Build(deps-dev): bump onnxruntime-genai from 0.15.2 to 0.18.0 in /python - #14600

Sourced from onnxruntime-genai's releases.

v0.18.0

No release notes provided.

v0.17.1

No release notes provided.

v0.17.0

Version 0.17.0 makes repeated prompts faster, adds new vision and audio models, and brings more ways to tune the Engine for different GPUs.

Highlights

• Faster repeated prompts with prefix caching - The dynamic Engine can now reuse matching prompt prefixes across requests. Prefix caching is enabled automatically for supported paged KV-cache layouts.
• Runtime profiles for different GPUs - A single model package can select Engine settings and model variants based on available GPU memory. New capability APIs also make it easier for applications to discover Engine limits.
• New LFM2 models - Added support for LFM2-VL and LFM2.5-VL vision models, LFM2-MoE, and LFM2-Audio and LFM2.5-Audio with speech input and output.
• Moonshine streaming speech recognition - Added Moonshine small and tiny streaming ASR models, designed for fast transcription with low CPU use.
• Faster DFlash decoding - DFlash now supports CUDA graph capture, prepacked weights, optional projection fusion, sampled speculative decoding, and direct Qwen 3.8 package export.
• Lower GPU memory pressure - Embedding lookups can be offloaded to CPU while keeping CUDA graph capture enabled for decoding.
• Less work during generation - Paged decoders can compute logits only for the token rows needed for generation and speculative verification.
• Safer conversation editing - Engine requests can rewind to the start of an earlier turn and continue from there without creating a new request.
• A better Model Builder configuration - The new structured configuration organizes target, drafter, speculative decoding, runtime, and search settings in one validated format.
• More Engine flexibility - Added configurable paged block sizes, improved speculative draft controls, and APIs for maximum request length.
• Reliability improvements - Includes fixes for WebGPU graph capture, decoder pipeline KV-cache shapes, paged prefill settings, legacy execution providers, and prefill logits.

v0.16.0

This is a big release! Version 0.16.0 brings major upgrades for high-throughput serving, faster decoding, and the latest generation of Qwen models.

Highlights

• A much more capable Engine - Continuous batching now supports chunked prefill, paged attention, multi-turn conversations, mixed workloads, and smarter token scheduling. This improves throughput while making better use of GPU memory.
• Faster generation with speculative decoding - Added support for draft models, n-gram speculation, MTP self-speculation, and DFlash2 and DSpark block drafting.
• Tool calling and constrained decoding - The Engine can generate tool calls, continue after tool results without replaying the full prompt, and enforce structured output with guidance.
• Better multi-turn control - Requests can retain their KV cache between turns. Sampling, token limits, seeds, stop strings, and guidance can be changed for each turn.
• More Qwen support - The model builder now supports Qwen2.5-VL, Qwen3-VL, and Qwen3.5 dense and MoE models, with additional export and speculative-decoding support for Qwen3.6 and Qwen3.8.
• More efficient KV caches - Added paged, windowed, sparse, and quantized KV-cache support, including INT4, INT8, and FP8 paths.
• New quantized model formats - Added native support for NVFP4 and FP8 checkpoints from NVIDIA Model Optimizer and compressed-tensors, plus the quant_auto format.
• New hardware support - Added the AMDGPU execution provider and WebGPU paged-attention exports.
• New model support - Added Granite models and improved Marian batching, multimodal prefill, Nemotron audio, and multi-image vision handling.
• Stronger reliability - This release includes broad validation and recovery improvements across the Engine, speculative decoding, vision and audio inputs, cache management, and process shutdown.

• 00d66ef Version 0.18.0
• beb6c98 Fix stale results in CUDA distributed Top-K selection (#2722)
• 83817d3 Bound leased-checkpoint scans during prefix-cache planning (#2699)
• 0deac7c Make DFlash snapshot retention configurable in GenAI profiles (#2696)
• 5c19002 Update Extensions and expose literal tokenizer encoding (#2697)
• e3e8d72 Support plugin EPs in the C++ model benchmark (#2670)
• 77b2693 Merge pipeline config overlays by model ID (#2553)
• 2472a36 Keep alternating hybrid prefixes warm to avoid repeated prefill
• c975ef9 Remove unnecessary compliance code (#2694)
• 0fb858c Support Gemma 4 12B Unified native multimodal generation (#2685)
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