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Python module

max.pipelines.architectures.llama3

Llama 3 transformer architecture for text generation.

Llama3Config​

class max.pipelines.architectures.llama3.Llama3Config(*, hidden_size, num_attention_heads, num_key_value_heads, num_hidden_layers, rope_theta, rope_scaling_params, max_seq_len, intermediate_size, interleaved_rope_weights, vocab_size, dtype, model_quantization_encoding, quantization_config, kv_params, return_logits=ReturnLogits.LAST_TOKEN, norm_method='rms_norm', norm_dtype=None, attention_bias=False, rms_norm_eps=None, tie_word_embeddings=False, stacked_mlp=False, stacked_qkv=False, attention_multiplier, embedding_multiplier, residual_multiplier, devices, clip_qkv, quant_config=None, lora_config=None, longrope_scaling_params=None, logits_scaling=1.0, return_hidden_states=ReturnHiddenStates.NONE, use_subgraphs=True, data_parallel_degree=1)

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Bases: ArchConfigWithKVCache

Model configuration for Llama3 graph construction/execution.

Parameters:

attention_bias​

attention_bias: bool = False

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attention_multiplier​

attention_multiplier: float

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calculate_attention_multiplier()​

static calculate_attention_multiplier(huggingface_config)

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The attention multiplier is a scalar that scales the attention scores. It is used to control the variance of the attention scores.

This function is used to get the attention multiplier from the huggingface config. If the attention multiplier is not set, it will be calculated as the square root of 1.0 divided by the head dimension.

Parameters:

huggingface_config (AutoConfig)

Return type:

float

calculate_max_seq_len()​

static calculate_max_seq_len(pipeline_config, huggingface_config, model_config=None)

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Parameters:

Return type:

int

clip_qkv​

clip_qkv: float | None

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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

data_parallel_degree​

data_parallel_degree: int = 1

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devices​

devices: list[DeviceRef]

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dtype​

dtype: DType

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embedding_multiplier​

embedding_multiplier: float

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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

hidden_size​

hidden_size: int

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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

initialize_from_config()​

classmethod initialize_from_config(pipeline_config, huggingface_config, model_config=None)

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Parameters:

Return type:

Self

interleaved_rope_weights​

interleaved_rope_weights: bool

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intermediate_size​

intermediate_size: int

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kv_params​

kv_params: KVCacheParams

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logits_scaling​

logits_scaling: float = 1.0

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longrope_scaling_params​

longrope_scaling_params: LongRoPEScalingParams | None = None

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lora_config​

lora_config: LoRAConfig | None = None

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max_seq_len​

max_seq_len: int

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model_quantization_encoding​

model_quantization_encoding: QuantizationEncoding | None

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norm_dtype​

norm_dtype: DType | None = None

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norm_method​

norm_method: Literal['rms_norm', 'layer_norm'] = 'rms_norm'

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num_attention_heads​

num_attention_heads: int

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num_hidden_layers​

num_hidden_layers: int

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num_key_value_heads​

num_key_value_heads: int

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quant_config​

quant_config: QuantConfig | None = None

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quantization_config​

quantization_config: QuantizationConfig | None

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residual_multiplier​

residual_multiplier: float

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return_hidden_states​

return_hidden_states: ReturnHiddenStates = 'none'

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return_logits​

return_logits: ReturnLogits = 'last_token'

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rms_norm_eps​

rms_norm_eps: float | None = None

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rope_scaling_params​

rope_scaling_params: Llama3RopeScalingParams | None

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rope_theta​

rope_theta: float

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stacked_mlp​

stacked_mlp: bool = False

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stacked_qkv​

stacked_qkv: bool = False

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tie_word_embeddings​

tie_word_embeddings: bool = False

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use_subgraphs​

use_subgraphs: bool = True

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vocab_size​

vocab_size: int

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Llama3Inputs​

class max.pipelines.architectures.llama3.Llama3Inputs(tokens, input_row_offsets, signal_buffers, return_n_logits, lora_grouped_offsets=None, num_active_loras=None, lora_end_idx=None, batch_seq_len=None, lora_ids_kv=None, lora_grouped_offsets_kv=None, data_parallel_splits=None, *, kv_cache_inputs=None, lora_ids=None, lora_ranks=None, hidden_states=None)

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Bases: ModelInputs

A class representing inputs for the Llama3 model.

This class encapsulates the input tensors required for the Llama3 model execution.

Parameters:

batch_seq_len​

batch_seq_len: Buffer | None = None

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buffers​

property buffers: tuple[Buffer, ...]

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Returns positional Buffer inputs for model ABI calls.

data_parallel_splits​

data_parallel_splits: Buffer | Sequence[Sequence[int]] | None = None

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Tensor containing the data parallel splits.

input_row_offsets​

input_row_offsets: Buffer

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Tensor containing the offsets for each row in the ragged input sequence.

lora_end_idx​

lora_end_idx: Buffer | None = None

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lora_grouped_offsets​

lora_grouped_offsets: Buffer | None = None

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lora_grouped_offsets_kv​

lora_grouped_offsets_kv: Buffer | None = None

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lora_ids_kv​

lora_ids_kv: Buffer | None = None

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num_active_loras​

num_active_loras: Buffer | None = None

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return_n_logits​

return_n_logits: Buffer

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signal_buffers​

signal_buffers: list[Buffer]

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Device buffers used for synchronization in communication collectives.

tokens​

tokens: Buffer

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Tensor containing the input token IDs.

Llama3Model​

class max.pipelines.architectures.llama3.Llama3Model(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: LlamaModelBase

Llama 3 pipeline model implementation.

Parameters:

config_class​

config_class

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alias of Llama3Config

norm_method​

norm_method: Literal['rms_norm'] | Literal['layer_norm'] = 'rms_norm'

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Normalization layer.

LlamaModelBase​

class max.pipelines.architectures.llama3.LlamaModelBase(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: LogProbabilitiesMixin, PipelineModelWithKVCache[TextContext]

Base Llama pipeline model implementation.

Parameters:

attention_bias​

attention_bias: bool = False

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Whether to use attention bias.

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

execute()​

execute(model_inputs)

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Executes the graph with the given inputs.

Parameters:

model_inputs (ModelInputs) – The model inputs to execute, containing tensors and any other required data for model execution.

Returns:

ModelOutputs containing the pipeline’s output tensors.

Return type:

ModelOutputs

This is an abstract method that must be implemented by concrete PipelineModels to define their specific execution logic.

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(session)

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Parameters:

session (InferenceSession)

Return type:

Model

model​

model: Model

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Compiled and initialized model ready for inference.

norm_method​

norm_method: Literal['rms_norm'] | Literal['layer_norm']

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Normalization layer.

prepare_initial_token_inputs()​

prepare_initial_token_inputs(replica_batches, kv_cache_inputs=None, return_n_logits=1)

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Prepare the inputs for the first pass in multistep execution.

Parameters:

Return type:

Llama3Inputs

prepare_next_token_inputs()​

prepare_next_token_inputs(next_tokens, prev_model_inputs)

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Prepare the inputs for the next token in multistep execution. This should avoid any device synchronization or copy operations.

Parameters:

Return type:

Llama3Inputs

state_dict​

state_dict: dict[str, Any]

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Weights to load into the model.

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