[Docs] Fix warnings in docs build (#22588)

Signed-off-by: Harry Mellor <[email protected]>

Author: hmellor

docs/api/summary.md

@@ -1,7 +1,5 @@
 # Summary
 
-[](){ #configuration }
-
 ## Configuration
 
 API documentation for vLLM's configuration classes.

docs/configuration/tpu.md

@@ -96,7 +96,7 @@ Although it’s common to do this with GPUs, don't try to fragment 2 or 8 differ
 
 ### Tune your workloads
 
-Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](../../benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
+Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](gh-file:benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
 
 ### Future Topics We'll Cover
 

docs/contributing/model/multimodal.md

@@ -540,8 +540,10 @@ return a schema of the tensors outputted by the HF processor that are related to
     The shape of `image_patches` outputted by `FuyuImageProcessor` is therefore
     `(1, num_images, num_patches, patch_width * patch_height * num_channels)`.
 
-    In order to support the use of [MultiModalFieldConfig.batched][] like in LLaVA,
-    we remove the extra batch dimension by overriding [BaseMultiModalProcessor._call_hf_processor][]:
+    In order to support the use of
+    [MultiModalFieldConfig.batched][vllm.multimodal.inputs.MultiModalFieldConfig.batched]
+    like in LLaVA, we remove the extra batch dimension by overriding
+    [BaseMultiModalProcessor._call_hf_processor][vllm.multimodal.processing.BaseMultiModalProcessor._call_hf_processor]:
 
     ??? code
 
@@ -816,7 +818,7 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
 After you have defined [BaseProcessingInfo][vllm.multimodal.processing.BaseProcessingInfo] (Step 2),
 [BaseDummyInputsBuilder][vllm.multimodal.profiling.BaseDummyInputsBuilder] (Step 3),
 and [BaseMultiModalProcessor][vllm.multimodal.processing.BaseMultiModalProcessor] (Step 4),
-decorate the model class with [MULTIMODAL_REGISTRY.register_processor][vllm.multimodal.processing.MultiModalRegistry.register_processor]
+decorate the model class with [MULTIMODAL_REGISTRY.register_processor][vllm.multimodal.registry.MultiModalRegistry.register_processor]
 to register them to the multi-modal registry:
 
 ```diff

docs/models/generative_models.md

@@ -4,7 +4,7 @@ vLLM provides first-class support for generative models, which covers most of LL
 
 In vLLM, generative models implement the[VllmModelForTextGeneration][vllm.model_executor.models.VllmModelForTextGeneration] interface.
 Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
-which are then passed through [Sampler][vllm.model_executor.layers.Sampler] to obtain the final text.
+which are then passed through [Sampler][vllm.model_executor.layers.sampler.Sampler] to obtain the final text.
 
 ## Configuration
 
@@ -19,7 +19,7 @@ Run a model in generation mode via the option `--runner generate`.
 ## Offline Inference
 
 The [LLM][vllm.LLM] class provides various methods for offline inference.
-See [configuration][configuration] for a list of options when initializing the model.
+See [configuration](../api/summary.md#configuration) for a list of options when initializing the model.
 
 ### `LLM.generate`
 

docs/models/pooling_models.md

@@ -81,7 +81,7 @@ which takes priority over both the model's and Sentence Transformers's defaults.
 ## Offline Inference
 
 The [LLM][vllm.LLM] class provides various methods for offline inference.
-See [configuration][configuration] for a list of options when initializing the model.
+See [configuration](../api/summary.md#configuration) for a list of options when initializing the model.
 
 ### `LLM.embed`
 

docs/models/supported_models.md

@@ -770,7 +770,7 @@ The following table lists those that are tested in vLLM.
 Cross-encoder and reranker models are a subset of classification models that accept two prompts as input.
 These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) API.
 
-| Architecture                        | Models             | Inputs   | Example HF Models        | [LoRA][lora-adapter]   | [PP][parallelism-scaling]   | [V1](gh-issue:8779)   |
+| Architecture                        | Models             | Inputs   | Example HF Models        | [LoRA](../features/lora.md)   | [PP](../serving/parallelism_scaling.md)   | [V1](gh-issue:8779)   |
 |-------------------------------------|--------------------|----------|--------------------------|------------------------|-----------------------------|-----------------------|
 | `JinaVLForSequenceClassification` | JinaVL-based | T + I<sup>E+</sup> | `jinaai/jina-reranker-m0`, etc. | | | ✅︎ |
 

vllm/attention/layers/__init__.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from .data import (DecoderOnlyInputs, EmbedsInputs, EncoderDecoderInputs,
-                   ExplicitEncoderDecoderPrompt, ProcessorInputs, PromptType,
-                   SingletonInputs, SingletonPrompt, TextPrompt, TokenInputs,
-                   TokensPrompt, build_explicit_enc_dec_prompt, embeds_inputs,
+from .data import (DecoderOnlyInputs, EmbedsInputs, EmbedsPrompt,
+                   EncoderDecoderInputs, ExplicitEncoderDecoderPrompt,
+                   ProcessorInputs, PromptType, SingletonInputs,
+                   SingletonPrompt, TextPrompt, TokenInputs, TokensPrompt,
+                   build_explicit_enc_dec_prompt, embeds_inputs,
                    to_enc_dec_tuple_list, token_inputs, zip_enc_dec_prompts)
 from .registry import (DummyData, InputContext, InputProcessingContext,
                        InputRegistry)
@@ -24,6 +25,7 @@
     "ExplicitEncoderDecoderPrompt",
     "TokenInputs",
     "EmbedsInputs",
+    "EmbedsPrompt",
     "token_inputs",
     "embeds_inputs",
     "DecoderOnlyInputs",

vllm/inputs/__init__.py

@@ -103,113 +103,89 @@ class SamplingParams(
     Overall, we follow the sampling parameters from the OpenAI text completion
     API (https://platform.openai.com/docs/api-reference/completions/create).
     In addition, we support beam search, which is not supported by OpenAI.
-
-    Args:
-        n: Number of output sequences to return for the given prompt.
-        best_of: Number of output sequences that are generated from the prompt.
-            From these `best_of` sequences, the top `n` sequences are returned.
-            `best_of` must be greater than or equal to `n`. By default,
-            `best_of` is set to `n`. Warning, this is only supported in V0.
-        presence_penalty: Float that penalizes new tokens based on whether they
-            appear in the generated text so far. Values > 0 encourage the model
-            to use new tokens, while values < 0 encourage the model to repeat
-            tokens.
-        frequency_penalty: Float that penalizes new tokens based on their
-            frequency in the generated text so far. Values > 0 encourage the
-            model to use new tokens, while values < 0 encourage the model to
-            repeat tokens.
-        repetition_penalty: Float that penalizes new tokens based on whether
-            they appear in the prompt and the generated text so far. Values > 1
-            encourage the model to use new tokens, while values < 1 encourage
-            the model to repeat tokens.
-        temperature: Float that controls the randomness of the sampling. Lower
-            values make the model more deterministic, while higher values make
-            the model more random. Zero means greedy sampling.
-        top_p: Float that controls the cumulative probability of the top tokens
-            to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
-        top_k: Integer that controls the number of top tokens to consider. Set
-            to 0 (or -1) to consider all tokens.
-        min_p: Float that represents the minimum probability for a token to be
-            considered, relative to the probability of the most likely token.
-            Must be in [0, 1]. Set to 0 to disable this.
-        seed: Random seed to use for the generation.
-        stop: list of strings that stop the generation when they are generated.
-            The returned output will not contain the stop strings.
-        stop_token_ids: list of tokens that stop the generation when they are
-            generated. The returned output will contain the stop tokens unless
-            the stop tokens are special tokens.
-        bad_words: list of words that are not allowed to be generated.
-            More precisely, only the last token of a corresponding
-            token sequence is not allowed when the next generated token
-            can complete the sequence.
-        include_stop_str_in_output: Whether to include the stop strings in
-            output text. Defaults to False.
-        ignore_eos: Whether to ignore the EOS token and continue generating
-            tokens after the EOS token is generated.
-        max_tokens: Maximum number of tokens to generate per output sequence.
-        min_tokens: Minimum number of tokens to generate per output sequence
-            before EOS or stop_token_ids can be generated
-        logprobs: Number of log probabilities to return per output token.
-            When set to None, no probability is returned. If set to a non-None
-            value, the result includes the log probabilities of the specified
-            number of most likely tokens, as well as the chosen tokens.
-            Note that the implementation follows the OpenAI API: The API will
-            always return the log probability of the sampled token, so there
-            may be up to `logprobs+1` elements in the response.
-            When set to -1, return all `vocab_size` log probabilities.
-        prompt_logprobs: Number of log probabilities to return per prompt token.
-        detokenize: Whether to detokenize the output. Defaults to True.
-        skip_special_tokens: Whether to skip special tokens in the output.
-        spaces_between_special_tokens: Whether to add spaces between special
-            tokens in the output.  Defaults to True.
-        logits_processors: list of functions that modify logits based on
-            previously generated tokens, and optionally prompt tokens as
-            a first argument.
-        truncate_prompt_tokens: If set to -1, will use the truncation size
-            supported by the model. If set to an integer k, will use only
-            the last k tokens from the prompt (i.e., left truncation).
-            Defaults to None (i.e., no truncation).
-        guided_decoding: If provided, the engine will construct a guided
-            decoding logits processor from these parameters. Defaults to None.
-        logit_bias: If provided, the engine will construct a logits processor
-            that applies these logit biases. Defaults to None.
-        allowed_token_ids: If provided, the engine will construct a logits
-            processor which only retains scores for the given token ids.
-            Defaults to None.
-        extra_args: Arbitrary additional args, that can be used by custom
-            sampling implementations, plugins, etc. Not used by any in-tree
-            sampling implementations.
     """
 
     n: int = 1
+    """Number of output sequences to return for the given prompt."""
     best_of: Optional[int] = None
+    """Number of output sequences that are generated from the prompt. From
+    these `best_of` sequences, the top `n` sequences are returned. `best_of`
+    must be greater than or equal to `n`. By default, `best_of` is set to `n`.
+    Warning, this is only supported in V0."""
     _real_n: Optional[int] = None
     presence_penalty: float = 0.0
+    """Penalizes new tokens based on whether they appear in the generated text
+    so far. Values > 0 encourage the model to use new tokens, while values < 0
+    encourage the model to repeat tokens."""
     frequency_penalty: float = 0.0
+    """Penalizes new tokens based on their frequency in the generated text so
+    far. Values > 0 encourage the model to use new tokens, while values < 0
+    encourage the model to repeat tokens."""
     repetition_penalty: float = 1.0
+    """Penalizes new tokens based on whether they appear in the prompt and the
+    generated text so far. Values > 1 encourage the model to use new tokens,
+    while values < 1 encourage the model to repeat tokens."""
     temperature: float = 1.0
+    """Controls the randomness of the sampling. Lower values make the model
+    more deterministic, while higher values make the model more random. Zero
+    means greedy sampling."""
     top_p: float = 1.0
+    """Controls the cumulative probability of the top tokens to consider. Must
+    be in (0, 1]. Set to 1 to consider all tokens."""
     top_k: int = 0
+    """Controls the number of top tokens to consider. Set to 0 (or -1) to
+    consider all tokens."""
     min_p: float = 0.0
+    """Represents the minimum probability for a token to be considered,
+    relative to the probability of the most likely token. Must be in [0, 1].
+    Set to 0 to disable this."""
     seed: Optional[int] = None
+    """Random seed to use for the generation."""
     stop: Optional[Union[str, list[str]]] = None
+    """String(s) that stop the generation when they are generated. The returned
+    output will not contain the stop strings."""
     stop_token_ids: Optional[list[int]] = None
+    """Token IDs that stop the generation when they are generated. The returned
+    output will contain the stop tokens unless the stop tokens are special
+    tokens."""
     ignore_eos: bool = False
+    """Whether to ignore the EOS token and continue generating
+    tokens after the EOS token is generated."""
     max_tokens: Optional[int] = 16
+    """Maximum number of tokens to generate per output sequence."""
     min_tokens: int = 0
+    """Minimum number of tokens to generate per output sequence before EOS or
+    `stop_token_ids` can be generated"""
     logprobs: Optional[int] = None
+    """Number of log probabilities to return per output token. When set to
+    `None`, no probability is returned. If set to a non-`None` value, the
+    result includes the log probabilities of the specified number of most
+    likely tokens, as well as the chosen tokens. Note that the implementation
+    follows the OpenAI API: The API will always return the log probability of
+    the sampled token, so there may be up to `logprobs+1` elements in the
+    response. When set to -1, return all `vocab_size` log probabilities."""
     prompt_logprobs: Optional[int] = None
+    """Number of log probabilities to return per prompt token."""
     # NOTE: This parameter is only exposed at the engine level for now.
     # It is not exposed in the OpenAI API server, as the OpenAI API does
     # not support returning only a list of token IDs.
     detokenize: bool = True
+    """Whether to detokenize the output."""
     skip_special_tokens: bool = True
+    """Whether to skip special tokens in the output."""
     spaces_between_special_tokens: bool = True
+    """Whether to add spaces between special tokens in the output."""
     # Optional[list[LogitsProcessor]] type. We use Any here because
     # Optional[list[LogitsProcessor]] type is not supported by msgspec.
     logits_processors: Optional[Any] = None
+    """Functions that modify logits based on previously generated tokens, and
+    optionally prompt tokens as a first argument."""
     include_stop_str_in_output: bool = False
+    """Whether to include the stop strings in output text."""
     truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=1)]] = None
+    """If set to -1, will use the truncation size supported by the model. If
+    set to an integer k, will use only the last k tokens from the prompt
+    (i.e., left truncation). If set to `None`, truncation is disabled."""
     output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE
 
     # The below fields are not supposed to be used as an input.
@@ -219,12 +195,24 @@ class SamplingParams(
 
     # Fields used to construct logits processors
     guided_decoding: Optional[GuidedDecodingParams] = None
+    """If provided, the engine will construct a guided decoding logits
+    processor from these parameters."""
     logit_bias: Optional[dict[int, float]] = None
+    """If provided, the engine will construct a logits processor that applies
+    these logit biases."""
     allowed_token_ids: Optional[list[int]] = None
+    """If provided, the engine will construct a logits processor which only
+    retains scores for the given token ids."""
     extra_args: Optional[dict[str, Any]] = None
+    """Arbitrary additional args, that can be used by custom sampling
+    implementations, plugins, etc. Not used by any in-tree sampling
+    implementations."""
 
     # Fields used for bad words
     bad_words: Optional[list[str]] = None
+    """Words that are not allowed to be generated. More precisely, only the
+    last token of a corresponding token sequence is not allowed when the next
+    generated token can complete the sequence."""
     _bad_words_token_ids: Optional[list[list[int]]] = None
 
     @staticmethod