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Python module

max.pipelines.architectures.olmo2_modulev3

OLMo 2 transformer architecture for text generation.

Olmo2Config​

class max.pipelines.architectures.olmo2_modulev3.Olmo2Config(*, vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, num_key_value_heads, head_dim, max_position_embeddings, rms_norm_eps, rope_theta, attention_bias, tie_word_embeddings, attention_multiplier, embedding_multiplier, residual_multiplier, dtype, devices, interleaved_rope_weights, return_logits, kv_params)

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Bases: ArchConfigWithKVCache

Configuration for Olmo2 models.

Contains parameters specific to the Olmo2 architecture, typically extracted from a HuggingFace configuration object.

Parameters:

attention_bias​

attention_bias: bool

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Whether to use a bias in the attention projection layers.

attention_multiplier​

attention_multiplier: float

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Scalar applied to attention scores.

calculate_attention_multiplier()​

static calculate_attention_multiplier(huggingface_config)

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Parameters:

huggingface_config (AutoConfig)

Return type:

float

calculate_max_seq_len()​

static calculate_max_seq_len(pipeline_config, huggingface_config)

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Parameters:

Return type:

int

construct_kv_params()​

static construct_kv_params(huggingface_config, pipeline_config, devices, kv_cache_config, cache_dtype)

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Parameters:

Return type:

KVCacheParams

devices​

devices: list[DeviceRef]

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Devices to run the model with.

dtype​

dtype: DType

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DType of the model weights and input.

embedding_multiplier​

embedding_multiplier: float

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Scalar applied to embeddings.

finalize()​

finalize(huggingface_config, state_dict, return_logits, return_hidden_states=ReturnHiddenStates.NONE, norm_method='rms_norm', attention_bias=False)

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Define parameters that can’t be determined just from the pipeline config.

Parameters:

Return type:

None

get_head_dim()​

static get_head_dim(huggingface_config)

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Parameters:

huggingface_config (AutoConfig)

Return type:

int

get_kv_params()​

get_kv_params()

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KV cache parameters to use when running the model.

Return type:

KVCacheParams

get_max_seq_len()​

get_max_seq_len()

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Returns the default maximum sequence length for the model.

Subclasses should determine whether this value can be overridden by setting the --max-length (pipeline_config.model.max_length) flag.

Return type:

int

get_num_layers()​

static get_num_layers(huggingface_config)

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Parameters:

huggingface_config (AutoConfig)

Return type:

int

head_dim​

head_dim: int

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Dimension of each attention head.

hidden_size​

hidden_size: int

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Dimension of the hidden representations.

initialize()​

classmethod initialize(pipeline_config, model_config=None)

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Initialize the config from a PipelineConfig.

Parameters:

  • pipeline_config (PipelineConfig) – The pipeline configuration.
  • model_config (MAXModelConfig | None) – The model configuration to read from. When None (the default), pipeline_config.model is used. Pass an explicit config (e.g. pipeline_config.draft_model) to initialize the arch config for a different model.

Return type:

Self

interleaved_rope_weights​

interleaved_rope_weights: bool

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True if the rope weights are in interleaved complex format.

intermediate_size​

intermediate_size: int

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Dimension of the MLP representations.

kv_params​

kv_params: KVCacheParams

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KV cache parameters.

max_position_embeddings​

max_position_embeddings: int

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The maximum sequence length that this model might ever be used with.

num_attention_heads​

num_attention_heads: int

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Number of attention heads for each attention layer.

num_hidden_layers​

num_hidden_layers: int

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Number of hidden layers in the Transformer decoder.

num_key_value_heads​

num_key_value_heads: int

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Number of key_value heads for Grouped Query Attention.

residual_multiplier​

residual_multiplier: float

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Scalar applied to residual connections.

return_logits​

return_logits: ReturnLogits

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Whether to return the last token, all logits, or a variable number of logits.

rms_norm_eps​

rms_norm_eps: float

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The epsilon used by the rms normalization layers.

rope_theta​

rope_theta: float

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The base period of the RoPE embeddings.

tie_word_embeddings​

tie_word_embeddings: bool

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Whether to tie weight embeddings.

vocab_size​

vocab_size: int

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Vocabulary size of the Olmo2 model.

Olmo2Model​

class max.pipelines.architectures.olmo2_modulev3.Olmo2Model(pipeline_config, session, devices, kv_cache_config, weights, adapter=None, return_logits=ReturnLogits.LAST_TOKEN, return_hidden_states=ReturnHiddenStates.NONE)

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Bases: Llama3Model

An Olmo2 pipeline model for text generation.

Parameters:

calculate_max_seq_len()​

classmethod calculate_max_seq_len(pipeline_config, huggingface_config)

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Calculates the optimal max sequence length for the model.

Models are expected to implement this method. The following example shows how to implement it for a Mistral model:

class MistralModel(PipelineModel):
    @classmethod
    def calculate_max_seq_len(cls, pipeline_config, huggingface_config) -> int:
        try:
            return upper_bounded_default(
                upper_bound=huggingface_config.max_seq_len,
                default=pipeline_config.model.max_length,
            )
        except ValueError as e:
            raise ValueError(
                "Unable to infer max_length for Mistral, the provided "
                f"max_length ({pipeline_config.model.max_length}) exceeds the "
                f"model's max_seq_len ({huggingface_config.max_seq_len})."
            ) from e

Parameters:

  • pipeline_config (PipelineConfig) – Configuration for the pipeline.
  • huggingface_config (AutoConfig) – Hugging Face model configuration.

Returns:

The maximum sequence length to use.

Return type:

int

get_kv_params()​

classmethod get_kv_params(huggingface_config, pipeline_config, devices, kv_cache_config, cache_dtype)

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Returns the KV cache params for the pipeline model.

Parameters:

Return type:

KVCacheParams

load_model()​

load_model()

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Return type:

Callable[[…], Any]

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