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feat: GPU float16 monkey-patches to fix TITAN OOM on large IMPACT slides#133

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feat/titan-get-alibi-gpu
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feat: GPU float16 monkey-patches to fix TITAN OOM on large IMPACT slides#133
raylim wants to merge 24 commits into
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feat/titan-get-alibi-gpu

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@raylim raylim commented Jun 16, 2026

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Problem

TITAN's get_alibi() creates O(N²) numpy float64 arrays on CPU, causing SLURM OOM for large IMPACT resection specimens (>25k patches). For N=33k: ~82 GB CPU RAM peak. Affects ~15% of IMPACT slides.

See Confluence: https://mskconfluence.mskcc.org/pages/viewpage.action?pageId=259719289

Changes — mussel/models/conch.py

Three monkey-patches applied in TitanSlideEncoderModel.get_model_fun():

  1. _titan_get_alibi_gpu_float16 — replaces numpy float64 broadcast with torch.cdist float16 on GPU. Eliminates the 17 GB intermediate (N,N,2) array. Peak: 82 GB CPU → 8 GB GPU for N=18k (typical IMPACT foreground patches).

  2. _titan_forward_features_efficient — replaces .repeat(B,1,1,1) with .expand() in forward_features. Avoids a 22 GB copy of the bias tensor.

  3. sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION) context in model_fun — forces xformers tiled attention, prevents QK^T matrix materialisation (~22 GB per layer with math kernel).

Memory budget (A100 80 GB, N_fg=18k typical IMPACT foreground)

Component Before After
get_alibi CPU RAM 82 GB ~0 GB
bias on GPU 52 GB (float32) 7.8 GB (float16)
QK^T per layer 22 GB (math) <1 GB (efficient tiled)
Total peak GPU OOM ~12 GB

Testing

  • Unit tests: 13/13 pass (CPU, no model weights)
  • Integration test (V100): 3/3 pass — VRAM peak 3.2 GB for N=5k, CPU RAM delta 0.0 GB for N=10k
  • Full suite: 458 passed, 0 failed, 3 skipped

raylim and others added 24 commits June 16, 2026 14:08
@raylim
feat: GPU float16 monkey-patch for TITAN get_alibi to fix CPU RAM OOM
c3d5055 
TITAN's get_alibi() creates O(N²) numpy float64 arrays on CPU, causing SLURM
OOM for large IMPACT resection specimens (>25k patches, ~82 GB for N=33k).

Fix (two monkey-patches applied in TitanSlideEncoderModel.get_model_fun()):

1. _titan_get_alibi_gpu_float16: replaces numpy float64 broadcast with
   torch.cdist in float16 on GPU. Eliminates the 17 GB (N,N,2) intermediate.
   Peak memory: 82 GB CPU → 26 GB GPU for N=33k. Covers N≤45k on A100.

2. _titan_attention_forward_efficient: wraps SDPA in SDPBackend.EFFICIENT_ATTENTION,
   avoiding materialization of the QK^T matrix (saves ~26 GB for N=33k).

GPU float16 peak for representative N values:
  N=7k (median IMPACT):  1.2 GB  ✓
  N=33k:                26 GB   ✓
  N=45k:                49 GB   ✓
  N=62k (max observed): 92 GB   ✗ (needs Phase 2 / slide_max_patches guard)

Patches are applied lazily per-call via types.MethodType and do not modify
the upstream TITAN repository.

Unit tests: 13/13 pass (CPU, no model weights needed).
Integration test: submitted as SLURM job 3532188 (premium QOS) — pending.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: use modules_list to iterate CustomSequential blocks in TITAN
3ebcad1 
TITAN's VisionTransformer uses a CustomSequential that wraps blocks in
a .modules_list (nn.ModuleList), not directly iterable. Use .modules_list
when patching Attention.forward per block.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: rename dev → device in _titan_get_alibi_gpu_float16
4c597ea 
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: use CONCH_DIM=768 and compact grid coords in GPU integration test
ff00e47 
- TITAN mlp_patch_embed_dim=768 → CONCH output must be 768-dim not 1024
- Diagonal coords created N×N-cell bounding box (900M cells for N=30k);
  use compact rectangular grid instead

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: replace repeat() with expand() in forward_features to avoid 22 G…
4ab4436 
…B copy

attn_bias.repeat(B, 1, 1, 1) creates a full copy of the (1, H, N+1, N+1) bias
tensor — 22 GB for N=30k — causing OOM even on A100 when added to the existing
22 GB bias. Replace with expand() (zero-copy view) in a new monkey-patch
_titan_forward_features_efficient that also inlines the expand into the flow.

Use .to(dtype, device) with no-op guard so no copy is made when the bias is
already float16 on the correct GPU (the common case with our get_alibi patch).

Also: use CONCH_DIM=768 and compact grid coords in integration test.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: remove stale _titan_attention_forward_efficient reference from g…
f79dd69 
…et_model_fun

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: set expandable_segments + empty_cache to reduce VRAM fragmentation
6e64f4b 
The A100 OOM was from allocator fragmentation: 41 GB reserved but only 17 GB
free when trying to allocate 20 GB for QK^T attention matrix. Setting
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True allows the allocator to grow
existing segments instead of requiring contiguous free blocks.
Also calls empty_cache() after model load to clear any reserved-but-unused
memory before the large bias allocation.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: restore EFFICIENT_ATTENTION wrapper per Attention block
1d75f6f 
Without SDPBackend.EFFICIENT_ATTENTION, the math kernel materializes
QK^T (22 GB) + QK^T+bias intermediate (22 GB) = 44 GB additional PER LAYER
during SDPA. With 6 layers and 22 GB bias, this easily exceeds A100's 80 GB.

EFFICIENT_ATTENTION processes attention in tiles without materializing QK^T,
reducing peak VRAM from 22→<1 GB for the attention computation per layer.
Memory budget: bias (22 GB) + model (2 GB) + tile intermediates (~1 GB) ≈ 25 GB.

Three monkey-patches now applied:
1. get_alibi → GPU float16 (eliminates 82 GB CPU RAM)
2. forward_features → expand() instead of repeat() (saves 22 GB GPU copy)
3. Attention.forward → EFFICIENT_ATTENTION (saves 44 GB per layer peak)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: use realistic sparse tissue mask in large-N GPU test
42f71b9 
The previous test used N=30k with 100% foreground (no bg_mask), giving
N_fg=30k → bias=22 GB → tight on A100. Real IMPACT slides have 40-50%
background excluded by neural segmentation, so N_fg ≈ 15-18k in practice.

Updated test 2: 30k total patches × 60% tissue = N_fg=18k → bias=7.8 GB →
expected peak <50 GB. This accurately models real workloads.

Note: 100% foreground 30k-patch slides (N_fg=30k) still need Phase 2
(slide_max_patches=40k guard covers this extreme case).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: apply SDPBackend.EFFICIENT_ATTENTION as context manager in model…
fcb33ca 
…_fun

Monkey-patching block.attn.forward doesn't work in PyTorch (Module.__call__
bypasses instance attribute lookup in some paths). Instead, wrap the entire
inference call in sdpa_kernel(EFFICIENT_ATTENTION) context manager, which
forces all F.scaled_dot_product_attention calls to use the tiled xformers
kernel that doesn't materialize the full QK^T matrix.

This is simpler and more robust than per-block patching.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: add quick integration test (N=5k, any GPU) for CI validation
00c5bf6 
3/3 pass on V100 — confirms all 3 patches work end-to-end:
- VRAM peak 3.2 GB for N=5k (vs 22 GB+ without EFFICIENT_ATTENTION context)
- CPU RAM delta 0.0 GB for N=10k (vs ~7 GB without GPU float16 get_alibi)

The large-N test (N=30k, A100 required) is in test_titan_gpu_integration.py
and validated mathematically for N_fg=18k (typical IMPACT foreground patches).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: add TITAN slide encoder regression tests and update snapshot
859c3b5 
- Add TestTitanSlideEncoderModelFun and TestGigapathSlideEncoderModelFun
  unit tests to test_model_classes.py
- Add test_slide_encoder_matches_snapshot parametrized regression test
  to test_encoder_integration.py; restructured so assertion errors
  are not swallowed by _skip_on_load_failure (digit substrings like
  '401'/'403' in float array reprs were triggering false-positive skips)
- Add TITAN_SLIDE.npy golden snapshot generated from patched code
- Add flash-attn container development docs to README

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: update CONCH1_5 snapshot for flash_attention_2 on A100
d54193c 
The existing snapshot was generated with sdpa (no flash-attn installed).
flash_attention_2 produces slightly different values (~5e-4 max diff)
due to different tiling/accumulation — expected float16 behavior, not
a regression.

Re-generated on tllihpcgpu2 (A100, compute 8.0) inside mussel-fastattn.sif
with flash_attention_2 confirmed active. Verified PASSED on second run.

Also add slurm_conch_fa2_regression.sh and slurm_conch_fa2_regen.sh for
repeating this check on A100 hardware.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: add patched vs unpatched TITAN numerical regression comparison
98ecde0 
Runs TITAN slide encoder with identical synthetic input (N=32, seed=42)
under both unpatched (77d016d) and patched (HEAD) code on A100.

Results (job 3538760, tllihpcgpu2, A100 80GB):
  max abs diff  : 0.000732   (float16 precision noise)
  mean abs diff : 0.000159
  cosine sim    : 1.000000
  allclose(rtol=1e-2, atol=1e-3): True

PASS -- no numerical regression from the GPU float16 get_alibi patch,
expand() refactor, or EFFICIENT_ATTENTION wrapper.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: remove dead code and ineffective env var from TITAN monkey-patches
43a0dd1 
Three issues found in code review:

1. Dead code (lines 290-338): Orphaned body of the abandoned per-block
   _titan_attention_forward_efficient patching strategy was stranded inside
   _titan_forward_features_efficient after its return statement. Deleted.

2. _titan_attention_forward_efficient was defined but never applied — the
   current approach uses a context manager in get_model_fun instead. Deleted
   the function and removed the corresponding test assertion that was checking
   for its existence rather than its effect.

3. os.environ.setdefault('PYTORCH_CUDA_ALLOC_CONF', ...) in get_model_fun
   had no effect: the CUDA allocator reads this env var once at initialization,
   before any model is loaded, so setting it here is too late. Removed the
   call; updated the docstring to document that it must be set in the process
   environment before Python starts.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: gate EFFICIENT_ATTENTION on CUDA compute >= 8.0
aec491d 
The previous code unconditionally created the sdpa_kernel(EFFICIENT_ATTENTION)
context manager at get_model_fun() time; the try/except only guarded the import
of sdpa_kernel, not the actual runtime use. On compute 6.1 (P40) and 7.0 (V100),
EFFICIENT_ATTENTION is not available, so sdpa_kernel raises RuntimeError at
forward-pass time: 'No viable backend for scaled_dot_product_attention'.

Fix: check torch.cuda.get_device_capability() before enabling EFFICIENT_ATTENTION,
mirroring the pattern already used in get_best_attn_implementation() in base.py.
On compute < 8.0, nullcontext is used (default SDPA kernel selection).

Also regenerate TITAN_SLIDE snapshot from P40 (default SDPA path).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: make integration tests generic and exclude from default run
de8800a 
- Add `-m "not integration"` to addopts so `pytest tests/` only runs
  unit tests; integration tests require `pytest -m integration`
- Add `_skip_if_no_testdata` skipif guard to the three patch-encoder
  tests that require 948176.svs / 948176.patch.h5 (graceful skip on
  machines without test slide data)
- Rename ad-hoc GPU scripts in tests/integration/ from test_*.py to
  run_*.py so pytest does not attempt to collect them (they caused
  INTERNALERROR due to module-level sys.exit and hardcoded paths)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: replace hardcoded on-prem path with relative path in TestMonkey…
6927304 
…PatchApplied

Derive conch.py path from __file__ instead of absolute /gpfs path.
Also remove dead importlib.util lines (spec/mod were never executed).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
test: remove on-prem specific scripts and internal tooling
939d02a 
Deleted files contained hardcoded /gpfs paths pointing to internal
MSK cluster infrastructure, making them unsuitable for a public repo.
The regression results they produced are preserved in checkpoint history.

Removed:
- tests/integration/  (SLURM scripts for internal cluster)
- tests/regression/   (standalone scripts referencing internal REEF data)
- tests/mussel/test_wsi_pipeline_comparison.py  (refs internal ref pipeline)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
merge: resolve README conflict with main — keep both Nextflow and Dev…
dd94692 
…elopment sections

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
refactor: DRY fixes in TITAN monkey-patch code
eeeca0e 
- Extract _get_slopes() from nested closure to module level so tests
  can import it directly; remove _get_slopes_ref duplicate from tests
- Move contextlib import to module top (was inside get_model_fun)
- Remove dead 'import math as _math' from _titan_forward_features_efficient
  (math already at module top; _math was never referenced)
- Tighten except clause: Exception -> (ImportError, RuntimeError)
- Guard EFFICIENT_ATTENTION on self.device.type == 'cuda' instead of
  torch.cuda.is_available() to avoid ValueError on CPU devices

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
docs: note flash-attn is required for GigaPath slide encoder too
2571f6b 
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
fix: pin TITAN HuggingFace revision to validated commit
728cb00 
Our monkey-patches override get_alibi() and forward_features() on the
TITAN VisionTransformer.  Without a revision pin, from_pretrained()
always pulls main, meaning an upstream change to either method's
signature or logic could silently break the patches.

Pins to dac6773 (current main as of 2026-06-18), verified against our
numerical regression test (cosine sim 1.000000 vs unpatched baseline).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@raylim
feat: add patch_oom=False escape hatch to bypass TITAN monkey-patches…
5e3ecae 
… and revision pin

- Gate monkey-patches in get_model_fun on getattr(self, '_patch_oom', True)
- When patch_oom=False: no revision pin, no get_alibi/forward_features patches
- Add **kwargs forwarding in ModelFactory and _SimpleModelFactory.get_model
- Add test_patch_oom_false_skips_monkey_patches to TestTitanSlideEncoderModelFun

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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@raylim