mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-10 20:04:52 +00:00
Merge branch 'main' into safetensors_docs
This commit is contained in:
Merve Noyan
GitHub
commit
0ef535e77e
@ -8,7 +8,7 @@ members = [
|
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]
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|
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[workspace.package]
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version = "1.0.1"
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version = "1.0.3"
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edition = "2021"
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authors = ["Olivier Dehaene"]
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homepage = "https://github.com/huggingface/text-generation-inference"
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@ -67,6 +67,7 @@ to power Hugging Chat, the Inference API and Inference Endpoint.
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- [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b)
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- [MPT](https://huggingface.co/mosaicml/mpt-30b)
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- [Llama V2](https://huggingface.co/meta-llama)
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- [Code Llama](https://huggingface.co/codellama)
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Other architectures are supported on a best effort basis using:
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@ -86,7 +87,7 @@ The easiest way of getting started is using the official Docker container:
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model=tiiuae/falcon-7b-instruct
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.1 --model-id $model
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.3 --model-id $model
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```
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**Note:** To use GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 11.8 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
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@ -153,7 +154,7 @@ model=meta-llama/Llama-2-7b-chat-hf
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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token=<your cli READ token>
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docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.1 --model-id $model
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docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.3 --model-id $model
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```
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### A note on Shared Memory (shm)
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@ -37,6 +37,7 @@ pub(crate) async fn generation_task(
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batch_size: Vec<u32>,
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sequence_length: u32,
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decode_length: u32,
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top_n_tokens: Option<u32>,
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n_runs: usize,
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warmups: usize,
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parameters: NextTokenChooserParameters,
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@ -48,7 +49,7 @@ pub(crate) async fn generation_task(
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// End task if a message is received on shutdown_receiver
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// _shutdown_guard_sender will be dropped once the task is finished
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tokio::select! {
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res = generate_runs(tokenizer, batch_size, sequence_length, decode_length, n_runs, warmups, parameters, client, run_sender.clone()) => {
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res = generate_runs(tokenizer, batch_size, sequence_length, decode_length, top_n_tokens, n_runs, warmups, parameters, client, run_sender.clone()) => {
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if let Err(err) = res {
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run_sender.send(Err(err)).await.unwrap_or(());
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}
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@ -64,6 +65,7 @@ async fn generate_runs(
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batch_size: Vec<u32>,
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sequence_length: u32,
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decode_length: u32,
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top_n_tokens: Option<u32>,
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n_runs: usize,
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warmups: usize,
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parameters: NextTokenChooserParameters,
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@ -82,6 +84,7 @@ async fn generate_runs(
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b,
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decode_length,
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parameters.clone(),
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top_n_tokens,
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&mut client,
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)
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.await?;
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@ -97,6 +100,7 @@ async fn generate_runs(
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b,
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decode_length,
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parameters.clone(),
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top_n_tokens,
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&mut client,
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)
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.await?;
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@ -130,6 +134,7 @@ async fn prefill(
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batch_size: u32,
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decode_length: u32,
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parameters: NextTokenChooserParameters,
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top_n_tokens: Option<u32>,
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client: &mut ShardedClient,
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) -> Result<(Prefill, CachedBatch), ClientError> {
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// Create requests
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@ -145,6 +150,7 @@ async fn prefill(
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stop_sequences: vec![],
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ignore_eos_token: true, // Will not stop even if a eos token is generated
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}),
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top_n_tokens: top_n_tokens.unwrap_or(0),
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})
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.collect();
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@ -22,6 +22,7 @@ pub async fn run(
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batch_size: Vec<u32>,
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sequence_length: u32,
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decode_length: u32,
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top_n_tokens: Option<u32>,
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n_runs: usize,
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warmups: usize,
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temperature: Option<f32>,
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@ -70,6 +71,7 @@ pub async fn run(
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batch_size.clone(),
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sequence_length,
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decode_length,
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top_n_tokens,
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n_runs,
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warmups,
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parameters,
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@ -130,6 +132,7 @@ pub async fn run(
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tokenizer_name,
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sequence_length,
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decode_length,
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top_n_tokens,
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n_runs,
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warmups,
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temperature,
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@ -93,6 +93,11 @@ struct Args {
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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do_sample: bool,
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/// Generation parameter in case you want to specifically test/debug particular
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/// decoding strategies, for full doc refer to the `text-generation-server`
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#[clap(long, env)]
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top_n_tokens: Option<u32>,
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}
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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@ -117,6 +122,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
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watermark,
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do_sample,
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master_shard_uds_path,
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top_n_tokens,
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} = args;
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let batch_size = batch_size.unwrap_or(vec![1, 2, 4, 8, 16, 32]);
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@ -173,6 +179,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
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batch_size,
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sequence_length,
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decode_length,
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top_n_tokens,
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runs,
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warmups,
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temperature,
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@ -7,6 +7,7 @@ pub(crate) fn parameters_table(
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tokenizer_name: String,
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sequence_length: u32,
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decode_length: u32,
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top_n_tokens: Option<u32>,
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n_runs: usize,
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warmups: usize,
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temperature: Option<f32>,
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@ -24,6 +25,7 @@ pub(crate) fn parameters_table(
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builder.push_record(["Model", &tokenizer_name]);
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builder.push_record(["Sequence Length", &sequence_length.to_string()]);
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builder.push_record(["Decode Length", &decode_length.to_string()]);
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builder.push_record(["Top N Tokens", &format!("{top_n_tokens:?}")]);
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builder.push_record(["N Runs", &n_runs.to_string()]);
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builder.push_record(["Warmups", &warmups.to_string()]);
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builder.push_record(["Temperature", &format!("{temperature:?}")]);
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@ -12,7 +12,7 @@ repository = "https://github.com/huggingface/text-generation-inference"
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[tool.poetry.dependencies]
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python = "^3.7"
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pydantic = "^1.10"
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pydantic = "> 1.10, < 3"
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aiohttp = "^3.8"
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huggingface-hub = ">= 0.12, < 1.0"
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@ -12,7 +12,7 @@
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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__version__ = "0.3.0"
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__version__ = "0.6.0"
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from text_generation.client import Client, AsyncClient
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from text_generation.inference_api import InferenceAPIClient, InferenceAPIAsyncClient
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@ -75,6 +75,7 @@ class Client:
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typical_p: Optional[float] = None,
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watermark: bool = False,
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decoder_input_details: bool = False,
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top_n_tokens: Optional[int] = None,
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) -> Response:
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"""
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Given a prompt, generate the following text
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@ -113,6 +114,8 @@ class Client:
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Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
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decoder_input_details (`bool`):
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Return the decoder input token logprobs and ids
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top_n_tokens (`int`):
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Return the `n` most likely tokens at each step
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Returns:
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Response: generated response
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@ -134,6 +137,7 @@ class Client:
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typical_p=typical_p,
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watermark=watermark,
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decoder_input_details=decoder_input_details,
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top_n_tokens=top_n_tokens
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)
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request = Request(inputs=prompt, stream=False, parameters=parameters)
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@ -164,6 +168,7 @@ class Client:
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truncate: Optional[int] = None,
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typical_p: Optional[float] = None,
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watermark: bool = False,
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top_n_tokens: Optional[int] = None,
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) -> Iterator[StreamResponse]:
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"""
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Given a prompt, generate the following stream of tokens
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@ -198,6 +203,8 @@ class Client:
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See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
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watermark (`bool`):
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Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
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top_n_tokens (`int`):
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Return the `n` most likely tokens at each step
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Returns:
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Iterator[StreamResponse]: stream of generated tokens
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@ -219,6 +226,7 @@ class Client:
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truncate=truncate,
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typical_p=typical_p,
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watermark=watermark,
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top_n_tokens=top_n_tokens,
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)
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request = Request(inputs=prompt, stream=True, parameters=parameters)
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@ -317,6 +325,7 @@ class AsyncClient:
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typical_p: Optional[float] = None,
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watermark: bool = False,
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decoder_input_details: bool = False,
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top_n_tokens: Optional[int] = None,
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) -> Response:
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"""
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Given a prompt, generate the following text asynchronously
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@ -355,6 +364,8 @@ class AsyncClient:
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Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
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decoder_input_details (`bool`):
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Return the decoder input token logprobs and ids
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top_n_tokens (`int`):
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Return the `n` most likely tokens at each step
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|
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Returns:
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Response: generated response
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@ -376,6 +387,7 @@ class AsyncClient:
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truncate=truncate,
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typical_p=typical_p,
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watermark=watermark,
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top_n_tokens=top_n_tokens,
|
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)
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request = Request(inputs=prompt, stream=False, parameters=parameters)
|
||||
|
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@ -404,6 +416,7 @@ class AsyncClient:
|
||||
truncate: Optional[int] = None,
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typical_p: Optional[float] = None,
|
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watermark: bool = False,
|
||||
top_n_tokens: Optional[int] = None,
|
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) -> AsyncIterator[StreamResponse]:
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"""
|
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Given a prompt, generate the following stream of tokens asynchronously
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@ -438,6 +451,8 @@ class AsyncClient:
|
||||
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
||||
watermark (`bool`):
|
||||
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
||||
top_n_tokens (`int`):
|
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Return the `n` most likely tokens at each step
|
||||
|
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Returns:
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AsyncIterator[StreamResponse]: stream of generated tokens
|
||||
@ -459,6 +474,7 @@ class AsyncClient:
|
||||
truncate=truncate,
|
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typical_p=typical_p,
|
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watermark=watermark,
|
||||
top_n_tokens=top_n_tokens,
|
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)
|
||||
request = Request(inputs=prompt, stream=True, parameters=parameters)
|
||||
|
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|
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@ -18,27 +18,29 @@ class Parameters(BaseModel):
|
||||
# Stop generating tokens if a member of `stop_sequences` is generated
|
||||
stop: List[str] = []
|
||||
# Random sampling seed
|
||||
seed: Optional[int]
|
||||
seed: Optional[int] = None
|
||||
# The value used to module the logits distribution.
|
||||
temperature: Optional[float]
|
||||
temperature: Optional[float] = None
|
||||
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
|
||||
top_k: Optional[int]
|
||||
top_k: Optional[int] = None
|
||||
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
|
||||
# higher are kept for generation.
|
||||
top_p: Optional[float]
|
||||
top_p: Optional[float] = None
|
||||
# truncate inputs tokens to the given size
|
||||
truncate: Optional[int]
|
||||
truncate: Optional[int] = None
|
||||
# Typical Decoding mass
|
||||
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
||||
typical_p: Optional[float]
|
||||
typical_p: Optional[float] = None
|
||||
# Generate best_of sequences and return the one if the highest token logprobs
|
||||
best_of: Optional[int]
|
||||
best_of: Optional[int] = None
|
||||
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
||||
watermark: bool = False
|
||||
# Get generation details
|
||||
details: bool = False
|
||||
# Get decoder input token logprobs and ids
|
||||
decoder_input_details: bool = False
|
||||
# Return the N most likely tokens at each step
|
||||
top_n_tokens: Optional[int]
|
||||
|
||||
@validator("best_of")
|
||||
def valid_best_of(cls, field_value, values):
|
||||
@ -101,12 +103,18 @@ class Parameters(BaseModel):
|
||||
raise ValidationError("`typical_p` must be > 0.0 and < 1.0")
|
||||
return v
|
||||
|
||||
@validator("top_n_tokens")
|
||||
def valid_top_n_tokens(cls, v):
|
||||
if v is not None and v <= 0:
|
||||
raise ValidationError("`top_n_tokens` must be strictly positive")
|
||||
return v
|
||||
|
||||
|
||||
class Request(BaseModel):
|
||||
# Prompt
|
||||
inputs: str
|
||||
# Generation parameters
|
||||
parameters: Optional[Parameters]
|
||||
parameters: Optional[Parameters] = None
|
||||
# Whether to stream output tokens
|
||||
stream: bool = False
|
||||
|
||||
@ -125,9 +133,7 @@ class Request(BaseModel):
|
||||
and parameters.best_of > 1
|
||||
and field_value
|
||||
):
|
||||
raise ValidationError(
|
||||
"`best_of` != 1 is not supported when `stream` == True"
|
||||
)
|
||||
raise ValidationError("`best_of` != 1 is not supported when `stream` == True")
|
||||
return field_value
|
||||
|
||||
|
||||
@ -139,7 +145,7 @@ class InputToken(BaseModel):
|
||||
text: str
|
||||
# Logprob
|
||||
# Optional since the logprob of the first token cannot be computed
|
||||
logprob: Optional[float]
|
||||
logprob: Optional[float] = None
|
||||
|
||||
|
||||
# Generated tokens
|
||||
@ -174,11 +180,13 @@ class BestOfSequence(BaseModel):
|
||||
# Number of generated tokens
|
||||
generated_tokens: int
|
||||
# Sampling seed if sampling was activated
|
||||
seed: Optional[int]
|
||||
seed: Optional[int] = None
|
||||
# Decoder input tokens, empty if decoder_input_details is False
|
||||
prefill: List[InputToken]
|
||||
# Generated tokens
|
||||
tokens: List[Token]
|
||||
# Most likely tokens
|
||||
top_tokens: Optional[List[List[Token]]]
|
||||
|
||||
|
||||
# `generate` details
|
||||
@ -188,13 +196,15 @@ class Details(BaseModel):
|
||||
# Number of generated tokens
|
||||
generated_tokens: int
|
||||
# Sampling seed if sampling was activated
|
||||
seed: Optional[int]
|
||||
seed: Optional[int] = None
|
||||
# Decoder input tokens, empty if decoder_input_details is False
|
||||
prefill: List[InputToken]
|
||||
# Generated tokens
|
||||
tokens: List[Token]
|
||||
# Most likely tokens
|
||||
top_tokens: Optional[List[List[Token]]]
|
||||
# Additional sequences when using the `best_of` parameter
|
||||
best_of_sequences: Optional[List[BestOfSequence]]
|
||||
best_of_sequences: Optional[List[BestOfSequence]] = None
|
||||
|
||||
|
||||
# `generate` return value
|
||||
@ -212,19 +222,21 @@ class StreamDetails(BaseModel):
|
||||
# Number of generated tokens
|
||||
generated_tokens: int
|
||||
# Sampling seed if sampling was activated
|
||||
seed: Optional[int]
|
||||
seed: Optional[int] = None
|
||||
|
||||
|
||||
# `generate_stream` return value
|
||||
class StreamResponse(BaseModel):
|
||||
# Generated token
|
||||
token: Token
|
||||
# Most likely tokens
|
||||
top_tokens: Optional[List[Token]]
|
||||
# Complete generated text
|
||||
# Only available when the generation is finished
|
||||
generated_text: Optional[str]
|
||||
generated_text: Optional[str] = None
|
||||
# Generation details
|
||||
# Only available when the generation is finished
|
||||
details: Optional[StreamDetails]
|
||||
details: Optional[StreamDetails] = None
|
||||
|
||||
|
||||
# Inference API currently deployed model
|
||||
|
||||
@ -10,7 +10,7 @@
|
||||
"name": "Apache 2.0",
|
||||
"url": "https://www.apache.org/licenses/LICENSE-2.0"
|
||||
},
|
||||
"version": "1.0.1"
|
||||
"version": "1.0.3"
|
||||
},
|
||||
"paths": {
|
||||
"/": {
|
||||
|
||||
@ -23,4 +23,6 @@
|
||||
title: Streaming
|
||||
- local: conceptual/safetensors
|
||||
title: Safetensors
|
||||
- local: conceptual/flash_attention
|
||||
title: Flash Attention
|
||||
title: Conceptual Guides
|
||||
|
||||
@ -75,6 +75,81 @@ To serve both ChatUI and TGI in same environment, simply add your own endpoints
|
||||
|
||||
|
||||
|
||||
## Gradio
|
||||
|
||||
Gradio is a Python library that helps you build web applications for your machine learning models with a few lines of code. It has a `ChatInterface` wrapper that helps create neat UIs for chatbots. Let's take a look at how to create a chatbot with streaming mode using TGI and Gradio. Let's install Gradio and Hub Python library first.
|
||||
|
||||
```bash
|
||||
pip install huggingface-hub gradio
|
||||
```
|
||||
|
||||
Assume you are serving your model on port 8080, we will query through [InferenceClient](consuming_tgi#inference-client).
|
||||
|
||||
```python
|
||||
import gradio as gr
|
||||
from huggingface_hub import InferenceClient
|
||||
|
||||
client = InferenceClient(model="http://127.0.0.1:8080")
|
||||
|
||||
def inference(message, history):
|
||||
partial_message = ""
|
||||
for token in client.text_generation(message, max_new_tokens=20, stream=True):
|
||||
partial_message += token
|
||||
yield partial_message
|
||||
|
||||
gr.ChatInterface(
|
||||
inference,
|
||||
chatbot=gr.Chatbot(height=300),
|
||||
textbox=gr.Textbox(placeholder="Chat with me!", container=False, scale=7),
|
||||
description="This is the demo for Gradio UI consuming TGI endpoint with LLaMA 7B-Chat model.",
|
||||
title="Gradio 🤝 TGI",
|
||||
examples=["Are tomatoes vegetables?"],
|
||||
retry_btn="Retry",
|
||||
undo_btn="Undo",
|
||||
clear_btn="Clear",
|
||||
).queue().launch()
|
||||
```
|
||||
|
||||
The UI looks like this 👇
|
||||
|
||||
<div class="flex justify-center">
|
||||
<img
|
||||
class="block dark:hidden"
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/gradio-tgi.png"
|
||||
/>
|
||||
<img
|
||||
class="hidden dark:block"
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tgi/gradio-tgi-dark.png"
|
||||
/>
|
||||
</div>
|
||||
|
||||
You can try the demo directly here 👇
|
||||
|
||||
<div class="block dark:hidden">
|
||||
<iframe
|
||||
src="https://merve-gradio-tgi-2.hf.space?__theme=light"
|
||||
width="850"
|
||||
height="750"
|
||||
></iframe>
|
||||
</div>
|
||||
<div class="hidden dark:block">
|
||||
<iframe
|
||||
src="https://merve-gradio-tgi-2.hf.space?__theme=dark"
|
||||
width="850"
|
||||
height="750"
|
||||
></iframe>
|
||||
</div>
|
||||
|
||||
|
||||
You can disable streaming mode using `return` instead of `yield` in your inference function, like below.
|
||||
|
||||
```python
|
||||
def inference(message, history):
|
||||
return client.text_generation(message, max_new_tokens=20)
|
||||
```
|
||||
|
||||
You can read more about how to customize a `ChatInterface` [here](https://www.gradio.app/guides/creating-a-chatbot-fast).
|
||||
|
||||
## API documentation
|
||||
|
||||
You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route. The Swagger UI is also available [here](https://huggingface.github.io/text-generation-inference).
|
||||
|
||||
12
docs/source/conceptual/flash_attention.md
Normal file
12
docs/source/conceptual/flash_attention.md
Normal file
@ -0,0 +1,12 @@
|
||||
# Flash Attention
|
||||
|
||||
Scaling the transformer architecture is heavily bottlenecked by the self-attention mechanism, which has quadratic time and memory complexity. Recent developments in accelerator hardware mainly focus on enhancing compute capacities and not memory and transferring data between hardware. This results in attention operation having a memory bottleneck. **Flash Attention** is an attention algorithm used to reduce this problem and scale transformer-based models more efficiently, enabling faster training and inference.
|
||||
|
||||
Standard attention mechanism uses High Bandwidth Memory (HBM) to store, read and write keys, queries and values. HBM is large in memory, but slow in processing, meanwhile SRAM is smaller in memory, but faster in operations. In the standard attention implementation, the cost of loading and writing keys, queries, and values from HBM is high. It loads keys, queries, and values from HBM to GPU on-chip SRAM, performs a single step of the attention mechanism, writes it back to HBM, and repeats this for every single attention step. Instead, Flash Attention loads keys, queries, and values once, fuses the operations of the attention mechanism, and writes them back.
|
||||
|
||||
|
||||
|
||||
It is implemented for supported models. You can check out the complete list of models that support Flash Attention [here](https://github.com/huggingface/text-generation-inference/tree/main/server/text_generation_server/models), for models with flash prefix.
|
||||
|
||||
You can learn more about Flash Attention by reading the paper in this [link](https://arxiv.org/abs/2205.14135).
|
||||
|
||||
@ -121,9 +121,9 @@ If you're using the free Inference API, you can use `HfInference`. If you're usi
|
||||
We can create a `HfInferenceEndpoint` providing our endpoint URL and credential.
|
||||
|
||||
```js
|
||||
import { HfInference } from '@huggingface/inference'
|
||||
import { HfInferenceEndpoint } from '@huggingface/inference'
|
||||
|
||||
const hf = new HfInference('https://YOUR_ENDPOINT.endpoints.huggingface.cloud', 'hf_YOUR_TOKEN')
|
||||
const hf = new HfInferenceEndpoint('https://YOUR_ENDPOINT.endpoints.huggingface.cloud', 'hf_YOUR_TOKEN')
|
||||
|
||||
// prompt
|
||||
const prompt = 'What can you do in Nuremberg, Germany? Give me 3 Tips'
|
||||
@ -143,6 +143,4 @@ SSEs are different than:
|
||||
* Polling: where the client keeps calling the server to get data. This means that the server might return empty responses and cause overhead.
|
||||
* Webhooks: where there is a bi-directional connection. The server can send information to the client, but the client can also send data to the server after the first request. Webhooks are more complex to operate as they don’t only use HTTP.
|
||||
|
||||
One of the limitations of Server-Sent Events is that they limit how many concurrent requests can handle by the server. Instead of timing out when there are too many SSE connections, TGI returns a HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g. it could display a busy error to the user or it could retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing to handle backpressure.
|
||||
|
||||
One of the limitations of Server-Sent Events is that they limit how many concurrent requests can handle by the server. Instead of timing out when there are too many SSE connections, TGI returns an HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g., it could display a busy error to the user or retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing clients to handle backpressure.
|
||||
If there are too many requests at the same time, TGI returns an HTTP Error with an `overloaded` error type (`huggingface_hub` returns `OverloadedError`). This allows the client to manage the overloaded server (e.g., it could display a busy error to the user or retry with a new request). To configure the maximum number of concurrent requests, you can specify `--max_concurrent_requests`, allowing clients to handle backpressure.
|
||||
|
||||
@ -8,7 +8,7 @@ Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/
|
||||
model=tiiuae/falcon-7b-instruct
|
||||
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:1.0.1 --model-id $model
|
||||
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.3 --model-id $model
|
||||
```
|
||||
|
||||
<Tip warning={true}>
|
||||
@ -85,7 +85,7 @@ 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:1.0.1 --help
|
||||
docker run ghcr.io/huggingface/text-generation-inference:1.0.3 --help
|
||||
```
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -11,52 +11,52 @@
|
||||
},
|
||||
{
|
||||
"id": 4911,
|
||||
"logprob": -5.3632812,
|
||||
"logprob": -5.7773438,
|
||||
"text": "User"
|
||||
},
|
||||
{
|
||||
"id": 29901,
|
||||
"logprob": -0.00762558,
|
||||
"logprob": -0.0069999695,
|
||||
"text": ":"
|
||||
},
|
||||
{
|
||||
"id": 32000,
|
||||
"logprob": -0.7739258,
|
||||
"logprob": -0.8125,
|
||||
"text": "<fake_token_around_image>"
|
||||
},
|
||||
{
|
||||
"id": 32001,
|
||||
"logprob": -9.775162e-05,
|
||||
"logprob": -6.651878e-05,
|
||||
"text": "<image>"
|
||||
},
|
||||
{
|
||||
"id": 32000,
|
||||
"logprob": -1.1920929e-07,
|
||||
"logprob": -3.5762787e-07,
|
||||
"text": "<fake_token_around_image>"
|
||||
},
|
||||
{
|
||||
"id": 1815,
|
||||
"logprob": -4.4140625,
|
||||
"logprob": -4.2265625,
|
||||
"text": "Can"
|
||||
},
|
||||
{
|
||||
"id": 366,
|
||||
"logprob": -0.01436615,
|
||||
"logprob": -0.013977051,
|
||||
"text": "you"
|
||||
},
|
||||
{
|
||||
"id": 2649,
|
||||
"logprob": -4.9414062,
|
||||
"logprob": -4.4375,
|
||||
"text": "tell"
|
||||
},
|
||||
{
|
||||
"id": 592,
|
||||
"logprob": -0.3005371,
|
||||
"logprob": -0.29077148,
|
||||
"text": "me"
|
||||
},
|
||||
{
|
||||
"id": 263,
|
||||
"logprob": -3.5703125,
|
||||
"logprob": -4.2109375,
|
||||
"text": "a"
|
||||
},
|
||||
{
|
||||
@ -66,37 +66,37 @@
|
||||
},
|
||||
{
|
||||
"id": 3273,
|
||||
"logprob": -1.9111328,
|
||||
"logprob": -1.8671875,
|
||||
"text": "short"
|
||||
},
|
||||
{
|
||||
"id": 5828,
|
||||
"logprob": -0.28881836,
|
||||
"logprob": -0.26586914,
|
||||
"text": "story"
|
||||
},
|
||||
{
|
||||
"id": 2729,
|
||||
"logprob": -3.4179688,
|
||||
"logprob": -3.7460938,
|
||||
"text": "based"
|
||||
},
|
||||
{
|
||||
"id": 373,
|
||||
"logprob": -0.00056886673,
|
||||
"logprob": -0.0005350113,
|
||||
"text": "on"
|
||||
},
|
||||
{
|
||||
"id": 278,
|
||||
"logprob": -0.14123535,
|
||||
"logprob": -0.13867188,
|
||||
"text": "the"
|
||||
},
|
||||
{
|
||||
"id": 1967,
|
||||
"logprob": -0.053985596,
|
||||
"logprob": -0.06842041,
|
||||
"text": "image"
|
||||
},
|
||||
{
|
||||
"id": 29973,
|
||||
"logprob": -0.15771484,
|
||||
"logprob": -0.15319824,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
@ -104,25 +104,25 @@
|
||||
"tokens": [
|
||||
{
|
||||
"id": 32002,
|
||||
"logprob": -0.004295349,
|
||||
"logprob": -0.0019445419,
|
||||
"special": true,
|
||||
"text": "<end_of_utterance>"
|
||||
},
|
||||
{
|
||||
"id": 29871,
|
||||
"logprob": -7.43866e-05,
|
||||
"logprob": -8.404255e-05,
|
||||
"special": false,
|
||||
"text": " "
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"logprob": -2.3126602e-05,
|
||||
"logprob": -1.7881393e-05,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 7900,
|
||||
"logprob": -3.9339066e-06,
|
||||
"logprob": -3.0994415e-06,
|
||||
"special": false,
|
||||
"text": "Ass"
|
||||
},
|
||||
@ -134,35 +134,36 @@
|
||||
},
|
||||
{
|
||||
"id": 29901,
|
||||
"logprob": -2.6226044e-06,
|
||||
"logprob": -3.2186508e-06,
|
||||
"special": false,
|
||||
"text": ":"
|
||||
},
|
||||
{
|
||||
"id": 319,
|
||||
"logprob": -0.87841797,
|
||||
"logprob": -0.9057617,
|
||||
"special": false,
|
||||
"text": " A"
|
||||
},
|
||||
{
|
||||
"id": 521,
|
||||
"logprob": -1.3837891,
|
||||
"id": 696,
|
||||
"logprob": -1.2314453,
|
||||
"special": false,
|
||||
"text": " ch"
|
||||
"text": " ro"
|
||||
},
|
||||
{
|
||||
"id": 21475,
|
||||
"logprob": -0.00051641464,
|
||||
"id": 15664,
|
||||
"logprob": -0.00024914742,
|
||||
"special": false,
|
||||
"text": "icken"
|
||||
"text": "oster"
|
||||
},
|
||||
{
|
||||
"id": 338,
|
||||
"logprob": -1.1435547,
|
||||
"id": 15028,
|
||||
"logprob": -1.1621094,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
"text": " stands"
|
||||
}
|
||||
]
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": "\nAssistant: A chicken is"
|
||||
"generated_text": "\nAssistant: A rooster stands"
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
982
integration-tests/poetry.lock
generated
Normal file
982
integration-tests/poetry.lock
generated
Normal file
@ -0,0 +1,982 @@
|
||||
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.8.5"
|
||||
description = "Async http client/server framework (asyncio)"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a94159871304770da4dd371f4291b20cac04e8c94f11bdea1c3478e557fbe0d8"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:13bf85afc99ce6f9ee3567b04501f18f9f8dbbb2ea11ed1a2e079670403a7c84"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2ce2ac5708501afc4847221a521f7e4b245abf5178cf5ddae9d5b3856ddb2f3a"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:96943e5dcc37a6529d18766597c491798b7eb7a61d48878611298afc1fca946c"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2ad5c3c4590bb3cc28b4382f031f3783f25ec223557124c68754a2231d989e2b"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0c413c633d0512df4dc7fd2373ec06cc6a815b7b6d6c2f208ada7e9e93a5061d"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df72ac063b97837a80d80dec8d54c241af059cc9bb42c4de68bd5b61ceb37caa"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c48c5c0271149cfe467c0ff8eb941279fd6e3f65c9a388c984e0e6cf57538e14"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:368a42363c4d70ab52c2c6420a57f190ed3dfaca6a1b19afda8165ee16416a82"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:7607ec3ce4993464368505888af5beb446845a014bc676d349efec0e05085905"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:0d21c684808288a98914e5aaf2a7c6a3179d4df11d249799c32d1808e79503b5"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:312fcfbacc7880a8da0ae8b6abc6cc7d752e9caa0051a53d217a650b25e9a691"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:ad093e823df03bb3fd37e7dec9d4670c34f9e24aeace76808fc20a507cace825"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-win32.whl", hash = "sha256:33279701c04351a2914e1100b62b2a7fdb9a25995c4a104259f9a5ead7ed4802"},
|
||||
{file = "aiohttp-3.8.5-cp310-cp310-win_amd64.whl", hash = "sha256:6e4a280e4b975a2e7745573e3fc9c9ba0d1194a3738ce1cbaa80626cc9b4f4df"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:ae871a964e1987a943d83d6709d20ec6103ca1eaf52f7e0d36ee1b5bebb8b9b9"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:461908b2578955045efde733719d62f2b649c404189a09a632d245b445c9c975"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:72a860c215e26192379f57cae5ab12b168b75db8271f111019509a1196dfc780"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cc14be025665dba6202b6a71cfcdb53210cc498e50068bc088076624471f8bb9"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8af740fc2711ad85f1a5c034a435782fbd5b5f8314c9a3ef071424a8158d7f6b"},
|
||||
{file = "aiohttp-3.8.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:841cd8233cbd2111a0ef0a522ce016357c5e3aff8a8ce92bcfa14cef890d698f"},
|
||||
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||||
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||||
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|
||||
|
||||
[package.dependencies]
|
||||
aiosignal = ">=1.1.2"
|
||||
async-timeout = ">=4.0.0a3,<5.0"
|
||||
attrs = ">=17.3.0"
|
||||
charset-normalizer = ">=2.0,<4.0"
|
||||
frozenlist = ">=1.1.1"
|
||||
multidict = ">=4.5,<7.0"
|
||||
yarl = ">=1.0,<2.0"
|
||||
|
||||
[package.extras]
|
||||
speedups = ["Brotli", "aiodns", "cchardet"]
|
||||
|
||||
[[package]]
|
||||
name = "aiosignal"
|
||||
version = "1.3.1"
|
||||
description = "aiosignal: a list of registered asynchronous callbacks"
|
||||
optional = false
|
||||
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|
||||
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||||
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||||
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|
||||
|
||||
[package.dependencies]
|
||||
frozenlist = ">=1.1.0"
|
||||
|
||||
[[package]]
|
||||
name = "async-timeout"
|
||||
version = "4.0.3"
|
||||
description = "Timeout context manager for asyncio programs"
|
||||
optional = false
|
||||
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|
||||
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|
||||
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "23.1.0"
|
||||
description = "Classes Without Boilerplate"
|
||||
optional = false
|
||||
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||||
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||||
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||||
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||||
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|
||||
|
||||
[package.extras]
|
||||
cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
|
||||
dev = ["attrs[docs,tests]", "pre-commit"]
|
||||
docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
|
||||
tests = ["attrs[tests-no-zope]", "zope-interface"]
|
||||
tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2023.7.22"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"},
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||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.2.0"
|
||||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
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|
||||
{file = "yarl-1.9.2-cp311-cp311-win32.whl", hash = "sha256:a60347f234c2212a9f0361955007fcf4033a75bf600a33c88a0a8e91af77c0e8"},
|
||||
{file = "yarl-1.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:be6b3fdec5c62f2a67cb3f8c6dbf56bbf3f61c0f046f84645cd1ca73532ea051"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:38a3928ae37558bc1b559f67410df446d1fbfa87318b124bf5032c31e3447b74"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac9bb4c5ce3975aeac288cfcb5061ce60e0d14d92209e780c93954076c7c4367"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3da8a678ca8b96c8606bbb8bfacd99a12ad5dd288bc6f7979baddd62f71c63ef"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:13414591ff516e04fcdee8dc051c13fd3db13b673c7a4cb1350e6b2ad9639ad3"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf74d08542c3a9ea97bb8f343d4fcbd4d8f91bba5ec9d5d7f792dbe727f88938"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6e7221580dc1db478464cfeef9b03b95c5852cc22894e418562997df0d074ccc"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:494053246b119b041960ddcd20fd76224149cfea8ed8777b687358727911dd33"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:52a25809fcbecfc63ac9ba0c0fb586f90837f5425edfd1ec9f3372b119585e45"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:e65610c5792870d45d7b68c677681376fcf9cc1c289f23e8e8b39c1485384185"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:1b1bba902cba32cdec51fca038fd53f8beee88b77efc373968d1ed021024cc04"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:662e6016409828ee910f5d9602a2729a8a57d74b163c89a837de3fea050c7582"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-win32.whl", hash = "sha256:f364d3480bffd3aa566e886587eaca7c8c04d74f6e8933f3f2c996b7f09bee1b"},
|
||||
{file = "yarl-1.9.2-cp37-cp37m-win_amd64.whl", hash = "sha256:6a5883464143ab3ae9ba68daae8e7c5c95b969462bbe42e2464d60e7e2698368"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5610f80cf43b6202e2c33ba3ec2ee0a2884f8f423c8f4f62906731d876ef4fac"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b9a4e67ad7b646cd6f0938c7ebfd60e481b7410f574c560e455e938d2da8e0f4"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:83fcc480d7549ccebe9415d96d9263e2d4226798c37ebd18c930fce43dfb9574"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5fcd436ea16fee7d4207c045b1e340020e58a2597301cfbcfdbe5abd2356c2fb"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84e0b1599334b1e1478db01b756e55937d4614f8654311eb26012091be109d59"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3458a24e4ea3fd8930e934c129b676c27452e4ebda80fbe47b56d8c6c7a63a9e"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:838162460b3a08987546e881a2bfa573960bb559dfa739e7800ceeec92e64417"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f4e2d08f07a3d7d3e12549052eb5ad3eab1c349c53ac51c209a0e5991bbada78"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:de119f56f3c5f0e2fb4dee508531a32b069a5f2c6e827b272d1e0ff5ac040333"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:149ddea5abf329752ea5051b61bd6c1d979e13fbf122d3a1f9f0c8be6cb6f63c"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:674ca19cbee4a82c9f54e0d1eee28116e63bc6fd1e96c43031d11cbab8b2afd5"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:9b3152f2f5677b997ae6c804b73da05a39daa6a9e85a512e0e6823d81cdad7cc"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5415d5a4b080dc9612b1b63cba008db84e908b95848369aa1da3686ae27b6d2b"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-win32.whl", hash = "sha256:f7a3d8146575e08c29ed1cd287068e6d02f1c7bdff8970db96683b9591b86ee7"},
|
||||
{file = "yarl-1.9.2-cp38-cp38-win_amd64.whl", hash = "sha256:63c48f6cef34e6319a74c727376e95626f84ea091f92c0250a98e53e62c77c72"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:75df5ef94c3fdc393c6b19d80e6ef1ecc9ae2f4263c09cacb178d871c02a5ba9"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c027a6e96ef77d401d8d5a5c8d6bc478e8042f1e448272e8d9752cb0aff8b5c8"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f3b078dbe227f79be488ffcfc7a9edb3409d018e0952cf13f15fd6512847f3f7"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:59723a029760079b7d991a401386390c4be5bfec1e7dd83e25a6a0881859e716"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b03917871bf859a81ccb180c9a2e6c1e04d2f6a51d953e6a5cdd70c93d4e5a2a"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8c56986609b057b4839968ba901944af91b8e92f1725d1a2d77cbac6972b9ed1"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2c315df3293cd521033533d242d15eab26583360b58f7ee5d9565f15fee1bef4"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:066c163aec9d3d073dc9ffe5dd3ad05069bcb03fcaab8d221290ba99f9f69ee3"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8288d7cd28f8119b07dd49b7230d6b4562f9b61ee9a4ab02221060d21136be80"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-win32.whl", hash = "sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623"},
|
||||
{file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
|
||||
{file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
idna = ">=2.0"
|
||||
multidict = ">=4.0"
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.13"
|
||||
content-hash = "bdad1d22d29138010cd6b11e1b92dc0630b35634422413a8456dc85a15bee05e"
|
||||
13
integration-tests/pyproject.toml
Normal file
13
integration-tests/pyproject.toml
Normal file
@ -0,0 +1,13 @@
|
||||
[tool.poetry]
|
||||
name = "text-generation-integration-tests"
|
||||
version = "1.0.3"
|
||||
description = "Text Generation Inference integration tests"
|
||||
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<3.13"
|
||||
syrupy = "4.0.1"
|
||||
text-generation = "^0.6.0"
|
||||
pytest = "^7.4.0"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
docker = "^6.1.3"
|
||||
@ -1,5 +1,33 @@
|
||||
syrupy
|
||||
text-generation
|
||||
pytest
|
||||
pytest-asyncio==0.17.2
|
||||
docker
|
||||
aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "3.13"
|
||||
aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
certifi==2023.7.22 ; python_version >= "3.9" and python_version < "3.13"
|
||||
charset-normalizer==3.2.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
|
||||
colored==1.4.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
docker==6.1.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
exceptiongroup==1.1.3 ; python_version >= "3.9" and python_version < "3.11"
|
||||
filelock==3.12.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
frozenlist==1.4.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
iniconfig==2.0.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
multidict==6.0.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
packaging==23.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pluggy==1.3.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pydantic==1.10.12 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pytest-asyncio==0.21.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pytest==7.4.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pywin32==306 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
|
||||
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
syrupy==4.0.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
text-generation==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tomli==2.0.1 ; python_version >= "3.9" and python_version < "3.11"
|
||||
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
urllib3==2.0.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
websocket-client==1.6.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
yarl==1.9.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
||||
@ -159,6 +159,14 @@ struct Args {
|
||||
#[clap(default_value = "4", long, env)]
|
||||
max_stop_sequences: usize,
|
||||
|
||||
/// This is the maximum allowed value for clients to set `top_n_tokens`.
|
||||
/// `top_n_tokens is used to return information about the the `n` most likely
|
||||
/// tokens at each generation step, instead of just the sampled token. This
|
||||
/// information can be used for downstream tasks like for classification or
|
||||
/// ranking.
|
||||
#[clap(default_value = "5", long, env)]
|
||||
max_top_n_tokens: u32,
|
||||
|
||||
/// This is the maximum allowed input length (expressed in number of tokens)
|
||||
/// for users. The larger this value, the longer prompt users can send which
|
||||
/// can impact the overall memory required to handle the load.
|
||||
@ -929,6 +937,8 @@ fn spawn_webserver(
|
||||
args.max_best_of.to_string(),
|
||||
"--max-stop-sequences".to_string(),
|
||||
args.max_stop_sequences.to_string(),
|
||||
"--max-top-n-tokens".to_string(),
|
||||
args.max_top_n_tokens.to_string(),
|
||||
"--max-input-length".to_string(),
|
||||
args.max_input_length.to_string(),
|
||||
"--max-total-tokens".to_string(),
|
||||
|
||||
@ -91,6 +91,8 @@ message Request {
|
||||
StoppingCriteriaParameters stopping_parameters = 5;
|
||||
/// Return prefill logprobs
|
||||
bool prefill_logprobs = 6;
|
||||
/// Return most likely n tokens
|
||||
uint32 top_n_tokens = 7;
|
||||
}
|
||||
|
||||
message Batch {
|
||||
@ -141,6 +143,17 @@ message PrefillTokens {
|
||||
repeated string texts = 3;
|
||||
}
|
||||
|
||||
message TopTokens {
|
||||
/// Top Token IDs
|
||||
repeated uint32 ids = 1;
|
||||
/// Top Logprobs
|
||||
repeated float logprobs = 2;
|
||||
/// Top Token Texts
|
||||
repeated string texts = 3;
|
||||
/// If the tokens are special
|
||||
repeated bool is_special = 6;
|
||||
}
|
||||
|
||||
message Generation {
|
||||
/// Request ID
|
||||
uint64 request_id = 1;
|
||||
@ -156,6 +169,8 @@ message Generation {
|
||||
bool token_is_special = 6;
|
||||
/// Complete generated text
|
||||
optional GeneratedText generated_text = 7;
|
||||
/// Top tokens
|
||||
TopTokens top_tokens = 8;
|
||||
}
|
||||
|
||||
message FilterBatchRequest {
|
||||
|
||||
@ -131,6 +131,7 @@ impl Client {
|
||||
ignore_eos_token: false,
|
||||
}),
|
||||
prefill_logprobs: true,
|
||||
top_n_tokens: 20,
|
||||
});
|
||||
n_tokens += max_input_length;
|
||||
}
|
||||
|
||||
@ -50,6 +50,7 @@ impl Health {
|
||||
stop_sequences: vec![],
|
||||
ignore_eos_token: false,
|
||||
}),
|
||||
top_n_tokens: 0,
|
||||
};
|
||||
let batch = Batch {
|
||||
id: BATCH_ID,
|
||||
|
||||
@ -138,12 +138,15 @@ impl Infer {
|
||||
&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, 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;
|
||||
@ -164,7 +167,10 @@ impl Infer {
|
||||
.collect();
|
||||
}
|
||||
// Push last token
|
||||
InferStreamResponse::Token(token) => result_tokens.push(token),
|
||||
InferStreamResponse::Intermediate { token, top_tokens } => {
|
||||
result_tokens.push(token);
|
||||
result_top_tokens.push(top_tokens);
|
||||
}
|
||||
// Final message
|
||||
// Set return values
|
||||
InferStreamResponse::End {
|
||||
@ -172,8 +178,10 @@ impl Infer {
|
||||
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)
|
||||
@ -191,6 +199,11 @@ impl Infer {
|
||||
generated_text,
|
||||
queued,
|
||||
start,
|
||||
top_tokens: if use_top_tokens {
|
||||
result_top_tokens
|
||||
} else {
|
||||
Vec::new()
|
||||
},
|
||||
})
|
||||
} else {
|
||||
let err = InferError::IncompleteGeneration;
|
||||
@ -520,6 +533,26 @@ fn send_responses(
|
||||
special: generation.token_is_special,
|
||||
};
|
||||
|
||||
// generation.top_tokens
|
||||
|
||||
let mut top_tokens = Vec::new();
|
||||
if let Some(top_tokens_) = generation.top_tokens {
|
||||
top_tokens.extend(
|
||||
top_tokens_
|
||||
.ids
|
||||
.into_iter()
|
||||
.zip(top_tokens_.logprobs.into_iter())
|
||||
.zip(top_tokens_.texts.into_iter())
|
||||
.zip(top_tokens_.is_special.into_iter())
|
||||
.map(|(((id, logprob), text), special)| Token {
|
||||
id,
|
||||
text,
|
||||
logprob,
|
||||
special,
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
if let Some(generated_text) = generation.generated_text {
|
||||
// Generation has ended
|
||||
stopped = true;
|
||||
@ -527,6 +560,7 @@ fn send_responses(
|
||||
entry.response_tx.send_timeout(
|
||||
Ok(InferStreamResponse::End {
|
||||
token,
|
||||
top_tokens,
|
||||
generated_text,
|
||||
queued: entry.queue_time,
|
||||
start: entry.batch_time.unwrap(),
|
||||
@ -536,7 +570,7 @@ fn send_responses(
|
||||
} else {
|
||||
// Send message
|
||||
entry.response_tx.send_timeout(
|
||||
Ok(InferStreamResponse::Token(token)),
|
||||
Ok(InferStreamResponse::Intermediate { token, top_tokens }),
|
||||
Duration::from_millis(10),
|
||||
)?;
|
||||
}
|
||||
@ -566,10 +600,14 @@ pub(crate) enum InferStreamResponse {
|
||||
// Optional first message
|
||||
Prefill(PrefillTokens),
|
||||
// Intermediate messages
|
||||
Token(Token),
|
||||
Intermediate {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
},
|
||||
// Last message
|
||||
End {
|
||||
token: Token,
|
||||
top_tokens: Vec<Token>,
|
||||
generated_text: GeneratedText,
|
||||
start: Instant,
|
||||
queued: Instant,
|
||||
@ -583,6 +621,7 @@ pub(crate) struct InferResponse {
|
||||
pub(crate) generated_text: GeneratedText,
|
||||
pub(crate) queued: Instant,
|
||||
pub(crate) start: Instant,
|
||||
pub(crate) top_tokens: Vec<Vec<Token>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Error)]
|
||||
|
||||
@ -135,6 +135,9 @@ pub(crate) struct GenerateParameters {
|
||||
example = "null"
|
||||
)]
|
||||
pub seed: Option<u64>,
|
||||
#[serde(default)]
|
||||
#[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 5)]
|
||||
pub top_n_tokens: Option<u32>,
|
||||
}
|
||||
|
||||
fn default_max_new_tokens() -> u32 {
|
||||
@ -158,6 +161,7 @@ fn default_parameters() -> GenerateParameters {
|
||||
details: false,
|
||||
decoder_input_details: false,
|
||||
seed: None,
|
||||
top_n_tokens: None,
|
||||
}
|
||||
}
|
||||
|
||||
@ -235,6 +239,8 @@ pub(crate) struct BestOfSequence {
|
||||
pub seed: Option<u64>,
|
||||
pub prefill: Vec<PrefillToken>,
|
||||
pub tokens: Vec<Token>,
|
||||
#[serde(skip_serializing_if = "Vec::is_empty")]
|
||||
pub top_tokens: Vec<Vec<Token>>,
|
||||
}
|
||||
|
||||
#[derive(Serialize, ToSchema)]
|
||||
@ -249,6 +255,8 @@ pub(crate) struct Details {
|
||||
pub tokens: Vec<Token>,
|
||||
#[serde(skip_serializing_if = "Option::is_none")]
|
||||
pub best_of_sequences: Option<Vec<BestOfSequence>>,
|
||||
#[serde(skip_serializing_if = "Vec::is_empty")]
|
||||
pub top_tokens: Vec<Vec<Token>>,
|
||||
}
|
||||
|
||||
#[derive(Serialize, ToSchema)]
|
||||
@ -272,6 +280,8 @@ pub(crate) struct StreamDetails {
|
||||
#[derive(Serialize, ToSchema)]
|
||||
pub(crate) struct StreamResponse {
|
||||
pub token: Token,
|
||||
#[serde(skip_serializing_if = "Vec::is_empty")]
|
||||
pub top_tokens: Vec<Token>,
|
||||
#[schema(nullable = true, default = "null", example = "test")]
|
||||
pub generated_text: Option<String>,
|
||||
#[schema(nullable = true, default = "null")]
|
||||
|
||||
@ -29,6 +29,8 @@ struct Args {
|
||||
max_best_of: usize,
|
||||
#[clap(default_value = "4", long, env)]
|
||||
max_stop_sequences: usize,
|
||||
#[clap(default_value = "5", long, env)]
|
||||
max_top_n_tokens: u32,
|
||||
#[clap(default_value = "1024", long, env)]
|
||||
max_input_length: usize,
|
||||
#[clap(default_value = "2048", long, env)]
|
||||
@ -75,6 +77,7 @@ fn main() -> Result<(), RouterError> {
|
||||
max_concurrent_requests,
|
||||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
waiting_served_ratio,
|
||||
@ -259,6 +262,7 @@ fn main() -> Result<(), RouterError> {
|
||||
max_concurrent_requests,
|
||||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
waiting_served_ratio,
|
||||
|
||||
@ -235,6 +235,7 @@ impl State {
|
||||
truncate: entry.request.truncate,
|
||||
parameters: Some(entry.request.parameters.clone()),
|
||||
stopping_parameters: Some(entry.request.stopping_parameters.clone()),
|
||||
top_n_tokens: entry.request.top_n_tokens,
|
||||
});
|
||||
// Set batch_time
|
||||
entry.batch_time = Some(Instant::now());
|
||||
@ -328,6 +329,7 @@ mod tests {
|
||||
max_new_tokens: 1,
|
||||
stop_sequences: vec![],
|
||||
},
|
||||
top_n_tokens: 0,
|
||||
},
|
||||
response_tx,
|
||||
span: info_span!("entry"),
|
||||
|
||||
@ -158,7 +158,7 @@ async fn generate(
|
||||
add_prompt = Some(req.inputs.clone());
|
||||
}
|
||||
|
||||
let details = req.parameters.details || req.parameters.decoder_input_details;
|
||||
let details: bool = req.parameters.details || req.parameters.decoder_input_details;
|
||||
|
||||
// Inference
|
||||
let (response, best_of_responses) = match req.parameters.best_of {
|
||||
@ -191,6 +191,7 @@ async fn generate(
|
||||
generated_tokens: response.generated_text.generated_tokens,
|
||||
prefill: response.prefill,
|
||||
tokens: response.tokens,
|
||||
top_tokens: response.top_tokens,
|
||||
seed: response.generated_text.seed,
|
||||
}
|
||||
})
|
||||
@ -204,6 +205,7 @@ async fn generate(
|
||||
tokens: response.tokens,
|
||||
seed: response.generated_text.seed,
|
||||
best_of_sequences,
|
||||
top_tokens: response.top_tokens,
|
||||
})
|
||||
}
|
||||
false => None,
|
||||
@ -385,12 +387,16 @@ async fn generate_stream(
|
||||
// Prefill is ignored
|
||||
InferStreamResponse::Prefill(_) => {}
|
||||
// Yield event for every new token
|
||||
InferStreamResponse::Token(token) => {
|
||||
InferStreamResponse::Intermediate{
|
||||
token,
|
||||
top_tokens,
|
||||
} => {
|
||||
tracing::debug!(parent: &span, "Token: {:?}", token);
|
||||
|
||||
// StreamResponse
|
||||
let stream_token = StreamResponse {
|
||||
token,
|
||||
top_tokens: top_tokens,
|
||||
generated_text: None,
|
||||
details: None,
|
||||
};
|
||||
@ -403,6 +409,7 @@ async fn generate_stream(
|
||||
generated_text,
|
||||
start,
|
||||
queued,
|
||||
top_tokens,
|
||||
} => {
|
||||
// Token details
|
||||
let details = match details {
|
||||
@ -451,6 +458,7 @@ async fn generate_stream(
|
||||
|
||||
let stream_token = StreamResponse {
|
||||
token,
|
||||
top_tokens: top_tokens,
|
||||
generated_text: Some(output_text),
|
||||
details
|
||||
};
|
||||
@ -509,6 +517,7 @@ pub async fn run(
|
||||
max_concurrent_requests: usize,
|
||||
max_best_of: usize,
|
||||
max_stop_sequences: usize,
|
||||
max_top_n_tokens: u32,
|
||||
max_input_length: usize,
|
||||
max_total_tokens: usize,
|
||||
waiting_served_ratio: f32,
|
||||
@ -571,6 +580,7 @@ pub async fn run(
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
|
||||
@ -15,6 +15,7 @@ pub struct Validation {
|
||||
/// Validation parameters
|
||||
max_best_of: usize,
|
||||
max_stop_sequences: usize,
|
||||
max_top_n_tokens: u32,
|
||||
max_input_length: usize,
|
||||
max_total_tokens: usize,
|
||||
/// Channel to communicate with the background tokenization task
|
||||
@ -27,6 +28,7 @@ impl Validation {
|
||||
tokenizer: Option<Tokenizer>,
|
||||
max_best_of: usize,
|
||||
max_stop_sequences: usize,
|
||||
max_top_n_tokens: u32,
|
||||
max_input_length: usize,
|
||||
max_total_tokens: usize,
|
||||
) -> Self {
|
||||
@ -54,6 +56,7 @@ impl Validation {
|
||||
max_best_of,
|
||||
sender,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
}
|
||||
@ -142,6 +145,7 @@ impl Validation {
|
||||
seed,
|
||||
watermark,
|
||||
decoder_input_details,
|
||||
top_n_tokens,
|
||||
..
|
||||
} = request.parameters;
|
||||
|
||||
@ -218,6 +222,15 @@ impl Validation {
|
||||
}
|
||||
};
|
||||
|
||||
let top_n_tokens = top_n_tokens
|
||||
.map(|value| {
|
||||
if value > self.max_top_n_tokens {
|
||||
return Err(ValidationError::TopNTokens(self.max_top_n_tokens, value));
|
||||
}
|
||||
Ok(value)
|
||||
})
|
||||
.unwrap_or(Ok(0))?;
|
||||
|
||||
// Check if inputs is empty
|
||||
if request.inputs.is_empty() {
|
||||
return Err(EmptyInput);
|
||||
@ -263,6 +276,7 @@ impl Validation {
|
||||
truncate: truncate.unwrap_or(self.max_input_length) as u32,
|
||||
parameters,
|
||||
stopping_parameters,
|
||||
top_n_tokens: top_n_tokens,
|
||||
})
|
||||
}
|
||||
|
||||
@ -336,6 +350,7 @@ pub(crate) struct ValidGenerateRequest {
|
||||
pub decoder_input_details: bool,
|
||||
pub parameters: NextTokenChooserParameters,
|
||||
pub stopping_parameters: StoppingCriteriaParameters,
|
||||
pub top_n_tokens: u32,
|
||||
}
|
||||
|
||||
#[derive(Error, Debug)]
|
||||
@ -350,6 +365,10 @@ pub enum ValidationError {
|
||||
BestOfSeed,
|
||||
#[error("`best_of` != 1 is not supported when streaming tokens")]
|
||||
BestOfStream,
|
||||
#[error("`top_n_tokens` must be >= 0 and <= {0}. Given: {1}")]
|
||||
TopNTokens(u32, u32),
|
||||
#[error("`top_n_tokens` != 0 is not allowed for this endpoint")]
|
||||
TopNTokensDisabled,
|
||||
#[error("`decoder_input_details` == true is not supported when streaming tokens")]
|
||||
PrefillDetailsStream,
|
||||
#[error("`temperature` must be strictly positive")]
|
||||
@ -391,14 +410,16 @@ mod tests {
|
||||
let tokenizer = None;
|
||||
let max_best_of = 2;
|
||||
let max_stop_sequence = 3;
|
||||
let max_input_length = 4;
|
||||
let max_total_tokens = 5;
|
||||
let max_top_n_tokens = 4;
|
||||
let max_input_length = 5;
|
||||
let max_total_tokens = 6;
|
||||
let workers = 1;
|
||||
let validation = Validation::new(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequence,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
@ -418,14 +439,16 @@ mod tests {
|
||||
let tokenizer = Some(get_tokenizer().await);
|
||||
let max_best_of = 2;
|
||||
let max_stop_sequence = 3;
|
||||
let max_input_length = 4;
|
||||
let max_total_tokens = 5;
|
||||
let max_top_n_tokens = 4;
|
||||
let max_input_length = 5;
|
||||
let max_total_tokens = 6;
|
||||
let workers = 1;
|
||||
let validation = Validation::new(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequence,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
@ -435,7 +458,7 @@ mod tests {
|
||||
.validate_input("Hello".to_string(), None, max_new_tokens)
|
||||
.await
|
||||
{
|
||||
Err(ValidationError::MaxTotalTokens(5, 1, 10)) => (),
|
||||
Err(ValidationError::MaxTotalTokens(6, 1, 10)) => (),
|
||||
_ => panic!("Unexpected not max new tokens"),
|
||||
}
|
||||
}
|
||||
@ -445,14 +468,16 @@ mod tests {
|
||||
let tokenizer = Some(get_tokenizer().await);
|
||||
let max_best_of = 2;
|
||||
let max_stop_sequence = 3;
|
||||
let max_input_length = 4;
|
||||
let max_total_tokens = 5;
|
||||
let max_top_n_tokens = 4;
|
||||
let max_input_length = 5;
|
||||
let max_total_tokens = 6;
|
||||
let workers = 1;
|
||||
let validation = Validation::new(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequence,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
@ -477,14 +502,16 @@ mod tests {
|
||||
let tokenizer = Some(get_tokenizer().await);
|
||||
let max_best_of = 2;
|
||||
let max_stop_sequence = 3;
|
||||
let max_input_length = 4;
|
||||
let max_total_tokens = 5;
|
||||
let max_top_n_tokens = 4;
|
||||
let max_input_length = 5;
|
||||
let max_total_tokens = 6;
|
||||
let workers = 1;
|
||||
let validation = Validation::new(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequence,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
@ -531,4 +558,75 @@ mod tests {
|
||||
// top_p == 1.0 is invalid for users to ask for but it's the default resolved value.
|
||||
assert_eq!(valid_request.parameters.top_p, 1.0);
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_validation_top_n_tokens() {
|
||||
let tokenizer = Some(get_tokenizer().await);
|
||||
let max_best_of = 2;
|
||||
let max_stop_sequences = 3;
|
||||
let max_top_n_tokens = 4;
|
||||
let max_input_length = 5;
|
||||
let max_total_tokens = 6;
|
||||
let workers = 1;
|
||||
let validation = Validation::new(
|
||||
workers,
|
||||
tokenizer,
|
||||
max_best_of,
|
||||
max_stop_sequences,
|
||||
max_top_n_tokens,
|
||||
max_input_length,
|
||||
max_total_tokens,
|
||||
);
|
||||
match validation
|
||||
.validate(GenerateRequest {
|
||||
inputs: "Hello".to_string(),
|
||||
parameters: GenerateParameters {
|
||||
top_n_tokens: Some(5),
|
||||
..default_parameters()
|
||||
},
|
||||
})
|
||||
.await
|
||||
{
|
||||
Err(ValidationError::TopNTokens(4, 5)) => (),
|
||||
_ => panic!("Unexpected top_n_tokens"),
|
||||
}
|
||||
|
||||
validation
|
||||
.validate(GenerateRequest {
|
||||
inputs: "Hello".to_string(),
|
||||
parameters: GenerateParameters {
|
||||
top_n_tokens: Some(4),
|
||||
max_new_tokens: 1,
|
||||
..default_parameters()
|
||||
},
|
||||
})
|
||||
.await
|
||||
.unwrap();
|
||||
|
||||
validation
|
||||
.validate(GenerateRequest {
|
||||
inputs: "Hello".to_string(),
|
||||
parameters: GenerateParameters {
|
||||
top_n_tokens: Some(0),
|
||||
max_new_tokens: 1,
|
||||
..default_parameters()
|
||||
},
|
||||
})
|
||||
.await
|
||||
.unwrap();
|
||||
|
||||
let valid_request = validation
|
||||
.validate(GenerateRequest {
|
||||
inputs: "Hello".to_string(),
|
||||
parameters: GenerateParameters {
|
||||
top_n_tokens: None,
|
||||
max_new_tokens: 1,
|
||||
..default_parameters()
|
||||
},
|
||||
})
|
||||
.await
|
||||
.unwrap();
|
||||
|
||||
assert_eq!(valid_request.top_n_tokens, 0);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
vllm_commit := d284b831c17f42a8ea63369a06138325f73c4cf9
|
||||
vllm_commit := e86af624d059969b0fb07b075b1d338bf10c3365
|
||||
|
||||
vllm:
|
||||
# Clone vllm
|
||||
@ -10,4 +10,4 @@ build-vllm: vllm
|
||||
|
||||
install-vllm: build-vllm
|
||||
pip uninstall vllm -y || true
|
||||
cd vllm && python setup.py install
|
||||
cd vllm && python setup.py install
|
||||
|
||||
334
server/poetry.lock
generated
334
server/poetry.lock
generated
@ -298,13 +298,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.1.6"
|
||||
version = "8.1.7"
|
||||
description = "Composable command line interface toolkit"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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||||
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|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -421,18 +421,21 @@ test = ["pytest (>=6)"]
|
||||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.12.2"
|
||||
version = "3.12.3"
|
||||
description = "A platform independent file lock."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
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|
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|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
|
||||
docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "frozenlist"
|
||||
@ -562,13 +565,13 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
|
||||
|
||||
[[package]]
|
||||
name = "grpc-interceptor"
|
||||
version = "0.15.2"
|
||||
version = "0.15.3"
|
||||
description = "Simplifies gRPC interceptors"
|
||||
optional = false
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
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|
||||
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|
||||
|
||||
[package.dependencies]
|
||||
@ -754,13 +757,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.14.1"
|
||||
version = "0.16.4"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
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|
||||
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|
||||
|
||||
[package.dependencies]
|
||||
@ -773,15 +776,16 @@ tqdm = ">=4.42.1"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
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|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
|
||||
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|
||||
inference = ["aiohttp", "pydantic"]
|
||||
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|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "gradio", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
torch = ["torch"]
|
||||
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
|
||||
typing = ["pydantic", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
@ -1409,13 +1413,13 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
|
||||
|
||||
[[package]]
|
||||
name = "pluggy"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
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||||
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|
||||
|
||||
[package.extras]
|
||||
@ -1424,24 +1428,24 @@ testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
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|
||||
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||||
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|
||||
|
||||
[[package]]
|
||||
@ -1472,36 +1476,40 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
|
||||
|
||||
[[package]]
|
||||
name = "pyarrow"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
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[package.dependencies]
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@ -1876,18 +1916,18 @@ files = [
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[[package]]
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||||
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[[package]]
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@ -2052,18 +2092,18 @@ telegram = ["requests"]
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||||
|
||||
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||||
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@ -2076,18 +2116,18 @@ tqdm = ">=4.27"
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|
||||
dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.2.8,!=0.3.2,<=0.4.13)", "jaxlib (>=0.1.65,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.4.13)", "jaxlib (>=0.1.65,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
|
||||
docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
|
||||
docs-specific = ["hf-doc-builder"]
|
||||
fairscale = ["fairscale (>0.3)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.2.8,!=0.3.2,<=0.4.13)", "jaxlib (>=0.1.65,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
integrations = ["optuna", "ray[tune]", "sigopt"]
|
||||
@ -2115,7 +2155,7 @@ tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
|
||||
torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (<10.0.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)", "decord (==0.6.0)"]
|
||||
vision = ["Pillow (<10.0.0)"]
|
||||
|
||||
@ -2465,4 +2505,4 @@ quantize = ["accelerate", "datasets", "texttable"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.13"
|
||||
content-hash = "91a848038c08a44c67acfb4257781440ccc1e74a4b82f09513e75588fa33f72b"
|
||||
content-hash = "f2ef5f41a172d14985367a385ad6ce844c8c05b2d68d9ddcc11b41f581921c96"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "text-generation-server"
|
||||
version = "1.0.1"
|
||||
version = "1.0.3"
|
||||
description = "Text Generation Inference Python gRPC Server"
|
||||
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
|
||||
|
||||
@ -25,8 +25,8 @@ opentelemetry-instrumentation-grpc = "^0.36b0"
|
||||
hf-transfer = "^0.1.2"
|
||||
sentencepiece = "^0.1.97"
|
||||
tokenizers = "^0.13.3"
|
||||
huggingface-hub = "^0.14.1"
|
||||
transformers = "^4.31.0"
|
||||
huggingface-hub = "^0.16.4"
|
||||
transformers = "^4.32.1"
|
||||
einops = "^0.6.1"
|
||||
texttable = { version = "^1.6.7", optional = true }
|
||||
datasets = { version = "^2.14.0", optional = true }
|
||||
|
||||
@ -1,25 +1,36 @@
|
||||
accelerate==0.20.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "3.13"
|
||||
aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
bitsandbytes==0.41.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
certifi==2023.7.22 ; python_version >= "3.9" and python_version < "3.13"
|
||||
charset-normalizer==3.2.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
click==8.1.6 ; python_version >= "3.9" and python_version < "3.13"
|
||||
click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
|
||||
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
|
||||
datasets==2.14.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
|
||||
dill==0.3.7 ; python_version >= "3.9" and python_version < "3.13"
|
||||
einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
filelock==3.12.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
filelock==3.12.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
frozenlist==1.4.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
fsspec[http]==2023.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
googleapis-common-protos==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpc-interceptor==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpc-interceptor==0.15.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpcio-reflection==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpcio-status==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
grpcio==1.57.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.14.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
jinja2==3.1.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
markupsafe==2.1.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
mpmath==1.3.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
multidict==6.0.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
multiprocess==0.70.15 ; python_version >= "3.9" and python_version < "3.13"
|
||||
networkx==3.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
numpy==1.25.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
@ -32,24 +43,33 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13
|
||||
opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
packaging==23.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pandas==2.0.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
peft==0.4.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pillow==10.0.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
protobuf==4.24.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
protobuf==4.24.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
psutil==5.9.5 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pyarrow==13.0.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pytz==2023.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
regex==2023.8.8 ; python_version >= "3.9" and python_version < "3.13"
|
||||
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
safetensors==0.3.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
scipy==1.11.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
scipy==1.11.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
|
||||
setuptools==68.0.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
setuptools==68.1.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
six==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
sympy==1.12 ; python_version >= "3.9" and python_version < "3.13"
|
||||
texttable==1.6.7 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
torch==2.0.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers==4.31.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
transformers==4.32.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "3.13"
|
||||
tzdata==2023.3 ; python_version >= "3.9" and python_version < "3.13"
|
||||
urllib3==2.0.4 ; python_version >= "3.9" and python_version < "3.13"
|
||||
win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
|
||||
wrapt==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
xxhash==3.3.0 ; python_version >= "3.9" and python_version < "3.13"
|
||||
yarl==1.9.2 ; python_version >= "3.9" and python_version < "3.13"
|
||||
|
||||
@ -1,7 +1,9 @@
|
||||
import torch
|
||||
from text_generation_server.utils.tokens import (
|
||||
StopSequenceCriteria,
|
||||
StoppingCriteria,
|
||||
FinishReason,
|
||||
batch_top_tokens,
|
||||
)
|
||||
|
||||
|
||||
@ -42,3 +44,22 @@ def test_stopping_criteria_max():
|
||||
assert criteria(1, "") == (False, None)
|
||||
assert criteria(1, "") == (False, None)
|
||||
assert criteria(1, "") == (True, FinishReason.FINISH_REASON_LENGTH)
|
||||
|
||||
def test_batch_top_tokens():
|
||||
top_n_tokens = [0, 2, 3, 4, 5]
|
||||
top_n_tokens_tensor = torch.tensor(top_n_tokens)
|
||||
inp_logprobs = torch.tensor([[-1., -3., -4., -2., -3.]] * 5)
|
||||
|
||||
topn_tok_ids, topn_tok_logprobs = batch_top_tokens(top_n_tokens, top_n_tokens_tensor, inp_logprobs)
|
||||
|
||||
assert topn_tok_ids[0] == []
|
||||
assert topn_tok_ids[1] == [0, 3]
|
||||
assert topn_tok_ids[2] == [0, 3, 1, 4]
|
||||
assert topn_tok_ids[3] == [0, 3, 1, 4]
|
||||
assert topn_tok_ids[4] == [0, 3, 1, 4, 2]
|
||||
|
||||
assert topn_tok_logprobs[0] == []
|
||||
assert topn_tok_logprobs[1] == [-1, -2]
|
||||
assert topn_tok_logprobs[2] == [-1, -2, -3, -3]
|
||||
assert topn_tok_logprobs[3] == [-1, -2, -3, -3]
|
||||
assert topn_tok_logprobs[4] == [-1, -2, -3, -3, -4]
|
||||
|
||||
@ -125,6 +125,9 @@ def download_weights(
|
||||
try:
|
||||
adapter_config_filename = hf_hub_download(model_id, revision=revision, filename="adapter_config.json")
|
||||
utils.download_and_unload_peft(model_id, revision, trust_remote_code=trust_remote_code)
|
||||
is_local_model = True
|
||||
utils.weight_files(model_id, revision, extension)
|
||||
return
|
||||
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
from text_generation_server.utils.tokens import batch_top_tokens
|
||||
import torch
|
||||
import inspect
|
||||
|
||||
@ -12,6 +13,7 @@ from text_generation_server.models.types import (
|
||||
PrefillTokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
TopTokens,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
@ -42,6 +44,8 @@ class CausalLMBatch(Batch):
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
stopping_criterias: List[StoppingCriteria]
|
||||
top_n_tokens: List[int]
|
||||
top_n_tokens_tensor: torch.Tensor
|
||||
|
||||
# Metadata used for padding
|
||||
max_input_length: int
|
||||
@ -72,6 +76,7 @@ class CausalLMBatch(Batch):
|
||||
inputs = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
requests_idx_mapping = {}
|
||||
@ -88,6 +93,7 @@ class CausalLMBatch(Batch):
|
||||
r.stopping_parameters, tokenizer
|
||||
)
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
top_n_tokens.append(r.top_n_tokens)
|
||||
max_truncation = max(max_truncation, r.truncate)
|
||||
max_decode_tokens += stopping_criteria.max_new_tokens
|
||||
padding_right_offset = max(
|
||||
@ -121,6 +127,9 @@ class CausalLMBatch(Batch):
|
||||
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
|
||||
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
|
||||
top_n_tokens_tensor = torch.tensor(
|
||||
top_n_tokens, device=device, dtype=torch.int64
|
||||
)
|
||||
|
||||
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
|
||||
|
||||
@ -138,6 +147,8 @@ class CausalLMBatch(Batch):
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
max_input_length=max_input_length.item(),
|
||||
padding_right_offset=padding_right_offset,
|
||||
max_tokens=max_tokens,
|
||||
@ -163,6 +174,7 @@ class CausalLMBatch(Batch):
|
||||
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
|
||||
total_remaining_decode_tokens = 0
|
||||
new_padding_right_offset = 0
|
||||
@ -184,6 +196,7 @@ class CausalLMBatch(Batch):
|
||||
next_token_choosers.append(self.next_token_choosers[idx])
|
||||
stopping_criteria = self.stopping_criterias[idx]
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
top_n_tokens.append(self.top_n_tokens[idx])
|
||||
remaining_decode_tokens = (
|
||||
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
|
||||
)
|
||||
@ -223,6 +236,7 @@ class CausalLMBatch(Batch):
|
||||
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
|
||||
del past_values
|
||||
|
||||
top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
|
||||
max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
|
||||
|
||||
self.requests = requests
|
||||
@ -235,6 +249,8 @@ class CausalLMBatch(Batch):
|
||||
self.read_offsets = read_offsets
|
||||
self.next_token_choosers = next_token_choosers
|
||||
self.stopping_criterias = stopping_criterias
|
||||
self.top_n_tokens = top_n_tokens
|
||||
self.top_n_tokens_tensor = top_n_tokens_tensor
|
||||
self.max_input_length = max_input_length
|
||||
self.padding_right_offset = new_padding_right_offset
|
||||
self.max_tokens = max_tokens
|
||||
@ -262,6 +278,7 @@ class CausalLMBatch(Batch):
|
||||
all_input_ids = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
max_tokens = 0
|
||||
|
||||
# Batch tensors
|
||||
@ -269,6 +286,7 @@ class CausalLMBatch(Batch):
|
||||
attention_mask = None
|
||||
position_ids = None
|
||||
past_key_values = []
|
||||
top_n_tokens_tensor = None
|
||||
|
||||
# Used for slicing correctly inside the tensors
|
||||
# Equivalent to a cumsum on batch sizes
|
||||
@ -281,6 +299,7 @@ class CausalLMBatch(Batch):
|
||||
all_input_ids.extend(batch.all_input_ids)
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
top_n_tokens.extend(batch.top_n_tokens)
|
||||
|
||||
if i == 0:
|
||||
requests_idx_mapping = batch.requests_idx_mapping
|
||||
@ -310,6 +329,12 @@ class CausalLMBatch(Batch):
|
||||
(total_batch_size, max_input_length + padding_right_offset),
|
||||
)
|
||||
|
||||
if top_n_tokens_tensor is None:
|
||||
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
|
||||
total_batch_size,
|
||||
)
|
||||
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
|
||||
|
||||
# We need to slice the attention mask to remove padding from previous steps
|
||||
# and to remove unused allocated space
|
||||
left_offset = max_input_length - batch.max_input_length
|
||||
@ -438,6 +463,8 @@ class CausalLMBatch(Batch):
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
max_input_length=max_input_length,
|
||||
padding_right_offset=padding_right_offset,
|
||||
keys_head_dim_last=batches[0].keys_head_dim_last,
|
||||
@ -549,6 +576,12 @@ class CausalLM(Model):
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
|
||||
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
||||
batch.top_n_tokens,
|
||||
batch.top_n_tokens_tensor,
|
||||
torch.softmax(logits[:, -1], -1),
|
||||
)
|
||||
|
||||
# Zipped iterator
|
||||
iterator = zip(
|
||||
batch.requests,
|
||||
@ -559,6 +592,9 @@ class CausalLM(Model):
|
||||
batch.next_token_choosers,
|
||||
batch.stopping_criterias,
|
||||
batch.all_input_ids,
|
||||
batch.top_n_tokens,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
@ -571,6 +607,9 @@ class CausalLM(Model):
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
top_n_tokens,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Select next token
|
||||
next_token_id, logprobs = next_token_chooser(
|
||||
@ -637,6 +676,24 @@ class CausalLM(Model):
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
if top_n_tokens > 0:
|
||||
toptoken_texts = self.tokenizer.batch_decode(
|
||||
top_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
special_toptokens,
|
||||
)
|
||||
else:
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
@ -645,6 +702,7 @@ class CausalLM(Model):
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
@ -64,6 +64,7 @@ class LlamaConfig(PretrainedConfig):
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=False,
|
||||
rope_scaling=None,
|
||||
rope_theta=10000.0,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
@ -84,6 +85,7 @@ class LlamaConfig(PretrainedConfig):
|
||||
self.pretraining_tp = pretraining_tp
|
||||
self.use_cache = use_cache
|
||||
self.rope_scaling = rope_scaling
|
||||
self.rope_theta = rope_theta
|
||||
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
@ -189,7 +191,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
||||
# config=config, prefix=f"{prefix}.rotary_emb", weights=weights
|
||||
# )
|
||||
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||
config=config, dim=self.head_size, base=10000.0, device=weights.device
|
||||
config=config, dim=self.head_size, base=config.rope_theta, device=weights.device
|
||||
)
|
||||
|
||||
self.softmax_scale = self.head_size**-0.5
|
||||
|
||||
@ -241,19 +241,21 @@ class FlashRWLargeAttention(torch.nn.Module):
|
||||
|
||||
hidden_size = config.hidden_size
|
||||
num_heads = config.n_head
|
||||
num_heads_kv = config.n_head_kv
|
||||
# num_heads_kv = config.n_head_kv
|
||||
num_groups = config.n_head_kv
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
self.head_size = hidden_size // num_heads
|
||||
self.num_groups = num_groups
|
||||
|
||||
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||
config=config, dim=self.head_size, base=10000.0, device=weights.device
|
||||
)
|
||||
self.softmax_scale = self.head_size ** (-0.5)
|
||||
|
||||
self.num_groups = num_heads // (num_heads_kv * 2)
|
||||
# self.num_groups = num_heads // (num_heads_kv * 2)
|
||||
self.num_heads = num_heads // self.num_groups
|
||||
self.num_heads_kv = num_heads_kv // self.num_groups
|
||||
# self.num_heads_kv = num_heads_kv // self.num_groups
|
||||
process_group = weights.process_group
|
||||
|
||||
if process_group.size() > self.num_groups:
|
||||
@ -264,6 +266,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
||||
raise NotImplementedError(
|
||||
f"Tensor Parallelism is not implemented for {self.num_groups} not divisible by {process_group.size()}"
|
||||
)
|
||||
|
||||
self.num_groups = self.num_groups // process_group.size()
|
||||
|
||||
self.query_key_value = TensorParallelColumnLinear.load(
|
||||
|
||||
@ -51,7 +51,7 @@ class IdeficsVisionConfig(PretrainedConfig):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
image_num_channels (`int`, *optional*, defaults to `3`):
|
||||
Number of image channels.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
|
||||
@ -80,7 +80,7 @@ class IdeficsVisionConfig(PretrainedConfig):
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=16,
|
||||
num_channels=3,
|
||||
hidden_act="quick_gelu",
|
||||
hidden_act="gelu",
|
||||
layer_norm_eps=1e-5,
|
||||
attention_dropout=0.0,
|
||||
initializer_range=0.02,
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import math
|
||||
import itertools
|
||||
from text_generation_server.utils.tokens import batch_top_tokens
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
@ -16,6 +17,7 @@ from text_generation_server.models.types import (
|
||||
PrefillTokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
TopTokens,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
|
||||
@ -165,6 +167,8 @@ class FlashCausalLMBatch(Batch):
|
||||
# Generation helpers
|
||||
next_token_chooser: HeterogeneousNextTokenChooser
|
||||
stopping_criterias: List[StoppingCriteria]
|
||||
top_n_tokens: List[int]
|
||||
top_n_tokens_tensor: torch.Tensor
|
||||
|
||||
# Number of blocks in this batch
|
||||
blocks: int
|
||||
@ -217,6 +221,7 @@ class FlashCausalLMBatch(Batch):
|
||||
|
||||
next_token_chooser_parameters = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
|
||||
# Cumulative length
|
||||
cumulative_length = 0
|
||||
@ -259,6 +264,7 @@ class FlashCausalLMBatch(Batch):
|
||||
)
|
||||
max_new_tokens = stopping_criteria.max_new_tokens
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
top_n_tokens.append(r.top_n_tokens)
|
||||
|
||||
# Paged attention
|
||||
# Remove one as the first token des not have a past
|
||||
@ -352,6 +358,9 @@ class FlashCausalLMBatch(Batch):
|
||||
prefill_next_token_indices = torch.tensor(
|
||||
prefill_next_token_indices, dtype=torch.int64, device=device
|
||||
)
|
||||
top_n_tokens_tensor = torch.tensor(
|
||||
top_n_tokens, device=device, dtype=torch.int64
|
||||
)
|
||||
|
||||
return cls(
|
||||
batch_id=pb.id,
|
||||
@ -378,6 +387,8 @@ class FlashCausalLMBatch(Batch):
|
||||
all_input_ids_tensor=all_input_ids_tensor,
|
||||
next_token_chooser=next_token_chooser,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
)
|
||||
@ -417,6 +428,7 @@ class FlashCausalLMBatch(Batch):
|
||||
read_offsets = []
|
||||
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
|
||||
blocks = 0
|
||||
max_blocks = 0
|
||||
@ -443,6 +455,8 @@ class FlashCausalLMBatch(Batch):
|
||||
stopping_criteria = self.stopping_criterias[idx]
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
|
||||
top_n_tokens.append(self.top_n_tokens[idx])
|
||||
|
||||
remaining_tokens = (
|
||||
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
|
||||
)
|
||||
@ -487,6 +501,7 @@ class FlashCausalLMBatch(Batch):
|
||||
input_lengths_tensor = self.input_lengths_tensor[indices]
|
||||
slots = self.slots[slot_filtering_indices]
|
||||
next_token_chooser = self.next_token_chooser.filter(indices)
|
||||
top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
|
||||
|
||||
start_slots = torch.tensor(start_slots, dtype=torch.int64)
|
||||
|
||||
@ -518,6 +533,8 @@ class FlashCausalLMBatch(Batch):
|
||||
all_input_ids_tensor=all_input_ids_tensor,
|
||||
next_token_chooser=next_token_chooser,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
)
|
||||
@ -566,6 +583,9 @@ class FlashCausalLMBatch(Batch):
|
||||
all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
|
||||
(total_batch_size, max_length)
|
||||
)
|
||||
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
|
||||
total_batch_size,
|
||||
)
|
||||
|
||||
start_slots = []
|
||||
block_tables = []
|
||||
@ -577,6 +597,7 @@ class FlashCausalLMBatch(Batch):
|
||||
|
||||
next_token_chooser_parameters = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
|
||||
# Cumulative length
|
||||
cumulative_batch_size = 0
|
||||
@ -602,6 +623,7 @@ class FlashCausalLMBatch(Batch):
|
||||
position_ids[start_index:end_index] = batch.position_ids
|
||||
slot_indices[start_index:end_index] = batch.slot_indices + cumulative_slots
|
||||
input_lengths_tensor[start_index:end_index] = batch.input_lengths_tensor
|
||||
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
|
||||
slots[slots_start_index:slots_end_index] = batch.slots
|
||||
|
||||
all_input_ids_tensor[
|
||||
@ -624,6 +646,8 @@ class FlashCausalLMBatch(Batch):
|
||||
next_token_chooser_parameters.extend([r.parameters for r in batch.requests])
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
|
||||
top_n_tokens.extend(batch.top_n_tokens)
|
||||
|
||||
# Update
|
||||
cumulative_batch_size += len(batch)
|
||||
cumulative_slots += len(batch.slots)
|
||||
@ -666,6 +690,8 @@ class FlashCausalLMBatch(Batch):
|
||||
all_input_ids_tensor=all_input_ids_tensor,
|
||||
next_token_chooser=next_token_chooser,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
blocks=blocks,
|
||||
max_blocks=max_blocks,
|
||||
)
|
||||
@ -831,10 +857,14 @@ class FlashCausalLM(Model):
|
||||
else:
|
||||
next_token_logits = out
|
||||
|
||||
next_input_ids, next_token_logprobs = batch.next_token_chooser(
|
||||
next_input_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
|
||||
batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits
|
||||
)
|
||||
|
||||
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
||||
batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs
|
||||
)
|
||||
|
||||
if prefill:
|
||||
if len(batch) > 1 and prefill_logprobs:
|
||||
# We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
|
||||
@ -931,8 +961,11 @@ class FlashCausalLM(Model):
|
||||
batch.all_input_ids,
|
||||
batch.next_token_chooser.do_sample,
|
||||
batch.next_token_chooser.seeds,
|
||||
batch.top_n_tokens,
|
||||
next_token_ids,
|
||||
next_token_logprobs,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
@ -945,8 +978,11 @@ class FlashCausalLM(Model):
|
||||
all_input_ids,
|
||||
do_sample,
|
||||
seed,
|
||||
top_n_tokens,
|
||||
next_token_id,
|
||||
next_token_logprob,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Append next token to all tokens
|
||||
all_input_ids.append(next_token_id)
|
||||
@ -1005,6 +1041,24 @@ class FlashCausalLM(Model):
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
if top_n_tokens > 0:
|
||||
toptoken_texts = self.tokenizer.batch_decode(
|
||||
top_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
special_toptokens,
|
||||
)
|
||||
else:
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
@ -1013,6 +1067,7 @@ class FlashCausalLM(Model):
|
||||
next_token_text,
|
||||
next_token_id in self.all_special_ids,
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
@ -54,7 +54,10 @@ class FlashRWSharded(FlashCausalLM):
|
||||
device,
|
||||
dtype,
|
||||
process_group=self.process_group,
|
||||
aliases={"lm_head.weight": ["transformer.word_embeddings.weight"]},
|
||||
aliases={
|
||||
"lm_head.weight": ["transformer.word_embeddings.weight"],
|
||||
"transformer.word_embeddings.weight": ["lm_head.weight"],
|
||||
},
|
||||
)
|
||||
|
||||
config.quantize = quantize
|
||||
|
||||
@ -763,6 +763,8 @@ class IdeficsCausalLM(Model):
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
top_tokens=None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
@ -771,6 +773,7 @@ class IdeficsCausalLM(Model):
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
generated_text,
|
||||
top_tokens
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
from text_generation_server.utils.tokens import batch_top_tokens
|
||||
import torch
|
||||
|
||||
from dataclasses import dataclass
|
||||
@ -11,6 +12,7 @@ from text_generation_server.models.types import (
|
||||
Batch,
|
||||
Generation,
|
||||
PrefillTokens,
|
||||
TopTokens,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
@ -48,6 +50,8 @@ class Seq2SeqLMBatch(Batch):
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
stopping_criterias: List[StoppingCriteria]
|
||||
top_n_tokens: List[int]
|
||||
top_n_tokens_tensor: torch.Tensor
|
||||
|
||||
# Metadata used for padding
|
||||
max_input_length: int
|
||||
@ -78,7 +82,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
inputs = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
|
||||
top_n_tokens = []
|
||||
decoder_input_lengths = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
@ -97,6 +101,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
r.stopping_parameters, tokenizer
|
||||
)
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
top_n_tokens.append(r.top_n_tokens)
|
||||
max_truncation = max(max_truncation, r.truncate)
|
||||
max_decode_tokens += stopping_criteria.max_new_tokens
|
||||
padding_right_offset = max(
|
||||
@ -126,6 +131,9 @@ class Seq2SeqLMBatch(Batch):
|
||||
prefix_offsets.append(0)
|
||||
read_offsets.append(1)
|
||||
all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
|
||||
top_n_tokens_tensor = torch.tensor(
|
||||
top_n_tokens, device=device, dtype=torch.int64
|
||||
)
|
||||
|
||||
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
|
||||
|
||||
@ -146,6 +154,8 @@ class Seq2SeqLMBatch(Batch):
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
max_input_length=max_input_length.item(),
|
||||
max_decoder_input_length=1,
|
||||
padding_right_offset=padding_right_offset,
|
||||
@ -173,6 +183,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
|
||||
max_input_length = 0
|
||||
max_decoder_input_length = 0
|
||||
@ -204,6 +215,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
next_token_choosers.append(self.next_token_choosers[idx])
|
||||
stopping_criteria = self.stopping_criterias[idx]
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
top_n_tokens.append(self.top_n_tokens[idx])
|
||||
remaining_decode_tokens = (
|
||||
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
|
||||
)
|
||||
@ -239,6 +251,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
layer[2] = layer[2][keep_indices, :, -max_input_length:]
|
||||
layer[3] = layer[3][keep_indices, :, -max_input_length:]
|
||||
|
||||
top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
|
||||
max_tokens = (
|
||||
len(request_ids) * (max_input_length + max_decoder_input_length)
|
||||
+ remaining_decode_tokens
|
||||
@ -254,6 +267,8 @@ class Seq2SeqLMBatch(Batch):
|
||||
self.read_offsets = read_offsets
|
||||
self.next_token_choosers = next_token_choosers
|
||||
self.stopping_criterias = stopping_criterias
|
||||
self.top_n_tokens = top_n_tokens
|
||||
self.top_n_tokens_tensor = top_n_tokens_tensor
|
||||
self.max_input_length = max_input_length
|
||||
self.max_decoder_input_length = max_decoder_input_length
|
||||
self.padding_right_offset = padding_right_offset
|
||||
@ -289,6 +304,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
read_offsets = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
top_n_tokens = []
|
||||
max_tokens = 0
|
||||
|
||||
# Batch tensors
|
||||
@ -296,6 +312,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
decoder_input_ids = None
|
||||
decoder_attention_mask = None
|
||||
encoder_last_hidden_state = None
|
||||
top_n_tokens_tensor = None
|
||||
past_key_values = []
|
||||
|
||||
# Used for slicing correctly inside the tensors
|
||||
@ -312,6 +329,7 @@ class Seq2SeqLMBatch(Batch):
|
||||
read_offsets.extend(batch.read_offsets)
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
top_n_tokens.extend(batch.top_n_tokens)
|
||||
|
||||
if i == 0:
|
||||
requests_idx_mapping = batch.requests_idx_mapping
|
||||
@ -384,6 +402,12 @@ class Seq2SeqLMBatch(Batch):
|
||||
),
|
||||
)
|
||||
|
||||
if top_n_tokens_tensor is None:
|
||||
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
|
||||
total_batch_size,
|
||||
)
|
||||
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
|
||||
|
||||
# Copy to correct indices
|
||||
encoder_last_hidden_state[
|
||||
start_index:end_index, -batch.max_input_length :, :
|
||||
@ -488,6 +512,8 @@ class Seq2SeqLMBatch(Batch):
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
max_input_length=max_input_length,
|
||||
max_decoder_input_length=max_decoder_input_length,
|
||||
padding_right_offset=padding_right_offset,
|
||||
@ -613,6 +639,12 @@ class Seq2SeqLM(Model):
|
||||
batch.past_key_values,
|
||||
)
|
||||
|
||||
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
||||
batch.top_n_tokens,
|
||||
batch.top_n_tokens_tensor,
|
||||
torch.softmax(logits[:, -1], -1),
|
||||
)
|
||||
|
||||
# Finished requests
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
@ -628,6 +660,9 @@ class Seq2SeqLM(Model):
|
||||
batch.next_token_choosers,
|
||||
batch.stopping_criterias,
|
||||
batch.all_decoder_input_ids,
|
||||
batch.top_n_tokens,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
@ -641,6 +676,9 @@ class Seq2SeqLM(Model):
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_decoder_input_ids,
|
||||
top_n_tokens,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Select next token
|
||||
next_token_id, logprobs = next_token_chooser(
|
||||
@ -698,6 +736,24 @@ class Seq2SeqLM(Model):
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
if top_n_tokens > 0:
|
||||
toptoken_texts = self.tokenizer.batch_decode(
|
||||
top_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
special_toptokens = [
|
||||
token_id in self.all_special_ids for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = TopTokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
special_toptokens,
|
||||
)
|
||||
else:
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
@ -706,6 +762,7 @@ class Seq2SeqLM(Model):
|
||||
next_token_text,
|
||||
next_token_id_squeezed.item() in self.all_special_ids,
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
from functools import total_ordering
|
||||
import torch
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
@ -71,6 +72,25 @@ class PrefillTokens:
|
||||
return len(self.token_ids)
|
||||
|
||||
|
||||
@dataclass
|
||||
class TopTokens:
|
||||
token_ids: List[int]
|
||||
logprobs: List[float]
|
||||
texts: List[str]
|
||||
is_special: List[bool]
|
||||
|
||||
def to_pb(self) -> generate_pb2.TopTokens:
|
||||
return generate_pb2.TopTokens(
|
||||
ids=self.token_ids,
|
||||
logprobs=self.logprobs,
|
||||
texts=self.texts,
|
||||
is_special=self.is_special,
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.token_ids)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Generation:
|
||||
request_id: int
|
||||
@ -80,6 +100,8 @@ class Generation:
|
||||
token_text: str
|
||||
token_is_special: bool
|
||||
generated_text: Optional[GeneratedText]
|
||||
# Optional for now, since it's not yet supported for every model.
|
||||
top_tokens: Optional[TopTokens]
|
||||
|
||||
def to_pb(self) -> generate_pb2.Generation:
|
||||
return generate_pb2.Generation(
|
||||
@ -94,4 +116,5 @@ class Generation:
|
||||
generated_text=self.generated_text.to_pb()
|
||||
if self.generated_text is not None
|
||||
else None,
|
||||
top_tokens=self.top_tokens.to_pb() if self.top_tokens is not None else None,
|
||||
)
|
||||
|
||||
@ -18,13 +18,20 @@ from accelerate import init_empty_weights
|
||||
|
||||
from text_generation_server.utils.gptq.quant_linear import QuantLinear
|
||||
|
||||
HAS_EXLLAMA = True
|
||||
try:
|
||||
major, _minor = torch.cuda.get_device_capability()
|
||||
except Exception:
|
||||
major = 1
|
||||
HAS_EXLLAMA = False
|
||||
CAN_EXLLAMA = major >= 8
|
||||
if os.getenv("DISABLE_EXLLAMA") == "True":
|
||||
HAS_EXLLAMA = False
|
||||
try:
|
||||
from text_generation_server.utils.gptq.exllama import Ex4bitLinear
|
||||
except ImportError:
|
||||
HAS_EXLLAMA = False
|
||||
elif CAN_EXLLAMA:
|
||||
try:
|
||||
from text_generation_server.utils.gptq.exllama import Ex4bitLinear
|
||||
HAS_EXLLAMA = True
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
from typing import Optional
|
||||
|
||||
|
||||
@ -1,24 +1,20 @@
|
||||
import re
|
||||
from typing import Callable, List, Optional, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
RepetitionPenaltyLogitsProcessor,
|
||||
PreTrainedTokenizerBase,
|
||||
)
|
||||
from typing import List, Tuple, Optional
|
||||
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.pb.generate_pb2 import FinishReason
|
||||
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
|
||||
from text_generation_server.utils.logits_process import (
|
||||
static_warper,
|
||||
HeterogeneousProcessorWrapper,
|
||||
HeterogeneousRepetitionPenaltyLogitsProcessor,
|
||||
HeterogeneousTemperatureLogitsWarper,
|
||||
HeterogeneousTopKLogitsWarper,
|
||||
HeterogeneousTopPLogitsWarper,
|
||||
HeterogeneousTypicalLogitsWarper,
|
||||
HeterogeneousProcessorWrapper,
|
||||
static_warper,
|
||||
)
|
||||
from text_generation_server.utils.watermark import WatermarkLogitsProcessor
|
||||
from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
|
||||
|
||||
|
||||
class NextTokenChooser:
|
||||
@ -229,11 +225,10 @@ class HeterogeneousNextTokenChooser:
|
||||
scores = warper(input_ids, scores)
|
||||
|
||||
next_ids = self.choice(scores)
|
||||
next_logprobs = torch.gather(
|
||||
torch.log_softmax(scores, -1), 1, next_ids.view(-1, 1)
|
||||
).view(-1)
|
||||
logprobs = torch.log_softmax(scores, -1)
|
||||
next_logprobs = torch.gather(logprobs, 1, next_ids.view(-1, 1)).view(-1)
|
||||
|
||||
return next_ids, next_logprobs
|
||||
return next_ids, next_logprobs, logprobs
|
||||
|
||||
def filter(self, indices):
|
||||
if self.watermark_processor is not None:
|
||||
@ -339,3 +334,51 @@ class HeterogeneousSampling:
|
||||
self.greedy_indices = new_greedy_indices
|
||||
self.sampling_mapping = new_sampling_mapping
|
||||
return self
|
||||
|
||||
|
||||
def batch_top_tokens(
|
||||
top_n_tokens: list[int], top_n_tokens_tensor: torch.Tensor, logprobs: torch.Tensor
|
||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||
"""Find the top n most likely tokens for a batch of generations.
|
||||
|
||||
When multiple tokens have equal probabilities and they don't all fit, the
|
||||
remaining tokens are also returned.
|
||||
"""
|
||||
max_top_n = max(top_n_tokens)
|
||||
# Early exit when top_n_tokens is not used
|
||||
if max_top_n == 0:
|
||||
return [[]] * len(top_n_tokens), [[]] * len(top_n_tokens)
|
||||
|
||||
# Ensure top_n doesn't exceed vocab size
|
||||
top_n_tokens = [min(tok, logprobs.size(-1)) for tok in top_n_tokens]
|
||||
|
||||
# Parallel kthvalue adapted from https://discuss.pytorch.org/t/how-to-efficiently-get-the-k-th-largest-values-in-parallel/160529/2
|
||||
# Sorted topk is faster than torch.sort() since we only need a small subset
|
||||
sorted_top_k = torch.topk(logprobs, k=max_top_n, dim=1, sorted=True).values
|
||||
nth_highest = torch.gather(
|
||||
sorted_top_k, 1, (top_n_tokens_tensor - 1).clip(min=0).unsqueeze(1)
|
||||
)
|
||||
nth_highest[nth_highest == -float("inf")] = torch.finfo(logprobs.dtype).min
|
||||
|
||||
# Find the new "fuzzy" top n values
|
||||
top_n_indices = (logprobs >= nth_highest).nonzero()
|
||||
_, top_n_ishes = torch.unique_consecutive(top_n_indices[:, 0], return_counts=True)
|
||||
|
||||
k = 1 if top_n_ishes.numel() == 0 else top_n_ishes.max()
|
||||
# Take a new topk for these new max n values
|
||||
top_k = torch.topk(logprobs, k=k, dim=1, sorted=True)
|
||||
|
||||
top_n_ishes = top_n_ishes.tolist()
|
||||
top_indices = top_k.indices.tolist()
|
||||
top_values = top_k.values.tolist()
|
||||
|
||||
return (
|
||||
[
|
||||
idxs[:n] if req_n > 0 else []
|
||||
for idxs, n, req_n in zip(top_indices, top_n_ishes, top_n_tokens)
|
||||
],
|
||||
[
|
||||
vals[:n] if req_n > 0 else []
|
||||
for vals, n, req_n in zip(top_values, top_n_ishes, top_n_tokens)
|
||||
],
|
||||
)
|
||||
|
||||
@ -170,13 +170,14 @@ class Weights:
|
||||
"Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
|
||||
)
|
||||
|
||||
from text_generation_server.utils.layers import HAS_EXLLAMA
|
||||
from text_generation_server.utils.layers import HAS_EXLLAMA, CAN_EXLLAMA
|
||||
|
||||
if use_exllama:
|
||||
if not HAS_EXLLAMA:
|
||||
logger.warning(
|
||||
"Exllama GPTQ cuda kernels (which are faster) could have been used, but are not currently installed, try using BUILD_EXTENSIONS=True"
|
||||
)
|
||||
if CAN_EXLLAMA:
|
||||
logger.warning(
|
||||
"Exllama GPTQ cuda kernels (which are faster) could have been used, but are not currently installed, try using BUILD_EXTENSIONS=True"
|
||||
)
|
||||
use_exllama = False
|
||||
else:
|
||||
logger.info("Using exllama kernels")
|
||||
@ -222,7 +223,7 @@ class Weights:
|
||||
return bits, groupsize
|
||||
|
||||
def _set_gptq_params(self, model_id):
|
||||
filename = "quantize_config.json"
|
||||
filename = "config.json"
|
||||
try:
|
||||
if os.path.exists(os.path.join(model_id, filename)):
|
||||
filename = os.path.join(model_id, filename)
|
||||
@ -230,7 +231,18 @@ class Weights:
|
||||
filename = hf_hub_download(model_id, filename=filename)
|
||||
with open(filename, "r") as f:
|
||||
data = json.load(f)
|
||||
self.gptq_bits = data["bits"]
|
||||
self.gptq_groupsize = data["group_size"]
|
||||
self.gptq_bits = data["quantization_config"]["bits"]
|
||||
self.gptq_groupsize = data["quantization_config"]["group_size"]
|
||||
except Exception:
|
||||
pass
|
||||
filename = "quantize_config.json"
|
||||
try:
|
||||
if os.path.exists(os.path.join(model_id, filename)):
|
||||
filename = os.path.join(model_id, filename)
|
||||
else:
|
||||
filename = hf_hub_download(model_id, filename=filename)
|
||||
with open(filename, "r") as f:
|
||||
data = json.load(f)
|
||||
self.gptq_bits = data["bits"]
|
||||
self.gptq_groupsize = data["group_size"]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
Loading…
Reference in New Issue
Block a user