feat: Expose Multi-Sequence Speculative Decoding & MTP to Public C API

[zsogitbe]

Context: I am submitting this on behalf of the LLamaSharp maintainer team (cc @martindevans). We needed a way to cleanly expose speculative decoding and MTP to our C# users without bloating the upstream core. This standalone C API has been heavily battle-tested across both C++ and C# environments.

Overview

This PR introduces a standalone, dependency-free C API for multi-sequence speculative decoding and Multi-Token Prediction (MTP). It extracts the core speculative execution loop from the internal common/speculative.cpp utilities into a public-facing implementation (src/llama-speculative.cpp). This enables downstream bindings (such as llama-cpp-python, LLamaSharp, etc.) to easily integrate draft-simple and Draft-MTP capabilities via native pointers, without needing to recreate complex draft/verify loops or context routing mechanics.

Additional information

Key Architectural Changes

• Public C API (include/llama.h): Introduced llama_speculative_init, llama_speculative_decode, llama_speculative_free, and the llama_speculative_result struct.
• MTP Staging Integration: Exposed llama_set_embeddings_nextn and llama_get_embeddings_nextn_ith to the public header. These are strictly required to route hidden states across MTP projection heads for architectures like DeepSeek-R1 and Qwen.
• Standalone Engine (src/llama-speculative.cpp): Implemented a core multi-sequence engine relying solely on llama.h and the C++ standard library, keeping the footprint minimal and avoiding common dependencies.
• Continuous Batching: Dynamically tracks batch->n_seq_id to draft and verify multiple independent sequences concurrently, mapping accepted tokens back to their origin sequence ID.
• M-RoPE / RNN Safe Rollback: Engineered an unconditional byte-level llama_state_seq backup mechanism prior to verification. If a sequence is rejected, the engine performs a hard memory wipe and transparent re-decodes accepted tokens. This specifically prevents fatal X < Y M-RoPE position collision crashes on hybrid recurrent architectures.
• Verification Alignment: Ensures the sampled base token is explicitly prepended to the verification batch so the target model evaluates draft tokens with perfect context alignment.

Validation

• Added tests/test-speculative.cpp and integrated it into CMakeLists.txt using the test-download-model fixture.
• The test uses a self-speculation approach (where target and draft contexts share the same model) to validate decoding loops, cache rollbacks, sequence mapping, and state recovery in CI, requiring zero multi-model external dependencies.
• Cross-Ecosystem Testing: (Note: This PR is paired with a companion PR in LLamaSharp feat: Native Speculative Decoding & MTP (Multi-Token Prediction) - #1431. The API and underlying engine have been exhaustively tested across both C++ and C# environments. This includes the native CI test provided here, alongside extensive managed C# unit tests validating multi-sequence multiplexing, KV cache deduplication, and real-world interactive Tokens-Per-Second (TPS) benchmarks.)

Requirements

• I have read and agree with the contributing guidelines
• AI usage disclosure: YES - I used AI assistants to help brainstorm architectural approaches, assist with the C++ implementation, and structure the PR description. However, all testing, benchmarking, CI validation, and cross-ecosystem integration (C++/C#) were conducted manually, and I take full responsibility for the accuracy and maintenance of this code.

ANNEX: Model Selection & Evaluation

1. Model Compatibility & Selection Rules

• Dual-Model Speculation: The target and draft models must share the exact same tokenizer architecture and vocabulary size to prevent immediate cache desynchronization crashes. Crucially, the larger the target model and the smaller/faster the draft model, the higher the resulting speedup.

• Multi-Token Prediction (MTP): This requires a single model with pre-trained speculative projection layers (nextn_predict_layers >= 1). The more draft heads the model has, the higher the potential speedup, as it can verify more tokens per native API call without context-switching overhead. The draft budget must match the available heads.

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2. Real-World Benchmark Examples

Draft-Simple (Expected Speedup)
Pairing Meta-Llama-3-8B-Instruct-Q8_0.gguf (Target) with Llama-3.2-1B-Instruct-Q4_0.gguf (Draft) yielded a 1.26x speedup (+25.7%). Even on a fast GPU, the 1B draft model was lightweight enough to outpace the compute overhead of the 8B target.

MTP Speculation (Hardware Slowdown)
Running Qwen3.5-4B-MTP-Q4_K_M.gguf (1 MTP head) resulted in a 0.47x slowdown (-53.2%). Because the 4B base model is computed incredibly fast on high-end hardware, the API overhead and CUDA graph launch latency of evaluating the MTP head completely overshadowed the memory bandwidth savings. To achieve a speedup instead, you must use a much larger model (like 14B or 32B) where the GPU's memory bandwidth becomes a true bottleneck as it fetches massive weight matrices from VRAM for every single token. Alternatively, using an MTP model with multiple prediction heads allows you to verify several tokens in a single API roundtrip, making the overhead worthwhile.

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3. Hardware & Workload Dynamics

• 100% VRAM Offloading: Both models (or the full MTP model) must fit entirely within GPU VRAM. If layers spill over to the CPU, parallel batch verification turns into serialized matrix multiplication, causing a severe net slowdown.

• The Size Threshold: Speculative decoding is an optimization for memory-bandwidth-bound workloads. Speedups reliably appear on mid-to-large models (8B, 14B, 32B, 70B) where reading massive weight matrices per token is the actual bottleneck.

• Task Predictability: Deterministic tasks (code completion, JSON extraction) achieve high draft acceptance rates (>70%), maximizing throughput. Creative writing and high-temperature sampling trigger frequent draft rejections, wasting compute cycles.

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4. Quick Selection & Viability Matrix

Strategy	Required Model Setup	Minimum Target Size	Ideal Workload	Expected Result
Draft-Simple	Target + Draft (Same Vocab & Family)	>= 8B (100% VRAM)	Code, JSON, Structured Tasks	1.4x – 2.2x Speedup
Draft-Simple	Target + Draft (Different Vocab)	Any	Any	Immediate Crash
MTP	Single Model (nextn_predict_layers >= 1)	>= 8B–14B (100% VRAM)	Low-Temperature / High-Confidence	1.3x – 1.8x Speedup
Any Speculative	Model partially offloaded to CPU	Any	Any	Slowdown
Any Speculative	Models <= 4B on High-End GPU	<= 4B (100% VRAM)	High-Entropy / Creative Prompts	Slowdown

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[zsogitbe]

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