huggingface/text-generation-inference
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Merge branch 'main' into ci_amd3

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This commit is contained in:
fxmarty 2024-07-01 14:14:46 +02:00
commit 59849777de
34 changed files with 7255 additions and 6516 deletions
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2
.github/workflows/build.yaml vendored
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@ -225,7 +225,7 @@ jobs:
runs-on: ["self-hosted", "${{ needs.build-and-push.outputs.runs_on }}", "multi-gpu"]
if: needs.build-and-push.outputs.runs_on != 'ubuntu-latest'
env:
PYTEST_FLAGS: ${{ github.ref == 'refs/heads/main' && '--release' || '' }}
PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main') && '--release' || '' }}
steps:
- name: Checkout repository
uses: actions/checkout@v4

599
Cargo.lock generated
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2
Cargo.toml
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@ -9,7 +9,7 @@ members = [
resolver = "2"
[workspace.package]
version = "2.0.5-dev0"
version = "2.1.1-dev0"
edition = "2021"
authors = ["Olivier Dehaene"]
homepage = "https://github.com/huggingface/text-generation-inference"

8
README.md
View File

@ -75,9 +75,11 @@ For a detailed starting guide, please see the [Quick Tour](https://huggingface.c
```shell
model=HuggingFaceH4/zephyr-7b-beta
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
# share a volume with the Docker container to avoid downloading weights every run
volume=$PWD/data
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id $model
```
And then you can make requests like
@ -91,7 +93,7 @@ curl 127.0.0.1:8080/generate_stream \
**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0-rocm --model-id $model` instead of the command above.
**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.1.0-rocm --model-id $model` instead of the command above.
To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
```

2
docs/source/installation_amd.md
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@ -11,7 +11,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
--device=/dev/kfd --device=/dev/dri --group-add video \
--ipc=host --shm-size 256g --net host -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:2.0.4-rocm \
ghcr.io/huggingface/text-generation-inference:2.1.0-rocm \
--model-id $model
```

2
docs/source/installation_nvidia.md
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@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 64g -p 8080:80 -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:2.0.4 \
ghcr.io/huggingface/text-generation-inference:2.1.0 \
--model-id $model
```

4
docs/source/quicktour.md
View File

@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:2.0.4 \
ghcr.io/huggingface/text-generation-inference:2.1.0 \
--model-id $model
```
@ -88,7 +88,7 @@ curl 127.0.0.1:8080/generate \
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
```bash
docker run ghcr.io/huggingface/text-generation-inference:2.0.4 --help
docker run ghcr.io/huggingface/text-generation-inference:2.1.0 --help
```
</Tip>

1
docs/source/supported_models.md
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@ -10,6 +10,7 @@ Text Generation Inference enables serving optimized models on specific hardware
- [Llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- [Gemma](https://huggingface.co/google/gemma-7b)
- [Gemma2](https://huggingface.co/google/gemma2-9b)
- [Cohere](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
- [Dbrx](https://huggingface.co/databricks/dbrx-instruct)
- [Mamba](https://huggingface.co/state-spaces/mamba-2.8b-slimpj)

84
integration-tests/models/__snapshots__/test_flash_llama_gptq_marlin/test_flash_llama_gptq_marlin.json
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@ -1,84 +0,0 @@
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84
integration-tests/models/__snapshots__/test_flash_llama_gptq_marlin/test_flash_llama_gptq_marlin_all_params.json
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@ -1,84 +0,0 @@
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}

338
integration-tests/models/__snapshots__/test_flash_llama_gptq_marlin/test_flash_llama_gptq_marlin_load.json
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@ -1,338 +0,0 @@
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11124
integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json
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20
integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_simple.json
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@ -8,61 +8,61 @@
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38
integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_two_images.json
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@ -8,115 +8,115 @@
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"special": true,
"text": "<end_of_utterance>"
},

46
launcher/src/main.rs
View File

@ -898,13 +898,20 @@ enum LauncherError {
WebserverCannotStart,
}
fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), LauncherError> {
fn download_convert_model(
model_id: &str,
revision: Option<&str>,
trust_remote_code: bool,
huggingface_hub_cache: Option<&str>,
weights_cache_override: Option<&str>,
running: Arc<AtomicBool>,
) -> Result<(), LauncherError> {
// Enter download tracing span
let _span = tracing::span!(tracing::Level::INFO, "download").entered();
let mut download_args = vec![
"download-weights".to_string(),
args.model_id.to_string(),
model_id.to_string(),
"--extension".to_string(),
".safetensors".to_string(),
"--logger-level".to_string(),
@ -913,13 +920,13 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
];
// Model optional revision
if let Some(revision) = &args.revision {
if let Some(revision) = &revision {
download_args.push("--revision".to_string());
download_args.push(revision.to_string())
}
// Trust remote code for automatic peft fusion
if args.trust_remote_code {
if trust_remote_code {
download_args.push("--trust-remote-code".to_string());
}
@ -934,7 +941,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
// If huggingface_hub_cache is set, pass it to the download process
// Useful when running inside a docker container
if let Some(ref huggingface_hub_cache) = args.huggingface_hub_cache {
if let Some(ref huggingface_hub_cache) = huggingface_hub_cache {
envs.push(("HUGGINGFACE_HUB_CACHE".into(), huggingface_hub_cache.into()));
};
@ -952,7 +959,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
// If args.weights_cache_override is some, pass it to the download process
// Useful when running inside a HuggingFace Inference Endpoint
if let Some(weights_cache_override) = &args.weights_cache_override {
if let Some(weights_cache_override) = &weights_cache_override {
envs.push((
"WEIGHTS_CACHE_OVERRIDE".into(),
weights_cache_override.into(),
@ -960,7 +967,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
};
// Start process
tracing::info!("Starting download process.");
tracing::info!("Starting check and download process for {model_id}");
let mut download_process = match Command::new("text-generation-server")
.args(download_args)
.env_clear()
@ -1002,7 +1009,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
loop {
if let Some(status) = download_process.try_wait().unwrap() {
if status.success() {
tracing::info!("Successfully downloaded weights.");
tracing::info!("Successfully downloaded weights for {model_id}");
break;
}
@ -1557,7 +1564,28 @@ fn main() -> Result<(), LauncherError> {
.expect("Error setting Ctrl-C handler");
// Download and convert model weights
download_convert_model(&args, running.clone())?;
download_convert_model(
&args.model_id,
args.revision.as_deref(),
args.trust_remote_code,
args.huggingface_hub_cache.as_deref(),
args.weights_cache_override.as_deref(),
running.clone(),
)?;
// Download and convert lora adapters if any
if let Some(lora_adapters) = &args.lora_adapters {
for adapter in lora_adapters.split(',') {
download_convert_model(
adapter,
None,
args.trust_remote_code,
args.huggingface_hub_cache.as_deref(),
args.weights_cache_override.as_deref(),
running.clone(),
)?;
}
}
if !running.load(Ordering::SeqCst) {
// Launcher was asked to stop

7
router/Cargo.toml
View File

@ -22,9 +22,10 @@ text-generation-client = { path = "client" }
clap = { version = "4.4.5", features = ["derive", "env"] }
futures = "0.3.28"
hf-hub = { workspace = true }
itertools = "0.10"
jsonschema = { version = "0.17.1", features = ["draft202012"] }
metrics = "0.21.1"
metrics-exporter-prometheus = { version = "0.12.1", features = [] }
metrics-exporter-prometheus = { version = "0.15.1", features = [] }
nohash-hasher = "0.2.0"
opentelemetry = { version = "0.20.0", features = ["rt-tokio"] }
opentelemetry-otlp = "0.13.0"
@ -37,9 +38,9 @@ tokenizers = { workspace = true}
tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
tokio-stream = "0.1.14"
tower-http = { version = "0.5.1", features = ["cors"] }
tracing = "0.1.37"
tracing = "0.1.40"
tracing-opentelemetry = "0.21.0"
tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] }
tracing-subscriber = { version = "0.3.18", features = ["json", "env-filter"] }
utoipa = { version = "4.2.0", features = ["axum_extras"] }
utoipa-swagger-ui = { version = "6.0.0", features = ["axum"] }
ngrok = { version = "0.13.1", features = ["axum"], optional = true }

13
router/src/config.rs
View File

@ -71,10 +71,12 @@ fn get_unpadded_features(
let current_aspect_ratio: f64 = current_width as f64 / current_height as f64;
let (current_height, current_width) = if aspect_ratio > current_aspect_ratio {
let new_height = (height * current_width) / width;
(new_height, current_width)
let padding = (current_height - new_height) / 2;
(current_height - (2 * padding), current_width)
} else {
let new_width = (width * current_height) / height;
(current_height, new_width)
let padding = (current_width - new_width) / 2;
(current_height, current_width - (2 * padding))
};
let unpadded_features = current_height * current_width;
@ -88,7 +90,9 @@ impl LlavaNext {
let patch_size = self.vision_config.patch_size;
assert!(image_size % patch_size == 0);
let npatches = image_size / patch_size;
let (num_patch_height, num_patch_width) =
// Dimensions are intentionally swapped to be bug-compatible with
// upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
let (num_patch_width, num_patch_height) =
get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size);
let (unpadded_features, newline_features) =
@ -112,7 +116,7 @@ pub struct Idefics2 {}
impl Idefics2 {
pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize {
320
64
}
}
@ -158,6 +162,7 @@ pub enum Config {
Baichuan,
Paligemma(Paligemma),
Gemma,
Gemma2,
Cohere,
Drbx,
Falcon,

22
router/src/lib.rs
View File

@ -61,6 +61,9 @@ pub struct HubTokenizerConfig {
pub bos_token: Option<String>,
#[serde(deserialize_with = "token_serde::deserialize")]
pub eos_token: Option<String>,
pub tokenizer_class: Option<String>,
pub add_bos_token: Option<bool>,
pub add_eos_token: Option<bool>,
}
impl HubTokenizerConfig {
@ -70,6 +73,25 @@ impl HubTokenizerConfig {
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "processor_class")]
pub enum HubPreprocessorConfig {
Idefics2Processor(Idefics2Preprocessor),
}
impl HubPreprocessorConfig {
pub fn from_file<P: AsRef<std::path::Path>>(filename: P) -> Option<Self> {
let content = std::fs::read_to_string(filename).ok()?;
serde_json::from_str(&content).ok()
}
}
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct Idefics2Preprocessor {
#[serde(default)]
do_image_splitting: bool,
}
#[derive(Debug, Clone, Deserialize, Default)]
pub struct HubProcessorConfig {
pub chat_template: Option<ChatTemplateVersions>,

135
router/src/main.rs
View File

@ -13,9 +13,11 @@ use std::io::BufReader;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::path::{Path, PathBuf};
use text_generation_router::config::Config;
use text_generation_router::{server, HubModelInfo, HubProcessorConfig, HubTokenizerConfig};
use text_generation_router::{
server, HubModelInfo, HubPreprocessorConfig, HubProcessorConfig, HubTokenizerConfig,
};
use thiserror::Error;
use tokenizers::Tokenizer;
use tokenizers::{processors::template::TemplateProcessing, Tokenizer};
use tower_http::cors::AllowOrigin;
use tracing_subscriber::layer::SubscriberExt;
use tracing_subscriber::util::SubscriberInitExt;
@ -214,6 +216,7 @@ async fn main() -> Result<(), RouterError> {
tokenizer_filename,
config_filename,
tokenizer_config_filename,
preprocessor_config_filename,
processor_config_filename,
model_info,
) = match api {
@ -221,6 +224,7 @@ async fn main() -> Result<(), RouterError> {
Some(local_path.join("tokenizer.json")),
Some(local_path.join("config.json")),
Some(local_path.join("tokenizer_config.json")),
Some(local_path.join("preprocessor_config.json")),
Some(local_path.join("processor_config.json")),
None,
),
@ -237,6 +241,7 @@ async fn main() -> Result<(), RouterError> {
};
let config_filename = api_repo.get("config.json").await.ok();
let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok();
let preprocessor_config_filename = api_repo.get("preprocessor_config.json").await.ok();
let processor_config_filename = api_repo.get("processor_config.json").await.ok();
let model_info = if let Some(model_info) = get_model_info(&api_repo).await {
@ -249,6 +254,7 @@ async fn main() -> Result<(), RouterError> {
tokenizer_filename,
config_filename,
tokenizer_config_filename,
preprocessor_config_filename,
processor_config_filename,
model_info,
)
@ -263,13 +269,12 @@ async fn main() -> Result<(), RouterError> {
repo.get("tokenizer.json"),
repo.get("config.json"),
repo.get("tokenizer_config.json"),
repo.get("preprocessor_config.json"),
repo.get("processor_config.json"),
None,
)
}
};
let tokenizer: Option<Tokenizer> =
tokenizer_filename.and_then(|filename| Tokenizer::from_file(filename).ok());
let config: Option<Config> = config_filename.and_then(|filename| {
std::fs::read_to_string(filename)
.ok()
@ -300,6 +305,23 @@ async fn main() -> Result<(), RouterError> {
HubTokenizerConfig::default()
});
let tokenizer: Option<Tokenizer> = tokenizer_filename.and_then(|filename| {
let mut tokenizer = Tokenizer::from_file(filename).ok();
if let Some(tokenizer) = &mut tokenizer {
if let Some(class) = &tokenizer_config.tokenizer_class {
if class == "LlamaTokenizer" || class == "LlamaTokenizerFast"{
if let Ok(post_processor) = create_post_processor(tokenizer, &tokenizer_config) {
tracing::info!("Overriding LlamaTokenizer with TemplateProcessing to follow python override defined in https://github.com/huggingface/transformers/blob/4aa17d00690b7f82c95bb2949ea57e22c35b4336/src/transformers/models/llama/tokenization_llama_fast.py#L203-L205");
tokenizer.with_post_processor(post_processor);
}
}
}
}
tokenizer
});
let preprocessor_config =
preprocessor_config_filename.and_then(HubPreprocessorConfig::from_file);
let processor_config = processor_config_filename
.and_then(HubProcessorConfig::from_file)
.unwrap_or_default();
@ -361,6 +383,7 @@ async fn main() -> Result<(), RouterError> {
ngrok_authtoken,
ngrok_edge,
tokenizer_config,
preprocessor_config,
processor_config,
messages_api_enabled,
disable_grammar_support,
@ -504,6 +527,77 @@ pub async fn get_tokenizer_config(api_repo: &ApiRepo) -> Option<HubTokenizerConf
Some(tokenizer_config)
}
/// Create a post_processor for the LlamaTokenizer
pub fn create_post_processor(
tokenizer: &Tokenizer,
tokenizer_config: &HubTokenizerConfig,
) -> Result<TemplateProcessing, tokenizers::processors::template::TemplateProcessingBuilderError> {
let add_bos_token = tokenizer_config.add_bos_token.unwrap_or(true);
let add_eos_token = tokenizer_config.add_eos_token.unwrap_or(false);
let bos_token = tokenizer_config.bos_token.as_ref();
let eos_token = tokenizer_config.eos_token.as_ref();
if add_bos_token && bos_token.is_none() {
panic!("add_bos_token = true but bos_token is None");
}
if add_eos_token && eos_token.is_none() {
panic!("add_eos_token = true but eos_token is None");
}
let mut single = Vec::new();
let mut pair = Vec::new();
let mut special_tokens = Vec::new();
if add_bos_token {
if let Some(bos) = bos_token {
let bos_token_id = tokenizer
.token_to_id(bos)
.expect("Should have found the bos token id");
special_tokens.push((bos.clone(), bos_token_id));
single.push(format!("{}:0", bos));
pair.push(format!("{}:0", bos));
}
}
single.push("$A:0".to_string());
pair.push("$A:0".to_string());
if add_eos_token {
if let Some(eos) = eos_token {
let eos_token_id = tokenizer
.token_to_id(eos)
.expect("Should have found the eos token id");
special_tokens.push((eos.clone(), eos_token_id));
single.push(format!("{}:0", eos));
pair.push(format!("{}:0", eos));
}
}
if add_bos_token {
if let Some(bos) = bos_token {
pair.push(format!("{}:1", bos));
}
}
pair.push("$B:1".to_string());
if add_eos_token {
if let Some(eos) = eos_token {
pair.push(format!("{}:1", eos));
}
}
let post_processor = TemplateProcessing::builder()
.try_single(single)?
.try_pair(pair)?
.special_tokens(special_tokens)
.build()?;
Ok(post_processor)
}
#[derive(Debug, Error)]
enum RouterError {
#[error("Argument validation error: {0}")]
@ -513,3 +607,36 @@ enum RouterError {
#[error("Tokio runtime failed to start: {0}")]
Tokio(#[from] std::io::Error),
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_create_post_processor() {
let tokenizer_config = HubTokenizerConfig {
add_bos_token: None,
add_eos_token: None,
bos_token: Some("<s>".to_string()),
eos_token: Some("</s>".to_string()),
chat_template: None,
tokenizer_class: None,
completion_template: None,
};
let tokenizer =
Tokenizer::from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", None).unwrap();
let post_processor = create_post_processor(&tokenizer, &tokenizer_config).unwrap();
let expected = TemplateProcessing::builder()
.try_single("<s>:0 $A:0 <s>:1")
.unwrap()
.try_pair("<s>:0 $A:0 $B:1")
.unwrap()
.special_tokens(vec![("<s>".to_string(), 1)])
.build()
.unwrap();
assert_eq!(post_processor, expected);
}
}

8
router/src/server.rs
View File

@ -12,9 +12,9 @@ use crate::kserve::{
use crate::validation::ValidationError;
use crate::{
BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse,
Usage, Validation,
GenerateResponse, GrammarType, HubModelInfo, HubPreprocessorConfig, HubProcessorConfig,
HubTokenizerConfig, Info, Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse,
Token, TokenizeResponse, Usage, Validation,
};
use crate::{
ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
@ -1423,6 +1423,7 @@ pub async fn run(
_ngrok_authtoken: Option<String>,
_ngrok_edge: Option<String>,
tokenizer_config: HubTokenizerConfig,
preprocessor_config: Option<HubPreprocessorConfig>,
processor_config: HubProcessorConfig,
messages_api_enabled: bool,
grammar_support: bool,
@ -1636,6 +1637,7 @@ pub async fn run(
validation_workers,
tokenizer,
config,
preprocessor_config,
max_best_of,
max_stop_sequences,
max_top_n_tokens,

242
router/src/validation.rs
View File

@ -1,13 +1,16 @@
/// Payload validation logic
use crate::config::Config;
use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
use crate::{GenerateParameters, GenerateRequest, GrammarType};
use crate::{
GenerateParameters, GenerateRequest, GrammarType, HubPreprocessorConfig, Idefics2Preprocessor,
};
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 std::iter;
use text_generation_client::{Chunk, Image, InputChunk};
use thiserror::Error;
use tokenizers::tokenizer::Tokenizer;
@ -36,6 +39,7 @@ impl Validation {
workers: usize,
tokenizer: Option<Tokenizer>,
config: Option<Config>,
preprocessor_config: Option<HubPreprocessorConfig>,
max_best_of: usize,
max_stop_sequences: usize,
max_top_n_tokens: u32,
@ -53,12 +57,18 @@ impl Validation {
for _ in 0..workers {
let tokenizer_clone = tokenizer.clone();
let config_clone = config.clone();
let preprocessor_config_clone = preprocessor_config.clone();
let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
senders.push(tokenizer_sender);
// Spawn worker
tokio::task::spawn_blocking(move || {
tokenizer_worker(tokenizer_clone, config_clone, tokenizer_receiver)
tokenizer_worker(
tokenizer_clone,
config_clone,
preprocessor_config_clone,
tokenizer_receiver,
)
});
}
@ -422,13 +432,20 @@ async fn round_robin_task(
fn tokenizer_worker(
tokenizer: Tokenizer,
config: Option<Config>,
preprocessor_config: Option<HubPreprocessorConfig>,
mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
) {
// Loop over requests
while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() {
parent_span.in_scope(|| {
response_tx
.send(prepare_input(inputs, truncate, &tokenizer, &config))
.send(prepare_input(
inputs,
truncate,
&tokenizer,
config.as_ref(),
preprocessor_config.as_ref(),
))
.unwrap_or(())
})
}
@ -508,16 +525,67 @@ fn fetch_image(input: &str) -> Result<(Vec<u8>, String, usize, usize), Validatio
}
}
fn image_tokens(
config: &Config,
preprocessor_config: Option<&HubPreprocessorConfig>,
height: usize,
width: usize,
) -> String {
use Config::*;
use HubPreprocessorConfig::*;
match config {
Idefics => "<image>".to_string(),
Idefics2(config) => {
const FAKE: &str = "<fake_token_around_image>";
const IMAGE: &str = "<image>";
let slots = config.get_number_of_features(height, width);
let mut image_string = String::with_capacity(2 * FAKE.len() + slots * IMAGE.len());
image_string.push_str(FAKE);
image_string.extend(iter::repeat(IMAGE).take(slots));
image_string.push_str(FAKE);
if matches!(
preprocessor_config,
Some(Idefics2Processor(Idefics2Preprocessor {
do_image_splitting: true,
..
}))
) {
image_string = image_string.repeat(5);
};
image_string
}
Paligemma(config) => "<image>".repeat(config.get_number_of_features(height, width)),
LlavaNext(config) => "<image>".repeat(config.get_number_of_features(height, width)),
_ => unimplemented!("Images tokens are not supported for this model configuration"),
}
}
fn image_tokens_fixup(config: &Config, text: String) -> String {
match config {
Config::Idefics2(_) => {
const FAKE: &str = "<fake_token_around_image>";
text.replace(&format!("{FAKE}{FAKE}"), FAKE)
}
_ => text,
}
}
/// Get input length and optionally truncate it
fn prepare_input(
inputs: String,
_truncate: Option<usize>,
tokenizer: &Tokenizer,
config: &Option<Config>,
config: Option<&Config>,
preprocessor_config: Option<&HubPreprocessorConfig>,
) -> Result<(tokenizers::Encoding, Vec<InputChunk>), ValidationError> {
use Config::*;
static RE: Lazy<Regex> = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap());
let (tokenizer_query, input_chunks) = match config {
Some(Config::LlavaNext(config)) => {
Some(config @ (Idefics | Idefics2(_) | Paligemma(_) | LlavaNext(_))) => {
let mut input_chunks = Vec::new();
let mut tokenizer_query = String::with_capacity(inputs.len());
let mut start = 0;
@ -529,88 +597,17 @@ fn prepare_input(
tokenizer_query.push_str(&inputs[start..chunk_start]);
}
let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
let slots = config.get_number_of_features(height, width);
input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
tokenizer_query.push_str(&"<image>".repeat(slots));
tokenizer_query.push_str(&image_tokens(config, preprocessor_config, height, width));
start = chunk_end;
}
if start != inputs.len() {
input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
tokenizer_query.push_str(&inputs[start..]);
}
(tokenizer_query, input_chunks)
}
Some(Config::Paligemma(config)) => {
let mut input_chunks = Vec::new();
let mut tokenizer_query = String::with_capacity(inputs.len());
let mut start = 0;
for chunk in RE.find_iter(&inputs) {
let chunk_start = chunk.start();
let chunk_end = chunk.end();
if chunk_start != start {
input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
tokenizer_query.push_str(&inputs[start..chunk_start]);
}
let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
let slots = config.get_number_of_features(height, width);
input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
tokenizer_query.push_str(&"<image>".repeat(slots));
start = chunk_end;
}
if start != inputs.len() {
input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
tokenizer_query.push_str(&inputs[start..]);
}
(tokenizer_query, input_chunks)
}
Some(Config::Idefics2(config)) => {
let mut input_chunks = Vec::new();
let mut tokenizer_query = String::with_capacity(inputs.len());
let mut start = 0;
for chunk in RE.find_iter(&inputs) {
let chunk_start = chunk.start();
let chunk_end = chunk.end();
if chunk_start != start {
input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
tokenizer_query.push_str(&inputs[start..chunk_start]);
}
let (data, mimetype, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?;
let slots = config.get_number_of_features(height, width);
tokenizer_query.push_str("<fake_token_around_image>");
tokenizer_query.push_str(&"<image>".repeat(slots));
tokenizer_query.push_str("<fake_token_around_image>");
input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
start = chunk_end;
}
if start != inputs.len() {
input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
tokenizer_query.push_str(&inputs[start..]);
}
(tokenizer_query, input_chunks)
}
Some(Config::Idefics) => {
let mut input_chunks = Vec::new();
let mut tokenizer_query = String::with_capacity(inputs.len());
let mut start = 0;
for chunk in RE.find_iter(&inputs) {
let chunk_start = chunk.start();
let chunk_end = chunk.end();
if chunk_start != start {
input_chunks.push(Chunk::Text(inputs[start..chunk_start].to_string()).into());
tokenizer_query.push_str(&inputs[start..chunk_start]);
}
let (data, mimetype, _height, _width) =
fetch_image(&inputs[chunk_start..chunk_end])?;
let slots = 1;
tokenizer_query.push_str(&"<image>".repeat(slots));
input_chunks.push(Chunk::Image(Image { data, mimetype }).into());
start = chunk_end;
}
if start != inputs.len() {
input_chunks.push(Chunk::Text(inputs[start..].to_string()).into());
tokenizer_query.push_str(&inputs[start..]);
}
tokenizer_query = image_tokens_fixup(config, tokenizer_query);
(tokenizer_query, input_chunks)
}
_ => (inputs.clone(), vec![Chunk::Text(inputs).into()]),
@ -750,7 +747,7 @@ pub enum ValidationError {
#[cfg(test)]
mod tests {
use super::*;
use crate::config::{PaliTextConfig, Paligemma};
use crate::config::{Idefics2, PaliTextConfig, Paligemma};
use crate::default_parameters;
use crate::tests::get_tokenizer;
@ -769,6 +766,7 @@ mod tests {
workers,
tokenizer,
config,
None,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
@ -803,6 +801,7 @@ mod tests {
workers,
tokenizer,
config,
None,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
@ -836,6 +835,7 @@ mod tests {
workers,
tokenizer,
config,
None,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
@ -874,6 +874,7 @@ mod tests {
workers,
tokenizer,
config,
None,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
@ -941,6 +942,7 @@ mod tests {
workers,
tokenizer,
config,
None,
max_best_of,
max_stop_sequences,
max_top_n_tokens,
@ -1026,6 +1028,7 @@ mod tests {
workers,
tokenizer,
Some(config),
None,
max_best_of,
max_stop_sequence,
max_top_n_tokens,
@ -1058,4 +1061,83 @@ mod tests {
"Failed to process images",
);
}
#[tokio::test]
async fn test_idefics2_correct_n_fake_tokens() {
let pixel_data = STANDARD.decode(PIXEL_GIF).unwrap();
let tokenizer = Some(get_tokenizer().await);
let max_best_of = 2;
let max_stop_sequence = 3;
let max_top_n_tokens = 4;
let max_input_length = 5;
let max_total_tokens = 6;
let disable_grammar_support = true;
let workers = 1;
let config = Config::Idefics2(Idefics2 {});
let validation = Validation::new(
workers,
tokenizer,
Some(config),
Some(HubPreprocessorConfig::Idefics2Processor(
Idefics2Preprocessor {
do_image_splitting: true,
},
)),
max_best_of,
max_stop_sequence,
max_top_n_tokens,
max_input_length,
max_total_tokens,
disable_grammar_support,
);
let (encoding, chunks) = match validation
.tokenize(
format!(
"test![](data:image/gif;base64,{})![](data:image/gif;base64,{})",
PIXEL_GIF, PIXEL_GIF
),
None,
)
.await
{
Ok(Some((encoding, chunks))) => (encoding, chunks),
_ => panic!("Unexpected tokenization failure"),
};
assert!(
chunks
== vec![
Chunk::Text("test".to_string()).into(),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
})
.into(),
Chunk::Image(Image {
data: pixel_data.clone(),
mimetype: "image/gif".to_string()
})
.into()
],
"Failed to process images",
);
// Verify the number of fake tokens:
//
// - Two images surrounded/separated by a fake token = 3.
// - Both are split in 5 subimages, separated by a fake token: 2 * 4
//
// Fake tokens get split up by the testing tokenizer, but we don't care.
assert_eq!(
encoding
.get_tokens()
.iter()
.filter(|t| *t == "fake")
.count(),
11
);
}
}

10
server/text_generation_server/layers/gptq/__init__.py
View File

@ -7,6 +7,16 @@ from text_generation_server.utils.import_utils import (
)
@dataclass
class GPTQParams:
bits: int
checkpoint_format: Optional[str]
groupsize: int
desc_act: bool
quant_method: str
sym: bool
@dataclass
class GPTQWeight:
qweight: torch.Tensor

27
server/text_generation_server/layers/linear.py
View File

@ -166,12 +166,17 @@ def get_linear(weight, bias, quantize):
elif quantize == "gptq":
from text_generation_server.layers.gptq import GPTQWeight
if not isinstance(weight, GPTQWeight):
raise NotImplementedError(
f"The passed weight is not `gptq` compatible, loader needs to be updated."
from text_generation_server.layers.marlin import (
GPTQMarlinLinear,
GPTQMarlinWeight,
)
if isinstance(weight, GPTQMarlinWeight):
linear = GPTQMarlinLinear(
weight=weight,
bias=bias,
)
elif isinstance(weight, GPTQWeight):
if weight.use_exllama:
try:
from text_generation_server.layers.gptq import (
@ -195,6 +200,11 @@ def get_linear(weight, bias, quantize):
weight.bits,
weight.groupsize,
)
else:
raise NotImplementedError(
f"The passed weight is not `gptq` compatible, loader needs to be updated."
)
elif quantize == "awq":
from text_generation_server.layers.gptq import GPTQWeight
@ -226,18 +236,11 @@ def get_linear(weight, bias, quantize):
from text_generation_server.layers.marlin import (
GPTQMarlin24Linear,
GPTQMarlin24Weight,
GPTQMarlinLinear,
GPTQMarlinWeight,
MarlinLinear,
MarlinWeight,
)
if isinstance(weight, GPTQMarlinWeight):
linear = GPTQMarlinLinear(
weight=weight,
bias=bias,
)
elif isinstance(weight, GPTQMarlin24Weight):
if isinstance(weight, GPTQMarlin24Weight):
linear = GPTQMarlin24Linear(
weight=weight,
bias=bias,

15
server/text_generation_server/layers/marlin.py
View File

@ -3,6 +3,8 @@ from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from text_generation_server.layers.gptq import GPTQParams
from text_generation_server.utils.import_utils import SYSTEM
try:
@ -22,6 +24,19 @@ GPTQ_MARLIN_GROUP_SIZES = [-1, 32, 64, 128]
MARLIN_TILE_SIZE = 16
def can_use_gptq_marlin(gptq_params: GPTQParams, quantize: str) -> bool:
return (
SYSTEM == "cuda"
and marlin_kernels is not None
and has_sm_8_0
and quantize == "gptq"
and gptq_params.quant_method == "gptq"
and gptq_params.bits in GPTQ_MARLIN_BITS
and gptq_params.groupsize in GPTQ_MARLIN_GROUP_SIZES
and gptq_params.sym
)
def _check_marlin_kernels():
if not (SYSTEM == "cuda" and has_sm_8_0):
raise NotImplementedError(

30
server/text_generation_server/models/__init__.py
View File

@ -68,6 +68,9 @@ try:
from text_generation_server.models.flash_gemma import (
FlashGemma,
)
from text_generation_server.models.flash_gemma2 import (
FlashGemma2,
)
from text_generation_server.models.pali_gemma import (
PaliGemma,
)
@ -102,6 +105,7 @@ if FLASH_ATTENTION:
__all__.append(FlashQwen2)
__all__.append(FlashStarcoder2)
__all__.append(FlashGemma)
__all__.append(FlashGemma2)
__all__.append(FlashCohere)
MAMBA_AVAILABLE = True
@ -145,6 +149,11 @@ class ModelType(enum.Enum):
"name": "Gemma",
"url": "https://huggingface.co/google/gemma-7b",
}
GEMMA2 = {
"type": "gemma2",
"name": "Gemma2",
"url": "https://huggingface.co/google/gemma2-9b",
}
COHERE = {
"type": "cohere",
"name": "Cohere",
@ -637,6 +646,27 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == GEMMA2:
if FLASH_ATTENTION:
return FlashGemma2(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma2"))
else:
return CausalLM(
model_id,
revision,
quantize=quantize,
speculator=speculator,
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == COHERE:
if FLASH_ATTENTION:

500
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py Normal file
View File

@ -0,0 +1,500 @@
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.layers.attention import (
paged_attention,
attention,
reshape_and_cache,
)
from text_generation_server.layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
TensorParallelEmbedding,
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
)
class Gemma2Config(PretrainedConfig):
def __init__(
self,
vocab_size=256128,
hidden_size=3072,
intermediate_size=24576,
num_hidden_layers=28,
num_attention_heads=16,
num_key_value_heads=16,
head_dim=256,
hidden_act="gelu_pytorch_tanh",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.head_dim = head_dim
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class Gemma2FastRMSNorm(FastRMSNorm):
@classmethod
def load(cls, prefix, weights, eps=1e-6):
dtype = weights.dtype
weights.dtype = torch.float32
weight = weights.get_tensor(f"{prefix}.weight") + 1
weights.dtype = dtype
new = cls(weight, eps)
new.dtype = dtype
return new
# perform the multiplication in full precision and downcast after
def forward(self, hidden_states, residual=None):
if residual is not None:
hidden_states += residual
residual = hidden_states
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * self.weight
return hidden_states.to(self.dtype), residual
def load_attention(config, prefix, weights):
if config.num_attention_heads != config.num_key_value_heads:
return _load_gqa(config, prefix, weights)
else:
return TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
dim=0,
weights=weights,
bias=False,
)
def _load_gqa(config, prefix: str, weights):
assert config.num_attention_heads % weights.process_group.size() == 0
weight = weights.get_multi_weights_col(
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
quantize=config.quantize,
dim=0,
)
if config.quantize not in ["gptq", "awq", "marlin"]:
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
head_size = config.head_dim
num_heads = config.num_attention_heads // weights.process_group.size()
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
assert list(weight.shape) == [
(num_heads + 2 * num_key_value_heads) * head_size,
config.hidden_size,
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
return TensorParallelColumnLinear(
get_linear(weight, bias=None, quantize=config.quantize)
)
class FlashGemma2Attention(torch.nn.Module):
def __init__(self, prefix: str, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.num_heads = config.num_attention_heads
self.head_size = config.head_dim
self.causal = causal
if is_sliding:
self.window_size = config.sliding_window
else:
self.window_size = -1
self.rotary_emb = PositionRotaryEmbedding.static(
config=config,
dim=self.head_size,
base=config.rope_theta,
device=weights.device,
)
# self.softmax_scale = self.head_size**-0.5
self.softmax_scale = config.query_pre_attn_scalar**-0.5
if self.num_heads % weights.process_group.size() != 0:
raise ValueError(
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
f"and `num_shards`: {weights.process_group.size()}"
)
self.num_heads = self.num_heads // weights.process_group.size()
self.num_key_value_heads = (
config.num_key_value_heads // weights.process_group.size()
)
self.query_key_value = load_attention(config, prefix, weights)
self.o_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.o_proj",
weights=weights,
bias=False,
)
self.num_groups = self.num_heads // self.num_key_value_heads
self.kv_head_mapping = torch.arange(
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
).repeat_interleave(self.num_groups)
def forward(
self,
hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
qkv = self.query_key_value(hidden_states)
query, kv = qkv.split(
[
self.head_size * self.num_heads,
2 * self.head_size * self.num_key_value_heads,
],
dim=1,
)
query = query.view(-1, self.num_heads, self.head_size)
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
# output tensor
attn_output = torch.empty_like(query)
# Prefill
if cu_seqlen_prefill is not None:
# flash attention
attention(
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
attn_output,
cu_seqlen_prefill,
max_s,
self.softmax_scale,
causal=self.causal,
window_size_left=self.window_size,
)
# Decode
else:
paged_attention(
attn_output,
query,
kv_cache[0],
kv_cache[1],
self.kv_head_mapping,
self.softmax_scale,
block_tables,
input_lengths,
max_s,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
class Gemma2MLP(nn.Module):
def __init__(self, prefix, config, weights):
super().__init__()
act = config.hidden_act
self.act = (
ACT2FN[act]
if "gelu" not in act
else lambda x: torch.nn.functional.gelu(
x,
approximate=(
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
),
)
)
# Fuse gate and up proj
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
config,
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
weights=weights,
dim=0,
bias=False,
)
self.down_proj = TensorParallelRowLinear.load(
config,
prefix=f"{prefix}.down_proj",
weights=weights,
bias=False,
)
self.intermediate_size = (
config.intermediate_size // weights.process_group.size()
)
def forward(self, hidden_states):
gate_up_states = self.gate_up_proj(hidden_states)
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
class FlashGemma2Layer(nn.Module):
def __init__(self, prefix, config, weights, causal: bool, is_sliding: bool):
super().__init__()
self.self_attn = FlashGemma2Attention(
prefix=f"{prefix}.self_attn",
config=config,
weights=weights,
causal=causal,
is_sliding=is_sliding,
)
self.mlp = Gemma2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
self.input_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
)
self.post_attention_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_attention_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.pre_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.pre_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
self.post_feedforward_layernorm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.post_feedforward_layernorm",
weights=weights,
eps=config.rms_norm_eps,
)
def forward(
self,
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
):
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
# Self Attention
attn_output = self.self_attn(
normed_hidden_states,
cos,
sin,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
# faster post attention rms norm
normed_attn_res_output, _ = self.post_attention_layernorm(attn_output)
normed_attn_res_output = normed_attn_res_output + res
res = normed_attn_res_output
pre_normed, _ = self.pre_feedforward_layernorm(normed_attn_res_output)
mlp_output = self.mlp(pre_normed)
post_hidden_states, _ = self.post_feedforward_layernorm(mlp_output)
return post_hidden_states, normed_attn_res_output
class FlashGemma2Model(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
process_group = weights.process_group
self.tp_rank = process_group.rank()
self.tp_world_size = process_group.size()
self.layers = nn.ModuleList(
[
FlashGemma2Layer(
prefix=f"{prefix}.layers.{layer_id}",
config=config,
weights=weights,
causal=causal,
is_sliding=layer_id % 2 == 0,
)
for layer_id in range(config.num_hidden_layers)
]
)
self.norm = Gemma2FastRMSNorm.load(
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
def forward(
self,
inputs_embeds: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
) -> torch.Tensor:
hidden_states = inputs_embeds
# Get rotary cos and sin for this forward
# Avoid to index in each layer
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
position_ids, max_s, hidden_states.dtype
)
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states,
residual,
cos,
sin,
cu_seqlen_prefill,
kv_cache[i],
block_tables,
slots,
input_lengths,
max_s,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class FlashGemma2ForCausalLM(torch.nn.Module):
def __init__(self, prefix, config, weights, causal: bool):
super().__init__()
embed_norm = config.hidden_size**0.5
if not prefix:
prefix = "model"
else:
prefix = f"{prefix}.model"
self.embed_tokens = TensorParallelEmbedding(
prefix=f"{prefix}.embed_tokens", weights=weights
)
self.embed_tokens.weight *= embed_norm
self.model = FlashGemma2Model(
prefix=prefix, config=config, weights=weights, causal=causal
)
self.lm_head = SpeculativeHead.load(
prefix=(
f"{prefix}.embed_tokens"
if config.tie_word_embeddings
else f"{prefix}.lm_head"
),
config=config,
weights=weights,
)
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
block_tables: torch.Tensor,
slots: torch.Tensor,
input_lengths: torch.Tensor,
max_s: int,
prefill_cache_indices: Optional[torch.Tensor],
lm_head_indices: Optional[torch.Tensor] = None,
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
input_embeds = self.embed_tokens(input_ids)
hidden_states = self.model(
input_embeds,
position_ids,
cu_seqlen_prefill,
kv_cache,
block_tables,
slots,
input_lengths,
max_s,
)
if lm_head_indices is not None:
hidden_states = hidden_states[lm_head_indices]
logits, speculative_logits = self.lm_head(hidden_states)
return logits, speculative_logits

2
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -375,8 +375,6 @@ class FlashGemmaModel(torch.nn.Module):
prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
)
self.gradient_checkpointing = False
self.head_size = self.layers[0].self_attn.head_size
self.num_heads = self.layers[0].self_attn.num_heads
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads

9
server/text_generation_server/models/custom_modeling/llava_next.py
View File

@ -39,7 +39,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
Args:
image_size (`tuple`):
The size of the input image in the format (width, height).
The size of the input image in the format (height, width).
grid_pinpoints (`List`):
A list containing possible resolutions. Each item in the list should be a tuple or list
of the form `(height, width)`.
@ -47,7 +47,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
The size of each image patch.
Returns:
tuple: The shape of the image patch grid in the format (width, height).
tuple: The shape of the image patch grid in the format (height, width).
"""
if not isinstance(grid_pinpoints, list):
raise ValueError("grid_pinpoints should be a list of tuples or lists")
@ -230,7 +230,10 @@ class LlavaNextForConditionalGeneration(nn.Module):
raise ValueError(
"The number of patches is not consistent with the image size."
)
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
# Dimensions are intentionally swapped to be bug-compatible with
# upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
num_patch_width, num_patch_height = get_anyres_image_grid_shape(
image_sizes[image_idx],
self.config.image_grid_pinpoints,
self.config.vision_config.image_size,

16
server/text_generation_server/models/flash_causal_lm.py
View File

@ -28,8 +28,12 @@ from text_generation_server.models.types import (
GeneratedText,
)
from text_generation_server.pb import generate_pb2
import text_generation_server.models.globals as tgi_globals
from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS
from text_generation_server.models.globals import (
MEM_POOL,
CUDA_GRAPHS,
get_adapter_to_index,
MODEL_ID,
)
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
from text_generation_server.utils.dist import MEMORY_FRACTION
from text_generation_server.utils.segments import SegmentConcatBuilder, find_segments
@ -233,7 +237,8 @@ class FlashCausalLMBatch(Batch):
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
adapter_index = tgi_globals.ADAPTER_TO_INDEX.get(r.adapter_id, 0)
ADAPTER_TO_INDEX = get_adapter_to_index()
adapter_index = ADAPTER_TO_INDEX.get(r.adapter_id, 0)
adapter_indices_list.append(torch.full((input_length,), adapter_index))
adapter_set.add(adapter_index)
@ -499,9 +504,8 @@ class FlashCausalLMBatch(Batch):
top_n_tokens.append(self.top_n_tokens[idx])
adapter_index = tgi_globals.ADAPTER_TO_INDEX.get(
self.requests[idx].adapter_id, 0
)
ADAPTER_TO_INDEX = get_adapter_to_index()
adapter_index = ADAPTER_TO_INDEX.get(self.requests[idx].adapter_id, 0)
adapter_set.add(adapter_index)
remaining_tokens = (

75
server/text_generation_server/models/flash_gemma2.py Normal file
View File

@ -0,0 +1,75 @@
import torch
import torch.distributed
from opentelemetry import trace
from typing import Optional
from transformers import PretrainedConfig, AutoTokenizer
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
FlashGemma2ForCausalLM,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
tracer = trace.get_tracer(__name__)
class FlashGemma2(FlashCausalLM):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGemma2 is only available on GPU")
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = PretrainedConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
if config.quantize in ["gptq", "awq", "marlin"]:
weights._set_gptq_params(model_id, revision)
# TODO hardcoded
prefix = ""
model = FlashGemma2ForCausalLM(prefix, config, weights, causal=True)
torch.distributed.barrier(group=self.process_group)
super(FlashGemma2, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
num_layers=len(model.model.layers),
num_kv_heads=model.model.num_key_value_heads,
head_size=model.model.head_size,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)

5
server/text_generation_server/models/globals.py
View File

@ -34,3 +34,8 @@ ADAPTER_TO_INDEX: Dict[str, int] = None
def set_adapter_to_index(adapter_to_index: Dict[str, int]):
global ADAPTER_TO_INDEX
ADAPTER_TO_INDEX = adapter_to_index
def get_adapter_to_index():
global ADAPTER_TO_INDEX
return ADAPTER_TO_INDEX

4
server/text_generation_server/models/pali_gemma.py
View File

@ -39,7 +39,9 @@ class PaliGemmaBatch(VlmCausalLMBatch):
# TODO do_convert_RGB should be on by default ?
image = image.convert("RGB")
image_input = processor.image_processor(image, return_tensors="pt")
full_text += image_text_replacement(image_input, config, image_id)
full_text += image_text_replacement(
processor, image_input, config, image_id
)
image_inputs.append(image_input)
else:
raise RuntimeError(f"Invalid chunk type {chunk_type}")

57
server/text_generation_server/models/vlm_causal_lm.py
View File

@ -1,3 +1,4 @@
from itertools import repeat
import torch
from PIL import Image
from io import BytesIO
@ -15,6 +16,9 @@ from text_generation_server.models.flash_mistral import (
tracer = trace.get_tracer(__name__)
IDEFICS2_FAKE_TOKEN = "<fake_token_around_image>"
IDEFICS2_IMAGE_TOKEN = "<image>"
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
"""
@ -22,7 +26,7 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
Args:
image_size (`tuple`):
The size of the input image in the format (width, height).
The size of the input image in the format (height, width).
grid_pinpoints (`List`):
A list containing possible resolutions. Each item in the list should be a tuple or list
of the form `(height, width)`.
@ -39,15 +43,13 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
return height // patch_size, width // patch_size
def image_text_replacement(image_input, config, image_id) -> str:
def image_text_replacement(processor, image_input, config, image_id: int) -> str:
if config.model_type == "idefics2":
# TODO technically depends on image splitting which is not implemented.
num_features = 320
return (
"<fake_token_around_image>"
+ "<image>" * num_features
+ "<fake_token_around_image>"
)
image_seq_len = 64
image_str = f"{IDEFICS2_FAKE_TOKEN}{IDEFICS2_IMAGE_TOKEN * image_seq_len}{IDEFICS2_FAKE_TOKEN}"
if processor.image_processor.do_image_splitting:
image_str *= 5
return image_str
elif config.model_type == "llava_next":
height, width = image_input["image_sizes"][image_id]
num_features = get_number_of_features(height, width, config)
@ -64,20 +66,35 @@ def image_text_replacement(image_input, config, image_id) -> str:
raise RuntimeError(f"Unknown config {config.model_type} for multimodal")
def image_text_replacement_fixup(config, text: str) -> str:
if config.model_type == "idefics2":
return text.replace(
f"{IDEFICS2_FAKE_TOKEN}{IDEFICS2_FAKE_TOKEN}", IDEFICS2_FAKE_TOKEN
)
return text
def get_unpadded_features(
height: int, width: int, npatches: int, num_patch_height: int, num_patch_width: int
original_height: int,
original_width: int,
npatches: int,
num_patch_height: int,
num_patch_width: int,
) -> Tuple[int, int]:
current_height = npatches * num_patch_height
current_width = npatches * num_patch_width
aspect_ratio: float = width / height
aspect_ratio: float = original_width / original_height
current_aspect_ratio: float = current_width / current_height
if aspect_ratio > current_aspect_ratio:
new_height = (height * current_width) // width
current_height = new_height
new_height = (original_height * current_width) // original_width
padding = (current_height - new_height) // 2
current_height = current_height - (2 * padding)
else:
new_width = (width * current_height) // height
current_width = new_width
new_width = (original_width * current_height) // original_height
padding = (current_width - new_width) // 2
current_width = current_width - (2 * padding)
unpadded_features = current_height * current_width
newline_features = current_height
@ -96,7 +113,9 @@ def get_number_of_features(height: int, width: int, config) -> int:
npatches = image_size // patch_size
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
# Dimensions are intentionally swapped to be bug-compatible with
# upstream: https://github.com/LLaVA-VL/LLaVA-NeXT/issues/59
num_patch_width, num_patch_height = get_anyres_image_grid_shape(
[height, width],
image_grid_pinpoints,
image_size,
@ -168,9 +187,13 @@ class VlmCausalLMBatch(FlashCausalLMBatch):
if chunk_type == "text":
full_text += chunk.text
elif chunk_type == "image":
full_text += image_text_replacement(image_inputs, config, image_id)
full_text += image_text_replacement(
processor, image_inputs, config, image_id
)
image_id += 1
full_text = image_text_replacement_fixup(config, full_text)
batch_inputs.append(full_text)
max_truncation = max(max_truncation, r.truncate)

200
server/text_generation_server/utils/weights.py
View File

@ -1,25 +1,15 @@
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
from typing import Dict, List, Optional, Union
from safetensors import safe_open, SafetensorError
import torch
from loguru import logger
from huggingface_hub import hf_hub_download
import json
from text_generation_server.layers.gptq import GPTQParams
from text_generation_server.utils.log import log_once
@dataclass
class _GPTQParams:
bits: int
checkpoint_format: Optional[str]
groupsize: int
desc_act: bool
quant_method: str
sym: bool
class Weights:
def __init__(
self,
@ -212,6 +202,10 @@ class Weights:
"""
if quantize in ["gptq", "awq"]:
from text_generation_server.layers.gptq import GPTQWeight
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
try:
qweight = self.get_packed_sharded(
@ -221,17 +215,28 @@ class Weights:
raise RuntimeError(
f"Cannot load `{quantize}` weight, make sure the model is already quantized."
)
gptq_params = self._get_gptq_params()
qzeros = self.get_packed_sharded(
f"{prefix}.qzeros", dim=1, block_sizes=block_sizes
)
scales = self.get_packed_sharded(
f"{prefix}.scales", dim=1, block_sizes=block_sizes
)
scales = scales.to(dtype=self.dtype)
gptq_params = self._get_gptq_params()
if can_use_gptq_marlin(gptq_params, quantize):
g_idx = self.get_tensor(f"{prefix}.g_idx")
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=False,
)
qzeros = self.get_packed_sharded(
f"{prefix}.qzeros", dim=1, block_sizes=block_sizes
)
if quantize == "gptq" and gptq_params.quant_method == "gptq":
g_idx = self.get_tensor(f"{prefix}.g_idx")
elif quantize == "gptq" and gptq_params.quant_method == "awq":
@ -269,7 +274,6 @@ class Weights:
repack_gptq_for_marlin,
)
quant_method = getattr(self, "quant_method", "marlin")
is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
if is_marlin_24:
B = self.get_packed_sharded(
@ -286,31 +290,6 @@ class Weights:
weight = GPTQMarlin24Weight(
B=B, B_meta=B_meta, s=s, bits=gptq_params.bits
)
elif quant_method == "gptq":
gptq_params = self._get_gptq_params()
try:
qweight = self.get_packed_sharded(
f"{prefix}.qweight", dim=1, block_sizes=block_sizes
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight for GPTQ -> Marlin repacking, make sure the model is already quantized"
)
scales = self.get_packed_sharded(
f"{prefix}.scales", dim=1, block_sizes=block_sizes
)
g_idx = self.get_tensor(f"{prefix}.g_idx")
weight = repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=False,
)
else:
B = self.get_packed_sharded(
f"{prefix}.B", dim=1, block_sizes=block_sizes
@ -356,6 +335,10 @@ class Weights:
raise ValueError("get_multi_weights_col is not supported for exl2")
elif quantize in ["gptq", "awq"]:
from text_generation_server.layers.gptq import GPTQWeight
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
try:
qweight = torch.cat(
@ -366,14 +349,31 @@ class Weights:
f"Cannot load `{quantize}` weight, make sure the model is already quantized"
)
qzeros = torch.cat(
[self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1
)
scales = torch.cat(
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
gptq_params = self._get_gptq_params()
if can_use_gptq_marlin(gptq_params, quantize):
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=False,
)
qzeros = torch.cat(
[self.get_sharded(f"{p}.qzeros", dim=1) for p in prefixes], dim=1
)
from text_generation_server.layers.gptq import HAS_EXLLAMA
@ -425,10 +425,8 @@ class Weights:
from text_generation_server.layers.marlin import (
GPTQMarlin24Weight,
MarlinWeight,
repack_gptq_for_marlin,
)
quant_method = getattr(self, "quant_method", "marlin")
is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
if is_marlin_24:
try:
@ -452,36 +450,6 @@ class Weights:
weight = GPTQMarlin24Weight(
B=B, B_meta=B_meta, s=s, bits=gptq_params.bits
)
elif quant_method == "gptq":
gptq_params = self._get_gptq_params()
try:
qweight = torch.cat(
[self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes],
dim=1,
)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight for GPTQ -> Marlin repacking, make sure the model is already quantized"
)
scales = torch.cat(
[self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
)
w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
weight = repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=False,
)
else:
try:
B = torch.cat(
@ -544,9 +512,41 @@ class Weights:
)
elif quantize == "gptq":
use_exllama = True
gptq_params = self._get_gptq_params()
from text_generation_server.layers.marlin import (
can_use_gptq_marlin,
repack_gptq_for_marlin,
)
gptq_params = self._get_gptq_params()
if can_use_gptq_marlin(gptq_params, quantize):
log_once(logger.info, "Using GPTQ-Marlin kernels")
try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight for GPTQ -> Marlin repacking, make sure the model is already quantized"
)
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
if gptq_params.desc_act or gptq_params.groupsize == -1:
scales = self.get_tensor(f"{prefix}.scales")
else:
scales = self.get_sharded(f"{prefix}.scales", dim=0)
sharded_in_features = self.process_group.size() > 1
return repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=sharded_in_features,
)
use_exllama = True
if gptq_params.bits != 4:
use_exllama = False
@ -672,10 +672,8 @@ class Weights:
from text_generation_server.layers.marlin import (
GPTQMarlin24Weight,
MarlinWeight,
repack_gptq_for_marlin,
)
quant_method = getattr(self, "quant_method", "marlin")
is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
if is_marlin_24:
try:
@ -698,35 +696,6 @@ class Weights:
weight = GPTQMarlin24Weight(
B=B, B_meta=B_meta, s=s, bits=gptq_params.bits
)
elif quant_method == "gptq":
log_once(logger.info, "Converting GPTQ model to Marlin packing format.")
gptq_params = self._get_gptq_params()
try:
qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
except RuntimeError:
raise RuntimeError(
f"Cannot load `{quantize}` weight for GPTQ -> Marlin repacking, make sure the model is already quantized"
)
g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
if gptq_params.desc_act or gptq_params.groupsize == -1:
scales = self.get_tensor(f"{prefix}.scales")
else:
scales = self.get_sharded(f"{prefix}.scales", dim=0)
sharded_in_features = self.process_group.size() > 1
weight = repack_gptq_for_marlin(
qweight=qweight,
scales=scales,
g_idx=g_idx,
bits=gptq_params.bits,
desc_act=gptq_params.desc_act,
groupsize=gptq_params.groupsize,
sym=gptq_params.sym,
sharded_infeatures=sharded_in_features,
)
else:
try:
B = self.get_sharded(f"{prefix}.B", dim=0)
@ -743,18 +712,17 @@ class Weights:
else:
s = self.get_sharded(f"{prefix}.s", dim=0)
weight = MarlinWeight(B=B, s=s)
else:
weight = self.get_sharded(f"{prefix}.weight", dim=1)
return weight
def _get_gptq_params(self) -> _GPTQParams:
def _get_gptq_params(self) -> GPTQParams:
try:
bits = self.get_tensor("gptq_bits").item()
groupsize = self.get_tensor("gptq_groupsize").item()
checkpoint_format = getattr(self, "gptq_checkpoint_format", None)
desc_act = False
sym = True
sym = False
quant_method = "gptq"
except (SafetensorError, RuntimeError) as e:
try:
@ -767,7 +735,7 @@ class Weights:
except Exception:
raise e
return _GPTQParams(
return GPTQParams(
bits=bits,
checkpoint_format=checkpoint_format,
desc_act=desc_act,