Two compounding bugs in ctx.launch's dim-translation path surfaced
by multi_user_* panels of milestone-gqa-llama70b (sub-cycle 4c step 2):
Bug A: _compute_local_shape divided by self._num_cubes (the topology's
cube count, 16 in default topology.yaml) instead of the DPPolicy's
effective num_cubes (4 for validation-scale multi_user). The tensor
allocator at context.py:471-484 already honored dp.num_cubes; the
parallel computation inside launch was out of sync. Fix mirrors the
allocator's eff_num_cubes precedence pattern.
Bug B: dim_map was keyed by value, so any scalar whose value
coincidentally equaled a global tensor dim got rewritten to that dim's
local value — e.g. d_head=64 colliding with K's global M=64 in
multi_user mode. Legacy bench kernels (va_offset etc.) rely on this
remap, so the fix is opt-out: ctx.launch(..., _auto_dim_remap=False)
preserves scalars exactly as passed. Default remains True.
Tests: 3 new dim-translation tests + 4-panel diag harness covers
single_user_* (PASS) and multi_user_* (advances to new SFR/axis layer
failure, tracked separately). va_offset + full attention spec suite
unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
mukesh
d9e767d048