Merge branch 'main' into fix_triton

Author: MengqingCao

.buildkite/run-xpu-test.sh

@@ -12,10 +12,11 @@ docker build -t ${image_name} -f Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() { 
-  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true;
+  docker rm -f "${container_name}" || true; 
+  docker image rm -f "${image_name}" || true;
+  docker system prune -f || true;
 }
 trap remove_docker_container EXIT
-remove_docker_container
 
 # Run the image and test offline inference/tensor parallel
 docker run \
@@ -25,6 +26,6 @@ docker run \
     --name "${container_name}" \
     "${image_name}" \
     sh -c '
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/test-pipeline.yaml

@@ -295,6 +295,7 @@ steps:
   # these tests need to be separated, cannot combine
   - pytest -v -s compile/piecewise/test_simple.py
   - pytest -v -s compile/piecewise/test_toy_llama.py
+  - pytest -v -s compile/test_pass_manager.py
 
 - label: PyTorch Fullgraph Test # 18min
   source_file_dependencies:

Dockerfile.xpu

@@ -1,11 +1,7 @@
-FROM intel/deep-learning-essentials:2025.0.1-0-devel-ubuntu22.04 AS vllm-base
+# oneapi 2025.0.2 docker base image use rolling 2448 package. https://dgpu-docs.intel.com/releases/packages.html?release=Rolling+2448.13&os=Ubuntu+22.04, and we don't need install driver manually.
+FROM intel/deep-learning-essentials:2025.0.2-0-devel-ubuntu22.04 AS vllm-base
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
+RUN rm /etc/apt/sources.list.d/intel-graphics.list
 
 RUN apt-get update -y && \
     apt-get install -y --no-install-recommends --fix-missing \
@@ -21,8 +17,6 @@ RUN apt-get update -y && \
     python3 \
     python3-dev \
     python3-pip \
-    libze-intel-gpu-dev \
-    libze-intel-gpu1 \
     wget
 
 WORKDIR /workspace/vllm

README.md

@@ -13,9 +13,17 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
+---
+
+[2025/03] We are collaborating with Ollama to host an [Inference Night](https://lu.ma/vllm-ollama) at Y Combinator in San Francisco on Thursday, March 27, at 6 PM. Discuss all things inference local or data center!
+
+[2025/04] We're hosting our first-ever *vLLM Asia Developer Day* in Singapore on *April 3rd*! This is a full-day event (9 AM - 9 PM SGT) in partnership with SGInnovate, AMD, and Embedded LLM. Meet vLLM's Cyrus Leung and Chen Zhang, and learn about LLM inference for RL, MI300X, and more! [Register Now](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)
+
+---
+
 *Latest News* 🔥
 
-- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit#slide=id.g33fb1ff286e_0_29).
+- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
 - [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).

benchmarks/benchmark_serving_structured_output.py

@@ -732,8 +732,11 @@ def main(args: argparse.Namespace):
         api_url = f"http://{args.host}:{args.port}{args.endpoint}"
         base_url = f"http://{args.host}:{args.port}"
 
-    tokenizer = get_tokenizer(tokenizer_id,
-                              trust_remote_code=args.trust_remote_code)
+    tokenizer = get_tokenizer(
+        tokenizer_id,
+        trust_remote_code=args.trust_remote_code,
+        tokenizer_mode=args.tokenizer_mode,
+    )
 
     if args.dataset == 'grammar':
         args.structure_type = 'guided_grammar'
@@ -876,6 +879,13 @@ def main(args: argparse.Namespace):
         help=
         "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
+    parser.add_argument(
+        "--tokenizer-mode",
+        type=str,
+        default="auto",
+        help=
+        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
+    )
     parser.add_argument(
         "--num-prompts",
         type=int,

benchmarks/run_structured_output_benchmark.sh

@@ -54,6 +54,7 @@ for qps in "${QPS_VALUES[@]}"; do
   python "$SCRIPT_DIR/benchmark_serving_structured_output.py" $COMMON_PARAMS \
     --request-rate $qps \
     --result-filename "$FILENAME" \
+    --tokenizer-mode ${TOKENIZER_MODE:-"auto"} \
     --port ${PORT:-8000}
 
   echo "Completed benchmark with QPS: $qps"

docs/source/deployment/docker.md

@@ -34,11 +34,11 @@ If you need to use those dependencies (having accepted the license terms),
 create a custom Dockerfile on top of the base image with an extra layer that installs them:
 
 ```Dockerfile
-FROM vllm/vllm-openai:v0.7.3
+FROM vllm/vllm-openai:v0.8.0
 
 # e.g. install the `audio` and `video` optional dependencies
 # NOTE: Make sure the version of vLLM matches the base image!
-RUN uv pip install --system vllm[audio,video]==0.7.3
+RUN uv pip install vllm[audio,video]==0.8.0
 ```
 
 :::
@@ -52,7 +52,7 @@ with an extra layer that installs their code from source:
 ```Dockerfile
 FROM vllm/vllm-openai:latest
 
-RUN uv pip install --system git+https://github.com/huggingface/transformers.git
+RUN uv pip install git+https://github.com/huggingface/transformers.git
 ```
 
 :::

docs/source/models/supported_models.md

@@ -768,7 +768,7 @@ See [this page](#generative-models) for more information on how to use generativ
   * `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc.
   * ✅︎
   * ✅︎
-  *
+  * ⚠️
 - * `GLM4VForCausalLM`<sup>^</sup>
   * GLM-4V
   * T + I
@@ -951,13 +951,10 @@ V0 correctly implements the model's attention pattern:
 
 V1 currently uses a simplified attention pattern:
 - Uses causal attention for all tokens, including image tokens
-- Generates reasonable outputs but does not match the original model's attention for text + image inputs
+- Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when `{"do_pan_and_scan": True}`
 - Will be updated in the future to support the correct behavior
-- Does not support `"do_pan_and_scan": True`
 
 This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.
-
-For these reasons, `Gemma3ForConditionalGeneration` is supported only on V0 at the moment.
 :::
 
 :::{note}

docs/source/serving/distributed_serving.md

@@ -83,7 +83,7 @@ Since this is a ray cluster of **containers**, all the following commands should
 
 Then, on any node, use `docker exec -it node /bin/bash` to enter the container, execute `ray status` and `ray list nodes` to check the status of the Ray cluster. You should see the right number of nodes and GPUs.
 
-After that, on any node, use `docker exec -it node /bin/bash` to enter the container again. **In the container**, you can use vLLM as usual, just as you have all the GPUs on one node. The common practice is to set the tensor parallel size to the number of GPUs in each node, and the pipeline parallel size to the number of nodes. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 8 and the pipeline parallel size to 2:
+After that, on any node, use `docker exec -it node /bin/bash` to enter the container again. **In the container**, you can use vLLM as usual, just as you have all the GPUs on one node: vLLM will be able to leverage GPU resources of all nodes in the Ray cluster, and therefore, only run the `vllm` command on this node but not other nodes. The common practice is to set the tensor parallel size to the number of GPUs in each node, and the pipeline parallel size to the number of nodes. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 8 and the pipeline parallel size to 2:
 
 ```console
  vllm serve /path/to/the/model/in/the/container \

requirements/test.in

@@ -30,7 +30,7 @@ matplotlib # required for qwen-vl test
 mistral_common[opencv] >= 1.5.4 # required for pixtral test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.4 # required for model evaluation test
-transformers==4.48.2 
+transformers==4.48.2
 # quantization
 bitsandbytes>=0.45.3
 buildkite-test-collector==0.1.9

requirements/tpu.txt

@@ -17,9 +17,9 @@ ray[data]
 --find-links https://storage.googleapis.com/libtpu-releases/index.html
 --find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
 --find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
-torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250314%2Bcxx11-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
-torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250314%2Bcxx11-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
-torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250314%2Bcxx11-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250314%2Bcxx11-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250314%2Bcxx11-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250314%2Bcxx11-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
+torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
+torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
+torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"

tests/compile/test_pass_manager.py

@@ -4,34 +4,38 @@
 
 import pytest
 import torch
-from torch._inductor.codecache import BypassFxGraphCache
 
-from vllm.compilation.config import CompilationConfig
-from vllm.compilation.inductor_pass import (CallableInductorPass,
-                                            as_inductor_pass)
+from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
 from vllm.compilation.pass_manager import PostGradPassManager
+from vllm.config import CompilationConfig
 
 
 def simple_callable(graph: torch.fx.Graph):
     pass
 
 
-@as_inductor_pass(files=(__file__, ))
-def callable_decorated(graph: torch.fx.Graph):
-    pass
+callable_uuid = CallableInductorPass(simple_callable,
+                                     InductorPass.hash_source(__file__))
 
 
 @pytest.mark.parametrize(
     "works, callable",
-    [(False, simple_callable), (True, callable_decorated),
-     (True, CallableInductorPass(simple_callable, "simple_callable"))])
+    [
+        (False, simple_callable),
+        (True, callable_uuid),
+        (True, CallableInductorPass(simple_callable)),
+    ],
+)
 def test_pass_manager(works: bool, callable):
     config = CompilationConfig().pass_config
-    pass_manager = PostGradPassManager([callable])
-    pass_manager.configure(config)  # Adds default passes
 
+    pass_manager = PostGradPassManager()
+    pass_manager.configure(config)
+
+    # Try to add the callable to the pass manager
     if works:
+        pass_manager.add(callable)
         pickle.dumps(pass_manager)
     else:
-        with pytest.raises(BypassFxGraphCache):
-            pickle.dumps(pass_manager)
+        with pytest.raises(AssertionError):
+            pass_manager.add(callable)

tests/multimodal/test_processing.py

@@ -7,19 +7,25 @@
 
 import numpy as np
 import pytest
+import torch
 from transformers import ProcessorMixin
 
 from vllm.config import ModelConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalFieldElem, MultiModalKwargs,
+                                    MultiModalKwargsItem,
+                                    MultiModalSharedField)
 # yapf conflicts with isort for this block
 # yapf: disable
 from vllm.multimodal.processing import (PlaceholderFeaturesInfo,
-                                        PromptIndexTargets, PromptInsertion,
-                                        PromptReplacement, apply_text_matches,
+                                        ProcessingCache, PromptIndexTargets,
+                                        PromptInsertion, PromptReplacement,
+                                        apply_text_matches,
                                         apply_token_matches,
                                         find_mm_placeholders,
                                         find_text_matches, find_token_matches,
-                                        iter_token_matches)
+                                        iter_token_matches,
+                                        replace_token_matches)
 # yapf: enable
 from vllm.multimodal.profiling import MultiModalProfiler
 from vllm.transformers_utils.tokenizer import (AnyTokenizer,
@@ -89,6 +95,58 @@ def test_iter_token_matches(token_ids, match_ids, expected):
     assert all(match_len == len(match_ids) for match_len in match_lens)
 
 
+# yapf: disable
+@pytest.mark.parametrize(
+    ("token_ids", "match_ids", "new_ids", "expected"),
+    [
+        ([], [], [-1], []),
+        ([], [32000], [-1], []),
+        (
+            [32000, 32000, 32000],
+            [32000],
+            [-1],
+            [-1, -1, -1],
+        ),
+        (
+            [32000, 32000, 32000],
+            [32000, 32000],
+            [-1],
+            [-1, 32000],
+        ),
+        (
+            [32000, 32000, 32000],
+            [32000, 32000, 32000],
+            [-1],
+            [-1],
+        ),
+        (
+            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
+            [28747, 32000],
+            [-1],
+            [9833, -1, 32000, 32000, 9833, -1, 32000, 918],
+        ),
+        (
+            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
+            [28747, 32000, 32000, 32000],
+            [-1],
+            [9833, -1, 9833, 28747, 32000, 32000, 918],
+        ),
+        (
+            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
+            [28747, 0, 32000],
+            [-1],
+            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
+        ),
+    ],
+)
+# yapf: enable
+def test_replace_token_matches(token_ids, match_ids, new_ids, expected):
+    result = replace_token_matches(token_ids, match_ids, new_ids)
+
+    # Manually constructed results
+    assert result == expected
+
+
 # yapf: disable
 @pytest.mark.parametrize(
     ("prompt", "target_by_key", "expected_by_key"),
@@ -837,6 +895,45 @@ def test_find_mm_placeholders(
     assert result == expected
 
 
+def _dummy_elem(modality: str, key: str, size: int):
+    return MultiModalFieldElem(
+        modality=modality,
+        key=key,
+        data=torch.empty((size, ), dtype=torch.int8),
+        field=MultiModalSharedField(1),
+    )
+
+
+def _dummy_item(modality: str, size_by_key: dict[str, int]):
+    return MultiModalKwargsItem.from_elems([
+        _dummy_elem(modality, key, size) for key, size in size_by_key.items()
+    ])
+
+
+def _dummy_kw(size_by_key_modality: dict[str, dict[str, int]]):
+    return MultiModalKwargs.from_items([
+        _dummy_item(modality, size_by_key)
+        for modality, size_by_key in size_by_key_modality.items()
+    ])
+
+
+# yapf: disable
+@pytest.mark.parametrize(
+    ("item", "expected_size"),
+    [
+        (_dummy_item("a", {"a1": 100}), 100),
+        (_dummy_item("a", {"a1": 100, "a2": 110}), 210),
+        (_dummy_kw({"a": {"a1": 100, "a2": 110}, "b": {"b1": 120, "b2": 130}}), 460),  # noqa: E501
+    ],
+)
+# yapf: enable
+def test_cache_item_size(item, expected_size):
+    cache = ProcessingCache.get_lru_cache(2048, type(item))
+    cache[""] = item
+
+    assert cache.currsize == expected_size
+
+
 @pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
 @pytest.mark.parametrize(
     ("limit", "num_supported", "is_valid"),