Changelog

0.5.0

• feat: allow AOT modules to declare explicit dynamic input shapes
• feat: Add MinLatencyStrategy tune strategy that profiles all backends and selects the one with the lowest latency
• feat: Add LatencyBudgetStrategy tune strategy that maximizes throughput while satisfying a latency budget
• feat: validate model correctness at min and max dynamic shape boundaries
• feat: capture JIT inspection details and config in tuning data reports - full inspection for deferred mode and basic for the eager mode
• feat: defer JIT deferred-mode tuning until the next normal forward pass after aitune.torch.jit.tune.deferred()
• breaking change: treat calls that pass the same forward parameters positionally or by keyword as the same graph; existing tuned checkpoints must be regenerated
• breaking change: use forward parameter paths instead of args_* and kwargs_* names in metadata reports and TensorRT optimization profiles; custom profiles must use the new paths
• breaking change: aitune.torch.jit.tune.deferred() now marks tuning to run on the next forward pass instead of tuning immediately
• breaking change: replace FindMaxBatchSizeMixinConfig and PerformanceValidationMixinConfig with a single strategy-level ProfilingConfig

0.4.1

• fix: use the recorded global batch size for baseline performance profiling
• fix: align Torch-TensorRT defaults and JIT inspection CUDA synchronization
• fix: remove memory spike during measuring performance baseline
• fix: remove memory spike for torch inductor jit backend with autocast
• fix: default profiling measurements to fixed-step sampling after warmup
• fix: compute profiling throughput from mean latency
• fix: stabilize optional CV-window profiling with warmup exclusion, max-sample bounds, and clearer failure errors
• fix: relax the default stable-window CV threshold to 10%
• fix: validate ratio and positive profiling configuration values
• breaking change: rename profiling and inspection threshold arguments to ratio-based names
• breaking change: replace enable_validate_against_baseline(False) with enable_performance_validation(False) as the single performance validation toggle

0.4.0

• feat: JIT tuning supports tune strategy selection via aitune.torch.jit_config.strategy (e.g. MaxThroughputStrategy); default remains FirstWinsStrategy
• feat: Add performance validation to tuning strategies. Backends with no speedup relative to TorchEagerBackend are skipped.
• feat: Deferred mode for Just-in-Time (JIT) tuning - explicit tuning trigger post whole pipeline or model pass, useful for image and video generation pipelines
• feat: Torch Inductor Ahead-of-Time (AoT) backend support added
• feat: ONNXRuntime backend support - CUDA and TensorRT execution providers
• feat: Extend TorchAO backend for NVFP4DQ and MXFP8DQ dynamic quantization options
• feat: Extend TensorRT backend for NVFP4 quantization options
• feat: Dynamo worker support - serve AITune-tuned models as Dynamo endpoints
• feat: New hardware metrics collection via subprocess-based hardware monitor
• feat: Hardware metrics output path can be configured via AITUNE_HARDWARE_METRICS_PATH environment variable
• feat: Locator now traverses collections.UserDict subclasses (e.g. transformers.BatchEncoding) like plain dict, restoring tensor shape tracking for sentence-transformers 5.4.0
• feat: JIT tuning can exclude packages or module classes from automatic tuning via aitune.torch.jit_config.patch_exclude
• feat: Add hardware metrics snapshot and enable/disable them at runtime
• feat: Tuning data collection emits a JSON report covering run, module, graph, and backend build stages - controlled via AITUNE_TUNING_DATA_COLLECTION environment variable
• feat: Tuning data output path can be configured via AITUNE_TUNING_DATA_PATH environment variable
• feat: Display speedup summary at WARNING level so it’s visible at the default Python log level
• fix: Preserve externally-registered forward hooks (e.g. capture_outputs from transformers≥ 5) across the AOT and JIT save/restore cycle — hooks registered after AITune wraps a module are no longer lost on the first forward pass
• fix: Remove transformers<5 restriction — AITune now supports transformers 5.x
• fix: Drop enabled_precisions={float16} default from Torch-TensorRT AOT and JIT backends; engine now matches the model’s loaded dtype
• fix: Store checkpoint backend artifacts and SHA entries with relative paths so .ait archives remain portable after copy or move
• chore: Split hardware metrics table if there is a multi-gpu system
• chore: Renamed TorchInductorBackend to TorchInductorJitBackend - breaking change
• breaking change: NVTX_ENABLE environment variable renamed to AITUNE_NVTX_EVENTS
• breaking change: Removed SystemMonitor class from public API; use new hardware metrics collection instead
• breaking change: Removed system_resource_monitor function from public API
• breaking change: Removed enable_gpu_memory_logging function from public API
• breaking change: Removed aitune.torch.jit_config.backends — set config.strategy = FirstWinsStrategy(backends=[...]) to customize backends

0.3.0

• feat: JIT tuning requires single sample only - tune on first model call
• feat: JIT tuning default fallback to Torch Inductor backend
• feat: allow to override tuning device in JIT tuning, when set to none, use the module device
• feat: add support for multi-profile engine with auto generated and user provided profiles in TensorRT backend
• fix: handling dynamic shapes in TorchTensorRT AoT backend
• fix: creating calibration data for ModelOpt ONNX PTQ in TensorRT backend
• misc: added documentation

0.2.0

• feat: introduce Just-in-Time (JIT) tuning: no-code model tuning controlled through import or environment flag
• feat: introduce Just-in-Time (JIT) inspect: no-code model analysis controlled through import or environment flag
• feat: module inspect considers lists and dicts for Torch module containers
• feat: add support for forward hooks for AOT and JIT tuning
• feat: add support for CUDA graphs for TensorRT backend
• feat: changing default ONNX export path to dynamo (torch.onnx.export(dynamo=True))
• feat: add ONNX AutoCast in TensorRT Backend for mixed precision through TensorRT ModelOpt
• feat: extend collecting profiling metrics through nvtx annotations
• feat: suppress console output during tuning and save logs to file - controlled through verbose flag
• feat: add support for dataclasses and user custom object in module.forward arguments
• feat: add support for kv cache for LLMs
• feat: add support for Static/Dynamic HuggingFace for TorchInductor backend
• feat: optimize handling input/output metadata
• feat: reduce CPU/GPU memory usage during tuning offloading modules to meta
• fix: prevent cache dir override when there are two similar modules in JIT tuning
• fix: dynamic shapes configuration for ONNX Dynamo export path in TensorRT Backend
• fix: bfloat16 support in TensorRT Backend
• fix: profiling of models without batching supported
• misc: extends examples and improved dependencies

0.1.0

• feat: add AITune features scoped for the first release
• feat: introduce Ahead-of-Time tuning for low-code model inspection and tuning