Add trt decoder

[wsttiger]

Add TensorRT Decoder Plugin for Quantum Error Correction

Overview

This PR introduces a new TensorRT-based decoder plugin for quantum error correction, leveraging NVIDIA TensorRT for accelerated neural network inference in QEC applications.

Key Features

• TensorRT Integration: Full TensorRT runtime integration with support for both ONNX model loading and pre-built engine loading
• Flexible Precision Support: Configurable precision modes (fp16, bf16, int8, fp8, tf32, best) with automatic hardware capability detection
• Memory Management: Efficient CUDA memory allocation and stream-based execution
• Parameter Validation: Comprehensive input validation with clear error messages
• Python Utilities: ONNX to TensorRT engine conversion script for model preprocessing

Technical Implementation

• Core Decoder Class: trt_decoder implementing the decoder interface with TensorRT backend
• Hardware Detection: Automatic GPU capability detection for optimal precision selection
• Error Handling: Robust error handling with graceful fallbacks and informative error messages
• Plugin Architecture: CMake-based plugin system with conditional TensorRT linking

Files Added/Modified

• libs/qec/include/cudaq/qec/trt_decoder_internal.h - Internal API declarations
• libs/qec/lib/decoders/plugins/trt_decoder/trt_decoder.cpp - Main decoder implementation
• libs/qec/lib/decoders/plugins/trt_decoder/CMakeLists.txt - Plugin build configuration
• libs/qec/python/cudaq_qec/plugins/tensorrt_utils/build_engine_from_onnx.py - Python utility
• libs/qec/unittests/test_trt_decoder.cpp - Comprehensive unit tests
• Updated CMakeLists.txt files for integration

Testing

• ✅ All 8 unit tests passing
• Parameter validation tests
• File loading utility tests
• Edge case handling tests
• Error condition testing

Usage Example

// Load from ONNX model
cudaqx::heterogeneous_map params;
params.insert("onnx_load_path", "model.onnx");
params.insert("precision", "fp16");
auto decoder = std::make_unique<trt_decoder>(H, params);

// Or load pre-built engine
params.clear();
params.insert("engine_load_path", "model.trt");
auto decoder = std::make_unique<trt_decoder>(H, params);

Dependencies

• TensorRT 10.13.3.9+
• CUDA 12.0+
• NVIDIA GPU with appropriate compute capability

Performance Benefits

• GPU-accelerated inference for QEC decoding
• Optimized precision selection based on hardware capabilities
• Efficient memory usage with CUDA streams
• Reduced latency compared to CPU-based decoders

This implementation provides a production-ready TensorRT decoder plugin that can significantly accelerate quantum error correction workflows while maintaining compatibility with the existing CUDA-Q QEC framework.

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[bmhowe23]

Is this file exposed as part of the wheel such that regular users will be able to use this file?

[wsttiger]

/ok to test fb16b36

[copy-pr-bot]

/ok to test fb16b36

@wsttiger, there was an error processing your request: E2

See the following link for more information: https://docs.gha-runners.nvidia.com/cpr/e/2/

[wsttiger]

/ok to test c9e563f