Python module

registry

Model registry, for tracking various model variants.

`PipelineRegistry`

class max.pipelines.lib.registry.PipelineRegistry(architectures)

Parameters:: architectures (list[SupportedArchitecture])

`get_active_huggingface_config()`

get_active_huggingface_config(huggingface_repo)

Retrieves or creates a cached HuggingFace AutoConfig for the given model configuration.

This method maintains a cache of HuggingFace configurations to avoid reloading them unnecessarily which incurs a huggingface hub API call. If a config for the given model hasn’t been loaded before, it will create a new one using AutoConfig.from_pretrained() with the model’s settings.

Parameters:: huggingface_repo (HuggingFaceRepo) – The HuggingFaceRepo containing the model.
Returns:: The HuggingFace configuration object for the model.
Return type:: AutoConfig

`get_active_tokenizer()`

get_active_tokenizer(huggingface_repo)

Retrieves or creates a cached HuggingFace AutoTokenizer for the given model configuration.

This method maintains a cache of HuggingFace tokenizers to avoid reloading them unnecessarily which incurs a huggingface hub API call. If a tokenizer for the given model hasn’t been loaded before, it will create a new one using AutoTokenizer.from_pretrained() with the model’s settings.

Parameters:: huggingface_repo (HuggingFaceRepo) – The HuggingFaceRepo containing the model.
Returns:: The HuggingFace tokenizer for the model.
Return type:: PreTrainedTokenizer | PreTrainedTokenizerFast

`register()`

register(architecture, *, allow_override=False)

Add new architecture to registry.

Parameters:

architecture (SupportedArchitecture)
allow_override (bool)

Return type:

None

`reset()`

reset()

Return type:: None

`retrieve()`

retrieve(pipeline_config, task=PipelineTask.TEXT_GENERATION, override_architecture=None)

Parameters:

pipeline_config (PipelineConfig)
task (PipelineTask)
override_architecture (str | None)

Return type:

tuple[PipelineTokenizer, PipelineTypes]

`retrieve_architecture()`

retrieve_architecture(huggingface_repo)

Parameters:: huggingface_repo (HuggingFaceRepo)
Return type:: SupportedArchitecture | None

`retrieve_factory()`

retrieve_factory(pipeline_config, task=PipelineTask.TEXT_GENERATION, override_architecture=None)

Parameters:

pipeline_config (PipelineConfig)
task (PipelineTask)
override_architecture (str | None)

Return type:

tuple[PipelineTokenizer, Callable[[], PipelineTypes]]

`retrieve_tokenizer()`

retrieve_tokenizer(pipeline_config, override_architecture=None)

Retrieves a tokenizer for the given pipeline configuration.

Parameters:

pipeline_config (PipelineConfig) – Configuration for the pipeline
override_architecture (str | None) – Optional architecture override string

Returns:

The configured tokenizer

Return type:

PipelineTokenizer

Raises:

ValueError – If no architecture is found or if engine is not MAX

`SupportedArchitecture`

class max.pipelines.lib.registry.SupportedArchitecture(name, example_repo_ids, default_encoding, supported_encodings, pipeline_model, task, tokenizer, default_weights_format, multi_gpu_supported=False, rope_type=RopeType.none, weight_adapters=None)

Represents a model architecture configuration for MAX pipelines.

This class defines all the necessary components and settings required to support a specific model architecture within the MAX pipeline system. Each SupportedArchitecture instance encapsulates the model implementation, tokenizer, supported encodings, and other architecture-specific configuration.

New architectures should be registered into the PipelineRegistry using the register() method.

my_architecture = SupportedArchitecture(
    name="MyModelForCausalLM",  # Must match your Hugging Face model class name
    example_repo_ids=[
        "your-org/your-model-name",  # Add example model repository IDs
    ],
    default_encoding=SupportedEncoding.q4_k,
    supported_encodings={
        SupportedEncoding.q4_k: [KVCacheStrategy.PAGED],
        SupportedEncoding.bfloat16: [KVCacheStrategy.PAGED],
        # Add other encodings your model supports
    },
    pipeline_model=MyModel,
    tokenizer=TextTokenizer,
    default_weights_format=WeightsFormat.safetensors,
    multi_gpu_supported=True,  # Set based on your implementation capabilities
    weight_adapters={
        WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict,
        # Add other weight formats if needed
    },
    task=PipelineTask.TEXT_GENERATION,
)
my_architecture = SupportedArchitecture(
    name="MyModelForCausalLM",  # Must match your Hugging Face model class name
    example_repo_ids=[
        "your-org/your-model-name",  # Add example model repository IDs
    ],
    default_encoding=SupportedEncoding.q4_k,
    supported_encodings={
        SupportedEncoding.q4_k: [KVCacheStrategy.PAGED],
        SupportedEncoding.bfloat16: [KVCacheStrategy.PAGED],
        # Add other encodings your model supports
    },
    pipeline_model=MyModel,
    tokenizer=TextTokenizer,
    default_weights_format=WeightsFormat.safetensors,
    multi_gpu_supported=True,  # Set based on your implementation capabilities
    weight_adapters={
        WeightsFormat.safetensors: weight_adapters.convert_safetensor_state_dict,
        # Add other weight formats if needed
    },
    task=PipelineTask.TEXT_GENERATION,
)

Parameters:

name (str) – The name of the model architecture that must match the Hugging Face model class name.
example_repo_ids (list[str]) – A list of Hugging Face repository IDs that use this architecture for testing and validation purposes.
default_encoding (SupportedEncoding) – The default quantization encoding to use when no specific encoding is requested.
supported_encodings (dict[SupportedEncoding, list[KVCacheStrategy]]) – A dictionary mapping supported quantization encodings to their compatible KV cache strategies.
pipeline_model (type[PipelineModel]) – The PipelineModel class that defines the model graph structure and execution logic.
task (PipelineTask) – The pipeline task type that this architecture supports.
tokenizer (Callable[..., PipelineTokenizer]) – A callable that returns a PipelineTokenizer instance for preprocessing model inputs.
default_weights_format (WeightsFormat) – The weights format expected by the pipeline_model.
multi_gpu_supported (bool) – Whether the architecture supports multi-GPU execution.
rope_type (RopeType) – The type of RoPE (Rotary Position Embedding) used by the model.
weight_adapters (dict[WeightsFormat, WeightsAdapter] | None) – A dictionary of weight format adapters for converting checkpoints from different formats to the default format.

`tokenizer_cls`

property tokenizer_cls: type[PipelineTokenizer]

`get_pipeline_for_task()`

max.pipelines.lib.registry.get_pipeline_for_task(task, pipeline_config)

Parameters:

task (PipelineTask)
pipeline_config (PipelineConfig)

Return type:

type[TextGenerationPipeline] | type[EmbeddingsPipeline] | type[SpeculativeDecodingTextGenerationPipeline] | type[AudioGeneratorPipeline] | type[SpeechTokenGenerationPipeline]

PipelineRegistry​

get_active_huggingface_config()​

get_active_tokenizer()​

register()​

reset()​

retrieve()​

retrieve_architecture()​

retrieve_factory()​

retrieve_tokenizer()​

SupportedArchitecture​

tokenizer_cls​

get_pipeline_for_task()​

`PipelineRegistry`

`get_active_huggingface_config()`

`get_active_tokenizer()`

`register()`

`reset()`

`retrieve()`

`retrieve_architecture()`

`retrieve_factory()`

`retrieve_tokenizer()`

`SupportedArchitecture`

`tokenizer_cls`

`get_pipeline_for_task()`