From f4e48eccecc4846295b222a94029e5fa6e2dd3a2 Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Mon, 11 May 2026 15:40:12 -0400 Subject: [PATCH 1/6] Squashed commit of the following: MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit commit 6a6629f1c4f444e20859ab905ba2d950d66bbb0b Merge: 2fb3d047 3670381b Author: Peter Sharpe Date: Mon May 11 14:42:49 2026 -0400 Merge branch 'main' into psharpe/GLOBE-dev commit 2fb3d0473e8d66536c04e2b9b78965a8c5a7d3bf Author: Peter Sharpe Date: Mon May 11 12:02:10 2026 -0400 Allows CPU-based tree build, if desired commit ada167d9c52b45487dafdba3e6d2872c1ac925fd Author: Peter Sharpe Date: Mon May 11 11:24:36 2026 -0400 bump tensordict version commit 34e14584b2092ab49c724b75a861989d66910f35 Author: Peter Sharpe Date: Mon May 11 11:17:12 2026 -0400 Makes `psutil` a core dependency commit 7e31b1c5e4e033e50c380f594ae206d3115596b6 Author: Peter Sharpe Date: Mon May 11 01:36:17 2026 -0400 Optimize indexing in _expand_dual_leaf_hits to reduce GPU synchronization by consolidating boolean mask operations into single nonzero calls, improving performance in the ClusterTree implementation. commit ebb2b0a611e57e6804b7f3a8cfad0294da68f38b Author: Peter Sharpe Date: Mon May 11 05:35:46 2026 +0000 Refactor gradient checkpointing in BarnesHutKernel to apply in both training and evaluation phases. This change improves memory efficiency and addresses a TensorDict bug related to batch size propagation during tracing. commit ce4ab79d649c674d24165aaedccf953984b23feb Author: Peter Sharpe Date: Sun May 10 18:29:41 2026 +0000 Adds chunking support on CPU too. commit 2b326c1ddb498cb7358be78c794360f6a9f73348 Author: Peter Sharpe Date: Sun May 10 14:14:58 2026 -0400 Refactor chunk memory budget calculation to use integer arithmetic and introduce integer ceiling division function to enhance compatibility with torch.compile. This change prevents crashes related to symbolic float handling during chunk size derivation. commit fce4b188a930f3431c44fd686a807c44a9c332ab Author: Peter Sharpe Date: Sun May 10 13:48:04 2026 -0400 eliminates graph break commit c5cff850b6d00122d866c225d354e9166c3f2462 Author: Peter Sharpe Date: Sun May 10 16:26:25 2026 +0000 fixes smoothing_radius graph break using buffer. commit 3fb11547a2fa2b59143efed608e1004fd470fd9f Merge: 835ea142 aa6d2208 Author: Peter Sharpe Date: Sun May 10 15:36:50 2026 +0000 Merge branch 'psharpe/GLOBE-dev-perf' of https://github.com/peterdsharpe/physicsnemo into psharpe/GLOBE-dev-perf commit aa6d220864ae24a81e68273aa25a3af1ca60c387 Author: Peter Sharpe Date: Sun May 10 11:36:06 2026 -0400 Enhance numerical stability in ClusterTree and BarnesHutKernel by switching cumulative sum operations to double precision (fp64) to prevent catastrophic cancellation in small-leaf scenarios. Added tests to validate precision at scale for both implementations, ensuring consistency between fp32 and fp64 results. commit 835ea142a27961d2a3af9609d7c911ac5ff9bbd0 Author: Peter Sharpe Date: Sat May 9 05:53:52 2026 +0000 further simplification to smoothing_radius commit ccf4d85c179617557349e44b8ae70b0cae069a71 Author: Peter Sharpe Date: Sat May 9 05:27:28 2026 +0000 removes torch.tensor() break in smoothing_radius commit de1b8c93a2603bd1c228e71dd76ea436f351371a Author: Peter Sharpe Date: Sat May 9 04:49:02 2026 +0000 Large refactor to reduce GPU syncs in field_kernel and cluster_tree; plus tests commit 8d5e6f2604be7852a909830ad392b84d0914dfa5 Author: Peter Sharpe Date: Sat May 9 02:14:40 2026 +0000 Removes GPU syncs during chunking commit b0f1db4410ea591a5b90ad15eec0ac323e76bff4 Author: Peter Sharpe Date: Sat May 9 01:35:22 2026 +0000 pipe through gradient checkpointing option, still default to true commit 9d5515c0539ad05ac162be8c57e8c10da9e75495 Author: Peter Sharpe Date: Sat May 9 01:32:01 2026 +0000 Removes GradScaler commit 57f2c6ce2dfb405b9a5c704aabb1b355fd1f8f03 Author: Peter Sharpe Date: Sat May 9 01:29:49 2026 +0000 env var setting commit 59e4b6c94292ce20c8641e7e65417de270c7fae3 Author: Peter Sharpe Date: Fri May 8 20:07:04 2026 +0000 Squashed commit of the following: commit 6bb41cc5ff9a79e97478b1a1e478245599929c7e Author: Peter Sharpe Date: Fri May 8 19:29:35 2026 +0000 Add validation for data ranks in GLOBE model - Introduced `validate_data_contains_ranks` function to ensure that incoming data contains all declared leaves with matching ranks, enhancing error handling for missing or mismatched data. - Updated the GLOBE model to utilize this validation during the forward pass, ensuring robustness against incorrect input configurations. - Added comprehensive tests to verify the behavior of the model with respect to extra and missing data keys, as well as rank mismatches. commit 97b1d3e9fffec31af513dcc5df724b94ae51f9ee Author: Peter Sharpe Date: Fri May 8 19:10:54 2026 +0000 fixes normal handling commit fdca989212fced6cabdf9da5eaebea9d3dd203c4 Author: Peter Sharpe Date: Fri May 8 17:18:21 2026 +0000 fixes issue where precision was casting the data in addition to the model, not just the model internals commit 80c76380c8b4ca158e5cbbb21f53140e5df77377 Merge: 96b17855 c8f21f16 Author: Peter Sharpe Date: Fri May 8 17:14:06 2026 +0000 Merge branch 'psharpe/add-GLOBE-to-unified-recipe' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE-to-unified-recipe commit 96b178550b819cce9b99e758e353ebb9e9d1f409 Author: Peter Sharpe Date: Fri May 8 17:08:34 2026 +0000 autocast minor fixes commit c8f21f1680dce763347099fab0a040aec03fbd3d Merge: a0a2bc70 5f2940f9 Author: Peter Sharpe Date: Fri May 8 12:30:27 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-to-unified-recipe commit 2b5106d70fce298d77727b63afd3a8835a07c8e5 Author: Peter Sharpe Date: Fri May 8 15:28:53 2026 +0000 sync to latest run commit a0a2bc70b8a7157a788bd33fff07567e9ceec475 Merge: e4c6b52f 121e0b31 Author: Peter Sharpe Date: Fri May 8 10:55:54 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-to-unified-recipe commit e4c6b52f00de81bb50f4f753180d1f299494f54e Author: Peter Sharpe Date: Fri May 8 10:54:55 2026 -0400 Squashed commit of the following: commit 95cfcd5e653cb691d034b8a1c7fc05ef6f843938 Author: Peter Sharpe Date: Fri May 8 10:52:56 2026 -0400 pre-commit formatting commit 881c2f67954a52b8441c4c682d9d9fc5648ae874 Merge: 224eef57 121e0b31 Author: Peter Sharpe Date: Fri May 8 10:52:22 2026 -0400 Merge branch 'main' into psharpe/update-unified-recipe commit 224eef57dab884308c72e11b2f0d273528083ddf Author: Peter Sharpe Date: Fri May 8 10:50:50 2026 -0400 Adds tests commit 2a191773f9354660c09db0feb04a6c9ceb56f6d5 Author: Peter Sharpe Date: Thu May 7 23:39:56 2026 -0400 Refactor freestream scale calculations to use the `freestream_scales` function for consistency and precision. Update docstring for TensorBoard logging function to clarify usage and improve readability. commit f677107cd7793542d99b52634281925e18b105e3 Author: Peter Sharpe Date: Thu May 7 23:38:27 2026 -0400 Cleanup pass commit f9ac280ab8644f0a653859f92d2369789bb72d51 Author: Peter Sharpe Date: Thu May 7 23:29:13 2026 -0400 inlines function commit f8773b2b2a24a993954472a54a031b024fc33a08 Author: Peter Sharpe Date: Thu May 7 23:25:51 2026 -0400 strips out apply_to_tensordict_mesh (now dead code; easily a one-liner with td.apply()) commit 705e46bd2d556df6500a903aa7c654f2590fb276 Merge: d6b96f6c e33028cd Author: Peter Sharpe Date: Fri May 8 03:15:23 2026 +0000 Merge branch 'psharpe/update-unified-recipe' of https://github.com/peterdsharpe/physicsnemo into psharpe/update-unified-recipe commit e33028cd47c94b8b0c010abc331fb2113069e5ff Author: Peter Sharpe Date: Thu May 7 23:13:29 2026 -0400 Switches to be tensordict-native commit e302f3c0515f34edae293c5fdf1c2ef6ec048076 Author: Peter Sharpe Date: Thu May 7 23:10:08 2026 -0400 more conversions to "associations" commit d705007ba22c0cc5ae2284aea6f89c7b6666d9d5 Author: Peter Sharpe Date: Thu May 7 23:07:19 2026 -0400 Standardize on the name "association" commit 9c5c5282623fa9b43269ba1c739051b01fa7717e Author: Peter Sharpe Date: Thu May 7 23:01:40 2026 -0400 pipes through new imports commit 0b7268926a2fd43d45ace9917efb10cb09576804 Author: Peter Sharpe Date: Thu May 7 23:00:59 2026 -0400 docs commit eb9807d023685e2e7613115a71629ad781f90dc0 Author: Peter Sharpe Date: Thu May 7 23:00:17 2026 -0400 adds docstring commit 51c7d2ceb38b8f36fa5bc521eaa345d6a8570051 Author: Peter Sharpe Date: Thu May 7 22:55:27 2026 -0400 Migrates functions per review commit 8a1aea97e58caee97278a89f9d0734aeb9b9d610 Author: Peter Sharpe Date: Thu May 7 22:52:57 2026 -0400 Enhance error handling in normalize_output_to_tensordict to raise explicit ValueError for all-None tensor outputs, improving clarity on model misconfiguration. commit 3cf398188221317f2b71604f638386db02c76a95 Author: Peter Sharpe Date: Thu May 7 22:52:23 2026 -0400 fixes forward_kwargs bool coercion commit d6b96f6c0119ffe7d9a0392cb986637b473df49e Author: Peter Sharpe Date: Fri May 8 02:49:03 2026 +0000 syncs with new name commit 1e86dae3a1aeb4b2f3cbb62e89356a1c23637403 Author: Peter Sharpe Date: Fri May 8 02:45:12 2026 +0000 Update surface pattern in drivaer_ml_surface.yaml to reflect vehicle boundaries commit 121e0b31e8ee4bd796f70f0ce96e9c313972c03f Author: Corey adams <6619961+coreyjadams@users.noreply.github.com> Date: Thu May 7 15:54:35 2026 -0500 Testing and validating ci with data from huggingface (#1601) * Testing and validating ci with data from huggingface * Update ci data download action * Remove get data command * Make sure the singleton states clear out to fix tests. commit bb0e19a45476f650aef4fcda5179cb19aca26c84 Author: Peter Sharpe Date: Thu May 7 15:58:55 2026 -0400 Partial merge from psharpe/add-GLOBE-to-unified-recipe commit a44697351dbad6e2d9feeb711408a95e4abee6bd Author: Peter Sharpe Date: Fri May 8 10:49:04 2026 -0400 sync tests commit 10beae03c2ee04dd844cdbe89257ef30bfa5c66c Author: Peter Sharpe Date: Fri May 8 10:48:37 2026 -0400 readme updates commit 26a91d0f5655b07399808d7af9021b6eb0fa62c0 Author: Peter Sharpe Date: Thu May 7 23:54:54 2026 -0400 Squashed commit of the following: commit 2a191773f9354660c09db0feb04a6c9ceb56f6d5 Author: Peter Sharpe Date: Thu May 7 23:39:56 2026 -0400 Refactor freestream scale calculations to use the `freestream_scales` function for consistency and precision. Update docstring for TensorBoard logging function to clarify usage and improve readability. commit f677107cd7793542d99b52634281925e18b105e3 Author: Peter Sharpe Date: Thu May 7 23:38:27 2026 -0400 Cleanup pass commit f9ac280ab8644f0a653859f92d2369789bb72d51 Author: Peter Sharpe Date: Thu May 7 23:29:13 2026 -0400 inlines function commit f8773b2b2a24a993954472a54a031b024fc33a08 Author: Peter Sharpe Date: Thu May 7 23:25:51 2026 -0400 strips out apply_to_tensordict_mesh (now dead code; easily a one-liner with td.apply()) commit 705e46bd2d556df6500a903aa7c654f2590fb276 Merge: d6b96f6c e33028cd Author: Peter Sharpe Date: Fri May 8 03:15:23 2026 +0000 Merge branch 'psharpe/update-unified-recipe' of https://github.com/peterdsharpe/physicsnemo into psharpe/update-unified-recipe commit e33028cd47c94b8b0c010abc331fb2113069e5ff Author: Peter Sharpe Date: Thu May 7 23:13:29 2026 -0400 Switches to be tensordict-native commit e302f3c0515f34edae293c5fdf1c2ef6ec048076 Author: Peter Sharpe Date: Thu May 7 23:10:08 2026 -0400 more conversions to "associations" commit d705007ba22c0cc5ae2284aea6f89c7b6666d9d5 Author: Peter Sharpe Date: Thu May 7 23:07:19 2026 -0400 Standardize on the name "association" commit 9c5c5282623fa9b43269ba1c739051b01fa7717e Author: Peter Sharpe Date: Thu May 7 23:01:40 2026 -0400 pipes through new imports commit 0b7268926a2fd43d45ace9917efb10cb09576804 Author: Peter Sharpe Date: Thu May 7 23:00:59 2026 -0400 docs commit eb9807d023685e2e7613115a71629ad781f90dc0 Author: Peter Sharpe Date: Thu May 7 23:00:17 2026 -0400 adds docstring commit 51c7d2ceb38b8f36fa5bc521eaa345d6a8570051 Author: Peter Sharpe Date: Thu May 7 22:55:27 2026 -0400 Migrates functions per review commit 8a1aea97e58caee97278a89f9d0734aeb9b9d610 Author: Peter Sharpe Date: Thu May 7 22:52:57 2026 -0400 Enhance error handling in normalize_output_to_tensordict to raise explicit ValueError for all-None tensor outputs, improving clarity on model misconfiguration. commit 3cf398188221317f2b71604f638386db02c76a95 Author: Peter Sharpe Date: Thu May 7 22:52:23 2026 -0400 fixes forward_kwargs bool coercion commit d6b96f6c0119ffe7d9a0392cb986637b473df49e Author: Peter Sharpe Date: Fri May 8 02:49:03 2026 +0000 syncs with new name commit 1e86dae3a1aeb4b2f3cbb62e89356a1c23637403 Author: Peter Sharpe Date: Fri May 8 02:45:12 2026 +0000 Update surface pattern in drivaer_ml_surface.yaml to reflect vehicle boundaries commit bb0e19a45476f650aef4fcda5179cb19aca26c84 Author: Peter Sharpe Date: Thu May 7 15:58:55 2026 -0400 Partial merge from psharpe/add-GLOBE-to-unified-recipe commit a513e024635a4fd89437c524141ae31b8c772be9 Merge: 8e98aaee b15dae48 Author: Peter Sharpe Date: Thu May 7 15:53:49 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-to-unified-recipe commit 8e98aaee30cf9e753d209287db28b2647aec4880 Author: Peter Sharpe Date: Wed May 6 19:44:43 2026 -0400 formatting commit 596f527a1d38e680f80d7db37990c1eb819af984 Author: Peter Sharpe Date: Wed May 6 19:06:43 2026 -0400 edits comments commit ceda0b3757218fb347af3330eb2791419980e388 Author: Peter Sharpe Date: Wed May 6 13:51:14 2026 -0400 type hints commit dfafc47c5a7d06dc23173e7e071c87de285c0a4c Author: Peter Sharpe Date: Wed May 6 13:38:40 2026 -0400 simplifications commit 49827b15fedf68a9cd1828ec234775b550e6fcec Author: Peter Sharpe Date: Wed May 6 13:20:03 2026 -0400 Adds progress commit 4756955f21a82b2be309d05d170a8149575c21c5 Author: Peter Sharpe Date: Wed May 6 10:55:38 2026 -0400 review pass commit e6b7345c5e70bcf5bc867f5467d32c113c4b301a Author: Peter Sharpe Date: Wed May 6 09:17:46 2026 -0400 simplifications throughout commit 9e0c1e86f12e0f6beb49742aec01159be45deeac Author: Peter Sharpe Date: Wed May 6 08:27:10 2026 -0400 Notes domino is WIP commit 8c2374a4fdbd30981233f74048aa71dacbbd5c4d Author: Peter Sharpe Date: Wed May 6 08:26:58 2026 -0400 Notes DoMINO is WIP commit 9982de0205903857e2c7cf63f52e0a8c8f630c2e Author: Peter Sharpe Date: Wed May 6 08:26:46 2026 -0400 add nontrivial default commit 66c3c3a361ef8af2e94a2d32a1bc38005f42683b Author: Peter Sharpe Date: Wed May 6 08:26:16 2026 -0400 dead code sweep commit bb4eb8ee381169b44e7ad58ff875b9da135ad6c2 Author: Peter Sharpe Date: Tue May 5 17:51:58 2026 -0400 formatting commit cd9a95a3787ca516d9c0dc6aa41a24715f7aa80d Author: Peter Sharpe Date: Tue May 5 17:51:46 2026 -0400 cleanup commit abba05443c2a6230f9c1329adeebb700f677eac2 Merge: 2ee96eb4 b5f075a9 Author: Peter Sharpe Date: Tue May 5 17:44:01 2026 -0400 Merge remote-tracking branch 'origin/psharpe/DomainMesh-apply-fix' into psharpe/add-GLOBE-to-unified-recipe commit b5f075a9366d471066bd6a3fa9a878352a29d19c Author: Peter Sharpe Date: Tue May 5 17:39:50 2026 -0400 renames DomainMesh.apply to DomainMesh.apply_to_meshes, to mitigate shadowing. commit 2ee96eb40c9dc73a0adaaaf6a4a88333088d02b0 Author: Peter Sharpe Date: Tue May 5 16:50:42 2026 -0400 adds tests commit 64352f1e50b0bada49a061027a0f8c4e1ebf23b0 Author: Peter Sharpe Date: Tue May 5 15:59:38 2026 -0400 Refactor external aerodynamics recipe to use TensorDict interface - Updated README.md to reflect changes in output normalization and loss/metric calculators, transitioning from dict-based to TensorDict-based structures. - Modified training configuration files to standardize boundary naming as `boundaries.vehicle` across surface and volume datasets. - Adjusted dataset YAML files to ensure consistent boundary definitions and removed outdated comments. - Enhanced collate functions to support TensorDict, ensuring proper batch dimension handling for targets. - Updated loss and metric calculators to operate on TensorDict inputs, improving compatibility and performance. - Revised tests to validate TensorDict functionality and ensure expected behavior across various configurations. commit 3b8760985a8ccf45a47679ad8fc8db948cb95237 Merge: c7b27c09 70701e18 Author: Peter Sharpe Date: Tue May 5 15:27:27 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-to-unified-recipe commit c7b27c093a00c3bf2bf4c6cbc1b3ad2a0cc829bb Merge: 03d91803 64fecb91 Author: Peter Sharpe Date: Tue May 5 11:05:57 2026 -0400 Merge branch 'psharpe/add-GLOBE-drivaerml-standalone' into psharpe/add-GLOBE-to-unified-recipe commit 03d9180347a68bf1071efee03bf90ef7b0aad883 Merge: 4f79a781 cbb1bf16 Author: Mehdi Ataei Date: Mon May 4 16:22:50 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-to-unified-recipe commit 4f79a781ceb87868a7f9f6b1723e4a2fea6951ae Author: Peter Sharpe Date: Mon May 4 16:21:38 2026 -0400 Adds first cut of updating recipe to support GLOBE, and to be Mesh-native. commit 4e33dc0b2c2be47ee098fa5b738d2f7cce00b16c Author: Peter Sharpe Date: Mon May 4 10:44:27 2026 -0400 import sort commit 80b760788e617482bddb62f8b0d8301c921584b8 Merge: 5716659f 260ddfb1 Author: Peter Sharpe Date: Fri May 8 19:54:07 2026 +0000 Merge branch 'main' into psharpe/GLOBE-dev commit 5716659f3209a9b4c5e35ffa076320081ffb0163 Author: Peter Sharpe Date: Fri May 8 12:40:27 2026 -0400 fixes old renames commit 73312c90befd8567e3c9bcd9827b8907769ea994 Merge: 76f2a747 fb402c99 Author: Peter Sharpe Date: Wed May 6 13:24:25 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 76f2a74752f66c6d41b2f032cdd29836036ef209 Merge: bbaad544 8cbd6cb6 Author: Peter Sharpe Date: Tue May 5 12:26:44 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit bbaad544f6ba6de16d0005906f9f34a9673b9c89 Merge: 560f5ff7 64fecb91 Author: Peter Sharpe Date: Tue May 5 11:15:08 2026 -0400 Merge branch 'psharpe/add-GLOBE-drivaerml-standalone' into psharpe/add-GLOBE-3D-BarnesHut commit 64fecb919d16758e087c2551f4b15ef79297b73c Merge: 87c64c22 9f279dbc Author: Peter Sharpe Date: Tue May 5 11:04:51 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-drivaerml-standalone commit 87c64c228869d731173fb0e29e9fd1a2d8c7dfa6 Author: Peter Sharpe Date: Tue May 5 11:02:19 2026 -0400 Adds a edge case guard commit 2958dcea5e0a7b6ba4dd797f56aa20f57c52d783 Author: Peter Sharpe Date: Tue May 5 10:43:59 2026 -0400 docs on sample IDs commit a1b8337a56fdd950796978aedc11d2e6bfe01f2c Author: Peter Sharpe Date: Tue May 5 10:42:27 2026 -0400 fixes patience edge case commit 29813c2f450029cf8147790582076509dcd7708b Author: Peter Sharpe Date: Tue May 5 10:09:23 2026 -0400 fix old arg name commit c4da8f27ae6321e5da50f497423fb4e5bd4b29f7 Author: Peter Sharpe Date: Tue May 5 10:00:53 2026 -0400 Add expected training behavior section to drivaer README - Included details on reference hardware, wall time per epoch, peak VRAM usage, and training/validation loss metrics at various epochs. - This addition aims to provide users with a benchmark for evaluating their training runs. commit f9ac3c633d86c4da588913f8b151afbb8d71a766 Author: Peter Sharpe Date: Tue May 5 09:51:54 2026 -0400 Integrate Profiler into training scripts for enhanced performance monitoring - Added Profiler import and initialization in train.py for both airfrans and drivaer examples. - Updated profiling logic to enable and configure the profiler based on conditions. - Adjusted the training loop to ensure profiler steps are called correctly. This enhancement allows for better performance tracking during model training. commit 733e5a0f456b46d35c73ae8859cd6b18f027ae64 Merge: 5f78ba6e 93d13424 Author: Peter Sharpe Date: Tue May 5 09:13:47 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-drivaerml-standalone commit 5f78ba6e877d4a944be4093fd210a390c42bf30e Author: Peter Sharpe Date: Mon May 4 18:59:09 2026 -0400 adds dataset link commit 560f5ff743c4abdd41b50283bacd3340a153f5f7 Author: Peter Sharpe Date: Sun May 3 21:16:06 2026 -0400 Squashed commit of the following: commit 91a942b6e0e75951907d2a21b49d78a3c837e78f Author: Peter Sharpe Date: Sun May 3 21:12:32 2026 -0400 Adds Greptile minor fixes commit b24f9b6e99e2abed1e9b6f530a108cbe1a99ca44 Author: Peter Sharpe Date: Sun May 3 20:56:24 2026 -0400 Back-merges dataset interrogate fix commit 6ddfb5ae569e59b7872184fe0f4838b3039a3735 Author: Peter Sharpe Date: Sun May 3 20:43:01 2026 -0400 Removes accidentally-commited benchmarks; these will come later commit 9fa0b5d2744471d5b33baf7a6927ec19f2aa0153 Merge: 3e67057c 4c52a453 Author: Peter Sharpe Date: Sun May 3 20:37:57 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-drivaerml-standalone commit 3e67057c596760d9aecf0505b5f9de19e98cc9bd Author: Peter Sharpe Date: Sun May 3 20:35:32 2026 -0400 Partial merge from add-GLOBE-3D-BarnesHut commit 4c52a4534c87290a4e0f9149c2942cbe097f95ad Author: Peter Sharpe Date: Sun May 3 16:38:28 2026 -0400 Synchronizes `GLOBE` model progress for 26.05 (#1595) * Migrate cached_dataset.py * verified model arch new features (self_regularization_beta) * minor formatting syncs * Adds nonregression testing * Adds compile_logging utilities and prefetching utilities * Adds self to pade.py codeowners * Syncs AirFRANS updates * corrects a docstring * Strips out broken ram caching * Adds helpful error messages * Adds helpful error messages * docs * Refactor compile logging in training script - Removed the CompileDiagnosticsCollector and replaced it with a new utility function, silence_compile_logs_on_non_zero_ranks, to suppress non-error logs from torch.compile on all ranks except rank 0. - Updated the training script to call this new function, improving log clarity during distributed training. - Adjusted logging levels for the globe logger to ensure proper diagnostics are captured only during the first launch. * Enhance DataLoader worker configuration for distributed training - Updated the logic for auto-computing `num_workers` in the `AirFRANSDataSet` class to consider CPU affinity and local world size, improving efficiency in distributed environments. - Adjusted logging to provide detailed information about the computed `num_workers`, including CPU count and GPU visibility. - Modified the run script comments to reflect the new method of calculating `num_workers`, ensuring clarity on process-level parallelism. * Partial merge from add-GLOBE-3D-BarnesHut commit 4cb586ab3750c2a79fa23c39a6929ccf62fcbc53 Merge: 645701fc ed855da4 Author: Peter Sharpe Date: Sun May 3 15:23:50 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-model-progress commit 645701fc7af2fd0ba8ffc97da9b9c8698d9dec64 Author: Peter Sharpe Date: Sun May 3 15:06:55 2026 -0400 Partial merge from add-GLOBE-3D-BarnesHut commit 15d7913a1623f5f9afe61e0257007898babf4a72 Author: Peter Sharpe Date: Sun May 3 13:45:06 2026 -0400 Enhance DataLoader worker configuration for distributed training - Updated the logic for auto-computing `num_workers` in the `AirFRANSDataSet` class to consider CPU affinity and local world size, improving efficiency in distributed environments. - Adjusted logging to provide detailed information about the computed `num_workers`, including CPU count and GPU visibility. - Modified the run script comments to reflect the new method of calculating `num_workers`, ensuring clarity on process-level parallelism. commit 65675a42e1b4617f088fd079f999bec3efe3a4b0 Author: Peter Sharpe Date: Fri May 1 17:53:47 2026 -0400 Refactor compile logging in training script - Removed the CompileDiagnosticsCollector and replaced it with a new utility function, silence_compile_logs_on_non_zero_ranks, to suppress non-error logs from torch.compile on all ranks except rank 0. - Updated the training script to call this new function, improving log clarity during distributed training. - Adjusted logging levels for the globe logger to ensure proper diagnostics are captured only during the first launch. commit 948da86713ea492c599d14e9f55981ab1e3d7549 Author: Peter Sharpe Date: Fri May 1 15:40:31 2026 -0400 docs commit ed855da4116d87174a17db0911413648f383cef3 Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Thu Apr 30 20:44:37 2026 -0700 Implements Predictor specialization for multi-diffusion (#1573) * Implements Predictor specialization for multi-diffusion Signed-off-by: Charlelie Laurent * Compile denoiser in multi-diffusion sampling compile tests Compiling the predictor instance directly was producing divergent results under torch 2.10 in the sample() loop (euler cases only). Follow the same pattern as test_samplers.py::TestSampleCompile and compile the denoiser closure instead — tracing through it still verifies that the predictor's __call__ path is compile-compatible. Signed-off-by: Charlelie Laurent * Avoid fullgraph compile in multi-diffusion sampling test torch 2.10 Dynamo crashes with Fatal Python error: Aborted when tracing the nested MultiDiffusionPredictor -> MultiDiffusionModel2D call chain inside sample() with fullgraph=True. Allow graph breaks here; the predictor compile contract is still tested in isolation by test_multi_diffusion_predictor.py::TestCompile. Signed-off-by: Charlelie Laurent * Flatten MultiDiffusionPredictor hot path for torch.compile Dispatch on pos_embd presence and model_kwargs is now resolved once at __init__ into a specialized closure, so __call__ is branch-free and the no-kwargs path avoids ** expansion. This keeps fullgraph=True compile cleanly traceable under torch 2.10 (which was hitting a Dynamo abort on the nested MultiDiffusionPredictor -> MultiDiffusionModel2D call chain when the denoiser closure was compiled in the sample() loop). Signed-off-by: Charlelie Laurent * Loosen torch.compile euler check in multi-diffusion sampling tests Reverts the two earlier CI-fix attempts (compile-denoiser switch, predictor hot-path flatten) since neither actually fixed the divergence. The underlying issue is an upstream torch>=2.10 Dynamo bug: euler + compiled MultiDiffusionPredictor produces numerically divergent results. Heun works, predictor compiles correctly in isolation. For euler we now assert only shape + isfinite until the upstream bug is resolved. Signed-off-by: Charlelie Laurent * Force contiguous t_cur/t_next in Euler solvers sample() passes t.expand(B) (a stride-0 non-contiguous tensor) into solver.step(). HeunSolver already forces .contiguous() on both tensors to prevent torch.compile from specializing on the stride pattern of the first call and then either mis-firing guards or silently recompiling on subsequent calls with different underlying storage. EulerSolver and EDMStochasticEulerSolver had no such guard, which was a latent bug exposed by torch 2.10 (stricter stride tracking) in the multi-diffusion compiled sample loop — producing 90%+ element divergence vs eager on the first call and a Dynamo abort on the second call. Apply the same fix uniformly across all four solver steps. Also revert the temporary loosened euler assertion in test_multi_diffusion_sampling.py now that the real fix is in place. Signed-off-by: Charlelie Laurent * Drop dead is_compiling guard and inherit from Predictor in MultiDiffusionPredictor Signed-off-by: Charlelie Laurent * Narrow _patching type and tighten multi-diffusion tests Move the _patching None check out of the is_compiling guard in MultiDiffusionModel2D so the type checker narrows self._patching to RandomPatching2D | GridPatching2D for the rest of each method, and route fuse/reset_patch_indices through isinstance. Streamline TestConstructor to only exercise the public contract (.fuse, .model, setter round-trip) and drop assertions on private caches. Compile the denoiser instead of the predictor in TestMultiDiffusionSampleCompile and add TestMultiDiffusionFullSamplerCompile mirroring test_samplers.py. Signed-off-by: Charlelie Laurent * Force contiguous pos_embd before patching pos_embd.unsqueeze(0).expand(B, -1, -1, -1) produces a stride-0 view (all B copies share storage). Passing this through nn.ReflectionPad2d and F.unfold inside image_batching triggers a glibc heap corruption on torch 2.10 (CI, not locally on torch 2.8) when the first non-regression posembd_sin test runs. Same class of fix as the earlier euler solver. Signed-off-by: Charlelie Laurent * Use functional F.pad in image_batching Instantiating torch.nn.ReflectionPad2d inside image_batching on every call creates a fresh nn.Module each time, which torch.compile / AOT autograd struggles to trace cleanly under fullgraph=True on torch 2.10. Switch to torch.nn.functional.pad which is a plain functional call and traces without allocating a module. Same result semantically. Signed-off-by: Charlelie Laurent * Replace einops.rearrange with native torch reshape+permute einops.rearrange goes through a pattern-matched lowering path that torch.compile / inductor on torch 2.10 handles fragilely in the image_batching / image_fuse hot paths. The underlying transform is a plain view + permute + view, so express it directly: this gives inductor a straightforward sequence of ops to trace, and drops the einops dependency from this module. Signed-off-by: Charlelie Laurent * Materialise returned tensors in multi-diffusion fuse path Under torch.compile / inductor on torch 2.10, a compiled sample() call through MultiDiffusionPredictor was returning a tensor whose metadata was valid but whose data pointer was dangling (use-after-free) — the caller SIGABRTed on the first read of the tensor data. Add .contiguous() at the two boundaries that returned a view: image_fuse returns x_folded[...] / overlap_count[...], and MultiDiffusionModel2D.forward returns the (possibly fused) inner-model output. Forcing fresh storage on each boundary prevents the returned tensor from aliasing a buffer whose lifetime ends with the compiled frame. Signed-off-by: Charlelie Laurent * Use clone instead of contiguous at fuse boundary The second torch.compile call of a fused MultiDiffusionPredictor was segfaulting (SIGSEGV) while the first succeeded. .contiguous() is a no-op when the tensor is already contiguous, so inductor could still see the returned tensor as aliasing an internal buffer across calls. .clone() always allocates fresh storage, so successive compiled calls get independent outputs. Also drop the redundant .contiguous() added earlier in MultiDiffusionModel2D.forward now that image_fuse owns that boundary. Signed-off-by: Charlelie Laurent * Revert speculative fuse-boundary copies and xfail full-sampler compile on torch>=2.10 Revert commits 3dfcdb51, 746518ff and a007c469 (native-torch rearrange in image_batching/image_fuse, .contiguous() on returned tensors, .clone() at fuse boundary) since they did not resolve the torch 2.10 inductor codegen segfault in TestMultiDiffusionFullSamplerCompile. Keep commits 7e1db11c (pos_embd .contiguous() for the glibc heap corruption in posembd_sin non-regression tests) and feb0d9e4 (ReflectionPad2d → F.pad). Gate TestMultiDiffusionFullSamplerCompile with xfail(run=False) when torch>=2.10 so the SIGSEGV does not bring down the pytest process. TestMultiDiffusionSampleCompile (per-step denoiser compile) still runs. Signed-off-by: Charlelie Laurent * Minor updates to predictor.py Signed-off-by: Charlelie Laurent * Drop redundant _patching_type and add test-time-only docstring warning Signed-off-by: Charlelie Laurent --------- Signed-off-by: Charlelie Laurent commit 16a336f9ac267ffa6b2fee8e02d0d21dd104007a Author: Peter Harrington <48932392+pzharrington@users.noreply.github.com> Date: Thu Apr 30 00:03:24 2026 -0700 FSDP optimizer state channels last fix (#1597) * Fix channels last FSDP optimizer state load bug * lint * Catch use_orig_params=True case commit 845906f4d0846061b516749d0f1ee450c16aca91 Author: Peter Harrington <48932392+pzharrington@users.noreply.github.com> Date: Wed Apr 29 23:59:29 2026 -0700 Add HealDA dataloader protocols and init recipe (#1555) * Add healda protocols and loaders to experimental * Cleanup and address imports * Update precommit for examples tests * integrate restartable sampler, other updates, migrate tests * move imports, cleanup * ruff check fix * skip prefetch on CPU * Rename to local_platform * Revert precommit change * greptile feedback * Migrate CSVs and deps to example * lockfile fix commit 91a942b6e0e75951907d2a21b49d78a3c837e78f Author: Peter Sharpe Date: Sun May 3 21:12:32 2026 -0400 Adds Greptile minor fixes commit b24f9b6e99e2abed1e9b6f530a108cbe1a99ca44 Author: Peter Sharpe Date: Sun May 3 20:56:24 2026 -0400 Back-merges dataset interrogate fix commit f03f7218c02cfbd517d176ffc722accdbf343501 Author: Peter Sharpe Date: Sun May 3 20:54:30 2026 -0400 Refactor DrivAerMLSample class to inherit from TensorClass and clean up unused imports. This change enhances type handling and maintains code clarity. commit 6ddfb5ae569e59b7872184fe0f4838b3039a3735 Author: Peter Sharpe Date: Sun May 3 20:43:01 2026 -0400 Removes accidentally-commited benchmarks; these will come later commit 9fa0b5d2744471d5b33baf7a6927ec19f2aa0153 Merge: 3e67057c 4c52a453 Author: Peter Sharpe Date: Sun May 3 20:37:57 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-drivaerml-standalone commit 3e67057c596760d9aecf0505b5f9de19e98cc9bd Author: Peter Sharpe Date: Sun May 3 20:35:32 2026 -0400 Partial merge from add-GLOBE-3D-BarnesHut commit 9d582ad73a7204797c859bf0fa195bee63c20064 Author: Peter Sharpe Date: Sun May 3 22:38:53 2026 +0000 default to expand far targets commit a297fb9f3e332a442649a09c0621915089c43abe Author: Peter Sharpe Date: Sun May 3 22:35:13 2026 +0000 sync with airfrans commit 0c0c0280fd86f499403c0171fa371eeada4b4e38 Author: Peter Sharpe Date: Sun May 3 22:33:34 2026 +0000 sync with airfrans commit 4cb586ab3750c2a79fa23c39a6929ccf62fcbc53 Merge: 645701fc ed855da4 Author: Peter Sharpe Date: Sun May 3 15:23:50 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-model-progress commit 645701fc7af2fd0ba8ffc97da9b9c8698d9dec64 Author: Peter Sharpe Date: Sun May 3 15:06:55 2026 -0400 Partial merge from add-GLOBE-3D-BarnesHut commit b0fb9d299046fb65c24e781433d849f3c644b58a Author: Peter Sharpe Date: Sun May 3 19:00:02 2026 +0000 Squashed commit of the following: commit 15d7913a1623f5f9afe61e0257007898babf4a72 Author: Peter Sharpe Date: Sun May 3 13:45:06 2026 -0400 Enhance DataLoader worker configuration for distributed training - Updated the logic for auto-computing `num_workers` in the `AirFRANSDataSet` class to consider CPU affinity and local world size, improving efficiency in distributed environments. - Adjusted logging to provide detailed information about the computed `num_workers`, including CPU count and GPU visibility. - Modified the run script comments to reflect the new method of calculating `num_workers`, ensuring clarity on process-level parallelism. commit 65675a42e1b4617f088fd079f999bec3efe3a4b0 Author: Peter Sharpe Date: Fri May 1 17:53:47 2026 -0400 Refactor compile logging in training script - Removed the CompileDiagnosticsCollector and replaced it with a new utility function, silence_compile_logs_on_non_zero_ranks, to suppress non-error logs from torch.compile on all ranks except rank 0. - Updated the training script to call this new function, improving log clarity during distributed training. - Adjusted logging levels for the globe logger to ensure proper diagnostics are captured only during the first launch. commit 948da86713ea492c599d14e9f55981ab1e3d7549 Author: Peter Sharpe Date: Fri May 1 15:40:31 2026 -0400 docs commit 4f2254d0de5991a835f5617111ac11ce588efff4 Author: Peter Sharpe Date: Sun May 3 18:42:52 2026 +0000 Refactor Kernel class in field_kernel.py by removing the add_semantics method and optimizing the output processing. The changes streamline the handling of tensor semantics and improve performance by constructing per-field outputs in a single pass, addressing potential issues with PyTorch's tensor operations. commit 459e3c0a3e63ae21a08c05c639665ff96c19a47e Author: Peter Sharpe Date: Sun May 3 17:47:39 2026 +0000 sync onto td 0.12.2 commit 15d7913a1623f5f9afe61e0257007898babf4a72 Author: Peter Sharpe Date: Sun May 3 13:45:06 2026 -0400 Enhance DataLoader worker configuration for distributed training - Updated the logic for auto-computing `num_workers` in the `AirFRANSDataSet` class to consider CPU affinity and local world size, improving efficiency in distributed environments. - Adjusted logging to provide detailed information about the computed `num_workers`, including CPU count and GPU visibility. - Modified the run script comments to reflect the new method of calculating `num_workers`, ensuring clarity on process-level parallelism. commit 65675a42e1b4617f088fd079f999bec3efe3a4b0 Author: Peter Sharpe Date: Fri May 1 17:53:47 2026 -0400 Refactor compile logging in training script - Removed the CompileDiagnosticsCollector and replaced it with a new utility function, silence_compile_logs_on_non_zero_ranks, to suppress non-error logs from torch.compile on all ranks except rank 0. - Updated the training script to call this new function, improving log clarity during distributed training. - Adjusted logging levels for the globe logger to ensure proper diagnostics are captured only during the first launch. commit 948da86713ea492c599d14e9f55981ab1e3d7549 Author: Peter Sharpe Date: Fri May 1 15:40:31 2026 -0400 docs commit 7992acc211470de1e21d446a9659a4fb70901a21 Merge: dd801555 aec5a3aa Author: Peter Sharpe Date: Wed Apr 29 16:11:47 2026 -0400 Merge branch 'psharpe/add-GLOBE-model-progress' into psharpe/add-GLOBE-3D-BarnesHut commit aec5a3aa5dfae3717c5daf97b24a196ed056286a Merge: 973bdd53 645fb0aa Author: Peter Sharpe Date: Wed Apr 29 16:10:53 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-model-progress commit 973bdd53c4cee8b575e75172085f33bf7435a485 Author: Peter Sharpe Date: Wed Apr 29 15:57:31 2026 -0400 Adds helpful error messages commit 65a9c54418be92af19324d66062e53406a6b1137 Author: Peter Sharpe Date: Wed Apr 29 15:56:58 2026 -0400 Adds helpful error messages commit 8bde77a319a64744c2fc6028a58b7fd43726a5d8 Merge: 9a8f056c a8a0739a Author: Peter Sharpe Date: Wed Apr 29 10:00:32 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-model-progress commit dd8015555b4a5d368a816e29a79d1d5e283bb921 Merge: 9d63b124 9a8f056c Author: Peter Sharpe Date: Mon Apr 27 17:13:46 2026 -0400 Merge branch 'psharpe/add-GLOBE-model-progress' into psharpe/add-GLOBE-3D-BarnesHut commit 9a8f056cde5589601568fd2ed784f0f515e79e4e Merge: 61f5cc00 fc088545 Author: Peter Sharpe Date: Mon Apr 27 17:13:26 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-model-progress commit 9d63b124c33074ec5447afc76389ba78d55b9791 Merge: 56db224a 61f5cc00 Author: Peter Sharpe Date: Mon Apr 27 16:49:27 2026 -0400 Merge psharpe/add-GLOBE-model-progress into psharpe/add-GLOBE-3D-BarnesHut Brings in the corrected Pade docstring, the AirFRANSSample TensorClass- inheritance refactor, and the removal of the broken RAM caching path. Note: git's recursive auto-merge of cached_dataset.py duplicated the 'sample_paths is empty' validation block (line aligner confused by both branches removing self.use_ram_caching above it). One copy was removed during conflict resolution. Resulting blob is byte-identical to MERGE_HEAD. commit 61f5cc00b27e848a6a48efc2c03d52affdf29cc9 Author: Peter Sharpe Date: Mon Apr 27 16:27:06 2026 -0400 Strips out broken ram caching commit dd331c9e0aeadd520fa5aa282f11483cedd61875 Author: Peter Sharpe Date: Mon Apr 27 15:25:09 2026 -0400 corrects a docstring commit 7d4a113d5312a8170a7d9f7374f7f83aa9bcec6a Author: Peter Sharpe Date: Mon Apr 27 12:05:29 2026 -0400 Syncs AirFRANS updates commit ebaa00ea1f1cb05bb651bc5b95bcd8039cd02e55 Author: Peter Sharpe Date: Mon Apr 27 12:00:05 2026 -0400 Adds self to pade.py codeowners commit fd7d4695a6384551c5903f6c707ec61bb3a06088 Author: Peter Sharpe Date: Mon Apr 27 11:55:32 2026 -0400 Adds compile_logging utilities and prefetching utilities commit 783b849378313126a01e0012dc5c8ea7f882f7ed Author: Peter Sharpe Date: Mon Apr 27 11:50:48 2026 -0400 Adds nonregression testing commit d1209c0225d4c75c8b3551ea636bea0dc052ec0e Author: Peter Sharpe Date: Mon Apr 27 11:47:49 2026 -0400 minor formatting syncs commit 83cd333133cc8ced126abdd54f2bbd282965bf45 Author: Peter Sharpe Date: Mon Apr 27 11:47:33 2026 -0400 verified model arch new features (self_regularization_beta) commit f69179b5442226b3bb6e4317f03c92f3d8010995 Author: Peter Sharpe Date: Mon Apr 27 11:45:59 2026 -0400 Migrate cached_dataset.py commit 56db224aed395535ab0b790babb9846d78d0bcff Merge: 096d3913 3c396d43 Author: Peter Sharpe Date: Mon Apr 27 11:12:59 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 096d3913862726bb4210edb5e3c1b7e78b1b0a8d Author: Peter Sharpe Date: Fri Apr 24 15:28:29 2026 +0000 default to 128x3 commit 69db12e4ed2046f016f8b8e00460a8594684df46 Author: Peter Sharpe Date: Fri Apr 24 15:27:32 2026 +0000 forward expand far targets commit 86757d757ca801870cd5d01787b165621b475a04 Author: Peter Sharpe Date: Fri Apr 24 15:26:43 2026 +0000 removes smooth_pade commit 34dcff5cc4e77a0410e9a274d29024044df8afdf Merge: ce0dbb85 8a02bced Author: Peter Sharpe Date: Fri Apr 24 15:22:55 2026 +0000 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit ce0dbb8500f3d085e2ac193d744308a6b27fef08 Author: Peter Sharpe Date: Fri Apr 24 15:21:42 2026 +0000 syncs changes to train.py commit af594ccc9b84d91d143f1085af86dd5efff45451 Author: Peter Sharpe Date: Fri Apr 24 15:20:10 2026 +0000 Adds `prediction_chunk_size` parameter to GLOBE model for memory-efficient evaluation. This allows chunked processing of prediction points, improving performance when handling large datasets. Updates documentation to reflect new parameter and its usage. commit 62e870e79a7ded5444de303b81d0b60a302181b9 Author: Peter Sharpe Date: Fri Apr 24 15:01:01 2026 +0000 formatting commit b15a414d44826ed7d918b43bb43c430e7de2b66b Author: Peter Sharpe Date: Fri Apr 24 15:00:33 2026 +0000 formatting commit 002cc44d2729746304499075d4fd8880fdc7be94 Author: Peter Sharpe Date: Fri Apr 24 14:59:32 2026 +0000 adds new params to airfrans commit 99955a114de94731e43d1c0d25c24e24c305949f Author: Peter Sharpe Date: Fri Apr 24 14:51:07 2026 +0000 formatting commit fc4761d8187863d10e0475a01a51bcded8224886 Author: Peter Sharpe Date: Fri Apr 24 14:49:21 2026 +0000 Adds in more pade kernel capabilities commit fd68c8f624caaed8fa186bfa6a9c35b355f9a88d Merge: 6a8ab360 59321561 Author: Peter Sharpe Date: Tue Apr 21 09:50:44 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 6a8ab36035c2640013fae55fdb55cae57b46ac0f Author: Peter Sharpe Date: Mon Apr 20 16:25:00 2026 +0000 switch airfrans to theta 0 commit 7dfb8cafc1bfef610d396bfd5e0995f2732cabd0 Author: Peter Sharpe Date: Mon Apr 20 15:12:26 2026 +0000 formatting commit 57e06a48ad13c5ef1d30a37b3d77612210f7fc82 Author: Peter Sharpe Date: Mon Apr 20 15:11:29 2026 +0000 formatting commit ceeee52be6c448b342cb4bffd5a471e39b6eae74 Merge: 625e43e2 8b1e3e8b Author: Peter Sharpe Date: Wed Apr 8 11:42:27 2026 -0400 Merge branch 'psharpe/add-GLOBE-3D-BarnesHut' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE-3D-BarnesHut commit 625e43e24da748556927df9701795a64103279b2 Author: Peter Sharpe Date: Mon Apr 6 14:38:40 2026 -0400 Update theta effect visualization script to enhance configuration and error evaluation - Increased the number of source points from 20 to 60 and grid resolution from 128 to 256 for improved accuracy. - Refactored mode configurations to use tuples for better clarity and added new labels and colors for modes. - Updated error evaluation logic to accommodate the new mode structure, ensuring consistent handling across all configurations. - Enhanced plotting logic to reflect changes in mode handling and improve visualization clarity. commit 8b1e3e8b23bfccc86a5f9f650c1ef1b4d0c2b984 Author: Peter Sharpe Date: Mon Apr 6 18:27:17 2026 +0000 shutdown earlier commit cf58894e0f78e622231b14d51a6232bc621cb7a0 Merge: 21a99804 f124d964 Author: Peter Sharpe Date: Wed Apr 1 14:32:13 2026 +0000 Merge branch 'psharpe/add-GLOBE-3D-BarnesHut' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE-3D-BarnesHut commit 21a99804427a75f2eea06c1b4a0b4043be88f522 Author: Peter Sharpe Date: Wed Apr 1 14:31:58 2026 +0000 Refactor hyperparameter logging in GLOBE examples - Removed reliance on `inspect` for constructor parameters in `log_hyperparameters`. - Updated to use canonical constructor arguments from `model._args["__args__"]`, improving robustness and avoiding silent drops of untracked parameters. commit ca805ad45240e8ba51cd8d6880af5b3e677ce5f7 Author: Peter Sharpe Date: Wed Apr 1 14:18:49 2026 +0000 Adds a backwards-compat self-regularization-beta parameter. commit 06482befa2c6d7f47a108a09e682303253096dd8 Author: Peter Sharpe Date: Wed Apr 1 14:18:18 2026 +0000 Adds benchmark_accuracy commit f124d964ff1c57ccc7d7e6015e70dae69f66325b Author: Peter Sharpe Date: Tue Mar 31 22:06:11 2026 -0400 Refactor 3D visualization setup in theta effect scripts - Updated the figure creation process in `theta_effect.py` and `theta_effect_nonuniform_strength.py` to use `p.figure3d` for improved aspect ratio handling. - Removed redundant box aspect setting to streamline the visualization code. commit 7436d7026c522cabbca2f3d0f11962848bb6997a Author: Peter Sharpe Date: Tue Mar 31 22:04:27 2026 -0400 Add expand_far_targets option to ClusterTree and kernel classes - Introduced an optional parameter `expand_far_targets` in the `ClusterTree`, `BarnesHutKernel`, and `MultiscaleKernel` classes to allow for the expansion of far-field target nodes to individual points. This change enhances the accuracy of interactions by eliminating target-side centroid approximations at the cost of additional kernel evaluations. - Updated relevant documentation to reflect the new parameter and its implications on performance and accuracy. commit c5c942af35f33a86b35e9070d87a3352f6b18568 Author: Peter Sharpe Date: Tue Mar 31 21:51:20 2026 -0400 Add visualization script for the effect of the Barnes-Hut theta parameter on GLOBE's field kernel - Introduced a new Python script that generates multiple visualizations, including ClusterTree spatial hierarchies and kernel scalar field comparisons. - The script evaluates the impact of varying theta values on computational cost and approximation errors, enhancing understanding of the Barnes-Hut algorithm's performance. commit a631ef89047d93a76a875fce7ff56814cd1d8b47 Author: Peter Sharpe Date: Tue Mar 31 20:44:17 2026 -0400 Adds visualization scripts commit 379c02197d9dbdc5fdc8c0825276f863bc329b5f Author: Peter Sharpe Date: Thu Mar 26 15:22:27 2026 +0000 docs commit c6e0b9de2c7c461b3b0dcd01ccc924d9f29938a0 Author: Peter Sharpe Date: Thu Mar 26 15:19:44 2026 +0000 Update MLflow launch scripts to specify ports for UI instances - Added port specifications to the MLflow UI launch commands in both `mlflow_launch.sh` scripts for `airfrans` and `drivaer`, ensuring that each instance runs on a unique port (5001 for airfrans and 5002 for drivaer). commit bf43029c1588c93a7b96ffd3224425cdbcbf48ac Author: Peter Sharpe Date: Wed Mar 25 19:21:31 2026 -0400 Adds Mesh dimensionality annotations for readability. commit 3dac89498109d1684bbbf744e78c0a0512cfdbdf Author: Peter Sharpe Date: Tue Mar 24 16:34:36 2026 +0000 Moves benchmarks into an explicitly-resource benchmarking, to distinguish from accuracy benchmarks. commit 07ce3efb1f3231e1877cc4ba149918d17b99adf3 Author: Peter Sharpe Date: Mon Mar 23 00:45:11 2026 +0000 Disable bytecode-compile; let this happen lazily commit 0f7ac796387cf2a3ca7dec2cb1976b8b993bbc41 Author: Peter Sharpe Date: Sun Mar 22 16:16:14 2026 -0400 Refactor dataclass usage to tensorclass in ClusterTree - Replaced `@dataclass` with `@tensorclass` for `DualInteractionPlan` and `SourceAggregates` to enhance performance and compatibility with tensor operations. - This change aligns with the ongoing optimization efforts in the GLOBE model's data handling. commit 1f5d7656d8815b6de72f834a998e3f34179eb55e Author: Peter Sharpe Date: Sun Mar 22 20:14:37 2026 +0000 Optimize source scalar and vector gathering in BarnesHutKernel - Refactored the gathering of source scalars and vectors to reduce GPU kernel calls, improving performance. - Implemented pre-flattening of source scalars and vectors using `concatenate_leaves`, followed by efficient indexing. - Adjusted the handling of gathered vectors to ensure compatibility with the feature engineering pipeline in `_evaluate_interactions`. commit 6dd835845b4edae2f22ab04b392040900f07cd37 Author: Peter Sharpe Date: Sun Mar 22 12:38:04 2026 -0400 EXPERIMENTAL changes that disable lazy compilation from GLOBE kernels commit 7948120728cc8604fe58e79dbcc957704aec8ab0 Author: Peter Sharpe Date: Sun Mar 22 16:31:33 2026 +0000 Enhance CompileDiagnosticsCollector for improved logging of graph breaks - Introduced a new internal class `_BreakRecord` to encapsulate details of graph break events, including the reason, user caller, and raw message. - Updated the `filter` method to parse and store detailed information about graph breaks and recompiles, improving the clarity of logged messages. - Added a new method `_extract_user_caller` to identify the nearest user code caller from the traceback, enhancing the context provided in logs. - Modified the `summary` method to display user caller information alongside graph break reasons, aiding in debugging and analysis. commit f223f790633c5a55db5c8dbeb8a88dcc3f3c3941 Author: Peter Sharpe Date: Sun Mar 22 15:49:49 2026 +0000 Add non-regression tests for GLOBE model - Introduced a new test suite in `test_nonregression.py` to validate the GLOBE model's forward pass against saved reference outputs, ensuring numerical consistency. - Implemented functionality to generate and save reference data on first run, with subsequent tests comparing outputs to this reference. - Added a gradient flow test to verify that gradients propagate correctly through the model during training. - Created a binary reference output file `globe_nonregression_output.pth` for comparison in tests. commit 851ce5f2d85d8e430a2b137d46020163493e83bc Author: Peter Sharpe Date: Sun Mar 22 15:47:58 2026 +0000 save every 1 commit 9f4d49f8037030c3fda1beb2a0e8465f6764b9a2 Author: Peter Sharpe Date: Sun Mar 22 11:34:56 2026 -0400 Add validation method to DualInteractionPlan and corresponding tests - Implemented a `validate` method in the `DualInteractionPlan` class to check for internal consistency, including shape pairing, non-negativity, and bounds for broadcast tensors. - Added unit tests for the `validate` method to ensure correct behavior, including checks for valid plans, shape mismatches, out-of-bounds errors, and negative counts. - Integrated validation call in the `find_dual_interaction_pairs` method to enforce checks during execution. commit 53f6a1a9ccd46b306f91e547b5d6290cfb1308f2 Author: Peter Sharpe Date: Sun Mar 22 11:09:07 2026 -0400 Add fixture to disable torch.compile during tests - Introduced a new pytest fixture `_disable_torch_compile` to prevent per-test compilation overhead by disabling torch.compile. - This change ensures that tests run with zero compilation cost while maintaining identical numerical behavior. commit 652b0c28be48bbe70cbb39629fa89bf0b57a656a Author: Peter Sharpe Date: Sun Mar 22 01:22:53 2026 -0400 Remove unused total parameter from _ragged_arange call in BarnesHutKernel class commit b42927132780a65457cd8cbb73a35eb56b5f9aa8 Author: Peter Sharpe Date: Sun Mar 22 05:02:43 2026 +0000 Add leaf node ID and segment ID attributes to ClusterTree - Introduced `leaf_node_ids` and `leaf_seg_ids` to optimize source aggregation by precomputing leaf indices during tree construction. - Updated relevant methods to utilize these new attributes, improving performance and maintaining compatibility with `torch.compile`. - Enhanced documentation to reflect the new properties and their usage. commit 0ad3271344bb9aaa11f87d6f629d3564707aafa4 Author: Peter Sharpe Date: Sun Mar 22 04:57:32 2026 +0000 sets a default LR of 1e-2. commit 71ab33296b3ea74e34597d3ef54de95f3942ee95 Author: Peter Sharpe Date: Sun Mar 22 04:56:46 2026 +0000 Expands out kwarg splat, which was causing a torch.compile graph break. commit 56607fc99dfcaf43e0aaaa905f39c1d88ddf26ee Author: Peter Sharpe Date: Sun Mar 22 04:52:10 2026 +0000 Add precomputed leaf field consistency tests for ClusterTree - Introduced tests for verifying leaf_node_ids and leaf_seg_ids against expected values. - Added checks for empty trees and single-point trees to ensure correct behavior. - Utilized _ragged_arange for leaf_seg_ids computation consistency. commit fd65d17815d9b6d6ed5cc707b0278065ea52c9fe Author: Peter Sharpe Date: Sun Mar 22 04:51:54 2026 +0000 Fixes ragged_arange to allow for counts of 0 while still preserving compile compatibility commit 082a9c7fc65c5343a9e5669689fd8ce30450215c Author: Peter Sharpe Date: Sat Mar 21 22:00:26 2026 +0000 Tweaks compile diagnostics + reduced log verbosity commit e704d4bb245316ee4c3e498a2c5aea8fe04481c2 Author: Peter Sharpe Date: Sat Mar 21 21:32:28 2026 +0000 Fixes LR scaling from Moonshot paper commit 5ab066bb1de1d07ce007e1741c632772978ab149 Author: Peter Sharpe Date: Sat Mar 21 21:30:56 2026 +0000 update paths commit 713674cb6b540262bb7c7a5e52dbe309c2b4aa01 Author: Peter Sharpe Date: Sat Mar 21 21:30:24 2026 +0000 update paths commit 179bf7ed75fb0d27c406f36d33bfff3e96edd23f Author: Peter Sharpe Date: Sat Mar 21 21:29:22 2026 +0000 Moves a lot of non-GLOBE-specific code to experimental.utils commit 59139e4ced1d956f8914edc0a15325c546916ef6 Author: Peter Sharpe Date: Sat Mar 21 21:10:21 2026 +0000 Cleans up verbose logs commit d7a6b3015dabd13ce8524841c5a4423fd22e639f Author: Peter Sharpe Date: Sat Mar 21 21:03:15 2026 +0000 Adds CompileDiagnosticsCollector commit eb8d912ece968656fdd039a91e01914c71e4337a Author: Peter Sharpe Date: Sat Mar 21 21:00:19 2026 +0000 Filters warnings by default commit d479c39f96689ab6df25dce39c1a9bfddf218d39 Author: Peter Sharpe Date: Sat Mar 21 20:57:42 2026 +0000 Fixes a graph break in ragged commit fea587889b934526d3a067eebb9990260205dfde Author: Peter Sharpe Date: Sat Mar 21 20:40:50 2026 +0000 reverts erroneous rdvz changes commit c76b78dec29d2f3d416d70b90db7490b7ed3797f Author: Peter Sharpe Date: Sat Mar 21 19:41:25 2026 +0000 Add `patience_steps` parameter to training scripts for dynamic learning rate adjustment. Update `ReduceLROnPlateau` scheduler to use calculated patience based on training data size. Refactor world-size learning rate adjustment for consistency across different GPU configurations. commit bc434d4a44be34ba207ff882a4615f4019fea155 Author: Peter Sharpe Date: Sat Mar 21 19:04:32 2026 +0000 default to less-frequent saves commit 1729b1a0d4cd0596059487ec02be39d906a6f0b4 Author: Peter Sharpe Date: Sat Mar 21 18:58:56 2026 +0000 Syncs lr behavior when changing world size mid-training commit d8ff1e6286c8352515eefbe2a75011140ad848eb Author: Peter Sharpe Date: Sat Mar 21 18:41:37 2026 +0000 Refactor training scripts in `train.py` for improved clarity and functionality. Rename `error_scales` to `error_scale_config` for better context. Consolidate logging for training resumption. Implement world-size learning rate portability adjustments in both `airfrans` and `drivaer` scripts to ensure consistent performance across different GPU counts. Update default parameters in `drivaer` for enhanced model configuration. commit 297b2f49c29c48a62839856bbc10e597fde82d10 Author: Peter Sharpe Date: Sat Mar 21 18:39:35 2026 +0000 Fixes rdvz id collision risk commit 263d1f118f14d8b8f282c51fbd885de54b01a0b7 Author: Peter Sharpe Date: Sat Mar 21 03:35:42 2026 +0000 Adjust prefetch factor in `DrivAerMLDataSet` to improve data loading efficiency by setting it to 1 when using multiple workers. commit 2ba5538c3a449aeecbac56746f47d563baaefaa1 Author: Peter Sharpe Date: Sat Mar 21 03:35:32 2026 +0000 Refactor prefetching mechanism in `prefetch.py` by introducing `_PrefetchMap` class for improved one-step lookahead functionality. The new implementation preserves the length of the source iterable, enhancing compatibility with progress indicators. commit dcd1e0dd97a742834f00964be585743519937077 Author: Peter Sharpe Date: Fri Mar 20 23:17:20 2026 -0400 Update reference lengths in DrivAerMLSample and DrivAerMLDataSet to include turbulent boundary layer thickness; adjust training script accordingly for consistency. commit 4de5e137121dc19f818c453f1ead748e61f85476 Author: Peter Sharpe Date: Fri Mar 20 22:04:04 2026 -0400 Add `prepare` method to `DrivAerMLSample` for efficient subsampling and device transfer; refactor inference and training scripts to utilize new methods for sample preparation and visualization. commit b28213b454e783690a5a5a0cf516c35aeada21e7 Author: Peter Sharpe Date: Fri Mar 20 21:13:44 2026 -0400 make subsample_mesh public; separate viz code; re-use point_to_cell commit 28675de77490d2fbb9a97115e7e2c9643d5f5e62 Author: Peter Sharpe Date: Fri Mar 20 21:00:55 2026 -0400 Adds floor BC commit 0b454e94d5890dfd8fcf885ec157613ccade6553 Merge: 95445f70 8f3dff56 Author: Peter Sharpe Date: Fri Mar 20 18:07:50 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 95445f70a2e009c57a8233a84adcba848331b97e Merge: 22bd7f46 bf6c1392 Author: Peter Sharpe Date: Fri Mar 20 18:06:04 2026 -0400 Merge branch 'psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit bf6c13920c0bc9aad6b99f2661facd7083e3fb52 Merge: 55122f04 7e87e7b7 Author: Peter Sharpe Date: Fri Mar 20 14:48:30 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit 55122f0412cee0c323d27feecf93210425ff3d54 Author: Peter Sharpe Date: Fri Mar 20 14:48:22 2026 -0400 Enhance global data handling in MultiscaleKernel by adding a copy operation to prevent unintended modifications. commit fd84ec0643221205c93e5788e8d3fbdc15ed157a Merge: 6466b54d 739188c3 Author: Peter Sharpe Date: Fri Mar 20 14:34:11 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit 22bd7f468ac7e3c7213ea99444f41ea09ba1fc36 Merge: 92f0a1cd 34c99e71 Author: Peter Sharpe Date: Fri Mar 20 13:16:53 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 92f0a1cde4eac508e06aac11c6e2b439bc48a176 Author: Peter Sharpe Date: Fri Mar 20 09:46:07 2026 -0400 Simplification + removes unused functions commit b6bca98d003c9b08c5dbfafab9ce2aa9d77bec46 Author: Peter Sharpe Date: Thu Mar 19 19:55:56 2026 -0400 refactors imports commit 168d19d62226807d4e02dab66423a317ea52fd92 Author: Peter Sharpe Date: Thu Mar 19 16:23:35 2026 -0700 Splits find_candidate_cells into validation + traversal steps commit 6a6cf1e4ec887a731e944dd01804036d2393be94 Author: Peter Sharpe Date: Thu Mar 19 16:23:08 2026 -0700 Updates partition and find_nearest_cells tests for new functions commit 5638b8971b5b424142d7f3d7a339679594370775 Author: Peter Sharpe Date: Thu Mar 19 16:21:25 2026 -0700 Massive speedups with nn.functional.knn commit 1f50be5673aa1405c505dbb0ef4fe973dcea3f9b Author: Peter Sharpe Date: Thu Mar 19 19:09:52 2026 -0400 Enhance subsampling in DrivAerMLDataSet by adding Voronoi area correction option. The `_subsample_mesh` method now supports two strategies: Voronoi correction for accurate local area representation and uniform rescaling for faster performance. Updated docstrings to clarify usage and implications of each method. commit a2756662a9c7e145d3dad716312f79391dfff6e3 Author: Peter Sharpe Date: Thu Mar 19 17:58:52 2026 -0400 finishes a dropped merge commit 1aa0eee5ba1a2bd4b6b43bdd15798292789057d7 Author: Peter Sharpe Date: Thu Mar 19 17:57:53 2026 -0400 Refactor triangulation in structured_grid.py to use fully vectorized operations for improved performance and clarity. The new implementation replaces the previous loop-based approach with a meshgrid method, enhancing efficiency in cell creation. commit aa98a432ce07a6e001b2f2954ac0292f185b7a53 Merge: 35ce50fb 16d5fda7 Author: Peter Sharpe Date: Thu Mar 19 14:38:05 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 6466b54d09ac336acdb9151c090d865fa92cbf3d Merge: a26e7d4f 16d5fda7 Author: Peter Sharpe Date: Thu Mar 19 08:54:38 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit a26e7d4f9fb821256429772c6f480f1da56d38aa Merge: 7201fc64 125cf76e Author: Peter Sharpe Date: Mon Mar 16 17:59:51 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit 7201fc64caed41931ef1214d18ddb9291192fe12 Author: Peter Sharpe Date: Mon Mar 16 17:59:03 2026 -0400 Docs fixes, and properly abstracts _ragged.py to deduplicate code. commit 35ce50fbdc21a1555dffcea6c1f54773aba90fd7 Merge: 6197f28d 125cf76e Author: Peter Sharpe Date: Mon Mar 16 17:47:57 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 6197f28d89528eb9c12b0d4e96e7fec0c349553f Author: Peter Sharpe Date: Mon Mar 16 14:36:51 2026 -0700 Improves test robustness commit bc28cbbc39388bc3c0e16ea0afd6107c3c161b31 Merge: 5719901e af0f0cb7 Author: Peter Sharpe Date: Sun Mar 15 19:42:42 2026 -0400 Merge branch 'psharpe/add-mesh-remeshing-partitioning' into psharpe/add-GLOBE-3D-BarnesHut commit 5719901ea7985bdaba7e8720b2cc744a0ebe713f Merge: e2abb4fe ca41fad4 Author: Peter Sharpe Date: Sun Mar 15 19:40:51 2026 -0400 Merge branch 'psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit e2abb4fe72f73228919b099d6d6fbff592142855 Merge: fafab859 1532e626 Author: Peter Sharpe Date: Sun Mar 15 19:01:35 2026 -0400 Merge branch 'psharpe/add-mesh-cached-neighbors' into psharpe/add-GLOBE-3D-BarnesHut commit 1532e626f27f4a68566664b3264ad076f1f59f64 Author: Peter Sharpe Date: Sun Mar 15 19:01:04 2026 -0400 Update topology cache access in geometric transformations and tests - Changed the access method for the 'topology' key in the geometric transformations to use `get` for safer retrieval, providing a default empty `TensorDict` if not found. - Updated the test for topology cache keys to handle cases where 'topology' may not exist, ensuring robustness in cache key retrieval. commit fafab8596bdd8b5f72c78a73b13d56f74323b374 Author: Peter Sharpe Date: Sun Mar 15 15:24:21 2026 -0700 compile by default commit aca5b2003ed6c87dac86f51a0b7c58b19f1c1562 Author: Peter Sharpe Date: Sun Mar 15 18:22:30 2026 -0400 Refactor ClusterTree and introduce _ragged_arange utility - Updated the ClusterTree class to enhance its data structure by adding per-node fields necessary for the Barnes-Hut algorithm. - Moved the _ragged_arange function to a new _ragged.py module for better organization and reusability across different components. - Adjusted imports in field_kernel.py and bvh.py to utilize the new _ragged_arange function, improving code clarity and reducing redundancy. commit b4143958d38e0ac79ebd24deb54457dcd1357cbf Merge: 092e22c7 528876b8 Author: Peter Sharpe Date: Sun Mar 15 14:54:31 2026 -0700 Merge branch 'psharpe/add-mesh-cached-neighbors' into psharpe/add-GLOBE-3D-BarnesHut commit 528876b81fb4ca0296d0a243fee40a7abb1456dd Author: Peter Sharpe Date: Sun Mar 15 14:53:49 2026 -0700 Refactor cache initialization in Mesh class - Simplified the cache structure initialization by removing the overlay logic for provided caches. - The default cache is now only created if it is not already set, improving clarity and efficiency in cache management. commit 092e22c75aa388a7c6b277a3a8e95f15f18490d1 Author: Peter Sharpe Date: Sun Mar 15 16:16:35 2026 -0400 removes unused import commit ec05b82f16682e37928c587182df954a6dcc240b Author: Peter Sharpe Date: Sun Mar 15 16:15:58 2026 -0400 formatting commit ca41fad480623ac0d9f521c61afa13d5b092c4b9 Author: Peter Sharpe Date: Sun Mar 15 16:04:24 2026 -0400 Adds theta and leaf_size forwarding commit b06dfab7589d189d7268ff00570468d09dc791b2 Author: Peter Sharpe Date: Sun Mar 15 16:03:22 2026 -0400 Enhance ClusterTree and BarnesHutKernel for improved internal node handling - Added internal_level_ids and internal_level_offsets to ClusterTree for efficient storage of internal node IDs in CSR-packed level order. - Introduced internal_nodes_per_level property to retrieve internal node IDs grouped by tree depth. - Updated _propagate_centroids_bottom_up to utilize cached internal node levels, improving performance. - Modified BarnesHutKernel to leverage cached level ordering for bottom-up propagation, enhancing efficiency in node strength calculations. - Adjusted lazy compilation settings for MLP and evaluation pipeline to optimize performance during execution. commit af0f0cb792a74cc05da36243b4809ce629cbcff3 Merge: 54cf9e6d 3a7ffcad Author: Peter Sharpe Date: Sun Mar 15 15:57:32 2026 -0400 Merge branch 'psharpe/bugfix-mesh-sampling' into psharpe/add-mesh-remeshing-partitioning commit 3a7ffcad931b7c9f26596156530e3df38d6b0304 Author: Peter Sharpe Date: Sun Mar 15 15:57:17 2026 -0400 reverts accidental future changelog items commit 54cf9e6defea14091c9ab3db8a47533ed11c4870 Author: Peter Sharpe Date: Sun Mar 15 15:51:24 2026 -0400 update changelog commit 32ed2d76eacb4a8c5781b7b32bb27e79e58461b6 Author: Peter Sharpe Date: Sun Mar 15 15:49:52 2026 -0400 Adds discrete cell partitioning algorithm to mesh.remeshing. commit 34f19dc7dc071182f040ed658d21ef7bc190ef6d Author: Peter Sharpe Date: Sun Mar 15 15:44:41 2026 -0400 Update changelog commit 18a6c1fb7cf3185f6ff2313ae340772691aedcfc Author: Peter Sharpe Date: Sun Mar 15 15:43:09 2026 -0400 Adds bugfix for highly skewed meshes, mixing up L2 and Linf norm by accident. commit d16e99a6ed227c6c035a1b9668139d514b3a68d1 Author: Peter Sharpe Date: Sun Mar 15 15:38:09 2026 -0400 Enhance mesh subsampling in DrivAerMLDataSet to compute Voronoi-corrected areas and normals - Updated the `_subsample_mesh` method to utilize the new `partition_cells` function for improved area and normal calculations. - Modified documentation to reflect the changes in subsampling methodology, emphasizing the use of Voronoi diagrams for more accurate geometric properties. - Ensured that the subsampled mesh retains effective area approximations, enhancing the fidelity of the mesh representation. commit fef8fb9bd4e71033d65a1a6f46892216789d4c61 Author: Peter Sharpe Date: Sun Mar 15 15:30:25 2026 -0400 Implement mesh cell partitioning and enhance remeshing documentation - Introduced `partition_cells` function for discrete restricted Voronoi partitioning of mesh cells, allowing for efficient assignment of cells to seed points and accumulation of geometric properties. - Updated module documentation to reflect the new functionality and clarify the relationship between cell partitioning and ACVD remeshing. - Added tests for `partition_cells` to validate assignment correctness, area conservation, and normal accumulation, ensuring robustness of the new feature. commit 41120cb4a6b808c67cddf3309592d7220b29e8cc Author: Peter Sharpe Date: Sun Mar 15 15:29:42 2026 -0400 Enhance BVH-accelerated nearest centroid search in sample_data.py. Increased maximum search rounds from 20 to 25, allowing for a broader search radius. Improved handling of candidate cells and updated best distance tracking to ensure more accurate results. Added detailed comments for clarity on the search process and resolution criteria. commit f1bf4db67df07eeee797acb7348c72041b996935 Merge: 81f6e98e 17596789 Author: Peter Sharpe Date: Sun Mar 15 11:04:32 2026 -0700 Merge branch 'main' into psharpe/add-mesh-cached-neighbors commit 23fc5b3d8945668f053027bd646308143af638aa Merge: d8157668 17596789 Author: Peter Sharpe Date: Sun Mar 15 11:01:15 2026 -0700 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit d81576689f0607d73dc0f04828e7ba11f7e9c48c Author: Peter Sharpe Date: Sun Mar 15 13:01:23 2026 -0400 Refactor data subsampling in AirFRANS and DrivAer models to use `torch.randint` for mask generation and convert prediction mesh to point cloud before slicing. This improves the sampling process and ensures compatibility with updated mesh structures. commit b6584d285855a2c676572f71a540facf9f0e52d3 Author: Peter Sharpe Date: Sun Mar 15 12:55:03 2026 -0400 Implement prefetching for sample preparation in AirFRANS and DrivAer models to enhance data loading efficiency. Introduce `prefetch_map` utility for overlapping CPU-bound tasks with GPU processing, improving overall training performance. commit 593933b8e6e54e0f2085dd9ecbfe1dfb6809423e Author: Peter Sharpe Date: Sun Mar 15 12:45:25 2026 -0400 Adds tenacity dep commit f7cbd91f204f5a304e194a61e05f9ea4785e24e7 Author: Peter Sharpe Date: Sun Mar 15 12:45:15 2026 -0400 syncs tenacity commit 0af3beb89bd3a82d5fe4ab056f9030c05ccae79c Author: Peter Sharpe Date: Sun Mar 15 12:44:12 2026 -0400 syncing mutual example improvements commit c5ddfb39ab16fd711ce69148dfe7b61b18424701 Merge: cd4449be 6f5982b9 Author: Peter Sharpe Date: Sun Mar 15 10:53:35 2026 -0400 Merge branch 'psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit cd4449bea1ca6342c3297c61c0d208d47b5fb05f Author: Peter Sharpe Date: Sun Mar 15 10:53:07 2026 -0400 docs commit 77651ef242eaf4d6bd161c956fff255738507207 Author: Peter Sharpe Date: Sun Mar 15 10:47:40 2026 -0400 Adds helpful docs notes on why this is the compile point. commit 6f50143984823314211be2155351af6f146ab2f9 Author: Peter Sharpe Date: Sun Mar 15 09:42:34 2026 -0400 Implement lazy compilation for interaction evaluation in BarnesHutKernel to optimize GPU performance. This change fuses feature engineering, MLP, and postprocessing into larger kernels, enhancing efficiency during the first call. commit ee2b319530cacb11bd910e5f8ebc4cd114ba3a03 Author: Peter Sharpe Date: Sun Mar 15 00:12:29 2026 -0400 Add internal node handling to ClusterTree and optimize centroid propagation in BarnesHutKernel - Introduced `internal_level_ids` and `internal_level_offsets` to `ClusterTree` for efficient internal node management. - Added `internal_nodes_per_level` property to retrieve internal node IDs grouped by depth. - Updated centroid propagation in `BarnesHutKernel` to utilize cached internal node levels, improving performance by avoiding BFS traversal. commit ccebb1f0305ba633d80c48756275e7e514567170 Author: Peter Sharpe Date: Sat Mar 14 23:45:13 2026 -0400 Traces a later batch to avoid compile artifacts commit 2f72332dba60d3aa96bd2afc9e2f395f7b79c68a Author: Peter Sharpe Date: Sat Mar 14 23:33:31 2026 -0400 long profiler traces per-batch, not per-epoch. commit fce56451ad759fed98b2a406d2084d22443c709e Author: Peter Sharpe Date: Sat Mar 14 21:19:37 2026 -0400 Update field_kernel.py to change torch.compile mode to 'max-autotune-no-cudagraphs' for enhanced performance tuning. commit 17bd04f3e2f103a4a645945f23da32fae1195033 Author: Peter Sharpe Date: Sat Mar 14 21:04:36 2026 -0400 benchmark on epoch 2 commit 81f6e98e33f78180406d4df5a276d0a0c5abd51b Author: Peter Sharpe Date: Sat Mar 14 17:44:13 2026 -0400 formatting commit 325bd457e1b92ebb45f6b0478fe9b714362b8093 Author: Peter Sharpe Date: Sat Mar 14 17:39:51 2026 -0400 Refactor TensorDict initialization in geometric transformations - Removed unnecessary device specification in TensorDict initialization for 'cell' and 'point' caches in the transform and translate functions, simplifying the code and improving readability. commit a4d24711c482a72095fabd76780b46797520ca85 Author: Peter Sharpe Date: Sat Mar 14 17:23:25 2026 -0400 changelog update commit c34e475a02812737fd12a36480baf437729e0b10 Author: Peter Sharpe Date: Sat Mar 14 17:20:22 2026 -0400 Add topology to transformation outputs and enhance adjacency tests - Included the 'topology' in the output of the transform and translate functions to improve data handling. - Updated adjacency tests to assert that offsets remain consistent after transformations, ensuring integrity of adjacency data across operations. commit 273278a96bc7886f5ea02501e3028443d03ad65c Author: Peter Sharpe Date: Sat Mar 14 17:18:18 2026 -0400 Refactor caching mechanism in Mesh class for adjacency computations - Simplified the default cache structure initialization and updated the caching logic to overlay provided caches. - Introduced a new method `_cached_adjacency` to streamline adjacency retrieval and caching. - Enhanced documentation for adjacency methods to clarify caching behavior and efficiency. - Added comprehensive tests to validate caching functionality and ensure correctness across various adjacency computations. commit 0905ff894cbe8b66bbeb770f477d4035d27df020 Author: Peter Sharpe Date: Sat Mar 14 16:44:32 2026 -0400 propagate caches commit 511614c4147cc9435dc2245e803e7054afaff833 Author: Peter Sharpe Date: Sat Mar 14 16:14:59 2026 -0400 Strips out tons of needless batch_size=[] commit 02c39a2cdeab3f42703a13b0b06f9b0239f163af Merge: 573b3a47 a3a9f19f Author: Peter Sharpe Date: Sat Mar 14 12:53:27 2026 -0700 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit f533062c87d2823e5bf6c220ea060d81a2fae2cc Author: Peter Sharpe Date: Sat Mar 14 15:37:14 2026 -0400 Update test for Mesh class serialization to reflect detailed dimension representation - Modified the assertion in the test case to check for the updated string format that includes explicit manifold and spatial dimensions. - Ensured consistency with recent changes in the Mesh class representation. commit 03bcbf13d32ff67bd459b53a31e0ae54f1808a6c Author: Peter Sharpe Date: Sat Mar 14 12:15:23 2026 -0400 Update mesh representation and test cases to include detailed dimension information - Modified the string representation of the Mesh class to specify manifold and spatial dimensions explicitly. - Updated related test cases to reflect the new format in expected outputs, ensuring consistency across representations. commit c99604b5d3b64e5c2377894913759a602b570605 Author: Peter Sharpe Date: Sat Mar 14 10:34:51 2026 -0400 formatting fix commit b394376b40d97506293defcb0533ec3b39fda369 Author: Peter Sharpe Date: Sat Mar 14 10:34:22 2026 -0400 Updates all primitives to have as-narrow type returns as possible commit c9461fe251a041afaa052151e2a099780fff1271 Author: Peter Sharpe Date: Sat Mar 14 10:31:21 2026 -0400 Enhance dimension validation in MeshDims class - Improved error handling for symbolic dimensions in the MeshDims class by adding checks to ensure paired dimensions are provided. - Updated parsing logic to cache results for symbolic dimensions, enhancing performance and clarity. - Added new tests to validate the behavior of symbolic dimensions and ensure proper error messages are raised when constraints are not met. commit 75716ec28d43f62981c99c5bdb84a0126ed855a2 Author: Peter Sharpe Date: Fri Mar 13 23:29:56 2026 -0400 Minor review fixes commit 32c1b63fbf4ea506371fd36a43a9f4d2aab3117c Author: Peter Sharpe Date: Fri Mar 13 23:10:28 2026 -0400 Refactor dimension parsing in MeshDims class to ignore type checks - Removed string type assertions for manifold and spatial dimensions in the MeshDims class. - Updated dimension parsing to include type ignore comments for better compatibility with type checkers. commit 0bc136c2e847748093fb8ef3dda46849e485d877 Author: Peter Sharpe Date: Fri Mar 13 23:09:47 2026 -0400 Refactor conditional check in MeshDims class for improved readability - Updated the conditional check for string types in the MeshDims class to enhance code clarity by formatting the line breaks. commit 4e9b8c07ad02e22adb7d00beeac6c35c4cbec222 Author: Peter Sharpe Date: Fri Mar 13 23:05:30 2026 -0400 Add parametric mesh type specifications and convenience aliases - Introduced `MeshDims` class for dimension-aware type hints in the `Mesh` class. - Implemented `__class_getitem__` method in `Mesh` to support subscript syntax for specifying manifold and spatial dimensions. - Added convenience aliases: `PointCloud` for 0-manifold and `Graph` for 1-manifold. - Updated mesh representation formatting to include dimension information. - Added comprehensive tests for the new functionality, including validation of dimension constraints and type checks. commit 6f5982b9fb8516bc800c67cbfe41d04703dd1251 Author: Peter Sharpe Date: Thu Mar 12 20:50:07 2026 -0400 Update run.sh script for improved configuration and compatibility - Introduced variables for output name and directory to enhance flexibility. - Updated the AIRFRANS_DATA_DIR path for consistency with dataset location. - Set OMP_NUM_THREADS to 1 to prevent thread oversubscription during data loading. - Simplified head node retrieval for multi-node training setup. commit 963df9b25a6d436ce4568b77b1c26c883a232237 Merge: ad859f35 a3a9f19f Author: Peter Sharpe Date: Thu Mar 12 17:11:07 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit ad859f35c1a9504eb00d3fe2e62fca12f5c33ca4 Author: Peter Sharpe Date: Thu Mar 12 13:39:08 2026 -0400 Enhance GLOBE model to support cross-boundary condition (BC) interactions. Updated documentation to reflect changes in self-interaction and cross-BC interaction handling. Modified the `GLOBE` class to compute dual interaction plans for all (source BC, destination BC) pairs, improving efficiency in communication layers. Added tests for multi-BC inference to validate functionality. commit 573b3a474fdb28dde5def20825acdbd837b835e3 Merge: 1130b320 2722e646 Author: Peter Sharpe Date: Thu Mar 12 09:46:48 2026 -0700 Merge remote-tracking branch 'origin/psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit 2722e646f056e92d2293513b4559fa10249eb1b1 Merge: 9f5a2525 e4fdb581 Author: Peter Sharpe Date: Thu Mar 12 12:46:15 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit 1130b320794c715624dfa3407b7c39e5563099e1 Merge: 00cb6318 9f5a2525 Author: Peter Sharpe Date: Thu Mar 12 09:35:14 2026 -0700 Merge remote-tracking branch 'origin/psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit 9f5a2525a6e5a6b3595a8c1d1395c135543111c3 Author: Peter Sharpe Date: Thu Mar 12 12:34:56 2026 -0400 Update MetaData class in model.py to disable JIT and CUDA graphs for improved compatibility with torch.compile and dynamic input handling. commit 00cb6318a6131fc64b551537dd0209f8fb781f71 Merge: 921d3a99 b45ca1be Author: Peter Sharpe Date: Thu Mar 12 09:31:39 2026 -0700 Merge remote-tracking branch 'origin/psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit b45ca1be5a0fd16a0089da1f6b1153e6cc0d6fa1 Author: Peter Sharpe Date: Thu Mar 12 12:29:50 2026 -0400 Update BarnesHutKernel to use appropriate dtype for zero-valued tensors, ensuring compatibility with AMP autocast settings. commit 553cf08a4a0a0d5a63c7afac46b0d5b8320b69d3 Author: Peter Sharpe Date: Thu Mar 12 12:26:58 2026 -0400 Docstring fix commit fd1bcaf9d3657184e85ee0f265ca6937786e4b08 Author: Peter Sharpe Date: Thu Mar 12 12:26:48 2026 -0400 Grammar fix commit 921d3a99b8dcb88069c83b771b57ade761878d12 Merge: f929d353 e4fdb581 Author: Peter Sharpe Date: Thu Mar 12 12:21:17 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit f929d353f85e9a59a8728544365fa0a6b8742b58 Merge: 59c153b6 137905f9 Author: Peter Sharpe Date: Thu Mar 12 12:20:53 2026 -0400 Merge branch 'psharpe/stacked-add-GLOBE-3D-DTT-model-changes' into psharpe/add-GLOBE-3D-BarnesHut commit 137905f96859b1a388558a7ea100a0777429eefc Merge: a984c127 fb4f1595 Author: Peter Sharpe Date: Thu Mar 12 10:16:44 2026 -0400 Merge branch 'main' into psharpe/stacked-add-GLOBE-3D-DTT-model-changes commit a984c12742906aa2b63e0416b7b2af7a3f73fb2f Author: Peter Sharpe Date: Thu Mar 12 10:16:29 2026 -0400 Squashed commit of the following: commit fb4f15956dbb32ed83082a99f9b99471874ab7b5 Author: Peter Sharpe Date: Wed Mar 11 23:11:04 2026 -0400 Adds the PhysicsNeMo-Mesh changes required for GLOBE 3D (#1483) * Adds the PhysicsNeMo-Mesh changes required for GLOBE 3D * Fixes docstring example for compute_cell_normals to reflect correct normal vector output in 2D case. * Refactor compute_cell_areas and compute_cell_normals functions to use match-case syntax for improved readability and maintainability. commit 219aca32d62f1817b99501c9b55d2cf017fcf110 Author: Peter Harrington <48932392+pzharrington@users.noreply.github.com> Date: Wed Mar 11 17:23:16 2026 -0700 Fix window shift in pangu, fengwu (#1492) * Fix window shift in pangu, fengwu * changelog commit 26fcdce0a267d0149fa7f66c0f4491143c3e6692 Author: Kaustubh Tangsali Date: Wed Mar 11 20:20:33 2026 +0000 fix linting issues commit ca15f470c3639acef9e6d100bae50b5ee6b865ff Author: Charlelie Laurent Date: Wed Mar 11 12:08:12 2026 -0700 Resolved conflicts in checkpoint.py Signed-off-by: Charlelie Laurent commit 95eca0cf54083c27fe3c77f714b32959c72caa11 Author: Kaustubh Tangsali Date: Tue Mar 10 23:27:40 2026 +0000 remove the conflict block commit 33d9a7e246e3c6ad8376bb10f3525dadb095db80 Author: Kaustubh Tangsali Date: Tue Mar 10 23:22:22 2026 +0000 update versioins commit fbfb896ef6cb75d57ee70ceebcebf0fba94ef7fd Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Mon Mar 9 16:42:38 2026 -0700 Improved docs for module.py + multiple cleanups in docs (#1478) * Improved docs for module.py Signed-off-by: Charlelie Laurent * Fix in save_checkpoint and load_checkpoint docstrings Signed-off-by: Charlelie Laurent * Addressed PR comments Signed-off-by: Charlelie Laurent * Improvements in docs Signed-off-by: Charlelie Laurent * Moved down section about static capture in physicsnemo.utils.rst Signed-off-by: Charlelie Laurent --------- Signed-off-by: Charlelie Laurent commit 13c6fb4b0f1f2145985e3e4cb72e235ccbdece53 Author: Corey adams <6619961+coreyjadams@users.noreply.github.com> Date: Mon Mar 9 11:25:44 2026 -0500 Update Datapipes API (#1468) * Trying again with datapipes check in * Update docs/api/datapipes/physicsnemo.datapipes.cae.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.cae.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Update docs/api/datapipes/physicsnemo.datapipes.transforms.rst Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Resolve api commits for datapipes * Remove old datapipes api * Add link to the datapipe docs. --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> commit 32f226153b248938814e8d29b6c4493b93e80e45 Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Fri Mar 6 15:31:53 2026 -0800 Fix unresolved conflict (#1477) Signed-off-by: Charlelie Laurent commit 9a32517ee0d86d0935180c42862779bf214e5ef1 Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Fri Mar 6 15:09:50 2026 -0800 Diffusion API docs (#1473) * New API docs for diffusion Signed-off-by: Charlelie Laurent * Some fixes in nested API references Signed-off-by: Charlelie Laurent * Revert some changes Signed-off-by: Charlelie Laurent * Some fixes Signed-off-by: Charlelie Laurent * Some clarifications in introduction.rst Signed-off-by: Charlelie Laurent * Some clarification in diffusion models.rst Signed-off-by: Charlelie Laurent * Fixed note sections in preconditioners.py Signed-off-by: Charlelie Laurent * Fix some broken short-form refs in losses.py Signed-off-by: Charlelie Laurent * Updated DPSScorePredictor class name in samplers.rst Signed-off-by: Charlelie Laurent * Enhance clarity and structure of introduction section Refactor introduction section for clarity and readability. Improve formatting and organization of key concepts related to the PhysicsNeMo diffusion framework. * Refactor metrics.rst for improved clarity and formatting Reformatted the description of the module to use bullet points for clarity. Adjusted wording for consistency and readability. * Fix punctuation and enhance clarity in models.rst Corrected punctuation and improved clarity in the documentation. * Addressed PR comments Signed-off-by: Charlelie Laurent --------- Signed-off-by: Charlelie Laurent Co-authored-by: megnvidia commit d7237671c47570a6121c42a60c9fadd695970d8f Author: Kaustubh Tangsali <71059996+ktangsali@users.noreply.github.com> Date: Thu Mar 5 18:56:46 2026 -0800 Minor edits to the install guide (#1470) * minor edits to the install guide * add more details * minor doc fix * add transolver to the api index commit 6bb6d0448d94d534097f407d9424422ca11c0c76 Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Thu Mar 5 12:38:13 2026 -0800 Fixes and renaming in dps_guidance.py (#1471) Signed-off-by: Charlelie Laurent commit 1e14a56f19c43308cf46d64e1d661393e89ae37d Author: Corey adams <6619961+coreyjadams@users.noreply.github.com> Date: Wed Mar 4 15:46:15 2026 -0600 Update API docs and structure. (#1337) * Update API docs and structure. * clean-up and re-organization of docs * fix based on new api * remove unused sections * update image paths * Update docs/api/models/diffusion.rst Co-authored-by: megnvidia * Update docs/api/models/operators.rst Co-authored-by: megnvidia * Update docs/api/models/weather.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.core.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.diffusion.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.diffusion.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * Update docs/api/physicsnemo.utils.rst Co-authored-by: megnvidia * fix formatting --------- Co-authored-by: Kaustubh Tangsali <71059996+ktangsali@users.noreply.github.com> Co-authored-by: Kaustubh Tangsali Co-authored-by: megnvidia commit f22cfbf76ecfe8e9b6885b224b331141086e7d31 Author: Peter Sharpe Date: Mon Mar 2 17:56:32 2026 -0500 Adds PhysicsNeMo-Mesh API Docs (#1461) * Adds mesh docs on top of RC branch * Update docs/mesh/boundaries.rst Co-authored-by: megnvidia * Apply suggestions from code review Co-authored-by: megnvidia * Refine documentation for mesh geometry functions to clarify usage and API exposure for advanced cases. * better subdivision descriptions * clearer docs --------- Co-authored-by: megnvidia commit 1e9fdd0c0c7a24e0bf3ab803ae5f8f9ddf48c383 Author: Corey adams <6619961+coreyjadams@users.noreply.github.com> Date: Fri Feb 27 12:12:02 2026 -0600 GeoTransolver: Fix attention and turn off feature broadcasting. (#1415) * Fix attention and turn off feature broadcasting. * Fix scalar loading shapes * Update volume.yaml Ensure the volume example works out of the box. * Fix Geotransolver inference tests commit 4f0a3cb9a5b0abb7743904c8128f648385c14daa Author: Kaustubh Tangsali <71059996+ktangsali@users.noreply.github.com> Date: Thu Feb 26 15:00:42 2026 -0800 Wandb fixes (#1458) * Add wandb to requirements * Modify requirements for trimesh and add wandb Updated trimesh version constraint and added wandb. commit 082cd36882719aec8e97e8d8d2048472b5b3fc2c Author: Kaustubh Tangsali <71059996+ktangsali@users.noreply.github.com> Date: Thu Feb 26 15:00:16 2026 -0800 Fixes from SFB builds / testing (#1459) * Remove 'perf' extra from physicsnemo installation because NGC containers already include transformer-engine * update deterministic settings commit f6ca8181f77d74347a7578781b41b2a9c022b68f Author: Charlelie Laurent <84199758+CharlelieLrt@users.noreply.github.com> Date: Wed Feb 25 13:20:46 2026 -0800 Fix broken cross-ref links in docstrings (#1454) Signed-off-by: Charlelie Laurent commit 47b8ff09ad0c96d07d6f3c09af6b75f19e3bbac9 Author: Peter Sharpe Date: Wed Mar 11 15:21:31 2026 -0400 Deprecates `physicsnemo.utils.mesh.py` (#1487) * Adds DeprecationWarning on module. * Changelog update for deprecations commit 74c91f943a96b6e60c96b58613eccdb438c73c18 Author: Peter Sharpe Date: Wed Mar 11 14:20:25 2026 -0400 Adds docstrings to CombinedOptimizer tests. (#1486) commit 1cc74e01c31f63c6d7898e700419c2a11f83d32e Author: Peter Sharpe Date: Wed Mar 11 12:34:18 2026 -0400 Adds DTT-related changes to AirFRANS train.py commit 1f471ff2ff807e10c1610b32d81a368cf1f6e005 Author: Peter Sharpe Date: Wed Mar 11 12:23:21 2026 -0400 Add dual-tree traversal algorithm to GLOBE model for O(N) kernel evaluations - Introduced a new `ClusterTree` class for spatial decomposition, enabling efficient dual-tree traversal. - Updated `GLOBE` model to utilize the new clustering mechanism, significantly reducing kernel evaluation complexity. - Enhanced `CHANGELOG.md` to reflect the addition of the dual-tree algorithm and its impact on performance. - Added comprehensive tests for the `BarnesHutKernel` and `ClusterTree` functionalities to ensure correctness and performance. - Refactored existing kernel evaluation methods to integrate the new dual-tree approach, improving overall efficiency. This update is crucial for handling large mesh scales effectively, particularly in scenarios with 800k+ faces. commit 231851425d03c0ab5d56a0b4a360117d74220672 Merge: 6344bfcc dfa2e2a8 Author: Peter Sharpe Date: Wed Mar 11 08:51:07 2026 -0400 Merge branch 'main' into psharpe/add-mesh-improvements-for-GLOBE-3D commit 59c153b6850c9943830c4a1f5894f20e9808a8f2 Author: Peter Sharpe Date: Tue Mar 10 14:46:15 2026 -0700 formatting commit aeaea94079fc51d85d4f019cf49c96f09b65831c Author: Peter Sharpe Date: Tue Mar 10 14:42:39 2026 -0700 Add tenacity and matplotlib dependencies; implement resilient logging in training - Updated requirements.txt to include tenacity and matplotlib for enhanced functionality. - Introduced a resilient logging mechanism in train.py to handle transient database errors during MLflow operations. - Added a retry decorator in utilities.py to improve robustness of logging functions. commit 6344bfccd61cdb6744fa6e69a1f32f9735c24049 Author: Peter Sharpe Date: Tue Mar 10 15:46:52 2026 -0400 Adds the PhysicsNeMo-Mesh changes required for GLOBE 3D commit 474ae53054566bc639c7f054acb47ab62b40dcab Author: Peter Sharpe Date: Tue Mar 10 13:23:46 2026 -0400 Enhance type hinting in globe model files - Updated imports in field_kernel.py and model.py to include TYPE_CHECKING for better type hinting without runtime overhead. - This change improves code clarity and maintains compatibility with type checkers. commit 6b2dc5a46a7fe292cda5cafc769b9bab29d055a3 Author: Peter Sharpe Date: Tue Mar 10 13:18:34 2026 -0400 Refactor test_barnes_hut_kernel.py to simplify list comprehensions - Removed unnecessary intermediate variables and replaced nested loops with list comprehensions for better readability and performance in the TestClusterTree class and test_self_interaction_source_coverage function. commit 053f88d99b3873a3f451766850d2d25d6ed83342 Author: Peter Sharpe Date: Tue Mar 10 13:18:18 2026 -0400 Fix formatting in hierarchical_acceleration.md to escape asterisks in mathematical expressions for clarity. commit 676f6f3164396c725bd2e4f222d0b687cdceb120 Author: Peter Sharpe Date: Tue Mar 10 13:17:12 2026 -0400 Add ValueError exception for empty sample_paths in CachedPreprocessingDataset - Introduced a check in the CachedPreprocessingDataset class to raise a ValueError if the sample_paths list is empty, ensuring that the dataset contains at least one sample before proceeding with initialization. This enhances error handling and improves user feedback when the dataset is improperly configured. commit c01ad1ac9dd4d8dca04e580fa9ab41bf06d05a33 Author: Peter Sharpe Date: Tue Mar 10 13:16:01 2026 -0400 Ruff fixes commit f332b566db3807432e6739ecbb78e83464895c05 Author: Peter Sharpe Date: Tue Mar 10 13:14:03 2026 -0400 cleanup commit b22a3be36530c0e9b6c65f82442afeda4c2064a2 Merge: 12ffe927 064f95db Author: Peter Sharpe Date: Tue Mar 10 13:10:26 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 12ffe9270fffe6fc3f118843331ec21c8024a709 Author: Peter Sharpe Date: Tue Mar 10 10:04:34 2026 -0700 update changelog commit e2d628e955ea858f09bb29c98f36519f78e52e77 Merge: 70bf4d34 7ff39a79 Author: Peter Sharpe Date: Tue Mar 10 12:51:02 2026 -0400 Merge branch 'main' into psharpe/add-GLOBE-3D-BarnesHut commit 70bf4d34d69d1c9eb9545151746c5efb7c501adb Author: Peter Sharpe Date: Tue Mar 10 09:48:59 2026 -0700 typo fix commit 0e27960fa2ad598539a8da0d5d9d0d0f2feac860 Author: Peter Sharpe Date: Sun Mar 8 11:51:08 2026 -0700 updates benchmarks commit d743ada9158cd3ac107327b80c33a252fa2d2df4 Author: Peter Sharpe Date: Sun Mar 8 11:50:23 2026 -0700 training stability fixes commit 6ba571eab32a202da3795257c47000b8b398202d Author: Peter Sharpe Date: Sat Mar 7 11:50:22 2026 -0800 setting new defaults commit 074ce7e444e60d0bbbd5534391aba2bab4bdee30 Author: Peter Sharpe Date: Sat Mar 7 11:09:50 2026 -0800 Adds train benchmarking script commit 162b478f487d02e01fcefeb7b3450cf1c2c0d5a2 Author: Peter Sharpe Date: Sat Mar 7 11:04:17 2026 -0800 Changes the default leaf_size from 32 to 1. commit d06f8b3aa2f0c9b05b08504790ef505bfb3b663b Author: Peter Sharpe Date: Sat Mar 7 08:04:07 2026 -0800 import ordering commit 0eaf0457b39f0f327924d12ba6d9977cf19c5fee Author: Peter Sharpe Date: Sat Mar 7 08:03:46 2026 -0800 performance improvements commit f91c8007395c0d0901c43df4b5d655e85944668f Author: Peter Sharpe Date: Sat Mar 7 08:01:26 2026 -0800 Code cleanup, some renaming commit 9185cd1ff4acb2db3eb6d579c574957e09449b09 Author: Peter Sharpe Date: Sat Mar 7 07:22:50 2026 -0800 Switches LR scaling with world size to be linear, not sqrt commit 1a50f0880e94716d843012b46ad2493b0bd1f21c Author: Peter Sharpe Date: Sat Mar 7 07:21:56 2026 -0800 Update points_per_iter handling in DrivAer training script - Changed the type of `points_per_iter` to allow `None`, defaulting to `boundary_n_faces` if not specified. - Adjusted learning rate calculation to remove dependency on `points_per_iter`, enhancing clarity and flexibility in training configuration. commit d5ded9d4d9bfbc14ee21efe705d6e869ede5e89b Author: Peter Sharpe Date: Fri Mar 6 21:06:25 2026 -0800 Enhances the dual-tree approach with far -> near interactions. commit 2a904c219a2ae810684b99decaa5650fce1c1e25 Author: Peter Sharpe Date: Fri Mar 6 19:45:19 2026 -0800 Enhances the dual-tree approach with near -> far interactions. This restores performance parity with Barnes-Hut (lower-bound) commit 39232f4d38d5ca74b8f01ba4c298bbf4f9b50db6 Author: Peter Sharpe Date: Fri Mar 6 14:57:29 2026 -0500 update theory doc commit ac58f8a911abd770be96c2b49f06ec143d09833b Author: Peter Sharpe Date: Fri Mar 6 14:28:02 2026 -0500 EXPERIMENTAL: Adds Dual Tree Traversal enhancement. commit ec20b96fcf62a93ce5dd949c828b0951d9060cc9 Author: Peter Sharpe Date: Thu Mar 5 21:48:56 2026 -0800 Refactor barrier condition in training scripts - Simplified the condition for invoking the barrier in both AirFRANS and DrivAer training scripts by removing the check for `use_profiler`. The barrier is now called directly when the world size is greater than 1, enhancing code clarity while maintaining functionality during distributed training. commit 0ce908cfcca84c51519c0b502f57f00b57784e10 Author: Peter Sharpe Date: Thu Mar 5 21:30:05 2026 -0800 Refactor profiler barrier handling in training scripts - Updated the condition for invoking the barrier in both AirFRANS and DrivAer training scripts to check for `use_profiler` before calling `barrier()`, ensuring that the barrier is only applied when profiling is enabled and the world size is greater than 1. This change improves clarity and maintains performance during distributed training. commit 4e00d3e339476568279a208c7ff4bd5927dfee43 Author: Peter Sharpe Date: Thu Mar 5 21:20:26 2026 -0800 Refactor tensor conversion in AirFRANSDataSet - Updated tensor conversion methods to ensure consistent float type handling when converting to numpy arrays. - Improved performance by removing unnecessary type casting, enhancing the efficiency of data processing in the dataset class. commit 7ecc32068dc845ac00a8173a00d261af0ea53fde Author: Peter Sharpe Date: Thu Mar 5 21:12:06 2026 -0800 performance improvements commit cd46134f9b7468bf2e30dceac368292bc63bdd39 Author: Peter Sharpe Date: Thu Mar 5 20:47:33 2026 -0800 Refactor boundary mesh handling in DrivAer training script - Removed the boundary mesh padding logic to streamline the process of handling boundary meshes. - Updated the code to precompute boundary mesh geometry using lazy properties, improving efficiency during training. - Adjusted the handling of centroids, areas, and normals to work directly with the original mesh, enhancing clarity and performance. commit 7b7fed50f0d71dd4f7ce0c6a533302db4f06f81e Author: Peter Sharpe Date: Thu Mar 5 20:37:24 2026 -0800 manageable profiler size commit 84172be327e99958c252b78186a1665cea4430fb Author: Peter Sharpe Date: Thu Mar 5 20:32:24 2026 -0800 Enhance mesh cleaning method documentation - Updated the docstring for the `clean` method in the `Mesh` class to provide a detailed explanation of the cleaning operations, including merging duplicate points, removing duplicate cells, and dropping unused points. - Clarified the parameters and their effects on performance, emphasizing the utility of the method after importing meshes from external sources. commit 19697788d8d9bb1079a0ec5b1e23edfad1217e23 Author: Peter Sharpe Date: Thu Mar 5 19:53:08 2026 -0800 Implement enhanced profiling and error handling in GLOBE training scripts - Updated the `benchmark.py` to improve profiling of the GLOBE training pipeline, including detailed error handling for `torch.cuda.OutOfMemoryError` during various profiling stages. - Introduced a new `benchmark_dataset.py` script to benchmark the CPU-bound data pipeline for DrivAerML GLOBE training, profiling each sub-operation to identify performance bottlenecks. - Added comprehensive timing breakdowns for data loading and preprocessing phases, facilitating optimization of the data pipeline. commit 8f2782110029f49499de43ac8df38ebd267da37a Author: Peter Sharpe Date: Thu Mar 5 19:52:50 2026 -0800 Enhance boundary cleaning in DrivAerMLDataSet - Updated the `clean` method in the `slice_cells` function to include additional parameters: `merge_points`, `remove_duplicate_cells`, and `remove_unused_points`. This change aims to improve the quality of the boundary mesh by ensuring that unnecessary points and cells are effectively managed during the cleaning process. commit d3f3e5a6da70d2fea1576c1f929ecd43397608eb Author: Peter Sharpe Date: Thu Mar 5 19:46:48 2026 -0800 better dataloader handling commit 15e23f4568498de74a0100fb5825d90621cf6e77 Author: Peter Sharpe Date: Thu Mar 5 17:17:25 2026 -0800 Replace `benchmark_barnes_hut.py` with comprehensive `benchmark.py` for GLOBE pipeline - Removed the `benchmark_barnes_hut.py` script and introduced a new `benchmark.py` that consolidates phase-level profiling, deep tracing, and parameter sensitivity analysis for the Barnes-Hut GLOBE pipeline. - The new benchmark script supports various configurations and outputs detailed performance metrics, enhancing the diagnostic capabilities for model evaluation. - Added a `run.sh` script for streamlined execution in a SLURM environment, ensuring proper resource allocation and CUDA version detection. commit 7a51bac4b1b25e4b9bfee86e2482aef9b6c64a1a Author: Peter Sharpe Date: Thu Mar 5 19:52:10 2026 -0500 Update profiling schedule in DrivAer training script - Modified the `torch.profiler.schedule` parameters in `train.py` to set the wait time to 0, allowing for immediate profiling of the training process. - This change aims to enhance the granularity of performance monitoring during the model's training phase. commit 30543b1ed8d890630524221de8df989697100201 Author: Peter Sharpe Date: Thu Mar 5 19:43:59 2026 -0500 Add benchmark script for Barnes-Hut GLOBE pipeline - Introduced a new script `benchmark_barnes_hut.py` to profile each phase of the Barnes-Hut GLOBE pipeline using synthetic mesh data. - The script measures wall-clock time, GPU time, interaction statistics, and peak memory usage, providing insights into performance bottlenecks. - It includes configurable parameters for mesh generation and benchmarking trials, facilitating detailed performance analysis of the GLOBE model's operations. commit dab5863bb67686d848e3836ff2509150cec72d16 Author: Peter Sharpe Date: Thu Mar 5 19:41:00 2026 -0500 Add profiling to training process in DrivAer model - Integrated `torch.profiler.record_function` around key operations in the training loop, including forward pass, backward pass, and optimizer step, to enhance performance monitoring. - This addition aims to facilitate performance analysis and identify potential bottlenecks during model training. commit 3a105d97118e4c0e0cbc1094c2ec433a81023289 Author: Peter Sharpe Date: Thu Mar 5 19:37:04 2026 -0500 Add profiling to ClusterTree and Kernel computations - Integrated `torch.profiler.record_function` to key sections of the `ClusterTree`, `Kernel`, and `GLOBE` classes to enhance performance monitoring and debugging capabilities. - Wrapped critical operations such as Morton code sorting, tree construction, and evaluation phases in profiling contexts to facilitate performance analysis. - This addition aims to provide insights into computational bottlenecks and optimize the overall efficiency of the models. commit a9a91b03b2c95d9c2cc63c41492ae5c07ca33391 Author: Peter Sharpe Date: Thu Mar 5 19:05:40 2026 -0500 Disalbe compilation of BarnesHutKernel.forward() commit a04b6de3dfdc83843141422911e2514c8f7cdff9 Author: Peter Sharpe Date: Thu Mar 5 14:53:35 2026 -0500 docs cleanup commit a78ebdc400a2dcfcdc0c02d2f6dc4919b3f868dd Author: Peter Sharpe Date: Thu Mar 5 14:43:24 2026 -0500 Add Barnes-Hut variant documentation for GLOBE model - Introduced a comprehensive markdown document detailing the Barnes-Hut tree acceleration applied to GLOBE's field kernel evaluation, outlining the motivation, monopole approximation, spatial data structure, and interaction pair finding. - The document emphasizes the reduction of computational complexity from O(N^2) to O(N log N) and explains the implementation details, including the construction of the ClusterTree and the interaction plan. - This addition aims to enhance understanding and usability of the Barnes-Hut variant within the GLOBE framework. commit 7c7d146b5816a52f13a6bc727ff386a39fe0849a Author: Peter Sharpe Date: Thu Mar 5 14:43:02 2026 -0500 Update theta parameter in various models and documentation for clarity - Changed the default value of the `theta` parameter from 0.5 to 1.0 in the `train.py` scripts for both GLOBE and DrivAer models, reflecting a more aggressive approximation strategy. - Updated documentation across multiple classes, including `ClusterTree`, `BarnesHutKernel`, and `MultiscaleKernel`, to clarify the role of `theta` as a Barnes-Hut opening angle rather than a far-field distance threshold. - Adjusted related test cases to ensure they accurately reflect the new understanding of `theta` and its impact on interaction pair evaluations. commit 895bda42c7db6bf799b12766eedffdf17273c278 Author: Peter Sharpe Date: Thu Mar 5 13:26:19 2026 -0500 license header commit ba206956ab5c7d724a345eb6bd4f15d30fa0e0b4 Author: Peter Sharpe Date: Thu Mar 5 13:17:28 2026 -0500 format commit baa376d8bfd2e34db55288a43b239eddc1428221 Author: Peter Sharpe Date: Thu Mar 5 13:16:56 2026 -0500 Refactor interaction plan tests in ClusterTree to ensure source coverage - Replaced the previous test for interaction plan coverage with a more comprehensive test that verifies each target's near and far pairs cover all sources exactly once. - Introduced parameterization for dimensions and theta values to enhance test flexibility and coverage. - Implemented a depth-first search utility to collect leaf sources, ensuring accurate validation of source counting and coverage assertions. commit b41feca406372e03f3e3e820adac88701f347f17 Author: Peter Sharpe Date: Thu Mar 5 13:15:44 2026 -0500 Enhance cluster tree and kernel functionality with new utility and documentation updates - Introduced `_ragged_arange` function to expand segment descriptors into flat index arrays, improving the handling of segment data. - Updated `_segmented_weighted_sum` and `_expand_leaf_hits` functions to utilize the new utility for better performance and clarity. - Enhanced comments and documentation throughout the `ClusterTree` and `BarnesHutKernel` classes to clarify the logic and improve code readability. - Refactored the handling of leaf node IDs and strengths in the `BarnesHutKernel` to streamline computations and memory usage. commit 585ec9bfaf10dcf3fc28325caec91ad724f88dad Author: Peter Sharpe Date: Thu Mar 5 12:34:55 2026 -0500 docs fix commit 46efb612f22a1456ddad954e2a96792d4e33caa8 Author: Peter Sharpe Date: Thu Mar 5 12:34:45 2026 -0500 Enhance GLOBE model with improved kernel parameter descriptions and refactor interaction tests - Updated comments and documentation for the `theta` parameter in both `ClusterTree` and `BarnesHutKernel` classes to clarify its role as a far-field distance threshold. - Refactored test cases to better reflect the behavior of far-field interactions based on `theta` values, ensuring accurate assertions in the `TestClusterTree` class. - Introduced a new test for handling nested source data keys, ensuring compatibility with GLOBE's data structure and validating the aggregation process. commit 4aad383f081b4c354dbf0242f2cdff363e239427 Author: Peter Sharpe Date: Thu Mar 5 10:57:51 2026 -0500 Refactor Barnes-Hut kernel evaluation in GLOBE model - Enhanced the handling of near and far-field interactions by introducing chunked evaluation, improving memory management during kernel computations. - Consolidated the gathering of source scalars and vectors for both near and far interactions, optimizing data processing. - Updated the logic for assembling TensorDicts and evaluating interactions, ensuring efficient handling of large datasets while maintaining precision. commit 6609be02076a763005890c5586141af52afd7282 Author: Peter Sharpe Date: Thu Mar 5 00:03:40 2026 -0500 Add tree and plan building methods to GLOBE model - Introduced `_build_trees_and_plans` and `_build_prediction_plans` methods to encapsulate the logic for constructing cluster trees and interaction plans, improving code organization and readability. - These methods are designed to run outside of `torch.compile` due to the irregular control flow involved in tree construction, ensuring compatibility and performance. - Updated the main evaluation logic to utilize these new methods, streamlining the process of building communication and prediction plans. commit 82e2b32e9ed6172e7ee21aaf9274744618c93ac1 Author: Peter Sharpe Date: Wed Mar 4 20:55:09 2026 -0800 Enhance GLOBE model with Barnes-Hut kernel parameters - Updated the `MultiscaleKernel` class to include new parameters for Barnes-Hut kernel configuration, such as `theta`, `cluster_tree`, `interaction_plan`, and `source_areas`, improving flexibility in kernel evaluations. - Modified the `GLOBE` model to utilize `leaf_size` when creating `ClusterTree` instances, ensuring better performance in spatial decomposition. - Removed the `chunk_size` parameter from the inference test, aligning with recent refactoring efforts to streamline the model's interface. commit 9b79d88d0f4b1dd2c4ef5586c17596f3bbf71843 Author: Peter Sharpe Date: Wed Mar 4 20:54:42 2026 -0800 Refactor inference and training scripts to remove chunk_size parameter - Eliminated the `chunk_size` argument from model inference calls in both `inference.py` files for GLOBE and DrivAer models, simplifying the function signatures. - Updated related documentation and comments to reflect the removal of chunking, enhancing clarity and maintainability. - Adjusted default `compile_mode` in training scripts to improve compatibility with specific use cases. commit c5c47078127f439e37b110122aaae513719be4a8 Author: Peter Sharpe Date: Wed Mar 4 20:51:08 2026 -0800 Update training script parameters for GLOBE model - Changed default `compile_mode` from "max-autotune" to "max-autotune-no-cudagraphs" to improve compatibility with specific use cases. - Enabled dynamic compilation in the training function to enhance performance during model training. commit e37b96059f7dbf4ed1dfaaef5356dba3bb802dd4 Merge: 63a47110 091c14ab Author: Peter Sharpe Date: Wed Mar 4 23:40:56 2026 -0500 Merge branch 'psharpe/add-GLOBE-3D' into psharpe/add-GLOBE-3D-BarnesHut commit 091c14ab5002aecaa6e6cf21e33dfc3c47f957c5 Merge: 2a3f9a18 42002510 Author: Peter Sharpe Date: Wed Mar 4 23:36:32 2026 -0500 Merge branch 'main' into psharpe/add-GLOBE-3D commit 63a47110aecae61739c681f3f38fcca1ed4f3f7a Author: Peter Sharpe Date: Wed Mar 4 23:29:25 2026 -0500 Refactor GLOBE model parameters to streamline kernel configuration - Removed the `kernel_class` and `kernel_class_kwargs` parameters from the `GLOBE` model, simplifying its interface. - Introduced `leaf_size` as a direct parameter to enhance clarity in kernel configuration. - Adjusted related code to ensure compatibility with the updated model structure, improving maintainability. commit 27228b6896defcf6d0d43bdb3ced454e1e513f9f Author: Peter Sharpe Date: Wed Mar 4 23:29:10 2026 -0500 Refactor kernel references in tests to use Kernel class - Updated test files to replace `ChunkedKernel` with `Kernel`, reflecting recent changes in the codebase. - Adjusted function signatures and test descriptions to align with the new kernel structure, ensuring consistency and clarity in testing. - Removed obsolete test cases related to chunking, streamlining the test suite. commit 8b908d5543cd2d2232cbe3b54b4fc2de6ff8cc19 Merge: 152d581f 2a3f9a18 Author: Peter Sharpe Date: Wed Mar 4 23:17:42 2026 -0500 Merge branch 'psharpe/add-GLOBE-3D' into psharpe/add-GLOBE-3D-BarnesHut commit 152d581fba691683245abc2f1ed5b91398ad028b Author: Peter Sharpe Date: Wed Mar 4 23:17:25 2026 -0500 Remove chunk_size parameter from GLOBE model - Eliminated the `chunk_size` parameter from the `GLOBE` model, simplifying the model's interface and reducing potential confusion regarding memory management during kernel evaluation. - This change aligns with recent refactoring efforts to streamline the codebase and enhance maintainability. commit b35f7784aee616112e9ba1be9b9cede7a6778c24 Author: Peter Sharpe Date: Wed Mar 4 23:17:11 2026 -0500 Remove ChunkedKernel from globe model and field kernel - Eliminated the `ChunkedKernel` class and its references from the `field_kernel` and `GLOBE` model, streamlining the codebase. - Updated imports and `__all__` definitions to reflect the removal, ensuring no residual dependencies remain. - Adjusted the `GLOBE` model's parameters and internal logic to accommodate the absence of the `ChunkedKernel`, enhancing clarity and maintainability. commit c9cab844d677a26286da97110fd937b5db0a9554 Author: Peter Sharpe Date: Wed Mar 4 23:16:27 2026 -0500 Implement Barnes-Hut kernel acceleration and introduce ClusterTree structure - Added a new `ClusterTree` class for efficient spatial decomposition, enabling O(N log N) kernel evaluations. - Integrated `BarnesHutKernel` for accelerated interactions, replacing the previous all-to-all approach with a mix of exact and approximate evaluations. - Updated the `GLOBE` model to utilize the new kernel and tree structures, enhancing performance and scalability. - Introduced interaction plans to optimize target-source pair evaluations, improving computational efficiency. - Added comprehensive tests for the new kernel and tree functionalities to ensure correctness and performance. commit 2a3f9a18414492e6d8b2a3119117888bbb79f574 Author: Peter Sharpe Date: Wed Mar 4 20:15:12 2026 -0800 Add disk cell visualization in Matplotlib for DrivAerMLDataSet - Introduced a new function `_draw_disk_cells` to render mesh cells as oriented disks in 3D plots, improving visualization of sparse subsampled meshes. - Updated `_visualize_matplotlib` to utilize the new disk rendering function, enhancing the representation of subsampled cells and addressing the sub-pixel triangle issue. - Adjusted training script to ensure correct area scaling for subsampled meshes, maintaining total surface area consistency for accurate visualizations. commit 740a0f2999cb3e3e27cdc1b485d8ad10ee9f02c5 Author: Peter Sharpe Date: Wed Mar 4 19:15:45 2026 -0800 fixes edge case commit 6145b811ab4d308fc3020313ce34d47496087b64 Author: Peter Sharpe Date: Wed Mar 4 17:52:04 2026 -0800 Refactor network compilation in Kernel class - Updated the network compilation logic to utilize a cached compiled network, improving performance and avoiding unnecessary recompilation. - Enhanced the handling of dynamic compilation based on the compilation state, ensuring efficient execution during forward passes. - This change maintains the integrity of the original network while optimizing for varying input sizes. commit f2d97e313406f48c3f8ce7e40d0f675dcfe65daa Author: Peter Sharpe Date: Wed Mar 4 12:02:03 2026 -0800 Refactor memory estimation in Kernel class - Renamed `_approx_peak_bytes_per_interaction` to `_floats_per_interaction` to better reflect its purpose of counting identifiable float allocations during interactions. - Updated the method to provide a lower bound for memory estimation, accounting for autograd overhead. - Enhanced chunk size calculation in `ChunkedKernel` to utilize the new method, improving memory management during kernel operations. - Changed `torch.compile` to use dynamic compilation for better performance with varying chunk sizes. commit 19f68b7095a57e30babbe923f9566a3c26b2998d Author: Peter Sharpe Date: Wed Mar 4 10:10:26 2026 -0800 Update DrivAer training script and enhance Kernel class performance - Modified the SLURM job parameters in `run.sh` for improved resource allocation. - Added `--no-use-compile` argument to training arguments for better control over compilation behavior. - Updated the `DRIVAER_DATA_DIR` environment variable to reflect new dataset path. - Implemented lazy compilation in the `Kernel` class to optimize performance on memory-bound workloads, achieving a ~12% speedup. commit 746efaf6cac9b1d97745ba7f9bce230ab82949c4 Author: Peter Sharpe Date: Wed Mar 4 09:44:15 2026 -0800 Remove torch.compile from Kernel class initialization - Eliminated the `torch.compile` call from the `Kernel` class constructor, simplifying the network setup process. - This change streamlines the initialization of the network, potentially improving performance and maintainability. commit fe288788d9510e1f8e5b65102e1b4bd6082c45e0 Author: Peter Sharpe Date: Wed Mar 4 09:32:15 2026 -0800 Enhance memory estimation in Kernel class - Introduced a new method `_approx_peak_bytes_per_interaction` to estimate peak memory usage during interactions, aiding in efficient chunk sizing for the `ChunkedKernel`. - Updated the chunk size calculation to utilize the new memory estimation method, improving memory management during kernel operations. - Removed gradient checkpointing logic from the forward pass to streamline the evaluation process, ensuring consistent output handling. commit cf72c3fda37d78dc440430a5498527b3aff7452e Author: Peter Sharpe Date: Wed Mar 4 07:31:22 2026 -0800 Enhance MLflow integration in training script - Updated the `start_run` function calls in `train.py` to include the `log_system_metrics` parameter, enabling automatic logging of system metrics during MLflow runs. - This change improves the tracking of system performance metrics alongside model training, facilitating better monitoring and analysis. commit 31f75c8ca60db44e64cf8954583868bf57dae629 Author: Peter Sharpe Date: Wed Mar 4 05:57:33 2026 -0800 Add save checkpoint frequency parameter in training script - Introduced a `save_every` parameter in `train.py` to allow users to specify the frequency of checkpoint saving during training. - Updated the logging and checkpointing logic to utilize this new parameter, enhancing flexibility in model training management. - Improved documentation to reflect the addition of the new parameter and its purpose. commit 03ec9755caeb7750f2b46b0732ac6e8e4c900dfd Author: Peter Sharpe Date: Tue Mar 3 19:41:29 2026 -0800 Enhance visualization capabilities in DrivAerMLDataSet - Added a `backend` parameter to the `visualize_comparison` method, allowing users to choose between "matplotlib" and "pyvista" for rendering. - Implemented separate visualization functions for both backends, improving flexibility and compatibility with headless nodes. - Updated the training script to utilize the new backend option, defaulting to "matplotlib" for rendering comparisons. commit fd6bcc41d31b5663270fd50104900daec4bec8c1 Author: Peter Sharpe Date: Tue Mar 3 19:41:01 2026 -0800 Update scalar value conversion in draw_mesh_pyvista function for consistency - Modified the conversion of point and cell scalar values to include the `.float()` method before transferring to NumPy arrays, ensuring data type consistency. - This change aligns with recent updates for improved compatibility in mesh data processing. commit 43cff1b717e3e6a344123cff59fa490ce1f1298c Author: Peter Sharpe Date: Tue Mar 3 19:37:39 2026 -0800 Enhance scalar value handling in draw_mesh_matplotlib function - Updated the conversion of point and cell scalar values to ensure they are NumPy-compatible by applying the `.float()` method. - This change improves data type consistency and aligns with recent updates for better compatibility in mesh data processing. commit 62b83bc227b0936f103375ceff3a65944164e34d Author: Peter Sharpe Date: Tue Mar 3 19:37:11 2026 -0800 Update point data conversion in to_pyvista function for improved compatibility - Changed the conversion of mesh points to use `mesh.points.float().cpu().numpy()` to ensure proper data type handling. - This adjustment aligns with recent updates for VTK compatibility and enhances the robustness of the mesh data processing pipeline. commit 36ff6c15b0a723ca69a01e48c92fc9f824751990 Author: Peter Sharpe Date: Tue Mar 3 19:24:00 2026 -0800 Update data conversion in to_pyvista function for VTK compatibility - Changed the conversion of point, cell, and global data arrays to use `v.float().cpu().numpy()` instead of `v.cpu().numpy()`, ensuring proper data type handling for VTK compatibility. - This adjustment enhances the robustness of the mesh data processing pipeline. commit 0d74a37b750b7545e354d39a9ea34bf1ee2fae0b Author: Peter Sharpe Date: Wed Mar 4 01:24:14 2026 +0000 Refactor mesh processing in AirFRANS and DrivAer examples by removing downsampling parameters - Eliminated the `face_downsampling_ratio` parameter from `compute_max_mesh_sizes` and training scripts, simplifying the mesh processing logic. - Updated the handling of boundary mesh cells to directly use the full count, enhancing clarity and consistency across the dataset and training scripts. - Adjusted comments and documentation to reflect the changes in downsampling strategy, improving overall code readability. commit cb4e6f5af7dad4c6ade7b5f1045a73d1fb35e553 Author: Peter Sharpe Date: Wed Mar 4 01:01:05 2026 +0000 Add caching mechanism for mesh padding in Mesh class - Introduced a caching system in the Mesh class to optimize the padding of cell and point tensors. - Implemented the `_cache` attribute to store padded tensors, enhancing performance during mesh processing. - Utilized the `_pad_with_value` function to ensure consistent padding with a specified value, improving data handling efficiency. commit 9e1cf7e17d49a06600deed6e4bbba80a0e0103c4 Author: Peter Sharpe Date: Wed Mar 4 00:52:18 2026 +0000 Enhance inference process in DrivAer example by updating sample preprocessing - Modified the inference script to load hyperparameters and preprocess the sample more effectively. - Added boundary mesh subsampling directly in the inference process, ensuring compatibility with hyperparameter settings. - Improved clarity in comments regarding the preprocessing steps and boundary mesh handling. commit 7d110a8f49ad780e7a47e11c907b1751f444e3d3 Author: Peter Sharpe Date: Tue Mar 3 16:15:07 2026 -0800 Update hyperparameter logging condition in training scripts for AirFRANS and DrivAer - Modified the hyperparameter logging condition in `train.py` for both AirFRANS and DrivAer examples to log only during the first epoch, ensuring cleaner logging and reducing unnecessary entries. commit e47721669b6277ee887a3ec5fde32b7c0f16073d Author: Peter Sharpe Date: Tue Mar 3 16:08:16 2026 -0800 Refactor training scripts and dataset handling for improved mesh processing - Updated `train.py` in both AirFRANS and DrivAer examples to replace direct sample path access with dataset indexing for visualization. - Introduced a barrier synchronization in distributed training to ensure proper coordination across ranks. - Removed the deprecated `compute_max_mesh_sizes` function from `dataset.py`, streamlining the dataset processing pipeline. - Adjusted boundary mesh handling in `train.py` to enhance clarity and maintain consistency with the updated dataset structure. commit 64c90d2711aca0769120ecb19a3b938d72d13eae Author: Peter Sharpe Date: Tue Mar 3 14:36:31 2026 -0800 adds mlflow launch script commit 6add8dd89c917c79791d110218a47755b46d4064 Author: Peter Sharpe Date: Tue Mar 3 14:36:24 2026 -0800 docs commit 47182e6273a9e13d8dad30218c7369c3425d25ff Author: Peter Sharpe Date: Tue Mar 3 14:36:12 2026 -0800 Refactor DrivAerML dataset and training script to improve boundary mesh handling - Updated `dataset.py` to read VTP car body surfaces and triangulate them, enhancing the data processing pipeline. - Changed the boundary mesh key from "car_body" to "no_slip" for consistency across the dataset and training script. - Removed deprecated car body extraction functions to streamline the codebase. - Adjusted `train.py` to reflect the updated boundary mesh key, ensuring compatibility with the new dataset structure. commit 666713ab6b7116e3366576a2ee2b02bdf7a9e105 Author: Peter Sharpe Date: Tue Mar 3 14:34:38 2026 -0800 Adds slurm dir fallback commit cf0f76c9848b919d0e04daa0e3681badd2159ae9 Author: Peter Sharpe Date: Tue Mar 3 10:46:34 2026 -0800 Enhance DrivAerML dataset handling and training script - Updated `dataset.py` to create the GLOBE boundary mesh at load time by randomly subsampling cells from the cached surface mesh, improving efficiency. - Removed unnecessary preprocessing parameters and clarified docstrings for better understanding. - Modified `train.py` to include a new `max_viz_points` parameter for controlling visualization point limits, optimizing performance during inference. - Implemented subsampling of the prediction surface to maintain visual quality while speeding up processing. commit fc59a9e30791b8c48cbd5e3282c9d936fa7e888b Author: Peter Sharpe Date: Tue Mar 3 10:39:48 2026 -0800 Remove docstring from the `to` method in `AirFRANSSample` class to streamline the code and improve readability. commit 3e1a972407172b06208d28af48488ef4725f1735 Author: Peter Sharpe Date: Mon Mar 2 17:22:24 2026 -0800 Add cell normal computation for codimension-1 simplices - Introduced a new module `physicsnemo.mesh.geometry._cell_normals` for computing unit normal vectors for codimension-1 simplices using dimension-specific methods. - Updated the `Mesh` class to utilize the new `compute_cell_normals` function for normal calculations. - Enhanced documentation for the `cell_normals` method to clarify the computation process and its applications. - Added comprehensive tests for the new functionality, covering 2D edges, 3D triangles, and higher-dimensional cases, ensuring correctness and unit length of normals. commit 25146d0584dd811d20a7fa6861810dc0a844cffa Author: Peter Sharpe Date: Mon Mar 2 15:27:06 2026 -0800 Add cell area computation and tests for n-simplices - Introduced a new module for computing cell areas (n-simplex volumes) in `physicsnemo.mesh.geometry._cell_areas`, utilizing dimension-specific methods for efficiency. - Updated the `Mesh` class to leverage the new `compute_cell_areas` function for area calculations. - Added comprehensive tests for the new functionality, covering edge lengths, triangle areas, tetrahedron volumes, and general n-simplex volumes across various dimensions. - Enhanced documentation to clarify the computation methods and their applications. commit 9d7eccc23c128d1d96f37ef7bcb4ff55833986ac Author: Peter Sharpe Date: Mon Mar 2 15:01:42 2026 -0800 Refactor volume computation in Mesh class to disable autocasting - Updated the volume calculation in the Mesh class to disable autocasting for torch.matmul, ensuring that geometry calculations remain in the native dtype of the points. - Improved clarity by restructuring the computation of relative vectors and the Gram matrix within the autocast context. commit 559e2680de16ccd2d7e82be52f56b09d9149f0f4 Author: Peter Sharpe Date: Fri Feb 27 07:09:01 2026 -0800 Update DrivAer training script and field kernel for improved performance - Modified the `train.py` script to automatically set the `chunk_size` parameter in the model's forward pass. - Changed the loss evaluation from "test" to "validation" to better reflect the training process. - Enhanced the `ChunkedKernel` class in `field_kernel.py` to support gradient checkpointing during training, optimizing memory usage. commit 200ce595020c75d9bada1d334cef57000a3876ba Author: Peter Sharpe Date: Fri Feb 27 03:10:00 2026 +0000 Update run scripts for AirFRANS and DrivAer with new account details and dataset paths - Changed SLURM account and job names to 'coreai_modulus_cae' for both run.sh scripts. - Reduced job time limit from 4 hours to 2 hours and adjusted node and GPU allocation. - Updated dataset directory paths to reflect new structure under 'coreai_modulus_cae'. - Simplified head node retrieval method for multi-node training. commit dc97ff8479c22f02507cfa190a3bc4194819dc13 Author: Peter Sharpe Date: Thu Feb 26 18:56:12 2026 -0800 script fixes commit 2b7665b9ad9337836dda38559024f295d39f6051 Author: Peter Sharpe Date: Fri Feb 27 02:46:48 2026 +0000 Adds scrubbed run.sh commit de36740c99433c3fd1f285d04e6a289b1ec9d899 Author: Peter Sharpe Date: Fri Feb 27 02:44:49 2026 +0000 Add GLOBE DrivAerML example with training, inference, and dataset handling - Introduced a comprehensive example for training a GLOBE model on the DrivAerML dataset, including scripts for training (`train.py`), inference (`inference.py`), and dataset management (`dataset.py`). - Added a README.md file detailing the problem description, dataset structure, usage instructions, and model architecture. - Implemented preprocessing utilities and logging for hyperparameters and metrics. - Included requirements.txt for necessary dependencies and CSV files for training and validation splits. - Enhanced visualization capabilities for comparing predictions against ground truth. commit 711763b43cbdf61df739ede59bbb5c628731aa9a Author: Peter Sharpe Date: Fri Feb 27 01:05:51 2026 +0000 Enhance AirFRANSDataSet with detailed documentation and refactor postprocess method - Added comprehensive docstring to the `load_sample` method, detailing its functionality and parameters. - Refactored the `postprocess` method to accept a preprocessed sample instead of a true mesh, improving clarity and usability. - Updated inference and training scripts to reflect changes in the postprocess method, including visualization of combined results and logging of surface force coefficients. commit 361243b5bddf0eceb74926340bd642bbdaa6ee81 Author: Peter Sharpe Date: Thu Feb 26 18:22:01 2026 +0000 Remove unused logging imports and main execution block from utilities.py to streamline the code. commit 2821a296c6421e416ea8176d5f9480740f3af499 Author: Peter Sharpe Date: Thu Feb 26 08:16:55 2026 -0800 Syncs lockfile commit 5be5c703b782b21b5a11d190f8312de8bb2bb7ca Author: Peter Sharpe Date: Thu Feb 26 08:02:19 2026 -0800 Refactor get_physicsnemo_pkg_info function into core module - Moved the get_physicsnemo_pkg_info function from utilities.py to physicsnemo.core for better organization. - Updated imports in train.py and utilities.py to reflect the new location of the function. - Ensured that the function is still accessible for logging package version and git hash. commit 62b77dfae357bc2f0909ee79b90d64373104bdf3 Author: Peter Sharpe Date: Thu Feb 26 08:00:35 2026 -0800 Add get_physicsnemo_pkg_info function for package version and git hash retrieval - Introduced get_physicsnemo_pkg_info in version_check.py to fetch the package version and current git commit hash. - Updated module.py to utilize the new function for saving metadata. - Included get_physicsnemo_pkg_info in the __all__ exports of __init__.py. commit e71efbf806de031ed72f118b58324ec0b8487613 Merge: 271e04e4 7dcace14 Author: Peter Sharpe Date: Thu Feb 26 06:39:31 2026 -0800 Merge branch 'psharpe/add-GLOBE' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE commit 271e04e41594283498864a1cec74d5f724f253c5 Author: Peter Sharpe Date: Thu Feb 26 06:39:26 2026 -0800 lint fix commit 7dcace14f5d48cd9dc4ff6b67c511b33d62c27ff Merge: 8bb4329b 10831c18 Author: Peter Sharpe Date: Thu Feb 26 02:46:09 2026 +0000 Merge branch 'main' into psharpe/add-GLOBE commit 8bb4329bcaeb42464f72578d051dccc750a6da42 Author: Peter Sharpe Date: Wed Feb 25 18:35:06 2026 -0800 Converts to dict for logging commit c3e3f01a1a5c4bac3670dd076368a1c5f5693c8e Author: Peter Sharpe Date: Wed Feb 25 18:23:00 2026 -0800 fixes for early termination commit 49ad869e9837d566095ebe8ff1b9a78f1b325f54 Author: Peter Sharpe Date: Wed Feb 25 18:17:17 2026 -0800 docs cleanup + higher recompile limit commit 012dec4b292ad39e6633350d80eb505ae6a0e94d Author: Peter Sharpe Date: Wed Feb 25 18:05:54 2026 -0800 docs improvements commit 54a2c181f0c08cbe1d9974550c5db3e4815d67ea Merge: f69bda72 10c7f0da Author: Peter Sharpe Date: Wed Feb 25 12:27:15 2026 -0800 Merge branch 'main' into psharpe/add-GLOBE commit f69bda7244ab04943b3b31904df86eb43d19afca Author: Peter Sharpe Date: Wed Feb 25 12:17:31 2026 -0800 linting commit fcf403be3aa990220fd50b928e57c6b17ac84988 Author: Peter Sharpe Date: Wed Feb 25 12:03:38 2026 -0800 Refactor training script by removing signal handling and adding shutdown file check - Removed the `install_graceful_shutdown` function and related signal handling code from `utilities.py`. - Introduced a shutdown file mechanism in `train.py` to handle graceful shutdown requests. - Added a new `_compute_loss` function to compute per-field losses without recompilation issues. - Updated comments and code structure for clarity and maintainability. commit a63acb466944522ea0234a0be987a252c2c41203 Merge: 19fc5b66 c7549a91 Author: Peter Sharpe Date: Wed Feb 25 11:58:33 2026 -0800 Merge branch 'psharpe/add-GLOBE' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE commit 19fc5b66eb46d0eb01f424eb2921ce66bcb5fb56 Author: Peter Sharpe Date: Wed Feb 25 11:57:39 2026 -0800 remove extraneous line commit c696c3b35779f389e0aaf535a0c4abee4dd378e7 Author: Peter Sharpe Date: Wed Feb 25 11:56:25 2026 -0800 Migrates cached_dataset to core, since this will be re-used for 3D cases. commit c7549a918617bf733904dbe85cf5984579b909f7 Author: Peter Sharpe Date: Wed Feb 25 19:41:09 2026 +0000 update changelog commit a57c0a806cb732df769ec2515ff8f8984b78b2fc Merge: bc0547ae ae489190 Author: Peter Sharpe Date: Wed Feb 25 19:39:25 2026 +0000 Merge branch 'main' into psharpe/add-GLOBE commit bc0547aed65f8f6cb71735e9da56bd7a153bca68 Author: Peter Sharpe Date: Wed Feb 25 17:38:43 2026 +0000 API changes in running_norm commit e2e16c73698af6e69aef033d1e1d9e69eb7e856c Author: Peter Sharpe Date: Wed Feb 25 17:30:19 2026 +0000 removes GLOBE from importlinter, since it's now in experimental commit d2ddc39b26c82ce680a007d4a4d15d5d6529723e Author: Peter Sharpe Date: Wed Feb 25 17:01:58 2026 +0000 Pr review fixes commit 3f6539d2cb7c686c00c6122a16b827f1d7b20e29 Author: Peter Sharpe Date: Wed Feb 25 17:01:10 2026 +0000 Adds PR review fixes commit 6cbd938df95e9120d307aa61795767a406cdb242 Author: Peter Sharpe Date: Wed Feb 25 16:59:04 2026 +0000 partial PR review fixes commit b637086df520a14211e1ad836fa8700da925d634 Author: Peter Sharpe Date: Wed Feb 25 16:54:02 2026 +0000 clarifying comment commit 93445bcd8ec1265b1c595597199ae1801298570d Author: Peter Sharpe Date: Wed Feb 25 16:34:38 2026 +0000 Misc cleanup commit bec925e660f9f84d85ad16d3f9632db435783ae1 Author: Peter Sharpe Date: Wed Feb 25 16:33:37 2026 +0000 misc cleanup commit c9a35f49d88ea2fb444dd58ce249a9f2eeff8d38 Author: Peter Sharpe Date: Wed Feb 25 16:19:33 2026 +0000 delete dangling cuda memory setting commit 0f3412699a36a80dba14a3d9e9e3955cc7a1fbeb Author: Peter Sharpe Date: Wed Feb 25 02:29:20 2026 +0000 consolidate MLFlow logging calls commit 4d6d898386e06fe5ecd6eaf744e458ea1d58fa3f Author: Peter Sharpe Date: Wed Feb 25 00:48:39 2026 +0000 Adds paper link commit 49a41da90657b31c08f4a8843face7087a0ef431 Author: Peter Sharpe Date: Wed Feb 25 00:47:39 2026 +0000 remove dangling module outside experimental commit c5bf1bd9729bb45afe286bee77b8707c4949f18b Author: Peter Sharpe Date: Tue Feb 24 20:51:49 2026 +0000 linter fixes commit c7caac29e3e08799800ed975bdcac1a492127c3d Author: Peter Sharpe Date: Tue Feb 24 20:12:42 2026 +0000 scrubs run.sh details commit b75c502a579a4860b35cb5d38889dcec53f139b0 Author: Peter Sharpe Date: Tue Feb 24 18:39:18 2026 +0000 Adds review-requested comment commit b1753d40142c8f4100b261a34e551fd286910074 Author: Peter Sharpe Date: Tue Feb 24 18:28:39 2026 +0000 Switches `profile` to `use_profiler` for readability commit c9aa9a54680b8e62fdf56699a2935ad6ab84af33 Author: Peter Sharpe Date: Tue Feb 24 18:28:14 2026 +0000 adds option to compile bytecode in uv sync commit cebae767166ffe67ba84377102e8843f138cd650 Author: Peter Sharpe Date: Tue Feb 24 18:10:50 2026 +0000 Update requirements.txt to specify version constraints for dependencies commit eb5d3fad0ee6a02ca4363241720e119e775b1a37 Author: Peter Sharpe Date: Tue Feb 24 18:10:21 2026 +0000 Split out cached preprocessing dataset and airfrans specific dataset into separate files commit aa1d9f3b9e60a62ef56eff2ea36d4f30eb5cd6f5 Author: Peter Sharpe Date: Tue Feb 24 17:52:11 2026 +0000 Adds jaxtyping commit 090c6d35fcc0ff98d54d9db2e8c1045304e15ac0 Author: Peter Sharpe Date: Tue Feb 24 17:47:30 2026 +0000 typo commit 609455ae810aa31d9065c987b9a8be146272307e Author: Peter Sharpe Date: Tue Feb 24 17:47:10 2026 +0000 Enhances the `legendre_polynomials` function documentation and adds input validation. Updates docstring to clarify behavior for non-positive `n` and includes a ValueError for negative inputs. Adjusts implementation to handle edge cases more explicitly. commit 23db9c3f88670db6f6be806d038f8154678ea6cb Author: Peter Sharpe Date: Tue Feb 24 17:21:48 2026 +0000 Fixes an incorrect docstring, which stated that knn only works in 3D - none of the backends require this; it's dimensionally-generic. commit 0c426d4e3a7f1141f1c089409bd5a87403af8ed6 Author: Peter Sharpe Date: Tue Feb 24 17:21:14 2026 +0000 formatting commit cd18e4be452e8cead8cc2f6720802110436b05a8 Author: Peter Sharpe Date: Tue Feb 24 17:20:56 2026 +0000 Switch to physicsnemo knn commit d649428e77a24fd7de99a4d95d6bd988bbe0e8f8 Author: Peter Sharpe Date: Tue Feb 24 17:04:02 2026 +0000 Pulls out magic numbers commit 72dc3dbb8887b5a4b6111b8997ce9a26b2f12a16 Author: Peter Sharpe Date: Tue Feb 24 17:01:19 2026 +0000 docs commit 2adfe5d8bb0f7a8ad8f69108c9376f863213512b Author: Peter Sharpe Date: Tue Feb 24 03:34:41 2026 +0000 signal handling fixes commit 35943e2f9b779d6b72c71a803f43af5bc99e41c1 Author: Peter Sharpe Date: Tue Feb 24 02:42:58 2026 +0000 Adds a config option commit 6e2f1c3ea7d713abeeb0e63e85dbd541ada28702 Author: Peter Sharpe Date: Tue Feb 24 02:41:22 2026 +0000 Adds fix for nested tensordicts commit 668e788dbdca4586c736bf2b1db1a430bd25851c Author: Peter Sharpe Date: Tue Feb 24 02:39:45 2026 +0000 fix for face centroid randomization commit dcba4c16f9d91d47bc452f8568d60369e1c85b5f Author: Peter Sharpe Date: Tue Feb 24 02:37:46 2026 +0000 fixes for graph breaks commit 4d3a59162e4e88064b760d379ce0777fd099966b Author: Peter Sharpe Date: Tue Feb 24 02:22:39 2026 +0000 fix recompiling due to length mutation commit e79838f15ef9314ea24dad0df2d5b946246ca330 Author: Peter Sharpe Date: Tue Feb 24 01:04:12 2026 +0000 silence extraneous logging commit f6ee127e11632c6d82ef6b093dc8f94bc10e13c0 Author: Peter Sharpe Date: Mon Feb 23 23:25:48 2026 +0000 nests the reference length ratios commit ea381d3415c08bf31f56049146c7f097a19e502a Author: Peter Sharpe Date: Mon Feb 23 23:21:02 2026 +0000 Disambiguates RankDict to be clearer. commit 35dbaf56728e20917246f08a43a28a657d3c7fe3 Author: Peter Sharpe Date: Mon Feb 23 23:16:00 2026 +0000 Switches back to Python-side rank dict specs for compile compat commit 4904703766ae0d7ef3c7aba0ec7294680453dc47 Author: Peter Sharpe Date: Mon Feb 23 22:24:11 2026 +0000 Adds fix for point normals commit 4607c0aed31738b81e879220c6487ffa2d94baff Author: Peter Sharpe Date: Mon Feb 23 22:22:52 2026 +0000 Adds second half of unstructured grid support commit 046517cde9055ea41e982bdb9515360992266171 Author: Peter Sharpe Date: Mon Feb 23 22:22:07 2026 +0000 Bugfix for 2D unstructured grid handling commit 8accf8907766f628a9689895abfc25322a575011 Author: Peter Sharpe Date: Mon Feb 23 21:06:41 2026 +0000 pre-commit fix commit eb6083aaec8f3fe0212e5a6765f2636493d88e69 Author: Peter Sharpe Date: Sun Feb 22 17:15:49 2026 +0000 docs commit 99f783ce3e16681cb0156efddfe2d8c5a486dbb3 Author: Peter Sharpe Date: Sun Feb 22 17:07:55 2026 +0000 rework to use tensordict commit 4836730f9f21a24b344d67bd9384dae21441071f Author: Peter Sharpe Date: Sun Feb 22 16:59:41 2026 +0000 Refines documentation for TensorDict structures by removing redundant batch size references and clarifying descriptions in Kernel and GLOBE classes. commit cf8da261e695745c01de3801ee96a1dab45de0ef Author: Peter Sharpe Date: Sun Feb 22 16:47:04 2026 +0000 cleaner dict mapping commit a14745e3df31764084ef08c3b89876fea031697f Author: Peter Sharpe Date: Sun Feb 22 16:16:42 2026 +0000 fix ty check errors commit 90b357ed6a3c2233415ef5e5db82fbaf84ea2060 Author: Peter Sharpe Date: Sun Feb 22 15:38:17 2026 +0000 Adds docs commit a5b59cf5b9938ed0836cb635b81658c9624d014a Author: Peter Sharpe Date: Sun Feb 22 15:35:47 2026 +0000 Refactors rank counting algorithm commit 8be4527a18c492c89f334d4de9fee2712bb06ec7 Author: Peter Sharpe Date: Sun Feb 22 15:29:15 2026 +0000 approved ty changes commit 1f9cbbe8230eef8053d6a9d70e0fbc8ae43f6b7e Author: Peter Sharpe Date: Sun Feb 22 15:21:05 2026 +0000 approved ty changes commit fbe707b262cfa1413b6ed8408bd04ca73062d74f Author: Peter Sharpe Date: Sun Feb 22 15:19:37 2026 +0000 approved ty changes commit adfe9cec72d8e064fad013c4e47837cdc1ecb716 Author: Peter Sharpe Date: Sun Feb 22 15:17:56 2026 +0000 approved ty changes commit e677961430e7e708d1b5dcc1594f80fdf129c3df Author: Peter Sharpe Date: Sun Feb 22 06:14:33 2026 +0000 format commit 447b1f63685a95ee4c697289a1aa67dbc771c8cd Author: Peter Sharpe Date: Sun Feb 22 06:13:30 2026 +0000 format commit 1493cc8c540c5e59ab51c12660eea450e758496e Author: Peter Sharpe Date: Sun Feb 22 06:09:18 2026 +0000 Refactor GLOBE model and field kernel to use rank-spec TensorDicts - Replaced output_fields, boundary_condition_n_source_scalars, and boundary_condition_n_source_vectors with rank-spec TensorDicts in the GLOBE model and field kernel. - Introduced new utility functions for deriving and counting ranks in TensorDicts, enhancing data handling and clarity. - Updated tests to align with the new data structure, ensuring compatibility and correctness in model inference and kernel operations. commit e8a195c74e26b7ebf333d94b19b4ab363c163108 Author: Peter Sharpe Date: Sun Feb 22 05:41:07 2026 +0000 Add dynamic attribute access to PythonLogger - Implemented __getattr__ method in PythonLogger to allow dynamic access to logger attributes, enhancing flexibility in logging operations. commit 90283ff28e3735335dd24ac77bf58c6e0ab1dff6 Author: Peter Sharpe Date: Sun Feb 22 05:40:44 2026 +0000 Refactor GLOBE model and field kernel data handling - Unified source and global data structures in the GLOBE model and field kernel, replacing separate scalars and vectors with a single TensorDict for each. - Updated input handling in tests to accommodate the new data structure, ensuring compatibility with the refactored model. - Enhanced documentation and comments for clarity on the new data organization and its implications for model functionality. commit 7f76170aa370925655cbaf9f0ca7301ead72863c Author: Peter Sharpe Date: Sun Feb 22 05:10:38 2026 +0000 Refactor AirFRANS data handling and GLOBE model input structure - Updated AirFRANSSample and AirFRANSDataSet to incorporate dimensional constants and streamline global data handling. - Replaced global scalars and vectors with a unified global_data structure in the GLOBE model, ensuring all conditioning features are nondimensional. - Enhanced documentation for clarity on the new data structures and their usage. commit f20c491ff15cb3bcbdaacb95172721ab17e0ddb1 Author: Peter Sharpe Date: Sun Feb 22 04:51:23 2026 +0000 Adds tensordict type hint improvements commit 8d909e21be2b12e0e977c2691a6144d172641ee1 Author: Peter Sharpe Date: Sat Feb 21 22:38:55 2026 +0000 readability enhancements commit f8810880576d0b79b5e6968810f9d37610fbeb65 Author: Peter Sharpe Date: Sat Feb 21 22:17:54 2026 +0000 fix splits commit c29b26547a265bb4bdc93b699b3b2791c4eb4731 Author: Peter Sharpe Date: Sat Feb 21 22:17:49 2026 +0000 more Mesh consolidation commit 0b0c847f16826f644b06a2ed0b573f94872f158d Author: Peter Sharpe Date: Sat Feb 21 22:16:15 2026 +0000 simplify splits logic commit 4e8b730d5958c78f015781685a052a9f279fa829 Author: Peter Sharpe Date: Sat Feb 21 21:58:50 2026 +0000 Further Mesh incorporation commit e90c235d24d77bbba304a4516b5fdcbd6549327e Author: Peter Sharpe Date: Sat Feb 21 21:18:26 2026 +0000 cleaner point cloud returns commit 1d62cefdaebb793dd06eb4b8363074ec8a81e9e7 Author: Peter Sharpe Date: Sat Feb 21 21:17:12 2026 +0000 allows cleaner point cloud creation commit b801f5513c6e87ceb264879c2aedcaac24632731 Author: Peter Sharpe Date: Sat Feb 21 21:16:48 2026 +0000 Refactor GLOBE model output structure and enhance feature extraction - Updated output format to return a Mesh object instead of TensorDict, containing predicted fields in point_data. - Renamed latent data keys for clarity and consistency. - Refactored data extraction from source meshes to utilize enriched cell_data, improving feature handling. - Added a new method for boundary-to-boundary communication, enhancing model capabilities. commit 370c3eedcdf638a15eb162d76254ac4d2c12e97c Author: Peter Sharpe Date: Sat Feb 21 20:11:11 2026 +0000 fixes serialization bug commit f22f3a8116394ef73fb0be3235a65897e8f5625d Author: Peter Sharpe Date: Sat Feb 21 19:57:04 2026 +0000 Fixes MLFlow too-long error commit 40c7e27869c0d509a20f7ee391e756887297ab17 Author: Peter Sharpe Date: Sat Feb 21 14:41:52 2026 -0500 simplify verbosity control with logging commit 1d2356e70dd2dca91d973cbf8a4def9297e12496 Author: Peter Sharpe Date: Sat Feb 21 14:33:04 2026 -0500 Switch to using logger commit 4042a0b0b3470fc972cf58cf9b7ef757f5d61e92 Author: Peter Sharpe Date: Sat Feb 21 14:20:14 2026 -0500 Adds proper documentation to checkpoint.py commit a7a01ad3e6de6d9146e18ea99a2123f4392a2ae9 Author: Peter Sharpe Date: Sat Feb 21 14:09:00 2026 -0500 switches to core checkpointing utils commit ed0871e9e2fb4b18b360a3addf726846df2b2fb7 Author: Peter Sharpe Date: Sat Feb 21 13:28:29 2026 -0500 Refactor Kernel class to improve network feature handling - Replaced `network_layer_sizes` with `network_in_features` and `network_out_features` for clarity. - Updated methods to compute input and output feature sizes directly, enhancing readability and maintainability. - Adjusted related tensor shape checks to align with the new feature definitions. commit 0aa5d22e7f3a12b2b9239ae5d23662e465f550de Author: Peter Sharpe Date: Sat Feb 21 12:59:27 2026 -0500 fixes old MLP API commit 563251f02219db3e93624f2124604f42d2e53dd1 Author: Peter Sharpe Date: Sat Feb 21 12:58:59 2026 -0500 Refactor GLOBE model imports and remove legacy MLP implementation - Updated import paths for MLP and Pade classes to the new `physicsnemo.nn` module. - Removed the legacy MLP implementation from the experimental globe module. - Adjusted the GLOBE model to utilize the new Mlp class from the nn module. - Cleaned up the __init__.py files to reflect the changes in available classes. commit 8a5bf82a7180d94470c0d3e6ebaad9d3abab4eb0 Author: Peter Sharpe Date: Sat Feb 21 12:16:41 2026 -0500 Refactor GLOBE model structure and update imports to experimental namespace - Moved GLOBE and related classes (MLP, Pade, ChunkedKernel, MultiscaleKernel) to `physicsnemo.experimental.models.globe`. - Updated import paths in `dataset.py`, `inference.py`, and `train.py` to reflect the new structure. - Added new utility functions for TensorDict manipulation in `utilities/tensordict_utils.py`. - Introduced README documentation for the GLOBE architecture, detailing its mathematical properties and usage. - Added new files for field kernel and MLP implementations, enhancing the model's capabilities. commit dc098f16216d867c8544e857e7324f827c61b6d3 Merge: 38a2b2bb 07629e85 Author: Peter Sharpe Date: Sat Feb 21 05:04:17 2026 +0000 Merge branch 'psharpe/add-GLOBE' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE commit 07629e8552c42741754e78048cfb4d43ccd56c25 Author: Peter Sharpe Date: Sat Feb 21 00:00:11 2026 -0500 simplify commit 38a2b2bbc04ede51eaab93f1a8e5a3e2645c707b Author: Peter Sharpe Date: Sat Feb 21 02:57:49 2026 +0000 Adds SLURM env var fallbacks commit fbad69090a830699c7fdabe84cd6db29acded329 Author: Peter Sharpe Date: Sat Feb 21 02:56:01 2026 +0000 Adds OMP_NUM_THREADS commit 78c15b56dcd86791091e87f568a96635e281f884 Author: Peter Sharpe Date: Sat Feb 21 02:53:42 2026 +0000 inexact sync commit 102b324ca663bbefeca6b8eb9af631f068d0a16a Author: Peter Sharpe Date: Fri Feb 20 21:51:21 2026 -0500 fixes dict class commit 5f9769f211a59c780238e05d9421c66e6d8987ec Author: Peter Sharpe Date: Fri Feb 20 21:17:50 2026 -0500 Refactor onto tensorclass data structure commit 6c97b4ce9cc92f8322689d12e2e3c787ceff740a Author: Peter Sharpe Date: Fri Feb 20 21:01:54 2026 -0500 cleanup commit 96939ada2600683b72f8991e57f01026ec2bd274 Author: Peter Sharpe Date: Fri Feb 20 17:26:28 2026 -0500 migrate to AIRFRANS_DATA_DIR env var commit 41fd5f83b631f5a2e030dfe778bb0c548fb5389f Author: Peter Sharpe Date: Fri Feb 20 15:22:09 2026 -0500 adds accountname commit f68412c704e7e0b64b806f196ca47f9d80cbba90 Author: Peter Sharpe Date: Fri Feb 20 13:34:49 2026 -0500 cleanups commit f4d063269ff3609e9a304743cbaf8f00a069c5d4 Author: Peter Sharpe Date: Fri Feb 20 13:32:11 2026 -0500 cleanup commit ea9b1286602d9f19fc41a0dc4097c878f4d420de Author: Peter Sharpe Date: Fri Feb 20 13:31:59 2026 -0500 removes need for reduce over ranks commit 705567cb9372055a40bd714b5c9e0f61d4f37eac Author: Peter Sharpe Date: Fri Feb 20 12:07:50 2026 -0500 path cleanups commit d23993fc374c768014ee0c477e25ae542bb34e88 Author: Peter Sharpe Date: Fri Feb 20 11:47:27 2026 -0500 Adds CUDA version detection logic for sync commit 043b343e47dd66dd9e7087f6fc2b86499c25cde6 Author: Peter Sharpe Date: Fri Feb 20 11:43:14 2026 -0500 clearer context logic commit c7205c63e8bddb5fcbda2163d03f34998be54c66 Author: Peter Sharpe Date: Fri Feb 20 11:26:01 2026 -0500 minor cleanups commit 0e9373156eff78e3377a8a2d6c84ff1453bcaa6b Author: Peter Sharpe Date: Fri Feb 20 11:25:01 2026 -0500 simplify path handling commit 86cabc52a2469d2c2ffe0d7e90e5d923b26e1c71 Author: Peter Sharpe Date: Thu Feb 19 23:38:55 2026 +0000 Disables TF32 in GLOBE tests commit b3e33113b2df9dabf181d3586237a7ccab08b6cb Author: Peter Sharpe Date: Thu Feb 19 15:16:25 2026 -0500 reflects new structure of examples commit d6d46d87f6ac73a6faaa1408e908ffffb3cc5929 Author: Peter Sharpe Date: Thu Feb 19 15:00:44 2026 -0500 adds a few more jaxtyping hints commit 336be2cd788944c3248b93f273dbb72d8c64a939 Author: Peter Sharpe Date: Thu Feb 19 12:51:22 2026 -0500 markdownlint commit c7579886badc56cb69384d677fb660cefc7a6edb Author: Peter Sharpe Date: Thu Feb 19 08:36:56 2026 -0800 formatting commit c6550d991585c7c49a9d16d7c81c1e29611081ee Author: Peter Sharpe Date: Thu Feb 19 07:11:08 2026 -0800 Refactor onto new caching syntax commit a9cd60991d07c7a28700015aaec2318cf7d66f5e Merge: 2a753a50 70b06edd Author: Peter Sharpe Date: Thu Feb 19 06:55:47 2026 -0800 Merge branch 'main' into psharpe/add-GLOBE commit 2a753a505d2ba742a684aee796a7b121d662eb43 Merge: fa81e92b 864462ba Author: Peter Sharpe Date: Wed Feb 18 10:14:39 2026 -0800 Merge branch 'psharpe/add-GLOBE' of https://github.com/peterdsharpe/physicsnemo into psharpe/add-GLOBE commit fa81e92bed5635503ff9e230d52ca7ccdfad2c80 Author: Peter Sharpe Date: Wed Feb 18 10:01:07 2026 -0800 minor readability enhancements commit 2509408a9fa85a9a53d4623a0210ca5fe67a4ed9 Author: Peter Sharpe Date: Wed Feb 18 09:58:29 2026 -0800 Bugfix in instantiation cache commit defca40681c8bcc33d55fd1c39fa0c0ffbf1c944 Author: Peter Sharpe Date: Wed Feb 18 09:55:54 2026 -0800 Adds required hyperlayer evaluation function commit 03c1f8aae8a32434ec1ac6f893d63c3d30ba1591 Author: Peter Sharpe Date: Wed Feb 18 09:55:16 2026 -0800 fixes TODO in docs. commit 15524ade8a4674ae77dd309b8a26d29d090e9070 Author: Peter Sharpe Date: Wed Feb 18 09:55:00 2026 -0800 Adds README.md to airfrans example. commit d4cbb3cf40a3fe0c8174ba4144f385e683ebe558 Author: Peter Sharpe Date: Wed Feb 18 09:54:51 2026 -0800 Simplifies hyperlayer evaluation syntax for readability commit a647ff9a37a0944cd6eddd96895e4bb30bd7d8ed Author: Peter Sharpe Date: Wed Feb 18 09:13:35 2026 -0800 Reduces allocation commit 7f00faebc583b5d9152b9223f4b0a850f1698eef Author: Peter Sharpe Date: Wed Feb 18 09:11:47 2026 -0800 Validated changes commit 864462ba9d4f14a07e2591b645f85bba8d47181d Author: Peter Sharpe Date: Wed Feb 18 12:06:34 2026 -0500 jaxtyping fixes commit f31f15dd273f3d691d64d70d2620b5fa6bbf48dd Author: Peter Sharpe Date: Wed Feb 18 12:05:59 2026 -0500 license headers commit bf7d2659683dddd9e042d024c06884970ab1f083 Merge: 5421b25a 82664751 Author: Peter Sharpe Date: Wed Feb 18 11:59:12 2026 -0500 Merge branch 'main' into psharpe/add-GLOBE commit 5421b25aaccded7f2d5d86cb4200caf9ed9514ae Author: Peter Sharpe Date: Wed Feb 18 08:39:50 2026 -0800 migrate folders to allow for drivaerml support commit 0b196e1da2980278d10375a0adcf6101d4fa39a5 Author: Peter Sharpe Date: Wed Feb 18 08:30:56 2026 -0800 simplification commit 2866c1ad49142f70e1d3f62c0c1d715d8e95c7c2 Author: Peter Sharpe Date: Wed Feb 18 08:19:08 2026 -0800 validated changes commit d5afa83c5f973ad2ab82c61195312dbfd30ab08a Author: Peter Sharpe Date: Wed Feb 18 06:13:27 2026 -0800 license headers commit b626fa143e11828b0405efab193c268f188f30a3 Author: Peter Sharpe Date: Wed Feb 18 06:05:35 2026 -0800 cleanup commit d6d4cc9644cd12dfd0b8bebf84fdedcbd56b620f Author: Peter Sharpe Date: Tue Feb 17 15:43:57 2026 -0800 default to 4 nodes commit de8a78f54357664c969d454e00432abf0f33d2fc Author: Peter Sharpe Date: Tue Feb 17 15:43:46 2026 -0800 Adds an ignore for new MLflow DB-based approach commit bfef0041c5c94eb11dfba047e2ec216d091290da Author: Peter Sharpe Date: Tue Feb 17 10:29:00 2026 -0800 defaulting to scarce, which is faster to train commit 2719c661146628ec29810e8a0b36ff50fb0f3763 Author: Peter Sharpe Date: Tue Feb 17 10:09:28 2026 -0800 Reduce recompilation by early-triggering normal computation caching commit 93b9ed52d2f045878999b50cc69d7890f523075f Author: Peter Sharpe Date: Tue Feb 17 10:09:11 2026 -0800 rewrite of mesh cell normals to remove torch.compile graph break commit 8b305ac763f3dd99294ca40b9b9f0e6b24698a2f Author: Peter Sharpe Date: Tue Feb 17 09:30:29 2026 -0800 Adds requirements commit 3b73bfc412fe9aca7c2862df87e95ee925dc7c89 Author: Peter Sharpe Date: Tue Feb 17 09:30:18 2026 -0800 Adds uv sync comment first commit 376dc03cf54b023d41149dc9ef31305d4754ae31 Author: Peter Sharpe Date: Tue Feb 17 11:57:47 2026 -0500 Adds output dir to gitignores commit 654f2e2635ff6a56d95e2f6d22bf75f1b6d4dd8c Merge: 99ff9f0e b4c0b2b6 Author: Peter Sharpe Date: Tue Feb 17 11:41:23 2026 -0500 Merge branch 'main' into psharpe/add-GLOBE commit 99ff9f0e0fee87909d071f49ce7c63feb9490271 Author: Peter Sharpe Date: Tue Feb 17 10:03:08 2026 -0500 add cache/ dirs to ignore list commit ba2fe9d5658c1902844c2dba700b253c2ea5114e Author: Peter Sharpe Date: Wed Feb 11 10:11:31 2026 -0500 dedup commit cbee2b913c35106ada7ef7e4b2e20fff5965d92c Merge: 48e53242 37b3e5c2 Author: Peter Sharpe Date: Wed Feb 11 10:09:04 2026 -0500 Merge branch 'main' into psharpe/add-GLOBE commit 48e532420bb607f1ce14d3784eabe295eaaae824 Author: Peter Sharpe Date: Wed Feb 11 10:00:50 2026 -0500 Updates boundary mesh handling to use cells instead of faces, improving data processing in distributed training. Adjusts related computations and caching for mesh properties. commit 082080e79c6181582882a13c16d65a5a3332883f Author: Peter Sharpe Date: Tue Feb 10 17:21:09 2026 -0500 refactors out BoundaryMesh in favor of Mesh commit 13a5994b177b4cdbf846181f74147ee48a06817b Author: Peter Sharpe Date: Tue Feb 10 15:34:33 2026 -0500 Adds ability to project data too commit 83c2ee218e1bdd86918648717ce9823a571d24b7 Author: Peter Sharpe Date: Tue Feb 10 13:14:47 2026 -0500 model compliance commit bf3306952062c60f0071037d9abc4beffcf9191c Author: Peter Sharpe Date: Tue Feb 10 12:41:28 2026 -0500 Initial GLOBE + tests + AirFRANS example addition. commit a752813cfe5d09c2646575c62e994bbe17e24b49 Author: Peter Sharpe Date: Tue Feb 10 10:24:42 2026 -0500 modifies importlinter commit 0d528836f5d0ff1c18794a193913275e5c8c2445 Merge: b4f11a2b 65318afb Author: Peter Sharpe Date: Tue Feb 10 09:45:58 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit b4f11a2b99ac1912f85d69b2c11e5be462910a2b Author: Peter Sharpe Date: Tue Feb 10 09:42:46 2026 -0500 license headers commit 0958f9977ee1fc8fd7a2d2de7ef0470f74edcd93 Author: Peter Sharpe Date: Tue Feb 10 09:41:53 2026 -0500 format commit 70710c0f3c647ad69881d9ff6ba3933136f7b0cd Author: Peter Sharpe Date: Tue Feb 10 09:41:27 2026 -0500 Adds mesh benchmarks commit 278ead77b7c85d28d554b5b5cf038867f265e860 Author: Peter Sharpe Date: Mon Feb 9 17:52:55 2026 -0500 adds viz commit 085caeaa0d48e5df1a0fe77cd465df04b1aab2c9 Author: Peter Sharpe Date: Mon Feb 9 17:49:03 2026 -0500 Adds viz commit 860fc7eff57a24f48df47efac97040a255581772 Author: Peter Sharpe Date: Mon Feb 9 17:44:01 2026 -0500 Fixes a null type commit 9f4b0f09bf6ae2adae765cbf73a4f286ce31b09c Author: Peter Sharpe Date: Mon Feb 9 17:29:18 2026 -0500 viz improvements commit eb55f048b65d5bd0a38677c4fd1e8333c924e031 Author: Peter Sharpe Date: Mon Feb 9 17:19:10 2026 -0500 Adds a fix for curl on lsq reconstruction. commit ba8b69b4be72f14a89877e3505e88f514d2df6ac Author: Peter Sharpe Date: Mon Feb 9 17:18:42 2026 -0500 Adds shading commit f88a3abcfa552fef197bb9e8f3fef385e0e14ff4 Author: Peter Sharpe Date: Mon Feb 9 17:00:36 2026 -0500 Adds shading commit 902b7a9747af63f2c74a4f228eb46f4ad2ba3c2b Author: Peter Sharpe Date: Mon Feb 9 16:24:11 2026 -0500 Update gradient computation to allow zero minimum neighbors and clarify documentation commit 00deaf92f915cf921df98297ebf007192b2c0cc0 Author: Peter Sharpe Date: Mon Feb 9 16:23:39 2026 -0500 makes 2D and 3D shading consistent commit 93b431bf8d5db06b4d3ff040100c1988a7443436 Author: Peter Sharpe Date: Mon Feb 9 16:18:15 2026 -0500 support for 3D data viz commit f6d21a96b24b613810bdf152c4c579e943d200e4 Author: Peter Sharpe Date: Mon Feb 9 16:13:58 2026 -0500 Adds a fix for 3D matplotlib viz commit 77fd304a40dcac7b131b58bfd3aa07ccadb26c44 Author: Peter Sharpe Date: Mon Feb 9 15:39:17 2026 -0500 docs commit 86f45593a4a0283dba761474b526ead09b4436c2 Author: Peter Sharpe Date: Mon Feb 9 15:02:41 2026 -0500 post-review updates commit 431f50fb18297b776ab07a9dcf4ab89f6bb59dda Author: Peter Sharpe Date: Mon Feb 9 14:45:43 2026 -0500 addresses review comments commit 19bc4758e74ed8a0ffa862addae3f3b894bae1a0 Author: Peter Sharpe Date: Mon Feb 9 11:43:23 2026 -0500 notebook updates commit 92c7687d0f5e861f150a0d24e2980e64728c9fd2 Author: Peter Sharpe Date: Fri Feb 6 16:24:38 2026 -0500 tutorial sync commit 149ec61cedffc95b6c4831c913596f20322064df Author: Peter Sharpe Date: Fri Feb 6 15:24:21 2026 -0500 Update license year commit fddceb49748d5201cc9e2457138960d8fc16e24e Merge: 8fec6c96 e94bea88 Author: Peter Sharpe Date: Fri Feb 6 15:21:52 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit 8fec6c962b088377a43de419ed9207a9bc19c61b Author: Peter Sharpe Date: Fri Feb 6 15:20:33 2026 -0500 tutorial progress commit fa407d79ff7c2fc69cb7c3cf397d61f3a764263e Author: Peter Sharpe Date: Fri Feb 6 15:19:32 2026 -0500 fixes missing license header commit d702c389577f2057302ca31d7ba5cd40473bdd52 Author: Peter Sharpe Date: Fri Feb 6 15:18:43 2026 -0500 Deduplication commit 7a5ae0042989a6d189b3540450613f9234340ba0 Author: Peter Sharpe Date: Fri Feb 6 15:09:17 2026 -0500 test fixes commit 3b1c797f677fb6d0892cc1d262143f3b7ef6c152 Author: Peter Sharpe Date: Fri Feb 6 15:02:42 2026 -0500 formatting commit 6a788cf09f6f0c4fc5d40430a3af574f9e24574c Author: Peter Sharpe Date: Fri Feb 6 15:00:46 2026 -0500 dtype fix commit 282c008382c71cd8538f7fea624e5bc717a11e9b Author: Peter Sharpe Date: Fri Feb 6 14:59:03 2026 -0500 cleanup commit 27e329688a51f754fb0baf45f9c038489a1a425d Author: Peter Sharpe Date: Fri Feb 6 14:58:13 2026 -0500 formatting commit f5f2ce27a7f3462352946e400f5c1caef5ccfbe5 Author: Peter Sharpe Date: Fri Feb 6 14:55:51 2026 -0500 __init__.py changes commit c08153f51e9c15a8c3a301b3abfdcb9fe1d81080 Author: Peter Sharpe Date: Fri Feb 6 14:55:18 2026 -0500 Review pass commit f95fc1a377dd44612fc6a653b658752a026b3f5f Author: Peter Sharpe Date: Fri Feb 6 14:13:45 2026 -0500 visualization fixes in 3D commit 9d68601bdba366c97dc06ea1b3f298c4fcd103a5 Author: Peter Sharpe Date: Fri Feb 6 14:01:44 2026 -0500 Adds perf notes commit cf0609b6e632284e52dcef032c3601cbd838958d Author: Peter Sharpe Date: Fri Feb 6 13:50:16 2026 -0500 cleans up no-longer-needed type hint ignores commit 78db9cf94ce3a1f61a76195b0cd7f726a86d1815 Author: Peter Sharpe Date: Fri Feb 6 13:50:00 2026 -0500 Adds pyvista import for type checking commit b07bfc2b05247a05c3b0a0797a7cce66bff39cb1 Author: Peter Sharpe Date: Fri Feb 6 13:24:10 2026 -0500 formatting commit c4f17122e9a7159c69968bae271b7be3ff4ed1e3 Author: Peter Sharpe Date: Fri Feb 6 13:23:14 2026 -0500 Ruff check fixes commit b882e92ac7311e93559af50cf7be78ca3b1b6296 Author: Peter Sharpe Date: Fri Feb 6 13:20:16 2026 -0500 markdownlint noqa commit 31a317a3558cc3b1715318b453331f0510dcf64e Author: Peter Sharpe Date: Fri Feb 6 13:17:51 2026 -0500 formatting commit 79c924e318518948e9d716ac9a890f48dd776712 Author: Peter Sharpe Date: Fri Feb 6 13:15:59 2026 -0500 Refactors mesh data handling by enhancing key exclusion logic, optimizing data aggregation methods, and improving error handling for key conflicts in cell and point data conversions. commit 1e7678460c0e4e93e3f287fd5c3feb527177012d Author: Peter Sharpe Date: Fri Feb 6 13:05:52 2026 -0500 Consolidates interior angle calculation commit ae935bc61a6c97ca26495e62eeded9e90f54b57d Author: Peter Sharpe Date: Fri Feb 6 12:28:35 2026 -0500 fixes test for first-order composition accuracy commit 8a0ef4bf2ee3cc389cc01205d05b6cd4fccd7e4b Author: Peter Sharpe Date: Fri Feb 6 11:26:31 2026 -0500 Refactor of BVH to use LBVH - goes from O(N) Python loops to O(log N). commit 7b77babc40cefe86f2f200f930de239b4925e04b Author: Peter Sharpe Date: Fri Feb 6 11:20:33 2026 -0500 Unifies loops commit 6982f1d0d9b69e5e75daac43934c4774b75be3e9 Author: Peter Sharpe Date: Fri Feb 6 11:16:39 2026 -0500 Splits embedding and projection tests commit 9497bb1601664ca5d6d5f2ab0f9bc2d37da10843 Author: Peter Sharpe Date: Fri Feb 6 11:12:15 2026 -0500 Refactor spatial dimension operations: rename embed_in_spatial_dims to embed, enhance documentation, and introduce project function for reducing mesh dimensions. commit e031e3bf8205334d42b87e74349806219320d4f7 Author: Peter Sharpe Date: Fri Feb 6 11:06:15 2026 -0500 vectorized commit 7387e7250d591efb2c27456c9de818e36580d48c Author: Peter Sharpe Date: Fri Feb 6 10:27:25 2026 -0500 Further consolidation in test_facet_extraction.py commit f5149299c6154599ecc61b96d3202223f4c606e0 Author: Peter Sharpe Date: Fri Feb 6 10:23:59 2026 -0500 Consolidation of facet extraction tests commit cee6ececba9a353c5cbe9689b79bfc4cb0bdc03b Author: Peter Sharpe Date: Fri Feb 6 10:20:24 2026 -0500 Deduplicates unique edge identification commit f07f6456ddb030009b5d19052fe431b2363ea17f Author: Peter Sharpe Date: Fri Feb 6 10:18:36 2026 -0500 Test deduplication commit 4ab53f259f05051bd97ee25bacdd93537f7b9e9f Author: Peter Sharpe Date: Fri Feb 6 10:14:51 2026 -0500 Vectorizes primitive generation commit 2a21d970c54a9ac06cb3d39ba3be3679986af60b Author: Peter Sharpe Date: Fri Feb 6 10:14:09 2026 -0500 Deduplication pass commit 0df49cab8f2f21b39848b60043562db52732e7f6 Author: Peter Sharpe Date: Fri Feb 6 09:42:43 2026 -0500 Part A1 and A3 fix. commit 96b8932391e70bec1f201b9e9865e65a8b859c2a Author: Peter Sharpe Date: Fri Feb 6 09:41:26 2026 -0500 Docs fix commit 3df6a3e98e4400391b6adbc5f3e007e0fbce16af Author: Peter Sharpe Date: Fri Feb 6 09:40:57 2026 -0500 Part E: Numerical performance commit 86f65d9c77def13d907ac6dc6dad5b80eed2b43b Author: Peter Sharpe Date: Fri Feb 6 09:39:17 2026 -0500 fix for cell-based gaussian curvature commit 4bca64dac22a8eacb6f15c13dd37a13dafee0e51 Author: Peter Sharpe Date: Fri Feb 6 09:33:07 2026 -0500 Part B: Deduplication commit 08b28b98f714771b27c5e6ad9005a5b64999b55b Author: Peter Sharpe Date: Fri Feb 6 09:26:42 2026 -0500 Part C: Missing __init__ commit a1a9833b424c819d8fa04fd261638294eada33e7 Author: Peter Sharpe Date: Fri Feb 6 09:26:17 2026 -0500 Part C: Reorganization commit a5000581f82fa7395437ccee9c26a1992929d710 Author: Peter Sharpe Date: Fri Feb 6 09:04:34 2026 -0500 Update test assertions for mesh repair and add skip condition for float16 in tolerance tests commit 7f8a70eca19a47c9572abe009d05e62939493be9 Author: Peter Sharpe Date: Fri Feb 6 08:59:15 2026 -0500 Fixes a few outdated API uses commit 57f62d856424c2f9953df38dc375f4bec38cf6dc Author: Peter Sharpe Date: Fri Feb 6 08:51:59 2026 -0500 Docs in examples commit c340e769faf58ec72d68b2b0a3a017bf3d972f44 Author: Peter Sharpe Date: Fri Feb 6 08:51:15 2026 -0500 Fixes for wing weights in newer butterfly scheme commit ca5e13a884540e7dece03ad1bfbf33ff0b564bf8 Author: Peter Sharpe Date: Fri Feb 6 08:50:39 2026 -0500 Branchless voronoi computations commit e3611719823be45bf8b1647f64f58f234645e483 Author: Peter Sharpe Date: Fri Feb 6 08:50:03 2026 -0500 Deduplication using FEM weights code commit f543b52c72f13318d24e513326707cd1fb02cd1a Author: Peter Sharpe Date: Fri Feb 6 08:49:51 2026 -0500 Rewrite of 3d vertex manifold check to be fully on the GPU commit ddbfbda84c70cb3ab5265483389563e3852b1d9f Author: Peter Sharpe Date: Fri Feb 6 08:49:18 2026 -0500 More consistent device preservation to eliminate torch.compile breaks commit 06888efcf8b86cae2d455f3ed375f0e1f874e87a Author: Peter Sharpe Date: Fri Feb 6 08:44:23 2026 -0500 bvh re-use commit 9481e02f2d159f96f4000269968e85716d4a1d96 Author: Peter Sharpe Date: Thu Feb 5 17:49:04 2026 -0500 Makes this CPU-side commit 99c90896bc62a74f9b8e511672e05f2421ade335 Author: Peter Sharpe Date: Thu Feb 5 17:45:17 2026 -0500 Part D, 3/4: Performance improvements commit c75aa6e46f509f11e0f297c879b7306e8ff0b292 Author: Peter Sharpe Date: Thu Feb 5 17:38:23 2026 -0500 Part A5: deduplication commit 68ff3bbfb5bd2faae237e58ed2118e534d1759e1 Author: Peter Sharpe Date: Thu Feb 5 17:21:56 2026 -0500 Part C fixes: deduplication commit 05554731573712c3a3eae0f1c587e13a42c7802f Author: Peter Sharpe Date: Thu Feb 5 17:15:08 2026 -0500 Part A fixes commit 5bcfd4d8ce2cb863bddeac4d7b01e0a2abe7d1cb Author: Peter Sharpe Date: Thu Feb 5 17:00:19 2026 -0500 Adds test fixes commit 465a15433b968afd4a29d5b4be162316b1b7ef02 Author: Peter Sharpe Date: Thu Feb 5 16:59:19 2026 -0500 types fixes commit a15bb995d6104a43c00d0321cfdfe61245d0a917 Author: Peter Sharpe Date: Thu Feb 5 16:58:12 2026 -0500 fixes for repo rules commit 76155ba69695a66bb05276bb6fb577173063800c Author: Peter Sharpe Date: Thu Feb 5 16:57:52 2026 -0500 docs commit cd6d83e1dea47891ecfb19be7dd071f22eb48420 Author: Peter Sharpe Date: Thu Feb 5 16:32:21 2026 -0500 sync tests commit 23dd3d53678218d9cec1a154586f73fee77846a5 Author: Peter Sharpe Date: Thu Feb 5 16:32:14 2026 -0500 deduplicate commit 4ce81cbd096f94ad14c4e7e028701660bed7abbb Author: Peter Sharpe Date: Thu Feb 5 16:31:55 2026 -0500 fixes for multiple holes commit 0569964cd27303807c6fedfdc3b9192a0d72accb Author: Peter Sharpe Date: Thu Feb 5 16:31:32 2026 -0500 major simplifications commit d93ff76d546a3db1fd831700244f821ec0d01224 Author: Peter Sharpe Date: Thu Feb 5 15:31:26 2026 -0500 consolidate sampling commit cb35e48741d57d7196d89e060bd70ce7ddd320ae Author: Peter Sharpe Date: Thu Feb 5 15:27:59 2026 -0500 deduplicate commit b94df8a458187e6390de95637baa067704e83e51 Author: Peter Sharpe Date: Thu Feb 5 15:27:45 2026 -0500 numpy docstrings commit 5b49621a5a0ec30dc75b82e4d2a8b61e42272abb Author: Peter Sharpe Date: Thu Feb 5 15:27:31 2026 -0500 dead code commit 8caf478ca2ad09aab4452a0eee2c92ebfe995b27 Author: Peter Sharpe Date: Thu Feb 5 15:27:07 2026 -0500 API link commit 172c21c24a34d58d4e257116a11855829ddcd4ce Author: Peter Sharpe Date: Thu Feb 5 15:24:23 2026 -0500 Adds sharp/flat fix commit d6d911a9211171444e201e8b2109bb9b95e29e5e Author: Peter Sharpe Date: Thu Feb 5 15:19:15 2026 -0500 migrate edge extraction to natural home commit afbdd6312ddfd07db268017edddc4e2c7b494cd7 Author: Peter Sharpe Date: Thu Feb 5 15:17:57 2026 -0500 adds utilities for duplicate detection and tols commit 835b72ecf37ee51f6a1d9149e129fee2776e979b Author: Peter Sharpe Date: Thu Feb 5 15:13:26 2026 -0500 merge API commit 362cef68f114de06849331985dccf3e3153b38ea Author: Peter Sharpe Date: Thu Feb 5 15:04:03 2026 -0500 deduplicate commit 794123ac9e865b84a1c5444bf42f08cd98c7149b Author: Peter Sharpe Date: Thu Feb 5 15:03:07 2026 -0500 NumPy-style docstrings commit 378edf2e6d9c73995386cd8f9385282e49fb201d Author: Peter Sharpe Date: Thu Feb 5 13:52:50 2026 -0500 comments cleanup commit 8de90fda5d359d1ab26943634f6b44070ac8835f Author: Peter Sharpe Date: Wed Feb 4 16:22:17 2026 -0500 Adds notes on mesh quality and repair commit 8f3e542ca4a786530534e144944ece84a8075b8c Author: Peter Sharpe Date: Wed Feb 4 16:21:28 2026 -0500 final notebooks commit c0b51663fd285009a535f4a40bafc2cbcbf24041 Author: Peter Sharpe Date: Wed Feb 4 16:12:26 2026 -0500 Enhances tutorial 3 by adding execution counts and outputs for gradient, Jacobian, divergence, and curl computations, improving clarity and usability of the notebook. commit 7ac6aa7043dd5024b036e5fbe2efffcea12d2948 Author: Peter Sharpe Date: Wed Feb 4 16:10:56 2026 -0500 Adds operations commit 24e23e783714f75f02d79a83aa1043dd4a499861 Author: Peter Sharpe Date: Wed Feb 4 16:10:13 2026 -0500 Adds fixes + tests for cube_volume commit 92ccde9d6754e7f268ee69e89aad925d27e45432 Author: Peter Sharpe Date: Wed Feb 4 16:01:30 2026 -0500 Adds test for boundary extraction commit c07c91c14141837310f07a16a0a0c7250bbf76cd Author: Peter Sharpe Date: Wed Feb 4 15:52:34 2026 -0500 adds updates to 1 commit f6b3eb6e1ed2c309689dcdb369c3a1999c2561e0 Author: Peter Sharpe Date: Wed Feb 4 15:51:49 2026 -0500 Adds ability to use string axes commit 64998181a7855e218b8e8bd175cebc95c0066031 Author: Peter Sharpe Date: Wed Feb 4 15:49:14 2026 -0500 Enhance draw_mesh_matplotlib function by adding scalar_label parameter for colorbar labeling and ensure tensor data is detached before conversion to numpy. commit f776da74fec89cb939f9b60ba0909456d73c1e68 Author: Peter Sharpe Date: Wed Feb 4 15:36:36 2026 -0500 finish demo 1 commit a5bd518f456d0900230fe29b4c6cdb6a75bf8873 Author: Peter Sharpe Date: Wed Feb 4 15:22:05 2026 -0500 adds higher-order tensor flattening commit d17909cd423706f37d8234f05df3f799ed4e2696 Author: Peter Sharpe Date: Wed Feb 4 15:21:06 2026 -0500 Adds counts property commit 45d420dd1196ae11ccb4a9269462ea7c05f5d475 Author: Peter Sharpe Date: Wed Feb 4 15:15:28 2026 -0500 API fixes commit 8b0a0995c99c341520e31e66ab3bd5ddec83c618 Author: Peter Sharpe Date: Wed Feb 4 15:06:19 2026 -0500 fixes naming conventions commit 9cd94db2a31bf11d2fd024080ae9f1a3b5f24131 Author: Peter Sharpe Date: Wed Feb 4 14:35:16 2026 -0500 Implement efficient duplicate vertex detection using spatial hashing in validate.py; update tests to reflect changes in duplicate check behavior for large meshes. commit bd0432baea3440867f84e159892bc5cc075a5812 Author: Peter Sharpe Date: Wed Feb 4 14:06:38 2026 -0500 cleanup from pr review commit d6c8e9c62e88452f3a70695f20c30b1d82c6c9da Author: Peter Sharpe Date: Wed Feb 4 13:46:27 2026 -0500 Adds _edge_lookup abstraction commit 1c1dfc6f8c361d7c9775196ca0e3189543dae9e1 Author: Peter Sharpe Date: Wed Feb 4 13:46:04 2026 -0500 sync tests with API changes commit 175a86e2325216aaab54b344217ee0ca4544b9f0 Author: Peter Sharpe Date: Wed Feb 4 13:45:09 2026 -0500 docs commit 9c353b598a32100f48448de71ce5725b985ed07e Author: Peter Sharpe Date: Wed Feb 4 13:44:17 2026 -0500 performance improvements commit 233b247d4ccd43bbe861fbc3f241695d4041c8bb Author: Peter Sharpe Date: Wed Feb 4 13:25:16 2026 -0500 deduplicates edge lookups commit 74c9117263effa242c9deb58f018593d79d9ba51 Author: Peter Sharpe Date: Wed Feb 4 12:53:03 2026 -0500 deduplicate functionality commit 946e4eaaeaa8542b6d05566fcb6f236654655858 Author: Peter Sharpe Date: Wed Feb 4 12:38:08 2026 -0500 improve vectorization commit a77488287260013fcb7d2e3b3c709468dcb62647 Author: Peter Sharpe Date: Wed Feb 4 11:27:52 2026 -0500 import fixes commit c16d417440e4f0ecf83e591bdf38cac7ac43cb29 Author: Peter Sharpe Date: Wed Feb 4 11:14:56 2026 -0500 Sensible upper-bound limits. commit e72991cccedba92ef18f2c0734f1c699d21a2cd6 Author: Peter Sharpe Date: Wed Feb 4 11:14:43 2026 -0500 standardize nomenclature commit d9d5bdbc87a835f2e5bb392709f0db712aad3bf9 Author: Peter Sharpe Date: Wed Feb 4 11:14:18 2026 -0500 Adds more sensible iteration upper-bound commit fa05b75591e410e82d386e0cbfeb7f006ee13202 Author: Peter Sharpe Date: Tue Feb 3 15:12:46 2026 -0500 fixes a flux weighting bug commit d0113598b02679c1dfc15cb65809c7db5a2535b7 Author: Peter Sharpe Date: Tue Feb 3 15:08:59 2026 -0500 adds fixes commit 85221ad2e70990af47eaa6eb56dd82928171109b Author: Peter Sharpe Date: Tue Feb 3 15:08:52 2026 -0500 adds first-pass on examples commit 422e494377a4070e2b95adcc30615cecdcc43d1c Author: Peter Sharpe Date: Tue Feb 3 13:54:21 2026 -0500 pre-commit commit 317a0224d9f4c07cca6d04fa9694e6979bf3dd61 Author: Peter Sharpe Date: Tue Feb 3 13:51:14 2026 -0500 cleaner cache usage commit 3ff9ad65af0fd4f85b0a1bea803de2dd12ed138d Author: Peter Sharpe Date: Tue Feb 3 12:35:05 2026 -0500 Exposes some interfaces commit 574bec9823a648c636a407d2a7cbbec760dfa60c Author: Peter Sharpe Date: Tue Feb 3 12:34:26 2026 -0500 updates tests for fixed sampling logic commit 7c7459605d784e5c4a446e526196dd8292e99a1c Author: Peter Sharpe Date: Tue Feb 3 12:34:10 2026 -0500 Standardizes some kwargs commit 5790474c989a66c5f465af9f43fe884dc9a9c144 Author: Peter Sharpe Date: Tue Feb 3 12:33:47 2026 -0500 Fixes a bug with codimension != 0 manifolds commit 7faacce442d8ced582c0dee6e064043d1a94cc78 Author: Peter Sharpe Date: Tue Feb 3 12:06:05 2026 -0500 Add tests for watertight mesh face deletion - Introduced `TestWatertightFaceDeletion` class to validate that deleting faces from a watertight mesh results in a non-watertight mesh. - Added parameterized tests to check various scenarios of face deletion from the `lumpy_sphere` mesh. - Ensured that the tests assert the expected behavior of the mesh before and after face deletions. commit bc7d9f09380f0a1e86ee684d341a70ecd3a8bebb Merge: b66b34e8 6362c652 Author: Peter Sharpe Date: Mon Feb 2 14:54:35 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit b66b34e818ed8439c983655a81b2be5942adefba Author: Peter Sharpe Date: Mon Feb 2 14:37:00 2026 -0500 doctest fixes commit f43da0831b8db6b2c20974bbc37c53fa65f34a36 Author: Peter Sharpe Date: Mon Feb 2 12:39:30 2026 -0500 adds requirements.txt commit 6f86ec52ad89f97f18d55e699eb7d81d44bcd90c Author: Peter Sharpe Date: Fri Jan 30 15:12:43 2026 -0500 Adds license headers commit 289023c8c61b204a8dd3ae1b79c1e3eee542edcd Author: Peter Sharpe Date: Fri Jan 30 13:08:13 2026 -0500 fixes a broken path commit 13d95080a440d8e7f3614fc27d82b8da5dc24a16 Author: Peter Sharpe Date: Thu Jan 29 11:56:50 2026 -0500 makes imports compliant commit c5ffd9d8e855d1729c13ccfd7ff0c2bb89d9a892 Author: Peter Sharpe Date: Thu Jan 29 10:56:14 2026 -0500 Guards import properly commit f91f3ac38863cacb4156de25d5a681deef40678b Author: Peter Sharpe Date: Thu Jan 29 09:14:12 2026 -0500 remove ugly exception commit 6776231e6169af02d97e57dfd40f00b516457ab3 Author: Peter Sharpe Date: Wed Jan 28 23:58:59 2026 -0500 markdownlints commit 2d97294679765477fba52ade15d77544e30aa924 Author: Peter Sharpe Date: Wed Jan 28 23:56:33 2026 -0500 pre-commit fixes commit 15752688d01dd42eb7bf0182e222875c7f4bb8f5 Author: Peter Sharpe Date: Wed Jan 28 23:04:16 2026 -0500 remove unused imports commit 8c593606cf4f756f9ecd762f310639a8c7d57afc Author: Peter Sharpe Date: Wed Jan 28 22:56:13 2026 -0500 formatting commit 6ad72e60bbdeb2017d1715df25222adf4d70bc75 Merge: 1f584c91 aa54bb2b Author: Peter Sharpe Date: Wed Jan 28 20:16:37 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit 1f584c91eb3d8e23c8bebf8ea8174a55ce1fa17f Author: Peter Sharpe Date: Wed Jan 28 18:24:40 2026 -0500 Combines disjoint test files commit cda6abe34c46779f92204a043310427874b48ea5 Author: Peter Sharpe Date: Wed Jan 28 17:06:23 2026 -0500 DEC Laplacian working! commit fab24a668e928678277ff072c145093dc331de6c Author: Peter Sharpe Date: Wed Jan 28 17:05:43 2026 -0500 fix marks commit d4186a4565ebdc7ddf5e66e20d50faff63c9c357 Author: Peter Sharpe Date: Wed Jan 28 16:35:57 2026 -0500 Add comprehensive tests for lumpy ball boundary extraction, including checks for cell count, watertightness, manifold properties, and noise robustness. commit 169687023a0227a66fdaadd9477bb42270c48bd6 Author: Peter Sharpe Date: Wed Jan 28 16:35:43 2026 -0500 Enhance lumpy ball mesh generation by ensuring consistent tetrahedron orientation and vertex sorting for canonical decomposition. Added checks to fix inverted tetrahedra during assembly. commit 8f34f8d0e6fb7cd3a3c4064655000f5fae18f121 Author: Peter Sharpe Date: Wed Jan 28 16:12:57 2026 -0500 Migrate tests to lumpy ball commit f1bb886eb56892bd9678aefd2221a52c71eeabba Author: Peter Sharpe Date: Wed Jan 28 16:12:49 2026 -0500 set nontrivial amplitude as default commit 75aed635aade95b71a31a862344827b85c569423 Author: Peter Sharpe Date: Wed Jan 28 15:51:53 2026 -0500 fixed subdivision algorithms commit 1cedb9f2d82c0c65a87f397838ee1b6a8f178aad Author: Peter Sharpe Date: Wed Jan 28 15:43:24 2026 -0500 vectorizes commit 20c45c17690e94dd7d10b0b827f31b5caf195f55 Author: Peter Sharpe Date: Wed Jan 28 15:40:04 2026 -0500 Adds lumpy ball primitive for test suite commit 0f1c17d507e3f74f589153171ee527777a59d59b Author: Peter Sharpe Date: Wed Jan 28 15:15:33 2026 -0500 test improvements commit ccbd50b93581e1bc26ade7a18384fa3f6ecb3dd1 Author: Peter Sharpe Date: Wed Jan 28 15:13:08 2026 -0500 deduplicates commit 1f8d5c9d70d7744a40e84f7a69fca8e4acd0257c Author: Peter Sharpe Date: Wed Jan 28 15:11:45 2026 -0500 deduplicate commit d7eeaccbab24aaec3afaa6ef6a5c46042d0c451c Author: Peter Sharpe Date: Wed Jan 28 15:11:31 2026 -0500 deduplicate commit 9d91ef99fcb91705cd79700cdf46f15c54c7090a Author: Peter Sharpe Date: Wed Jan 28 15:09:50 2026 -0500 removes old duplicates commit 8f72b77e4b8c2d73a31347920c7e74224b0c502c Author: Peter Sharpe Date: Wed Jan 28 15:07:28 2026 -0500 more asserts commit 8db6ff208197b02531a549435af8199b88e5de8c Author: Peter Sharpe Date: Wed Jan 28 15:06:51 2026 -0500 verified fixes commit 4bc6c56978af07bd20cadb1f2cba9a2cfc5e2d39 Author: Peter Sharpe Date: Wed Jan 28 15:05:44 2026 -0500 removes old importorskip commit 0a86a3890316d1980fe9915edff84fcc4550c7f7 Author: Peter Sharpe Date: Wed Jan 28 14:31:24 2026 -0500 Switch to lumpy sphere commit b0e809dcf8c5992f3ea390ba81736f83ce8df4e7 Author: Peter Sharpe Date: Wed Jan 28 14:31:05 2026 -0500 Standardizes to use interesting meshes commit 4d7da4e82303ab3503be01b1f8e76a0b75f44e66 Author: Peter Sharpe Date: Wed Jan 28 14:09:31 2026 -0500 matches error message + path fix commit 3e277a524be0492989235355d9357ab0f7a56186 Author: Peter Sharpe Date: Wed Jan 28 14:09:09 2026 -0500 matches error message commit 0d1e2ef2ec24f68a2c92adbfe81f51d49b917791 Author: Peter Sharpe Date: Wed Jan 28 14:08:27 2026 -0500 tol fixes commit 9883c91922963e29fb4e3c9828925c50e2e01f5c Author: Peter Sharpe Date: Wed Jan 28 14:08:10 2026 -0500 path fixes commit 70b929a0e92010b945e15ca48173bce07502ee24 Merge: d56fc03e 0a4238a0 Author: Peter Sharpe Date: Tue Jan 27 22:41:55 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit d56fc03e2b91b9a1f362464072c83a8ef022d228 Merge: ed6fe82c 6436e631 Author: Peter Sharpe Date: Tue Jan 27 13:16:38 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit ed6fe82c645bc6b7fde3d41238046a143ced77c3 Author: Peter Sharpe Date: Thu Jan 22 15:13:41 2026 -0500 Refactor calculus tests to use procedural mesh generation - Replaced instances of `from_pyvista` with procedural mesh generation for consistency and improved test coverage. - Updated test cases to utilize `lumpy_sphere` and `cube_volume` for various gradient and divergence tests. - Removed unused mesh fixtures to streamline the test setup. commit a47e93b543fdc2852d470eca3dc68c44bc6c228d Author: Peter Sharpe Date: Thu Jan 22 15:12:43 2026 -0500 Add icosahedral sphere surface implementation - Introduced a new module `sphere_icosahedral.py` for creating a sphere by subdividing an icosahedron and projecting vertices onto the sphere surface. - Enhanced documentation with detailed descriptions, parameters, and examples for the `load` function. - Implemented error handling for invalid radius and subdivision inputs. - Ensured compatibility with existing mesh structures in the physicsnemo library. commit db896cdbe7ff31cd9c03ac6c92ce2fd3d2358174 Author: Peter Sharpe Date: Thu Jan 22 15:11:26 2026 -0500 Refactor text module and enhance documentation - Added version requirement for matplotlib to ensure compatibility. - Improved docstrings for clarity, including return types for `_text_to_path`. - Updated imports to use `require_version_spec` for matplotlib. - Fixed import paths in `_group_letters` and `_winding_number` functions. - Added new procedural and surface primitives: `lumpy_sphere` and `sphere_icosahedral`. - Updated test files to skip tests if matplotlib or PyVista are not available. commit 097563cc2dcc98fe9cf9a9676df155da019a1850 Merge: 258d1a59 6c7b5116 Author: Peter Sharpe Date: Tue Jan 20 14:07:24 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit 258d1a59e7c649eb998e33818c7dff38c9af6b1d Merge: f99567d5 f393908e Author: Peter Sharpe Date: Fri Jan 16 15:28:17 2026 -0500 Merge branch 'main' into psharpe/add-physicsnemo-mesh commit f99567d506e8172f3c888ecdf60472568e982736 Author: Peter Sharpe Date: Wed Jan 14 11:52:44 2026 -0500 INITIAL WIP changes to Mesh. commit de2c5f185d099de4796752a29d84022f7ae78396 Author: Peter Sharpe Date: Thu Jan 8 15:47:18 2026 -0500 Fixes lumpy sphere commit 8ec71218a08472e80e8ca67f96a52f43de2a4b5d Author: Peter Sharpe Date: Thu Jan 8 15:09:31 2026 -0500 import ordering commit 537deb0a45a362b9cded8a137c4ca93b5c851edd Author: Peter Sharpe Date: Wed Jan 7 16:30:01 2026 -0500 Adds text and lumpy sphere commit e3ce39ebde2530e0092c16020195b5c3725657b0 Author: Peter Sharpe Date: Wed Jan 7 15:16:36 2026 -0500 fixes default label --- .../globe/airfrans/run.sh | 7 + .../globe/airfrans/train.py | 15 +- .../globe/drivaer/run.sh | 7 + .../globe/drivaer/train.py | 15 +- .../experimental/models/globe/cluster_tree.py | 869 +++--- .../experimental/models/globe/field_kernel.py | 602 ++-- .../models/globe/hierarchical_acceleration.md | 20 +- .../experimental/models/globe/model.py | 226 +- pyproject.toml | 3 +- test/models/globe/test_barnes_hut_kernel.py | 333 +- uv.lock | 2720 ++++++++--------- 11 files changed, 2428 insertions(+), 2389 deletions(-) diff --git a/examples/cfd/external_aerodynamics/globe/airfrans/run.sh b/examples/cfd/external_aerodynamics/globe/airfrans/run.sh index 73678e989b..f9737c80dd 100755 --- a/examples/cfd/external_aerodynamics/globe/airfrans/run.sh +++ b/examples/cfd/external_aerodynamics/globe/airfrans/run.sh @@ -53,6 +53,13 @@ CPUS_PER_NODE=${SLURM_CPUS_ON_NODE:-$(nproc)} export OMP_NUM_THREADS=1 echo "OMP_NUM_THREADS=$OMP_NUM_THREADS (process-level parallelism via DataLoader workers; ${CPUS_PER_NODE} CPUs / ${NUM_GPUS_PER_NODE} GPUs)" +### [CUDA Allocator] +# expandable_segments: avoids the synchronizing cudaMalloc/cudaFree round-trips +# that the default segment allocator performs when chunked kernel evaluations +# stress the cache. Lets the allocator grow segments instead. +export PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True" +echo "PYTORCH_CUDA_ALLOC_CONF=$PYTORCH_CUDA_ALLOC_CONF" + ### [Sync Dependencies] # Select the right CUDA extra based on the detected driver version, # then install both the project deps and example-specific requirements. diff --git a/examples/cfd/external_aerodynamics/globe/airfrans/train.py b/examples/cfd/external_aerodynamics/globe/airfrans/train.py index 33c4ca7b3e..1c6ac22238 100644 --- a/examples/cfd/external_aerodynamics/globe/airfrans/train.py +++ b/examples/cfd/external_aerodynamics/globe/airfrans/train.py @@ -104,6 +104,7 @@ def main( n_spherical_harmonics: int = 1, theta: float = 0.0, leaf_size: int = 1, + tree_build_device: Literal["cpu", "cuda"] | None = None, airfrans_task: Literal["full", "scarce", "reynolds", "aoa"] = "full", patience_steps: int = 1600, use_profiler: bool = True, @@ -140,6 +141,8 @@ def main( n_spherical_harmonics: Number of Legendre polynomial terms for angle features. theta: Barnes-Hut opening angle. Larger = more aggressive approximation. leaf_size: Maximum sources per leaf node in the Barnes-Hut tree. + tree_build_device: Device on which to build cluster trees and run the + dual-tree Barnes-Hut traversal. ``None`` (default) uses the input's device. airfrans_task: Which AirFRANS dataset task to train on. patience_steps: ReduceLROnPlateau patience expressed in gradient steps (world-size independent). Converted to epochs internally. @@ -270,6 +273,7 @@ def main( self_regularization_beta=self_regularization_beta, latent_compression_scale=latent_compression_scale, expand_far_targets=expand_far_targets, + tree_build_device=tree_build_device, ).to(device) logger0.info(f"{output_dir.name=!r}") @@ -347,8 +351,6 @@ def main( min_lr=learning_rate / 64, threshold=1e-3, ) - scaler = torch.amp.GradScaler(device=device.type, enabled=amp) - ### [Checkpoint Save/Load] metadata_dict: dict[str, Any] = {} epoch = load_checkpoint( @@ -356,7 +358,6 @@ def main( models=base_model, optimizer=optimizer, scheduler=scheduler, - scaler=scaler, metadata_dict=metadata_dict, device=dist.device, ) @@ -430,7 +431,6 @@ def main( **config_settings, "optimizer": optimizer.__class__.__name__, "scheduler": scheduler.__class__.__name__, - "scaler": scaler.__class__.__name__, "physicsnemo_pkg_info": get_physicsnemo_pkg_info(), "world_size": dist.world_size, **{f"n_{split}_samples": len(sample_paths[split]) for split in splits}, @@ -526,15 +526,13 @@ def prepare_sample(sample: AirFRANSSample) -> AirFRANSSample: if torch.isnan(batch_loss): warnings.warn(f"{batch_loss=} at: {dist.rank=}, {epoch=}") with record_function("backward"): - scaler.scale(batch_loss).backward() + batch_loss.backward() if gradient_clip_norm is not None: - scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_( model.parameters(), max_norm=gradient_clip_norm ) with record_function("optimizer_step"): - scaler.step(optimizer) - scaler.update() + optimizer.step() all_batch_losses.append(batch_loss.detach().clone()) for k, v in batch_loss_components.items(): all_batch_loss_components[k].append(v.detach().clone()) @@ -614,7 +612,6 @@ def save_ckpt() -> None: models=base_model, optimizer=optimizer, scheduler=scheduler, - scaler=scaler, epoch=epoch, metadata=checkpoint_metadata(), ) diff --git a/examples/cfd/external_aerodynamics/globe/drivaer/run.sh b/examples/cfd/external_aerodynamics/globe/drivaer/run.sh index b0acaca947..df10acf52c 100755 --- a/examples/cfd/external_aerodynamics/globe/drivaer/run.sh +++ b/examples/cfd/external_aerodynamics/globe/drivaer/run.sh @@ -51,6 +51,13 @@ CPUS_PER_NODE=${SLURM_CPUS_ON_NODE:-$(nproc)} export OMP_NUM_THREADS=1 echo "OMP_NUM_THREADS=$OMP_NUM_THREADS (process-level parallelism via DataLoader workers; ${CPUS_PER_NODE} CPUs / ${NUM_GPUS_PER_NODE} GPUs)" +### [CUDA Allocator] +# expandable_segments: avoids the synchronizing cudaMalloc/cudaFree round-trips +# that the default segment allocator performs when chunked kernel evaluations +# stress the cache. Lets the allocator grow segments instead. +export PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True" +echo "PYTORCH_CUDA_ALLOC_CONF=$PYTORCH_CUDA_ALLOC_CONF" + ### [Sync Dependencies] # Select the right CUDA extra based on the detected driver version, # then install both the project deps and example-specific requirements. diff --git a/examples/cfd/external_aerodynamics/globe/drivaer/train.py b/examples/cfd/external_aerodynamics/globe/drivaer/train.py index a5889791c4..98fa47bf99 100644 --- a/examples/cfd/external_aerodynamics/globe/drivaer/train.py +++ b/examples/cfd/external_aerodynamics/globe/drivaer/train.py @@ -111,6 +111,7 @@ def main( n_spherical_harmonics: int = 4, theta: float = 1.0, leaf_size: int = 1, + tree_build_device: Literal["cpu", "cuda"] | None = None, n_faces_per_boundary: int = 80_000, patience_steps: int = 1600, use_profiler: bool = True, @@ -152,6 +153,8 @@ def main( theta: Barnes-Hut opening angle. Larger values are more aggressive (more approximation, faster). 0 = exact. leaf_size: Maximum sources per leaf node in the Barnes-Hut tree. + tree_build_device: Device on which to build cluster trees and run the + dual-tree Barnes-Hut traversal. ``None`` (default) uses the input's device. n_faces_per_boundary: Target boundary mesh face count after decimation. patience_steps: ReduceLROnPlateau patience expressed in gradient steps (world-size independent). Converted to epochs internally. @@ -281,6 +284,7 @@ def main( self_regularization_beta=self_regularization_beta, latent_compression_scale=latent_compression_scale, expand_far_targets=expand_far_targets, + tree_build_device=tree_build_device, ).to(device) logger0.info(f"{output_dir.name=!r}") @@ -358,8 +362,6 @@ def main( min_lr=learning_rate / 64, threshold=1e-3, ) - scaler = torch.amp.GradScaler(device=device.type, enabled=amp) - ### [Checkpoint Save/Load] metadata_dict: dict[str, Any] = {} epoch = load_checkpoint( @@ -367,7 +369,6 @@ def main( models=base_model, optimizer=optimizer, scheduler=scheduler, - scaler=scaler, metadata_dict=metadata_dict, device=dist.device, ) @@ -438,7 +439,6 @@ def main( **config_settings, "optimizer": optimizer.__class__.__name__, "scheduler": scheduler.__class__.__name__, - "scaler": scaler.__class__.__name__, "physicsnemo_pkg_info": get_physicsnemo_pkg_info(), "world_size": dist.world_size, **{f"n_{split}_samples": len(sample_paths[split]) for split in splits}, @@ -505,15 +505,13 @@ def run_epoch(split: Split) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: if torch.isnan(batch_loss): warnings.warn(f"{batch_loss=} at: {dist.rank=}, {epoch=}") with record_function("backward"): - scaler.scale(batch_loss).backward() + batch_loss.backward() if gradient_clip_norm is not None: - scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_( model.parameters(), max_norm=gradient_clip_norm ) with record_function("optimizer_step"): - scaler.step(optimizer) - scaler.update() + optimizer.step() all_batch_losses.append(batch_loss.detach().clone()) for k, v in batch_loss_components.items(): @@ -594,7 +592,6 @@ def save_ckpt() -> None: models=base_model, optimizer=optimizer, scheduler=scheduler, - scaler=scaler, epoch=epoch, metadata=checkpoint_metadata(), ) diff --git a/physicsnemo/experimental/models/globe/cluster_tree.py b/physicsnemo/experimental/models/globe/cluster_tree.py index 7b0637a7a7..4462c58e75 100644 --- a/physicsnemo/experimental/models/globe/cluster_tree.py +++ b/physicsnemo/experimental/models/globe/cluster_tree.py @@ -184,46 +184,6 @@ def validate(self) -> None: ) -# --------------------------------------------------------------------------- -# Segmented reduction helpers -# --------------------------------------------------------------------------- - - -def _segmented_weighted_sum( - values: Float[torch.Tensor, "n *features"], - weights: Float[torch.Tensor, " n"], - seg_ids: Int[torch.Tensor, " n"], - n_segments: int, -) -> Float[torch.Tensor, "n_segments *features"]: - """Compute weighted sum per segment via scatter_add. - - Parameters - ---------- - values : torch.Tensor - Values to aggregate, shape ``(N,)`` or ``(N, F)``. - weights : torch.Tensor - Per-element weights, shape ``(N,)``. - seg_ids : torch.Tensor - Segment assignment for each element, shape ``(N,)``, int64. - n_segments : int - Total number of output segments. - - Returns - ------- - torch.Tensor - Weighted sums, shape ``(n_segments,)`` or ``(n_segments, F)``. - """ - weighted = values * (weights.unsqueeze(-1) if values.ndim > 1 else weights) - out = torch.zeros( - (n_segments,) + values.shape[1:], - dtype=values.dtype, - device=values.device, - ) - idx = seg_ids.unsqueeze(-1).expand_as(weighted) if weighted.ndim > 1 else seg_ids - out.scatter_add_(0, idx, weighted) - return out - - def _expand_dual_leaf_hits( target_leaf_ids: Int[torch.Tensor, " n_leaf_pairs"], source_leaf_ids: Int[torch.Tensor, " n_leaf_pairs"], @@ -271,12 +231,13 @@ def _expand_dual_leaf_hits( theta_sq = theta * theta n_pairs = target_leaf_ids.shape[0] - def _empty_result(): - e = torch.empty(0, dtype=torch.long, device=device) - return e, e.clone(), e.clone(), e.clone(), e.clone(), e.clone(), e.clone(), e.clone(), e.clone() - - if n_pairs == 0: - return _empty_result() + ### This function is intentionally written without ``if X.any():`` / + ### ``int(X.sum())`` early-exit branches. Each such branch was a + ### CPU-GPU sync point in the dual-traversal hot loop; the sync count + ### was the dominant CPU stall in profiling. All downstream operations + ### (boolean indexing, ``_ragged_arange``, ``argsort``, ``scatter``, + ### ``torch.bincount``) handle zero-element inputs correctly, so we + ### let empty intermediate tensors flow through unconditionally. t_starts = target_tree.leaf_start[target_leaf_ids] t_counts = target_tree.leaf_count[target_leaf_ids] @@ -299,18 +260,20 @@ def _empty_result(): dist_sq_t = (target_pts - clamped_t).pow(2).sum(dim=-1) target_is_far = dist_sq_t * theta_sq > source_tree.node_diameter_sq[src_leaf_per_target] - ### (near, far) output - nf_target_ids = target_point_ids[target_is_far] - nf_source_node_ids = src_leaf_per_target[target_is_far] - - ### Survivors: targets that failed the per-target test - surv_mask = ~target_is_far - if not surv_mask.any(): - e = torch.empty(0, dtype=torch.long, device=device) - return e, e.clone(), nf_target_ids, nf_source_node_ids, e.clone(), e.clone(), e.clone(), e.clone(), e.clone() - - surv_point_ids = target_point_ids[surv_mask] - surv_lp_ids = leaf_pair_ids_t[surv_mask] + ### (near, far) output. ``target_is_far`` is consumed by two + ### indexings; doing one ``nonzero`` and reusing the integer index + ### saves one sync (each ``tensor[bool_mask]`` lowers to a + ### synchronizing ``aten::nonzero`` to size the output). + far_idx_t = target_is_far.nonzero(as_tuple=True)[0] + nf_target_ids = target_point_ids[far_idx_t] + nf_source_node_ids = src_leaf_per_target[far_idx_t] + + ### Survivors: targets that failed the per-target test. Empty + ### survivors are fine; downstream ops produce empty tensors. + ### Same dedup trick as ``far_idx_t`` above. + surv_idx = (~target_is_far).nonzero(as_tuple=True)[0] + surv_point_ids = target_point_ids[surv_idx] + surv_lp_ids = leaf_pair_ids_t[surv_idx] # ================================================================== # Stage 2: per-source test against target leaf AABBs @@ -328,10 +291,13 @@ def _empty_result(): dist_sq_s = (src_pts - clamped_s).pow(2).sum(dim=-1) source_is_far = dist_sq_s * theta_sq > target_tree.node_diameter_sq[tgt_leaf_per_src] - ### (far, near) output: source points far from the target leaf - fn_source_ids = src_point_ids[source_is_far] - fn_target_node_ids = tgt_leaf_per_src[source_is_far] - fn_lp_ids = leaf_pair_ids_s[source_is_far] + ### (far, near) output: source points far from the target leaf. + ### Three indexings off the same mask collapse to one ``nonzero`` + + ### integer indexing, saving two syncs. + far_idx_s = source_is_far.nonzero(as_tuple=True)[0] + fn_source_ids = src_point_ids[far_idx_s] + fn_target_node_ids = tgt_leaf_per_src[far_idx_s] + fn_lp_ids = leaf_pair_ids_s[far_idx_s] # ================================================================== # Build (far, near) broadcast mapping @@ -341,13 +307,15 @@ def _empty_result(): # Only include survivors from leaf pairs that have fn sources; # survivors from all-close leaf pairs are not referenced by any # fn_broadcast_starts/counts entry. + # ``index_put_`` with empty indices is a no-op, so no shape branch. has_fn_source = torch.zeros(n_pairs, dtype=torch.bool, device=device) - if fn_lp_ids.numel() > 0: - has_fn_source[fn_lp_ids] = True + has_fn_source[fn_lp_ids] = True fn_active_mask = has_fn_source[surv_lp_ids] - active_surv_ids = surv_point_ids[fn_active_mask] - active_surv_lp_ids = surv_lp_ids[fn_active_mask] + ### Shared mask -> dedup the boolean indexing (saves one sync). + fn_active_idx = fn_active_mask.nonzero(as_tuple=True)[0] + active_surv_ids = surv_point_ids[fn_active_idx] + active_surv_lp_ids = surv_lp_ids[fn_active_idx] surv_sort = active_surv_lp_ids.argsort(stable=True) fn_broadcast_targets = active_surv_ids[surv_sort] @@ -361,40 +329,28 @@ def _empty_result(): # ================================================================== # Reduced Cartesian product: survivors × close sources only # ================================================================== - close_mask = ~source_is_far - close_src_ids = src_point_ids[close_mask] - close_lp_ids = leaf_pair_ids_s[close_mask] - - if close_src_ids.numel() == 0 or surv_point_ids.numel() == 0: - e = torch.empty(0, dtype=torch.long, device=device) - return ( - e, e.clone(), - nf_target_ids, nf_source_node_ids, - fn_target_node_ids, fn_source_ids, - fn_broadcast_targets, fn_broadcast_starts, fn_broadcast_counts, - ) - - ### Group close sources by leaf pair for contiguous access + ### Survivors of the per-source far test. Shared mask + dedup as + ### above. + close_idx = (~source_is_far).nonzero(as_tuple=True)[0] + close_src_ids = src_point_ids[close_idx] + close_lp_ids = leaf_pair_ids_s[close_idx] + + ### Group close sources by leaf pair for contiguous access. When + ### either side is empty the per-segment counts are all zero and + ### the expansion below produces empty (near, near) output - no + ### early-exit needed. close_sort = close_lp_ids.argsort(stable=True) sorted_close_srcs = close_src_ids[close_sort] close_counts_per_lp = torch.bincount(close_lp_ids, minlength=n_pairs) close_starts_per_lp = close_counts_per_lp.cumsum(0) - close_counts_per_lp - ### Each survivor expands against its leaf pair's close sources + ### Each survivor expands against its leaf pair's close sources. per_surv_close_counts = close_counts_per_lp[surv_lp_ids] - total_nn = int(per_surv_close_counts.sum()) - - if total_nn == 0: - e = torch.empty(0, dtype=torch.long, device=device) - return ( - e, e.clone(), - nf_target_ids, nf_source_node_ids, - fn_target_node_ids, fn_source_ids, - fn_broadcast_targets, fn_broadcast_starts, fn_broadcast_counts, - ) - - expanded_near_tgts = torch.repeat_interleave(surv_point_ids, per_surv_close_counts) per_surv_close_starts = close_starts_per_lp[surv_lp_ids] + + expanded_near_tgts = torch.repeat_interleave( + surv_point_ids, per_surv_close_counts + ) src_positions_nn, _ = _ragged_arange(per_surv_close_starts, per_surv_close_counts) expanded_near_srcs = sorted_close_srcs[src_positions_nn] @@ -458,15 +414,6 @@ class ClusterTree: Original source point coordinates, shape ``(n_sources, D)``. max_depth : torch.Tensor Scalar tensor storing the tree depth (for fixed-iteration traversal). - leaf_node_ids : torch.Tensor - Indices of leaf nodes, shape ``(n_leaves,)``. Precomputed during - tree construction so ``compute_source_aggregates`` avoids a - data-dependent ``torch.where`` that would break ``torch.compile``. - leaf_seg_ids : torch.Tensor - Per-source compact leaf segment ID in sorted order, shape - ``(n_sources,)``. Maps each source to the index of its - containing leaf within ``leaf_node_ids``, used for segmented - reductions in ``compute_source_aggregates``. """ node_aabb_min: torch.Tensor @@ -482,17 +429,6 @@ class ClusterTree: sorted_source_order: torch.Tensor source_points: torch.Tensor max_depth: torch.Tensor - internal_level_ids: torch.Tensor - internal_level_offsets: torch.Tensor - # internal_level_ids and internal_level_offsets store the tree's - # internal node IDs in CSR-packed level order (shallowest first). - # Computed once during from_points() and reused by all bottom-up - # propagation routines (_propagate_centroids_bottom_up, - # _compute_node_strengths) to avoid recomputing the BFS traversal - # that discovers this ordering. Stored as tensors (not a Python - # list) so they participate in tensorclass .to(device) moves. - leaf_node_ids: torch.Tensor - leaf_seg_ids: torch.Tensor @property def n_nodes(self) -> int: @@ -509,25 +445,6 @@ def n_spatial_dims(self) -> int: """Spatial dimensionality.""" return self.node_aabb_min.shape[1] - @property - def n_leaves(self) -> int: - """Number of leaf nodes in the tree.""" - return self.leaf_node_ids.shape[0] - - @property - def internal_nodes_per_level(self) -> list[torch.Tensor]: - """Internal node IDs grouped by tree depth, shallowest first. - - Reconstructed from CSR-packed ``internal_level_ids`` and - ``internal_level_offsets`` tensors that are computed once during - tree construction in :meth:`from_points`. - """ - offsets = self.internal_level_offsets - return [ - self.internal_level_ids[offsets[i] : offsets[i + 1]] - for i in range(len(offsets) - 1) - ] - @classmethod def from_points( cls, @@ -583,10 +500,6 @@ def from_points( sorted_source_order=empty_long, source_points=points, max_depth=torch.tensor(0, dtype=torch.long, device=device), - internal_level_ids=empty_long, - internal_level_offsets=torch.tensor([0], dtype=torch.long, device=device), - leaf_node_ids=empty_long, - leaf_seg_ids=empty_long, batch_size=torch.Size([]), ) @@ -633,6 +546,19 @@ def from_points( internal_nodes_per_level: list[torch.Tensor] = [] + ### Defer leaf-segment processing to a single end-of-loop pass. + # Per-iter ``torch.where(is_leaf_seg)[0]`` was a CPU-GPU sync + # point on every level of every tree (~16 levels x 4 trees x + # 28 samples). We instead accumulate (node_id, start, size, + # validity) for each segment seen during the loop and pay one + # boolean compaction at the end. ``torch.where(is_internal_seg)`` + # remains in-loop because the next iteration's active segments + # are derived from this iteration's internals. + leaf_seg_node_ids: list[torch.Tensor] = [] + leaf_seg_starts: list[torch.Tensor] = [] + leaf_seg_sizes: list[torch.Tensor] = [] + leaf_seg_validity: list[torch.Tensor] = [] + while len(seg_starts) > 0: seg_sizes = seg_ends - seg_starts @@ -647,30 +573,11 @@ def from_points( is_leaf_seg = seg_sizes <= leaf_size is_internal_seg = ~is_leaf_seg - ### Process leaf segments - leaf_indices = torch.where(is_leaf_seg)[0] - if len(leaf_indices) > 0: - leaf_nids = seg_node_ids[leaf_indices] - l_starts = seg_starts[leaf_indices] - l_sizes = seg_sizes[leaf_indices] - - leaf_start_buf[leaf_nids] = l_starts - leaf_count_buf[leaf_nids] = l_sizes - - # Compute leaf AABBs via segmented reduction - _fill_leaf_aabbs( - leaf_nids, - l_starts, - l_sizes, - sorted_points, - aabb_min_buf, - aabb_max_buf, - ) - - # Compute leaf total areas - _fill_leaf_total_areas( - leaf_nids, l_starts, l_sizes, sorted_areas, total_area_buf - ) + ### Defer leaf processing: accumulate, compact once at end. + leaf_seg_node_ids.append(seg_node_ids) + leaf_seg_starts.append(seg_starts) + leaf_seg_sizes.append(seg_sizes) + leaf_seg_validity.append(is_leaf_seg) ### Process internal segments: split at the midpoint of the # morton-sorted range. Because morton codes preserve spatial @@ -704,6 +611,28 @@ def from_points( seg_ends = torch.cat([midpoints, int_ends]) seg_node_ids = torch.cat([left_ids, right_ids]) + ### Single-pass leaf fill: one boolean compaction across all + ### levels, then one combined ``_fill_leaf_aggregates`` call + ### instead of one per level. + if leaf_seg_node_ids: + all_leaf_validity = torch.cat(leaf_seg_validity) + leaf_nids = torch.cat(leaf_seg_node_ids)[all_leaf_validity] + l_starts = torch.cat(leaf_seg_starts)[all_leaf_validity] + l_sizes = torch.cat(leaf_seg_sizes)[all_leaf_validity] + + leaf_start_buf[leaf_nids] = l_starts + leaf_count_buf[leaf_nids] = l_sizes + _fill_leaf_aggregates( + leaf_nids, + l_starts, + l_sizes, + sorted_points, + sorted_areas, + aabb_min_buf, + aabb_max_buf, + total_area_buf, + ) + # ----------------------------------------------------------- # Phase 2: Bottom-up AABB and area propagation # ----------------------------------------------------------- @@ -726,43 +655,12 @@ def from_points( aabb_max_trimmed = aabb_max_buf[:node_count] diameter_sq = (aabb_max_trimmed - aabb_min_trimmed).pow(2).sum(dim=-1) - ### Precompute leaf indices and per-source segment IDs so that - ### compute_source_aggregates() avoids a data-dependent - ### torch.where() that would break torch.compile tracing. leaf_count_trimmed = leaf_count_buf[:node_count] - _leaf_node_ids = torch.where(leaf_count_trimmed > 0)[0] - _leaf_starts = leaf_start_buf[_leaf_node_ids] - _leaf_counts = leaf_count_trimmed[_leaf_node_ids] - _positions, _compact_ids = _ragged_arange( - _leaf_starts, _leaf_counts, total=n_points, - ) - _leaf_seg_ids = torch.zeros(n_points, dtype=torch.long, device=device) - _leaf_seg_ids[_positions] = _compact_ids - logger.debug( - "ClusterTree: %d points -> %d nodes (%d leaves), " - "depth %d, leaf_size=%d", - n_points, node_count, _leaf_node_ids.shape[0], actual_depth, - leaf_size, + "ClusterTree: %d points -> %d nodes, depth %d, leaf_size=%d", + n_points, node_count, actual_depth, leaf_size, ) - ### Pack the per-level internal node IDs into CSR tensors so they - ### survive as tensorclass attributes (device-safe, no BFS needed later). - _level_ids = ( - torch.cat(internal_nodes_per_level) - if internal_nodes_per_level - else torch.empty(0, dtype=torch.long, device=device) - ) - _level_lengths = torch.tensor( - [len(t) for t in internal_nodes_per_level], - dtype=torch.long, - device=device, - ) - _level_offsets = torch.cat([ - torch.zeros(1, dtype=torch.long, device=device), - _level_lengths.cumsum(0), - ]) - return cls( node_aabb_min=aabb_min_trimmed, node_aabb_max=aabb_max_trimmed, @@ -777,10 +675,6 @@ def from_points( sorted_source_order=sorted_order, source_points=points, max_depth=torch.tensor(actual_depth, dtype=torch.long, device=device), - internal_level_ids=_level_ids, - internal_level_offsets=_level_offsets, - leaf_node_ids=_leaf_node_ids, - leaf_seg_ids=_leaf_seg_ids, batch_size=torch.Size([]), ) @@ -811,66 +705,85 @@ def compute_source_aggregates( SourceAggregates Per-node aggregated centroids and source data. """ - if self.n_nodes == 0: - D = source_points.shape[1] - device = source_points.device - dtype = source_points.dtype - return SourceAggregates( - node_centroid=torch.empty((0, D), dtype=dtype, device=device), - node_source_data=None, - ) - device = source_points.device dtype = source_points.dtype D = source_points.shape[1] n_nodes = self.n_nodes + if n_nodes == 0: + return SourceAggregates( + node_centroid=torch.empty((0, D), dtype=dtype, device=device), + node_source_data=None, + ) - ### Leaf aggregation: compute per-leaf centroids and source data. - ### leaf_node_ids and leaf_seg_ids were precomputed during tree - ### construction (from_points) to avoid data-dependent torch.where - ### and _ragged_arange calls that would break torch.compile. - with record_function("cluster_tree::leaf_aggregation"): - leaf_node_ids = self.leaf_node_ids - n_leaves = leaf_node_ids.shape[0] - seg_ids_compact = self.leaf_seg_ids - - sorted_points = source_points[self.sorted_source_order] - sorted_areas = areas[self.sorted_source_order] - - centroid_buf = torch.zeros(n_nodes, D, dtype=dtype, device=device) + ### Range-sum aggregation via morton-sorted prefix sums. + # Each node covers a contiguous range + # [node_range_start, node_range_start + node_range_count) in + # morton-sorted source order, so any node-subtree sum is just + # ``prefix[end] - prefix[start]``. This replaces the old + # leaf-aggregation + bottom-up Python loop, which were the + # dominant CPU + GPU costs in ``compute_source_aggregates`` + # (~2 s combined per training step in profiling). + # + # The cumsum and the range subtract are done in fp64 because fp32 + # suffers catastrophic cancellation when ``range_sum << cumsum_total``, + # which is the regime of small leaves (``leaf_size=1``) in a large + # tree built over offset (e.g. all-positive) coordinates. At + # drivaer scale (``N=1M``, coords ~5 m), fp32 leaf centroids had + # median ~2 % relative error and p99 ~100 % wrong. Lifting the + # cumsum to fp64 brings this back to fp32 epsilon (~1e-7) and adds + # <1 % wall-clock to the training step (cumsum is ~2.3x slower in + # fp64, but cumsum is a tiny fraction of step time). CUDA fp32 + # cumsum is also non-deterministic across runs (pytorch#75240); + # fp64 cumsum is much less affected. + sorted_points = source_points[self.sorted_source_order] + sorted_areas = areas[self.sorted_source_order] + weighted_points_64 = (sorted_points * sorted_areas.unsqueeze(-1)).double() + + ### Leading-zero padding makes ``prefix[i]`` the sum of the first + ### ``i`` elements, so subtraction gives the half-open range sum. + cumsum_weighted_points = torch.nn.functional.pad( + torch.cumsum(weighted_points_64, dim=0), (0, 0, 1, 0) + ) - leaf_centroids = _segmented_weighted_sum( - sorted_points, sorted_areas, seg_ids_compact, n_leaves - ) - leaf_total_areas = self.node_total_area[leaf_node_ids] - safe_areas = leaf_total_areas.clamp(min=1e-30) - leaf_centroids = leaf_centroids / safe_areas.unsqueeze(-1) - centroid_buf[leaf_node_ids] = leaf_centroids - - node_source_data: TensorDict | None = None - if source_data is not None: - sorted_source_data = source_data[self.sorted_source_order] - node_source_data = _aggregate_source_data_leaves( - sorted_source_data, - sorted_areas, - seg_ids_compact, - n_leaves, - leaf_node_ids, - leaf_total_areas, - n_nodes, - device, + starts = self.node_range_start + ends = starts + self.node_range_count + node_total_weighted_pts = ( + cumsum_weighted_points[ends] - cumsum_weighted_points[starts] + ) + ### ``self.node_total_area`` was filled during tree construction + ### via the bottom-up AABB pass; reuse it instead of recomputing. + ### Promote to fp64 here so the divide stays in fp64 alongside the + ### range-sum; cast back to ``source_points.dtype`` at the end. + safe_areas_64 = self.node_total_area.double().clamp(min=1e-30) + with record_function("cluster_tree::node_centroids"): + centroid_buf = ( + node_total_weighted_pts / safe_areas_64.unsqueeze(-1) + ).to(source_points.dtype) + + node_source_data: TensorDict | None = None + if source_data is not None: + sorted_source_data = source_data[self.sorted_source_order] + inv_safe_areas_64 = 1.0 / safe_areas_64 + + def _aggregate_via_prefix_sum(tensor: torch.Tensor) -> torch.Tensor: + trailing_shape = tensor.shape[1:] + ### Flatten trailing dims so the prefix sum is over a + ### single feature axis - avoids materialising a + ### per-feature kernel chain inside ``cumsum``. Same fp64 + ### upcast rationale as the centroid branch above. + flat = tensor.reshape(tensor.shape[0], -1) + weighted_64 = (flat * sorted_areas.unsqueeze(-1)).double() + cumsum_weighted = torch.nn.functional.pad( + torch.cumsum(weighted_64, dim=0), (0, 0, 1, 0) ) + node_weighted_sum = cumsum_weighted[ends] - cumsum_weighted[starts] + node_avg = node_weighted_sum * inv_safe_areas_64.unsqueeze(-1) + return node_avg.reshape((n_nodes,) + trailing_shape).to(tensor.dtype) - ### Bottom-up propagation: internal node centroids - with record_function("cluster_tree::bottom_up_propagation"): - _propagate_centroids_bottom_up( - centroid_buf, - node_source_data, - self.node_left_child, - self.node_right_child, - self.node_total_area, - self.internal_nodes_per_level, - ) + with record_function("cluster_tree::node_source_data"): + node_source_data = sorted_source_data.apply( + _aggregate_via_prefix_sum, batch_size=[n_nodes] + ) return SourceAggregates( node_centroid=centroid_buf, @@ -945,10 +858,21 @@ def find_dual_interaction_pairs( active_tgt_nodes = torch.zeros(1, dtype=torch.long, device=device) active_src_nodes = torch.zeros(1, dtype=torch.long, device=device) + ### Output streams. Far/(near,far)-from-far entries are appended + ### unfiltered together with a per-iteration ``is_far`` validity + ### mask; one boolean compaction at the very end of the function + ### eliminates the per-iteration sync that ``active[is_far]`` + ### would otherwise force. + far_tgt_unfiltered_list: list[torch.Tensor] = [] + far_src_unfiltered_list: list[torch.Tensor] = [] + far_validity_list: list[torch.Tensor] = [] + + ### Outputs from leaf-leaf expansion still get appended already- + ### filtered, since ``_expand_dual_leaf_hits`` works on a + ### compacted (target_leaf_id, source_leaf_id) batch. We accept + ### one boolean indexing sync per iteration here. near_target_list: list[torch.Tensor] = [] near_source_list: list[torch.Tensor] = [] - far_tgt_node_list: list[torch.Tensor] = [] - far_src_node_list: list[torch.Tensor] = [] nf_target_list: list[torch.Tensor] = [] nf_source_node_list: list[torch.Tensor] = [] fn_tgt_node_list: list[torch.Tensor] = [] @@ -958,10 +882,18 @@ def find_dual_interaction_pairs( fn_bcast_counts_list: list[torch.Tensor] = [] fn_bcast_offset = 0 - max_iters = int(target_tree.max_depth.item()) + int(source_tree.max_depth.item()) + 1 + ### Loop bound: every iteration descends at least one tree level + ### on at least one side, so ``2 * total_levels + safety`` is a + ### hard upper bound that requires no GPU->CPU read. Using + ### ``int(max_depth.item())`` as before would force two syncs + ### per call before we even start the loop. + n_src_levels = max(1, int(source_tree.n_sources).bit_length()) + n_tgt_levels = max(1, int(target_tree.n_sources).bit_length()) + max_iters = 2 * (n_src_levels + n_tgt_levels) + 4 depth = 0 for depth in range(max_iters): + ### ``numel()`` is a shape query (Python int), not a sync. if active_tgt_nodes.numel() == 0: break @@ -979,158 +911,159 @@ def find_dual_interaction_pairs( ) min_dist_sq = gap.pow(2).sum(dim=-1) - diam_T = target_tree.node_diameter_sq[active_tgt_nodes].sqrt() - diam_S = source_tree.node_diameter_sq[active_src_nodes].sqrt() + diam_sq_T = target_tree.node_diameter_sq[active_tgt_nodes] + diam_sq_S = source_tree.node_diameter_sq[active_src_nodes] + diam_T = diam_sq_T.sqrt() + diam_S = diam_sq_S.sqrt() combined_diam_sq = (diam_T + diam_S).pow(2) is_far = min_dist_sq * theta_sq > combined_diam_sq - ### Classify active pairs + ### Classify active pairs (boolean masks over the full + ### active set; combined later via ``need_split``). is_leaf_T = target_tree.leaf_count[active_tgt_nodes] > 0 is_leaf_S = source_tree.leaf_count[active_src_nodes] > 0 - - ### 1. Far-field: well-separated node pairs - if is_far.any(): - if expand_far_targets: - # Expand target nodes to individual points, - # converting (far,far) → (near,far). - far_tgt_nids = active_tgt_nodes[is_far] - far_src_nids = active_src_nodes[is_far] - starts = target_tree.node_range_start[far_tgt_nids] - counts = target_tree.node_range_count[far_tgt_nids] - positions, pair_ids = _ragged_arange(starts, counts) - nf_target_list.append( - target_tree.sorted_source_order[positions] - ) - nf_source_node_list.append(far_src_nids[pair_ids]) - else: - far_tgt_node_list.append(active_tgt_nodes[is_far]) - far_src_node_list.append(active_src_nodes[is_far]) - - ### 2. Near-field, both leaves: two-stage filtered expansion. - # Stage 1 (per-target) -> (near,far). - # Stage 2 (per-source) -> (far,near). - # Remainder -> (near,near). near_leaf_leaf = (~is_far) & is_leaf_T & is_leaf_S - if near_leaf_leaf.any(): - ( - nn_tgts, nn_srcs, - nf_tgts, nf_snids, - fn_tnids, fn_sids, - fn_btgts, fn_bstarts, fn_bcounts, - ) = _expand_dual_leaf_hits( - active_tgt_nodes[near_leaf_leaf], - active_src_nodes[near_leaf_leaf], - target_tree, - source_tree, - theta, - ) - near_target_list.append(nn_tgts) - near_source_list.append(nn_srcs) - nf_target_list.append(nf_tgts) - nf_source_node_list.append(nf_snids) - fn_tgt_node_list.append(fn_tnids) - fn_src_list.append(fn_sids) - fn_bcast_targets_list.append(fn_btgts) - fn_bcast_starts_list.append(fn_bstarts + fn_bcast_offset) - fn_bcast_counts_list.append(fn_bcounts) - fn_bcast_offset += fn_btgts.shape[0] - - ### 3. Need to split: at least one is internal, not far need_split = (~is_far) & (~near_leaf_leaf) - if not need_split.any(): - break - - split_tgt = active_tgt_nodes[need_split] - split_src = active_src_nodes[need_split] - split_is_leaf_T = is_leaf_T[need_split] - split_is_leaf_S = is_leaf_S[need_split] - split_diam_sq_T = target_tree.node_diameter_sq[split_tgt] - split_diam_sq_S = source_tree.node_diameter_sq[split_src] - - ### Splitting decision: split the larger node. - # If equal (including self-interaction T==S), split both. - # If one side is a leaf, can only split the other. - do_split_T = (~split_is_leaf_T) & ( - split_is_leaf_S | (split_diam_sq_T >= split_diam_sq_S) - ) - do_split_S = (~split_is_leaf_S) & ( - split_is_leaf_T | (split_diam_sq_S >= split_diam_sq_T) - ) - ### Generate child pairs for each split case - next_tgt_parts: list[torch.Tensor] = [] - next_src_parts: list[torch.Tensor] = [] - - # Case A: split T only (T internal, S leaf or T strictly larger) - case_T_only = do_split_T & (~do_split_S) - if case_T_only.any(): - t_ids = split_tgt[case_T_only] - s_ids = split_src[case_T_only] - left_T = target_tree.node_left_child[t_ids] - right_T = target_tree.node_right_child[t_ids] - for child_T in (left_T, right_T): - valid = child_T >= 0 - if valid.any(): - next_tgt_parts.append(child_T[valid]) - next_src_parts.append(s_ids[valid]) - - # Case B: split S only (S internal, T leaf or S strictly larger) - case_S_only = do_split_S & (~do_split_T) - if case_S_only.any(): - t_ids = split_tgt[case_S_only] - s_ids = split_src[case_S_only] - left_S = source_tree.node_left_child[s_ids] - right_S = source_tree.node_right_child[s_ids] - for child_S in (left_S, right_S): - valid = child_S >= 0 - if valid.any(): - next_tgt_parts.append(t_ids[valid]) - next_src_parts.append(child_S[valid]) - - # Case C: split both (both internal, equal diameter or T==S) - case_both = do_split_T & do_split_S - if case_both.any(): - t_ids = split_tgt[case_both] - s_ids = split_src[case_both] - left_T = target_tree.node_left_child[t_ids] - right_T = target_tree.node_right_child[t_ids] - left_S = source_tree.node_left_child[s_ids] - right_S = source_tree.node_right_child[s_ids] - for child_T in (left_T, right_T): - for child_S in (left_S, right_S): - valid = (child_T >= 0) & (child_S >= 0) - if valid.any(): - next_tgt_parts.append(child_T[valid]) - next_src_parts.append(child_S[valid]) - - if next_tgt_parts: - active_tgt_nodes = torch.cat(next_tgt_parts) - active_src_nodes = torch.cat(next_src_parts) + ### 1. Far-field: deferred-compaction path. + if expand_far_targets: + ### Mask counts to zero for non-far entries; the ragged + ### expansion then naturally skips them. ``pair_ids`` + ### indexes back into the *full* active set so we never + ### need a separate filtered ``far_src_nids`` tensor. + starts_full = target_tree.node_range_start[active_tgt_nodes] + counts_full = target_tree.node_range_count[active_tgt_nodes] + counts_masked = torch.where( + is_far, counts_full, torch.zeros_like(counts_full) + ) + positions, pair_ids = _ragged_arange( + starts_full, counts_masked + ) + nf_target_list.append( + target_tree.sorted_source_order[positions] + ) + nf_source_node_list.append(active_src_nodes[pair_ids]) else: - break - - ### Concatenate accumulated pairs - if near_target_list: - near_tgt = torch.cat(near_target_list) - near_src = torch.cat(near_source_list) - else: - near_tgt = torch.empty(0, dtype=torch.long, device=device) - near_src = torch.empty(0, dtype=torch.long, device=device) + far_tgt_unfiltered_list.append(active_tgt_nodes) + far_src_unfiltered_list.append(active_src_nodes) + far_validity_list.append(is_far) - if far_tgt_node_list: - far_tgt_nid = torch.cat(far_tgt_node_list) - far_src_nid = torch.cat(far_src_node_list) + ### 2. Near-field, both leaves: two-stage filtered expansion. + # ``_expand_dual_leaf_hits`` operates on a packed batch of + # (target_leaf_id, source_leaf_id) pairs. Two indexings + # share the ``near_leaf_leaf`` mask, so dedup the + # ``nonzero`` call: one sync per iteration instead of two. + nll_idx = near_leaf_leaf.nonzero(as_tuple=True)[0] + ( + nn_tgts, nn_srcs, + nf_tgts, nf_snids, + fn_tnids, fn_sids, + fn_btgts, fn_bstarts, fn_bcounts, + ) = _expand_dual_leaf_hits( + active_tgt_nodes[nll_idx], + active_src_nodes[nll_idx], + target_tree, + source_tree, + theta, + ) + near_target_list.append(nn_tgts) + near_source_list.append(nn_srcs) + nf_target_list.append(nf_tgts) + nf_source_node_list.append(nf_snids) + fn_tgt_node_list.append(fn_tnids) + fn_src_list.append(fn_sids) + fn_bcast_targets_list.append(fn_btgts) + fn_bcast_starts_list.append(fn_bstarts + fn_bcast_offset) + fn_bcast_counts_list.append(fn_bcounts) + fn_bcast_offset += fn_btgts.shape[0] + + ### 3. Generate next iteration's active set. + # We compute children over the FULL active set (n_active + # entries) and use validity masks per (T,S) child slot to + # encode the case-A / case-B / case-C splitting rules from + # the original implementation. After unioning the eight + # potential child slots we pay ONE boolean compaction + # instead of the original ~12 ``.any()``-gated indexings. + do_split_T = (~is_leaf_T) & ( + is_leaf_S | (diam_sq_T >= diam_sq_S) + ) + do_split_S = (~is_leaf_S) & ( + is_leaf_T | (diam_sq_S >= diam_sq_T) + ) + case_T_only = need_split & do_split_T & (~do_split_S) + case_S_only = need_split & do_split_S & (~do_split_T) + case_both = need_split & do_split_T & do_split_S + + left_T = target_tree.node_left_child[active_tgt_nodes] + right_T = target_tree.node_right_child[active_tgt_nodes] + left_S = source_tree.node_left_child[active_src_nodes] + right_S = source_tree.node_right_child[active_src_nodes] + left_T_ok = left_T >= 0 + right_T_ok = right_T >= 0 + left_S_ok = left_S >= 0 + right_S_ok = right_S >= 0 + + ### Eight child-pair slots: each is (t_ids, s_ids, validity) + ### where every tensor has shape ``(n_active,)``. + # 1: case_T_only, (left_T, parent_S) + # 2: case_T_only, (right_T, parent_S) + # 3: case_S_only, (parent_T, left_S) + # 4: case_S_only, (parent_T, right_S) + # 5: case_both, (left_T, left_S) + # 6: case_both, (left_T, right_S) + # 7: case_both, (right_T, left_S) + # 8: case_both, (right_T, right_S) + slot_t = torch.stack([ + left_T, right_T, + active_tgt_nodes, active_tgt_nodes, + left_T, left_T, right_T, right_T, + ]) + slot_s = torch.stack([ + active_src_nodes, active_src_nodes, + left_S, right_S, + left_S, right_S, left_S, right_S, + ]) + slot_v = torch.stack([ + case_T_only & left_T_ok, + case_T_only & right_T_ok, + case_S_only & left_S_ok, + case_S_only & right_S_ok, + case_both & left_T_ok & left_S_ok, + case_both & left_T_ok & right_S_ok, + case_both & right_T_ok & left_S_ok, + case_both & right_T_ok & right_S_ok, + ]) + + ### One sync per iteration: the boolean compaction below. + flat_v = slot_v.reshape(-1) + active_tgt_nodes = slot_t.reshape(-1)[flat_v] + active_src_nodes = slot_s.reshape(-1)[flat_v] + + ### Concatenate accumulated pairs. + ### Lists always have at least one element per iteration (we + ### always append, gated by validity), so we don't need the + ### empty-list fallbacks the previous implementation had. + near_tgt = torch.cat(near_target_list) if near_target_list else \ + torch.empty(0, dtype=torch.long, device=device) + near_src = torch.cat(near_source_list) if near_source_list else \ + torch.empty(0, dtype=torch.long, device=device) + + ### Far-field compaction: ONE sync for the entire traversal. + if far_validity_list: + far_tgt_full = torch.cat(far_tgt_unfiltered_list) + far_src_full = torch.cat(far_src_unfiltered_list) + far_validity_full = torch.cat(far_validity_list) + far_tgt_nid = far_tgt_full[far_validity_full] + far_src_nid = far_src_full[far_validity_full] else: far_tgt_nid = torch.empty(0, dtype=torch.long, device=device) far_src_nid = torch.empty(0, dtype=torch.long, device=device) - if nf_target_list: - nf_tgt = torch.cat(nf_target_list) - nf_snid = torch.cat(nf_source_node_list) - else: - nf_tgt = torch.empty(0, dtype=torch.long, device=device) - nf_snid = torch.empty(0, dtype=torch.long, device=device) + nf_tgt = torch.cat(nf_target_list) if nf_target_list else \ + torch.empty(0, dtype=torch.long, device=device) + nf_snid = torch.cat(nf_source_node_list) if nf_source_node_list else \ + torch.empty(0, dtype=torch.long, device=device) if fn_tgt_node_list: fn_tnid = torch.cat(fn_tgt_node_list) @@ -1225,26 +1158,36 @@ class SourceAggregates: # --------------------------------------------------------------------------- -def _fill_leaf_aabbs( +def _fill_leaf_aggregates( leaf_nids: Int[torch.Tensor, " n_leaves"], leaf_starts: Int[torch.Tensor, " n_leaves"], leaf_sizes: Int[torch.Tensor, " n_leaves"], sorted_points: Float[torch.Tensor, "n_sorted_sources n_dims"], + sorted_areas: Float[torch.Tensor, " n_sorted_sources"], aabb_min_buf: Float[torch.Tensor, "n_nodes n_dims"], aabb_max_buf: Float[torch.Tensor, "n_nodes n_dims"], + total_area_buf: Float[torch.Tensor, " n_nodes"], ) -> None: - """Fill AABB buffers for leaf nodes via segmented reduction (in-place).""" + """Fill leaf AABB and total-area buffers in one segmented reduction pass. + + AABB and area aggregations share the same per-source ``(positions, + seg_ids)`` mapping from ``_ragged_arange``; doing them together + halves the ragged-arange work and avoids a redundant + ``int(leaf_sizes.sum())`` sync that the previous separate + ``_fill_leaf_aabbs`` / ``_fill_leaf_total_areas`` helpers each paid. + Empty inputs (``n_leaves == 0``) are a no-op via the early return. + """ + n_leaves = leaf_nids.shape[0] + if n_leaves == 0: + return + device = leaf_nids.device D = sorted_points.shape[1] dtype = sorted_points.dtype - n_leaves = leaf_nids.shape[0] - total = int(leaf_sizes.sum()) - - if total == 0 or n_leaves == 0: - return positions, seg_ids = _ragged_arange(leaf_starts, leaf_sizes) - pts = sorted_points[positions] # (total, D) + pts = sorted_points[positions] + areas_per_pos = sorted_areas[positions] seg_min = torch.full((n_leaves, D), float("inf"), dtype=dtype, device=device) seg_max = torch.full((n_leaves, D), float("-inf"), dtype=dtype, device=device) @@ -1252,137 +1195,11 @@ def _fill_leaf_aabbs( seg_min.scatter_reduce_(0, exp_ids, pts, reduce="amin", include_self=True) seg_max.scatter_reduce_(0, exp_ids, pts, reduce="amax", include_self=True) + leaf_areas = torch.zeros(n_leaves, dtype=areas_per_pos.dtype, device=device) + leaf_areas.scatter_add_(0, seg_ids, areas_per_pos) + aabb_min_buf[leaf_nids] = seg_min aabb_max_buf[leaf_nids] = seg_max - - -def _fill_leaf_total_areas( - leaf_nids: Int[torch.Tensor, " n_leaves"], - leaf_starts: Int[torch.Tensor, " n_leaves"], - leaf_sizes: Int[torch.Tensor, " n_leaves"], - sorted_areas: Float[torch.Tensor, " n_sorted_sources"], - total_area_buf: Float[torch.Tensor, " n_nodes"], -) -> None: - """Compute total area per leaf node (in-place).""" - device = leaf_nids.device - n_leaves = leaf_nids.shape[0] - total = int(leaf_sizes.sum()) - - if total == 0 or n_leaves == 0: - return - - positions, seg_ids = _ragged_arange(leaf_starts, leaf_sizes) - areas = sorted_areas[positions] - - leaf_areas = torch.zeros(n_leaves, dtype=areas.dtype, device=device) - leaf_areas.scatter_add_(0, seg_ids, areas) - total_area_buf[leaf_nids] = leaf_areas -def _aggregate_source_data_leaves( - sorted_source_data: TensorDict, - sorted_areas: Float[torch.Tensor, " n_sorted_sources"], - seg_ids: Int[torch.Tensor, " n_sorted_sources"], - n_leaves: int, - leaf_node_ids: Int[torch.Tensor, " n_leaves"], - leaf_total_areas: Float[torch.Tensor, " n_leaves"], - n_nodes: int, - device: torch.device, -) -> TensorDict: - """Compute area-weighted average source data for leaf nodes. - - Returns a TensorDict with ``batch_size=(n_nodes,)`` where only - leaf entries are populated (internal nodes are zeros, filled by - bottom-up propagation). - """ - safe_areas = leaf_total_areas.clamp(min=1e-30) - - def _aggregate_leaf(tensor: torch.Tensor) -> torch.Tensor: - trailing_shape = tensor.shape[1:] - flat = tensor.reshape(tensor.shape[0], -1) # (n_sorted_sources, F) - - weighted_sum = _segmented_weighted_sum( - flat, sorted_areas, seg_ids, n_leaves - ) - avg = weighted_sum / safe_areas.unsqueeze(-1) - - out = torch.zeros( - (n_nodes,) + trailing_shape, - dtype=tensor.dtype, - device=device, - ) - out_flat = out.reshape(n_nodes, -1) - out_flat[leaf_node_ids] = avg - return out.reshape((n_nodes,) + trailing_shape) - - return sorted_source_data.apply(_aggregate_leaf, batch_size=[n_nodes]) - - -### Disabled for torch.compile: this function iterates over a -### variable-length list (depth_levels), whose length equals the tree -### depth. Dynamo unrolls this loop and specializes on the length, -### causing recompilation every time a new tree depth is encountered -### (each airfoil mesh produces a different-depth tree). Disabling -### compilation here produces one clean graph break at the function -### boundary instead of per-depth-level recompilation storms. -@torch.compiler.disable -def _propagate_centroids_bottom_up( - centroid_buf: Float[torch.Tensor, "n_nodes n_dims"], - node_source_data: TensorDict | None, - left_child: Int[torch.Tensor, " n_nodes"], - right_child: Int[torch.Tensor, " n_nodes"], - total_area: Float[torch.Tensor, " n_nodes"], - depth_levels: list[torch.Tensor], -) -> None: - """Propagate centroids and source data from leaves to root (in-place). - - Internal node centroid = area-weighted average of its children's centroids. - Internal node source data = area-weighted average of its children's data. - - Parameters - ---------- - centroid_buf : Float[torch.Tensor, "n_nodes n_dims"] - Buffer of per-node centroids (leaf values pre-filled, internal values - written by this function). - node_source_data : TensorDict or None - Per-node source data to propagate (same structure as centroid_buf). - left_child : Int[torch.Tensor, "n_nodes"] - Left child index per node (-1 for leaves). - right_child : Int[torch.Tensor, "n_nodes"] - Right child index per node (-1 for leaves). - total_area : Float[torch.Tensor, "n_nodes"] - Total source area in each node's subtree. - depth_levels : list[torch.Tensor] - Internal node IDs grouped by tree depth (shallowest first), - from :attr:`ClusterTree.internal_nodes_per_level`. - """ - for level_ids in reversed(depth_levels): - left = left_child[level_ids] - right = right_child[level_ids] - - left_area = total_area[left] - right_area = total_area[right] - total = (left_area + right_area).clamp(min=1e-30) - - # 1D base weights; each consumer unsqueezes as needed for its rank - w_left_1d = left_area / total # (n,) - w_right_1d = right_area / total # (n,) - - centroid_buf[level_ids] = ( - centroid_buf[left] * w_left_1d.unsqueeze(-1) - + centroid_buf[right] * w_right_1d.unsqueeze(-1) - ) - - if node_source_data is not None: - for key in node_source_data.keys(include_nested=True, leaves_only=True): - val_left = node_source_data[key][left] - val_right = node_source_data[key][right] - w_l = w_left_1d - w_r = w_right_1d - while w_l.ndim < val_left.ndim: - w_l = w_l.unsqueeze(-1) - w_r = w_r.unsqueeze(-1) - node_source_data[key][level_ids] = ( - val_left * w_l + val_right * w_r - ) diff --git a/physicsnemo/experimental/models/globe/field_kernel.py b/physicsnemo/experimental/models/globe/field_kernel.py index da76105994..294a54c621 100644 --- a/physicsnemo/experimental/models/globe/field_kernel.py +++ b/physicsnemo/experimental/models/globe/field_kernel.py @@ -17,13 +17,14 @@ import itertools import logging import operator -from functools import cached_property, reduce -from math import ceil, comb, prod -from typing import TYPE_CHECKING, Literal, Sequence +from functools import cache, cached_property, reduce +from math import comb, prod +from typing import TYPE_CHECKING, Final, Literal, Sequence +import psutil import torch import torch.nn as nn -from jaxtyping import Float +from jaxtyping import Float, Int from tensordict import TensorDict from torch.profiler import record_function from torch.utils.checkpoint import checkpoint @@ -57,6 +58,71 @@ ) +### Static, per-device memory-budget helpers used by chunk-size sizing. + +# Fraction of total device memory we are willing to spend on a single +# kernel evaluation's chunked autograd state (peak intermediate footprint +# for one (Phase A | B | C | D) call). Conservative because parameters, +# DDP buckets, and activations from other kernels coexist on-device. +# +# Expressed as an integer percent (rather than e.g. ``0.25``) so that +# ``_device_chunk_budget_bytes`` stays in pure-integer arithmetic. Under +# ``torch.compile`` with the default ``specialize_float=False``, a +# module-level Python float is treated as an unbacked symbolic float +# ("zf" symbol), which then infects every ``chunk_size`` derivation +# downstream and crashes Dynamo at the ``checkpoint`` boundary in +# ``_gather_and_evaluate`` with "Source of '...' is None when lifting it +# to input of top-level". +_CHUNK_MEMORY_BUDGET_PERCENT: Final[int] = 25 + + +def _ceil_div(a: int, b: int) -> int: + """Integer ceiling division: equivalent to ``math.ceil(a / b)`` for + non-negative ``a`` and positive ``b``, but with no Python ``float`` + intermediate. + + Used here because, under ``torch.compile`` with the default + ``specialize_float=False``, a Python ``float`` in the trace is treated + as an unbacked symbolic free variable that propagates into the chunk + size and crashes Dynamo at the ``checkpoint`` boundary in + :meth:`BarnesHutKernel._gather_and_evaluate`. ``-(-a // b)`` is the + standard Python idiom: floor division rounds toward negative infinity, + so negating both ends produces a ceiling on the original quotient. + """ + return -(-a // b) + + +@torch.compiler.disable +@cache +def _device_total_memory_bytes(device: torch.device) -> int: + """Cached lookup of total physical memory available on ``device``. + + On CUDA, returns ``torch.cuda.get_device_properties.total_memory``. + On CPU, returns total system RAM via :func:`psutil.virtual_memory`. + The CPU path is debug-only - production runs on CUDA. + + ``@torch.compiler.disable`` is layered above ``@cache`` so Dynamo + bails on the outer call and never traces the device query here. + Without this, Dynamo treats ``torch.cuda.get_device_properties`` + as an opaque Python function and tries to evaluate it as a + constant during graph capture, which crashes on CPU-only hosts + (``RuntimeError: Found no NVIDIA driver``) and produces unbacked + symbolic ints on CUDA hosts that poison downstream chunk-size + arithmetic. + """ + if device.type == "cuda": + return int(torch.cuda.get_device_properties(device).total_memory) + if device.type == "cpu": + return int(psutil.virtual_memory().total) + raise ValueError(f"Unsupported {device.type=!r}") + + +@torch.compiler.disable +def _device_chunk_budget_bytes(device: torch.device) -> int: + """Static memory budget for a single chunked kernel evaluation.""" + return _device_total_memory_bytes(device) * _CHUNK_MEMORY_BUDGET_PERCENT // 100 + + class Kernel(Module): r"""A kernel function for evaluating scalar and vector fields from source points. @@ -169,6 +235,22 @@ def __init__( self.n_spherical_harmonics = n_spherical_harmonics self.use_gradient_checkpointing = use_gradient_checkpointing + ### Pre-squared smoothing radius as a registered tensor buffer. + ### A buffer (rather than a Python float) is required because + ### ``_evaluate_interactions`` adds this scalar to a TensorDict + ### inside a checkpoint sub-graph; Python free variables cannot + ### be lifted across nested Dynamo SubgraphTracers (the lift + ### chain bottoms out at the root tracer with no parent and + ### asserts ``lift_tracked_freevar_to_input should not be + ### called on root SubgraphTracer``). As a buffer, the + ### tensor is a tracked module attribute that Dynamo treats as + ### a graph leaf, so no lift is needed. + self.register_buffer( + "_smoothing_radius_sq", + torch.tensor(smoothing_radius**2, dtype=torch.float32), + persistent=False, + ) + in_features = self.network_in_features hidden_features = list(self.hidden_layer_sizes) out_features = self.network_out_features @@ -282,6 +364,24 @@ def network_out_features(self) -> int: + 2 * (n_vectors_in - 1) # All non-r vectors ) + @cached_property + def _output_packing(self) -> tuple[tuple[str, ...], tuple[int, ...], int]: + """Canonical-ordered output keys, per-key feature widths, and total width. + + Used by :class:`BarnesHutKernel` to pack the per-phase scatter + targets into a single ``(n_targets, total_features)`` buffer. The + backward pass through ``aten::scatter_add`` was the largest single + GPU kernel time in profiling (``indexing_backward_kernel_stride_1``, + ~1 s out of 4.5 s total GPU time per training step), and packing + cuts that cost proportional to the number of output fields. + """ + ranks_dict = flatten_rank_spec(self.output_field_ranks) + keys = tuple(sorted(ranks_dict.keys())) + features_per_key = tuple( + 1 if ranks_dict[k] == 0 else self.n_spatial_dims for k in keys + ) + return keys, features_per_key, sum(features_per_key) + def forward( self, *, @@ -498,17 +598,11 @@ def _evaluate_interactions( dtype = torch.get_autocast_dtype(device.type) scalars = scalars.to(dtype=dtype) vectors = vectors.to(dtype=dtype) - else: - dtype = None - - smoothing_radius = torch.tensor( - self.smoothing_radius, device=device, dtype=dtype - ) ### Vector magnitude, direction, and log-magnitude features with record_function("kernel::feature_engineering"): vectors_mag_squared: TensorDict = ( - (vectors * vectors).sum(dim=-1).apply(lambda x: x + smoothing_radius**2) + (vectors * vectors).sum(dim=-1) + self._smoothing_radius_sq ) vectors_mag = vectors_mag_squared.sqrt() vectors_hat = vectors / vectors_mag.unsqueeze(-1) @@ -919,9 +1013,6 @@ def forward( 100.0 * n_near / max(n_dense, 1), ) - if n_near == 0 and n_nf == 0 and n_fn == 0 and n_far_nodes == 0: - return self._empty_result(n_targets, device) - ### Prepare aggregate data for far-field and (near,far) phases if n_far_nodes > 0 or n_nf > 0: if aggregates.node_source_data is not None: @@ -938,8 +1029,31 @@ def forward( [cluster_tree.n_nodes, self.n_spatial_dims] ) - ### Initialize output buffers - output_bufs: dict[str, torch.Tensor] = {} + ### Packed output buffer. All four phases scatter into this single + ### tensor; the per-phase loops over output keys (one ``scatter_add_`` + ### per key) are replaced with one packed ``scatter_add_`` per phase. + ### ``indexing_backward`` was the top GPU kernel by time in profiling + ### (~23% of total GPU time); packing cuts this by the number of + ### output fields (~4x for ``C_p`` + ``C_f`` on DrivAerML). + ### + ### Buffer dtype must match the dtype of ``weighted = chunk * weights`` + ### that the four phases will scatter in (``index_add_`` requires + ### exact dtype match). ``chunk`` comes from the MLP at the + ### autocast dtype (or ``source_points.dtype`` outside autocast); + ### ``weights`` carries ``source_strengths.dtype``. Their product + ### is the type-promoted dtype, which is what the previous + ### lazy-allocated buffers used to capture - we now compute it + ### eagerly so a single buffer can be allocated up front. + _, _, total_features = self._output_packing + chunk_dtype = ( + torch.get_autocast_dtype(device.type) + if torch.is_autocast_enabled(device.type) + else source_points.dtype + ) + buffer_dtype = torch.promote_types(chunk_dtype, source_strengths.dtype) + packed_buf = torch.zeros( + (n_targets, total_features), dtype=buffer_dtype, device=device + ) # ================================================================== # Phase A: Near-field (individual target-source pairs, chunked) @@ -966,39 +1080,19 @@ def forward( # the gathered float data (~300 bytes/pair). This is a # ~37x reduction in checkpoint-saved memory per branch. with record_function("bh_kernel::near_chunk"): - if self.training and self.use_gradient_checkpointing: - chunk_result = checkpoint( - self._gather_and_evaluate, - chunk_tgt_ids, chunk_src_ids, - target_points, source_points, - source_scalars, source_vectors, - global_scalars, global_vectors, - reference_length, device, - use_reentrant=False, - ) - else: - chunk_result = self._gather_and_evaluate( - chunk_tgt_ids, chunk_src_ids, - target_points, source_points, - source_scalars, source_vectors, - global_scalars, global_vectors, - reference_length, device, - ) + chunk_result = self._maybe_checkpointed_evaluate( + chunk_tgt_ids, chunk_src_ids, + target_points, source_points, + source_scalars, source_vectors, + global_scalars, global_vectors, + reference_length, device, + ) with record_function("bh_kernel::near_scatter"): chunk_strengths = source_strengths[chunk_src_ids] - for k, v in chunk_result.items(): - weighted = v * chunk_strengths.view(-1, *([1] * (v.ndim - 1))) - if k not in output_bufs: - output_bufs[k] = torch.zeros( - (n_targets,) + v.shape[1:], - dtype=weighted.dtype, - device=device, - ) - idx = chunk_tgt_ids.view( - -1, *([1] * (v.ndim - 1)) - ).expand_as(weighted) - output_bufs[k].scatter_add_(0, idx, weighted) + self._pack_and_scatter( + chunk_result, chunk_strengths, chunk_tgt_ids, packed_buf + ) # ================================================================== # Phase B: Far-field node pairs (evaluate once, broadcast to targets) @@ -1011,24 +1105,13 @@ def forward( # Same gather-inside-checkpoint pattern: the checkpoint saves # only the node ID indices, not the gathered aggregate data. with record_function("bh_kernel::far_node_evaluate"): - if self.training and self.use_gradient_checkpointing: - far_result = checkpoint( - self._gather_and_evaluate, - far_tgt_nids, far_src_nids, - target_centroids, aggregates.node_centroid, - agg_scalars, agg_vectors, - global_scalars, global_vectors, - reference_length, device, - use_reentrant=False, - ) - else: - far_result = self._gather_and_evaluate( - far_tgt_nids, far_src_nids, - target_centroids, aggregates.node_centroid, - agg_scalars, agg_vectors, - global_scalars, global_vectors, - reference_length, device, - ) + far_result = self._maybe_checkpointed_evaluate( + far_tgt_nids, far_src_nids, + target_centroids, aggregates.node_centroid, + agg_scalars, agg_vectors, + global_scalars, global_vectors, + reference_length, device, + ) ### Broadcast node-level results to individual targets. with record_function("bh_kernel::far_node_broadcast"): @@ -1039,19 +1122,14 @@ def forward( positions, pair_ids = _ragged_arange(node_starts, node_counts) expanded_tgt_ids = target_tree.sorted_source_order[positions] - for k, v in far_result.items(): - weighted = v * far_strengths.view(-1, *([1] * (v.ndim - 1))) - expanded = weighted[pair_ids] - if k not in output_bufs: - output_bufs[k] = torch.zeros( - (n_targets,) + v.shape[1:], - dtype=expanded.dtype, - device=device, - ) - idx = expanded_tgt_ids.view( - -1, *([1] * (v.ndim - 1)) - ).expand_as(expanded) - output_bufs[k].scatter_add_(0, idx, expanded) + ### Broadcast node-level outputs to individual targets via + ### ``pair_ids`` (which point back into the per-node result + ### tensor). Packing the broadcast lets us do one expand + + ### one scatter_add instead of one per output field. + self._pack_and_scatter( + far_result, far_strengths, expanded_tgt_ids, + packed_buf, broadcast_pair_ids=pair_ids, + ) # ================================================================== # Phase C: (near,far) - individual targets × source node centroids @@ -1063,39 +1141,19 @@ def forward( ### Same evaluation as Phase B (source centroids + aggregates), # but same scatter as Phase A (per-target, no broadcast). with record_function("bh_kernel::nf_evaluate"): - if self.training and self.use_gradient_checkpointing: - nf_result = checkpoint( - self._gather_and_evaluate, - nf_tgt_ids, nf_src_nids, - target_points, aggregates.node_centroid, - agg_scalars, agg_vectors, - global_scalars, global_vectors, - reference_length, device, - use_reentrant=False, - ) - else: - nf_result = self._gather_and_evaluate( - nf_tgt_ids, nf_src_nids, - target_points, aggregates.node_centroid, - agg_scalars, agg_vectors, - global_scalars, global_vectors, - reference_length, device, - ) + nf_result = self._maybe_checkpointed_evaluate( + nf_tgt_ids, nf_src_nids, + target_points, aggregates.node_centroid, + agg_scalars, agg_vectors, + global_scalars, global_vectors, + reference_length, device, + ) with record_function("bh_kernel::nf_scatter"): nf_strengths = node_total_strength[nf_src_nids] - for k, v in nf_result.items(): - weighted = v * nf_strengths.view(-1, *([1] * (v.ndim - 1))) - if k not in output_bufs: - output_bufs[k] = torch.zeros( - (n_targets,) + v.shape[1:], - dtype=weighted.dtype, - device=device, - ) - idx = nf_tgt_ids.view( - -1, *([1] * (v.ndim - 1)) - ).expand_as(weighted) - output_bufs[k].scatter_add_(0, idx, weighted) + self._pack_and_scatter( + nf_result, nf_strengths, nf_tgt_ids, packed_buf + ) # ================================================================== # Phase D: (far,near) - target node centroid × individual sources, @@ -1109,24 +1167,13 @@ def forward( # Uses target centroids (like Phase B) but individual source # points and data (like Phase A). with record_function("bh_kernel::fn_evaluate"): - if self.training and self.use_gradient_checkpointing: - fn_result = checkpoint( - self._gather_and_evaluate, - fn_tgt_nids, fn_src_ids, - target_centroids, source_points, - source_scalars, source_vectors, - global_scalars, global_vectors, - reference_length, device, - use_reentrant=False, - ) - else: - fn_result = self._gather_and_evaluate( - fn_tgt_nids, fn_src_ids, - target_centroids, source_points, - source_scalars, source_vectors, - global_scalars, global_vectors, - reference_length, device, - ) + fn_result = self._maybe_checkpointed_evaluate( + fn_tgt_nids, fn_src_ids, + target_centroids, source_points, + source_scalars, source_vectors, + global_scalars, global_vectors, + reference_length, device, + ) ### Broadcast to stage-1 survivors via the ragged mapping. with record_function("bh_kernel::fn_broadcast"): @@ -1138,35 +1185,156 @@ def forward( ) expanded_tgt_ids = dual_plan.fn_broadcast_targets[positions] - for k, v in fn_result.items(): - weighted = v * fn_strengths.view(-1, *([1] * (v.ndim - 1))) - expanded = weighted[pair_ids] - if k not in output_bufs: - output_bufs[k] = torch.zeros( - (n_targets,) + v.shape[1:], - dtype=expanded.dtype, - device=device, - ) - idx = expanded_tgt_ids.view( - -1, *([1] * (v.ndim - 1)) - ).expand_as(expanded) - output_bufs[k].scatter_add_(0, idx, expanded) + self._pack_and_scatter( + fn_result, fn_strengths, expanded_tgt_ids, + packed_buf, broadcast_pair_ids=pair_ids, + ) - if not output_bufs: - return self._empty_result(n_targets, device) + return self._unpack_buf(packed_buf, n_targets, device) + + def _maybe_checkpointed_evaluate(self, *args: object) -> TensorDict: + """Run :meth:`_gather_and_evaluate` with optional gradient checkpointing. + + The four BH-kernel phases (near, far_node, nf, fn) all wrap the + same ``_gather_and_evaluate`` call in an + ``if self.use_gradient_checkpointing`` branch with identical + checkpoint kwargs. Hoisting the branch removes ~30 lines of + duplicated forward/checkpoint plumbing across the four phases + and keeps the gradient-checkpointing policy in one place. + + The wrap fires in **both** training and eval, not just training, + for two reasons: + + 1. Memory savings (the original purpose). In eval no autograd + tape is active (``train.py`` wraps validation in + ``torch.no_grad()``), so ``checkpoint(use_reentrant=False)`` + degenerates to a near-no-op forward call - no recompute, no + extra memory, no extra compute. + 2. Workaround for a Dynamo+CUDA TensorDict bug. When the same + ``_gather_and_evaluate`` body is inlined into the parent + graph (i.e. without the checkpoint wrapper), FakeTensor + tracing fails to propagate ``TensorDict.batch_size`` through + ``(vectors * vectors).sum(dim=-1)``: leaf tensors get + reduced to 1D but the TD still reports its 2D pre-reduction + batch size, and any downstream op that consults + ``batch_size`` (``.unsqueeze(-1)``, ``concatenate_leaves``'s + reshape) trips on the inconsistency. Wrapping the call in + ``checkpoint`` gives Dynamo a fresh sub-tracer scope that + tracks the post-reduction batch size correctly, so the + identical eager-mode-correct body now also traces correctly. + """ + if self.use_gradient_checkpointing: + return checkpoint( + self._gather_and_evaluate, *args, use_reentrant=False + ) + return self._gather_and_evaluate(*args) - return TensorDict( - output_bufs, - batch_size=torch.Size([n_targets]), - device=device, - ) + def _pack_and_scatter( + self, + chunk_result: TensorDict, + weights: Float[torch.Tensor, " n_pairs"], + tgt_ids: Int[torch.Tensor, " n_pairs_or_expanded"], + packed_buf: Float[torch.Tensor, "n_targets total_features"], + *, + broadcast_pair_ids: Int[torch.Tensor, " n_pairs_or_expanded"] | None = None, + ) -> None: + """Pack output fields, weight, and ``scatter_add_`` into ``packed_buf``. + + Replaces the per-key ``scatter_add_`` loop inside each phase with + a single packed scatter. Packing collapses the per-key + ``indexing_backward`` kernel calls (the largest GPU cost in + profiling) into one per phase, which roughly halves backward + scatter cost on DrivAerML (``C_p``: 1 ch + ``C_f``: 3 ch = 4 chs). + + Parameters + ---------- + chunk_result : TensorDict + Output of one ``_evaluate_interactions`` call. Contains the + keys declared in ``self.output_field_ranks``; values have + ``batch_size=(n_pairs,)``. + weights : Float[torch.Tensor, "n_pairs"] + Per-pair scalar multipliers (e.g. source strengths). + tgt_ids : Int[torch.Tensor, "n_pairs_or_expanded"] + Target index for each scatter contribution. When + ``broadcast_pair_ids`` is ``None``, length matches + ``n_pairs``; otherwise it matches the broadcast-expanded + length and ``broadcast_pair_ids`` indexes back into the + ``n_pairs`` dimension. + packed_buf : Float[torch.Tensor, "n_targets total_features"] + Shared output buffer for the four-phase loop. + broadcast_pair_ids : Int[torch.Tensor, "n_pairs_or_expanded"] | None + Used by Phase B and Phase D, where each evaluated pair is + broadcast to many targets via a ragged-arange mapping. When + given, weighting is applied first (over ``n_pairs``) and + broadcasting second (lower memory than the reverse). + """ + keys, _, _ = self._output_packing + ### Pack output fields in canonical order; trailing feature dims + ### are flattened so we end up with a 2D ``(n_pairs, total_F)``. + parts = [ + chunk_result[k].reshape(chunk_result[k].shape[0], -1) for k in keys + ] + packed = torch.cat(parts, dim=-1) + + weighted = packed * weights.unsqueeze(-1) + if broadcast_pair_ids is not None: + weighted = weighted[broadcast_pair_ids] + + ### ``index_add_`` rather than ``scatter_add_`` with broadcasted + ### indices: equivalent semantics, but ``index_add_`` takes a 1-D + ### ``index`` and avoids the ``unsqueeze`` + ``expand_as`` overhead + ### that ``scatter_add_`` requires. In addition to the direct + ### speedup, ``index_add_`` has a more compact backward (no index + ### broadcasting in the saved tensors), which compounds with the + ### packing optimization above. + packed_buf.index_add_(0, tgt_ids, weighted) + + def _unpack_buf( + self, + packed_buf: Float[torch.Tensor, "n_targets total_features"], + n_targets: int, + device: torch.device, + ) -> TensorDict: + """Slice ``packed_buf`` back into a per-output-field TensorDict. + + Counterpart to :meth:`_pack_and_scatter`. Scalar fields lose the + trailing length-1 dim so the returned shapes are + ``(n_targets,)`` for scalars and ``(n_targets, D)`` for vectors. + Empty inputs flow through correctly: an all-zero ``packed_buf`` + unpacks to all-zero per-field tensors, so the four-phase loop + does not need a separate degenerate-case branch. + """ + keys, features_per_key, _ = self._output_packing + ranks_dict = flatten_rank_spec(self.output_field_ranks) + fields: dict[str, torch.Tensor] = {} + offset = 0 + for key, n_features in zip(keys, features_per_key): + slice_ = packed_buf[:, offset : offset + n_features] + if ranks_dict[key] == 0: + ### Scalar fields: drop the trailing length-1 dim so + ### shape stays ``(n_targets,)`` as before packing. + fields[key] = slice_.squeeze(-1) + else: + fields[key] = slice_ + offset += n_features + return TensorDict(fields, batch_size=torch.Size([n_targets]), device=device) def _compute_node_strengths( self, tree: "ClusterTree", source_strengths: Float[torch.Tensor, " n_sources"], ) -> Float[torch.Tensor, " n_nodes"]: - """Compute total source strength per tree node via bottom-up summation. + """Compute total source strength per tree node. + + Each node covers a contiguous range + ``[node_range_start, node_range_start + node_range_count)`` in + morton-sorted source order, so the total strength in a node's + subtree is just a range sum: ``prefix_sum[end] - prefix_sum[start]``. + This replaces the previous bottom-up Python loop over tree levels + - which launched a fresh batch of gather + add + scatter kernels at + every level - with a single ``cumsum`` + gather + subtract. In + profiling, the level loop was the largest single contributor to + ``cluster_tree::bottom_up_propagation`` GPU and CPU time. Parameters ---------- @@ -1182,62 +1350,23 @@ def _compute_node_strengths( """ device = source_strengths.device n_nodes = tree.n_nodes - node_strengths = torch.zeros(n_nodes, dtype=source_strengths.dtype, device=device) - - is_leaf = tree.leaf_count > 0 - leaf_ids = torch.where(is_leaf)[0] - - if leaf_ids.numel() == 0: - return node_strengths - - ### Sum strengths within each leaf - leaf_starts = tree.leaf_start[leaf_ids] - leaf_counts = tree.leaf_count[leaf_ids] - n_leaves = leaf_ids.shape[0] - - if int(leaf_counts.sum()) > 0: - from physicsnemo.mesh.spatial._ragged import _ragged_arange - - positions, seg_ids = _ragged_arange( - leaf_starts, leaf_counts, total=tree.n_sources, - ) - sorted_strengths = source_strengths[tree.sorted_source_order[positions]] - leaf_sums = torch.zeros(n_leaves, dtype=source_strengths.dtype, device=device) - leaf_sums.scatter_add_(0, seg_ids, sorted_strengths) - node_strengths[leaf_ids] = leaf_sums - - ### Bottom-up propagation using cached level ordering - for level_ids in reversed(tree.internal_nodes_per_level): - node_strengths[level_ids] = ( - node_strengths[tree.node_left_child[level_ids]] - + node_strengths[tree.node_right_child[level_ids]] - ) - - return node_strengths - - def _empty_result( - self, - n_targets: int, - device: torch.device, - ) -> TensorDict[str, Float[torch.Tensor, "n_targets ..."]]: - """Produce a zero-valued result TensorDict for the degenerate case.""" - # Match the dtype that AMP autocast would produce for real activations, - # so downstream ops don't hit a float32-vs-half mismatch. - dtype = ( - torch.get_autocast_dtype(device.type) - if torch.is_autocast_enabled(device.type) - else torch.float32 + if n_nodes == 0: + return torch.zeros(0, dtype=source_strengths.dtype, device=device) + + ### Cumsum and range-subtract in fp64 to avoid catastrophic + ### cancellation when ``cumsum_total >> range_sum`` - the regime + ### of small leaves in a large tree built over offset coordinates. + ### See the matching note in :meth:`ClusterTree.compute_source_aggregates`. + sorted_strengths_64 = source_strengths[tree.sorted_source_order].double() + ### Pad with a leading zero so that ``cumsum[i]`` is the sum of + ### sorted_strengths[:i] - both endpoints index identically. + prefix_sum = torch.nn.functional.pad( + torch.cumsum(sorted_strengths_64, dim=0), (1, 0) ) - ranks_dict = flatten_rank_spec(self.output_field_ranks) - fields: dict[str, torch.Tensor] = {} - for name, rank in sorted(ranks_dict.items()): - if rank == 0: - fields[name] = torch.zeros(n_targets, device=device, dtype=dtype) - else: - fields[name] = torch.zeros( - n_targets, self.n_spatial_dims, device=device, dtype=dtype - ) - return TensorDict(fields, batch_size=torch.Size([n_targets]), device=device) + + starts = tree.node_range_start + ends = starts + tree.node_range_count + return (prefix_sum[ends] - prefix_sum[starts]).to(source_strengths.dtype) def _gather_and_evaluate( self, @@ -1328,26 +1457,33 @@ def _gather_and_evaluate( return self._evaluate_interactions(scalars=scalars, vectors=vectors, device=device) + @torch.compiler.disable def _auto_chunk_size(self, n_total_pairs: int, device: torch.device) -> int: """Determine chunk size for pair-batched kernel evaluation. - Estimates peak memory per pair from the kernel's feature engineering - pipeline and sizes chunks to fit within ~50% of GPU memory. During - inference (no grad), the autograd overhead multiplier is dropped, - allowing larger chunks. - - Returns ``n_total_pairs`` (i.e., no chunking) when the estimated - peak fits comfortably, or when running on CPU. + Sizes chunks to fit within a fixed fraction of total device memory + from the kernel's per-pair feature-engineering footprint estimate. + During inference (no grad), the autograd overhead multiplier is + dropped, allowing larger chunks. + + Uses a static budget derived from + :func:`_device_total_memory_bytes` (cached) instead of + ``torch.cuda.mem_get_info``. ``mem_get_info`` is a synchronizing + driver query - calling it on every kernel evaluation produced + ~60 syncs per training step in profiling. Trading "exactly fits + in current free memory" for "fits in 25% of total device memory" + is fine when total_memory is large (e.g. 197 GB GB200) and the + non-kernel resident state (parameters + activations elsewhere) is + well under the remaining 75%. + + On CPU the same algorithm runs against system RAM (debug-only; + production runs on CUDA). Returns at least 1. """ - if device.type != "cuda": - return n_total_pairs - - if torch.is_autocast_enabled(device.type): - element_bytes = torch.tensor( - [], dtype=torch.get_autocast_dtype(device.type) - ).element_size() - else: - element_bytes = 4 # fp32 + element_bytes = ( + torch.get_autocast_dtype(device.type).itemsize + if torch.is_autocast_enabled(device.type) + else 4 # fp32 + ) autograd_overhead = 5 if torch.is_grad_enabled() else 1 approx_peak_bytes = ( @@ -1356,18 +1492,20 @@ def _auto_chunk_size(self, n_total_pairs: int, device: torch.device) -> int: * element_bytes * autograd_overhead ) - free_bytes, total_bytes = torch.cuda.mem_get_info(device) - target_bytes = free_bytes // 2 + target_bytes = _device_chunk_budget_bytes(device) - n_chunks = max(1, ceil(approx_peak_bytes / target_bytes)) - chunk_size = max(1, ceil(n_total_pairs / n_chunks)) + n_chunks = max(1, _ceil_div(approx_peak_bytes, target_bytes)) + chunk_size = max(1, _ceil_div(n_total_pairs, n_chunks)) if not torch.compiler.is_compiling(): logger.debug( "auto_chunk_size: %d pairs -> %d chunks of %d " - "(%.1f MB est. peak, %.1f MB free / %.1f MB total GPU)", + "(%.1f MB est. peak, %.1f MB budget / %.1f MB total %s)", n_total_pairs, n_chunks, chunk_size, - approx_peak_bytes / 1e6, free_bytes / 1e6, total_bytes / 1e6, + approx_peak_bytes / 1e6, + target_bytes / 1e6, + _device_total_memory_bytes(device) / 1e6, + device.type.upper(), ) return chunk_size @@ -1712,7 +1850,9 @@ def forward( # (int64 checkpoint-saved indices + multiply/scatter graph nodes). # Branch checkpointing avoids holding all branches' graphs # simultaneously, which is essential at large N (800k+ faces) - # but a pure compute overhead at small N (20k faces). + # but a pure compute overhead at small N. Compared against a + # static fraction of total device memory rather than free memory + # (mem_get_info is a synchronizing driver query). _AUTOGRAD_BYTES_PER_PAIR = 34 n_branches = len(self.reference_length_names) use_branch_ckpt = False @@ -1720,20 +1860,20 @@ def forward( n_total_pairs = dual_plan.n_near + dual_plan.n_nf + dual_plan.n_fn per_branch_bytes = n_total_pairs * _AUTOGRAD_BYTES_PER_PAIR all_branches_bytes = per_branch_bytes * n_branches - if device.type == "cuda": - free_bytes = torch.cuda.mem_get_info(device)[0] - use_branch_ckpt = all_branches_bytes > free_bytes * 0.1 - else: - use_branch_ckpt = False + ### Compares against a fraction of total device memory + ### (CUDA: VRAM; CPU: RAM). ``_device_chunk_budget_bytes`` + ### handles both via ``_device_total_memory_bytes``. + budget_bytes = _device_chunk_budget_bytes(device) + use_branch_ckpt = all_branches_bytes > budget_bytes if not torch.compiler.is_compiling(): logger.debug( "branch checkpoint: %s (est. %.1f MB/branch, " - "%.1f MB all branches, %.1f MB free, %d branches)", + "%.1f MB all branches, %.1f MB budget, %d branches)", "ENABLED" if use_branch_ckpt else "DISABLED", per_branch_bytes / 1e6, all_branches_bytes / 1e6, - free_bytes / 1e6 if device.type == "cuda" else 0, + budget_bytes / 1e6, n_branches, ) diff --git a/physicsnemo/experimental/models/globe/hierarchical_acceleration.md b/physicsnemo/experimental/models/globe/hierarchical_acceleration.md index eb95c33523..229bec70ec 100644 --- a/physicsnemo/experimental/models/globe/hierarchical_acceleration.md +++ b/physicsnemo/experimental/models/globe/hierarchical_acceleration.md @@ -360,15 +360,17 @@ The branch-level and chunk-level checkpoints nest correctly: ### 6.4 Chunk-Size Determinism -`_auto_chunk_size()` queries free GPU memory (`torch.cuda.mem_get_info`), -which changes between the forward pass and a checkpoint replay during -backward. If the chunk size changes, intermediate tensors have different -shapes, and the outer (branch-level) checkpoint raises `CheckpointError`. - -To prevent this, `MultiscaleKernel.forward()` precomputes each branch's chunk -size **outside** the checkpoint boundary and passes it as a fixed input via -the `near_chunk_size` kwarg. The checkpoint saves this value as an input and -replays with the identical value, regardless of current GPU memory state. +`_auto_chunk_size()` derives the chunk count from a static fraction of +total device memory (cached `torch.cuda.get_device_properties.total_memory`) +rather than current free memory. This avoids the synchronizing +`torch.cuda.mem_get_info` driver query and gives a deterministic chunk size +across forward and checkpoint-replay backward passes. + +`MultiscaleKernel.forward()` still precomputes each branch's chunk size +**outside** the checkpoint boundary and passes it as a fixed input via the +`near_chunk_size` kwarg, so future changes that re-introduce dynamic sizing +won't break the outer (branch-level) checkpoint by changing intermediate +shapes between the forward pass and its replay. --- diff --git a/physicsnemo/experimental/models/globe/model.py b/physicsnemo/experimental/models/globe/model.py index 04eac54c16..5ca8aa0578 100644 --- a/physicsnemo/experimental/models/globe/model.py +++ b/physicsnemo/experimental/models/globe/model.py @@ -65,16 +65,17 @@ class MetaData(ModelMetaData): class GLOBE(Module): r"""Green's-function-Like Operator for Boundary Element PDEs. - GLOBE is a neural surrogate architecture for boundary-driven elliptic PDEs that - combines learnable Green's-function-like kernels with equivariant ML. The model - represents solutions as superpositions of kernel evaluations from boundary faces - to target points, with communication hyperlayers enabling boundary-to-boundary - information propagation before final interior evaluation. + GLOBE is a neural surrogate architecture for boundary-driven elliptic PDEs + that combines learnable Green's-function-like kernels with equivariant ML. + The model represents solutions as superpositions of kernel evaluations from + boundary faces to target points, with communication hyperlayers enabling + boundary-to-boundary information propagation before final interior + evaluation. The architecture is designed to satisfy fundamental physical requirements: - - Translation-, rotation-, and parity-equivariant through relative positions and - local basis reprojection + - Translation-, rotation-, and parity-equivariant through relative positions + and local basis reprojection - Discretization-invariant via area-weighted boundary integrals - Units-invariant through rigorous nondimensionalization - Global receptive field through all-to-all boundary-to-target evaluation @@ -101,20 +102,21 @@ class GLOBE(Module): boundary_source_data_ranks : dict[str, TensorDict] Mapping of boundary condition type names to rank-spec TensorDicts describing the per-face source features for each BC type. The keys - implicitly define the set of boundary condition names. The face - normal vector is automatically added, so don't include it. + implicitly define the set of boundary condition names. The face normal + vector is automatically added, so don't include it. reference_length_names : Sequence[str] - Sequence of identifiers for reference length scales - (e.g., ``["viscous_length", "chord_length"]``). Each creates a separate - kernel branch in the multiscale composition. + Sequence of identifiers for reference length scales (e.g., + ``["viscous_length", "chord_length"]``). Each creates a separate kernel + branch in the multiscale composition. reference_area : float Scalar used to nondimensionalize face areas. Typically a characteristic area of the problem (e.g., chord^2 for airfoils). global_data_ranks : TensorDict or None, optional - Rank-spec TensorDict for global conditioning features. Defaults to - empty (no global conditioning). + Rank-spec TensorDict for global conditioning features. Defaults to empty + (no global conditioning). n_communication_hyperlayers : int, optional, default=2 - Number of boundary-to-boundary communication layers before final evaluation. + Number of boundary-to-boundary communication layers before final + evaluation. n_latent_scalars : int, optional, default=12 Number of scalar latent channels propagated between hyperlayers. n_latent_vectors : int, optional, default=6 @@ -129,11 +131,11 @@ class GLOBE(Module): kernel functions. theta : float, optional, default=1.0 Barnes-Hut opening angle controlling the near/far-field split in the - dual-tree traversal. The criterion is - :math:`(D_T + D_S) / r < \theta`, where :math:`D_T` and :math:`D_S` - are AABB diagonals and :math:`r` is the minimum inter-AABB distance. - Larger values approximate more aggressively; ``0`` forces all - interactions to be exact (no far-field approximation). + dual-tree traversal. The criterion is :math:`(D_T + D_S) / r < \theta`, + where :math:`D_T` and :math:`D_S` are AABB diagonals and :math:`r` is + the minimum inter-AABB distance. Larger values approximate more + aggressively; ``0`` forces all interactions to be exact (no far-field + approximation). leaf_size : int, optional, default=1 Maximum number of source points per leaf node in the cluster tree. Larger values produce shallower trees (fewer traversal iterations) at @@ -143,6 +145,22 @@ class GLOBE(Module): converting ``(far, far)`` pairs into ``(near, far)`` pairs. This eliminates the target-side approximation at the cost of more kernel evaluations. + use_gradient_checkpointing : bool, optional, default=True + If ``True``, applies ``torch.utils.checkpoint.checkpoint`` to each + kernel evaluation during training, trading compute for memory. See + :class:`~physicsnemo.experimental.models.globe.field_kernel.Kernel` for + details. + tree_build_device : torch.device or str or None, optional, default=None + Device on which to build cluster trees and run the dual-tree Barnes-Hut + traversal in :meth:`_build_trees_and_plans` and + :meth:`_build_prediction_plans`. Trees and plans are transferred back + to the input's device at function exit (a no-op when the devices + match). ``None`` (default) uses the input's device. ``"cpu"`` can be + faster for small problems (~a few thousand boundary cells) where CUDA + launch latency and ``cudaStreamSynchronize`` round-trips dominate the + real work in tree traversal; for large problems (hundreds of thousands + of cells) the GPU's parallelism wins and the input's device is the + right choice. Forward ------- @@ -164,22 +182,23 @@ class GLOBE(Module): A point-cloud :class:`~physicsnemo.mesh.Mesh` (0-dimensional manifold) whose ``.points`` attribute equals the input ``prediction_points``. The predicted fields are in ``.point_data``, keyed by the names from - ``output_field_ranks``. - Scalar fields have shape :math:`(N_{points},)`, vector fields have shape - :math:`(N_{points}, D)`. Cells are empty (shape ``(0, 1)``). - ``global_data`` is passed through from the input. + ``output_field_ranks``. Scalar fields have shape :math:`(N_{points},)`, + vector fields have shape :math:`(N_{points}, D)`. Cells are empty (shape + ``(0, 1)``). ``global_data`` is passed through from the input. Notes ----- - ``kernel_layers`` is a :class:`~torch.nn.ModuleList` of communication - hyperlayers, each containing a :class:`~torch.nn.ModuleDict` mapping BC type - names to :class:`~physicsnemo.experimental.models.globe.field_kernel.MultiscaleKernel` + hyperlayers, each containing a :class:`~torch.nn.ModuleDict` mapping BC + type names to + :class:`~physicsnemo.experimental.models.globe.field_kernel.MultiscaleKernel` instances. - ``final_field_transforms`` is a :class:`~torch.nn.ModuleList` of per-field linear calibration layers, ordered alphabetically by field name. - Cell areas are automatically normalized by ``reference_area`` to preserve discretization-invariance. - - The cell normal vector is automatically added to source data for each mesh. + - The cell normal vector is automatically added to source data for each + mesh. - The ``Mesh["n-1", "n"]`` type annotations assume the PDE domain fills the full ambient space (domain manifold dim = spatial dim), so boundary meshes are codimension-1 in the ambient space. For a PDE on a ``d``-dimensional @@ -227,6 +246,8 @@ def __init__( self_regularization_beta: float | None = None, latent_compression_scale: float | None = None, expand_far_targets: bool = False, + use_gradient_checkpointing: bool = True, + tree_build_device: torch.device | str | None = None, ): if hidden_layer_sizes is None: hidden_layer_sizes = [64, 64, 64] @@ -295,6 +316,10 @@ def __init__( self.network_type = network_type self.self_regularization_beta = self_regularization_beta self.expand_far_targets = expand_far_targets + self.use_gradient_checkpointing = use_gradient_checkpointing + self.tree_build_device: torch.device | None = ( + torch.device(tree_build_device) if tree_build_device is not None else None + ) ### Build the intermediate output-field rank spec for communication # hyperlayers. Only the final hyperlayer emits output_field_ranks. @@ -331,6 +356,7 @@ def __init__( leaf_size=leaf_size, network_type=network_type, self_regularization_beta=self_regularization_beta, + use_gradient_checkpointing=use_gradient_checkpointing, ) for bc_type in boundary_condition_names } @@ -404,40 +430,70 @@ def _build_trees_and_plans( """ from physicsnemo.experimental.models.globe.cluster_tree import ClusterTree - cluster_trees: dict[str, ClusterTree] = {} - bc_areas: dict[str, torch.Tensor] = {} - for bc_type, mesh in boundary_meshes.items(): - areas = mesh.cell_areas / self.reference_area - bc_areas[bc_type] = areas - cluster_trees[bc_type] = ClusterTree.from_points( - mesh.cell_centroids, leaf_size=self.leaf_size, areas=areas - ) + ### ``no_grad`` is safe: tree inputs (centroids, areas) carry no grad + ### and the outputs are consumed downstream as integer indices and as + ### a non-grad-tracked area divisor in ``compute_source_aggregates``. + ### Without it we'd pay autograd bookkeeping on dozens of tensor ops + ### inside the ``@torch.compiler.disable`` body for nothing. See the + ### ``tree_build_device`` docstring on :class:`GLOBE` for why building + ### on CPU can be faster than on CUDA at small ``N``. + original_device = ( + next(iter(boundary_meshes.values())).points.device + if boundary_meshes + else self.reference_area.device + ) + build_device = self.tree_build_device or original_device - ### Build interaction plans for all (source, destination) BC pairs. - comm_plans: dict[str, dict[str, DualInteractionPlan]] = {} - for dst_bc in boundary_meshes: - comm_plans[dst_bc] = { - src_bc: cluster_trees[src_bc].find_dual_interaction_pairs( - target_tree=cluster_trees[dst_bc], theta=self.theta, - expand_far_targets=self.expand_far_targets, + with torch.no_grad(): + ### Pass 1: build one tree per BC. Must complete before pass 2 + ### so every plan can see every tree. + cluster_trees_built: dict[str, ClusterTree] = {} + bc_areas_built: dict[str, torch.Tensor] = {} + for bc_type, mesh in boundary_meshes.items(): + centroids = mesh.cell_centroids.to(build_device) + areas = (mesh.cell_areas / self.reference_area).to(build_device) + bc_areas_built[bc_type] = areas + cluster_trees_built[bc_type] = ClusterTree.from_points( + centroids, leaf_size=self.leaf_size, areas=areas, ) - for src_bc in boundary_meshes - } - for dst_bc, plans_for_dst in comm_plans.items(): - n_dst = boundary_meshes[dst_bc].n_cells - for src_bc, plan in plans_for_dst.items(): - n_src = boundary_meshes[src_bc].n_cells - logger.logger.debug( - "comm plan [%s -> %s]: %d near + %d nf + %d fn + %d far_node " - "(%.2f%% near-field, %d src x %d dst faces, " - "theta=%.2f, leaf_size=%d)", - src_bc, dst_bc, - plan.n_near, plan.n_nf, plan.n_fn, plan.n_far_nodes, - 100.0 * plan.n_near / max(n_src * n_dst, 1), - n_src, n_dst, self.theta, self.leaf_size, - ) + ### Pass 2: build B^2 plans + log each in-line. + comm_plans_built: dict[str, dict[str, DualInteractionPlan]] = {} + for dst_bc, dst_mesh in boundary_meshes.items(): + n_dst = dst_mesh.n_cells + comm_plans_built[dst_bc] = {} + for src_bc, src_mesh in boundary_meshes.items(): + n_src = src_mesh.n_cells + plan = cluster_trees_built[src_bc].find_dual_interaction_pairs( + target_tree=cluster_trees_built[dst_bc], theta=self.theta, + expand_far_targets=self.expand_far_targets, + ) + comm_plans_built[dst_bc][src_bc] = plan + logger.logger.debug( + "comm plan [%s -> %s]: %d near + %d nf + %d fn + %d far_node " + "(%.2f%% near-field, %d src x %d dst faces, " + "theta=%.2f, leaf_size=%d)", + src_bc, dst_bc, + plan.n_near, plan.n_nf, plan.n_fn, plan.n_far_nodes, + 100.0 * plan.n_near / max(n_src * n_dst, 1), + n_src, n_dst, self.theta, self.leaf_size, + ) + ### Transfer to the original device. ``ClusterTree`` and + ### ``DualInteractionPlan`` are both ``@tensorclass``, so ``.to`` moves + ### all member tensors at once. No-op when devices already match. + cluster_trees = { + bc: t.to(original_device) # ty: ignore[unresolved-attribute] + for bc, t in cluster_trees_built.items() + } + bc_areas = {bc: a.to(original_device) for bc, a in bc_areas_built.items()} + comm_plans = { + dst: { + src: p.to(original_device) # ty: ignore[unresolved-attribute] + for src, p in plans.items() + } + for dst, plans in comm_plans_built.items() + } return cluster_trees, bc_areas, comm_plans @torch.compiler.disable @@ -461,27 +517,41 @@ def _build_prediction_plans( """ from physicsnemo.experimental.models.globe.cluster_tree import ClusterTree - pred_target_tree = ClusterTree.from_points( - prediction_points, leaf_size=self.leaf_size, - ) - pred_plans = { - bc_type: tree.find_dual_interaction_pairs( - target_tree=pred_target_tree, theta=self.theta, - expand_far_targets=self.expand_far_targets, - ) - for bc_type, tree in cluster_trees.items() - } + ### See ``_build_trees_and_plans`` for the ``no_grad`` + build-device + ### rationale. ``cluster_trees`` arrive on the caller's device from + ### that earlier call, so we move them to ``build_device`` for the + ### dual traversal and transfer both the new prediction-point tree + ### and the resulting plans back at function exit. + original_device = prediction_points.device + build_device = self.tree_build_device or original_device n_pred = prediction_points.shape[0] - for bc_type, plan in pred_plans.items(): - n_src = cluster_trees[bc_type].n_sources - logger.logger.debug( - "pred plan [%s]: %d near + %d nf + %d fn + %d far_node " - "(%d sources x %d targets, theta=%.2f)", - bc_type, plan.n_near, plan.n_nf, plan.n_fn, plan.n_far_nodes, - n_src, n_pred, self.theta, + with torch.no_grad(): + pred_target_tree_built = ClusterTree.from_points( + prediction_points.to(build_device), leaf_size=self.leaf_size, ) + ### Single pass: each iteration is independent - transfer the + ### source tree to ``build_device``, run the dual traversal + ### against the shared ``pred_target_tree_built``, log inline. + pred_plans_built: dict[str, DualInteractionPlan] = {} + for bc_type, tree in cluster_trees.items(): + plan = tree.to(build_device).find_dual_interaction_pairs( # ty: ignore[unresolved-attribute] + target_tree=pred_target_tree_built, theta=self.theta, + expand_far_targets=self.expand_far_targets, + ) + pred_plans_built[bc_type] = plan + logger.logger.debug( + "pred plan [%s]: %d near + %d nf + %d fn + %d far_node " + "(%d sources x %d targets, theta=%.2f)", + bc_type, plan.n_near, plan.n_nf, plan.n_fn, plan.n_far_nodes, + tree.n_sources, n_pred, self.theta, + ) + pred_target_tree = pred_target_tree_built.to(original_device) # ty: ignore[unresolved-attribute] + pred_plans = { + bc: p.to(original_device) # ty: ignore[unresolved-attribute] + for bc, p in pred_plans_built.items() + } return pred_target_tree, pred_plans def _evaluate_hyperlayer( @@ -556,9 +626,7 @@ def _evaluate_hyperlayer( kernel_source_keys = ( flatten_rank_spec(kernel.source_data_ranks).keys() - {"normals"} ) - source_data = _flatten_keys( - mesh.cell_data.exclude("strengths") - ).select(*kernel_source_keys) + source_data = _flatten_keys(mesh.cell_data).select(*kernel_source_keys) source_data["normals"] = mesh.cell_normals kernel_result: TensorDict[str, Float[torch.Tensor, "n_targets ..."]] = kernel( @@ -885,6 +953,6 @@ def forward( key = tuple(name.split(".")) t = output_mesh.point_data[key] output_mesh.point_data[key] = self.final_field_transforms[idx]( - t.view(-1, 1) - ).view(t.shape) + t.reshape(-1, 1) + ).reshape(t.shape) return output_mesh diff --git a/pyproject.toml b/pyproject.toml index 941636a791..f16767cae0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,9 +43,10 @@ dependencies = [ "jaxtyping>=0.3.3", "termcolor>=3.2.0", "hydra-core>=1.3.2", - "tensordict>=0.10.0", + "tensordict>=0.12.2", "omegaconf>=2.3.0", "importlib-metadata>=8.7.1", + "psutil>=6.0.0", ] [project.urls] diff --git a/test/models/globe/test_barnes_hut_kernel.py b/test/models/globe/test_barnes_hut_kernel.py index 86262707bf..272aa0627f 100644 --- a/test/models/globe/test_barnes_hut_kernel.py +++ b/test/models/globe/test_barnes_hut_kernel.py @@ -382,80 +382,6 @@ def test_aggregate_source_data_mixed(self): agg.node_source_data["v"][0], expected_v, atol=1e-5, rtol=1e-5 ) - # -- Precomputed leaf field consistency tests ---------------------------- - - @pytest.mark.parametrize( - "n_points, leaf_size, n_dims", - [ - (50, 4, 3), - (1, 4, 2), - (10, 100, 2), - (20, 1, 3), - ], - ids=["normal", "single_point", "root_only_leaf", "one_per_leaf"], - ) - def test_precomputed_leaf_node_ids( - self, - n_points: int, - leaf_size: int, - n_dims: int, - ): - """Precomputed leaf_node_ids matches torch.where(leaf_count > 0).""" - torch.manual_seed(DEFAULT_SEED) - points = torch.randn(n_points, n_dims) - tree = ClusterTree.from_points(points, leaf_size=leaf_size) - - expected = torch.where(tree.leaf_count > 0)[0] - assert torch.equal(tree.leaf_node_ids, expected) - assert tree.n_leaves == expected.shape[0] - - @pytest.mark.parametrize( - "n_points, leaf_size, n_dims", - [ - (50, 4, 3), - (1, 4, 2), - (10, 100, 2), - (20, 1, 3), - ], - ids=["normal", "single_point", "root_only_leaf", "one_per_leaf"], - ) - def test_precomputed_leaf_seg_ids( - self, - n_points: int, - leaf_size: int, - n_dims: int, - ): - """Precomputed leaf_seg_ids matches on-the-fly _ragged_arange computation.""" - torch.manual_seed(DEFAULT_SEED) - points = torch.randn(n_points, n_dims) - tree = ClusterTree.from_points(points, leaf_size=leaf_size) - - assert tree.leaf_seg_ids.shape == (n_points,) - assert tree.leaf_seg_ids.dtype == torch.long - if n_points > 0: - assert tree.leaf_seg_ids.max() < tree.n_leaves - - # Rebuild seg_ids from scratch and compare - leaf_starts = tree.leaf_start[tree.leaf_node_ids] - leaf_counts = tree.leaf_count[tree.leaf_node_ids] - positions, compact_ids = _ragged_arange( - leaf_starts, - leaf_counts, - total=n_points, - ) - expected = torch.zeros(n_points, dtype=torch.long) - expected[positions] = compact_ids - - assert torch.equal(tree.leaf_seg_ids, expected) - - def test_precomputed_leaf_fields_empty_tree(self): - """Empty tree has empty leaf_node_ids and leaf_seg_ids.""" - tree = ClusterTree.from_points(torch.empty(0, 2), leaf_size=4) - - assert tree.leaf_node_ids.numel() == 0 - assert tree.leaf_seg_ids.numel() == 0 - assert tree.n_leaves == 0 - def test_compute_source_aggregates_single_point(self): """Single-point tree centroid equals the point itself.""" point = torch.tensor([[3.0, -1.0, 7.0]]) @@ -473,7 +399,9 @@ def test_compute_source_aggregates_root_only_leaf(self): areas = torch.rand(n) + 0.1 tree = ClusterTree.from_points(points, leaf_size=100, areas=areas) - assert tree.n_leaves == 1, "Expected single leaf (root)" + ### leaf_size > n means the root is the only leaf (single node tree). + assert tree.n_nodes == 1 + assert int((tree.leaf_count > 0).sum()) == 1 agg = tree.compute_source_aggregates(points, areas) expected = (points * areas.unsqueeze(-1)).sum(0) / areas.sum() @@ -484,6 +412,79 @@ def test_compute_source_aggregates_root_only_leaf(self): rtol=1e-5, ) + @pytest.mark.parametrize( + "n_points, coord_offset, coord_scale, leaf_size", + [ + (10_000, 0.0, 5.0, 1), + (10_000, 5.0, 2.5, 1), + (10_000, 5.0, 2.5, 4), + (1_000, 50.0, 5.0, 1), + ], + ids=[ + "centered_large", + "offset_large", + "offset_larger_leaves", + "small_extreme_offset", + ], + ) + def test_compute_source_aggregates_precision_at_scale( + self, + n_points: int, + coord_offset: float, + coord_scale: float, + leaf_size: int, + ): + """Precision regression guard for the cumsum-cancellation regime. + + An earlier implementation (commit ``de1b8c93``) ran the cumsum + and the range-subtract in fp32, which produced 1-100 % relative + error on small-leaf centroids when ``cumsum_total >> range_sum`` + (large ``N`` with all-positive / offset coordinates - the regime + real car / airfoil surface meshes produce). The current + implementation lifts the cumsum to fp64 internally and casts back; + this test checks that fp32 inputs agree with fp64 inputs to + within fp32 epsilon, which would not be true under the old + fp32-cumsum implementation. + + CPU-only so the deterministic fp64 path is the reference (CUDA + cumsum has warp-level non-determinism even in fp64). + """ + torch.manual_seed(DEFAULT_SEED) + pts = (torch.rand(n_points, 3) - 0.5) * (2.0 * coord_scale) + coord_offset + areas = (torch.rand(n_points) * 0.9 + 0.1) * 1e-3 + normals = torch.nn.functional.normalize( + torch.randn(n_points, 3), dim=-1 + ) + sd = TensorDict({"face_normal": normals}, batch_size=[n_points]) + + tree = ClusterTree.from_points(pts, leaf_size=leaf_size, areas=areas) + actual = tree.compute_source_aggregates(pts, areas, source_data=sd) + expected = tree.compute_source_aggregates( + pts.double(), + areas.double(), + source_data=TensorDict( + {"face_normal": normals.double()}, batch_size=[n_points] + ), + ) + + torch.testing.assert_close( + actual.node_centroid.double(), + expected.node_centroid, + atol=1e-5, + rtol=1e-5, + ) + ### Looser tolerance for ``face_normal``: averaging unit vectors + ### whose directions partially cancel can leave near-zero + ### components, making the relative error metric sensitive even + ### in the precise-arithmetic limit. Absolute tolerance keeps + ### the assertion meaningful for non-degenerate components. + torch.testing.assert_close( + actual.node_source_data["face_normal"].double(), + expected.node_source_data["face_normal"], + atol=1e-4, + rtol=1e-4, + ) + # --------------------------------------------------------------------------- # BarnesHutKernel convergence tests @@ -1548,5 +1549,187 @@ def test_four_quadrant_per_category_accuracy( ) +# --------------------------------------------------------------------------- +# Autocast / mixed-precision regression guards +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "output_fields", + [ + {"p": "scalar"}, + {"u": "vector"}, + {"p": "scalar", "u": "vector"}, + ], + ids=["scalar_only", "vector_only", "scalar_and_vector"], +) +def test_bh_forward_under_bf16_autocast( + output_fields: dict[str, Literal["scalar", "vector"]], +) -> None: + """BarnesHutKernel.forward must run under bf16 autocast with fp32 strengths. + + Production training enables ``torch.autocast(..., dtype=torch.bfloat16)`` + around the forward pass and passes fp32 ``source_strengths``. The MLP + output is then bf16 while the strengths multiplicand is fp32, so + ``weighted = chunk * strengths`` is promoted to fp32 before + ``packed_buf.index_add_`` runs. This test catches the bug where the + packed scatter buffer was allocated in the bf16 autocast dtype, causing + ``index_add_`` to raise ``RuntimeError: self (BFloat16) and source + (Float) must have the same scalar type`` on the first BH call. + + The check is twofold: + + 1. The forward completes without raising (catches the dtype mismatch + directly). + 2. The output approximately matches the fp32 baseline within bf16 + tolerance (catches a future regression where the fix instead + silently downcasts the result, losing accumulation precision). + """ + bh_kernel, _, data = _make_bh_kernel_and_data( + n_spatial_dims=2, + n_source_scalars=0, + n_source_vectors=1, + output_fields=output_fields, + hidden_layer_sizes=[16, 16], + n_source_points=20, + n_target_points=15, + leaf_size=DEFAULT_LEAF_SIZE, + ) + + with torch.no_grad(): + baseline = bh_kernel(**data) + + with ( + torch.no_grad(), + torch.autocast(device_type="cpu", dtype=torch.bfloat16), + ): + autocast_output = bh_kernel(**data) + + ### Output keys + shapes must match the fp32 baseline exactly. + assert set(autocast_output.keys()) == set(baseline.keys()) + for key in baseline.keys(): + assert autocast_output[key].shape == baseline[key].shape, ( + f"shape mismatch for {key}: autocast={autocast_output[key].shape} " + f"baseline={baseline[key].shape}" + ) + + ### Values close within bf16 tolerance. bf16 has ~3 decimal digits of + ### mantissa precision, and our scatter accumulates O(n_pairs) values, + ### so a few percent relative error is expected. + for key in baseline.keys(): + torch.testing.assert_close( + autocast_output[key].float(), + baseline[key], + atol=5e-2, + rtol=5e-2, + msg=f"autocast output diverges from fp32 baseline for field {key!r}", + ) + + +def test_compute_node_strengths_precision_at_scale() -> None: + """Precision regression guard for ``BarnesHutKernel._compute_node_strengths``. + + Same cumsum-cancellation regime as + :py:meth:`TestClusterTree.test_compute_source_aggregates_precision_at_scale`, + but for the per-node strength sum used to weight far-field kernel + contributions in :class:`BarnesHutKernel`. The fp32-cumsum + implementation produced relative errors up to 186 % on individual + leaf strengths at drivaer scale; the current fp64-cumsum + implementation must agree with an fp64-input reference within fp32 + epsilon. CPU-only for the same reproducibility reasons. + """ + torch.manual_seed(DEFAULT_SEED) + n_points = 10_000 + pts = (torch.rand(n_points, 3) - 0.5) * 5.0 + 5.0 + areas = (torch.rand(n_points) * 0.9 + 0.1) * 1e-3 + strengths = areas.clone() + + tree = ClusterTree.from_points(pts, leaf_size=1, areas=areas) + bh, _, _ = _make_bh_kernel_and_data( + n_spatial_dims=3, + hidden_layer_sizes=[8, 8], + n_source_points=n_points, + n_target_points=10, + ) + + actual = bh._compute_node_strengths(tree, strengths) + expected = bh._compute_node_strengths(tree, strengths.double()) + torch.testing.assert_close( + actual.double(), expected, atol=1e-5, rtol=1e-5 + ) + + +# --------------------------------------------------------------------------- +# Sync-budget regression guard +# --------------------------------------------------------------------------- + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason="CUDA required for sync-event counting" +) +@pytest.mark.parametrize( + "n_points, leaf_size, theta", + [ + (200, 1, 1.0), + (200, 4, 1.0), + (1000, 4, 0.5), + ], +) +def test_dual_traversal_sync_budget( + n_points: int, leaf_size: int, theta: float +) -> None: + """Cluster-tree construction + dual traversal stays within a sync budget. + + Profiling identified ``cudaStreamSynchronize`` as the dominant CPU + stall in GLOBE training (9919 calls / ~3 s in a 10.5 s training step, + most of them from ``find_dual_interaction_pairs``). Phase 2 of the + perf optimization moved that section to a deferred-compaction + structure that pays at most a handful of syncs per call. + + This test counts ``cudaStreamSynchronize`` events inside one tree + build + one dual traversal and asserts the count stays under a + generous budget. The budget is set well above the current count to + accommodate small implementation variations, but tight enough to + catch the kind of regression we fixed (per-iteration ``.any()`` / + ``.item()`` / boolean-mask compactions in the traversal loop). + """ + device = torch.device("cuda") + torch.manual_seed(DEFAULT_SEED) + points = torch.randn(n_points, 3, device=device) + + ### Warm up: first calls allocate / JIT, which can fire one-off syncs + ### unrelated to the algorithmic structure under test. + tree_warm = ClusterTree.from_points(points, leaf_size=leaf_size) + tree_warm.find_dual_interaction_pairs(target_tree=tree_warm, theta=theta) + torch.cuda.synchronize() + + with torch.profiler.profile( + activities=[torch.profiler.ProfilerActivity.CUDA], + record_shapes=False, + ) as prof: + tree = ClusterTree.from_points(points, leaf_size=leaf_size) + tree.find_dual_interaction_pairs(target_tree=tree, theta=theta) + torch.cuda.synchronize() + + sync_count = sum( + 1 for ev in prof.events() if ev.name == "cudaStreamSynchronize" + ) + + ### Budget: ~3-5 syncs per traversal iteration is what the new + ### implementation pays (boolean compaction for the new active set, + ### one for ``active[near_leaf_leaf]``, one inside ``_ragged_arange``). + ### A 200-point leaf_size=1 tree takes ~10 traversal iterations; the + ### top_down build adds a handful more. Setting the budget at 100 + ### catches the ``.any()``-per-branch regression (~12 syncs / iter) + ### that this work eliminated, while leaving headroom. + budget = 100 + assert sync_count <= budget, ( + f"dual_traversal sync count {sync_count} exceeds budget {budget} for " + f"{n_points=}, {leaf_size=}, {theta=}. Did a `.any()`, `.item()`, or " + f"data-dependent boolean indexing creep back into the dual-traversal " + f"hot loop?" + ) + + if __name__ == "__main__": pytest.main() diff --git a/uv.lock b/uv.lock index bd5c3401aa..6cec6d2966 100644 --- a/uv.lock +++ b/uv.lock @@ -116,18 +116,9 @@ conflicts = [[ { package = "nvidia-physicsnemo", extra = "natten-cu13" }, ]] -[[package]] -name = "absl-py" -version = "2.4.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size 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'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", "(python_full_version < '3.12' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", "python_full_version >= '3.13' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", "python_full_version == '3.12.*' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", @@ -1184,46 +1178,46 @@ wheels = [ [package.optional-dependencies] cccl = [ - { name = "nvidia-cuda-cccl", version = "13.0.85", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'win32') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or sys_platform == 'linux' or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-cuda-cccl", version = "13.0.85", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'win32') or sys_platform == 'linux' or (sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] cublas = [ - { name = "nvidia-cublas", version = "13.1.0.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 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'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] cudart = [ - { name = "nvidia-cuda-runtime", version = "13.0.96", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 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(platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-cuda-runtime", version = "13.0.96", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] cufft = [ - { name = "nvidia-cufft", version = "12.0.0.61", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-cufft", version = "12.0.0.61", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] cufile = [ { name = "nvidia-cufile", marker = "(sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "(platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 'ARM64' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-cuda-cupti", marker = "(platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 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and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "(platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (platform_machine != 'ARM64' and platform_machine != 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 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sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'linux' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (extra 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== 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", - "python_full_version >= '3.13' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", 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and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", - "(python_full_version == '3.12.*' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')", - "python_full_version >= '3.13' and platform_machine == 's390x' and extra == 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'3.12.*' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", - "python_full_version >= '3.13' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and 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'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "(python_full_version == '3.12.*' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 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'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')", - "(python_full_version == '3.12.*' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')", - "python_full_version >= '3.13' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", - "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 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and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "(python_full_version < '3.12' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 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extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "(python_full_version == '3.12.*' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version < '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "(python_full_version < '3.12' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version >= '3.13' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version == '3.12.*' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version < '3.12' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", + "(python_full_version >= '3.13' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 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platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version >= '3.13' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version == '3.12.*' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version < '3.12' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "(python_full_version >= '3.13' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 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'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version >= '3.13' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version == '3.12.*' and platform_machine == 's390x' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 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platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version == '3.12.*' and 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}, marker = "extra == 'extra-18-nvidia-physicsnemo-cu13' or extra != 'extra-18-nvidia-physicsnemo-cu12' or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, { name = "typing-extensions" }, ] sdist = { url = "https://files.pythonhosted.org/packages/44/bd/8a391e7c356366224734efd24da929cc4796fff468bfb179fe1af6548535/numcodecs-0.16.5.tar.gz", hash = "sha256:0d0fb60852f84c0bd9543cc4d2ab9eefd37fc8efcc410acd4777e62a1d300318", size = 6276387, upload-time = "2025-11-21T02:49:48.986Z" } @@ -4009,12 +3672,15 @@ name = "numpy" version = "2.2.6" source = { registry = "https://pypi.org/simple" } resolution-markers = [ + "python_full_version >= '3.13' and platform_machine == 'ARM64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'ARM64' and sys_platform == 'win32'", "python_full_version >= '3.13' and platform_machine == 'aarch64'", "python_full_version >= '3.13' and platform_machine == 'x86_64'", 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and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "(python_full_version == '3.12.*' and platform_machine != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or (python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13')", + "python_full_version >= '3.13' and platform_machine == 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13'", + "python_full_version == '3.12.*' and platform_machine 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"(python_full_version >= '3.12' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "packaging", marker = "python_full_version >= '3.12' or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "typing-extensions", marker = "python_full_version >= '3.12' or (extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" }, +] + [[package]] name = "zipp" version = "3.23.1" From e0ab081f47aeffe2ccd562230ece818b480e0d8a Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Mon, 11 May 2026 16:25:36 -0400 Subject: [PATCH 2/6] formatting --- test/models/globe/test_barnes_hut_kernel.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/test/models/globe/test_barnes_hut_kernel.py b/test/models/globe/test_barnes_hut_kernel.py index 272aa0627f..ec8ac93e11 100644 --- a/test/models/globe/test_barnes_hut_kernel.py +++ b/test/models/globe/test_barnes_hut_kernel.py @@ -452,9 +452,7 @@ def test_compute_source_aggregates_precision_at_scale( torch.manual_seed(DEFAULT_SEED) pts = (torch.rand(n_points, 3) - 0.5) * (2.0 * coord_scale) + coord_offset areas = (torch.rand(n_points) * 0.9 + 0.1) * 1e-3 - normals = torch.nn.functional.normalize( - torch.randn(n_points, 3), dim=-1 - ) + normals = torch.nn.functional.normalize(torch.randn(n_points, 3), dim=-1) sd = TensorDict({"face_normal": normals}, batch_size=[n_points]) tree = ClusterTree.from_points(pts, leaf_size=leaf_size, areas=areas) @@ -1654,9 +1652,7 @@ def test_compute_node_strengths_precision_at_scale() -> None: actual = bh._compute_node_strengths(tree, strengths) expected = bh._compute_node_strengths(tree, strengths.double()) - torch.testing.assert_close( - actual.double(), expected, atol=1e-5, rtol=1e-5 - ) + torch.testing.assert_close(actual.double(), expected, atol=1e-5, rtol=1e-5) # --------------------------------------------------------------------------- @@ -1711,9 +1707,7 @@ def test_dual_traversal_sync_budget( tree.find_dual_interaction_pairs(target_tree=tree, theta=theta) torch.cuda.synchronize() - sync_count = sum( - 1 for ev in prof.events() if ev.name == "cudaStreamSynchronize" - ) + sync_count = sum(1 for ev in prof.events() if ev.name == "cudaStreamSynchronize") ### Budget: ~3-5 syncs per traversal iteration is what the new ### implementation pays (boolean compaction for the new active set, From 3838eca6494df3fb0d9481ff5b75c9153426c0c7 Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Tue, 12 May 2026 17:43:09 -0400 Subject: [PATCH 3/6] reductions to syncs --- .../experimental/models/globe/cluster_tree.py | 625 ++++++++++++------ test/models/globe/test_barnes_hut_kernel.py | 62 +- 2 files changed, 462 insertions(+), 225 deletions(-) diff --git a/physicsnemo/experimental/models/globe/cluster_tree.py b/physicsnemo/experimental/models/globe/cluster_tree.py index 4462c58e75..38b4d6d01c 100644 --- a/physicsnemo/experimental/models/globe/cluster_tree.py +++ b/physicsnemo/experimental/models/globe/cluster_tree.py @@ -41,9 +41,10 @@ """ import logging +from typing import NamedTuple import torch -from jaxtyping import Float, Int +from jaxtyping import Bool, Float, Int from tensordict import TensorDict, tensorclass from torch.profiler import record_function @@ -149,7 +150,7 @@ def validate(self) -> None: f"{name_b}.shape={b.shape!r}" ) - ### fn_broadcast tensors must be consistently sized + ### fn_broadcast tensors must be consistently sized AND non-negative. n_fn = self.fn_source_ids.shape[0] for name, tensor in [ ("fn_broadcast_starts", self.fn_broadcast_starts), @@ -159,12 +160,6 @@ def validate(self) -> None: raise ValueError( f"{name}.shape={tensor.shape!r}, expected ({n_fn},)" ) - - ### Non-negativity - for name, tensor in [ - ("fn_broadcast_starts", self.fn_broadcast_starts), - ("fn_broadcast_counts", self.fn_broadcast_counts), - ]: if tensor.numel() > 0 and (tensor < 0).any(): raise ValueError(f"{name} contains negative values") @@ -184,19 +179,75 @@ def validate(self) -> None: ) +class _ExpandedLeafHits(NamedTuple): + """Per-iteration output of :func:`_expand_dual_leaf_hits`. + + Three of the four interaction streams are returned in + *deferred-compaction* form: the per-element tensor is unfiltered + (length ``t_full`` or ``s_full``) and accompanied by a boolean + validity mask, so the caller can amortise compaction across all + traversal iterations into a single boolean indexing per stream. + + Fields + ------ + near_tgts, near_srcs : Int[Tensor, " n_near"] + (near, near) Cartesian-product pairs. Already compacted - + ``_ragged_arange`` sized the output by data anyway. + nf_tgts, nf_snids : Int[Tensor, " t_full"] + (near, far) target IDs / source-node IDs, length :math:`T_\\text{full} + = \\sum t_\\text{counts}`. + nf_validity : Bool[Tensor, " t_full"] + Mask selecting the (near, far) entries. Equals ``target_is_far``. + fn_sids, fn_tnids : Int[Tensor, " s_full"] + (far, near) source IDs / target-node IDs, length :math:`S_\\text{full} + = \\sum s_\\text{counts}`. + fn_validity : Bool[Tensor, " s_full"] + Mask selecting the (far, near) entries. Equals ``source_is_far``. + fn_bcast_starts, fn_bcast_counts : Int[Tensor, " s_full"] + Per-source start/count into ``fn_bcast_targets``. Aligned with + the fn stream; filter by ``fn_validity``. Non-fn entries have + arithmetically defined but unused values. + fn_bcast_targets : Int[Tensor, " t_full"] + Active survivor target IDs sorted by leaf pair, *sentinel-padded* + at the tail. Compact via ``fn_bcast_targets_validity``. + fn_bcast_targets_validity : Bool[Tensor, " t_full"] + Mask selecting active (non-sentinel) entries. + """ + + near_tgts: torch.Tensor + near_srcs: torch.Tensor + nf_tgts: torch.Tensor + nf_snids: torch.Tensor + nf_validity: torch.Tensor + fn_sids: torch.Tensor + fn_tnids: torch.Tensor + fn_validity: torch.Tensor + fn_bcast_starts: torch.Tensor + fn_bcast_counts: torch.Tensor + fn_bcast_targets: torch.Tensor + fn_bcast_targets_validity: torch.Tensor + + @classmethod + def empty(cls, device: torch.device) -> "_ExpandedLeafHits": + """All-empty hits, used as the ``n_pairs == 0`` short-circuit.""" + el = torch.empty(0, dtype=torch.long, device=device) + eb = torch.empty(0, dtype=torch.bool, device=device) + return cls( + near_tgts=el, near_srcs=el.clone(), + nf_tgts=el.clone(), nf_snids=el.clone(), nf_validity=eb, + fn_sids=el.clone(), fn_tnids=el.clone(), fn_validity=eb.clone(), + fn_bcast_starts=el.clone(), fn_bcast_counts=el.clone(), + fn_bcast_targets=el.clone(), fn_bcast_targets_validity=eb.clone(), + ) + + def _expand_dual_leaf_hits( target_leaf_ids: Int[torch.Tensor, " n_leaf_pairs"], source_leaf_ids: Int[torch.Tensor, " n_leaf_pairs"], target_tree: "ClusterTree", source_tree: "ClusterTree", theta: float, -) -> tuple[ - Int[torch.Tensor, " n_near"], Int[torch.Tensor, " n_near"], - Int[torch.Tensor, " n_nf"], Int[torch.Tensor, " n_nf"], - Int[torch.Tensor, " n_fn"], Int[torch.Tensor, " n_fn"], - Int[torch.Tensor, " n_fn_bcast"], - Int[torch.Tensor, " n_fn"], Int[torch.Tensor, " n_fn"], -]: +) -> _ExpandedLeafHits: """Expand ``(target_leaf, source_leaf)`` pairs with two-stage filtering. Applies two sequential per-point tests to classify each (target, source) @@ -214,30 +265,46 @@ def _expand_dual_leaf_hits( The two stages are independent (different AABBs) and sequential (stage 2 only applies to survivors), so no (target, source) pair is double-counted. + Three of the four output streams are returned in **deferred-compaction** + form on the result struct: the per-element tensor is unfiltered (length + ``t_full`` or ``s_full``) and accompanied by a boolean validity mask. + The caller accumulates these across traversal iterations and does ONE + boolean compaction at the end - mirroring the pattern already used for + the far-field stream in ``find_dual_interaction_pairs``. This + eliminates the five per-iter ``aten::nonzero`` syncs that the previous + eagerly-filtered version paid (one each for ``target_is_far``, + ``~target_is_far``, ``source_is_far``, ``fn_active_mask``, and + ``~source_is_far``). + Returns ------- - near_target_ids, near_source_ids : torch.Tensor - (near, near) individual target-source pairs. - nf_target_ids, nf_source_node_ids : torch.Tensor - (near, far) individual target to source-node pairs. - fn_target_node_ids, fn_source_ids : torch.Tensor - (far, near) target-node to individual source pairs. - fn_broadcast_targets : torch.Tensor - Survivor target IDs sorted by leaf pair, for (far, near) broadcast. - fn_broadcast_starts, fn_broadcast_counts : torch.Tensor - Per-fn-pair offset/count into ``fn_broadcast_targets``. + _ExpandedLeafHits + See :class:`_ExpandedLeafHits` for the per-field shapes and + deferred-compaction protocol. """ device = target_leaf_ids.device theta_sq = theta * theta n_pairs = target_leaf_ids.shape[0] - ### This function is intentionally written without ``if X.any():`` / - ### ``int(X.sum())`` early-exit branches. Each such branch was a - ### CPU-GPU sync point in the dual-traversal hot loop; the sync count - ### was the dominant CPU stall in profiling. All downstream operations - ### (boolean indexing, ``_ragged_arange``, ``argsort``, ``scatter``, - ### ``torch.bincount``) handle zero-element inputs correctly, so we - ### let empty intermediate tensors flow through unconditionally. + ### The early-return guard is a Python ``int`` comparison on a shape + ### attribute - zero CUDA cost. It matters because in the early + ### traversal iterations (top-of-tree) there are typically no + ### leaf-leaf pairs yet, and without this guard the three + ### ``_ragged_arange`` calls below would each pay a + ### ``torch.arange(scalar_tensor)`` host sync to size their empty + ### output. Saves ~3 syncs * (number of leaf-leaf-free early iters) + ### per traversal. + if n_pairs == 0: + return _ExpandedLeafHits.empty(device) + + ### The rest of this function is intentionally written without + ### ``if X.any():`` / ``int(X.sum())`` early-exit branches AND without + ### ``X.nonzero()`` compactions. Each such call was a CPU-GPU sync + ### point in the dual-traversal hot loop; the sync count was the + ### dominant CPU stall in profiling. All downstream operations + ### (``_ragged_arange``, ``argsort``, ``scatter_add_``, ``scatter_``) + ### handle zero-element inputs correctly, so we let empty intermediate + ### tensors flow through unconditionally. t_starts = target_tree.leaf_start[target_leaf_ids] t_counts = target_tree.leaf_count[target_leaf_ids] @@ -258,22 +325,13 @@ def _expand_dual_leaf_hits( max=source_tree.node_aabb_max[src_leaf_per_target], ) dist_sq_t = (target_pts - clamped_t).pow(2).sum(dim=-1) - target_is_far = dist_sq_t * theta_sq > source_tree.node_diameter_sq[src_leaf_per_target] - - ### (near, far) output. ``target_is_far`` is consumed by two - ### indexings; doing one ``nonzero`` and reusing the integer index - ### saves one sync (each ``tensor[bool_mask]`` lowers to a - ### synchronizing ``aten::nonzero`` to size the output). - far_idx_t = target_is_far.nonzero(as_tuple=True)[0] - nf_target_ids = target_point_ids[far_idx_t] - nf_source_node_ids = src_leaf_per_target[far_idx_t] - - ### Survivors: targets that failed the per-target test. Empty - ### survivors are fine; downstream ops produce empty tensors. - ### Same dedup trick as ``far_idx_t`` above. - surv_idx = (~target_is_far).nonzero(as_tuple=True)[0] - surv_point_ids = target_point_ids[surv_idx] - surv_lp_ids = leaf_pair_ids_t[surv_idx] + target_is_far = ( + dist_sq_t * theta_sq > source_tree.node_diameter_sq[src_leaf_per_target] + ) + + ### (near, far) stream is returned unfiltered. ``target_point_ids``, + ### ``src_leaf_per_target`` are length ``t_full``; the caller compacts + ### them with ``target_is_far`` (== ``nf_validity``) at end-of-traversal. # ================================================================== # Stage 2: per-source test against target leaf AABBs @@ -289,79 +347,197 @@ def _expand_dual_leaf_hits( max=target_tree.node_aabb_max[tgt_leaf_per_src], ) dist_sq_s = (src_pts - clamped_s).pow(2).sum(dim=-1) - source_is_far = dist_sq_s * theta_sq > target_tree.node_diameter_sq[tgt_leaf_per_src] + source_is_far = ( + dist_sq_s * theta_sq > target_tree.node_diameter_sq[tgt_leaf_per_src] + ) - ### (far, near) output: source points far from the target leaf. - ### Three indexings off the same mask collapse to one ``nonzero`` + - ### integer indexing, saving two syncs. - far_idx_s = source_is_far.nonzero(as_tuple=True)[0] - fn_source_ids = src_point_ids[far_idx_s] - fn_target_node_ids = tgt_leaf_per_src[far_idx_s] - fn_lp_ids = leaf_pair_ids_s[far_idx_s] + ### (far, near) stream is returned unfiltered: ``src_point_ids``, + ### ``tgt_leaf_per_src`` are length ``s_full``; the caller compacts + ### with ``source_is_far`` (== ``fn_validity``). # ================================================================== - # Build (far, near) broadcast mapping + # Build (far, near) broadcast mapping (sync-free, sentinel-padded) # ================================================================== - # Group survivors by leaf pair so each fn source can look up its - # broadcast targets (all survivors from the same leaf pair). - # Only include survivors from leaf pairs that have fn sources; - # survivors from all-close leaf pairs are not referenced by any - # fn_broadcast_starts/counts entry. - # ``index_put_`` with empty indices is a no-op, so no shape branch. - has_fn_source = torch.zeros(n_pairs, dtype=torch.bool, device=device) - has_fn_source[fn_lp_ids] = True - fn_active_mask = has_fn_source[surv_lp_ids] - - ### Shared mask -> dedup the boolean indexing (saves one sync). - fn_active_idx = fn_active_mask.nonzero(as_tuple=True)[0] - active_surv_ids = surv_point_ids[fn_active_idx] - active_surv_lp_ids = surv_lp_ids[fn_active_idx] - - surv_sort = active_surv_lp_ids.argsort(stable=True) - fn_broadcast_targets = active_surv_ids[surv_sort] - - surv_counts_per_lp = torch.bincount(active_surv_lp_ids, minlength=n_pairs) - surv_starts_per_lp = surv_counts_per_lp.cumsum(0) - surv_counts_per_lp - - fn_broadcast_starts = surv_starts_per_lp[fn_lp_ids] - fn_broadcast_counts = surv_counts_per_lp[fn_lp_ids] + # ``has_fn_source[lp]`` is True iff leaf pair ``lp`` has at least one + # fn source (i.e., a source that passed the per-source far test). + # Sync-free construction: scatter ``True`` into ``has_fn_source[lp]`` + # for every fn entry and into a sentinel slot for every non-fn entry. + # The original ``has_fn_source[fn_lp_ids] = True`` required a + # ``nonzero`` on ``source_is_far`` to compute the filtered + # ``fn_lp_ids``; ``torch.where`` + sentinel-slot scatter is + # data-flow-equivalent and pays zero CUDA syncs. + has_fn_source_buf = torch.zeros(n_pairs + 1, dtype=torch.bool, device=device) + safe_fn_lp = torch.where( + source_is_far, + leaf_pair_ids_s, + torch.full_like(leaf_pair_ids_s, n_pairs), + ) + has_fn_source_buf.scatter_(0, safe_fn_lp, True) + has_fn_source = has_fn_source_buf[:n_pairs] + + ### An "active" target is a stage-1 survivor whose leaf pair has at + ### least one fn source. Working on the unfiltered ``leaf_pair_ids_t`` + ### lets us build the validity mask without a ``nonzero`` over + ### ``~target_is_far``. + active_validity = (~target_is_far) & has_fn_source[leaf_pair_ids_t] + + ### Sort by ``(leaf_pair_id if active else n_pairs)`` so that within + ### the sorted target_point_ids the active entries come first, grouped + ### by leaf-pair, followed by all the inactive entries (which carry the + ### sentinel key ``n_pairs``). The caller drops the inactive tail via + ### ``fn_broadcast_targets_validity`` at end-of-traversal. + bcast_sort_key = torch.where( + active_validity, + leaf_pair_ids_t, + torch.full_like(leaf_pair_ids_t, n_pairs), + ) + bcast_sort_order = bcast_sort_key.argsort(stable=True) + fn_broadcast_targets = target_point_ids[bcast_sort_order] + fn_broadcast_targets_validity = active_validity[bcast_sort_order] + + ### Per-lp active count via weighted ``scatter_add_``. Weight = + ### ``active_validity.long()``, so non-active entries contribute zero. + active_counts_per_lp = torch.zeros(n_pairs, dtype=torch.long, device=device) + active_counts_per_lp.scatter_add_( + 0, leaf_pair_ids_t, active_validity.long() + ) + active_starts_per_lp = active_counts_per_lp.cumsum(0) - active_counts_per_lp + + ### Return broadcast_starts/counts aligned with the *full* per-source + ### axis (length ``s_full``). The caller filters by ``source_is_far``. + fn_broadcast_starts_full = active_starts_per_lp[leaf_pair_ids_s] + fn_broadcast_counts_full = active_counts_per_lp[leaf_pair_ids_s] # ================================================================== # Reduced Cartesian product: survivors × close sources only # ================================================================== - ### Survivors of the per-source far test. Shared mask + dedup as - ### above. - close_idx = (~source_is_far).nonzero(as_tuple=True)[0] - close_src_ids = src_point_ids[close_idx] - close_lp_ids = leaf_pair_ids_s[close_idx] - - ### Group close sources by leaf pair for contiguous access. When - ### either side is empty the per-segment counts are all zero and - ### the expansion below produces empty (near, near) output - no - ### early-exit needed. - close_sort = close_lp_ids.argsort(stable=True) - sorted_close_srcs = close_src_ids[close_sort] - close_counts_per_lp = torch.bincount(close_lp_ids, minlength=n_pairs) - close_starts_per_lp = close_counts_per_lp.cumsum(0) - close_counts_per_lp - - ### Each survivor expands against its leaf pair's close sources. - per_surv_close_counts = close_counts_per_lp[surv_lp_ids] - per_surv_close_starts = close_starts_per_lp[surv_lp_ids] - - expanded_near_tgts = torch.repeat_interleave( - surv_point_ids, per_surv_close_counts + ### Per-lp count of close sources via weighted ``scatter_add_``. + close_counts_per_lp = torch.zeros(n_pairs, dtype=torch.long, device=device) + close_counts_per_lp.scatter_add_( + 0, leaf_pair_ids_s, (~source_is_far).long() + ) + + ### Sort sources by ``(leaf_pair_id, source_is_far)`` so within each + ### lp's contiguous block the close sources come first (key + ### ``2*lp + 0``) followed by the far sources (key ``2*lp + 1``). + ### This avoids the per-iter ``(~source_is_far).nonzero()`` sync that + ### the previous filtered-then-sort version paid. Stable argsort + ### preserves the original within-lp order of close sources, matching + ### the previous implementation's output element-for-element. + src_sort_key = leaf_pair_ids_s * 2 + source_is_far.long() + src_sort_order = src_sort_key.argsort(stable=True) + sorted_src_ids = src_point_ids[src_sort_order] + + ### Start of lp's block in ``sorted_src_ids`` is the exclusive cumsum + ### of ``s_counts`` (the per-lp total source count, by construction). + total_lp_starts = s_counts.cumsum(0) - s_counts + + ### Per-target close counts: gate by ``(~target_is_far).long()`` so + ### non-survivors get count 0 and produce no Cartesian-product output. + ### The block start does not depend on survivor-ness. + per_target_close_counts = ( + close_counts_per_lp[leaf_pair_ids_t] * (~target_is_far).long() ) - src_positions_nn, _ = _ragged_arange(per_surv_close_starts, per_surv_close_counts) - expanded_near_srcs = sorted_close_srcs[src_positions_nn] - - return ( - expanded_near_tgts, expanded_near_srcs, - nf_target_ids, nf_source_node_ids, - fn_target_node_ids, fn_source_ids, - fn_broadcast_targets, fn_broadcast_starts, fn_broadcast_counts, + per_target_close_starts = total_lp_starts[leaf_pair_ids_t] + + ### Reuse ``_ragged_arange``'s second output (``seg_ids_nn``) as the + ### per-element survivor index instead of calling + ### ``torch.repeat_interleave(surv_point_ids, per_target_close_counts)``. + ### Both ops sync once to size their output; folding them into one + ### ``_ragged_arange`` halves that cost. Functionally identical: + ### ``repeat_interleave(x, counts)[k] == x[seg_ids[k]]`` by + ### definition of segment ids. + src_positions_nn, seg_ids_nn = _ragged_arange( + per_target_close_starts, per_target_close_counts + ) + expanded_near_tgts = target_point_ids[seg_ids_nn] + expanded_near_srcs = sorted_src_ids[src_positions_nn] + + return _ExpandedLeafHits( + near_tgts=expanded_near_tgts, + near_srcs=expanded_near_srcs, + nf_tgts=target_point_ids, + nf_snids=src_leaf_per_target, + nf_validity=target_is_far, + fn_sids=src_point_ids, + fn_tnids=tgt_leaf_per_src, + fn_validity=source_is_far, + fn_bcast_starts=fn_broadcast_starts_full, + fn_bcast_counts=fn_broadcast_counts_full, + fn_bcast_targets=fn_broadcast_targets, + fn_bcast_targets_validity=fn_broadcast_targets_validity, ) +# --------------------------------------------------------------------------- +# Deferred-compaction helpers (used by find_dual_interaction_pairs) +# --------------------------------------------------------------------------- + + +def _compact_deferred( + *tensor_lists: list[torch.Tensor], + validity_list: list[torch.Tensor], + device: torch.device, +) -> tuple[torch.Tensor, ...]: + """Concatenate per-iter accumulators and boolean-index by validity. + + Each ``tensor_lists[i]`` is the per-iter accumulator for one output + stream; ``validity_list`` is the shared per-iter validity mask. All + accumulators must be the same length within each iteration. Pays + exactly one ``aten::nonzero`` sync regardless of the number of + output streams - the sync is amortised across them by computing + the integer ``keep_idx`` once and reusing it for every stream. + + The empty-``validity_list`` case (no iteration ever contributed to + this stream) is handled explicitly because ``torch.cat([])`` raises. + """ + if not validity_list: + empty = torch.empty(0, dtype=torch.long, device=device) + return tuple(empty.clone() for _ in tensor_lists) + keep_idx = torch.cat(validity_list).nonzero(as_tuple=True)[0] + return tuple(torch.cat(L)[keep_idx] for L in tensor_lists) + + +def _compact_sentinel_padded( + padded_tensor: torch.Tensor, + referencing_indices: torch.Tensor, + validity: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + """Compact a sentinel-padded buffer and remap referencing indices. + + ``padded_tensor`` has don't-care entries wherever ``~validity``; + ``referencing_indices`` are integer indices into ``padded_tensor`` + that only ever reference *valid* positions. This returns + ``(padded_tensor[validity], remapped_indices)`` where the remapped + indices point at the same elements in the compacted buffer. + + The remap is the exclusive cumsum of ``validity``: position ``p`` in + the padded buffer maps to ``sum(validity[:p])`` in the compacted + buffer, which is its slot in ``padded_tensor[validity]``. Pays one + sync (the boolean indexing); the cumsum and integer-indexing remap + are sync-free. + """ + valid_long = validity.long() + new_pos = valid_long.cumsum(0) - valid_long + return padded_tensor[validity], new_pos[referencing_indices] + + +def _sort_by_key( + *tensors: torch.Tensor, + key: torch.Tensor, +) -> tuple[torch.Tensor, ...]: + """Stable-sort companion tensors by ``key``; no-op on empty input. + + Used at the end of ``find_dual_interaction_pairs`` to group each + output stream by source index (or source node) for coalesced + downstream gathers. + """ + if key.numel() == 0: + return tensors + order = key.argsort(stable=True) + return tuple(t[order] for t in tensors) + + # --------------------------------------------------------------------------- # ClusterTree tensorclass # --------------------------------------------------------------------------- @@ -858,28 +1034,53 @@ def find_dual_interaction_pairs( active_tgt_nodes = torch.zeros(1, dtype=torch.long, device=device) active_src_nodes = torch.zeros(1, dtype=torch.long, device=device) - ### Output streams. Far/(near,far)-from-far entries are appended - ### unfiltered together with a per-iteration ``is_far`` validity - ### mask; one boolean compaction at the very end of the function - ### eliminates the per-iteration sync that ``active[is_far]`` - ### would otherwise force. + ### Output streams. All per-iteration outputs use a + ### deferred-compaction protocol: the per-element tensor is + ### accumulated unfiltered together with a boolean validity + ### mask, and ONE compaction is paid per stream at the end of + ### the traversal. This trades five per-iteration ``nonzero`` + ### syncs inside the (near,far)/(far,near)/broadcast paths plus + ### two per-iteration syncs in the far path for a fixed handful + ### of end-of-loop syncs. far_tgt_unfiltered_list: list[torch.Tensor] = [] far_src_unfiltered_list: list[torch.Tensor] = [] far_validity_list: list[torch.Tensor] = [] - ### Outputs from leaf-leaf expansion still get appended already- - ### filtered, since ``_expand_dual_leaf_hits`` works on a - ### compacted (target_leaf_id, source_leaf_id) batch. We accept - ### one boolean indexing sync per iteration here. + ### (near,near) output from leaf-leaf expansion is already + ### compacted by the ``_ragged_arange`` inside the expansion + ### (its output size is set by the Cartesian total anyway), + ### so no per-stream validity mask is needed here. near_target_list: list[torch.Tensor] = [] near_source_list: list[torch.Tensor] = [] - nf_target_list: list[torch.Tensor] = [] - nf_source_node_list: list[torch.Tensor] = [] - fn_tgt_node_list: list[torch.Tensor] = [] - fn_src_list: list[torch.Tensor] = [] - fn_bcast_targets_list: list[torch.Tensor] = [] + + ### (near,far) stream has two append paths: + ### - ``expand_far_targets=True``: already-filtered entries + ### from the ``_ragged_arange``-with-masked-counts branch + ### below. No validity mask needed. + ### - ``_expand_dual_leaf_hits``: unfiltered targets + + ### ``nf_validity`` mask (length ``t_full`` per iter). + ### Kept in separate lists so the deferred path's compaction + ### at end-of-traversal does not touch the already-filtered + ### entries. + nf_filtered_target_list: list[torch.Tensor] = [] + nf_filtered_source_node_list: list[torch.Tensor] = [] + nf_deferred_target_list: list[torch.Tensor] = [] + nf_deferred_source_node_list: list[torch.Tensor] = [] + nf_deferred_validity_list: list[torch.Tensor] = [] + + ### (far,near) + broadcast mapping from ``_expand_dual_leaf_hits``. + ### Both the per-source tensors and the per-source broadcast + ### starts/counts are stored unfiltered against the same + ### ``fn_validity`` (``= source_is_far``); the broadcast + ### targets buffer carries its own ``fn_bcast_validity`` mask + ### that drops the sentinel-padded tail of each iter. + fn_deferred_tgt_node_list: list[torch.Tensor] = [] + fn_deferred_src_list: list[torch.Tensor] = [] + fn_deferred_validity_list: list[torch.Tensor] = [] fn_bcast_starts_list: list[torch.Tensor] = [] fn_bcast_counts_list: list[torch.Tensor] = [] + fn_bcast_targets_list: list[torch.Tensor] = [] + fn_bcast_validity_list: list[torch.Tensor] = [] fn_bcast_offset = 0 ### Loop bound: every iteration descends at least one tree level @@ -940,43 +1141,43 @@ def find_dual_interaction_pairs( positions, pair_ids = _ragged_arange( starts_full, counts_masked ) - nf_target_list.append( + nf_filtered_target_list.append( target_tree.sorted_source_order[positions] ) - nf_source_node_list.append(active_src_nodes[pair_ids]) + nf_filtered_source_node_list.append(active_src_nodes[pair_ids]) else: far_tgt_unfiltered_list.append(active_tgt_nodes) far_src_unfiltered_list.append(active_src_nodes) far_validity_list.append(is_far) - ### 2. Near-field, both leaves: two-stage filtered expansion. - # ``_expand_dual_leaf_hits`` operates on a packed batch of - # (target_leaf_id, source_leaf_id) pairs. Two indexings - # share the ``near_leaf_leaf`` mask, so dedup the - # ``nonzero`` call: one sync per iteration instead of two. + ### 2. Near-field, both leaves: two-stage deferred expansion. + # ``_expand_dual_leaf_hits`` returns the (near,far), + # (far,near), and broadcast streams unfiltered (with + # validity masks); the caller compacts them once at the + # end of the traversal. Only the (near,near) Cartesian- + # product output (whose size is data-dependent regardless) + # is already compacted. nll_idx = near_leaf_leaf.nonzero(as_tuple=True)[0] - ( - nn_tgts, nn_srcs, - nf_tgts, nf_snids, - fn_tnids, fn_sids, - fn_btgts, fn_bstarts, fn_bcounts, - ) = _expand_dual_leaf_hits( + hits = _expand_dual_leaf_hits( active_tgt_nodes[nll_idx], active_src_nodes[nll_idx], target_tree, source_tree, theta, ) - near_target_list.append(nn_tgts) - near_source_list.append(nn_srcs) - nf_target_list.append(nf_tgts) - nf_source_node_list.append(nf_snids) - fn_tgt_node_list.append(fn_tnids) - fn_src_list.append(fn_sids) - fn_bcast_targets_list.append(fn_btgts) - fn_bcast_starts_list.append(fn_bstarts + fn_bcast_offset) - fn_bcast_counts_list.append(fn_bcounts) - fn_bcast_offset += fn_btgts.shape[0] + near_target_list.append(hits.near_tgts) + near_source_list.append(hits.near_srcs) + nf_deferred_target_list.append(hits.nf_tgts) + nf_deferred_source_node_list.append(hits.nf_snids) + nf_deferred_validity_list.append(hits.nf_validity) + fn_deferred_tgt_node_list.append(hits.fn_tnids) + fn_deferred_src_list.append(hits.fn_sids) + fn_deferred_validity_list.append(hits.fn_validity) + fn_bcast_starts_list.append(hits.fn_bcast_starts + fn_bcast_offset) + fn_bcast_counts_list.append(hits.fn_bcast_counts) + fn_bcast_targets_list.append(hits.fn_bcast_targets) + fn_bcast_validity_list.append(hits.fn_bcast_targets_validity) + fn_bcast_offset += hits.fn_bcast_targets.shape[0] ### 3. Generate next iteration's active set. # We compute children over the FULL active set (n_active @@ -1036,73 +1237,81 @@ def find_dual_interaction_pairs( ]) ### One sync per iteration: the boolean compaction below. + ### Each ``tensor[bool_mask]`` lowers to ``aten::nonzero``; + ### computing ``keep_idx`` explicitly once and integer- + ### indexing both ``slot_t`` and ``slot_s`` collapses the + ### two nonzero syncs into one. flat_v = slot_v.reshape(-1) - active_tgt_nodes = slot_t.reshape(-1)[flat_v] - active_src_nodes = slot_s.reshape(-1)[flat_v] + keep_idx = flat_v.nonzero(as_tuple=True)[0] + active_tgt_nodes = slot_t.reshape(-1)[keep_idx] + active_src_nodes = slot_s.reshape(-1)[keep_idx] + + ### Concatenate accumulated pairs and pay one boolean + ### compaction per output stream, all at end-of-traversal. + ### See :func:`_compact_deferred` and + ### :func:`_compact_sentinel_padded` for the protocol. + empty_long = torch.empty(0, dtype=torch.long, device=device) - ### Concatenate accumulated pairs. - ### Lists always have at least one element per iteration (we - ### always append, gated by validity), so we don't need the - ### empty-list fallbacks the previous implementation had. near_tgt = torch.cat(near_target_list) if near_target_list else \ - torch.empty(0, dtype=torch.long, device=device) + empty_long.clone() near_src = torch.cat(near_source_list) if near_source_list else \ - torch.empty(0, dtype=torch.long, device=device) - - ### Far-field compaction: ONE sync for the entire traversal. - if far_validity_list: - far_tgt_full = torch.cat(far_tgt_unfiltered_list) - far_src_full = torch.cat(far_src_unfiltered_list) - far_validity_full = torch.cat(far_validity_list) - far_tgt_nid = far_tgt_full[far_validity_full] - far_src_nid = far_src_full[far_validity_full] - else: - far_tgt_nid = torch.empty(0, dtype=torch.long, device=device) - far_src_nid = torch.empty(0, dtype=torch.long, device=device) - - nf_tgt = torch.cat(nf_target_list) if nf_target_list else \ - torch.empty(0, dtype=torch.long, device=device) - nf_snid = torch.cat(nf_source_node_list) if nf_source_node_list else \ - torch.empty(0, dtype=torch.long, device=device) - - if fn_tgt_node_list: - fn_tnid = torch.cat(fn_tgt_node_list) - fn_sid = torch.cat(fn_src_list) - fn_btgts = torch.cat(fn_bcast_targets_list) - fn_bstarts = torch.cat(fn_bcast_starts_list) - fn_bcounts = torch.cat(fn_bcast_counts_list) + empty_long.clone() + + ### Far-field stream: deferred (unfiltered + validity). + far_tgt_nid, far_src_nid = _compact_deferred( + far_tgt_unfiltered_list, far_src_unfiltered_list, + validity_list=far_validity_list, device=device, + ) + + ### (near, far) stream: combine deferred entries from + ### ``_expand_dual_leaf_hits`` with the already-filtered + ### entries from the ``expand_far_targets=True`` branch. + nf_def_tgt, nf_def_snid = _compact_deferred( + nf_deferred_target_list, nf_deferred_source_node_list, + validity_list=nf_deferred_validity_list, device=device, + ) + nf_tgt = torch.cat( + [nf_def_tgt, *nf_filtered_target_list] + ) if nf_filtered_target_list else nf_def_tgt + nf_snid = torch.cat( + [nf_def_snid, *nf_filtered_source_node_list] + ) if nf_filtered_source_node_list else nf_def_snid + + ### (far, near) + broadcast streams. The fn tensors and + ### the per-source ``fn_bcast_starts/counts`` are aligned + ### with ``fn_validity`` (= ``source_is_far``) and compact + ### together. ``fn_broadcast_targets`` is sentinel-padded + ### on the *t_full* axis and compacts separately via its + ### own validity mask, with ``fn_bcast_starts`` remapped + ### into the compacted space. + if fn_deferred_validity_list: + fn_tnid, fn_sid, fn_bstarts_padded, fn_bcounts = _compact_deferred( + fn_deferred_tgt_node_list, fn_deferred_src_list, + fn_bcast_starts_list, fn_bcast_counts_list, + validity_list=fn_deferred_validity_list, device=device, + ) + fn_btgts, fn_bstarts = _compact_sentinel_padded( + torch.cat(fn_bcast_targets_list), + fn_bstarts_padded, + torch.cat(fn_bcast_validity_list), + ) else: - fn_tnid = torch.empty(0, dtype=torch.long, device=device) - fn_sid = torch.empty(0, dtype=torch.long, device=device) - fn_btgts = torch.empty(0, dtype=torch.long, device=device) - fn_bstarts = torch.empty(0, dtype=torch.long, device=device) - fn_bcounts = torch.empty(0, dtype=torch.long, device=device) - - ### Sort near pairs by source index for coalesced gather - if near_src.numel() > 0: - sort_order = near_src.argsort(stable=True) - near_tgt = near_tgt[sort_order] - near_src = near_src[sort_order] - - ### Sort far pairs by source node for coalesced aggregate gather - if far_src_nid.numel() > 0: - sort_order = far_src_nid.argsort(stable=True) - far_tgt_nid = far_tgt_nid[sort_order] - far_src_nid = far_src_nid[sort_order] - - ### Sort (near,far) pairs by source node for coalesced gather - if nf_snid.numel() > 0: - sort_order = nf_snid.argsort(stable=True) - nf_tgt = nf_tgt[sort_order] - nf_snid = nf_snid[sort_order] - - ### Sort (far,near) pairs by source index for coalesced gather - if fn_sid.numel() > 0: - sort_order = fn_sid.argsort(stable=True) - fn_tnid = fn_tnid[sort_order] - fn_sid = fn_sid[sort_order] - fn_bstarts = fn_bstarts[sort_order] - fn_bcounts = fn_bcounts[sort_order] + fn_tnid = empty_long.clone() + fn_sid = empty_long.clone() + fn_btgts = empty_long.clone() + fn_bstarts = empty_long.clone() + fn_bcounts = empty_long.clone() + + ### Group each output stream by source index (or source node) + ### for coalesced downstream gathers. See :func:`_sort_by_key`. + near_tgt, near_src = _sort_by_key(near_tgt, near_src, key=near_src) + far_tgt_nid, far_src_nid = _sort_by_key( + far_tgt_nid, far_src_nid, key=far_src_nid + ) + nf_tgt, nf_snid = _sort_by_key(nf_tgt, nf_snid, key=nf_snid) + fn_tnid, fn_sid, fn_bstarts, fn_bcounts = _sort_by_key( + fn_tnid, fn_sid, fn_bstarts, fn_bcounts, key=fn_sid + ) plan = DualInteractionPlan( near_target_ids=near_tgt, diff --git a/test/models/globe/test_barnes_hut_kernel.py b/test/models/globe/test_barnes_hut_kernel.py index ec8ac93e11..1c61f8643c 100644 --- a/test/models/globe/test_barnes_hut_kernel.py +++ b/test/models/globe/test_barnes_hut_kernel.py @@ -1678,16 +1678,43 @@ def test_dual_traversal_sync_budget( Profiling identified ``cudaStreamSynchronize`` as the dominant CPU stall in GLOBE training (9919 calls / ~3 s in a 10.5 s training step, - most of them from ``find_dual_interaction_pairs``). Phase 2 of the - perf optimization moved that section to a deferred-compaction - structure that pays at most a handful of syncs per call. - - This test counts ``cudaStreamSynchronize`` events inside one tree - build + one dual traversal and asserts the count stays under a - generous budget. The budget is set well above the current count to - accommodate small implementation variations, but tight enough to - catch the kind of regression we fixed (per-iteration ``.any()`` / - ``.item()`` / boolean-mask compactions in the traversal loop). + most of them from ``find_dual_interaction_pairs``). The perf-pass + moved every output stream (near/far/(near,far)/(far,near)/broadcast) + to a deferred-compaction protocol where the per-iteration tensors + are accumulated unfiltered with a validity mask, and a single + boolean compaction is paid per stream at the end of the traversal. + + With those changes the structural syncs remaining per call are: + + - Three ``_ragged_arange`` calls inside each ``_expand_dual_leaf_hits`` + invocation, each of which forces a ``torch.arange(scalar_tensor)`` + host read to size its output (data-dependent total). Iterations + without any leaf-leaf pair short-circuit before paying these. + - One ``aten::nonzero`` per dual-traversal iteration to materialise + the integer index for ``active_tgt_nodes[keep_idx]`` and + ``active_src_nodes[keep_idx]`` (the new active set), shared with + the symmetric source-side indexing. + - One ``aten::nonzero`` per dual-traversal iteration for + ``near_leaf_leaf.nonzero()`` (used to filter the leaf-leaf input + to ``_expand_dual_leaf_hits``; cannot be deferred because the + function requires strictly (leaf, leaf) pairs for the AABB tests + to be valid). + - One ``torch.where`` per ``from_points`` top-down level for + ``internal_indices = torch.where(is_internal_seg)[0]``. + - Four end-of-traversal compactions (one each for the far, (near,far), + (far,near), and fn-broadcast-targets streams). + - ~5-6 syncs from the eager-only ``DualInteractionPlan.validate()`` + call at the end of every traversal (``(t < 0).any()``, + ``nonzero.any()``, ``.max().item()``, plus a couple of boolean + indexings); zero under ``torch.compile``. + + The remaining syncs are structural - they read data-dependent shapes + that PyTorch cannot infer at trace time. Together they observe well + below ``budget`` for the parametrisations tested below. Headroom + above the observed values is sized to catch the kind of regression + we eliminated (a per-iteration ``.any()`` / ``.item()`` / + boolean-indexing regrowth, which would add ~5-10 syncs/iter and blow + past the budget on any non-trivial tree). """ device = torch.device("cuda") torch.manual_seed(DEFAULT_SEED) @@ -1709,13 +1736,14 @@ def test_dual_traversal_sync_budget( sync_count = sum(1 for ev in prof.events() if ev.name == "cudaStreamSynchronize") - ### Budget: ~3-5 syncs per traversal iteration is what the new - ### implementation pays (boolean compaction for the new active set, - ### one for ``active[near_leaf_leaf]``, one inside ``_ragged_arange``). - ### A 200-point leaf_size=1 tree takes ~10 traversal iterations; the - ### top_down build adds a handful more. Setting the budget at 100 - ### catches the ``.any()``-per-branch regression (~12 syncs / iter) - ### that this work eliminated, while leaving headroom. + ### Observed at time of test authorship on an RTX 4090 with + ### PyTorch 2.11: 59 / 67 / 83 syncs for the three parametrisations + ### below. Setting the budget at 100 gives ~20 % headroom for + ### platform-specific profiler-overhead variation (Kineto/CUPTI emit + ### a handful of extra ``cudaStreamSynchronize`` events at activity + ### boundaries that vary by driver / CUDA version), while staying + ### tight enough to catch a regression that re-introduces ~5+ + ### syncs/iter into the dual-traversal loop. budget = 100 assert sync_count <= budget, ( f"dual_traversal sync count {sync_count} exceeds budget {budget} for " From fd56e21698093056c8e06b248ac103d846d04362 Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Fri, 15 May 2026 16:29:57 -0400 Subject: [PATCH 4/6] Adds device fixture fix --- test/models/globe/test_barnes_hut_kernel.py | 31 +++++++++++---------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/test/models/globe/test_barnes_hut_kernel.py b/test/models/globe/test_barnes_hut_kernel.py index 1c61f8643c..72f12896ed 100644 --- a/test/models/globe/test_barnes_hut_kernel.py +++ b/test/models/globe/test_barnes_hut_kernel.py @@ -1660,9 +1660,6 @@ def test_compute_node_strengths_precision_at_scale() -> None: # --------------------------------------------------------------------------- -@pytest.mark.skipif( - not torch.cuda.is_available(), reason="CUDA required for sync-event counting" -) @pytest.mark.parametrize( "n_points, leaf_size, theta", [ @@ -1672,10 +1669,13 @@ def test_compute_node_strengths_precision_at_scale() -> None: ], ) def test_dual_traversal_sync_budget( - n_points: int, leaf_size: int, theta: float + device: str, n_points: int, leaf_size: int, theta: float ) -> None: """Cluster-tree construction + dual traversal stays within a sync budget. + Runs in ~3 s on an RTX-class GPU (one warm-up traversal plus three + profiled traversals); CUDA-only and skipped on CPU-only runners. + Profiling identified ``cudaStreamSynchronize`` as the dominant CPU stall in GLOBE training (9919 calls / ~3 s in a 10.5 s training step, most of them from ``find_dual_interaction_pairs``). The perf-pass @@ -1716,9 +1716,11 @@ def test_dual_traversal_sync_budget( boolean-indexing regrowth, which would add ~5-10 syncs/iter and blow past the budget on any non-trivial tree). """ - device = torch.device("cuda") + if not device.startswith("cuda"): + pytest.skip("CUDA required for sync-event counting") + device_obj = torch.device(device) torch.manual_seed(DEFAULT_SEED) - points = torch.randn(n_points, 3, device=device) + points = torch.randn(n_points, 3, device=device_obj) ### Warm up: first calls allocate / JIT, which can fire one-off syncs ### unrelated to the algorithmic structure under test. @@ -1736,14 +1738,15 @@ def test_dual_traversal_sync_budget( sync_count = sum(1 for ev in prof.events() if ev.name == "cudaStreamSynchronize") - ### Observed at time of test authorship on an RTX 4090 with - ### PyTorch 2.11: 59 / 67 / 83 syncs for the three parametrisations - ### below. Setting the budget at 100 gives ~20 % headroom for - ### platform-specific profiler-overhead variation (Kineto/CUPTI emit - ### a handful of extra ``cudaStreamSynchronize`` events at activity - ### boundaries that vary by driver / CUDA version), while staying - ### tight enough to catch a regression that re-introduces ~5+ - ### syncs/iter into the dual-traversal loop. + ### Observed at time of test authorship on an RTX 4090 Laptop with + ### PyTorch 2.11: 59 / 67 / 83 syncs and 2.0 / 0.5 / 0.3 s wall-clock + ### for the three parametrisations below. Setting the budget at 100 + ### gives ~20 % headroom for platform-specific profiler-overhead + ### variation (Kineto/CUPTI emit a handful of extra + ### ``cudaStreamSynchronize`` events at activity boundaries that vary + ### by driver / CUDA version), while staying tight enough to catch a + ### regression that re-introduces ~5+ syncs/iter into the + ### dual-traversal loop. budget = 100 assert sync_count <= budget, ( f"dual_traversal sync count {sync_count} exceeds budget {budget} for " From 088db88d02185973fefb19a2794fc403c186efba Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Fri, 15 May 2026 16:41:10 -0400 Subject: [PATCH 5/6] cuts the sync test --- test/models/globe/test_barnes_hut_kernel.py | 101 -------------------- 1 file changed, 101 deletions(-) diff --git a/test/models/globe/test_barnes_hut_kernel.py b/test/models/globe/test_barnes_hut_kernel.py index 72f12896ed..5da7badfa0 100644 --- a/test/models/globe/test_barnes_hut_kernel.py +++ b/test/models/globe/test_barnes_hut_kernel.py @@ -1655,106 +1655,5 @@ def test_compute_node_strengths_precision_at_scale() -> None: torch.testing.assert_close(actual.double(), expected, atol=1e-5, rtol=1e-5) -# --------------------------------------------------------------------------- -# Sync-budget regression guard -# --------------------------------------------------------------------------- - - -@pytest.mark.parametrize( - "n_points, leaf_size, theta", - [ - (200, 1, 1.0), - (200, 4, 1.0), - (1000, 4, 0.5), - ], -) -def test_dual_traversal_sync_budget( - device: str, n_points: int, leaf_size: int, theta: float -) -> None: - """Cluster-tree construction + dual traversal stays within a sync budget. - - Runs in ~3 s on an RTX-class GPU (one warm-up traversal plus three - profiled traversals); CUDA-only and skipped on CPU-only runners. - - Profiling identified ``cudaStreamSynchronize`` as the dominant CPU - stall in GLOBE training (9919 calls / ~3 s in a 10.5 s training step, - most of them from ``find_dual_interaction_pairs``). The perf-pass - moved every output stream (near/far/(near,far)/(far,near)/broadcast) - to a deferred-compaction protocol where the per-iteration tensors - are accumulated unfiltered with a validity mask, and a single - boolean compaction is paid per stream at the end of the traversal. - - With those changes the structural syncs remaining per call are: - - - Three ``_ragged_arange`` calls inside each ``_expand_dual_leaf_hits`` - invocation, each of which forces a ``torch.arange(scalar_tensor)`` - host read to size its output (data-dependent total). Iterations - without any leaf-leaf pair short-circuit before paying these. - - One ``aten::nonzero`` per dual-traversal iteration to materialise - the integer index for ``active_tgt_nodes[keep_idx]`` and - ``active_src_nodes[keep_idx]`` (the new active set), shared with - the symmetric source-side indexing. - - One ``aten::nonzero`` per dual-traversal iteration for - ``near_leaf_leaf.nonzero()`` (used to filter the leaf-leaf input - to ``_expand_dual_leaf_hits``; cannot be deferred because the - function requires strictly (leaf, leaf) pairs for the AABB tests - to be valid). - - One ``torch.where`` per ``from_points`` top-down level for - ``internal_indices = torch.where(is_internal_seg)[0]``. - - Four end-of-traversal compactions (one each for the far, (near,far), - (far,near), and fn-broadcast-targets streams). - - ~5-6 syncs from the eager-only ``DualInteractionPlan.validate()`` - call at the end of every traversal (``(t < 0).any()``, - ``nonzero.any()``, ``.max().item()``, plus a couple of boolean - indexings); zero under ``torch.compile``. - - The remaining syncs are structural - they read data-dependent shapes - that PyTorch cannot infer at trace time. Together they observe well - below ``budget`` for the parametrisations tested below. Headroom - above the observed values is sized to catch the kind of regression - we eliminated (a per-iteration ``.any()`` / ``.item()`` / - boolean-indexing regrowth, which would add ~5-10 syncs/iter and blow - past the budget on any non-trivial tree). - """ - if not device.startswith("cuda"): - pytest.skip("CUDA required for sync-event counting") - device_obj = torch.device(device) - torch.manual_seed(DEFAULT_SEED) - points = torch.randn(n_points, 3, device=device_obj) - - ### Warm up: first calls allocate / JIT, which can fire one-off syncs - ### unrelated to the algorithmic structure under test. - tree_warm = ClusterTree.from_points(points, leaf_size=leaf_size) - tree_warm.find_dual_interaction_pairs(target_tree=tree_warm, theta=theta) - torch.cuda.synchronize() - - with torch.profiler.profile( - activities=[torch.profiler.ProfilerActivity.CUDA], - record_shapes=False, - ) as prof: - tree = ClusterTree.from_points(points, leaf_size=leaf_size) - tree.find_dual_interaction_pairs(target_tree=tree, theta=theta) - torch.cuda.synchronize() - - sync_count = sum(1 for ev in prof.events() if ev.name == "cudaStreamSynchronize") - - ### Observed at time of test authorship on an RTX 4090 Laptop with - ### PyTorch 2.11: 59 / 67 / 83 syncs and 2.0 / 0.5 / 0.3 s wall-clock - ### for the three parametrisations below. Setting the budget at 100 - ### gives ~20 % headroom for platform-specific profiler-overhead - ### variation (Kineto/CUPTI emit a handful of extra - ### ``cudaStreamSynchronize`` events at activity boundaries that vary - ### by driver / CUDA version), while staying tight enough to catch a - ### regression that re-introduces ~5+ syncs/iter into the - ### dual-traversal loop. - budget = 100 - assert sync_count <= budget, ( - f"dual_traversal sync count {sync_count} exceeds budget {budget} for " - f"{n_points=}, {leaf_size=}, {theta=}. Did a `.any()`, `.item()`, or " - f"data-dependent boolean indexing creep back into the dual-traversal " - f"hot loop?" - ) - - if __name__ == "__main__": pytest.main() From 53dcad2854a8526095acb9d72f3c840e55cff103 Mon Sep 17 00:00:00 2001 From: Peter Sharpe Date: Fri, 15 May 2026 16:41:27 -0400 Subject: [PATCH 6/6] uv sync --upgrade --- uv.lock | 773 ++++++++++++++++++++++++++++++-------------------------- 1 file changed, 421 insertions(+), 352 deletions(-) diff --git a/uv.lock b/uv.lock index 6cec6d2966..b5eb71a211 100644 --- a/uv.lock +++ b/uv.lock @@ -499,7 +499,7 @@ version = "1.6.5" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-18-nvidia-physicsnemo-cu12' or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, - { name = "numpy", version = "2.4.4", source = { registry = 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name = "numpy", version = "2.4.5", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-18-nvidia-physicsnemo-cu13' or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/59/83/03139c7d9c0425ec4824d6269cfd9e1ac8ae1cc88f12540578a405113083/cuda_core-0.7.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:69946d1d5e2d96fc65b7bb36164d26c328cca75d9482b74ee7d61b2c1e1d33a7", size = 30374690, upload-time = "2026-04-08T17:03:08.962Z" }, @@ -1181,7 +1181,7 @@ cccl = [ { name = "nvidia-cuda-cccl", version = "13.0.85", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'win32') or sys_platform == 'linux' or (sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'win32' and extra != 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