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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: _KimiK2_5VisionCacheConfig, ArchVLConfigWithTextSubconfig, ArchConfigWithKVCache, ArchConfigWithVisionCache

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, *, max_seq_len)

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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, *, max_seq_len)

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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.
  • max_seq_len (int)

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, *, memory_plan, 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_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)

REASONING_END​

REASONING_END: ClassVar[str] = '</think>'

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Text delimiter that closes a reasoning span (e.g. "</think>").

See REASONING_START for the declaration contract.

REASONING_START​

REASONING_START: ClassVar[str] = '<think>'

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Text delimiter that opens a reasoning span (e.g. "<think>").

Subclasses declare their delimiters here and resolve token ids from them, so each model’s delimiters are written down exactly once. Consumers that work in the text domain rather than the token domain read them from here instead of restating them.

Declare both delimiters or neither. None means this parser has no text form at all, so a text-domain consumer cannot bound a reasoning span and will leave reasoning in the assistant’s content; declaring only one is a bug, since a span needs both ends. A parser whose chat template prefills the opening delimiter still declares it – whether a given turn emits it is a property of the request, not of the parser.

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, kv_params, devices, use_subgraphs=True, data_parallel_degree=1, quantization_encoding=None, vocab_size=129280, hidden_size=7168, intermediate_size=18432, moe_intermediate_size=2048, moe_layer_freq=1, num_hidden_layers=61, num_attention_heads=128, num_key_value_heads=128, n_shared_experts=1, n_routed_experts=256, routed_scaling_factor=2.5, kv_lora_rank=512, q_lora_rank=1536, qk_rope_head_dim=64, v_head_dim=128, qk_nope_head_dim=128, topk_method='greedy', n_group=8, topk_group=4, num_experts_per_tok=8, first_k_dense_replace=3, norm_topk_prob=True, hidden_act='silu', max_position_embeddings=4096, max_seq_len, rms_norm_eps=1e-06, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, rope_interleave=True, scoring_func='sigmoid', attention_bias=False, attention_dropout=0.0, norm_dtype=bfloat16, gate_dtype=None, correction_bias_dtype=None, max_batch_context_length=131072, quant_config=None, dense_mlp_layers_without_quant=frozenset({}), ep_config=None, graph_mode='auto', return_logits=ReturnLogits.LAST_TOKEN, return_hidden_states=ReturnHiddenStates.NONE, eagle_aux_hidden_state_layer_ids=None, eplb_profile_enabled=False)

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Bases: _KimiK2_5VisionCacheConfig, DeepseekV3Config, ArchConfigWithVisionCache

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(huggingface_config, model_config)

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Returns the resolved maximum sequence length.

Bounds or defaults the user’s model_config.max_length with the model’s own limits. Construction runs this once and stores the result on model_config.max_length; memory planning may lower it further, but only on the memory plan.

Parameters:

  • huggingface_config (AutoConfig) – The HuggingFace config to read model bounds from.
  • model_config (MAXModelConfig) – The model config whose max_length carries the user’s setting.

Return type:

int

initialize()​

classmethod initialize(pipeline_config, model_config=None, *, max_seq_len)

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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 constrained-decoding grammar for Kimi tool calls.

Returns a serialized xgrammar StructuralTag that frames the Kimi tool-call envelope and constrains each call’s arguments to that tool’s JSON schema. 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.
  • tokenizer (PipelineTokenizer[Any, Any, Any] | None) – Unused (the xgrammar tag references literal markers).
  • backend (str) – Structured-output backend; must be "xgrammar".
  • tool_choice (str | dict[str, Any] | None) – "auto", "required", or a named choice.
  • **kwargs (Any) – Ignored; accepts future kwargs.

Returns:

The StructuralTag serialized as a JSON string.

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]

source

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

source

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

source

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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