Stream checkpoints from disk during layerwise observation/pruning - #26
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aquilarubra wants to merge 2 commits into
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Stream checkpoints from disk during layerwise observation/pruning#26aquilarubra wants to merge 2 commits into
aquilarubra wants to merge 2 commits into
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Registers Qwen3_5MoeForConditionalGeneration in MODEL_ATTRS and generalizes get_moe() to walk model.model.language_model.layers, not just model.model.layers, for this architecture's nested multimodal wrapper. Adds a matching observer hook config (num_experts/top_k live on .experts/.gate submodules rather than the MoE block itself here). Generalizes the fused-expert pruning branch, which was hardcoded to Llama4's moe.router attribute and nn.Linear-style router with out_features, to also support Qwen3.5's .gate raw weight Parameter. Extracts select_retained_experts() as a standalone function from prune()'s inline body so the same selection logic can be reused outside an in-memory model (disk-based surgery). Signed-off-by: Fabrizio del Tin <devotedmystic@gmail.com>
REAP's layerwise-observer path was RAM-bounded only in appearance: the observer phase loaded the entire model onto CPU via device_map="cpu" before any block-wise logic ran, so peak RSS still scaled with total checkpoint size rather than one layer's size. This makes it possible to prune a checkpoint far larger than available RAM+VRAM. - New src/reap/disk_stream_util.py: reads/writes checkpoint tensors directly from safetensors shards, opening and closing each shard scoped to a single read (deliberately never caches file handles, since a held mmap handle keeps its touched pages resident in RSS). - layerwise_prune.py: replaces the full-RAM device_map="cpu" load with accelerate.init_empty_weights() (meta tensors) plus real materialization of only the embedding table, for local checkpoint directories (Hub model ids keep the original load path). - layerwise_observer.py: generalizes the fused_experts branch's router lookup (was hardcoded to moe.router) and adds a dense all-experts-all-tokens fallback for architectures whose experts need top-k routing args to call (Qwen3.5, unlike Llama4's simpler dense callable convention). Adds dual-mask support (ReplayBatch.causal_mask) so hybrid linear/full-attention models get the correct mask per layer type instead of replaying whichever mask block 0 received. Extends _move_block()/_offload_current_block() to materialize/free a block's real tensors from disk on demand via the new streaming primitive, instead of only supporting already-CPU-resident blocks. Signed-off-by: Fabrizio del Tin <devotedmystic@gmail.com>
This was referenced Jul 19, 2026
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Summary
Depends on #25 (Qwen3.5-MoE architecture support) — stacked on top of it, so the diff includes that commit until it merges; only the last commit is new here.
REAP's layerwise-observer path was RAM-bounded only in appearance: the observer phase loaded the entire model onto CPU via
device_map="cpu"before any block-wise logic ran, so peak RSS still scaled with total checkpoint size rather than one layer's size. This makes it possible to prune a checkpoint far larger than available RAM+VRAM.What's in this PR
src/reap/disk_stream_util.py(new): reads/writes checkpoint tensors directly from safetensors shards, opening and closing each shard scoped to a single read (deliberately never caches file handles, since a held mmap handle keeps its touched pages resident in RSS).src/reap/layerwise_prune.py: replaces the full-RAMdevice_map="cpu"load withaccelerate.init_empty_weights()(meta tensors) plus real materialization of only the embedding table, for local checkpoint directories (Hub model ids keep the original load path).src/reap/layerwise_observer.py: generalizes thefused_expertsbranch's router lookup (was hardcoded tomoe.router) and adds a dense all-experts-all-tokens fallback for architectures whose experts need top-k routing args to call (Qwen3.5, unlike Llama4's simpler dense-callable convention). Adds dual-mask support (ReplayBatch.causal_mask) so hybrid linear/full-attention models get the correct mask per layer type instead of replaying whichever mask block 0 received. Extends_move_block()/_offload_current_block()to materialize/free a block's real tensors from disk on demand, instead of only supporting already-CPU-resident blocks.Validation
Verified peak RSS stays flat (~2.2GB) whether the test model has 4 or 32 layers, that
el_prune_disk_surgery.py's output matches an in-memory reference prune bit-for-bit, and end-to-end at real ~807GB/60-layer/512-experts-per-layer scale (60 blocks processed, RSS stayed in a ~2-10GB band throughout).