LLM Engine Config

struct Gemma4MTPKVSharingEntry

Gemma4 MTP assistant-layer to target-layer shared-KV mapping.

Public Members

int32_t assistantLayerIdx = {-1}

Assistant local attention-layer index.

int32_t targetAttentionLayerIdx = {-1}

Target local attention-layer index.

int32_t targetAbsoluteLayerIdx = {-1}

Target absolute decoder-layer index, derived during validation.

struct LLMEngineConfig

Unified configuration for base, vanilla decode, and SpecDecode draft engines.

Replaces LLMEngineRunnerConfig and EagleDraftEngineRunnerConfig with a single structure that can be parsed once and used across all runtime components (KV cache, RoPE, LoRA, preprocessors, etc.).

Public Functions

InferenceDims prefillDims(

int64_t batch,

int64_t seqLen,

bool kvCacheAllEmpty

) const

Prefill dims (vanilla LLM, SpecDecode base, and SpecDecode draft). seqLen is the prompt length being processed this step. kvCacheAllEmpty signals whether this is the initial prefill of an empty KV cache — this drives autoregressive engines’ kvcache_start_index shape to [0] (engine’s “initial prefill” sentinel) instead of [batch]. DiffusionGemma keeps kvcache_start_index at [batch] and uses context_mask_selector as its attention-mask sentinel.

InferenceDims decodeDims(int64_t batch) const

Vanilla single-token decode dims. seqLen is always 1 here; packedMaskLen is 1 (no proposal mask in vanilla).

InferenceDims denoiseDims(int64_t batch, int64_t canvasLen) const

DiffusionGemma denoise dims. Uses existing chunked-prefill execution: seqLen/selectLen are the canvas length, kvcache_start_index is [batch], and context_mask_selector is [batch] to select non-causal PADDING mask.

InferenceDims diffusionCommitDims(

int64_t batch,

int64_t commitLen

) const

DiffusionGemma commit dims. Reruns finalized tokens causally before KV commit; context_mask_selector is [0], so AttentionPlugin keeps causal mask.

InferenceDims specVerifyDims(int64_t batch, int64_t verifySize) const

SpecDecode base verification dims. verifySize feeds three fields: seqLen, selectLen, and packedMaskLen. This is the only recipe where selectLen != 1.

InferenceDims proposalDims(

int64_t batch,

int64_t proposalSize,

int64_t draftTopK

) const

SpecDecode draft proposal dims. proposalSize feeds seqLen, attnMaskSeqLen, and packedMaskLen. draftTopK feeds selectLen — the draft proposal selects draftTopK tokens per sequence, matching the 3D logits output shape [batch, draftTopK, draftVocabSize].

InferenceDims acceptDims(int64_t batch, int64_t acceptLen) const

SpecDecode draft accept-token dims. acceptLen is in [1, draftingStep+1]; feeds seqLen and packedMaskLen.

InferenceDims resetDims() const

Clean-slate dims for CUDA-graph capture reset. All 1s except kvLen = maxKVCacheCapacity. ropeBatch is fixed at 1 even for MRope models — this matches the pre-migration behavior in both runtimes (reset is a binding placeholder, not an inference step).

Public Members

int32_t hiddenSize = {}

Model hidden dimension.

int32_t outputVocabSize = {}

Actual output vocab (reduced if vocab reduction active)

int32_t numAttentionLayers = {}

Number of attention layers needing KV cache.

int32_t numKVHeads = {}

Number of key-value heads.

int32_t headDim = {}

Dimension of each attention head.

int32_t maxSupportedBatchSize = {}

Maximum supported batch size.

int32_t maxSupportedInputLength = {}

Maximum supported input length.

int32_t maxKVCacheCapacity = {}

Maximum KV cache capacity (sequence length)

int64_t skipSoftmaxScaleOverride = {0}

skip-softmax scale-factor override (0 = disabled)

int32_t kvPoolPages = {}

Exact physical K-page count serialized in KV binding shapes.

int32_t rotaryDim = {}

Rotary embedding dimension.

int32_t numDecoderLayers = {}

Total decoder layers (attention + linear)

int32_t vocabSize = {}

Full vocabulary size.

int32_t reducedVocabSize = {0}

0 = no vocab reduction

int32_t diffusionCanvasLength = {0}

int32_t diffusionMaxDenoisingSteps = {0}

int32_t diffusionSelfConditioningSize = {0}

float diffusionTMax = {0.8F}

float diffusionTMin = {0.4F}

float diffusionEntropyBound = {0.1F}

float diffusionEntropyThreshold = {0.005F}

float rmsNormEps = {1.0e-6F}

int32_t diffusionStabilityWindow = {2}

bool isSpecDecodeBase = {false}

Base engine exposes speculative decoding verification bindings.

bool isDiffusionBackbone = {false}

DiffusionGemma phase-aware transformer backbone engine.

bool diffusionUnifiedConditioning = {false}

Backbone engine owns DiffusionGemma self-conditioning inputs.

bool contextMaskSelectorEnabled = {false}

Engine exposes context_mask_selector binding.

SpecDecodeMode specDecodeType{SpecDecodeMode::kNONE}

Speculative decoding strategy mode (parsed from spec_decode_type)

nvinfer1::DataType kvCacheDtype = {nvinfer1::DataType::kHALF}

KV cache data type. Parsed from required top-level kv_cache_dtype in config.json (written by llm_export.py). Accepted values: “fp16” → kHALF, “fp8” → kFP8, “int8” → kINT8, “bf16” → kBF16. The runtime validates this against the engine’s actual KV binding dtype.

nvinfer1::DataType recurrentStateDtype = {nvinfer1::DataType::kHALF}

Recurrent state data type (hybrid models only). Parsed from required top-level recurrent_state_dtype when numLinearAttnLayers > 0; left at the default otherwise. Runtime validates against the engine’s recurrent-state binding dtype.

nvinfer1::DataType convStateDtype = {nvinfer1::DataType::kHALF}

Conv state data type (hybrid models only). Same shape as recurrentStateDtype.

RopeConfig ropeConfig = {}

Full RoPE configuration.

bool useDualRope = {false}

Use separate RoPE caches for sliding/full attention.

RopeConfig slidingRopeConfig = {}

RoPE configuration for sliding attention layers.

RopeConfig fullRopeConfig = {}

RoPE configuration for full attention layers.

int32_t slidingRotaryDim = {}

Rotary dimension for sliding attention RoPE.

int32_t fullRotaryDim = {}

Rotary dimension for full attention RoPE.

bool useContextDependentRope = {false}

Use context-dependent RoPE.

int32_t numDeepstackFeatures = {0}

Deepstack features (Qwen3-VL/Qwen3-Omni)

bool pleEnabled = {false}

Gemma4 PLE runtime preprocessor enabled.

int32_t numPleInputs = {0}

Number of PLE tensors bound into the engine (0 = disabled)

int32_t pleHiddenSize = {0}

Hidden dimension of each PLE tensor (0 = disabled)

int32_t maxSupportedLoraRank = {0}

Maximum LoRA rank (0 = no LoRA)

std::vector<int32_t> eosTokenIds = {}

Additional EOS token IDs parsed from config.json eos_token_id array. Models like Gemma4 have multiple EOS tokens (e.g. [1, 106] for <eos> and <turn|>). Empty if eos_token_id is absent or scalar.

int32_t imageTokenId = {-1}

Special token ID for image (-1 = unused)

int32_t audioTokenId = {-1}

Special token ID for audio (-1 = unused)

bool useVisionBidirectionalAttention = {false}

Gemma4 Unified prefill uses block-causal image attention.

int32_t numLinearAttnLayers = {0}

Number of linear attention / recurrent layers.

int32_t recurrentStateNumHeads = {0}

Recurrent state heads (hv for GDN, mamba_num_heads for Mamba)

int32_t recurrentStateHeadDim = {0}

Recurrent state head dimension.

int32_t recurrentStateSize = {0}

Recurrent state dimension (v for GDN, dstate for Mamba)

int32_t recurrentStateNumGroups = {0}

Mamba B/C group count (spec-verify replay-B extent; 0 for GDN)

bool recurrentSpecVerifyUsesReplay = {false}

MTP spec-verify recurrent-state commit mode, parsed from recurrent_spec_verify_mode (“replay” → true, “snapshot”/absent → false).

int32_t convDim = {0}

Conv1d channel dimension.

int32_t convKernel = {0}

Conv1d kernel width.

int32_t maxVerifyTreeSize = {0}

Max seq_len the base engine accepts for proposal verification. Parsed from builder_config.max_verify_tree_size when isSpecDecodeBase == true; 0 otherwise. Consumers prefer the consolidated DeploymentConfig::specDecode when the deployment view is available.

int32_t maxDraftTreeSize = {0}

Max seq_len the draft engine accepts for proposal / draft generation. Parsed from builder_config.max_draft_tree_size by parseDraftEngineConfig; 0 on base / vanilla engines. Consumers prefer the consolidated DeploymentConfig::specDecode when the deployment view is available.

int32_t baseModelHiddenSize = {0}

Hidden dim the draft engine expects for its hidden_states_input binding (== the base engine’s hidden-state output dim as seen by the draft). Parsed from top-level base_model_hidden_size by parseDraftEngineConfig; left at 0 on base / vanilla engines. Differs from base.hiddenSize for EAGLE-3 (= base.hiddenSize * 3, multi-layer concat) and equals base.hiddenSize for MTP. The deployment factory copies this into DeploymentConfig::specDecode->baseOutputHiddenDim.

int32_t specDraftBlockSize = {0}

Cached draft proposal block size for DFlash/DSpark. Parsed from the mode-specific config object (dflash_config or dspark_config).

int32_t specDraftMaskTokenId = {0}

Mask token ID used to seed cached draft input blocks for DFlash/DSpark.

std::vector<int32_t> specTargetLayerIds = {}

Target decoder-layer IDs whose hidden states are concatenated for cached drafts. EAGLE base engines own this contract through eagle_hidden_state_layers; DFlash/DSpark keep their mode-specific target-layer metadata on the engine that exports it.

std::string modelType

Top-level model type string, if exported.

bool sharesTargetKV = {false}

Draft assistant reads target KV cache.

bool hasOwnKVCache = {true}

Draft assistant owns/writes its own KV cache.

bool constantDraftPositions = {false}

Assistant draft position IDs stay constant through a chain.

bool returnsFeedbackHidden = {false}

Assistant emits backbone-space feedback hidden states.

int32_t assistantHiddenSize = {0}

Assistant internal hidden dim, when distinct from hiddenSize.

std::vector<Gemma4MTPKVSharingEntry> gemma4MTPKVSharingMap = {}

Assistant -> target KV sharing map.

bool dsparkEnableConfidenceHead = {false}

DSpark Markov/confidence sidecar metadata.

bool dsparkConfidenceHeadWithMarkov = {false}

std::string dsparkMarkovHeadType = {}

int32_t dsparkMarkovRank = {0}

std::string dsparkHeadsFile = {}

std::string dsparkHeadsInfoFile = {}

std::vector<HybridCacheManager::LayerType> layerTypes = {}

Absolute decoder-layer -> attention|mamba. Populated either from the canonical layer_types field in config.json or by broadcasting scalar numAttentionLayers/numLinearAttnLayers for back-compat. Size equals the number of stateful decoder layers (mlp/moe excluded on the Python side).

std::vector<KVLayerConfig> kvLayerConfigs = {}

Per-attention-layer KV config. Size equals the attention count in layerTypes. Indexed by LOCAL attention-layer index (0..numAttn-1), NOT absolute decoder-layer index.

std::vector<int32_t> kvSharingDonors = {}

Per-attention-layer KV sharing donor index. Size equals the attention count in layerTypes. Value of -1 means the layer owns its own KV cache (normal). A value >= 0 means this layer shares the KV cache from the donor attention layer at that LOCAL index (the donor’s cache is bound to this layer’s KV input). Used for Gemma4’s KV sharing where the last N layers reuse a donor’s cache.

LLMEngineConfig trt_edgellm::rt::parseEngineConfig(

std::filesystem::path const &configPath

)

Parse a config.json file (the same format used by the existing runtime) into an LLMEngineConfig.

Parameters:

configPath – Path to config.json.

Throws:

std::runtime_error – if file cannot be opened/parsed or required fields are missing.

Returns:

Parsed configuration.

LLMEngineConfig trt_edgellm::rt::parseDraftEngineConfig(

std::filesystem::path const &configPath

)

Parse a SpecDecode draft engine’s config.json into an LLMEngineConfig.

The draft config carries a reduced field set (no builder_config.spec_base, its own draft_vocab_size). max_draft_tree_size is required and is parsed into cfg.maxDraftTreeSize; cfg.maxVerifyTreeSize stays at 0 on the draft side. isSpecDecodeBase is left false because this is the draft — not the base — engine. Cross-engine fields (draftHiddenSize, baseOutputHiddenDim) are not stored on LLMEngineConfig; they are derived in createDeploymentConfig and live on DeploymentConfig::specConfig.

Parameters:

configPath – Path to the draft engine’s config.json.

Throws:

std::runtime_error – on parse failure or missing required fields.

Returns:

Parsed configuration.

std::string trt_edgellm::rt::formatEngineConfig(

LLMEngineConfig const &config

)

Format the config as a human-readable string (for logging).

void trt_edgellm::rt::validateAgainstEngine(

LLMEngineConfig const &config,

EngineExecutor const &executor,

char const *engineLabel

)

Cross-check an engine’s KV / recurrent / conv binding dtypes against their parsed-config counterparts. The parsed config is the source of truth; this raises a runtime_error with an actionable message on any mismatch.

Parameters:

• config – Parsed LLMEngineConfig (from parseEngineConfig or similar).

• executor – Engine whose binding dtypes should match.

• engineLabel – Short label (“base” / “draft”) used in error messages.

Throws:

std::runtime_error – on any KV / recurrent / conv dtype mismatch.