Merge branch 'main' into optimize-lm-eval-large-tests

.github/dependabot.yml

@@ -14,6 +14,15 @@ updates:
     reviewers: ["khluu", "simon-mo"]
     allow:
       - dependency-type: "all"
+    ignore:
+      - dependency-name: "torch"
+      - dependency-name: "torchvision"
+      - dependency-name: "xformers"
+      - dependency-name: "lm-format-enforcer"
+      - dependency-name: "gguf"
+      - dependency-name: "compressed-tensors"
+      - dependency-name: "ray[adag]"
+      - dependency-name: "lm-eval"
     groups:
       patch-update:
         applies-to: version-updates

Dockerfile

@@ -206,7 +206,7 @@ FROM vllm-base AS vllm-openai
 
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install accelerate hf_transfer 'modelscope!=1.15.0' bitsandbytes>=0.44.0 timm==0.9.10
+    pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
 
 ENV VLLM_USAGE_SOURCE production-docker-image
 

Dockerfile.neuron

@@ -31,7 +31,7 @@ RUN --mount=type=bind,source=.git,target=.git \
     if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 
 RUN python3 -m pip install -U \
-        cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
+        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
         -r requirements-neuron.txt
 
 ENV VLLM_TARGET_DEVICE neuron

Dockerfile.ppc64le

@@ -21,7 +21,7 @@ RUN --mount=type=bind,source=.git,target=.git \
 # These packages will be in rocketce eventually
 RUN --mount=type=cache,target=/root/.cache/pip  \
     pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
-        cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
+        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
         torch==2.3.1 \
         -r requirements-cpu.txt \
         xformers uvloop==0.20.0

Dockerfile.rocm

@@ -52,7 +52,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
             python3 -m pip uninstall -y torch torchvision \
             && python3 -m pip install --pre \
                 torch==2.6.0.dev20240918 \
-                setuptools-scm>=8 \
+                'setuptools-scm>=8' \
                 torchvision==0.20.0.dev20240918 \
                 --extra-index-url https://download.pytorch.org/whl/nightly/rocm6.2;; \
         *) ;; esac

Dockerfile.tpu

@@ -25,7 +25,7 @@ ENV VLLM_TARGET_DEVICE="tpu"
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
     python3 -m pip install \
-        cmake>=3.26 ninja packaging setuptools-scm>=8 wheel jinja2 \
+        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
         -r requirements-tpu.txt
 RUN python3 setup.py develop
 

benchmarks/benchmark_serving.py

@@ -406,9 +406,9 @@ def calculate_metrics(
         median_itl_ms=np.median(itls or 0) * 1000,
         percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
                             for p in selected_percentiles],
-        mean_e2el_ms=np.median(e2els or 0) * 1000,
+        mean_e2el_ms=np.mean(e2els or 0) * 1000,
         std_e2el_ms=np.std(e2els or 0) * 1000,
-        median_e2el_ms=np.mean(e2els or 0) * 1000,
+        median_e2el_ms=np.median(e2els or 0) * 1000,
         percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
                              for p in selected_percentiles],
     )

docs/source/getting_started/tpu-installation.rst

@@ -1,35 +1,167 @@
 .. _installation_tpu:
 
+#####################
 Installation with TPU
-=====================
+#####################
 
-vLLM supports Google Cloud TPUs using PyTorch XLA.
+Tensor Processing Units (TPUs) are Google's custom-developed application-specific 
+integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs 
+are available in different versions each with different hardware specifications.
+For more information about TPUs, see `TPU System Architecture <https://cloud.google.com/tpu/docs/system-architecture-tpu-vm>`_. 
+For more information on the TPU versions supported with vLLM, see:
+
+* `TPU v6e <https://cloud.google.com/tpu/docs/v6e>`_
+* `TPU v5e <https://cloud.google.com/tpu/docs/v5e>`_
+* `TPU v5p <https://cloud.google.com/tpu/docs/v5p>`_
+* `TPU v4 <https://cloud.google.com/tpu/docs/v4>`_
+
+These TPU versions allow you to configure the physical arrangements of the TPU 
+chips. This can improve throughput and networking performance. For more 
+information see: 
+
+* `TPU v6e topologies <https://cloud.google.com/tpu/docs/v6e#configurations>`_
+* `TPU v5e topologies <https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config>`_
+* `TPU v5p topologies <https://cloud.google.com/tpu/docs/v5p#tpu-v5p-config>`_
+* `TPU v4 topologies <https://cloud.google.com/tpu/docs/v4#tpu-v4-config>`_
+
+In order for you to use Cloud TPUs you need to have TPU quota granted to your 
+Google Cloud Platform project. TPU quotas specify how many TPUs you can use in a
+GPC project and are specified in terms of TPU version, the number of TPU you 
+want to use, and quota type. For more information, see `TPU quota <https://cloud.google.com/tpu/docs/quota#tpu_quota>`_. 
+
+For TPU pricing information, see `Cloud TPU pricing <https://cloud.google.com/tpu/pricing>`_.
+
+You may need additional persistent storage for your TPU VMs. For more 
+information, see `Storage options for Cloud TPU data <https://cloud.devsite.corp.google.com/tpu/docs/storage-options>`_.
 
 Requirements
 ------------
 
-* Google Cloud TPU VM (single & multi host)
-* TPU versions: v5e, v5p, v4
-* Python: 3.10
+* Google Cloud TPU VM 
+* TPU versions: v6e, v5e, v5p, v4
+* Python: 3.10 or newer
+
+Provision Cloud TPUs
+====================
+
+You can provision Cloud TPUs using the `Cloud TPU API <https://cloud.google.com/tpu/docs/reference/rest>`_` 
+or the `queued resources <https://cloud.google.com/tpu/docs/queued-resources>`_` 
+API. This section shows how to create TPUs using the queued resource API. 
+For more information about using the Cloud TPU API, see `Create a Cloud TPU using the Create Node API <https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm#create-node-api>`_. 
+`Queued resources <https://cloud.devsite.corp.google.com/tpu/docs/queued-resources>`_
+enable you to request Cloud TPU resources in a queued manner. When you request 
+queued resources, the request is added to a queue maintained by the Cloud TPU 
+service. When the requested resource becomes available, it's assigned to your 
+Google Cloud project for your immediate exclusive use. 
+
+Provision a Cloud TPU with the queued resource API
+--------------------------------------------------
+Create a TPU v5e with 4 TPU chips:
+
+.. code-block:: console
+
+    gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
+    --node-id TPU_NAME \
+    --project PROJECT_ID \
+    --zone ZONE \
+    --accelerator-type ACCELERATOR_TYPE \
+    --runtime-version RUNTIME_VERSION \
+    --service-account SERVICE_ACCOUNT
+
+.. list-table:: Parameter descriptions
+    :header-rows: 1
+
+    * - Parameter name
+      - Description
+    * - QUEUED_RESOURCE_ID
+      - The user-assigned ID of the queued resource request.
+    * - TPU_NAME
+      - The user-assigned name of the TPU which is created when the queued 
+        resource request is allocated.
+    * - PROJECT_ID
+      - Your Google Cloud project
+    * - ZONE
+      - The `zone <https://cloud.google.com/tpu/docs/regions-zones>`_ where you 
+        want to create your Cloud TPU.
+    * - ACCELERATOR_TYPE
+      - The TPU version you want to use. Specify the TPU version, followed by a 
+        '-' and the number of TPU cores. For example `v5e-4` specifies a v5e TPU 
+        with 4 cores. For more information, see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
+    * - RUNTIME_VERSION
+      - The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
+    * - SERVICE_ACCOUNT
+      - The email address for your service account. You can find it in the IAM 
+        Cloud Console under *Service Accounts*. For example: 
+        `tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
+
+Connect to your TPU using SSH:
+
+.. code-block:: bash
+
+    gcloud compute tpus tpu-vm ssh TPU_NAME
+
+Create and activate a Conda environment for vLLM:
+
+.. code-block:: bash
 
-Installation options:
+    conda create -n vllm python=3.10 -y
+    conda activate vllm
 
-1. :ref:`Build a docker image with Dockerfile <build_docker_tpu>`.
-2. :ref:`Build from source <build_from_source_tpu>`.
+Clone the vLLM repository and go to the vLLM directory:
+
+.. code-block:: bash
+
+    git clone https://github.com/vllm-project/vllm.git && cd vllm
+
+Uninstall the existing `torch` and `torch_xla` packages:
+
+.. code-block:: bash
+
+    pip uninstall torch torch-xla -y
+
+Install `torch` and `torch_xla`
+
+.. code-block:: bash
+
+    pip install --pre torch==2.6.0.dev20241028+cpu torchvision==0.20.0.dev20241028+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
+    pip install 'torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev-cp310-cp310-linux_x86_64.whl' -f https://storage.googleapis.com/libtpu-releases/index.html
+
+Install JAX and Pallas:
+
+.. code-block:: bash
+
+    pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
+    pip install jaxlib==0.4.32.dev20240829 jax==0.4.32.dev20240829 -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
+
+Install other build dependencies:
+
+.. code-block:: bash
+
+    pip install -r requirements-tpu.txt
+    VLLM_TARGET_DEVICE="tpu" python setup.py develop
+    sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev 
+
+Provision Cloud TPUs with GKE 
+-----------------------------
+
+For more information about using TPUs with GKE, see 
+https://cloud.google.com/kubernetes-engine/docs/how-to/tpus
+https://cloud.google.com/kubernetes-engine/docs/concepts/tpus
+https://cloud.google.com/kubernetes-engine/docs/concepts/plan-tpus
 
 .. _build_docker_tpu:
 
 Build a docker image with :code:`Dockerfile.tpu`
 ------------------------------------------------
 
-`Dockerfile.tpu <https://github.com/vllm-project/vllm/blob/main/Dockerfile.tpu>`_ is provided to build a docker image with TPU support.
+You can use `Dockerfile.tpu <https://github.com/vllm-project/vllm/blob/main/Dockerfile.tpu>`_ 
+to build a Docker image with TPU support.
 
 .. code-block:: console
 
     $ docker build -f Dockerfile.tpu -t vllm-tpu .
 
-
-You can run the docker image with the following command:
+Run the Docker image with the following command:
 
 .. code-block:: console
 
@@ -75,14 +207,12 @@ Next, build vLLM from source. This will only take a few seconds:
 
     $ VLLM_TARGET_DEVICE="tpu" python setup.py develop
 
-
 .. note::
 
     Since TPU relies on XLA which requires static shapes, vLLM bucketizes the possible input shapes and compiles an XLA graph for each different shape.
     The compilation time may take 20~30 minutes in the first run.
     However, the compilation time reduces to ~5 minutes afterwards because the XLA graphs are cached in the disk (in :code:`VLLM_XLA_CACHE_PATH` or :code:`~/.cache/vllm/xla_cache` by default).
 
-
 .. tip::
 
     If you encounter the following error:
@@ -93,7 +223,7 @@ Next, build vLLM from source. This will only take a few seconds:
         ImportError: libopenblas.so.0: cannot open shared object file: No such file or directory
 
 
-    Please install OpenBLAS with the following command:
+    Install OpenBLAS with the following command:
 
     .. code-block:: console
 

docs/source/models/supported_models.rst

@@ -160,13 +160,13 @@ Text Generation
     -
     - ✅︎
   * - :code:`GraniteForCausalLM`
-    - PowerLM
-    - :code:`ibm/PowerLM-3b` etc.
+    - Granite 3.0, PowerLM
+    - :code:`ibm-granite/granite-3.0-2b-base`, :code:`ibm-granite/granite-3.0-8b-instruct`, :code:`ibm/PowerLM-3b`, etc.
     - ✅︎
     - ✅︎
   * - :code:`GraniteMoeForCausalLM`
-    - PowerMoE
-    - :code:`ibm/PowerMoE-3b` etc.
+    - Granite 3.0 MoE, PowerMoE
+    - :code:`ibm-granite/granite-3.0-1b-a400m-base`, :code:`ibm-granite/granite-3.0-3b-a800m-instruct`, :code:`ibm/PowerMoE-3b`, etc.
     - ✅︎
     - ✅︎
   * - :code:`InternLMForCausalLM`
@@ -440,6 +440,12 @@ Text Generation
     - :code:`THUDM/glm-4v-9b` etc.
     - 
     - ✅︎
+  * - :code:`H2OVLChatModel`
+    - H2OVL
+    - T + I\ :sup:`E+`
+    - :code:`h2oai/h2ovl-mississippi-800m`, :code:`h2oai/h2ovl-mississippi-2b`, etc.
+    - 
+    - ✅︎
   * - :code:`InternVLChatModel`
     - InternVL2
     - T + I\ :sup:`E+`

examples/offline_inference_audio_language.py

@@ -34,11 +34,7 @@ def run_ultravox(question: str, audio_count: int):
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    llm = LLM(model=model_name,
-              enforce_eager=True,
-              enable_chunked_prefill=False,
-              max_model_len=8192,
-              limit_mm_per_prompt={"audio": audio_count})
+    llm = LLM(model=model_name, limit_mm_per_prompt={"audio": audio_count})
     stop_token_ids = None
     return llm, prompt, stop_token_ids
 

examples/offline_inference_vision_language.py

@@ -176,6 +176,31 @@ def run_minicpmv(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# H2OVL-Mississippi
+def run_h2ovl(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "h2oai/h2ovl-mississippi-2b"
+
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=8192,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    messages = [{'role': 'user', 'content': f"<image>\n{question}"}]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    # Stop tokens for H2OVL-Mississippi
+    # https://huggingface.co/h2oai/h2ovl-mississippi-2b
+    stop_token_ids = [tokenizer.eos_token_id]
+    return llm, prompt, stop_token_ids
+
+
 # InternVL
 def run_internvl(question: str, modality: str):
     assert modality == "image"
@@ -363,6 +388,7 @@ def run_glm4v(question: str, modality: str):
     "chameleon": run_chameleon,
     "minicpmv": run_minicpmv,
     "blip-2": run_blip2,
+    "h2ovl_chat": run_h2ovl,
     "internvl_chat": run_internvl,
     "NVLM_D": run_nvlm_d,
     "qwen_vl": run_qwen_vl,
@@ -475,4 +501,4 @@ def main(args):
                         default=16,
                         help='Number of frames to extract from the video.')
     args = parser.parse_args()
-    main(args)
+    main(args)