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Run each partitioner’s edge transforms after caller-supplied passes so backends can use the standard lowering entry point. QNN contributes its existing transforms through the new hook.
Reserve names for constant tensors already present in the exported program before lifting new constants, so methods with folded tables retain distinct constants.
Choose the registered IOManager and its logits, method names and step sizes, rejecting unknown contracts and invalid metadata. Load and reset the selected methods; route prompt tails to decode with a side-effect-free capacity check. Preserve module and I/O-buffer lifetimes and expose a backend-neutral allocator hook.
Route backend options through QnnPartitioner.from_options and register an allocator that supplies QNN shared-memory buffers through the generic IOManager hook. The LLM runner remains backend-neutral.
Encode graph output positions and restore their order at both runtime tensor-allocation entry points. Preserve context-binary order and legacy mutable outputs, and ignore malformed or overflowing position labels without throwing. This runtime change needs Qualcomm review.
Move backend-neutral static-cache rewriting into the LLM extension and keep QNN partitioning and tuning in the backend. Detect sliding layers from model configuration, keep cache writes in place with standard reinplace, and register a versioned runner that handles fixed chunks, padding, counters and reset. Quantized caches, sha attention and full-length sliding mode are excluded.
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Export hf_static float-cache programs and register their Python runner
HF exports a variable-length static-cache graph that writes into caller-owned buffers. HTP needs fixed shapes and QNN cannot perform those input mutations. Export fixed
prefill/decodemethods, attend over incoming caches and current-token rows, and retain cache writes, masks and counters in CPU kernels with the existing reinplace pass.Each step keeps HF's tensor interface: token IDs, masks, positions and full K/V caches in; logits and those caches updated in place out. The fixed-shape padding/counter adaptation is hf_static v1. Decode is required; prefill is optional. Caches remain float16/float32 even with quantized compute. Quantized caches, sha and full-length sliding mode are excluded.
Backend-neutral rewriting and
cache_graph.pystay in the LLM extension. Tensor dtype and constant-method decoding live in their sole driver module. QNN partitioning, SeqMSE, shared activation encodings, calibration prompts and the hf_static-only CLI live inbackends/qualcomm/utils/hf_static.py. Their retained helper implementations and constants are unchanged. The shared Gemma folding and Qwen3 norm-layout tests remain covered here.The guarded transformers hook
transformers.exporters.register_io_contractis available only in the local transformers branch used for this work, not stock transformers. Without the hook, registration is skipped. The Python driver handles padding, absolute counters, reset, context-tail decode fallback and DLPack logits; it rejects planned/device-resident state. No zero-copy Python host API is claimed. External cache layout requires export-time validation.Review shared graph utilities and rewriting, Python driver/registration, backend lowering/tuning and the CLI, then tests. The C++ manager follows in PR 7. Cache inspection, adopt/fork/evict policies remain IOManager follow-ups.
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7cb492766bdd019340a865774dc8742489f10c6bon892e50425f5a3e90ff8d3c8802fdfb31f8599518. Validated locally with Python 3.12, pinned Torch/TorchAO, the local transformers exporter branch and QAIRT SDK 2.48.DLPack/non-Torch output subprocess: 3 passed + 2 subtests, zero failures. CLI help and forwarding checks pass. All 32 retained function/class definitions are structurally equivalent, with identical moved constants and calibration prompts.
No device/physical rpcmem, two-target FCB, real-model export or full QNN device-suite result is claimed. Buck could not start because the local launcher requires a missing
.buck2. The base already contains #23563 and #23183; neither is a separate prerequisite commit here.Related PRs (review in order)
Each PR includes the commits of the ones above it; review only its own top commit.