pypi / litert-torch
litert-torch
What changed in litert-torch 0.9, from its official release notes, upgrade guides and advisories. Sources checked 2026-10-18.
1 breaking22 new APIs and options5 behavior and defaults2 tooling10 fixes and other
See which of these apply to your code, ranked, in your agent:
npx @unstale/cli initlitert-torch 0.940 changes
Breaking changes 1
Stop token prefixes are expanded automatically, and additional stop tokens are inferred from the chat template.
New APIs and options 22
A tool was added for post-training/static range quantization.
This flag enables dynamic context length support for GPU cache.
This flag enables dynamic context length support for GPU prefill.
This experimental flag enables Jinja template transpilation for better LiteRT-LM support.
This experimental flag enables fp16/fp32 mixed precision support for GPU.
This experimental flag accompanies fp16/fp32 mixed precision support for GPU.
FFT lowering support was added to the converter.
Export support was added for the Gemma 4 architecture.
Experimental support was added for exporting models with an optimized GPU attention custom op.
A sampling executor was added to support inference with exported models.
A tokenizer patch adds Metaspace pre-tokenizer and BPE character support.
Constant-fusing patch updates are supported.
Lowering now supports 1D transposed convolutions, including
output_padding.Lowering now supports 1D Real-to-Complex FFT.
An experimental feature transpiles Jinja2 chat templates into Minijinja-compliant templates.
An experimental LiteRT-LM NPU compiler tool is available.
A package provides object-oriented management of
.litertlmcontainers.The Generative API adds a Mixture-of-Experts layer with FP32 and INT8 lowerings.
An OS NPU quantization calibration pipeline is available with sampling executors.
Split-cache models are now exportable.
The
torch.uint8tensor data type is now supported.Unified split-cache export now uses a single
valid_maskinput tensor.
Behavior and default changes 5
Converter legalization and lowering support was added for 1D transposed convolution.
The export_hf pipeline now supports ASR models.
ExecutorMetadata improves handling of sliding window attention and non-SDPA attentions.
The converter now supports the torch.uint8 dtype.
LoRA FlatBuffer export now packs rank metadata.
Tooling 2
An experimental bring-your-own-model NPU export workflow was added to the export_hf pipeline.
A tool was added to compile LiteRT-LM to a chosen NPU backend.
Fixes and other changes 10
Tokenizers using a Metaspace pre-tokenizer and BPE chars are fixed to correctly de-tokenize BPE tokens.
Constant cache lookup now distinguishes PyTorch tensors that share a data_ptr but have different offsets.
Argument flattening preserves dictionary keys and supports the updated PyTree API.
Gemma 4 unified models are now supported.
Key lookup collisions are fixed for PyTorch tensors sharing
data_ptrbut having different offsets.Static shape export no longer has state contamination for LFM2 ShortConv.
Input tensor shape handling is fixed for Parakeet speech models.
Conversion now has an explicit exception safeguard for already-quantized checkpoints.
The RMSNorm patch now supports FP16 mixed-precision export.
Export is supported for Qwen3-ASR with transformers v5, Whisper, Parakeet CTC/TDT, and Moonshine.