Expand TRT decoder YAML config for composite decoding [depends on PR #524]#536
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Expand TRT decoder YAML config for composite decoding [depends on PR #524]#536wsttiger wants to merge 10 commits into
wsttiger wants to merge 10 commits into
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…output
Add a "predecoder" execution mode to the TensorRT decoder so it can be
chained with a second decoder (e.g. PyMatching) and return logical-frame
observables directly. The TRT model is assumed to emit a single output
that concatenates [pre_L (num_observables entries), residual_dets (rest)].
New constructor parameters:
- "batch_size": required when the ONNX model has a dynamic batch dim.
Used to size the optimization profile and pre-allocate I/O buffers.
- "global_decoder" + "global_decoder_params": optional decoder name and
params for a follow-up decoder run on the residual_dets portion of
the TRT output. Created with the same H passed to trt_decoder.
- "O": observables matrix (num_observables x block_size). Enables
decode()/decode_batch() to return the predicted logical frame.
Number of observables is inferred from O.shape()[0].
Decode behavior matrix:
- no global_decoder, no O -> raw TRT output (unchanged).
- no global_decoder, O -> return the pre_L prefix only.
- global_decoder, no O -> entire output -> global_decoder.result.
- global_decoder, O -> residual -> global_decoder; return
pre_L XOR global_decoder.logical_frame.
Constructor validation when O is set:
- output_size_per_sample >= num_observables, and
- when global_decoder_ is set,
output_size_per_sample == num_observables + global_decoder.syndrome_size.
Other changes:
- Dynamic batch support: setInputShape per call when the model's batch
dim is -1; ONNX builder now installs a min/opt/max optimization
profile when "batch_size" is provided.
- Split decode_batch into a typed decode_batch_impl<float|uint8_t> for
cleaner dtype dispatch (engine I/O dtypes float32 / uint8 unchanged).
- Better INFO logging: total non-zero input vs residual detector counts
per batch to help diagnose predecoder behavior.
Signed-off-by: Ben Howe <bhowe@nvidia.com>
Add a realtime test/demo that initializes the TensorRT decoder from an ONNX predecoder model with PyMatching configured as the global decoder. The driver loads detector, observable, parity-check, observable, and prior data from the Stim export bundle, decodes samples through the composite TRT+PyMatching path, and reports latency, throughput, correctness, and residual-syndrome diagnostics. Register the new test_trt_decoder_composite target when TensorRT, realtime, and the TRT decoder plugin are available. Signed-off-by: Scott Thornton <wsttiger@gmail.com>
Add YAML/config support for TRT decoder runtime options including batch size, CUDA graph execution, global decoder selection, and PyMatching-specific global decoder parameters. Wire realtime decoder construction so TRT configs receive the top-level observable matrix from O_sparse, and pass the same O matrix into PyMatching global decoder params for composite observable decoding. Expose the new config fields through Python bindings and heterogeneous_map round-tripping. Extend YAML tests for TRT config round-trip, runtime parameter conversion, and O_sparse-to-O injection. Update test_trt_decoder_composite to support an optional --config-yaml path, allowing the existing composite demo to construct and run a real TRT+PyMatching decoder directly from YAML while preserving the original manual CLI path. Signed-off-by: Scott Thornton <wsttiger@gmail.com>
…yaml # Conflicts: # libs/qec/unittests/realtime/CMakeLists.txt # libs/qec/unittests/realtime/test_trt_decoder_composite.cpp
Replace the TRT decoder's hardcoded optional PyMatching global decoder params with a tagged global_decoder_config variant. Preserve PyMatching as the current supported concrete config while using std::monostate for the unset case. Update heterogeneous-map conversion, YAML mapping, and Python bindings so the existing PyMatching YAML/Python surface continues to round-trip. Extend the YAML unit test to verify the PyMatching variant arm is selected and still produces the expected runtime parameter map. Signed-off-by: Scott Thornton <wsttiger@gmail.com>
…yaml # Conflicts: # libs/qec/python/bindings/py_decoding_config.cpp
Signed-off-by: Scott Thornton <wsttiger@gmail.com>
Signed-off-by: Scott Thornton <wsttiger@gmail.com>
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Add YAML/config support for TRT decoder runtime options including batch size,
CUDA graph execution, global decoder selection, and PyMatching-specific global
decoder parameters. Wire realtime decoder construction so TRT configs receive
the top-level observable matrix from O_sparse, and pass the same O matrix into
PyMatching global decoder params for composite observable decoding.
Expose the new config fields through Python bindings and heterogeneous_map
round-tripping. Extend YAML tests for TRT config round-trip, runtime parameter
conversion, and O_sparse-to-O injection.
Update test_trt_decoder_composite to support an optional --config-yaml path,
allowing the existing composite demo to construct and run a real TRT+PyMatching
decoder directly from YAML while preserving the original manual CLI path.