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Python module

max.pipelines.architectures.kimik2_5

Kimi K2.5 mixture-of-experts architecture for text generation.

KimiK2_5Config​

class max.pipelines.architectures.kimik2_5.KimiK2_5Config(*, devices, dtype, quantization_encoding=None, bos_token_id, eos_token_id, ignore_index, media_placeholder_token_id, pad_token_id, tie_word_embeddings, use_unified_vision_chunk, video_placeholder, vision_config, llm_config)

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Bases: ArchVLConfigWithTextSubconfig, ArchConfigWithKVCache

Configuration for Kimi-K2.5 models.

Parameters:

DEFAULT_ENCODING​

DEFAULT_ENCODING: ClassVar[SupportedEncoding] = 'bfloat16'

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SUPPORTED_ENCODINGS​

SUPPORTED_ENCODINGS: ClassVar[set[SupportedEncoding]] = {'bfloat16', 'float4_e2m1fnx2', 'float8_e4m3fn'}

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bos_token_id​

bos_token_id: int

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ID of the beginning-of-sequence (BOS) token.

devices​

devices: list[DeviceRef]

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Devices that the Kimi-K2.5 model is parallelized over.

dtype​

dtype: DType

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DType of the Kimi-K2.5 model weights.

eos_token_id​

eos_token_id: int

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ID of the end-of-sequence (EOS) token.

get_kv_params()​

get_kv_params()

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Returns the KV cache parameters from the embedded LLM config.

Return type:

KVCacheParamInterface

get_num_layers()​

static get_num_layers(huggingface_config)

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Parameters:

huggingface_config (AutoConfig)

Return type:

int

ignore_index​

ignore_index: int

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Index that should be ignored when calculating loss (e.g., for padding).

initialize()​

classmethod initialize(pipeline_config, model_config=None)

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Initializes a Qwen3VLConfig instance from pipeline configuration.

Parameters:

Returns:

A Qwen3VLConfig instance with fields initialized from config.

Return type:

Self

initialize_from_config()​

classmethod initialize_from_config(pipeline_config, huggingface_config, llm_config=None)

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Initializes a KimiK2_5Config from pipeline and HuggingFace configs.

This method creates a config instance with all fields that can be determined from the pipeline and HuggingFace configurations, without needing the state_dict. Fields that depend on the state_dict should be set via the finalize() method.

Parameters:

  • pipeline_config (PipelineConfig) – The MAX Engine pipeline configuration.
  • huggingface_config (AutoConfig) – HuggingFace model configuration.
  • llm_config (KimiK2_5TextConfig | None) – Pre-initialized DeepseekV3 configuration.

Returns:

A KimiK2_5Config instance ready for finalization.

Return type:

Self

llm_config​

llm_config: KimiK2_5TextConfig

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Language model configuration using DeepseekV3 architecture.

media_placeholder_token_id​

media_placeholder_token_id: int

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Token ID used as a placeholder for media (e.g., images, video frames) within sequences.

pad_token_id​

pad_token_id: int

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Token ID used for padding sequences to uniform length.

quantization_encoding​

quantization_encoding: SupportedEncoding | None = None

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The resolved weight encoding the model runs with.

tie_word_embeddings​

tie_word_embeddings: bool

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Whether to share (tie) the input and output word embeddings in the language model.

use_unified_vision_chunk​

use_unified_vision_chunk: bool | None

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Whether to use a unified chunk for vision inputs.

video_placeholder​

video_placeholder: str | None

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Placeholder string used to represent video segments in input text.

vision_config​

vision_config: VisionConfig

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Vision encoder configuration.

KimiK2_5Model​

class max.pipelines.architectures.kimik2_5.KimiK2_5Model(pipeline_config, session, devices, kv_cache_config, weights, adapter=None, return_logits=ReturnLogits.ALL, return_hidden_states=ReturnHiddenStates.NONE, max_batch_size=1)

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Bases: AlwaysSignalBuffersMixin, MultiGraphPipelineModelWithKVCache[KimiK2_5TextAndVisionContext]

A Kimi-K2.5 pipeline model for multimodal text generation.

Parameters:

batch_processor_cls​

batch_processor_cls

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alias of KimiK2_5BatchProcessor

empty_vision_embeddings()​

empty_vision_embeddings(devices)

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Per-device [0, hidden] image embeddings for non-vision steps.

Cached: this is hit on every text-only / decode step, so it must not allocate per call.

Parameters:

devices (list[Device])

Return type:

list[Buffer]

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.

Delegates to model_config_cls.construct_kv_params(...). Subclasses with custom KV behavior should override this method.

Parameters:

Return type:

KVCacheParamInterface

language_model​

language_model: Model

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The compiled language model for text generation.

load_model()​

load_model(session)

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Build, compile, and load vision and language graphs into session.

Parameters:

session (InferenceSession)

Return type:

tuple[Model | None, Model]

model​

property model: Model

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Expose language model for graph capture/replay.

Only the language model is captured since vision runs during prefill

model_config_cls​

model_config_cls

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alias of KimiK2_5Config

pack_vision_inputs()​

pack_vision_inputs(selection, devices)

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Kimi packs inline in vision_execute() (chunked encode).

Parameters:

Return type:

None

prepare_initial_token_inputs()​

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

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Delegates to the batch processor; typed for Eagle subclasses.

Parameters:

Return type:

KimiK2_5ModelInputs

vision_execute()​

vision_execute(selection, devices, packed)

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Run the chunked vision encoder over the cache-selected images.

The chunked encode (packing + per-chunk graph runs + re-concatenation to per-image order) stays encapsulated in the batch processor; the cache only ever sees the per-image-ordered output.

Parameters:

Return type:

VisionEncodeResult

vision_model​

vision_model: Model | None

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The compiled vision model for processing images.

KimiK2_5ModelInputs​

class max.pipelines.architectures.kimik2_5.KimiK2_5ModelInputs(tokens, input_row_offsets, signal_buffers, host_input_row_offsets, batch_context_lengths, image_token_indices=None, precomputed_image_embeddings=None, pixel_values=None, grid_thws=None, cu_seqlens=None, max_seqlen=None, vision_position_ids=None, eplb_counter_buffers=<factory>, *, kv_cache_inputs=None, lora=None, lora_buffers=(), vision_embeddings=<factory>, vision_scatter_indices=<factory>, hidden_states=None, return_n_logits, data_parallel_splits, ep_inputs=())

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Bases: DeepseekV3Inputs

A class representing inputs for the KimiK2_5M model.

This class encapsulates the input tensors required for the KimiK2_5M model execution, including both text and vision inputs. Vision inputs are optional and can be None for text-only processing.

Parameters:

buffers​

property buffers: tuple[Buffer, ...]

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Returns the language model input ABI tuple.

cu_seqlens​

cu_seqlens: list[Buffer] | None = None

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Cumulative sequence lengths for full attention per device.

eplb_counter_buffers​

eplb_counter_buffers: list[Buffer]

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Per-device EP counter buffers for the language model graph.

grid_thws​

grid_thws: list[Buffer] | None = None

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Grid dimensions (temporal, height, width) for each image/video, shape (n_images, 3) per device.

has_vision_inputs​

property has_vision_inputs: bool

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Check if this input contains vision data.

image_token_indices​

image_token_indices: list[Buffer] | None = None

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Per-device pre-computed multimodal merge indices for the image embeddings.

These are the locations of the image_token_id in the inputs fed to the model.

Some indices may be negative, which means that they are ignored by the multimodal merge.

max_seqlen​

max_seqlen: list[Buffer] | None = None

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Maximum sequence length for full attention for vision inputs per device.

pixel_values​

pixel_values: list[Buffer] | None = None

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Pixel values for vision inputs.

precomputed_image_embeddings​

precomputed_image_embeddings: list[Buffer] | None = None

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Pre-computed image embeddings from VisionEncoderCache.

vision_position_ids​

vision_position_ids: list[Buffer] | None = None

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Vision rotary position IDs per device.

KimiK2_5ReasoningParser​

class max.pipelines.architectures.kimik2_5.KimiK2_5ReasoningParser(think_start_token_id, think_end_token_id, tool_section_start_token_id=None)

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Bases: ReasoningParser

Kimi K2.5 reasoning parser for … sections.

Per Moonshot’s “Interleaved Thinking” design (see https://platform.moonshot.ai/docs/guide/use-kimi-k2-thinking-model and https://huggingface.co/moonshotai/Kimi-K2.5), a single assistant turn can interleave multiple <think>...</think> blocks with <|tool_calls_section_begin|>...<|tool_calls_section_end|> blocks.

A reasoning span ends on </think> or <|tool_calls_section_begin|>; the model may open the tool-call section directly from inside the prefilled <think> block without a closing </think>. The section marker is left as content rather than consumed as a delimiter, so the tool parser (which only sees content) receives the whole section.

Reasoning may begin implicitly, without an explicit <think> token, when the chat template prefilled the assistant turn already inside a thinking block.

Reasoning can be disabled through the chat template by including a </think> token at the end of the prompt; this is detected by will_reason_after_prompt().

Parameters:

  • think_start_token_id (int)
  • think_end_token_id (int)
  • tool_section_start_token_id (int | None)

from_tokenizer()​

async classmethod from_tokenizer(tokenizer)

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Construct a reasoning parser from a tokenizer.

Parameters:

tokenizer (PipelineTokenizer[Any, Any, Any])

Return type:

KimiK2_5ReasoningParser

reasoning_end_token_id()​

async classmethod reasoning_end_token_id(tokenizer)

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Returns the </think> token id.

Parameters:

tokenizer (PipelineTokenizer[Any, Any, Any])

Return type:

int | None

stream()​

stream(delta_token_ids, is_currently_reasoning=True)

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Identify a reasoning span within a streaming delta chunk.

When is_currently_reasoning=False and the chunk contains no <think> opener, returns an empty span so non-reasoning chunks (turns where the chat template prefilled </think>, or any chunk after reasoning ended in a prior chunk) aren’t misclassified as reasoning.

Parameters:

Return type:

ParsedReasoningDelta

will_reason_after_prompt()​

will_reason_after_prompt(prompt_token_ids)

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Predicts whether the model will emit reasoning after this prompt.

Kimi K2.5 chat templates emit <think> to open the new assistant turn’s reasoning section, and </think> to close the prior assistant turn’s reasoning section.

Scan right-to-left and return based on the first delimiter seen:

  • <think> → reasoning is currently open → True.
  • </think> (or <|tool_calls_section_begin|>) → reasoning is currently closed → False.
  • No delimiters at all → reasoning is not in use → False.

Uses the same end-of-reasoning delimiters as stream() so both agree on where reasoning ends.

Parameters:

prompt_token_ids (Sequence[int])

Return type:

bool

KimiK2_5TextConfig​

class max.pipelines.architectures.kimik2_5.KimiK2_5TextConfig(*, dtype: 'DType', kv_params: 'KVCacheParamInterface', devices: 'list[DeviceRef]', use_subgraphs: 'bool' = True, data_parallel_degree: 'int' = 1, quantization_encoding: 'SupportedEncoding | None' = None, vocab_size: 'int' = 129280, hidden_size: 'int' = 7168, intermediate_size: 'int' = 18432, moe_intermediate_size: 'int' = 2048, moe_layer_freq: 'int' = 1, num_hidden_layers: 'int' = 61, num_attention_heads: 'int' = 128, num_key_value_heads: 'int' = 128, n_shared_experts: 'int' = 1, n_routed_experts: 'int' = 256, routed_scaling_factor: 'float' = 2.5, kv_lora_rank: 'int' = 512, q_lora_rank: 'int' = 1536, qk_rope_head_dim: 'int' = 64, v_head_dim: 'int' = 128, qk_nope_head_dim: 'int' = 128, topk_method: 'str' = 'greedy', n_group: 'int' = 8, topk_group: 'int' = 4, num_experts_per_tok: 'int' = 8, first_k_dense_replace: 'int' = 3, norm_topk_prob: 'bool' = True, hidden_act: 'str' = 'silu', max_position_embeddings: 'int' = 4096, max_seq_len: 'int' = 163840, rms_norm_eps: 'float' = 1e-06, tie_word_embeddings: 'bool' = False, rope_theta: 'float' = 10000.0, rope_scaling: 'dict[str, Any] | None' = None, rope_interleave: 'bool' = True, scoring_func: 'str' = 'sigmoid', attention_bias: 'bool' = False, attention_dropout: 'float' = 0.0, norm_dtype: 'DType' = bfloat16, gate_dtype: 'DType | None' = None, correction_bias_dtype: 'DType | None' = None, max_batch_context_length: 'int' = 131072, quant_config: 'QuantConfig | None' = None, dense_mlp_layers_without_quant: 'frozenset[int]' = frozenset(), ep_config: 'EPConfig | None' = None, graph_mode: 'str' = 'auto', return_logits: 'ReturnLogits' = <ReturnLogits.LAST_TOKEN: 'last_token'>, return_hidden_states: 'ReturnHiddenStates' = <ReturnHiddenStates.NONE: 'none'>, eagle_aux_hidden_state_layer_ids: 'list[int] | None' = None, eplb_profile_enabled: 'bool' = False)

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Bases: DeepseekV3Config

Parameters:

DEFAULT_ENCODING​

DEFAULT_ENCODING: ClassVar[SupportedEncoding] = 'bfloat16'

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SUPPORTED_ENCODINGS​

SUPPORTED_ENCODINGS: ClassVar[set[SupportedEncoding]] = {'bfloat16', 'float4_e2m1fnx2', 'float8_e4m3fn'}

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calculate_max_seq_len()​

classmethod calculate_max_seq_len(pipeline_config, huggingface_config, model_config=None)

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Parameters:

Return type:

int

initialize()​

classmethod initialize(pipeline_config, model_config=None)

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Initializes a DeepseekV3Config instance from pipeline configuration.

This method creates a config instance with all fields that can be determined from the pipeline configuration, without needing the state_dict. Fields that depend on the state_dict (like norm_dtype, quant_config, etc.) should be set via the finalize() method.

Parameters:

Returns:

An initialized DeepseekV3Config instance.

Return type:

Self

KimiToolParser​

class max.pipelines.architectures.kimik2_5.KimiToolParser

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Bases: StructuralTagToolParser

Parses Kimi K2.5-style tool calls from model responses.

Kimi K2.5 wraps tool calls in section/call markers and embeds the function name as a compound functions.{name}:{idx} identifier before a dedicated argument-begin marker. Arguments are raw JSON, which the base class can diff directly.

CALL_BEGIN​

CALL_BEGIN: ClassVar[str] = '<|tool_call_begin|>'

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CALL_END​

CALL_END: ClassVar[str] = '<|tool_call_end|>'

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SECTION_BEGIN​

SECTION_BEGIN: ClassVar[str] = '<|tool_calls_section_begin|>'

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SECTION_END​

SECTION_END: ClassVar[str] = '<|tool_calls_section_end|>'

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XGRAMMAR_FORMAT​

XGRAMMAR_FORMAT = 'kimi'

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generate_tool_call_grammar()​

static generate_tool_call_grammar(response_format_schema=None, tools=None, tokenizer=None, backend='xgrammar', tool_choice=None, **kwargs)

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Generates a grammar for constrained decoding of Kimi tool calls.

With the default backend="xgrammar" this returns a serialized xgrammar StructuralTag (which constrains each call’s arguments to that tool’s JSON schema). With backend="llguidance" it returns a Lark grammar whose argument body is freeform.

Structural markers and <|im_end|> are referenced as single-token symbols (<[id]>) resolved from tokenizer — they are atomic special tokens, so the freeform /[\s\S]*/ argument body terminates cleanly at the closing marker.

When response_format_schema is provided, the grammar also accepts a JSON response matching the schema (the model’s first tokens select the branch).

Parameters:

  • response_format_schema (dict[str, Any] | None) – Optional JSON schema dict. When provided, the grammar also accepts a JSON response matching the schema.
  • tools (list[dict[str, Any]] | None) – Optional list of OpenAI-style tool dicts. None accepts any length-capped identifier as the function name.
  • tokenizer (PipelineTokenizer[Any, Any, Any] | None) – Pipeline tokenizer used to resolve special-token IDs. Required.
  • **kwargs (Any) – Ignored; accepts future kwargs.
  • backend (str)
  • tool_choice (str | dict[str, Any] | None)
  • **kwargs

Returns:

A grammar string compatible with the selected backend.

Return type:

str

VisionConfig​

class max.pipelines.architectures.kimik2_5.VisionConfig(dtype, devices, init_pos_emb_height, init_pos_emb_time, init_pos_emb_width, merge_kernel_size, mm_hidden_size, patch_size, projector_ln_eps, text_hidden_size, vt_hidden_size, vt_intermediate_size, vt_num_attention_heads, vt_num_hidden_layers, merge_type=None, mm_projector_type=None, model_type='', pos_emb_type=None, projector_hidden_act=None, video_attn_type=None, has_bias=True, in_channels=3, rope_max_height=512, rope_max_width=512, rope_theta=10000.0)

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Bases: object

Vision configuration for Kimi-K2.5 models with required fields.

Parameters:

  • dtype (DType)
  • devices (list[DeviceRef])
  • init_pos_emb_height (int)
  • init_pos_emb_time (int)
  • init_pos_emb_width (int)
  • merge_kernel_size (list[int])
  • mm_hidden_size (int)
  • patch_size (int)
  • projector_ln_eps (float)
  • text_hidden_size (int)
  • vt_hidden_size (int)
  • vt_intermediate_size (int)
  • vt_num_attention_heads (int)
  • vt_num_hidden_layers (int)
  • merge_type (str | None)
  • mm_projector_type (str | None)
  • model_type (str)
  • pos_emb_type (str | None)
  • projector_hidden_act (str | None)
  • video_attn_type (str | None)
  • has_bias (bool)
  • in_channels (int)
  • rope_max_height (int)
  • rope_max_width (int)
  • rope_theta (float)

devices​

devices: list[DeviceRef]

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Devices that the Kimi-K2.5 vision encoder model is parallelized over.

dtype​

dtype: DType

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DType of the Kimi-K2.5 vision model weights.

finalize()​

finalize(vision_dtype)

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Finalize VisionConfig with state_dict dependent fields.

Parameters:

vision_dtype (DType)

Return type:

None

has_bias​

has_bias: bool = True

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Whether linear projections in the vision transformer include bias terms.

in_channels​

in_channels: int = 3

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Number of input image channels (3 for RGB).

init_pos_emb_height​

init_pos_emb_height: int

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Height of the initial position embedding.

init_pos_emb_time​

init_pos_emb_time: int

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Time of the initial position embedding.

init_pos_emb_width​

init_pos_emb_width: int

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Width of the initial position embedding.

initialize_from_config()​

classmethod initialize_from_config(pipeline_config, hf_vision_config, huggingface_config=None)

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Initialize VisionConfig from HuggingFace vision config.

Parameters:

  • pipeline_config (PipelineConfig) – MAX Engine pipeline configuration.
  • hf_vision_config (AutoConfig) – HuggingFace vision sub-config.
  • huggingface_config (AutoConfig | None) – Full HuggingFace model config, used to derive text_hidden_size from text_config.hidden_size when hf_vision_config does not carry the attribute directly (e.g. moonshotai/Kimi-VL-A3B-Instruct vs nvidia/Kimi-K2.5-NVFP4).

Return type:

VisionConfig

Note: dtype fields will be set to defaults and should be updated via finalize() once state_dict is available.

merge_kernel_size​

merge_kernel_size: list[int]

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Kernel size for the merge operation.

merge_type​

merge_type: str | None = None

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Type of the merge operation.

mm_hidden_size​

mm_hidden_size: int

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Hidden size of the multi-modal hidden layer.

mm_projector_type​

mm_projector_type: str | None = None

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Type of the multi-modal projector.

model_type​

model_type: str = ''

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Type of the model.

patch_size​

patch_size: int

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Size of the patch.

pos_emb_type​

pos_emb_type: str | None = None

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Type of the position embedding.

projector_hidden_act​

projector_hidden_act: str | None = None

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Activation function for the projector.

projector_ln_eps​

projector_ln_eps: float

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Epsilon for the layer normalization.

rope_max_height​

rope_max_height: int = 512

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Maximum grid height for RoPE frequency precomputation. Hardcoded to 512 in https://huggingface.co/nvidia/Kimi-K2.5-NVFP4/blob/main/modeling_kimi_k25.py#L571

rope_max_width​

rope_max_width: int = 512

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Maximum grid width for RoPE frequency precomputation. Hardcoded to 512 in https://huggingface.co/nvidia/Kimi-K2.5-NVFP4/blob/main/modeling_kimi_k25.py#L571

rope_theta​

rope_theta: float = 10000.0

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Base for the RoPE inverse-frequency exponent. Hardcoded to 10000 in https://huggingface.co/nvidia/Kimi-K2.5-NVFP4/blob/main/modeling_kimi_k25.py#L379

text_hidden_size​

text_hidden_size: int

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Hidden size of the text hidden layer.

video_attn_type​

video_attn_type: str | None = None

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Type of the video attention.

vt_hidden_size​

vt_hidden_size: int

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Hidden size of the video hidden layer.

vt_intermediate_size​

vt_intermediate_size: int

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Intermediate size of the video hidden layer.

vt_num_attention_heads​

vt_num_attention_heads: int

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Number of attention heads of the video hidden layer.

vt_num_hidden_layers​

vt_num_hidden_layers: int

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Number of hidden layers of the video hidden layer.

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