget);
|
||||
response_sender.send(next_batch).unwrap();
|
||||
metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
|
||||
}),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Queue State
|
||||
#[derive(Debug)]
|
||||
struct State {
|
||||
/// Queue entries organized in a Vec
|
||||
entries: VecDeque<(u64, Entry)>,
|
||||
|
||||
/// Id of the next entry
|
||||
next_id: u64,
|
||||
|
||||
/// Id of the next batch
|
||||
next_batch_id: u64,
|
||||
|
||||
/// Whether the model is using padding
|
||||
requires_padding: bool,
|
||||
|
||||
/// Paged Attention block size
|
||||
block_size: u32,
|
||||
|
||||
/// Sliding window
|
||||
window_size: Option<u32>,
|
||||
|
||||
/// Speculation amount
|
||||
speculate: u32,
|
||||
}
|
||||
|
||||
impl State {
|
||||
fn new(
|
||||
requires_padding: bool,
|
||||
block_size: u32,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
) -> Self {
|
||||
Self {
|
||||
entries: VecDeque::with_capacity(128),
|
||||
next_id: 0,
|
||||
next_batch_id: 0,
|
||||
requires_padding,
|
||||
block_size,
|
||||
window_size,
|
||||
speculate,
|
||||
}
|
||||
}
|
||||
|
||||
/// Append an entry to the queue
|
||||
fn append(&mut self, mut entry: Entry) {
|
||||
// Create a span that will live as long as the entry is in the queue waiting to be batched
|
||||
let queue_span = info_span!(parent: &entry.span, "queued");
|
||||
entry.temp_span = Some(queue_span);
|
||||
|
||||
// Push entry in the queue
|
||||
self.entries.push_back((self.next_id, entry));
|
||||
self.next_id += 1;
|
||||
}
|
||||
|
||||
// Get the next batch
|
||||
fn next_batch(
|
||||
&mut self,
|
||||
min_size: Option<usize>,
|
||||
max_size: Option<usize>,
|
||||
prefill_token_budget: u32,
|
||||
token_budget: u32,
|
||||
) -> Option<NextBatch> {
|
||||
if self.entries.is_empty() {
|
||||
tracing::debug!("No queue");
|
||||
return None;
|
||||
}
|
||||
|
||||
// Check if we have enough entries
|
||||
if let Some(min_size) = min_size {
|
||||
if self.entries.len() < min_size {
|
||||
tracing::debug!("Not enough entries");
|
||||
return None;
|
||||
}
|
||||
}
|
||||
|
||||
// Pad prefill_token_budget to be a multiple of block size
|
||||
let prefill_token_budget =
|
||||
((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size;
|
||||
|
||||
// Create span for this batch to add context to inference calls
|
||||
let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
|
||||
next_batch_span.follows_from(&Span::current());
|
||||
|
||||
let mut batch_requests = Vec::with_capacity(self.entries.len());
|
||||
let mut batch_entries =
|
||||
IntMap::with_capacity_and_hasher(self.entries.len(), BuildNoHashHasher::default());
|
||||
|
||||
let mut max_input_length = 0;
|
||||
let mut prefill_tokens: u32 = 0;
|
||||
let mut decode_tokens: u32 = 0;
|
||||
|
||||
// Pop entries starting from the front of the queue
|
||||
while let Some((id, mut entry)) = self.entries.pop_front() {
|
||||
// Filter entries where the response receiver was dropped (== entries where the request
|
||||
// was dropped by the client)
|
||||
if entry.response_tx.is_closed() {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
||||
tracing::debug!("Dropping entry");
|
||||
continue;
|
||||
}
|
||||
|
||||
if self.requires_padding {
|
||||
// We pad to max input length in the Python shards
|
||||
// We need to take these padding tokens into the equation
|
||||
max_input_length = max_input_length.max(entry.request.input_length);
|
||||
prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
|
||||
} else {
|
||||
// pad to block size
|
||||
prefill_tokens += ((entry.request.input_length + self.block_size - 1)
|
||||
/ self.block_size)
|
||||
* self.block_size;
|
||||
}
|
||||
|
||||
if self.requires_padding {
|
||||
decode_tokens += entry.request.stopping_parameters.max_new_tokens;
|
||||
} else {
|
||||
let max_new_tokens = match self.window_size {
|
||||
None => entry.request.stopping_parameters.max_new_tokens,
|
||||
Some(window_size) => min(
|
||||
window_size.saturating_sub(entry.request.input_length),
|
||||
entry.request.stopping_parameters.max_new_tokens,
|
||||
),
|
||||
};
|
||||
|
||||
// pad to block size
|
||||
decode_tokens +=
|
||||
((max_new_tokens + self.block_size - 1) / self.block_size) * self.block_size;
|
||||
}
|
||||
|
||||
if prefill_tokens > prefill_token_budget
|
||||
|| (prefill_tokens + decode_tokens + self.speculate) > token_budget
|
||||
{
|
||||
// Entry is over budget
|
||||
// Add it back to the front
|
||||
tracing::debug!("Over budget: prefill_tokens={prefill_tokens} > {prefill_token_budget} || {prefill_tokens} + {decode_tokens} + {} > {token_budget}", self.speculate);
|
||||
self.entries.push_front((id, entry));
|
||||
break;
|
||||
}
|
||||
|
||||
tracing::debug!("Accepting entry");
|
||||
// Create a new span to link the batch back to this entry
|
||||
let entry_batch_span = info_span!(parent: &entry.span, "infer");
|
||||
// Add relationships
|
||||
next_batch_span.follows_from(&entry_batch_span);
|
||||
entry_batch_span.follows_from(&next_batch_span);
|
||||
// Update entry
|
||||
entry.temp_span = Some(entry_batch_span);
|
||||
|
||||
batch_requests.push(Request {
|
||||
id,
|
||||
prefill_logprobs: entry.request.decoder_input_details,
|
||||
inputs: entry.request.inputs.chunks_to_string(),
|
||||
truncate: entry.request.truncate,
|
||||
parameters: Some(NextTokenChooserParameters::from(
|
||||
entry.request.parameters.clone(),
|
||||
)),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters::from(
|
||||
entry.request.stopping_parameters.clone(),
|
||||
)),
|
||||
top_n_tokens: entry.request.top_n_tokens,
|
||||
});
|
||||
// Set batch_time
|
||||
entry.batch_time = Some(Instant::now());
|
||||
// Insert in batch_entries IntMap
|
||||
batch_entries.insert(id, entry);
|
||||
|
||||
// Check if max_size
|
||||
if Some(batch_requests.len()) == max_size {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Empty batch
|
||||
if batch_requests.is_empty() {
|
||||
tracing::debug!("Filtered out all entries");
|
||||
return None;
|
||||
}
|
||||
|
||||
// Check if our batch is big enough
|
||||
if let Some(min_size) = min_size {
|
||||
// Batch is too small
|
||||
if batch_requests.len() < min_size {
|
||||
// Add back entries to the queue in the correct order
|
||||
for r in batch_requests.into_iter().rev() {
|
||||
let id = r.id;
|
||||
let entry = batch_entries.remove(&id).unwrap();
|
||||
self.entries.push_front((id, entry));
|
||||
}
|
||||
|
||||
return None;
|
||||
}
|
||||
}
|
||||
|
||||
// Final batch size
|
||||
let size = batch_requests.len() as u32;
|
||||
next_batch_span.record("batch_size", size);
|
||||
|
||||
let batch = Batch {
|
||||
id: self.next_batch_id,
|
||||
requests: batch_requests,
|
||||
size,
|
||||
max_tokens: (prefill_tokens + decode_tokens),
|
||||
};
|
||||
// Increment batch id
|
||||
self.next_batch_id += 1;
|
||||
|
||||
metrics::histogram!("tgi_batch_next_size", batch.size as f64);
|
||||
|
||||
Some((batch_entries, batch, next_batch_span))
|
||||
}
|
||||
}
|
||||
|
||||
type NextBatch = (IntMap<u64, Entry>, Batch, Span);
|
||||
|
||||
#[derive(Debug)]
|
||||
enum QueueCommand {
|
||||
Append(Box<Entry>, Span),
|
||||
NextBatch {
|
||||
min_size: Option<usize>,
|
||||
max_size: Option<usize>,
|
||||
prefill_token_budget: u32,
|
||||
token_budget: u32,
|
||||
response_sender: oneshot::Sender<Option<NextBatch>>,
|
||||
span: Span,
|
||||
},
|
||||
}
|
||||
|
||||
impl From<ValidParameters> for NextTokenChooserParameters {
|
||||
fn from(value: ValidParameters) -> Self {
|
||||
let (grammar, grammar_type) = match value.grammar {
|
||||
None => (String::new(), GrammarType::None),
|
||||
|
||||
Some(grammar) => match grammar {
|
||||
ValidGrammar::Json(grammar_string) => (grammar_string, GrammarType::Json),
|
||||
ValidGrammar::Regex(grammar_string) => (grammar_string, GrammarType::Regex),
|
||||
},
|
||||
};
|
||||
|
||||
Self {
|
||||
temperature: value.temperature,
|
||||
top_k: value.top_k,
|
||||
top_p: value.top_p,
|
||||
typical_p: value.typical_p,
|
||||
do_sample: value.do_sample,
|
||||
seed: value.seed,
|
||||
repetition_penalty: value.repetition_penalty,
|
||||
frequency_penalty: value.frequency_penalty,
|
||||
watermark: value.watermark,
|
||||
grammar,
|
||||
grammar_type: grammar_type.into(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<ValidStoppingParameters> for StoppingCriteriaParameters {
|
||||
fn from(value: ValidStoppingParameters) -> Self {
|
||||
Self {
|
||||
max_new_tokens: value.max_new_tokens,
|
||||
stop_sequences: value.stop_sequences,
|
||||
ignore_eos_token: value.ignore_eos_token,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use tracing::info_span;
|
||||
|
||||
fn default_entry() -> (
|
||||
Entry,
|
||||
mpsc::UnboundedReceiver<Result<InferStreamResponse, InferError>>,
|
||||
) {
|
||||
let (response_tx, receiver_tx) = mpsc::unbounded_channel();
|
||||
|
||||
let entry = Entry {
|
||||
request: ValidGenerateRequest {
|
||||
inputs: vec![],
|
||||
input_length: 0,
|
||||
truncate: 0,
|
||||
decoder_input_details: false,
|
||||
parameters: ValidParameters {
|
||||
temperature: 0.0,
|
||||
top_k: 0,
|
||||
top_p: 0.0,
|
||||
typical_p: 0.0,
|
||||
do_sample: false,
|
||||
seed: 0,
|
||||
repetition_penalty: 0.0,
|
||||
frequency_penalty: 0.0,
|
||||
watermark: false,
|
||||
grammar: None,
|
||||
},
|
||||
stopping_parameters: ValidStoppingParameters {
|
||||
ignore_eos_token: false,
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
},
|
||||
top_n_tokens: 0,
|
||||
},
|
||||
response_tx,
|
||||
span: info_span!("entry"),
|
||||
temp_span: None,
|
||||
queue_time: Instant::now(),
|
||||
batch_time: None,
|
||||
};
|
||||
(entry, receiver_tx)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_append() {
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry, _guard) = default_entry();
|
||||
|
||||
assert_eq!(state.next_id, 0);
|
||||
assert_eq!(state.entries.len(), 0);
|
||||
|
||||
state.append(entry);
|
||||
|
||||
assert_eq!(state.next_id, 1);
|
||||
assert_eq!(state.entries.len(), 1);
|
||||
let (id, _) = state.entries.remove(0).unwrap();
|
||||
assert_eq!(id, 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_next_batch_empty() {
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
|
||||
assert!(state.next_batch(None, None, 1, 1).is_none());
|
||||
assert!(state.next_batch(Some(1), None, 1, 1).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_next_batch_min_size() {
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
state.append(entry1);
|
||||
state.append(entry2);
|
||||
|
||||
let (entries, batch, _) = state.next_batch(None, None, 2, 2).unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.contains_key(&1));
|
||||
assert!(entries.get(&0).unwrap().batch_time.is_some());
|
||||
assert!(entries.get(&1).unwrap().batch_time.is_some());
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 2);
|
||||
|
||||
assert_eq!(state.next_id, 2);
|
||||
assert_eq!(state.entries.len(), 0);
|
||||
assert_eq!(state.next_batch_id, 1);
|
||||
|
||||
let (entry3, _guard3) = default_entry();
|
||||
state.append(entry3);
|
||||
|
||||
assert!(state.next_batch(Some(2), None, 2, 2).is_none());
|
||||
|
||||
assert_eq!(state.next_id, 3);
|
||||
assert_eq!(state.entries.len(), 1);
|
||||
let (id, _) = state.entries.remove(0).unwrap();
|
||||
assert_eq!(id, 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_next_batch_max_size() {
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
state.append(entry1);
|
||||
state.append(entry2);
|
||||
|
||||
let (entries, batch, _) = state.next_batch(None, Some(1), 2, 2).unwrap();
|
||||
assert_eq!(entries.len(), 1);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.get(&0).unwrap().batch_time.is_some());
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 1);
|
||||
|
||||
assert_eq!(state.next_id, 2);
|
||||
assert_eq!(state.entries.len(), 1);
|
||||
assert_eq!(state.next_batch_id, 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_next_batch_token_budget() {
|
||||
let mut state = State::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
state.append(entry1);
|
||||
state.append(entry2);
|
||||
|
||||
let (entries, batch, _) = state.next_batch(None, None, 1, 1).unwrap();
|
||||
assert_eq!(entries.len(), 1);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 1);
|
||||
|
||||
assert_eq!(state.next_id, 2);
|
||||
assert_eq!(state.entries.len(), 1);
|
||||
assert_eq!(state.next_batch_id, 1);
|
||||
|
||||
let (entry3, _guard3) = default_entry();
|
||||
state.append(entry3);
|
||||
|
||||
let (entries, batch, _) = state.next_batch(None, None, 3, 3).unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&1));
|
||||
assert!(entries.contains_key(&2));
|
||||
assert_eq!(batch.id, 1);
|
||||
assert_eq!(batch.size, 2);
|
||||
|
||||
assert_eq!(state.next_id, 3);
|
||||
assert_eq!(state.entries.len(), 0);
|
||||
assert_eq!(state.next_batch_id, 2);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_append() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry, _guard) = default_entry();
|
||||
queue.append(entry);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_empty() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
|
||||
assert!(queue.next_batch(None, None, 1, 1).await.is_none());
|
||||
assert!(queue.next_batch(Some(1), None, 1, 1).await.is_none());
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_min_size() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
queue.append(entry2);
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, None, 2, 2).await.unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.contains_key(&1));
|
||||
assert!(entries.get(&0).unwrap().batch_time.is_some());
|
||||
assert!(entries.get(&1).unwrap().batch_time.is_some());
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 2);
|
||||
|
||||
let (entry3, _guard3) = default_entry();
|
||||
queue.append(entry3);
|
||||
|
||||
// Not enough requests pending
|
||||
assert!(queue.next_batch(Some(2), None, 2, 2).await.is_none());
|
||||
// Not enough token budget
|
||||
assert!(queue.next_batch(Some(1), None, 0, 0).await.is_none());
|
||||
// Ok
|
||||
let (entries2, batch2, _) = queue.next_batch(Some(1), None, 2, 2).await.unwrap();
|
||||
assert_eq!(entries2.len(), 1);
|
||||
assert!(entries2.contains_key(&2));
|
||||
assert!(entries2.get(&2).unwrap().batch_time.is_some());
|
||||
assert_eq!(batch2.id, 1);
|
||||
assert_eq!(batch2.size, 1);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_max_size() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
queue.append(entry2);
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, Some(1), 2, 2).await.unwrap();
|
||||
assert_eq!(entries.len(), 1);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.get(&0).unwrap().batch_time.is_some());
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 1);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_token_budget() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
queue.append(entry2);
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, None, 1, 1).await.unwrap();
|
||||
assert_eq!(entries.len(), 1);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 1);
|
||||
|
||||
let (entry3, _guard3) = default_entry();
|
||||
queue.append(entry3);
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, None, 3, 3).await.unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&1));
|
||||
assert!(entries.contains_key(&2));
|
||||
assert_eq!(batch.id, 1);
|
||||
assert_eq!(batch.size, 2);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_token_speculate() {
|
||||
let queue = Queue::new(false, 1, None, 2);
|
||||
let (entry1, _guard1) = default_entry();
|
||||
let (entry2, _guard2) = default_entry();
|
||||
queue.append(entry1);
|
||||
queue.append(entry2);
|
||||
|
||||
// Budget of 1 is not enough
|
||||
assert!(queue.next_batch(None, None, 1, 1).await.is_none());
|
||||
|
||||
let (entries, batch, _) = queue.next_batch(None, None, 6, 6).await.unwrap();
|
||||
assert_eq!(entries.len(), 2);
|
||||
assert!(entries.contains_key(&0));
|
||||
assert!(entries.contains_key(&1));
|
||||
assert_eq!(batch.id, 0);
|
||||
assert_eq!(batch.size, 2);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_queue_next_batch_dropped_receiver() {
|
||||
let queue = Queue::new(false, 1, None, 0);
|
||||
let (entry, _) = default_entry();
|
||||
queue.append(entry);
|
||||
|
||||
assert!(queue.next_batch(None, None, 1, 1).await.is_none());
|
||||
}
|
||||
}
|
||||
|
|
@ -1,79 +1,46 @@
|
|||
/// Batching and inference logic
|
||||
use crate::validation::{Validation, ValidationError};
|
||||
use crate::{
|
||||
ChatTemplateInputs, ChatTemplateVersions, Entry, GenerateRequest, GenerateStreamResponse,
|
||||
HubProcessorConfig, HubTokenizerConfig, Message, MessageChunk, PrefillToken, Queue, Text,
|
||||
TextMessage, Token,
|
||||
use crate::infer::v2::queue::{Entry, Queue};
|
||||
use crate::infer::{
|
||||
GenerateStreamResponse, GeneratedText, InferError, InferStreamResponse, Scheduler,
|
||||
};
|
||||
use crate::{FunctionRef, FunctionsMap, GrammarType, Properties, Tool, ToolType, Tools};
|
||||
use futures::future::try_join_all;
|
||||
use minijinja::{Environment, ErrorKind, Template};
|
||||
use crate::validation::ValidGenerateRequest;
|
||||
use crate::{FinishReason, PrefillToken, Token};
|
||||
use nohash_hasher::IntMap;
|
||||
use serde_json::{json, Map, Value};
|
||||
use std::collections::HashMap;
|
||||
use std::sync::{
|
||||
atomic::{AtomicBool, Ordering},
|
||||
Arc,
|
||||
};
|
||||
use text_generation_client::{
|
||||
Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
|
||||
};
|
||||
use thiserror::Error;
|
||||
use text_generation_client::v2::{Batch, CachedBatch, Generation, ShardedClient};
|
||||
use text_generation_client::ClientError;
|
||||
use tokio::sync::mpsc::error::SendError;
|
||||
use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError};
|
||||
use tokio::sync::{mpsc, Notify, OwnedSemaphorePermit};
|
||||
use tokio::time::Instant;
|
||||
use tokio_stream::wrappers::UnboundedReceiverStream;
|
||||
use tokio_stream::StreamExt;
|
||||
use tracing::{info_span, instrument, Instrument, Span};
|
||||
|
||||
/// Inference struct
|
||||
#[derive(Clone)]
|
||||
pub struct Infer {
|
||||
/// Validation
|
||||
validation: Validation,
|
||||
pub(crate) struct SchedulerV2 {
|
||||
/// Request queue
|
||||
queue: Queue,
|
||||
/// Shared state
|
||||
shared: Arc<Shared>,
|
||||
/// Chat template
|
||||
chat_template: Option<ChatTemplate>,
|
||||
/// Inference limit
|
||||
limit_concurrent_requests: Arc<Semaphore>,
|
||||
/// Notify batcher on queue appends
|
||||
batching_task_notifier: Arc<Notify>,
|
||||
}
|
||||
|
||||
/// Infer shared state
|
||||
struct Shared {
|
||||
/// Batching background Tokio task notifier
|
||||
batching_task: Notify,
|
||||
}
|
||||
|
||||
/// Raise a exception (custom function) used in the chat templates
|
||||
fn raise_exception(err_text: String) -> Result<String, minijinja::Error> {
|
||||
Err(minijinja::Error::new(ErrorKind::SyntaxError, err_text))
|
||||
}
|
||||
|
||||
impl Infer {
|
||||
impl SchedulerV2 {
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub(crate) fn new(
|
||||
client: ShardedClient,
|
||||
validation: Validation,
|
||||
waiting_served_ratio: f32,
|
||||
max_batch_prefill_tokens: u32,
|
||||
max_batch_total_tokens: u32,
|
||||
max_waiting_tokens: usize,
|
||||
max_batch_size: Option<usize>,
|
||||
max_concurrent_requests: usize,
|
||||
requires_padding: bool,
|
||||
window_size: Option<u32>,
|
||||
speculate: u32,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
tokenizer_config: HubTokenizerConfig,
|
||||
processor_config: HubProcessorConfig,
|
||||
) -> Self {
|
||||
let queue = Queue::new(requires_padding, 16, window_size, speculate);
|
||||
let shared = Arc::new(Shared {
|
||||
batching_task: Notify::new(),
|
||||
});
|
||||
let batching_task_notifier = Arc::new(Notify::new());
|
||||
|
||||
// Spawn batching background task that contains all the inference logic
|
||||
tokio::spawn(batching_task(
|
||||
|
|
@ -84,72 +51,31 @@ impl Infer {
|
|||
max_waiting_tokens,
|
||||
max_batch_size,
|
||||
queue.clone(),
|
||||
shared.clone(),
|
||||
batching_task_notifier.clone(),
|
||||
generation_health,
|
||||
));
|
||||
|
||||
let chat_template = tokenizer_config
|
||||
.chat_template
|
||||
.or(processor_config.chat_template)
|
||||
.and_then(|t| match t {
|
||||
ChatTemplateVersions::Single(template) => Some(template),
|
||||
ChatTemplateVersions::Multiple(templates) => templates
|
||||
.into_iter()
|
||||
.find(|t| t.name == "default")
|
||||
.map(|t| t.template),
|
||||
})
|
||||
.map(|t| {
|
||||
// .strip() is not supported in minijinja
|
||||
// .capitalize() is not supported in minijinja but we can use | capitalize
|
||||
let t = t
|
||||
.replace(".strip()", " | trim")
|
||||
.replace(".capitalize()", " | capitalize");
|
||||
ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token)
|
||||
});
|
||||
|
||||
// Inference limit with a semaphore
|
||||
let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
|
||||
|
||||
Self {
|
||||
validation,
|
||||
queue,
|
||||
shared,
|
||||
chat_template,
|
||||
limit_concurrent_requests: semaphore,
|
||||
batching_task_notifier,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Add a new request to the queue and return a stream of InferStreamResponse
|
||||
impl Scheduler for SchedulerV2 {
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn generate_stream(
|
||||
fn schedule(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
request: ValidGenerateRequest,
|
||||
permit: OwnedSemaphorePermit,
|
||||
) -> Result<GenerateStreamResponse, InferError> {
|
||||
// Limit concurrent requests by acquiring a permit from the semaphore
|
||||
let permit = self
|
||||
.clone()
|
||||
.limit_concurrent_requests
|
||||
.try_acquire_owned()
|
||||
.map_err(|err| {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
|
||||
tracing::error!("{err}");
|
||||
err
|
||||
})?;
|
||||
|
||||
// Validate request
|
||||
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 input_length = valid_request.input_length;
|
||||
let input_length = request.input_length;
|
||||
|
||||
// Append the request to the queue
|
||||
self.queue.append(Entry {
|
||||
request: valid_request,
|
||||
request,
|
||||
response_tx,
|
||||
span: Span::current(),
|
||||
temp_span: None,
|
||||
|
|
@ -159,7 +85,7 @@ impl Infer {
|
|||
|
||||
// Notify the background task that we have a new entry in the queue that needs
|
||||
// to be batched
|
||||
self.shared.batching_task.notify_one();
|
||||
self.batching_task_notifier.notify_one();
|
||||
|
||||
// Return stream
|
||||
Ok((
|
||||
|
|
@ -168,343 +94,6 @@ impl Infer {
|
|||
UnboundedReceiverStream::new(response_rx),
|
||||
))
|
||||
}
|
||||
|
||||
/// Tokenizer the input
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn tokenize(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<Option<tokenizers::Encoding>, InferError> {
|
||||
// Tokenize request
|
||||
let inputs = request.inputs;
|
||||
let truncate = request.parameters.truncate;
|
||||
let encoding = self
|
||||
.validation
|
||||
.tokenize(inputs, truncate)
|
||||
.await
|
||||
.map_err(|err| {
|
||||
tracing::error!("Tokenization {err}");
|
||||
err
|
||||
})?;
|
||||
|
||||
// Return Encoding
|
||||
Ok(encoding.map(|(encoding, _)| encoding))
|
||||
}
|
||||
|
||||
/// Apply the chat template to the chat request
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) fn apply_chat_template(
|
||||
&self,
|
||||
messages: Vec<Message>,
|
||||
grammar_with_prompt: Option<(GrammarType, String)>,
|
||||
) -> Result<String, InferError> {
|
||||
self.chat_template
|
||||
.as_ref()
|
||||
.ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
|
||||
.apply(messages, grammar_with_prompt)
|
||||
.map_err(|e| {
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "template");
|
||||
tracing::error!("{e}");
|
||||
e
|
||||
})
|
||||
}
|
||||
|
||||
/// Add a new request to the queue and return a InferResponse
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) async fn generate(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
) -> Result<InferResponse, InferError> {
|
||||
let use_top_tokens = request.parameters.top_n_tokens.is_some_and(|x| x > 0);
|
||||
|
||||
// Create stream and keep semaphore permit as long as generate lives
|
||||
let (_permit, _input_length, mut stream) = self.generate_stream(request).await?;
|
||||
|
||||
// Return values
|
||||
let mut result_prefill = Vec::new();
|
||||
let mut result_tokens = Vec::new();
|
||||
let mut result_top_tokens = Vec::new();
|
||||
let mut result_generated_text = None;
|
||||
let mut result_start = None;
|
||||
let mut result_queued = None;
|
||||
|
||||
// Iterate on stream
|
||||
while let Some(response) = stream.next().await {
|
||||
match response? {
|
||||
// Add prefill tokens
|
||||
InferStreamResponse::Prefill(tokens) => {
|
||||
// Create Token objects
|
||||
// We do that here instead of in the Python code as Rust for loops are faster
|
||||
result_prefill = tokens
|
||||
.ids
|
||||
.into_iter()
|
||||
.zip(tokens.logprobs.into_iter())
|
||||
.zip(tokens.texts.into_iter())
|
||||
.map(|((id, logprob), text)| PrefillToken { id, text, logprob })
|
||||
.collect();
|
||||
}
|
||||
// Push last token
|
||||
InferStreamResponse::Intermediate { token, top_tokens } => {
|
||||
result_tokens.push(token);
|
||||
result_top_tokens.push(top_tokens);
|
||||
}
|
||||
// Final message
|
||||
// Set return values
|
||||
InferStreamResponse::End {
|
||||
token,
|
||||
generated_text,
|
||||
start,
|
||||
queued,
|
||||
top_tokens,
|
||||
} => {
|
||||
result_tokens.push(token);
|
||||
result_top_tokens.push(top_tokens);
|
||||
result_generated_text = Some(generated_text);
|
||||
result_start = Some(start);
|
||||
result_queued = Some(queued)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check that we received a `InferStreamResponse::End` message
|
||||
if let (Some(generated_text), Some(queued), Some(start)) =
|
||||
(result_generated_text, result_queued, result_start)
|
||||
{
|
||||
Ok(InferResponse {
|
||||
prefill: result_prefill,
|
||||
_input_length,
|
||||
tokens: result_tokens,
|
||||
generated_text,
|
||||
queued,
|
||||
start,
|
||||
top_tokens: if use_top_tokens {
|
||||
result_top_tokens
|
||||
} else {
|
||||
Vec::new()
|
||||
},
|
||||
})
|
||||
} else {
|
||||
let err = InferError::IncompleteGeneration;
|
||||
metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
|
||||
tracing::error!("{err}");
|
||||
Err(err)
|
||||
}
|
||||
}
|
||||
/// Add best_of new requests to the queue and return a InferResponse of the sequence with
|
||||
/// the highest log probability per token
|
||||
#[instrument(skip(self, request))]
|
||||
pub(crate) async fn generate_best_of(
|
||||
&self,
|
||||
request: GenerateRequest,
|
||||
best_of: usize,
|
||||
) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
|
||||
// validate best_of parameter separately
|
||||
let best_of = self.validation.validate_best_of(best_of)?;
|
||||
|
||||
// create multiple generate requests
|
||||
let mut infer_responses: Vec<InferResponse> =
|
||||
try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;
|
||||
|
||||
// get the sequence with the highest log probability per token
|
||||
let mut max_index = 0;
|
||||
let mut max_logprob: f32 = f32::MIN;
|
||||
|
||||
for (i, response) in infer_responses.iter().enumerate() {
|
||||
// mean logprobs of the generated tokens
|
||||
let sequence_logprob = response
|
||||
.tokens
|
||||
.iter()
|
||||
.map(|token| token.logprob)
|
||||
.sum::<f32>()
|
||||
/ response.tokens.len() as f32;
|
||||
|
||||
// set best sequence
|
||||
if sequence_logprob > max_logprob {
|
||||
max_index = i;
|
||||
max_logprob = sequence_logprob;
|
||||
}
|
||||
}
|
||||
let best_response = infer_responses.remove(max_index);
|
||||
Ok((best_response, infer_responses))
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
struct ChatTemplate {
|
||||
template: Template<'static, 'static>,
|
||||
bos_token: Option<String>,
|
||||
eos_token: Option<String>,
|
||||
use_default_tool_template: bool,
|
||||
}
|
||||
|
||||
impl ChatTemplate {
|
||||
fn new(template: String, bos_token: Option<String>, eos_token: Option<String>) -> Self {
|
||||
let mut env = Box::new(Environment::new());
|
||||
let template_str = template.into_boxed_str();
|
||||
env.add_function("raise_exception", raise_exception);
|
||||
|
||||
// check if contains the tools variable within the template
|
||||
let use_default_tool_template =
|
||||
!template_str.as_ref().replace(' ', "").contains("{{tools}}");
|
||||
// leaking env and template_str as read-only, static resources for performance.
|
||||
let template = Box::leak(env)
|
||||
.template_from_str(Box::leak(template_str))
|
||||
.unwrap();
|
||||
|
||||
Self {
|
||||
template,
|
||||
bos_token,
|
||||
eos_token,
|
||||
use_default_tool_template,
|
||||
}
|
||||
}
|
||||
|
||||
fn apply(
|
||||
&self,
|
||||
mut messages: Vec<Message>,
|
||||
grammar_with_prompt: Option<(GrammarType, String)>,
|
||||
) -> Result<String, InferError> {
|
||||
if self.use_default_tool_template {
|
||||
if let Some(last_message) = messages.last_mut() {
|
||||
if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt {
|
||||
last_message.content.push(MessageChunk::Text(Text {
|
||||
text: format!("\n---\n{}\n{}", tool_prompt, tools),
|
||||
}));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
|
||||
|
||||
self.template
|
||||
.render(ChatTemplateInputs {
|
||||
messages,
|
||||
bos_token: self.bos_token.as_deref(),
|
||||
eos_token: self.eos_token.as_deref(),
|
||||
add_generation_prompt: true,
|
||||
tools: None,
|
||||
tools_prompt: None,
|
||||
})
|
||||
.map_err(InferError::TemplateError)
|
||||
}
|
||||
}
|
||||
|
||||
pub struct ToolGrammar {}
|
||||
|
||||
impl ToolGrammar {
|
||||
pub fn apply(
|
||||
tools: Option<Vec<Tool>>,
|
||||
tool_choice: Option<ToolType>,
|
||||
) -> Result<Option<Tools>, InferError> {
|
||||
if let Some((req_tools, tool_choice)) = tools.zip(tool_choice) {
|
||||
// let tool_prompt = tool_prompt.unwrap_or_default();
|
||||
let tools_to_use = match tool_choice {
|
||||
ToolType::FunctionName(name) => {
|
||||
vec![req_tools
|
||||
.iter()
|
||||
.find(|tool| tool.function.name == *name)
|
||||
.unwrap_or_else(|| panic!("Tool with name {} not found", name))
|
||||
.clone()]
|
||||
}
|
||||
ToolType::OneOf => req_tools.to_owned(),
|
||||
};
|
||||
|
||||
// adds the error notification function for LLM feedback if required
|
||||
let mut text_response_properties = Map::new();
|
||||
text_response_properties.insert(
|
||||
"error".to_string(),
|
||||
serde_json::json!({
|
||||
"type": "string",
|
||||
"description": "The error or issue to notify"
|
||||
}),
|
||||
);
|
||||
text_response_properties.insert(
|
||||
"_name".to_string(),
|
||||
serde_json::json!({
|
||||
"type": "string",
|
||||
"const": "notify_error"
|
||||
}),
|
||||
);
|
||||
|
||||
let functions: HashMap<String, serde_json::Value> = tools_to_use
|
||||
.iter()
|
||||
.map(|tool| {
|
||||
let func = tool.function.clone();
|
||||
|
||||
// Clone the existing parameters, which are expected to be a JSON object
|
||||
let mut params = if let Value::Object(params) = &func.arguments {
|
||||
params.clone()
|
||||
} else {
|
||||
Map::new()
|
||||
};
|
||||
|
||||
// Insert the function's description at the top level, outside of properties
|
||||
params.insert(
|
||||
"description".to_string(),
|
||||
Value::String(func.description.clone().unwrap_or_default()),
|
||||
);
|
||||
|
||||
// Ensure 'properties' exists and is an object
|
||||
let properties = params
|
||||
.entry("properties".to_string())
|
||||
.or_insert_with(|| json!({}))
|
||||
.as_object_mut()
|
||||
.unwrap();
|
||||
|
||||
// Insert the constant for the function name inside 'properties'
|
||||
properties.insert(
|
||||
"_name".to_string(),
|
||||
json!({
|
||||
"type": "string",
|
||||
"const": func.name.clone(),
|
||||
// "description": "The name of the function"
|
||||
}),
|
||||
);
|
||||
|
||||
// Check if 'required' exists, and it is an array. If not, create an empty array.
|
||||
let required = params
|
||||
.entry("required".to_string())
|
||||
.or_insert_with(|| json!([]))
|
||||
.as_array_mut()
|
||||
.unwrap();
|
||||
|
||||
// Add 'name' to the 'required' array if it is not already present
|
||||
if !required.iter().any(|r| r == "_name") {
|
||||
required.push(json!("_name"));
|
||||
}
|
||||
|
||||
(func.name, Value::Object(params))
|
||||
})
|
||||
.chain([(
|
||||
"notify_error".to_string(),
|
||||
serde_json::json!({
|
||||
"properties": text_response_properties,
|
||||
"required": ["error", "_name"],
|
||||
"type": "object"
|
||||
}),
|
||||
)])
|
||||
.collect();
|
||||
|
||||
let tools = Tools {
|
||||
functions_map: FunctionsMap { functions },
|
||||
properties: Properties {
|
||||
function: tools_to_use
|
||||
.iter()
|
||||
.map(|tool| FunctionRef {
|
||||
ref_path: format!("#/$functions/{}", tool.function.name.clone()),
|
||||
})
|
||||
.chain(std::iter::once(FunctionRef {
|
||||
ref_path: "#/$functions/notify_error".to_string(),
|
||||
}))
|
||||
.collect(),
|
||||
},
|
||||
};
|
||||
|
||||
return Ok(Some(tools));
|
||||
}
|
||||
// Err(InferError::ToolError("No tools provided".to_string()))
|
||||
Ok(None)
|
||||
}
|
||||
}
|
||||
|
||||
/// Batching logic
|
||||
|
|
@ -512,7 +101,7 @@ impl ToolGrammar {
|
|||
///
|
||||
/// Batches requests and sends them to the inference server
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
async fn batching_task(
|
||||
pub(crate) async fn batching_task(
|
||||
mut client: ShardedClient,
|
||||
waiting_served_ratio: f32,
|
||||
max_batch_prefill_tokens: u32,
|
||||
|
|
@ -520,13 +109,13 @@ async fn batching_task(
|
|||
max_waiting_tokens: usize,
|
||||
max_batch_size: Option<usize>,
|
||||
queue: Queue,
|
||||
shared: Arc<Shared>,
|
||||
notifier: Arc<Notify>,
|
||||
generation_health: Arc<AtomicBool>,
|
||||
) {
|
||||
// Infinite loop
|
||||
loop {
|
||||
// Wait for a notification from the Infer struct
|
||||
shared.batching_task.notified().await;
|
||||
notifier.notified().await;
|
||||
|
||||
// Get the next batch from the queue
|
||||
// This batch might be smaller than the maximum batch size if there are not enough requests
|
||||
|
|
@ -792,6 +381,16 @@ fn send_responses(
|
|||
let mut stopped = false;
|
||||
|
||||
if let Some(prefill_tokens) = generation.prefill_tokens {
|
||||
// Create Token objects
|
||||
// We do that here instead of in the Python code as Rust for loops are faster
|
||||
let prefill_tokens = prefill_tokens
|
||||
.ids
|
||||
.into_iter()
|
||||
.zip(prefill_tokens.logprobs)
|
||||
.zip(prefill_tokens.texts)
|
||||
.map(|((id, logprob), text)| PrefillToken { id, text, logprob })
|
||||
.collect();
|
||||
|
||||
// Send message
|
||||
entry
|
||||
.response_tx
|
||||
|
|
@ -842,7 +441,7 @@ fn send_responses(
|
|||
entry.response_tx.send(Ok(InferStreamResponse::End {
|
||||
token,
|
||||
top_tokens,
|
||||
generated_text: generated_text.clone(),
|
||||
generated_text: GeneratedText::from(generated_text.clone()),
|
||||
queued: entry.queue_time,
|
||||
start: entry.batch_time.unwrap(),
|
||||
}))?;
|
||||
|
|
@ -877,64 +476,21 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
|
|||
});
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) enum InferStreamResponse {
|
||||
// Optional first message
|
||||
Prefill(Tokens),
|
||||
// Intermediate messages
|
||||
Intermediate {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
},
|
||||
// Last message
|
||||
End {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
generated_text: GeneratedText,
|
||||
start: Instant,
|
||||
queued: Instant,
|
||||
},
|
||||
}
|
||||
impl From<text_generation_client::v2::GeneratedText> for GeneratedText {
|
||||
fn from(value: text_generation_client::v2::GeneratedText) -> Self {
|
||||
let v2_finish_reason =
|
||||
text_generation_client::v2::FinishReason::try_from(value.finish_reason).unwrap();
|
||||
let finish_reason = match v2_finish_reason {
|
||||
text_generation_client::v2::FinishReason::Length => FinishReason::Length,
|
||||
text_generation_client::v2::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
|
||||
text_generation_client::v2::FinishReason::StopSequence => FinishReason::StopSequence,
|
||||
};
|
||||
|
||||
#[derive(Debug)]
|
||||
pub(crate) struct InferResponse {
|
||||
/// input_length is the input as perceived by the rust tokenizer in the
|
||||
/// validation pathway. It is redundant with prefill.len() but prefill
|
||||
/// has data only if the user asked for it. This will always be filled.
|
||||
pub(crate) _input_length: u32,
|
||||
pub(crate) prefill: Vec<PrefillToken>,
|
||||
pub(crate) tokens: Vec<Token>,
|
||||
pub(crate) generated_text: GeneratedText,
|
||||
pub(crate) queued: Instant,
|
||||
pub(crate) start: Instant,
|
||||
pub(crate) top_tokens: Vec<Vec<Token>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Error)]
|
||||
pub enum InferError {
|
||||
#[error("Request failed during generation: {0}")]
|
||||
GenerationError(String),
|
||||
#[error("Model is overloaded")]
|
||||
Overloaded(#[from] TryAcquireError),
|
||||
#[error("Input validation error: {0}")]
|
||||
ValidationError(#[from] ValidationError),
|
||||
#[error("Incomplete generation")]
|
||||
IncompleteGeneration,
|
||||
#[error("Template error: {0}")]
|
||||
TemplateError(#[from] minijinja::Error),
|
||||
#[error("Tool error: {0}")]
|
||||
ToolError(String),
|
||||
}
|
||||
|
||||
impl InferError {
|
||||
pub(crate) fn error_type(&self) -> &str {
|
||||
match self {
|
||||
InferError::GenerationError(_) => "generation",
|
||||
InferError::Overloaded(_) => "overloaded",
|
||||
InferError::ValidationError(_) => "validation",
|
||||
InferError::IncompleteGeneration => "incomplete_generation",
|
||||
InferError::TemplateError(_) => "template_error",
|
||||
InferError::ToolError(_) => "tool_error",
|
||||
Self {
|
||||
text: value.text,
|
||||
generated_tokens: value.generated_tokens,
|
||||
finish_reason,
|
||||
seed: value.seed,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1355,11 +911,11 @@ mod tests {
|
|||
chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
input: ChatTemplateInputs {
|
||||
messages: vec![
|
||||
TextMessage{
|
||||
TextMessage {
|
||||
role: "system".to_string(),
|
||||
content: "You are a friendly chatbot who always responds in the style of a pirate".to_string(),
|
||||
},
|
||||
TextMessage{
|
||||
TextMessage {
|
||||
role: "user".to_string(),
|
||||
content: "How many helicopters can a human eat in one sitting?".to_string(),
|
||||
},
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
mod queue;
|
||||
mod scheduler;
|
||||
|
||||
pub(crate) use scheduler::SchedulerV3;
|
||||
|
|
@ -1,12 +1,14 @@
|
|||
use crate::infer::InferError;
|
||||
use crate::infer::InferStreamResponse;
|
||||
use crate::validation::ValidGenerateRequest;
|
||||
use crate::infer::{InferError, InferStreamResponse};
|
||||
use crate::validation::{
|
||||
ValidGenerateRequest, ValidGrammar, ValidParameters, ValidStoppingParameters,
|
||||
};
|
||||
use nohash_hasher::{BuildNoHashHasher, IntMap};
|
||||
use std::cmp::min;
|
||||
use std::collections::VecDeque;
|
||||
use text_generation_client::ChunksToString;
|
||||
use text_generation_client::Input;
|
||||
use text_generation_client::{Batch, Request};
|
||||
use text_generation_client::v3::{
|
||||
Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
|
||||
};
|
||||
use text_generation_client::{ChunksToString, Input};
|
||||
use tokio::sync::{mpsc, oneshot};
|
||||
use tokio::time::Instant;
|
||||
use tracing::{info_span, instrument, Span};
|
||||
|
|
@ -57,7 +59,6 @@ impl Queue {
|
|||
Self { queue_sender }
|
||||
}
|
||||
|
||||
/// Append an entry to the queue
|
||||
#[instrument(skip_all)]
|
||||
pub(crate) fn append(&self, entry: Entry) {
|
||||
// Send append command to the background task managing the state
|
||||
|
|
@ -280,13 +281,17 @@ impl State {
|
|||
batch_requests.push(Request {
|
||||
id,
|
||||
prefill_logprobs: entry.request.decoder_input_details,
|
||||
inputs: entry.request.inputs.chunks_to_string(),
|
||||
input_chunks: Some(Input {
|
||||
chunks: entry.request.inputs.clone(),
|
||||
}),
|
||||
inputs: entry.request.inputs.chunks_to_string(),
|
||||
truncate: entry.request.truncate,
|
||||
parameters: Some(entry.request.parameters.clone()),
|
||||
stopping_parameters: Some(entry.request.stopping_parameters.clone()),
|
||||
parameters: Some(NextTokenChooserParameters::from(
|
||||
entry.request.parameters.clone(),
|
||||
)),
|
||||
stopping_parameters: Some(StoppingCriteriaParameters::from(
|
||||
entry.request.stopping_parameters.clone(),
|
||||
)),
|
||||
top_n_tokens: entry.request.top_n_tokens,
|
||||
});
|
||||
// Set batch_time
|
||||
|
|
@ -355,12 +360,46 @@ enum QueueCommand {
|
|||
},
|
||||
}
|
||||
|
||||
impl From<ValidParameters> for NextTokenChooserParameters {
|
||||
fn from(value: ValidParameters) -> Self {
|
||||
let (grammar, grammar_type) = match value.grammar {
|
||||
None => (String::new(), GrammarType::None),
|
||||
|
||||
Some(grammar) => match grammar {
|
||||
ValidGrammar::Json(grammar_string) => (grammar_string, GrammarType::Json),
|
||||
ValidGrammar::Regex(grammar_string) => (grammar_string, GrammarType::Regex),
|
||||
},
|
||||
};
|
||||
|
||||
Self {
|
||||
temperature: value.temperature,
|
||||
top_k: value.top_k,
|
||||
top_p: value.top_p,
|
||||
typical_p: value.typical_p,
|
||||
do_sample: value.do_sample,
|
||||
seed: value.seed,
|
||||
repetition_penalty: value.repetition_penalty,
|
||||
frequency_penalty: value.frequency_penalty,
|
||||
watermark: value.watermark,
|
||||
grammar,
|
||||
grammar_type: grammar_type.into(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<ValidStoppingParameters> for StoppingCriteriaParameters {
|
||||
fn from(value: ValidStoppingParameters) -> Self {
|
||||
Self {
|
||||
max_new_tokens: value.max_new_tokens,
|
||||
stop_sequences: value.stop_sequences,
|
||||
ignore_eos_token: value.ignore_eos_token,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use text_generation_client::{
|
||||
GrammarType as ProtoGrammarType, NextTokenChooserParameters, StoppingCriteriaParameters,
|
||||
};
|
||||
use tracing::info_span;
|
||||
|
||||
fn default_entry() -> (
|
||||
|
|
@ -375,7 +414,7 @@ mod tests {
|
|||
input_length: 0,
|
||||
truncate: 0,
|
||||
decoder_input_details: false,
|
||||
parameters: NextTokenChooserParameters {
|
||||
parameters: ValidParameters {
|
||||
temperature: 0.0,
|
||||
top_k: 0,
|
||||
top_p: 0.0,
|
||||
|
|
@ -385,10 +424,9 @@ mod tests {
|
|||
repetition_penalty: 0.0,
|
||||
frequency_penalty: 0.0,
|
||||
watermark: false,
|
||||
grammar: String::new(),
|
||||
grammar_type: ProtoGrammarType::None as i32,
|
||||
grammar: None,
|
||||
},
|
||||
stopping_parameters: StoppingCriteriaParameters {
|
||||
stopping_parameters: ValidStoppingParameters {
|
||||
ignore_eos_token: false,
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,27 +1,14 @@
|
|||
pub mod config;
|
||||
mod health;
|
||||
/// Text Generation Inference Webserver
|
||||
pub mod config;
|
||||
mod infer;
|
||||
mod queue;
|
||||
pub mod server;
|
||||
mod validation;
|
||||
|
||||
use infer::{Infer, InferError, InferStreamResponse};
|
||||
use queue::{Entry, Queue};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use tokio::sync::OwnedSemaphorePermit;
|
||||
use tokio_stream::wrappers::UnboundedReceiverStream;
|
||||
use tracing::warn;
|
||||
use utoipa::ToSchema;
|
||||
use validation::Validation;
|
||||
|
||||
/// Type alias for generation responses
|
||||
pub(crate) type GenerateStreamResponse = (
|
||||
OwnedSemaphorePermit,
|
||||
u32, // input_length
|
||||
UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
|
||||
);
|
||||
|
||||
#[derive(Clone, Deserialize, ToSchema)]
|
||||
pub(crate) struct VertexInstance {
|
||||
#[schema(example = "What is Deep Learning?")]
|
||||
|
|
@ -158,7 +145,7 @@ pub struct Info {
|
|||
#[schema(example = "4")]
|
||||
pub max_stop_sequences: usize,
|
||||
#[schema(example = "1024")]
|
||||
pub max_input_length: usize,
|
||||
pub max_input_tokens: usize,
|
||||
#[schema(example = "2048")]
|
||||
pub max_total_tokens: usize,
|
||||
#[schema(example = "1.2")]
|
||||
|
|
@ -1087,7 +1074,7 @@ pub struct SimpleToken {
|
|||
stop: usize,
|
||||
}
|
||||
|
||||
#[derive(Serialize, ToSchema)]
|
||||
#[derive(Debug, Serialize, ToSchema)]
|
||||
#[serde(rename_all(serialize = "snake_case"))]
|
||||
#[schema(example = "Length")]
|
||||
pub(crate) enum FinishReason {
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ use std::fs::File;
|
|||
use std::io::BufReader;
|
||||
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
|
||||
use std::path::{Path, PathBuf};
|
||||
use text_generation_client::{ClientError, ShardedClient};
|
||||
use text_generation_router::config::Config;
|
||||
use text_generation_router::{server, HubModelInfo, HubProcessorConfig, HubTokenizerConfig};
|
||||
use thiserror::Error;
|
||||
|
|
@ -315,59 +314,6 @@ async fn main() -> Result<(), RouterError> {
|
|||
Some(pipeline_tag) => pipeline_tag.as_str() == "text-generation",
|
||||
};
|
||||
|
||||
// Instantiate sharded client from the master unix socket
|
||||
let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
|
||||
.await
|
||||
.map_err(RouterError::Connection)?;
|
||||
// Clear the cache; useful if the webserver rebooted
|
||||
sharded_client
|
||||
.clear_cache(None)
|
||||
.await
|
||||
.map_err(RouterError::Cache)?;
|
||||
// Get info from the shard
|
||||
let shard_info = sharded_client.info().await.map_err(RouterError::Info)?;
|
||||
|
||||
// Warmup model
|
||||
tracing::info!("Warming up model");
|
||||
let max_supported_batch_total_tokens = match sharded_client
|
||||
.warmup(
|
||||
max_input_tokens as u32,
|
||||
max_batch_prefill_tokens,
|
||||
max_total_tokens as u32,
|
||||
max_batch_size,
|
||||
)
|
||||
.await
|
||||
.map_err(RouterError::Warmup)?
|
||||
{
|
||||
// Older models do not support automatic max-batch-total-tokens
|
||||
None => {
|
||||
let max_batch_total_tokens = max_batch_total_tokens
|
||||
.unwrap_or(16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)));
|
||||
tracing::warn!("Model does not support automatic max batch total tokens");
|
||||
max_batch_total_tokens
|
||||
}
|
||||
// Flash attention models return their max supported total tokens
|
||||
Some(max_supported_batch_total_tokens) => {
|
||||
// Warn if user added his own max-batch-total-tokens as we will ignore it
|
||||
if max_batch_total_tokens.is_some() {
|
||||
tracing::warn!(
|
||||
"`--max-batch-total-tokens` is deprecated for Flash \
|
||||
Attention models."
|
||||
);
|
||||
tracing::warn!(
|
||||
"Inferred max batch total tokens: {max_supported_batch_total_tokens}"
|
||||
);
|
||||
}
|
||||
if max_total_tokens as u32 > max_supported_batch_total_tokens {
|
||||
return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_supported_batch_total_tokens}")));
|
||||
}
|
||||
|
||||
max_supported_batch_total_tokens
|
||||
}
|
||||
};
|
||||
tracing::info!("Setting max batch total tokens to {max_supported_batch_total_tokens}");
|
||||
tracing::info!("Connected");
|
||||
|
||||
// Determine the server port based on the feature and environment variable.
|
||||
let port = if cfg!(feature = "google") {
|
||||
std::env::var("AIP_HTTP_PORT")
|
||||
|
|
@ -387,8 +333,8 @@ async fn main() -> Result<(), RouterError> {
|
|||
|
||||
// Run server
|
||||
server::run(
|
||||
master_shard_uds_path,
|
||||
model_info,
|
||||
shard_info,
|
||||
compat_return_full_text,
|
||||
max_concurrent_requests,
|
||||
max_best_of,
|
||||
|
|
@ -398,10 +344,9 @@ async fn main() -> Result<(), RouterError> {
|
|||
max_total_tokens,
|
||||
waiting_served_ratio,
|
||||
max_batch_prefill_tokens,
|
||||
max_supported_batch_total_tokens,
|
||||
max_batch_total_tokens,
|
||||
max_waiting_tokens,
|
||||
max_batch_size,
|
||||
sharded_client,
|
||||
tokenizer,
|
||||
config,
|
||||
validation_workers,
|
||||
|
|
@ -557,16 +502,8 @@ pub async fn get_tokenizer_config(api_repo: &ApiRepo) -> Option<HubTokenizerConf
|
|||
enum RouterError {
|
||||
#[error("Argument validation error: {0}")]
|
||||
ArgumentValidation(String),
|
||||
#[error("Unable to connect to the Python model shards: {0}")]
|
||||
Connection(ClientError),
|
||||
#[error("Unable to clear the Python model shards cache: {0}")]
|
||||
Cache(ClientError),
|
||||
#[error("Unable to get the Python model shards info: {0}")]
|
||||
Info(ClientError),
|
||||
#[error("Unable to warmup the Python model shards: {0}")]
|
||||
Warmup(ClientError),
|
||||
#[error("WebServer error: {0}")]
|
||||
WebServer(#[from] server::WebServerError),
|
||||
#[error("Tokio runtime failed to start: {0}")]
|
||||
Tokio(#[from] std::io::Error),
|
||||
#[error("Axum webserver failed: {0}")]
|
||||
Axum(#[from] axum::BoxError),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,13 +1,15 @@
|
|||
use crate::config::Config;
|
||||
/// HTTP Server logic
|
||||
use crate::health::Health;
|
||||
use crate::infer::{InferError, InferResponse, InferStreamResponse, ToolGrammar};
|
||||
use crate::config::Config;
|
||||
use crate::infer::v2::SchedulerV2;
|
||||
use crate::infer::v3::SchedulerV3;
|
||||
use crate::infer::{HealthCheck, Scheduler};
|
||||
use crate::infer::{Infer, InferError, InferResponse, InferStreamResponse, ToolGrammar};
|
||||
use crate::validation::ValidationError;
|
||||
use crate::{
|
||||
BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
|
||||
GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Infer,
|
||||
Info, Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token,
|
||||
TokenizeResponse, Usage, Validation,
|
||||
GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
|
||||
Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse,
|
||||
Usage, Validation,
|
||||
};
|
||||
use crate::{
|
||||
ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
|
||||
|
|
@ -34,7 +36,8 @@ use std::convert::Infallible;
|
|||
use std::net::SocketAddr;
|
||||
use std::sync::atomic::AtomicBool;
|
||||
use std::sync::Arc;
|
||||
use text_generation_client::{ShardInfo, ShardedClient};
|
||||
use text_generation_client::{v2, v3, ClientError, ShardInfo};
|
||||
use thiserror::Error;
|
||||
use tokenizers::Tokenizer;
|
||||
use tokio::select;
|
||||
use tokio::signal;
|
||||
|
|
@ -115,7 +118,9 @@ example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
|
|||
)]
|
||||
#[instrument(skip(health))]
|
||||
/// Health check method
|
||||
async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
|
||||
async fn health(
|
||||
mut health: Extension<HealthCheck>,
|
||||
) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
|
||||
match health.check().await {
|
||||
true => Ok(()),
|
||||
false => Err((
|
||||
|
|
@ -213,9 +218,7 @@ async fn generate_internal(
|
|||
|
||||
BestOfSequence {
|
||||
generated_text: output_text,
|
||||
finish_reason: FinishReason::from(
|
||||
response.generated_text.finish_reason,
|
||||
),
|
||||
finish_reason: response.generated_text.finish_reason,
|
||||
generated_tokens: response.generated_text.generated_tokens,
|
||||
prefill: response.prefill,
|
||||
tokens: response.tokens,
|
||||
|
|
@ -227,7 +230,7 @@ async fn generate_internal(
|
|||
});
|
||||
|
||||
Some(Details {
|
||||
finish_reason: FinishReason::from(response.generated_text.finish_reason),
|
||||
finish_reason: response.generated_text.finish_reason,
|
||||
generated_tokens: response.generated_text.generated_tokens,
|
||||
prefill: response.prefill,
|
||||
tokens: response.tokens,
|
||||
|
|
@ -468,7 +471,7 @@ async fn generate_stream_internal(
|
|||
// Token details
|
||||
let details = match details {
|
||||
true => Some(StreamDetails {
|
||||
finish_reason: FinishReason::from(generated_text.finish_reason),
|
||||
finish_reason: generated_text.finish_reason,
|
||||
generated_tokens: generated_text.generated_tokens,
|
||||
seed: generated_text.seed,
|
||||
}),
|
||||
|
|
@ -556,38 +559,38 @@ async fn generate_stream_internal(
|
|||
|
||||
/// Generate tokens
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/v1/completions",
|
||||
request_body = CompletionRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Chat Completion",
|
||||
content(
|
||||
("application/json" = Completion),
|
||||
("text/event-stream" = CompletionCompleteChunk),
|
||||
)),
|
||||
(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 = "/v1/completions",
|
||||
request_body = CompletionRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Chat Completion",
|
||||
content(
|
||||
("application/json" = Completion),
|
||||
("text/event-stream" = CompletionCompleteChunk),
|
||||
)),
|
||||
(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_all,
|
||||
fields(
|
||||
// parameters = ? req.parameters,
|
||||
total_time,
|
||||
validation_time,
|
||||
queue_time,
|
||||
inference_time,
|
||||
time_per_token,
|
||||
seed,
|
||||
)
|
||||
)]
|
||||
skip_all,
|
||||
fields(
|
||||
// parameters = ? req.parameters,
|
||||
total_time,
|
||||
validation_time,
|
||||
queue_time,
|
||||
inference_time,
|
||||
time_per_token,
|
||||
seed,
|
||||
)
|
||||
)]
|
||||
async fn completions(
|
||||
Extension(infer): Extension<Infer>,
|
||||
Extension(compute_type): Extension<ComputeType>,
|
||||
|
|
@ -961,38 +964,38 @@ async fn completions(
|
|||
|
||||
/// Generate tokens
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/v1/chat/completions",
|
||||
request_body = ChatRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Chat Completion",
|
||||
content(
|
||||
("application/json" = ChatCompletion),
|
||||
("text/event-stream" = ChatCompletionChunk),
|
||||
)),
|
||||
(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 = "/v1/chat/completions",
|
||||
request_body = ChatRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Chat Completion",
|
||||
content(
|
||||
("application/json" = ChatCompletion),
|
||||
("text/event-stream" = ChatCompletionChunk),
|
||||
)),
|
||||
(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_all,
|
||||
fields(
|
||||
// parameters = ? req.parameters,
|
||||
total_time,
|
||||
validation_time,
|
||||
queue_time,
|
||||
inference_time,
|
||||
time_per_token,
|
||||
seed,
|
||||
)
|
||||
)]
|
||||
skip_all,
|
||||
fields(
|
||||
// parameters = ? req.parameters,
|
||||
total_time,
|
||||
validation_time,
|
||||
queue_time,
|
||||
inference_time,
|
||||
time_per_token,
|
||||
seed,
|
||||
)
|
||||
)]
|
||||
async fn chat_completions(
|
||||
Extension(infer): Extension<Infer>,
|
||||
Extension(compute_type): Extension<ComputeType>,
|
||||
|
|
@ -1217,22 +1220,22 @@ async fn chat_completions(
|
|||
|
||||
/// Generate tokens from Vertex request
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/vertex",
|
||||
request_body = VertexRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = VertexResponse),
|
||||
(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 = "/vertex",
|
||||
request_body = VertexRequest,
|
||||
responses(
|
||||
(status = 200, description = "Generated Text", body = VertexResponse),
|
||||
(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_all,
|
||||
fields(
|
||||
|
|
@ -1310,16 +1313,16 @@ async fn vertex_compatibility(
|
|||
|
||||
/// Tokenize inputs
|
||||
#[utoipa::path(
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/tokenize",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Tokenized ids", body = TokenizeResponse),
|
||||
(status = 404, description = "No tokenizer found", body = ErrorResponse,
|
||||
example = json ! ({"error": "No fast tokenizer available"})),
|
||||
)
|
||||
)]
|
||||
post,
|
||||
tag = "Text Generation Inference",
|
||||
path = "/tokenize",
|
||||
request_body = GenerateRequest,
|
||||
responses(
|
||||
(status = 200, description = "Tokenized ids", body = TokenizeResponse),
|
||||
(status = 404, description = "No tokenizer found", body = ErrorResponse,
|
||||
example = json ! ({"error": "No fast tokenizer available"})),
|
||||
)
|
||||
)]
|
||||
#[instrument(skip_all)]
|
||||
async fn tokenize(
|
||||
Extension(infer): Extension<Infer>,
|
||||
|
|
@ -1372,21 +1375,20 @@ pub(crate) struct ComputeType(String);
|
|||
/// Serving method
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub async fn run(
|
||||
master_shard_uds_path: String,
|
||||
model_info: HubModelInfo,
|
||||
shard_info: ShardInfo,
|
||||
compat_return_full_text: bool,
|
||||
max_concurrent_requests: usize,
|
||||
max_best_of: usize,
|
||||
max_stop_sequences: usize,
|
||||
max_top_n_tokens: u32,
|
||||
max_input_length: usize,
|
||||
max_input_tokens: usize,
|
||||
max_total_tokens: usize,
|
||||
waiting_served_ratio: f32,
|
||||
max_batch_prefill_tokens: u32,
|
||||
max_batch_total_tokens: u32,
|
||||
max_batch_total_tokens: Option<u32>,
|
||||
max_waiting_tokens: usize,
|
||||
max_batch_size: Option<usize>,
|
||||
client: ShardedClient,
|
||||
tokenizer: Option<Tokenizer>,
|
||||
config: Option<Config>,
|
||||
validation_workers: usize,
|
||||
|
|
@ -1400,7 +1402,7 @@ pub async fn run(
|
|||
messages_api_enabled: bool,
|
||||
grammar_support: bool,
|
||||
max_client_batch_size: usize,
|
||||
) -> Result<(), axum::BoxError> {
|
||||
) -> Result<(), WebServerError> {
|
||||
// OpenAPI documentation
|
||||
#[derive(OpenApi)]
|
||||
#[openapi(
|
||||
|
|
@ -1470,6 +1472,141 @@ pub async fn run(
|
|||
struct ApiDoc;
|
||||
|
||||
// Create state
|
||||
|
||||
// Open connection, get model info and warmup
|
||||
let (scheduler, health_ext, shard_info, max_batch_total_tokens): (
|
||||
Arc<dyn Scheduler + Send + Sync>,
|
||||
HealthCheck,
|
||||
ShardInfo,
|
||||
u32,
|
||||
) = {
|
||||
// Helper function to check both v2 and v3
|
||||
let check_max_batch_total_tokens = |max_supported_batch_total_tokens: Option<u32>| {
|
||||
match max_supported_batch_total_tokens {
|
||||
// Older models do not support automatic max-batch-total-tokens
|
||||
None => {
|
||||
let max_batch_total_tokens = max_batch_total_tokens.unwrap_or(
|
||||
16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)),
|
||||
);
|
||||
tracing::warn!("Model does not support automatic max batch total tokens");
|
||||
Ok(max_batch_total_tokens)
|
||||
}
|
||||
// Flash attention models return their max supported total tokens
|
||||
Some(max_supported_batch_total_tokens) => {
|
||||
// Warn if user added his own max-batch-total-tokens as we will ignore it
|
||||
if max_batch_total_tokens.is_some() {
|
||||
tracing::warn!(
|
||||
"`--max-batch-total-tokens` is deprecated for Flash \
|
||||
Attention models."
|
||||
);
|
||||
tracing::warn!(
|
||||
"Inferred max batch total tokens: {max_supported_batch_total_tokens}"
|
||||
);
|
||||
}
|
||||
if max_total_tokens as u32 > max_supported_batch_total_tokens {
|
||||
return Err(WebServerError::NotEnoughMemory(max_total_tokens));
|
||||
}
|
||||
|
||||
Ok(max_supported_batch_total_tokens)
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
let generation_health = Arc::new(AtomicBool::new(false));
|
||||
|
||||
match v3::ShardedClient::connect_uds(master_shard_uds_path.clone()).await {
|
||||
Ok(mut sharded_client) => {
|
||||
// server is running on v3
|
||||
// Clear the cache; useful if the webserver rebooted
|
||||
sharded_client
|
||||
.clear_cache(None)
|
||||
.await
|
||||
.map_err(WebServerError::Cache)?;
|
||||
// Get info from the shard
|
||||
let shard_info = sharded_client.info().await.map_err(WebServerError::Info)?;
|
||||
|
||||
// Warmup model
|
||||
tracing::info!("Warming up model");
|
||||
let max_batch_total_tokens = check_max_batch_total_tokens(
|
||||
sharded_client
|
||||
.warmup(
|
||||
max_input_tokens as u32,
|
||||
max_batch_prefill_tokens,
|
||||
max_total_tokens as u32,
|
||||
max_batch_size,
|
||||
)
|
||||
.await
|
||||
.map_err(WebServerError::Warmup)?,
|
||||
)?;
|
||||
|
||||
let health_ext =
|
||||
HealthCheck::new(Arc::new(sharded_client.clone()), generation_health.clone());
|
||||
let scheduler = Arc::new(SchedulerV3::new(
|
||||
sharded_client,
|
||||
waiting_served_ratio,
|
||||
max_batch_prefill_tokens,
|
||||
max_batch_total_tokens,
|
||||
max_waiting_tokens,
|
||||
max_batch_size,
|
||||
shard_info.requires_padding,
|
||||
shard_info.window_size,
|
||||
shard_info.speculate,
|
||||
generation_health,
|
||||
));
|
||||
tracing::info!("Using scheduler V3");
|
||||
|
||||
(scheduler, health_ext, shard_info, max_batch_total_tokens)
|
||||
}
|
||||
Err(_) => {
|
||||
let mut sharded_client = v2::ShardedClient::connect_uds(master_shard_uds_path)
|
||||
.await
|
||||
.map_err(WebServerError::Connection)?;
|
||||
|
||||
// server is running on v2
|
||||
// Clear the cache; useful if the webserver rebooted
|
||||
sharded_client
|
||||
.clear_cache(None)
|
||||
.await
|
||||
.map_err(WebServerError::Cache)?;
|
||||
// Get info from the shard
|
||||
let shard_info = sharded_client.info().await.map_err(WebServerError::Info)?;
|
||||
|
||||
// Warmup model
|
||||
tracing::info!("Warming up model");
|
||||
let max_batch_total_tokens = check_max_batch_total_tokens(
|
||||
sharded_client
|
||||
.warmup(
|
||||
max_input_tokens as u32,
|
||||
max_batch_prefill_tokens,
|
||||
max_total_tokens as u32,
|
||||
max_batch_size,
|
||||
)
|
||||
.await
|
||||
.map_err(WebServerError::Warmup)?,
|
||||
)?;
|
||||
|
||||
let health_ext =
|
||||
HealthCheck::new(Arc::new(sharded_client.clone()), generation_health.clone());
|
||||
let scheduler = Arc::new(SchedulerV2::new(
|
||||
sharded_client,
|
||||
waiting_served_ratio,
|
||||
max_batch_prefill_tokens,
|
||||
max_batch_total_tokens,
|
||||
max_waiting_tokens,
|
||||
max_batch_size,
|
||||
shard_info.requires_padding,
|
||||
shard_info.window_size,
|
||||
shard_info.speculate,
|
||||
generation_health,
|
||||
));
|
||||
tracing::info!("Using scheduler V2");
|
||||
|
||||
(scheduler, health_ext, shard_info, max_batch_total_tokens)
|
||||
}
|
||||
}
|
||||
};
|
||||
tracing::info!("Setting max batch total tokens to {max_batch_total_tokens}");
|
||||
|
||||
let validation = Validation::new(
|
||||
validation_workers,
|
||||
tokenizer,
|
||||
|
|
@ -1477,25 +1614,15 @@ pub async fn run(
|
|||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_input_tokens,
|
||||
max_total_tokens,
|
||||
grammar_support,
|
||||
);
|
||||
let generation_health = Arc::new(AtomicBool::new(false));
|
||||
let health_ext = Health::new(client.clone(), generation_health.clone());
|
||||
|
||||
let infer = Infer::new(
|
||||
client,
|
||||
scheduler,
|
||||
validation,
|
||||
waiting_served_ratio,
|
||||
max_batch_prefill_tokens,
|
||||
max_batch_total_tokens,
|
||||
max_waiting_tokens,
|
||||
max_batch_size,
|
||||
max_concurrent_requests,
|
||||
shard_info.requires_padding,
|
||||
shard_info.window_size,
|
||||
shard_info.speculate,
|
||||
generation_health,
|
||||
tokenizer_config,
|
||||
processor_config,
|
||||
);
|
||||
|
|
@ -1514,7 +1641,7 @@ pub async fn run(
|
|||
// Input Length buckets
|
||||
let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
|
||||
let input_length_buckets: Vec<f64> = (0..100)
|
||||
.map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
|
||||
.map(|x| (max_input_tokens as f64 / 100.0) * (x + 1) as f64)
|
||||
.collect();
|
||||
// Generated tokens buckets
|
||||
let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
|
||||
|
|
@ -1568,7 +1695,7 @@ pub async fn run(
|
|||
max_concurrent_requests,
|
||||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_input_length,
|
||||
max_input_tokens,
|
||||
max_total_tokens,
|
||||
waiting_served_ratio,
|
||||
max_batch_total_tokens,
|
||||
|
|
@ -1664,6 +1791,8 @@ pub async fn run(
|
|||
.layer(OtelAxumLayer::default())
|
||||
.layer(cors_layer);
|
||||
|
||||
tracing::info!("Connected");
|
||||
|
||||
if ngrok {
|
||||
#[cfg(feature = "ngrok")]
|
||||
{
|
||||
|
|
@ -1686,7 +1815,8 @@ pub async fn run(
|
|||
let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
|
||||
axum::serve(listener, app)
|
||||
.with_graceful_shutdown(shutdown_signal())
|
||||
.await?;
|
||||
.await
|
||||
.map_err(|err| WebServerError::Axum(Box::new(err)))?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
|
@ -1719,17 +1849,6 @@ async fn shutdown_signal() {
|
|||
opentelemetry::global::shutdown_tracer_provider();
|
||||
}
|
||||
|
||||
impl From<i32> for FinishReason {
|
||||
fn from(finish_reason: i32) -> Self {
|
||||
let finish_reason = text_generation_client::FinishReason::try_from(finish_reason).unwrap();
|
||||
match finish_reason {
|
||||
text_generation_client::FinishReason::Length => FinishReason::Length,
|
||||
text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
|
||||
text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert to Axum supported formats
|
||||
impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
|
||||
fn from(err: InferError) -> Self {
|
||||
|
|
@ -1762,3 +1881,19 @@ impl From<InferError> for Event {
|
|||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Error)]
|
||||
pub enum WebServerError {
|
||||
#[error("Unable to connect to the Python model shards: {0}")]
|
||||
Connection(ClientError),
|
||||
#[error("Unable to clear the Python model shards cache: {0}")]
|
||||
Cache(ClientError),
|
||||
#[error("Unable to get the Python model shards info: {0}")]
|
||||
Info(ClientError),
|
||||
#[error("Unable to warmup the Python model shards: {0}")]
|
||||
Warmup(ClientError),
|
||||
#[error("Not enough memory to handle `max_total_tokens={0}`")]
|
||||
NotEnoughMemory(usize),
|
||||
#[error("Axum error: {0}")]
|
||||
Axum(#[from] axum::BoxError),
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,20 +1,16 @@
|
|||
use crate::config::Config;
|
||||
/// Payload validation logic
|
||||
use crate::config::Config;
|
||||
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
|
||||
use crate::{GenerateParameters, GenerateRequest, GrammarType};
|
||||
use base64::{engine::general_purpose::STANDARD, Engine};
|
||||
use image::{io::Reader as ImageReader, ImageFormat};
|
||||
use jsonschema::{Draft, JSONSchema};
|
||||
use rand::{thread_rng, Rng};
|
||||
use serde_json::Value;
|
||||
use std::io::Cursor;
|
||||
use text_generation_client::{
|
||||
Chunk, GrammarType as ProtoGrammarType, Image, InputChunk, NextTokenChooserParameters,
|
||||
StoppingCriteriaParameters,
|
||||
};
|
||||
use text_generation_client::{Chunk, Image, InputChunk};
|
||||
use thiserror::Error;
|
||||
use tokenizers::tokenizer::Tokenizer;
|
||||
// use tokenizers::TruncationDirection;
|
||||
use base64::{engine::general_purpose::STANDARD, Engine};
|
||||
use image::{io::Reader as ImageReader, ImageFormat};
|
||||
use tokio::sync::mpsc;
|
||||
use tokio::sync::oneshot;
|
||||
use tracing::{instrument, Span};
|
||||
|
|
@ -173,10 +169,6 @@ impl Validation {
|
|||
// Validate MaxNewTokens
|
||||
if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 {
|
||||
input_length = input_length.saturating_sub(max_new_tokens as usize);
|
||||
// return Err(ValidationError::MaxNewTokens(
|
||||
// self.max_total_tokens - self.max_input_length,
|
||||
// max_new_tokens,
|
||||
// ));
|
||||
}
|
||||
|
||||
Ok((
|
||||
|
|
@ -327,13 +319,13 @@ impl Validation {
|
|||
// compiler and use that to build the FSM here.
|
||||
|
||||
// Validate grammar and unpack the grammar and type for the proto message
|
||||
let (grammar, grammar_type) = match grammar {
|
||||
let grammar = match grammar {
|
||||
Some(grammar) => {
|
||||
// Ensure that grammar is not set if it's not supported
|
||||
if self.disable_grammar_support {
|
||||
return Err(ValidationError::Grammar);
|
||||
}
|
||||
match grammar {
|
||||
let valid_grammar = match grammar {
|
||||
GrammarType::Json(json) => {
|
||||
let json = match json {
|
||||
// if value is a string, we need to parse it again to make sure its
|
||||
|
|
@ -350,20 +342,20 @@ impl Validation {
|
|||
.compile(&json)
|
||||
.map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?;
|
||||
|
||||
(
|
||||
// Serialize json to string
|
||||
// Serialize json to string
|
||||
ValidGrammar::Json(
|
||||
serde_json::to_string(&json)
|
||||
.map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?,
|
||||
ProtoGrammarType::Json.into(),
|
||||
)
|
||||
}
|
||||
GrammarType::Regex(regex) => (regex, ProtoGrammarType::Regex.into()),
|
||||
}
|
||||
GrammarType::Regex(regex) => ValidGrammar::Regex(regex),
|
||||
};
|
||||
Some(valid_grammar)
|
||||
}
|
||||
None => (String::new(), ProtoGrammarType::None.into()),
|
||||
None => None,
|
||||
};
|
||||
|
||||
let parameters = NextTokenChooserParameters {
|
||||
let parameters = ValidParameters {
|
||||
temperature,
|
||||
repetition_penalty,
|
||||
frequency_penalty,
|
||||
|
|
@ -374,9 +366,8 @@ impl Validation {
|
|||
seed,
|
||||
watermark,
|
||||
grammar,
|
||||
grammar_type,
|
||||
};
|
||||
let stopping_parameters = StoppingCriteriaParameters {
|
||||
let stopping_parameters = ValidStoppingParameters {
|
||||
max_new_tokens,
|
||||
stop_sequences,
|
||||
ignore_eos_token: false,
|
||||
|
|
@ -458,6 +449,7 @@ fn format_from_mimetype(mimetype: &str) -> Option<ImageFormat> {
|
|||
_ => None,
|
||||
}
|
||||
}
|
||||
|
||||
fn format_to_mimetype(format: ImageFormat) -> String {
|
||||
match format {
|
||||
ImageFormat::Png => "image/png",
|
||||
|
|
@ -636,14 +628,55 @@ type TokenizerRequest = (
|
|||
Span,
|
||||
);
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub(crate) enum ValidGrammar {
|
||||
Json(String),
|
||||
Regex(String),
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub(crate) struct ValidParameters {
|
||||
/// / exponential scaling output probability distribution
|
||||
pub temperature: f32,
|
||||
/// / restricting to the k highest probability elements
|
||||
pub top_k: u32,
|
||||
/// / restricting to top tokens summing to prob_cut_off <= prob_cut_off
|
||||
pub top_p: f32,
|
||||
/// / restricting to top tokens summing to prob_cut_off <= prob_cut_off
|
||||
pub typical_p: f32,
|
||||
/// / apply sampling on the logits
|
||||
pub do_sample: bool,
|
||||
/// / random seed for sampling
|
||||
pub seed: u64,
|
||||
/// / repetition penalty
|
||||
pub repetition_penalty: f32,
|
||||
/// / frequency penalty
|
||||
pub frequency_penalty: f32,
|
||||
/// / token watermarking using "A Watermark for Large Language Models"
|
||||
pub watermark: bool,
|
||||
/// / grammar (applied if not empty)
|
||||
pub grammar: Option<ValidGrammar>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub(crate) struct ValidStoppingParameters {
|
||||
/// / Maximum number of generated tokens
|
||||
pub max_new_tokens: u32,
|
||||
/// / Optional stopping sequences
|
||||
pub stop_sequences: Vec<String>,
|
||||
/// / Ignore end of sequence token
|
||||
/// / used for benchmarking
|
||||
pub ignore_eos_token: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub(crate) struct ValidGenerateRequest {
|
||||
pub inputs: Vec<InputChunk>,
|
||||
pub input_length: u32,
|
||||
pub truncate: u32,
|
||||
pub decoder_input_details: bool,
|
||||
pub parameters: NextTokenChooserParameters,
|
||||
pub stopping_parameters: StoppingCriteriaParameters,
|
||||
pub parameters: ValidParameters,
|
||||
pub stopping_parameters: ValidStoppingParameters,
|
||||
pub top_n_tokens: u32,
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -12,8 +12,8 @@ gen-server:
|
|||
# Compile protos
|
||||
pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no-cache-dir
|
||||
mkdir text_generation_server/pb || true
|
||||
python -m grpc_tools.protoc -I../proto --python_out=text_generation_server/pb \
|
||||
--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/generate.proto
|
||||
python -m grpc_tools.protoc -I../proto/v3 --python_out=text_generation_server/pb \
|
||||
--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/v3/generate.proto
|
||||
find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
|
||||
touch text_generation_server/pb/__init__.py
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue