feat: per-environment collision-pair capacity override - #24
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Ports set_rbd_collisions_capacity() from perf/flat-narrow-phase (81c76bb) to the unified line: NexusState grows a setter that overrides the per-environment collision-pair floor used when the GPU state is allocated at finalize, plus the Python binding. Why: the 4096-pairs/env default is sized for one busy scene, not thousands of small batched envs — pair-keyed buffers scale as capacity x envs, so batching hits wgpu's 4 GiB max-buffer limit near 512 envs (the failing allocation is 4.7 GiB at 1024) and OOMs a 32 GiB card at 4096 envs on CUDA. The capacity is a floor, not a cap: the default RbdResizePolicy::Grow resizes off measured pair counts, so contacts are never lost — the LeRobot-legs biped MJCF produces bit-identical 400-step end frames at capacity 4, 16, and 4096. Measured on the RTX 5090 (capacity 16, 800-step windows, env-steps/s): cube 16,384 envs WebGPU 5.67M | CUDA+graph 5.28M (was: OOM) biped-pendulum 16,384 envs WebGPU 3.14M | CUDA+graph 2.93M (was: OOM) previous ceiling on main: ~170k env-steps/s at <=2048 envs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NTtT75BpS7XH9DoHVbdfwN
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This will be merged as part of #36 |
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* fix(rbd): apply the per-batch stride to collider_parent reads in the narrow phase Replaces #21 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * feat: per-environment collision-pair capacity override Replaces #24 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * feat(rbd): make the narrow-phase contact prediction distance configurable Replaces #28 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * fix(rbd): thread the configurable prediction distance through the brute-force broad phase Completes #28 * feat(python): per-environment MJCF insertion Replaces #16 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * fix(python): drop the duplicated collisions-capacity setter and pass the RbdCoupling to insert_rigid_body_in Completes #16 * feat(python): per-step MJCF actuator control + multibody state readback Replaces #12 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@1ms.ai> * fix(python): gate multibody control/readback on dim3, add the missing PyArray2 import Replaces #12 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * fix(rbd): decode the SoA link workspace for the multibody readback and drop the stale set_gravity copy Completes #12 * perf(rbd): dedupe shared TriMesh uploads in from_rapier Replaces #19 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * perf(rbd): optional GPU contact reduction, merging per-pair manifolds to <=4 points Replaces #17 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * fix(rbd): pass the prediction distance to manifold_reduction in the contact-reduction kernel Completes #17 * perf(rbd): flat 1-D narrow-phase dispatch, packing warps across batches Replaces #21 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * fix(rbd): restore the contacts capacity binding and import atomic_load_u32 for the flat dispatch Completes #21 * fix(rbd): drop the stale 2mm PREDICTION constant reintroduced by the flat-dispatch port Completes #21 * fix mpm feature-gating * feat(rbd): expose dof_state_mut, links_static, joint_constraints and link_of_body * feat(rbd): env-reset primitives, GPU motor scatter, contact sensors, actuator delay, encoded step, substep-refresh cadence and per-DoF armature/frictionloss * fix(rbd): guard against implicit-coriolis drifting from the batch_indices uniform * feat(rbd): cluster contact manifolds by normal, matching rapier, with a tunable threshold * feat(rbd): model multibody joint frictionloss as a constraint instead of a force * feat(rbd): seed per-DoF joint friction from rapier's Multibody::frictions * refactor(rbd): read the contact prediction distance from RbdSimParams instead of a dedicated uniform * chore: cargo fmt * refactor(rbd): move the contact merge cosine into RbdSimParams * refactor: move read_multibody_links onto NexusState and drive every env from control_multibody_motors * test(rbd): add a headless many-small-environments step-timing harness * revert(rbd): drop the flat 1-D narrow-phase dispatch Measured 7-21% slower on Metal. * chore: cleanup comments * fix: gate control_multibody_motors on dim3 so the 2D build still compiles * fix instability in joint-ball3 demo * chore: remove debug test files * chore: cleanups * fix(rbd): build the bench harness without the metal feature and only in 3D * fix(rbd): silence the clippy needless-borrow and unnecessary-mut lints * fix(rbd): split the joint-constraint back-solve into its own dispatch to fit 8 storage buffers * test(rbd): keep the bench harness under wgpu's default buffer-size limit * fix(rbd): split the batched env reset into pose and DoF passes to fit 8 storage buffers * chore: switch to the published rapier version * fix: make all envs share the same RbdSimParams * chore: clippy fixes --------- Co-authored-by: Haixuan Xavier Tao <tao.xavier@outlook.com> Co-authored-by: Haixuan Xavier Tao <tao.xavier@1ms.ai>
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Adds
NexusState::set_rbd_collisions_capacity()(+ Python binding): overrides the per-environment collision-pair floor used when the GPU rigid-body state is allocated atfinalize. 16 lines, no behavior change unless called.Why. The 4096-pairs/env default sizes every pair-keyed buffer as
capacity × envs. That's right for one busy scene, but batched RL-style workloads (one small robot per env) hit wgpu's 4 GiB max-buffer limit near 512 envs — the failing allocation is a single 4.7 GiB buffer at 1,024 envs for a 2-body scene — and OOM a 32 GiB card at 4,096 envs on the CUDA backend, while actual usage is ~7 pairs/env.Safety. The value is a floor, not a cap: the default
RbdResizePolicy::Growkeeps resizing off measured pair counts, so contacts are never dropped. Verified with a 12-DOF biped MJCF (real ground contacts throughout): bit-identical 400-step end frames at capacity 4, 16, and the default 4096.Measured (RTX 5090, capacity 16, 800-step sustained windows): the cube-per-env scene goes from a hard failure above ~512 envs to 5.67M env-steps/s at 16,384 envs (WebGPU) / 5.28M (CUDA+graph); a 10-link multibody pendulum per env reaches 3.14M / 2.93M. Numbers were taken on haixuanTao/nexus main (includes recent mb perf work), but the memory scaling this PR fixes is independent of those changes. Merged on the fork as haixuanTao#1; original implementation of the idea in the fork's
perf/flat-narrow-phaseline (81c76bb).🤖 Generated with Claude Code
https://claude.ai/code/session_01NTtT75BpS7XH9DoHVbdfwN