diff --git a/docs/source/design/multimodal/multimodal_index.rst b/docs/source/design/multimodal/multimodal_index.rst index 30f543abc20c7..c6d47f90b62d5 100644 --- a/docs/source/design/multimodal/multimodal_index.rst +++ b/docs/source/design/multimodal/multimodal_index.rst @@ -7,7 +7,7 @@ Multi-Modality vLLM provides experimental support for multi-modal models through the :mod:`vllm.multimodal` package. -Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models ` +Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models ` via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptType`. Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities @@ -15,9 +15,6 @@ by following :ref:`this guide `. Looking to add your own multi-modal model? Please follow the instructions listed :ref:`here `. -.. - TODO: Add usage of --limit-mm-per-prompt when multi-image input is officially supported - Guides ++++++ diff --git a/docs/source/index.rst b/docs/source/index.rst index 0692e949f1c77..86b1eed2d26ba 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -85,12 +85,8 @@ Documentation serving/deploying_with_nginx serving/distributed_serving serving/metrics - serving/env_vars - serving/usage_stats serving/integrations serving/tensorizer - serving/compatibility_matrix - serving/faq .. toctree:: :maxdepth: 1 @@ -99,12 +95,21 @@ Documentation models/supported_models models/adding_model models/enabling_multimodal_inputs - models/engine_args - models/lora - models/vlm - models/structured_outputs - models/spec_decode - models/performance + +.. toctree:: + :maxdepth: 1 + :caption: Usage + + usage/lora + usage/multimodal_inputs + usage/structured_outputs + usage/spec_decode + usage/compatibility_matrix + usage/performance + usage/faq + usage/engine_args + usage/env_vars + usage/usage_stats .. toctree:: :maxdepth: 1 diff --git a/docs/source/models/enabling_multimodal_inputs.rst b/docs/source/models/enabling_multimodal_inputs.rst index 49b5285c45590..5c1236e1a8972 100644 --- a/docs/source/models/enabling_multimodal_inputs.rst +++ b/docs/source/models/enabling_multimodal_inputs.rst @@ -3,7 +3,7 @@ Enabling Multimodal Inputs ========================== -This document walks you through the steps to extend a vLLM model so that it accepts :ref:`multi-modal ` inputs. +This document walks you through the steps to extend a vLLM model so that it accepts :ref:`multi-modal inputs `. .. seealso:: :ref:`adding_a_new_model` diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 9f3b6f59068e2..5b416e04da745 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -471,6 +471,8 @@ Sentence Pair Scoring .. note:: These models are supported in both offline and online inference via Score API. +.. _supported_mm_models: + Multimodal Language Models ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -489,8 +491,6 @@ On the other hand, modalities separated by :code:`/` are mutually exclusive. - e.g.: :code:`T / I` means that the model supports text-only and image-only inputs, but not text-with-image inputs. -.. _supported_vlms: - Text Generation --------------- @@ -646,6 +646,21 @@ Text Generation | :sup:`E` Pre-computed embeddings can be inputted for this modality. | :sup:`+` Multiple items can be inputted per text prompt for this modality. +.. important:: + To enable multiple multi-modal items per text prompt, you have to set :code:`limit_mm_per_prompt` (offline inference) + or :code:`--limit-mm-per-prompt` (online inference). For example, to enable passing up to 4 images per text prompt: + + .. code-block:: python + + llm = LLM( + model="Qwen/Qwen2-VL-7B-Instruct", + limit_mm_per_prompt={"image": 4}, + ) + + .. code-block:: bash + + vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt image=4 + .. note:: vLLM currently only supports adding LoRA to the language backbone of multimodal models. diff --git a/docs/source/serving/openai_compatible_server.md b/docs/source/serving/openai_compatible_server.md index c39cef85897ed..d75e90807ca1d 100644 --- a/docs/source/serving/openai_compatible_server.md +++ b/docs/source/serving/openai_compatible_server.md @@ -32,7 +32,7 @@ We currently support the following OpenAI APIs: - [Completions API](https://platform.openai.com/docs/api-reference/completions) - *Note: `suffix` parameter is not supported.* - [Chat Completions API](https://platform.openai.com/docs/api-reference/chat) - - [Vision](https://platform.openai.com/docs/guides/vision)-related parameters are supported; see [Using VLMs](../models/vlm.rst). + - [Vision](https://platform.openai.com/docs/guides/vision)-related parameters are supported; see [Multimodal Inputs](../usage/multimodal_inputs.rst). - *Note: `image_url.detail` parameter is not supported.* - We also support `audio_url` content type for audio files. - Refer to [vllm.entrypoints.chat_utils](https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/chat_utils.py) for the exact schema. @@ -41,7 +41,7 @@ We currently support the following OpenAI APIs: - [Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) - Instead of `inputs`, you can pass in a list of `messages` (same schema as Chat Completions API), which will be treated as a single prompt to the model according to its chat template. - - This enables multi-modal inputs to be passed to embedding models, see [Using VLMs](../models/vlm.rst). + - This enables multi-modal inputs to be passed to embedding models, see [this page](../usage/multimodal_inputs.rst) for details. - *Note: You should run `vllm serve` with `--task embedding` to ensure that the model is being run in embedding mode.* ## Score API for Cross Encoder Models diff --git a/docs/source/serving/compatibility_matrix.rst b/docs/source/usage/compatibility_matrix.rst similarity index 100% rename from docs/source/serving/compatibility_matrix.rst rename to docs/source/usage/compatibility_matrix.rst diff --git a/docs/source/models/engine_args.rst b/docs/source/usage/engine_args.rst similarity index 100% rename from docs/source/models/engine_args.rst rename to docs/source/usage/engine_args.rst diff --git a/docs/source/serving/env_vars.rst b/docs/source/usage/env_vars.rst similarity index 100% rename from docs/source/serving/env_vars.rst rename to docs/source/usage/env_vars.rst diff --git a/docs/source/serving/faq.rst b/docs/source/usage/faq.rst similarity index 99% rename from docs/source/serving/faq.rst rename to docs/source/usage/faq.rst index 9e858e612c8bf..ce327abd5fa20 100644 --- a/docs/source/serving/faq.rst +++ b/docs/source/usage/faq.rst @@ -1,3 +1,5 @@ +.. _faq: + Frequently Asked Questions =========================== diff --git a/docs/source/models/lora.rst b/docs/source/usage/lora.rst similarity index 99% rename from docs/source/models/lora.rst rename to docs/source/usage/lora.rst index ef0177eaf2162..c2c6fa2aebfaf 100644 --- a/docs/source/models/lora.rst +++ b/docs/source/usage/lora.rst @@ -1,7 +1,7 @@ .. _lora: -Using LoRA adapters -=================== +LoRA Adapters +============= This document shows you how to use `LoRA adapters `_ with vLLM on top of a base model. diff --git a/docs/source/models/vlm.rst b/docs/source/usage/multimodal_inputs.rst similarity index 62% rename from docs/source/models/vlm.rst rename to docs/source/usage/multimodal_inputs.rst index bcbe50a25fa09..c93f65327e31b 100644 --- a/docs/source/models/vlm.rst +++ b/docs/source/usage/multimodal_inputs.rst @@ -1,34 +1,31 @@ -.. _vlm: +.. _multimodal_inputs: -Using VLMs -========== +Multimodal Inputs +================= -vLLM provides experimental support for Vision Language Models (VLMs). See the :ref:`list of supported VLMs here `. -This document shows you how to run and serve these models using vLLM. +This page teaches you how to pass multi-modal inputs to :ref:`multi-modal models ` in vLLM. .. note:: - We are actively iterating on VLM support. See `this RFC `_ for upcoming changes, + We are actively iterating on multi-modal support. See `this RFC `_ for upcoming changes, and `open an issue on GitHub `_ if you have any feedback or feature requests. Offline Inference ----------------- -Single-image input -^^^^^^^^^^^^^^^^^^ - -The :class:`~vllm.LLM` class can be instantiated in much the same way as language-only models. - -.. code-block:: python - - llm = LLM(model="llava-hf/llava-1.5-7b-hf") - -To pass an image to the model, note the following in :class:`vllm.inputs.PromptType`: +To input multi-modal data, follow this schema in :class:`vllm.inputs.PromptType`: * ``prompt``: The prompt should follow the format that is documented on HuggingFace. * ``multi_modal_data``: This is a dictionary that follows the schema defined in :class:`vllm.multimodal.MultiModalDataDict`. +Image +^^^^^ + +You can pass a single image to the :code:`'image'` field of the multi-modal dictionary, as shown in the following examples: + .. code-block:: python + llm = LLM(model="llava-hf/llava-1.5-7b-hf") + # Refer to the HuggingFace repo for the correct format to use prompt = "USER: \nWhat is the content of this image?\nASSISTANT:" @@ -41,41 +38,6 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptT "multi_modal_data": {"image": image}, }) - for o in outputs: - generated_text = o.outputs[0].text - print(generated_text) - - # Inference with image embeddings as input - image_embeds = torch.load(...) # torch.Tensor of shape (1, image_feature_size, hidden_size of LM) - outputs = llm.generate({ - "prompt": prompt, - "multi_modal_data": {"image": image_embeds}, - }) - - for o in outputs: - generated_text = o.outputs[0].text - print(generated_text) - - # Inference with image embeddings as input with additional parameters - # Specifically, we are conducting a trial run of Qwen2VL and MiniCPM-V with the new input format, which utilizes additional parameters. - mm_data = {} - - image_embeds = torch.load(...) # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM) - # For Qwen2VL, image_grid_thw is needed to calculate positional encoding. - mm_data['image'] = { - "image_embeds": image_embeds, - "image_grid_thw": torch.load(...) # torch.Tensor of shape (1, 3), - } - # For MiniCPM-V, image_size_list is needed to calculate details of the sliced image. - mm_data['image'] = { - "image_embeds": image_embeds, - "image_size_list": [image.size] # list of image sizes - } - outputs = llm.generate({ - "prompt": prompt, - "multi_modal_data": mm_data, - }) - for o in outputs: generated_text = o.outputs[0].text print(generated_text) @@ -102,12 +64,7 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptT A code example can be found in `examples/offline_inference_vision_language.py `_. -Multi-image input -^^^^^^^^^^^^^^^^^ - -Multi-image input is only supported for a subset of VLMs, as shown :ref:`here `. - -To enable multiple multi-modal items per text prompt, you have to set ``limit_mm_per_prompt`` for the :class:`~vllm.LLM` class. +To substitute multiple images inside the same text prompt, you can pass in a list of images instead: .. code-block:: python @@ -118,10 +75,6 @@ To enable multiple multi-modal items per text prompt, you have to set ``limit_mm limit_mm_per_prompt={"image": 2}, # The maximum number to accept ) -Instead of passing in a single image, you can pass in a list of images. - -.. code-block:: python - # Refer to the HuggingFace repo for the correct format to use prompt = "<|user|>\n<|image_1|>\n<|image_2|>\nWhat is the content of each image?<|end|>\n<|assistant|>\n" @@ -169,30 +122,114 @@ Multi-image input can be extended to perform video captioning. We show this with generated_text = o.outputs[0].text print(generated_text) +Video +^^^^^ + +You can pass a list of NumPy arrays directly to the :code:`'video'` field of the multi-modal dictionary +instead of using multi-image input. + +Please refer to `examples/offline_inference_vision_language.py `_ for more details. + +Audio +^^^^^ + +You can pass a tuple :code:`(array, sampling_rate)` to the :code:`'audio'` field of the multi-modal dictionary. + +Please refer to `examples/offline_inference_audio_language.py `_ for more details. + +Embedding +^^^^^^^^^ + +To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model, +pass a tensor of shape :code:`(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary. + +.. code-block:: python + + # Inference with image embeddings as input + llm = LLM(model="llava-hf/llava-1.5-7b-hf") + + # Refer to the HuggingFace repo for the correct format to use + prompt = "USER: \nWhat is the content of this image?\nASSISTANT:" + + # Embeddings for single image + # torch.Tensor of shape (1, image_feature_size, hidden_size of LM) + image_embeds = torch.load(...) + + outputs = llm.generate({ + "prompt": prompt, + "multi_modal_data": {"image": image_embeds}, + }) + + for o in outputs: + generated_text = o.outputs[0].text + print(generated_text) + +For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings: + +.. code-block:: python + + # Construct the prompt based on your model + prompt = ... + + # Embeddings for multiple images + # torch.Tensor of shape (num_images, image_feature_size, hidden_size of LM) + image_embeds = torch.load(...) + + # Qwen2-VL + llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4}) + mm_data = { + "image": { + "image_embeds": image_embeds, + # image_grid_thw is needed to calculate positional encoding. + "image_grid_thw": torch.load(...), # torch.Tensor of shape (1, 3), + } + } + + # MiniCPM-V + llm = LLM("openbmb/MiniCPM-V-2_6", trust_remote_code=True, limit_mm_per_prompt={"image": 4}) + mm_data = { + "image": { + "image_embeds": image_embeds, + # image_size_list is needed to calculate details of the sliced image. + "image_size_list": [image.size for image in images], # list of image sizes + } + } + + outputs = llm.generate({ + "prompt": prompt, + "multi_modal_data": mm_data, + }) + + for o in outputs: + generated_text = o.outputs[0].text + print(generated_text) + Online Inference ---------------- -OpenAI Vision API -^^^^^^^^^^^^^^^^^ +Our OpenAI-compatible server accepts multi-modal data via the `Chat Completions API `_. + +.. important:: + A chat template is **required** to use Chat Completions API. + + Although most models come with a chat template, for others you have to define one yourself. + The chat template can be inferred based on the documentation on the model's HuggingFace repo. + For example, LLaVA-1.5 (``llava-hf/llava-1.5-7b-hf``) requires a chat template that can be found `here `__. + +Image +^^^^^ -You can serve vision language models with vLLM's HTTP server that is compatible with `OpenAI Vision API `_. +Image input is supported according to `OpenAI Vision API `_. +Here is a simple example using Phi-3.5-Vision. -Below is an example on how to launch the same ``microsoft/Phi-3.5-vision-instruct`` with vLLM's OpenAI-compatible API server. +First, launch the OpenAI-compatible server: .. code-block:: bash vllm serve microsoft/Phi-3.5-vision-instruct --task generate \ --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2 -.. important:: - Since OpenAI Vision API is based on `Chat Completions API `_, - a chat template is **required** to launch the API server. - - Although Phi-3.5-Vision comes with a chat template, for other models you may have to provide one if the model's tokenizer does not come with it. - The chat template can be inferred based on the documentation on the model's HuggingFace repo. - For example, LLaVA-1.5 (``llava-hf/llava-1.5-7b-hf``) requires a chat template that can be found `here `_. - -To consume the server, you can use the OpenAI client like in the example below: +Then, you can use the OpenAI client as follows: .. code-block:: python @@ -252,22 +289,59 @@ A full code example can be found in `examples/openai_chat_completion_client_for_ .. note:: - By default, the timeout for fetching images through http url is ``5`` seconds. You can override this by setting the environment variable: + By default, the timeout for fetching images through HTTP URL is ``5`` seconds. + You can override this by setting the environment variable: .. code-block:: console $ export VLLM_IMAGE_FETCH_TIMEOUT= -Chat Embeddings API -^^^^^^^^^^^^^^^^^^^ +Video +^^^^^ + +Instead of :code:`image_url`, you can pass a video file via :code:`video_url`. + +You can use `these tests `_ as reference. + +.. note:: + + By default, the timeout for fetching videos through HTTP URL url is ``30`` seconds. + You can override this by setting the environment variable: + + .. code-block:: console + + $ export VLLM_VIDEO_FETCH_TIMEOUT= -vLLM's Chat Embeddings API is a superset of OpenAI's `Embeddings API `_, -where a list of ``messages`` can be passed instead of batched ``inputs``. This enables multi-modal inputs to be passed to embedding models. +Audio +^^^^^ + +Instead of :code:`image_url`, you can pass an audio file via :code:`audio_url`. + +A full code example can be found in `examples/openai_chat_completion_client_for_multimodal.py `_. + +.. note:: + + By default, the timeout for fetching audios through HTTP URL is ``10`` seconds. + You can override this by setting the environment variable: + + .. code-block:: console + + $ export VLLM_AUDIO_FETCH_TIMEOUT= + +Embedding +^^^^^^^^^ + +vLLM's Embeddings API is a superset of OpenAI's `Embeddings API `_, +where a list of chat ``messages`` can be passed instead of batched ``inputs``. This enables multi-modal inputs to be passed to embedding models. .. tip:: The schema of ``messages`` is exactly the same as in Chat Completions API. + You can refer to the above tutorials for more details on how to pass each type of multi-modal data. -In this example, we will serve the ``TIGER-Lab/VLM2Vec-Full`` model. +Usually, embedding models do not expect chat-based input, so we need to use a custom chat template to format the text and images. +Refer to the examples below for illustration. + +Here is an end-to-end example using VLM2Vec. To serve the model: .. code-block:: bash @@ -279,10 +353,8 @@ In this example, we will serve the ``TIGER-Lab/VLM2Vec-Full`` model. Since VLM2Vec has the same model architecture as Phi-3.5-Vision, we have to explicitly pass ``--task embedding`` to run this model in embedding mode instead of text generation mode. -.. important:: - - VLM2Vec does not expect chat-based input. We use a `custom chat template `_ - to combine the text and images together. + The custom chat template is completely different from the original one for this model, + and can be found `here `__. Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level ``requests`` library: @@ -310,7 +382,7 @@ Since the request schema is not defined by OpenAI client, we post a request to t response_json = response.json() print("Embedding output:", response_json["data"][0]["embedding"]) -Here is an example for serving the ``MrLight/dse-qwen2-2b-mrl-v1`` model. +Below is another example, this time using the ``MrLight/dse-qwen2-2b-mrl-v1`` model. .. code-block:: bash @@ -319,8 +391,10 @@ Here is an example for serving the ``MrLight/dse-qwen2-2b-mrl-v1`` model. .. important:: - Like with VLM2Vec, we have to explicitly pass ``--task embedding``. Additionally, ``MrLight/dse-qwen2-2b-mrl-v1`` requires an EOS token for embeddings, - which is handled by the jinja template. + Like with VLM2Vec, we have to explicitly pass ``--task embedding``. + + Additionally, ``MrLight/dse-qwen2-2b-mrl-v1`` requires an EOS token for embeddings, which is handled + by `this custom chat template `__. .. important:: diff --git a/docs/source/models/performance.rst b/docs/source/usage/performance.rst similarity index 100% rename from docs/source/models/performance.rst rename to docs/source/usage/performance.rst diff --git a/docs/source/models/spec_decode.rst b/docs/source/usage/spec_decode.rst similarity index 98% rename from docs/source/models/spec_decode.rst rename to docs/source/usage/spec_decode.rst index d57ffec53215d..67e8ede7654b7 100644 --- a/docs/source/models/spec_decode.rst +++ b/docs/source/usage/spec_decode.rst @@ -1,7 +1,7 @@ .. _spec_decode: -Speculative decoding in vLLM -============================ +Speculative decoding +==================== .. warning:: Please note that speculative decoding in vLLM is not yet optimized and does @@ -182,7 +182,7 @@ speculative decoding, breaking down the guarantees into three key areas: 3. **vLLM Logprob Stability** - vLLM does not currently guarantee stable token log probabilities (logprobs). This can result in different outputs for the same request across runs. For more details, see the FAQ section - titled *Can the output of a prompt vary across runs in vLLM?* in the `FAQs <../serving/faq>`_. + titled *Can the output of a prompt vary across runs in vLLM?* in the :ref:`FAQs `. **Conclusion** @@ -197,7 +197,7 @@ can occur due to following factors: **Mitigation Strategies** -For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the `FAQs <../serving/faq>`_. +For mitigation strategies, please refer to the FAQ entry *Can the output of a prompt vary across runs in vLLM?* in the :ref:`FAQs `. Resources for vLLM contributors ------------------------------- diff --git a/docs/source/models/structured_outputs.rst b/docs/source/usage/structured_outputs.rst similarity index 100% rename from docs/source/models/structured_outputs.rst rename to docs/source/usage/structured_outputs.rst diff --git a/docs/source/serving/usage_stats.md b/docs/source/usage/usage_stats.md similarity index 100% rename from docs/source/serving/usage_stats.md rename to docs/source/usage/usage_stats.md diff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py index 9139c3c1314d8..19daeb729ee61 100644 --- a/vllm/attention/backends/rocm_flash_attn.py +++ b/vllm/attention/backends/rocm_flash_attn.py @@ -430,7 +430,7 @@ def forward( Returns: shape = [num_tokens, num_heads * head_size] """ - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if attn_type != AttentionType.DECODER: raise NotImplementedError("Encoder self-attention and " diff --git a/vllm/config.py b/vllm/config.py index 1cbab8ea30249..5c904914a71cf 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -509,7 +509,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, self.use_async_output_proc = False return - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if device_config.device_type not in ("cuda", "tpu", "xpu", "hpu"): logger.warning( @@ -525,7 +525,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, self.use_async_output_proc = False return - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if device_config.device_type == "cuda" and self.enforce_eager: logger.warning( @@ -540,7 +540,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, if self.task == "embedding": self.use_async_output_proc = False - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if speculative_config: logger.warning("Async output processing is not supported with" @@ -1704,7 +1704,7 @@ def verify_with_model_config(self, model_config: ModelConfig): model_config.quantization) def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if scheduler_config.chunked_prefill_enabled: raise ValueError("LoRA is not supported with chunked prefill yet.") diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 3b776c1d9d39f..0b304658f012c 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1111,7 +1111,7 @@ def create_engine_config(self, disable_logprobs=self.disable_logprobs_during_spec_decoding, ) - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if self.num_scheduler_steps > 1: if speculative_config is not None: diff --git a/vllm/engine/output_processor/multi_step.py b/vllm/engine/output_processor/multi_step.py index 7a6ebb430541f..a9b638ed02a1e 100644 --- a/vllm/engine/output_processor/multi_step.py +++ b/vllm/engine/output_processor/multi_step.py @@ -65,7 +65,7 @@ def process_prompt_logprob(self, seq_group: SequenceGroup, @staticmethod @functools.lru_cache def _log_prompt_logprob_unsupported_warning_once(): - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid logger.warning( "Prompt logprob is not supported by multi step workers. " diff --git a/vllm/executor/cpu_executor.py b/vllm/executor/cpu_executor.py index 336f9bc8efb20..6b4cb5a9a1d61 100644 --- a/vllm/executor/cpu_executor.py +++ b/vllm/executor/cpu_executor.py @@ -23,7 +23,7 @@ class CPUExecutor(ExecutorBase): def _init_executor(self) -> None: assert self.device_config.device_type == "cpu" - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid assert self.lora_config is None, "cpu backend doesn't support LoRA" diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py index b5333fbd6f502..680ee74129739 100644 --- a/vllm/platforms/cpu.py +++ b/vllm/platforms/cpu.py @@ -46,7 +46,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: import vllm.envs as envs from vllm.utils import GiB_bytes model_config = vllm_config.model_config - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if not model_config.enforce_eager: logger.warning( diff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py index 53634f7b0b366..ced7f53827665 100644 --- a/vllm/spec_decode/spec_decode_worker.py +++ b/vllm/spec_decode/spec_decode_worker.py @@ -104,7 +104,7 @@ def create_spec_worker(*args, **kwargs) -> "SpecDecodeWorker": return spec_decode_worker -# Reminder: Please update docs/source/serving/compatibility_matrix.rst +# Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid class SpecDecodeWorker(LoraNotSupportedWorkerBase): """Worker which implements speculative decoding. diff --git a/vllm/utils.py b/vllm/utils.py index 07bf82e24cbe6..6cee4847e57b4 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -47,7 +47,7 @@ # Exception strings for non-implemented encoder/decoder scenarios -# Reminder: Please update docs/source/serving/compatibility_matrix.rst +# Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid STR_NOT_IMPL_ENC_DEC_SWA = \ diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py index 3ee0fb4dc943e..3ca0d88a42183 100644 --- a/vllm/worker/multi_step_model_runner.py +++ b/vllm/worker/multi_step_model_runner.py @@ -817,7 +817,7 @@ def _pythonize_sampler_output( for sgdx, (seq_group, sample_result) in enumerate(zip(seq_groups, samples_list)): - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid # (Check for Guided Decoding) if seq_group.sampling_params.logits_processors: diff --git a/vllm/worker/utils.py b/vllm/worker/utils.py index f43635464ef00..5f71ec0c14df8 100644 --- a/vllm/worker/utils.py +++ b/vllm/worker/utils.py @@ -13,7 +13,7 @@ def assert_enc_dec_mr_supported_scenario( a supported scenario. ''' - # Reminder: Please update docs/source/serving/compatibility_matrix.rst + # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if enc_dec_mr.cache_config.enable_prefix_caching: