Rachel E.

Responsibilities:

• Optimized large language models and computer vision networks for edge devices through pruning, quantization, and distillation, using profiling to balance inference latency, memory footprint, and accuracy.

• Investigated a failure where a statically INT8-quantized model could not start, reproducing the issue and tracing it to the QuantizeLinear operator in opset 23.

• Mapped how the inference engine builds its execution graph and selects kernels for each node, to understand why a registered QuantizeLinear kernel was still not chosen.

• Analyzed how the model format uses Protobuf to describe tensors and type constraints, and compared QuantizeLinear’s official schema with the engine’s constraints to locate the mismatch.

• Aligned the QuantizeLinear kernel’s type constraints with the official schema, verified that INT8-quantized models now run correctly, and contributed the fix as an open-source pull request.

• Updated the MatMulNBits quantizer so it no longer rejects 3D weight tensors, adding logic that iterates over the third dimension and produces a correctly quantized weight slice for each batch.

• Adapted the MatMulNBits runtime kernel to treat 3D weights as a batched matrix, computing a common batch size and looping over the batch dimension so that each slice is multiplied with the input using the optimized N-bit GEMM path.

• Implemented GoogleTest and pytest unit and integration tests for quantized operators to ensure numerical correctness and protection against regressions.