nemo-mbridge-perf-activation-recompute

Stable docs: @docs/training/activation-recomputation.md Card: @skills/nemo-mbridge-perf-activation-recompute/card.yaml

<!-- NVSkills CI refresh: 2026-06-15. No instruction changes. -->

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 | |---|---|---|---|---| | selective | recompute_modules list (e.g. core_attn, mlp) | specific submodules within each layer | moderate (module-dependent) | low to high | | full | recompute_num_layers + recompute_method | entire transformer layers (N layers) | strongest | highest |

Note: MCore names these "selective" (submodule-level) vs "full" (layer-level). "Full" means recomputing full layers, not the full model — you still choose how many layers via recompute_num_layers.

Quick Decision

1. Rule out allocator fragmentation first with PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True; see @skills/nemo-mbridge-perf-memory-tuning/SKILL.md.
2. For activation pressure, start with selective recompute: recompute_granularity="selective" and recompute_modules=["core_attn"].
3. Add modules by cost: "layernorm" is cheap but saves little, while "mlp" saves much more memory at a clear throughput cost.
4. Use full-layer recompute only when selective recompute does not fit, and set all required fields: recompute_granularity="full", recompute_method, and recompute_num_layers.
5. With FP8 or TE-scoped CUDA graphs, avoid full-layer recompute unless graph scope is full_iteration; otherwise use selective recompute or disable TE graph capture.

CPU offloading (cpu_offloading=True) is an alternative that avoids recompute cost entirely, but it is incompatible with PP > 1.

Enablement

Selective recompute

cfg.model.recompute_granularity = "selective"
cfg.model.recompute_modules = ["core_attn"]  # add "layernorm", "mlp", or other valid modules as needed

Full-layer recompute

cfg.model.recompute_granularity = "full"
cfg.model.recompute_method = "uniform"
cfg.model.recompute_num_layers = 4

Available recompute_modules

| Module | What it recomputes | Compute cost | Memory savings | |---|---|---|---| | core_attn | attention softmax/dropout/QKV dot product | low (Flash Attention already recomputes internally) | moderate | | layernorm | layer normalization | negligible (~0%) | negligible | | mlp | full FFN block | high (~16% on Llama3 70B, hidden=28672) | ~3 GB | | moe | MoE expert dispatch | varies | varies | | moe_act | MoE activation functions | low | small | | shared_experts | shared expert layers | moderate | moderate | | mla_up_proj | Multi-Latent Attention up projection | moderate | moderate |

Performance harness CLI

uv run python scripts/performance/run_script.py \
  -m llama \
  -mr llama3_8b \
  --task pretrain \
  -g h100 \
  -c bf16 \
  -ng 8 \
  --recompute_modules core_attn,layernorm \
  ...

Compatibility and Constraints

• recompute_granularity=selective requires a non-empty recompute_modules list
• recompute_granularity=full requires recompute_method and recompute_num_layers
• Layer-level recompute (recompute_granularity="full" + recompute_num_layers) is incompatible with TE-scoped CUDA graphs. MCore calls this "full" granularity — the name refers to recomputing full transformer layers, not the full model. Even though you're selecting how many layers to recompute, MCore treats it differently from submodule recompute. Any TE-scoped scope (attn, mlp, moe_router, etc.) will assert. This commonly hits FP8 configs that enable TE-scoped graphs by default (e.g. LLAMA3_70B_SFT_CONFIG_H100_FP8_CS_V1 sets cuda_graph_impl="transformer_engine", cuda_graph_scope="mlp"). Options:
  • use submodule recompute (recompute_granularity="selective" + recompute_modules) — compatible with TE-scoped graphs
  • disable CUDA graphs (cuda_graph_impl="none") and use layer-level recompute
  • switch to cuda_graph_impl="local", cuda_graph_scope="full_iteration"
• distribute_saved_activations=True cannot be combined with sequence_parallel=True
• Combining mlp + core_attn recompute is slightly worse than mlp alone due to double recompute overhead

Measured Results

Llama3 70B SFT on 32x H100 80GB, FP8 (Current Scaling):

• Baseline: TP=4, PP=4, VPP=5, DP=2, MBS=1, GBS=32, seq_len=4096
• Golden GPU utilization: 709.93 TFLOP/s/GPU
• Regression threshold: 5%

| Experiment | recompute_modules | TFLOP/s/GPU | vs Golden | Peak Mem (GB) | Result | |---|---|---|---|---|---| | Baseline | [core_attn] | ~704 | -0.8% | 58.8 (OOM rank0) | OOM | | Exp 1 | [mlp] | 593.6 | -16.4% | 55.6 | Perf regression | | Exp 2 | [mlp, core_attn] | 586.8 | -17.3% | 55.6 | Perf regression | | Exp 3 | [core_attn, layernorm] | ~702 | -1.1% | 59.6 (OOM rank0) | OOM |

Key takeaways:

• layernorm recompute is nearly free compute-wise but saves negligible memory
• mlp recompute saves ~3 GB peak but costs ~16% because the Llama3 70B FFN (hidden=28672) is expensive to recompute
• Combining mlp + core_attn is slightly worse than mlp alone
• For this workload, the actual OOM fix was PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True (memory fragmentation, not capacity). See @skills/nemo-mbridge-perf-memory-tuning/SKILL.md.

Code Anchors

Recompute modules enum and selective checkpoint logic

# 3rdparty/Megatron-LM/megatron/core/transformer/transformer_block.py
# _checkpointed_forward() applies selective recompute based on recompute_modules

Recompute config validation

# 3rdparty/Megatron-LM/megatron/core/transformer/transformer_config.py
# Validates recompute_granularity, recompute_method, recompute_num_layers

Llama3 recipe defaults

    # Memory saving (recompute & offloading)
    cfg.model.recompute_granularity = None
    cfg.model.recompute_modules = None
    cfg.model.fine_grained_activation_offloading = False
    cfg.model.offload_modules = None

Full recompute + CUDA graph assertion (MCore)

            if self.recompute_granularity:
                if self.recompute_granularity != "selective":
                    assert self.cuda_graph_scope == [
                        CudaGraphScope.full_iteration
                    ], "full recompute is only supported with full iteration CUDA graph."

CPU offloading PP incompatibility (MCore)

        if self.cpu_offloading and self.pipeline_model_parallel_size > 1:
            raise ValueError(
                "Currently there is no support for Pipeline parallelism with CPU offloading"
            )

Failure Diagnosis

| Symptom | Cause | Confirm | Fix | |---|---|---|---| | >15% GPU utilization drop | mlp recompute on a large FFN | check whether recompute_modules includes mlp | remove mlp, lower micro batch size, or use CPU offload if PP=1 | | Still OOM after adding layernorm | layernorm activations are too small to move the peak materially | compare peak memory before/after | switch to a higher-impact module or full-layer recompute | | AssertionError: full recompute is only supported with full iteration CUDA graph | layer-level recompute with TE-scoped graph capture | check cuda_graph_impl and cuda_graph_scope | use selective, set cuda_graph_impl=none, or use local + full_iteration | | ValueError: PP + CPU offloading | cpu_offloading=True with pipeline_model_parallel_size > 1 | check PP config | disable CPU offloading or set PP=1 | | mlp+core_attn worse than mlp alone | double recompute overhead | compare Exp 1 vs Exp 2 | use mlp alone |

Known Limitations

• Per-module memory savings vary significantly by model architecture and hidden dimension
• No automatic module selection — users must choose which modules to recompute
• layernorm recompute is almost never worth it as a standalone fix
• CPU offloading (the zero-compute-cost alternative) is blocked when PP > 1

Verification

uv run python -m pytest \
  tests/unit_tests/training/test_config.py -k "recompute" -q

Success criteria:

• Unit tests pass for recompute config validation
• No assertion errors from config validation