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Python module

max.pipelines.architectures.mistral

Mistral transformer architecture for text generation.

MistralConfig​

class max.pipelines.architectures.mistral.MistralConfig(*, hidden_size, num_attention_heads, num_key_value_heads, num_hidden_layers, head_dim, vocab_size, rope_theta, max_seq_len, rms_norm_eps, feed_forward_length, dtype, kv_params, attention_multiplier, devices, return_logits=ReturnLogits.LAST_TOKEN)

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Bases: ArchConfigWithKVCache

Configuration for Mistral models.

Parameters:

attention_multiplier​

attention_multiplier: float

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calculate_max_seq_len()​

static calculate_max_seq_len(pipeline_config, huggingface_config)

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Calculates the maximum sequence length for the model.

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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dtype​

dtype: DType

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feed_forward_length​

feed_forward_length: int

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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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hidden_size​

hidden_size: int

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initialize()​

classmethod initialize(pipeline_config, model_config=None)

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Initializes a MistralConfig instance from pipeline configuration.

This method creates a config instance with all fields that can be determined from the pipeline configuration.

Parameters:

Returns:

An initialized MistralConfig instance.

Return type:

Self

initialize_from_config()​

classmethod initialize_from_config(pipeline_config, huggingface_config)

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Parameters:

Return type:

Self

kv_params​

kv_params: KVCacheParams

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max_seq_len​

max_seq_len: int

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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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return_logits​

return_logits: ReturnLogits = 'last_token'

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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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rope_theta​

rope_theta: float

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vocab_size​

vocab_size: int

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MistralModel​

class max.pipelines.architectures.mistral.MistralModel(pipeline_config, session, devices, kv_cache_config, weights, adapter=None, return_logits=ReturnLogits.LAST_TOKEN)

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Bases: PipelineModelWithKVCache[TextContext]

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

execute()​

execute(model_inputs)

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Runs the graph.

Parameters:

model_inputs (ModelInputs)

Return type:

ModelOutputs

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

graph_inputs()​

graph_inputs()

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Return type:

tuple[TensorType | BufferType, …]

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.

prepare_initial_token_inputs()​

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

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Prepares the initial inputs to be passed to execute().

The inputs and functionality can vary per model. For example, model inputs could include encoded tensors, unique IDs per tensor when using a KV cache manager, and kv_cache_inputs (or None if the model does not use KV cache). This method typically batches encoded tensors, claims a KV cache slot if needed, and returns the inputs and caches.

Parameters:

Return type:

MistralInputs

prepare_next_token_inputs()​

prepare_next_token_inputs(next_tokens, prev_model_inputs)

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Prepares the secondary inputs to be passed to execute().

While prepare_initial_token_inputs is responsible for managing the initial inputs. This function is responsible for updating the inputs, for each step in a multi-step execution pattern.

Parameters:

Return type:

MistralInputs

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