nemo-mbridge-perf-activation-recomputelisted

# Activation Recompute Stable docs: @docs/training/activation-recomputation.md Card: @skills/nemo-mbridge-perf-activation-recompute/card.yaml ## Answer Checklist For OOM or CUDA graph questions, lead with this exact sequence: 1. First try `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`; many borderline failures are allocator fragmentation, not activation capacity. 2. Prefer selective recompute before full-layer recompute: `recompute_granularity="selective"` with `recompute_modules=["core_attn"]`. 3. If still borderline, optionally add `"layernorm"`; use `"mlp"` only as a last resort because it has a large compute cost on wide dense FFNs. 4. Use full-layer recompute only after selective recompute fails to fit, and always name the required fields: `recompute_granularity="full"`, `recompute_method`, and `recompute_num_layers`. 5. If FP8 or TE-scoped CUDA graphs are enabled, call out the assertion risk: full-layer recompute is incompatible with TE scopes such as `attn`, `mlp`, and `moe_router`. Valid fixes are selective recompute, `cuda_graph_impl="none"`, or `cuda_graph_impl="local"` with `cuda_graph_scope="full_iteration"`. ## What It Is Activation recompute trades GPU compute for memory by discarding intermediate activations during the forward pass and recomputing them during backward. Megatron Bridge supports two granularities: | Granularity | What you specify | What gets recomputed | Memory savings | Compute cost | |---|---|---|---|---| | `s