Merge branch 'main' into feature/usage-stats

Author: ErikKaum

.github/workflows/autodocs.yaml

@@ -30,11 +30,16 @@ jobs:
       id: install-router
       run: cargo install --path router/
 
+    - uses: actions/setup-node@v4
+      with:
+        node-version: 22
+
     - name: Set up Python
       uses: actions/setup-python@v2
       with:
         python-version: '3.x'
 
     - name: Check that documentation is up-to-date
       run: |
+        npm install -g swagger-cli
         python update_doc.py --check

.github/workflows/build.yaml

@@ -11,6 +11,11 @@ on:
           # - rocm
           # - intel
         required: true
+      release-tests:
+        description: "Run release integration tests"
+        required: true
+        default: false
+        type: boolean
 
 jobs:
   build-and-push:
@@ -23,7 +28,7 @@ jobs:
       group: ${{ github.workflow }}-build-and-push-image-${{ inputs.hardware }}-${{ github.head_ref || github.run_id }}
       cancel-in-progress: true
     # TODO see with @Glegendre to get CPU runner here instead
-    runs-on: [self-hosted, nvidia-gpu , multi-gpu, 4-a10, ci]
+    runs-on: [self-hosted, intel-cpu, 32-cpu, 256-ram, ci]
     permissions:
       contents: write
       packages: write
@@ -131,8 +136,8 @@ jobs:
             DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}${{ env.LABEL }}
           tags: ${{ steps.meta.outputs.tags || steps.meta-pr.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }}
-          cache-from: type=registry,ref=registry-push.github-runners.huggingface.tech/api-inference/community/text-generation-inference:cache${{ env.LABEL }},mode=min
-          cache-to: type=registry,ref=registry-push.github-runners.huggingface.tech/api-inference/community/text-generation-inference:cache${{ env.LABEL }},mode=min
+          cache-from: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-generation-inference-cache${{ env.LABEL }},mode=min,access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=min
+          cache-to: type=s3,region=us-east-1,bucket=ci-docker-buildx-cache,name=text-generation-inference-cache${{ env.LABEL }},mode=min,access_key_id=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_ACCESS_KEY_ID }},secret_access_key=${{ secrets.S3_CI_DOCKER_BUILDX_CACHE_SECRET_ACCESS_KEY }},mode=min
       - name: Final
         id: final
         run: |
@@ -148,7 +153,7 @@ jobs:
     runs-on: ["self-hosted", "${{ needs.build-and-push.outputs.runs_on }}", "multi-gpu"]
     if: needs.build-and-push.outputs.runs_on != 'ubuntu-latest'
     env:
-      PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main') && '--release' || '' }}
+      PYTEST_FLAGS: ${{ (startsWith(github.ref, 'refs/tags/') || github.ref == 'refs/heads/main' || inputs.release-tests == true) && '--release' || '' }}
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4

.github/workflows/ci_build.yaml

@@ -20,7 +20,14 @@ on:
       - "Dockerfile_amd"
       - "Dockerfile_intel"
     branches:
-      - 'main'
+      - "main"
+  workflow_dispatch:
+    inputs:
+      release-tests:
+        description: "Run release integration tests"
+        required: true
+        default: false
+        type: boolean
 
 jobs:
   build:
@@ -33,4 +40,6 @@ jobs:
     uses: ./.github/workflows/build.yaml # calls the one above ^
     with:
       hardware: ${{ matrix.hardware }}
+      # https://github.com/actions/runner/issues/2206
+      release-tests: ${{ inputs.release-tests == true }}
     secrets: inherit

Cargo.lock

@@ -40,7 +40,9 @@ RUN cargo build --profile release-opt
 # Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile
 FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS pytorch-install
 
+# NOTE: When updating PyTorch version, beware to remove `pip install nvidia-nccl-cu12==2.22.3` below in the Dockerfile. Context: https://github.com/huggingface/text-generation-inference/pull/2099
 ARG PYTORCH_VERSION=2.3.0
+
 ARG PYTHON_VERSION=3.10
 # Keep in sync with `server/pyproject.toml
 ARG CUDA_VERSION=12.1
@@ -241,7 +243,10 @@ COPY server/Makefile server/Makefile
 RUN cd server && \
     make gen-server && \
     pip install -r requirements_cuda.txt && \
-    pip install ".[bnb, accelerate, quantize, peft, outlines]" --no-cache-dir
+    pip install ".[bnb, accelerate, quantize, peft, outlines]" --no-cache-dir && \
+    pip install nvidia-nccl-cu12==2.22.3
+
+ENV LD_PRELOAD=/opt/conda/lib/python3.10/site-packages/nvidia/nccl/lib/libnccl.so.2
 
 # Deps before the binaries
 # The binaries change on every build given we burn the SHA into them

Dockerfile

@@ -20,19 +20,20 @@ to power Hugging Chat, the Inference API and Inference Endpoint.
 
 ## Table of contents
 
-- [Get Started](#get-started)
-  - [API Documentation](#api-documentation)
-  - [Using a private or gated model](#using-a-private-or-gated-model)
-  - [A note on Shared Memory](#a-note-on-shared-memory-shm)
-  - [Distributed Tracing](#distributed-tracing)
-  - [Local Install](#local-install)
-  - [CUDA Kernels](#cuda-kernels)
-- [Optimized architectures](#optimized-architectures)
-- [Run Mistral](#run-a-model)
-  - [Run](#run)
-  - [Quantization](#quantization)
-- [Develop](#develop)
-- [Testing](#testing)
+  - [Get Started](#get-started)
+    - [Docker](#docker)
+    - [API documentation](#api-documentation)
+    - [Using a private or gated model](#using-a-private-or-gated-model)
+    - [A note on Shared Memory (shm)](#a-note-on-shared-memory-shm)
+    - [Distributed Tracing](#distributed-tracing)
+    - [Architecture](#architecture)
+    - [Local install](#local-install)
+  - [Optimized architectures](#optimized-architectures)
+  - [Run locally](#run-locally)
+    - [Run](#run)
+    - [Quantization](#quantization)
+  - [Develop](#develop)
+  - [Testing](#testing)
 
 Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and [more](https://huggingface.co/docs/text-generation-inference/supported_models). TGI implements many features, such as:
 

README.md

@@ -61,7 +61,7 @@ class ChoiceDeltaToolCall(BaseModel):
 class ChoiceDelta(BaseModel):
     role: str
     content: Optional[str] = None
-    tool_calls: Optional[ChoiceDeltaToolCall]
+    tool_calls: Optional[ChoiceDeltaToolCall] = None
 
 
 class Choice(BaseModel):

clients/python/text_generation/types.py

@@ -11,6 +11,8 @@
     title: Using TGI with Intel Gaudi
   - local: installation_inferentia
     title: Using TGI with AWS Inferentia
+  - local: installation_intel
+    title: Using TGI with Intel GPUs
   - local: installation
     title: Installation from source
   - local: supported_models

docs/source/_toctree.yml

@@ -103,6 +103,7 @@ Several variants of the model server exist that are actively supported by Huggin
 
 - By default, the model server will attempt building [a server optimized for Nvidia GPUs with CUDA](https://huggingface.co/docs/text-generation-inference/installation_nvidia). The code for this version is hosted in the [main TGI repository](https://github.com/huggingface/text-generation-inference).
 - A [version optimized for AMD with ROCm](https://huggingface.co/docs/text-generation-inference/installation_amd) is hosted in the main TGI repository. Some model features differ.
+- A [version optimized for Intel GPUs](https://huggingface.co/docs/text-generation-inference/installation_intel) is hosted in the main TGI repository. Some model features differ.
 - The [version for Intel Gaudi](https://huggingface.co/docs/text-generation-inference/installation_gaudi) is maintained on a forked repository, often resynchronized with the main [TGI repository](https://github.com/huggingface/tgi-gaudi).
 - A [version for Neuron (AWS Inferentia2)](https://huggingface.co/docs/text-generation-inference/installation_inferentia) is maintained as part of [Optimum Neuron](https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference).
 - A version for Google TPUs is maintained as part of [Optimum TPU](https://github.com/huggingface/optimum-tpu/tree/main/text-generation-inference).

docs/source/architecture.md

@@ -0,0 +1,19 @@
+# Using TGI with Intel GPUs
+
+TGI optimized models are supported on Intel Data Center GPU [Max1100](https://www.intel.com/content/www/us/en/products/sku/232876/intel-data-center-gpu-max-1100/specifications.html), [Max1550](https://www.intel.com/content/www/us/en/products/sku/232873/intel-data-center-gpu-max-1550/specifications.html), the recommended usage is through Docker.
+
+
+On a server powered by Intel GPUs, TGI can be launched with the following command:
+
+```bash
+model=teknium/OpenHermes-2.5-Mistral-7B
+volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
+
+docker run --rm --privileged --cap-add=sys_nice \
+    --device=/dev/dri \
+    --ipc=host --shm-size 1g --net host -v $volume:/data \
+    ghcr.io/huggingface/text-generation-inference:latest-intel \
+    --model-id $model --cuda-graphs 0
+```
+
+The launched TGI server can then be queried from clients, make sure to check out the [Consuming TGI](./basic_tutorials/consuming_tgi) guide.