biondizzle f13a81d48b CUDA graph: Fix per-call allocations in grouped_linear and quantize ...

1. grouped_linear.py: Pre-allocate _scale_a_buf for swizzle
   - Same fix as linear.py — avoids torch.zeros per call
   - Uses correctly-sized view for pad_and_swizzle_single

2. quantize.py: Replace torch.zeros_like with scalar 0.0
   - torch.zeros_like allocates a full tensor every call
   - torch.where(cond, 0.0, x) broadcasts scalar — no allocation

2026-06-03 17:39:20 +00:00

..

__init__.py

Restructure: cutedsl/ -> dsv4/ with proper layering

2026-05-21 17:30:44 +00:00

custom_ops.py

Stage E: head-packed MQA/GQA, batch dim, custom_op, integration API

2026-05-27 15:15:03 +00:00

gemm_runner.py

fix: import SF_VEC_SIZE from quantize in gemm_runner (was NameError)

2026-06-01 00:04:48 +00:00

layouts.py

Restructure: cutedsl/ -> dsv4/ with proper layering

2026-05-21 17:30:44 +00:00

quantize.py

CUDA graph: Fix per-call allocations in grouped_linear and quantize

2026-06-03 17:39:20 +00:00

rope_cuda.py

fix: rope_cuda path — kernels/cuda not ops/cuda

2026-06-02 09:06:36 +00:00

router.py

router: catch CuTeDSL warmup failures fast, don't let MLIR errors slow down init

2026-06-01 00:00:07 +00:00