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Traces data flow across different hardware backends to isolate kernels producing incorrect numerical outputs.
Distinct from Kernel Dispatchers: Focuses on comparing outputs across backends for correctness, unlike kernel dispatchers which manage task submission.
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IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Traces tensor flow and compares logs across backends to identify specific kernels causing incorrect output.