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..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") @@ -184,44 +179,66 @@ 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. +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. + """ - 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. + 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 - 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 + @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( @@ -230,13 +247,7 @@ def _expand_dual_leaf_hits( 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) @@ -254,29 +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] - 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() - + ### 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 _empty_result() + 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] @@ -297,20 +325,13 @@ def _empty_result(): 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 - 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() + target_is_far = ( + dist_sq_t * theta_sq > source_tree.node_diameter_sq[src_leaf_per_target] + ) - surv_point_ids = target_point_ids[surv_mask] - surv_lp_ids = leaf_pair_ids_t[surv_mask] + ### (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 @@ -326,84 +347,195 @@ def _empty_result(): 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 - 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) 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. - 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 - 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] - - 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 + # ``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 - fn_broadcast_starts = surv_starts_per_lp[fn_lp_ids] - fn_broadcast_counts = surv_counts_per_lp[fn_lp_ids] + ### 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 # ================================================================== - 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, - ) + ### 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() + ) - ### Group close sources by leaf pair for contiguous access - 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] - 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, - ) + ### 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() + ) + 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, + ) - expanded_near_tgts = torch.repeat_interleave(surv_point_ids, per_surv_close_counts) - per_surv_close_starts = close_starts_per_lp[surv_lp_ids] - 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, - ) +# --------------------------------------------------------------------------- +# 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) # --------------------------------------------------------------------------- @@ -458,15 +590,6 @@ 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 +605,6 @@ 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 +621,6 @@ 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 +676,6 @@ 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 +722,19 @@ 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 +749,11 @@ 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 +787,28 @@ 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 +831,12 @@ 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 +851,6 @@ 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 +881,85 @@ 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, @@ -945,23 +1034,67 @@ 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. 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] = [] + + ### (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] = [] - 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] = [] - 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 - 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 @@ -979,197 +1112,206 @@ 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) + ### 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_filtered_target_list.append( + target_tree.sorted_source_order[positions] + ) + 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 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] + hits = _expand_dual_leaf_hits( + active_tgt_nodes[nll_idx], + active_src_nodes[nll_idx], + target_tree, + source_tree, + theta, ) - do_split_S = (~split_is_leaf_S) & ( - split_is_leaf_T | (split_diam_sq_S >= split_diam_sq_T) + 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 + # 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. + ### 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) + 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) - ### 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) - 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) + near_tgt = torch.cat(near_target_list) if near_target_list else \ + empty_long.clone() + near_src = torch.cat(near_source_list) if near_source_list else \ + empty_long.clone() - if far_tgt_node_list: - far_tgt_nid = torch.cat(far_tgt_node_list) - far_src_nid = torch.cat(far_src_node_list) - else: - far_tgt_nid = torch.empty(0, dtype=torch.long, device=device) - far_src_nid = torch.empty(0, dtype=torch.long, device=device) + ### 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, + ) - 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) - - 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) + ### (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, @@ -1225,26 +1367,36 @@ 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 +1404,11 @@ 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..5da7badfa0 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 +412,77 @@ 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 +1547,113 @@ 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) + + if __name__ == "__main__": pytest.main() diff --git a/uv.lock b/uv.lock index bd5c3401aa..b5eb71a211 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 = { 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'extra-18-nvidia-physicsnemo-natten-cu12' 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-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'win32' 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')" }, ] @@ -1112,7 +1100,7 @@ cufile = [ { name = "nvidia-cufile-cu12", 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-cu12", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12') 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-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-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') or (extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-cuda-cupti-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'win32' 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')" }, ] curand = [ { name = "nvidia-curand-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'win32' 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')" }, @@ -1127,13 +1115,13 @@ nvcc = [ { name = "nvidia-cuda-nvcc-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine != 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12') 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-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-cu12') or (sys_platform != 'win32' and extra == 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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') or (extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, + { name = "nvidia-nvtx-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform == 'win32' 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')" }, ] [[package]] @@ -1147,14 +1135,20 @@ resolution-markers = [ "python_full_version >= '3.13' and 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(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-cublas", version = "13.1.0.3", 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')" }, ] 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 == '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-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 != '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')" }, ] curand = [ - { name = "nvidia-curand", version = "10.4.0.35", 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-curand", version = "10.4.0.35", 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')" }, ] cusolver = [ - { name = "nvidia-cusolver", version = "12.0.4.66", 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-cusolver", version = "12.0.4.66", 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')" }, ] cusparse = [ - { name = "nvidia-cusparse", version = "12.6.3.3", 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 == '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-cusparse", version = "12.6.3.3", 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')" }, ] nvcc = [ - { name = "nvidia-cuda-nvcc", version = "13.0.88", 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-nvcc", version = "13.0.88", 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')" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", version = "13.0.88", 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-nvjitlink", version = "13.0.88", 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')" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", version = "13.0.88", 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-cuda-nvrtc", version = "13.0.88", 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')" }, ] 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 == '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-nvtx", 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')" }, ] nvvm = [ - { name = "nvidia-nvvm", version = "13.0.88", 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-nvvm", version = "13.0.88", 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')" }, ] [[package]] @@ -1290,38 +1284,24 @@ nvvm = [ name = "cudf-cu12" version = "26.2.1" source = { registry = "https://pypi.org/simple" } -resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64'", - "python_full_version >= '3.13' and platform_machine == 'x86_64'", - "(python_full_version >= '3.13' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64') or (python_full_version >= '3.13' and platform_machine != 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(sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu12' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform == 'emscripten' 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 == 'emscripten' and extra != 'extra-18-nvidia-physicsnemo-cu13' 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-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, { name = "cupy-cuda13x" }, { name = "fsspec" }, { name = "libcudf-cu13" }, { name = "numba", version = "0.64.0", source = { registry = "https://pypi.org/simple" } }, { name = "numba-cuda", version = "0.28.2", source = { registry = "https://pypi.org/simple" }, extra = ["cu13"], marker = "extra == 'extra-18-nvidia-physicsnemo-cu13' or (extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13')" }, - { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" } }, + { name = "numpy", version = "2.4.5", source = { registry = "https://pypi.org/simple" } }, { name = "nvtx" }, { name = "packaging" }, { name = "pandas" }, @@ -1396,37 +1344,23 @@ wheels = [ name = "cuml-cu12" version = "26.2.0" source = { registry = "https://pypi.nvidia.com/" } -resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64'", - "python_full_version >= '3.13' and platform_machine == 'x86_64'", - "(python_full_version >= '3.13' and platform_machine != 'ARM64' and platform_machine != 'aarch64' and platform_machine != 's390x' 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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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'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 == '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 != '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 >= '3.13' and platform_machine != 's390x' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 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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 == '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'", @@ -4792,14 +4490,20 @@ resolution-markers = [ "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 != 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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 == 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"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 != '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 >= '3.13' 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'", @@ -4824,9 +4528,9 @@ resolution-markers = [ "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'", ] dependencies = [ - { name = "nvidia-cublas", version = "13.1.0.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 's390x' and sys_platform == 'emscripten' and extra != 'extra-18-nvidia-physicsnemo-cu12') 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-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (platform_machine != 'ARM64' and sys_platform == 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') 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-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-18-nvidia-physicsnemo-cu12') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12' and extra == 'extra-18-nvidia-physicsnemo-natten-cu13') or (sys_platform == 'emscripten' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra == 'extra-18-nvidia-physicsnemo-natten-cu12') or (sys_platform == 'emscripten' and extra == 'extra-18-nvidia-physicsnemo-cu13' and extra != 'extra-18-nvidia-physicsnemo-natten-cu13') or 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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 == '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 != 'ARM64' and platform_machine != 's390x' and extra != 'extra-18-nvidia-physicsnemo-cu12' and extra == 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'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 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 == 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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 == '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 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 != '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 != '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 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 != '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 != '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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