Fixes from running Section A detector on B200:
1. single_shot_inference.py: Use pinned CPU buffers for token/position transfer
- dec_tid_buf[0] = python_int causes CPU→GPU sync
- Fixed: write to pinned CPU buffer, then copy_ (async, graph-capturable)
2. grouped_linear.py: Fix expert_offsets Python loop
- expert_offsets[g] = python_int * padded_rows → CPU→GPU sync per iteration
- Fixed: element-wise multiply with pre-allocated range tensor (GPU-only)
3. grouped_linear.py: Vectorized output extraction for T=1 decode
- Python loop z[:, g, :] = out[...] → CPU sync for each slice
- Fixed: GPU gather with pre-computed indices for T=1
4. grouped_linear.py: Pre-allocate output buffer
- torch.empty() per call → allocation inside graph
- Fixed: use self._output_buf (pre-allocated at max size)
5. grouped_linear.py: Pre-allocate expert_offsets_range_buf
- torch.arange() per call → allocation inside graph
- Fixed: compute once at init, reuse via element-wise multiply
biondizzle
0ca7bed0e1