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viable/strict/1788603844: [cuBLAS] Always eagerly allocate cuBLAS(Lt) workspaces (#194311)

This release note describes a prototype change to eagerly allocate cuBLAS(Lt) workspaces as opposed to using a cache-based approach. Benchmark data shows small per-operation overhead when using eager workspaces, with performance improvements or regressions depending on the workload. The discussion notes ongoing considerations about graph-capture behavior and cache clearing strategies.

Why it matters: If adopted, this change could alter performance characteristics for cuBLAS-backed operations and affect debugging of graph captures; it may lead to simpler lifetime management of workspaces but with potential per-operation overhead changes.

Primary source: Release notes from pytorchOpen source ↗
Entities:cachecuBLAScuBLAS(Lt)graph-captureworkspace
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viable/strict/1788510868: Rename the CUPTI monitor to Cuspy (#195881)

The in-process CUPTI activity collection engine behind torch.profiler's experimental backend is renamed from CUPTI monitor to Cuspy. No behavioral changes or new code paths accompany the rename. Other CUPTI-related names remain unchanged, including libcupti, cupti-python, CuptiError, cupti* API calls, and related catalogs and options.

Why it matters: Standardizes naming to align with the new Cuspy component. Helps avoid ambiguity between CUPTI (NVIDIA) and the Cuspy monitoring engine, clarifying usage for users and contributors.

Primary source: Release notes from pytorchOpen source ↗
Entities:CUPTICuptiMonitorCuspylibcuptitorch.profiler
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viable/strict/1788331551: [ROCm][CK] Remove host-side FMHA sequence padding workaround (#195498)

Release notes describe removing host-side sequence padding workarounds for CK FMHA, passing original logical tensors directly to CK, and expanding operator coverage for unaligned sequences and related features. The change targets unpadded Q/K/V sequences and eliminates external padding and related scratch/outputs in the PyTorch CK wrapper. It includes test coverage across multiple ROCm architectures and configurations.

Why it matters: Removes padding-related overhead and clarifies data flow to CK, with broader validation across unaligned lengths and related features.

Primary source: Release notes from pytorchOpen source ↗
Entities:CKFMHAFMHA wrapperPyTorchQ/K/V sequencesROCm
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