June 2025 monthly summary for graphcore/pytorch-fork. Focused on correcting critical dynamic slicing behavior in the PyTorch fork. The main deliverable was a bug fix for slicing with dynamic input shapes and negative indices, preventing overflow errors and ensuring correct results. This work reduces runtime failures for models using dynamic shapes and improves reliability in production workloads.

In January 2025, delivered essential 8-bit rowwise quantization utilities in ROCm/FBGEMM, enabling efficient low-precision inference and reduced memory usage. Implemented abstract implementations and conversion utilities for Fused8BitRowwiseQuantizedToFloatOrHalf and related operations, with tests to ensure correctness. Added new functions for converting between float/half and 8-bit row-wise quantized formats, including dequantization paths. This work strengthens the quantization pipeline and lays groundwork for broader hardware support and performance improvements.