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Python class

PipelineArgs

PipelineArgs​

class max.pipelines.lib.PipelineArgs(*, models=None, config_file=None, section_name=None, model_override=<factory>, task=PipelineTask.UNDEFINED, debug_verify_replay=False, tokenizer_impl=None, model_path='', served_model_name=None, weight_path=<factory>, quantization_encoding=None, huggingface_model_revision='main', huggingface_weight_revision='main', trust_remote_code=False, subfolder=None, device_specs=<factory>, force_download=False, vision_config_overrides=<factory>, rope_type=None, sliding_window=None, enable_echo=False, chat_template=None, use_subgraphs=True, data_parallel_degree=1, pool_embeddings=True, max_length=None, kv_cache=<factory>, runtime=<factory>, denoising_cache=<factory>, sampling=<factory>, profiling=<factory>, lora=None, speculative=None, draft_model=None)

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Bases: ConfigFileModel

User-settable input arguments for a pipeline.

PipelineArgs is the user-facing input to the pipeline system. It holds flat model-level fields plus nested sub-configs mirroring the PipelineConfig schema (runtime, sampling, profiling) and a small number of cohesive sub-config objects (kv_cache, lora, speculative, draft_model).

Multi-component pipelines (e.g. diffusion) that require a pre-built ModelManifest may pass models=<manifest> to the constructor. That manifest is stored as a private override and used verbatim by PipelineConfig.from_args() instead of constructing one from the flat scalar fields.

Call PipelineConfig.from_args() to obtain a fully-constructed PipelineConfig ready for architecture-driven resolution.

Instances are immutable: assigning to a field after construction raises a pydantic ValidationError.

Parameters:

chat_template​

chat_template: Path | None

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config_file​

config_file: str | None

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Path to the configuration file.

data_parallel_degree​

data_parallel_degree: int

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debug_verify_replay​

debug_verify_replay: bool

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denoising_cache​

denoising_cache: DenoisingCacheSettings

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User denoising-cache settings. Construction fills unset fields.

device_specs​

device_specs: list[DeviceSpec]

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draft_model​

draft_model: MAXModelConfig | None

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enable_echo​

enable_echo: bool

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force_download​

force_download: bool

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from_flat_kwargs()​

classmethod from_flat_kwargs(**kwargs)

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Construct a PipelineArgs from a flat CLI kwargs namespace.

Owns the full flat-to-nested routing for CLI and legacy callers:

  • Flat sub-config kwargs (e.g. max_batch_size, enable_lora, num_speculative_tokens) are nested under their sub-config section (runtime, lora, speculative, …) before the --config-file merge, so CLI flags and config-file subtrees reconcile per field.
  • A config file’s model: section (the PipelineConfig schema shape) is folded into the flat model fields; explicit CLI kwargs win per field, --model-override entries win over both.
  • draft_-prefixed kwargs build draft_model, inheriting trust_remote_code/device_specs/data_parallel_degree from the target model when unset.
  • Multi-component (e.g. diffusion) model paths are detected via ModelManifest.from_model_path() and carried as a manifest override.

Parameters:

**kwargs (Any) – Flat keyword arguments, e.g. model_path, kv_cache_size, enable_lora.

Returns:

A PipelineArgs populated from the flat kwargs.

Return type:

Self

huggingface_model_revision​

huggingface_model_revision: str

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huggingface_weight_revision​

huggingface_weight_revision: str

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kv_cache​

kv_cache: KVCacheConfig

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lora​

lora: LoRAConfig | None

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main_architecture_name​

property main_architecture_name: str

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Returns the HuggingFace architecture class name for the main model.

Reads architectures[0] from the model’s HuggingFace config without constructing a full PipelineConfig.

Raises:

ValueError – If the architecture name cannot be determined.

max_length​

max_length: int | None

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model_config​

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'frozen': True, 'strict': False}

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Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_override​

model_override: list[str]

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model_path​

model_path: str

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model_post_init()​

model_post_init(context, /)

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This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.
  • context (Any) – The context.

Return type:

None

pool_embeddings​

pool_embeddings: bool

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profiling​

profiling: ProfilingConfig

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quantization_encoding​

quantization_encoding: SupportedEncoding | None

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rope_type​

rope_type: RopeType | None

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runtime​

runtime: PipelineRuntimeConfig

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sampling​

sampling: SamplingConfig

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section_name​

section_name: str | None

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Optional section name for comprehensive/multi-section config files.

If not provided, values are loaded from the YAML top-level (treating the file as an “individual config” file).

served_model_name​

served_model_name: str | None

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sliding_window​

sliding_window: int | None

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speculative​

speculative: SpeculativeConfig | None

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subfolder​

subfolder: str | None

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task​

task: PipelineTask

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tokenizer_impl​

tokenizer_impl: str | None

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trust_remote_code​

trust_remote_code: bool

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use_subgraphs​

use_subgraphs: bool

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vision_config_overrides​

vision_config_overrides: dict[str, Any]

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weight_path​

weight_path: list[Path]

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