From 7e69b65117054a2c7c7056fe75c55d0b704337d1 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Thu, 11 Dec 2025 15:21:53 +0200 Subject: [PATCH 01/80] cleaned up installation --- pyproject.toml | 57 ++++++++++--------------- setup.py | 112 ------------------------------------------------- 2 files changed, 22 insertions(+), 147 deletions(-) delete mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml index 0092a3cbc..f0306ad9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,15 +1,24 @@ [project] -name = "PLACEHOLDER" +name = "torchmd-net" description = "TorchMD-NET provides state-of-the-art neural networks potentials for biomolecular systems" authors = [{ name = "Acellera", email = "info@acellera.com" }] readme = "README.md" license = "MIT" -requires-python = ">=3.8" -dynamic = ["version", "dependencies"] +requires-python = ">=3.9" +dynamic = ["version"] classifiers = [ "Programming Language :: Python :: 3", "Operating System :: POSIX :: Linux", ] +dependencies = [ + "h5py", + # "nnpops", + "torch==2.7.1", + "torch_geometric", + "lightning", + "tqdm", + "numpy", +] [project.urls] "Homepage" = "https://github.com/torchmd/torchmd-net" @@ -27,40 +36,18 @@ include = ["torchmdnet*"] "*" = ["*.c", "*.cpp", "*.h", "*.cuh", "*.cu", ".gitignore"] [build-system] -requires = ["setuptools>=78", "setuptools-scm>=8", "torch==2.7.1", "numpy"] +requires = ["setuptools>=78", "setuptools-scm>=8"] build-backend = "setuptools.build_meta" - -[tool.cibuildwheel] -# Disable builds which can't support CUDA and pytorch -skip = [ - "cp38-*", - "cp314-*", - "cp314t-*", - "pp*", - "*win32", - "*armv7l", - "*_i686", - "*_ppc64le", - "*_s390x", - "*_universal2", - "*-musllinux_*", +[tool.uv.sources] +torch = [ + { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] -test-requires = ["pytest", "pytest-xdist"] -test-command = "pytest {project}/tests" -environment-pass = ["ACCELERATOR", "CI"] -# container-engine = "docker; create_args: --gpus all" - -[tool.cibuildwheel.linux] -before-all = "bash {project}/cibuildwheel_support/before_all_linux.sh" -repair-wheel-command = [ - "auditwheel repair --exclude libcudart.so.* --exclude libc10.so --exclude libc10_cuda.so --exclude libtorch.so --exclude libtorch_cuda.so --exclude libtorch_cpu.so --exclude libtorch_python.so -w {dest_dir} {wheel}", -] - -[tool.cibuildwheel.macos] -repair-wheel-command = [ - "delocate-wheel --ignore-missing-dependencies --require-archs {delocate_archs} -w {dest_dir} -v {wheel}", +torchvision = [ + { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] -[tool.cibuildwheel.windows] -before-all = "bash {project}/cibuildwheel_support/before_all_windows.sh" +[[tool.uv.index]] +name = "pytorch-cu126" +url = "https://download.pytorch.org/whl/cu126" +explicit = true diff --git a/setup.py b/setup.py deleted file mode 100644 index 716514642..000000000 --- a/setup.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org -# Distributed under the MIT License. -# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - -from setuptools import setup -import torch -from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension -import os -import platform - - -if os.environ.get("ACCELERATOR", None) is not None: - use_cuda = os.environ.get("ACCELERATOR", "").startswith("cu") -else: - use_cuda = torch.cuda._is_compiled() - - -def _replace_name(name): - import pathlib - - pyproject_path = pathlib.Path(__file__).parent / "pyproject.toml" - with open(pyproject_path, "r") as f: - pyproject_text = f.read() - pyproject_text = pyproject_text.replace("PLACEHOLDER", name) - with open(pyproject_path, "w") as f: - f.write(pyproject_text) - - -if os.getenv("ACCELERATOR", "").startswith("cpu"): - _replace_name("torchmd-net-cpu") -if os.getenv("ACCELERATOR", "").startswith("cu"): - cuda_ver = os.getenv("ACCELERATOR", "")[2:4] - _replace_name(f"torchmd-net-cu{cuda_ver}") - - -def set_torch_cuda_arch_list(): - """Set the CUDA arch list according to the architectures the current torch installation was compiled for. - This function is a no-op if the environment variable TORCH_CUDA_ARCH_LIST is already set or if torch was not compiled with CUDA support. - """ - if use_cuda and not os.environ.get("TORCH_CUDA_ARCH_LIST"): - arch_flags = torch._C._cuda_getArchFlags() - sm_versions = [x[3:] for x in arch_flags.split() if x.startswith("sm_")] - formatted_versions = ";".join([f"{y[:-1]}.{y[-1]}" for y in sm_versions]) - formatted_versions += "+PTX" - os.environ["TORCH_CUDA_ARCH_LIST"] = formatted_versions - - -set_torch_cuda_arch_list() - -extension_root = os.path.join("torchmdnet", "extensions") -neighbor_sources = ["neighbors_cpu.cpp"] -if use_cuda: - neighbor_sources.append("neighbors_cuda.cu") -neighbor_sources = [ - os.path.join(extension_root, "neighbors", source) for source in neighbor_sources -] - -runtime_library_dirs = None -if platform.system() == "Darwin": - runtime_library_dirs = [ - "@loader_path/../../torch/lib", - "@loader_path/../../nvidia/cuda_runtime/lib", - ] -elif platform.system() == "Linux": - runtime_library_dirs = [ - "$ORIGIN/../../torch/lib", - "$ORIGIN/../../nvidia/cuda_runtime/lib", - ] - -extra_deps = [] -if os.getenv("ACCELERATOR", "").startswith("cu"): - cuda_ver = os.getenv("ACCELERATOR")[2:4] - extra_deps = [f"nvidia-cuda-runtime-cu{cuda_ver}"] - -ExtensionType = CppExtension if not use_cuda else CUDAExtension -extensions = ExtensionType( - name="torchmdnet.extensions.torchmdnet_extensions", - sources=[os.path.join(extension_root, "torchmdnet_extensions.cpp")] - + neighbor_sources, - define_macros=[("WITH_CUDA", 1)] if use_cuda else [], - runtime_library_dirs=runtime_library_dirs, -) - -kwargs = {} -if "CI" in os.environ: - from setuptools_scm import get_version - - # Drop the dev version suffix because we modify pyproject.toml - # We do this only in CI because we need to upload to PyPI - - kwargs = {"version": ".".join(get_version().split(".")[:3])} - -if __name__ == "__main__": - setup( - ext_modules=[extensions], - cmdclass={ - "build_ext": BuildExtension.with_options( - no_python_abi_suffix=True, use_ninja=False - ) - }, - install_requires=[ - "h5py", - # "nnpops", - "torch==2.7.1", - "torch_geometric", - "lightning", - "tqdm", - "numpy", - ] - + extra_deps, - **kwargs, - ) From 016a744d86817715a0dc421729a9847985b625ac Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Thu, 11 Dec 2025 15:39:09 +0200 Subject: [PATCH 02/80] keep just brute tests --- tests/test_neighbors.py | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index fa3e06046..1099db7f6 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -71,10 +71,7 @@ def compute_ref_neighbors(pos, batch, loop, include_transpose, cutoff, box_vecto return ref_neighbors, ref_distance_vecs, ref_distances -@pytest.mark.parametrize( - ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], -) +@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4, 128]) @pytest.mark.parametrize("cutoff", [0.1, 1.0, 3.0, 4.9]) @pytest.mark.parametrize("loop", [True, False]) @@ -147,10 +144,7 @@ def test_neighbors( assert np.allclose(distance_vecs, ref_distance_vecs) -@pytest.mark.parametrize( - ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], -) +@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) @@ -259,10 +253,7 @@ def test_neighbor_grads( assert np.allclose(ref_pos_grad_sorted, pos_grad_sorted, atol=1e-8, rtol=1e-5) -@pytest.mark.parametrize( - ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], -) +@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @pytest.mark.parametrize("num_atoms", [1, 2, 10]) @@ -314,7 +305,7 @@ def test_neighbor_autograds( ) -@pytest.mark.parametrize("strategy", ["brute", "cell", "shared"]) +@pytest.mark.parametrize("strategy", ["brute"]) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4]) def test_large_size(strategy, n_batches): device = "cuda" @@ -373,7 +364,7 @@ def test_large_size(strategy, n_batches): @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], + [("cpu", "brute"), ("cuda", "brute")], ) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @@ -444,7 +435,7 @@ def test_jit_script_compatible( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) +@pytest.mark.parametrize("strategy", ["brute"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -527,7 +518,7 @@ def test_cuda_graph_compatible_forward( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) +@pytest.mark.parametrize("strategy", ["brute"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -600,9 +591,7 @@ def test_cuda_graph_compatible_backward( torch.cuda.synchronize() -@pytest.mark.parametrize( - ("device", "strategy"), [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared")] -) +@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("use_forward", [True, False]) def test_per_batch_box(device, strategy, n_batches, use_forward): From 79e6754ecc35e8733fb2b047a3cc34556f72848d Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Thu, 11 Dec 2025 15:39:17 +0200 Subject: [PATCH 03/80] add triton dep --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index f0306ad9e..be8e09000 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,7 @@ dependencies = [ "lightning", "tqdm", "numpy", + "triton", ] [project.urls] @@ -39,6 +40,9 @@ include = ["torchmdnet*"] requires = ["setuptools>=78", "setuptools-scm>=8"] build-backend = "setuptools.build_meta" +[dependency-groups] +dev = ["ipython>=8.18.1", "pytest>=8.4.2"] + [tool.uv.sources] torch = [ { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, From a2318575ba75448fcb297e345384ca1d52093046 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Thu, 11 Dec 2025 19:56:27 +0200 Subject: [PATCH 04/80] first triton and pytorch implementations --- torchmdnet/extensions/ops.py | 31 +- torchmdnet/extensions/triton_neighbors.py | 506 ++++++++++++++++++++++ 2 files changed, 536 insertions(+), 1 deletion(-) create mode 100644 torchmdnet/extensions/triton_neighbors.py diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index ab420de3a..1e8e67e78 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -12,6 +12,8 @@ from typing import Tuple from torch.library import register_fake +from . import triton_neighbors + __all__ = ["is_current_stream_capturing", "get_neighbor_pairs_kernel"] @@ -29,6 +31,17 @@ def is_current_stream_capturing(): return _is_current_stream_capturing() +@torch.jit.ignore +def _stream_is_capturing_runtime() -> bool: + try: + return torch.cuda.is_current_stream_capturing() + except Exception: + try: + return is_current_stream_capturing() + except Exception: + return False + + def get_neighbor_pairs_kernel( strategy: str, positions: Tensor, @@ -79,7 +92,23 @@ def get_neighbor_pairs_kernel( num_pairs : Tensor The number of pairs found. """ - return torch.ops.torchmdnet_extensions.get_neighbor_pairs( + use_triton = positions.is_cuda and strategy in ("brute", "shared", "cell") + + if torch.jit.is_scripting() or not use_triton: + return triton_neighbors.torch_neighbor_pairs( + strategy, + positions, + batch, + box_vectors, + use_periodic, + cutoff_lower, + cutoff_upper, + max_num_pairs, + loop, + include_transpose, + ) + + return triton_neighbors.triton_neighbor_pairs( strategy, positions, batch, diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py new file mode 100644 index 000000000..72549928d --- /dev/null +++ b/torchmdnet/extensions/triton_neighbors.py @@ -0,0 +1,506 @@ +import math +from typing import Optional, Tuple + +import torch +from torch import Tensor + +try: + import triton + import triton.language as tl + + _HAS_TRITON = True +except Exception: + triton = None + tl = None + _HAS_TRITON = False + + +def _round_nearest(x: Tensor) -> Tensor: + # Equivalent to torch.round but works for both float32/float64 and keeps TorchScript happy. + return torch.where(x >= 0, torch.floor(x + 0.5), torch.ceil(x - 0.5)) + + +def _validate_box(box_vectors: Tensor, cutoff_upper: float, n_batch: int) -> Tensor: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0).expand(n_batch, 3, 3) + if box_vectors.dim() != 3 or box_vectors.shape[1:] != (3, 3): + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + if box_vectors.size(0) != n_batch: + raise ValueError('Expected "box_vectors" first dimension to match batch size') + v = box_vectors[0] + c = float(cutoff_upper) + if not ( + v[0, 1] == 0 + and v[0, 2] == 0 + and v[1, 2] == 0 + and v[0, 0] >= 2 * c + and v[1, 1] >= 2 * c + and v[2, 2] >= 2 * c + and v[0, 0] >= 2 * v[1, 0] + and v[0, 0] >= 2 * v[2, 0] + and v[1, 1] >= 2 * v[2, 1] + ): + raise ValueError("Invalid box vectors") + return box_vectors + + +def _apply_pbc_torch(deltas: Tensor, box_for_pairs: Tensor) -> Tensor: + # box_for_pairs: (num_pairs, 3, 3) + scale3 = _round_nearest(deltas[:, 2] / box_for_pairs[:, 2, 2]) + deltas = deltas - scale3.unsqueeze(1) * box_for_pairs[:, 2] + scale2 = _round_nearest(deltas[:, 1] / box_for_pairs[:, 1, 1]) + deltas = deltas - scale2.unsqueeze(1) * box_for_pairs[:, 1] + scale1 = _round_nearest(deltas[:, 0] / box_for_pairs[:, 0, 0]) + deltas = deltas - scale1.unsqueeze(1) * box_for_pairs[:, 0] + return deltas + + +def _torch_neighbor_bruteforce( + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """Brute-force neighbor list using pure PyTorch. + + This implementation avoids nonzero() to be torch.compile compatible. + It uses argsort with infinity for invalid pairs to achieve fixed output shapes. + """ + if positions.dim() != 2 or positions.size(1) != 3: + raise ValueError('Expected "positions" to have shape (N, 3)') + if batch.dim() != 1 or batch.size(0) != positions.size(0): + raise ValueError('Expected "batch" to have shape (N,)') + if max_num_pairs <= 0: + raise ValueError('Expected "max_num_pairs" to be positive') + + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + if use_periodic: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0) + elif box_vectors.dim() != 3: + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + box_vectors = box_vectors.to(device=device, dtype=dtype) + + # Generate indices for all n² pairs + arange_n = torch.arange(n_atoms, device=device) + i_idx = arange_n.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) + j_idx = arange_n.view(1, -1).expand(n_atoms, n_atoms) # (n, n) + + # Compute deltas for ALL pairs: (n, n, 3) + pos_i = positions.unsqueeze(1) # (n, 1, 3) + pos_j = positions.unsqueeze(0) # (1, n, 3) + deltas_grid = pos_i - pos_j # (n, n, 3) + + # Apply PBC if needed + if use_periodic: + # Get batch indices for all pairs + batch_i = batch.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) + if box_vectors.size(0) == 1: + # Single box for all - broadcast + box_for_grid = box_vectors.expand(n_atoms, n_atoms, 3, 3) # (n, n, 3, 3) + else: + # Per-batch boxes - index by batch of atom i + box_for_grid = box_vectors[batch_i] # (n, n, 3, 3) + # Apply PBC: deltas_grid is (n, n, 3), box_for_grid is (n, n, 3, 3) + deltas_flat_pbc = deltas_grid.reshape(-1, 3) + box_flat_pbc = box_for_grid.reshape(-1, 3, 3) + deltas_flat_pbc = _apply_pbc_torch(deltas_flat_pbc, box_flat_pbc) + deltas_grid = deltas_flat_pbc.reshape(n_atoms, n_atoms, 3) + + # Compute distances for all pairs: (n, n) + dist_sq = (deltas_grid * deltas_grid).sum(dim=-1) + zero_mask = dist_sq == 0 + distances_grid = torch.where( + zero_mask, + torch.zeros_like(dist_sq), + torch.sqrt(dist_sq.clamp(min=1e-32)), + ) + + # Build validity mask (n, n) + if include_transpose: + valid_mask = torch.ones((n_atoms, n_atoms), device=device, dtype=torch.bool) + if not loop: + valid_mask = valid_mask & (i_idx != j_idx) + else: + valid_mask = (i_idx > j_idx) if not loop else (i_idx >= j_idx) + + # Apply batch constraint + if batch.numel() > 0: + same_batch = batch.view(-1, 1) == batch.view(1, -1) + valid_mask = valid_mask & same_batch + + # Apply cutoff constraints + valid_mask = ( + valid_mask & (distances_grid < cutoff_upper) & (distances_grid >= cutoff_lower) + ) + + # Flatten everything: (n²) + i_flat = i_idx.reshape(-1) + j_flat = j_idx.reshape(-1) + deltas_flat = deltas_grid.reshape(-1, 3) + distances_flat = distances_grid.reshape(-1) + valid_flat = valid_mask.reshape(-1) + + # Sort key: valid pairs by distance, invalid pairs get infinity (sorted last) + sort_key = torch.where( + valid_flat, + distances_flat, + torch.full_like(distances_flat, float("inf")), + ) + + # Sort and take top max_num_pairs (fixed output shape) + order = torch.argsort(sort_key)[:max_num_pairs] + + # Gather results using the sorted indices + i_out = i_flat.index_select(0, order) + j_out = j_flat.index_select(0, order) + deltas_out = deltas_flat.index_select(0, order) + distances_out = distances_flat.index_select(0, order) + valid_out = valid_flat.index_select(0, order) + + # Replace invalid entries with -1 (neighbors) and 0 (deltas/distances) + # Ensure gradients flow through valid entries + neighbors_out = torch.stack( + [ + torch.where(valid_out, i_out, torch.full_like(i_out, -1)), + torch.where(valid_out, j_out, torch.full_like(j_out, -1)), + ] + ) + # For deltas/distances, use where to preserve gradients for valid entries + zero_deltas = deltas_out.detach() * 0 + zero_distances = distances_out.detach() * 0 + deltas_out = torch.where(valid_out.unsqueeze(1), deltas_out, zero_deltas) + distances_out = torch.where(valid_out, distances_out, zero_distances) + + # Count valid pairs + num_pairs_tensor = valid_out.sum().clamp(max=max_num_pairs).view(1).to(torch.long) + return neighbors_out, deltas_out, distances_out, num_pairs_tensor + + +@triton.jit +def _tl_round(x): + return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) + + +@triton.jit +def _neighbor_brute_kernel( + pos_ptr, + batch_ptr, + box_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + stride_batch: tl.constexpr, + stride_box_b: tl.constexpr, + stride_box_r: tl.constexpr, + stride_box_c: tl.constexpr, + n_atoms: tl.constexpr, + use_periodic: tl.constexpr, + include_transpose: tl.constexpr, + loop: tl.constexpr, + max_pairs: tl.constexpr, + cutoff_lower: tl.constexpr, + cutoff_upper: tl.constexpr, + BLOCK: tl.constexpr, +): + """Brute-force neighbor list kernel using atomic operations for compaction.""" + pid = tl.program_id(axis=0) + start = pid * BLOCK + idx = start + tl.arange(0, BLOCK) + i = idx // n_atoms + j = idx - i * n_atoms + + valid = idx < n_atoms * n_atoms + tri_mask = j < i + if loop: + tri_mask = j <= i + valid = valid & tri_mask + batch_i = tl.load(batch_ptr + i * stride_batch, mask=valid, other=0) + batch_j = tl.load(batch_ptr + j * stride_batch, mask=valid, other=0) + valid = valid & (batch_i == batch_j) + + pos_ix = tl.load( + pos_ptr + i * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 + ) + pos_iy = tl.load( + pos_ptr + i * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 + ) + pos_iz = tl.load( + pos_ptr + i * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jx = tl.load( + pos_ptr + j * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jy = tl.load( + pos_ptr + j * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jz = tl.load( + pos_ptr + j * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 + ) + + dx = pos_ix - pos_jx + dy = pos_iy - pos_jy + dz = pos_iz - pos_jz + + if use_periodic: + box_base = box_ptr + batch_i * stride_box_b + + b20 = tl.load( + box_base + 2 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 + ) + b21 = tl.load( + box_base + 2 * stride_box_r + 1 * stride_box_c, mask=valid, other=0.0 + ) + b22 = tl.load( + box_base + 2 * stride_box_r + 2 * stride_box_c, mask=valid, other=1.0 + ) + b10 = tl.load( + box_base + 1 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 + ) + b11 = tl.load( + box_base + 1 * stride_box_r + 1 * stride_box_c, mask=valid, other=1.0 + ) + b00 = tl.load( + box_base + 0 * stride_box_r + 0 * stride_box_c, mask=valid, other=1.0 + ) + + scale3 = _tl_round(dz / b22) + dx = dx - scale3 * b20 + dy = dy - scale3 * b21 + dz = dz - scale3 * b22 + scale2 = _tl_round(dy / b11) + dx = dx - scale2 * b10 + dy = dy - scale2 * b11 + scale1 = _tl_round(dx / b00) + dx = dx - scale1 * b00 + + dist2 = dx * dx + dy * dy + dz * dz + dist = tl.sqrt(dist2) + valid = valid & (dist >= cutoff_lower) & (dist < cutoff_upper) + + valid_int = valid.to(tl.int32) + local_idx = tl.cumsum(valid_int, axis=0) - 1 + total_pairs = tl.sum(valid_int, axis=0) + + # For transpose, don't count self-loops (i == j) + if include_transpose: + is_not_self_loop = i != j + valid_transpose = valid & is_not_self_loop + total_transpose = tl.sum(valid_transpose.to(tl.int32), axis=0) + total_out = total_pairs + total_transpose + else: + total_out = total_pairs + + has_work = total_out > 0 + start_idx = tl.atomic_add(counter_ptr, total_out, mask=has_work) + start_idx = tl.where(has_work, start_idx, 0) + start_idx = tl.broadcast_to(start_idx, local_idx.shape) + + write_idx = start_idx + local_idx + mask_store = valid & (write_idx < max_pairs) + tl.store(neighbors0_ptr + write_idx, i, mask=mask_store) + tl.store(neighbors1_ptr + write_idx, j, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 0, dx, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 1, dy, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 2, dz, mask=mask_store) + tl.store(distances_ptr + write_idx, dist, mask=mask_store) + + if include_transpose: + # Don't add transpose for self-loops (i == j) + is_not_self_loop = i != j + valid_transpose = valid & is_not_self_loop + valid_t_int = valid_transpose.to(tl.int32) + local_idx_t = tl.cumsum(valid_t_int, axis=0) - 1 + total_pairs_t = tl.sum(valid_t_int, axis=0) + + write_idx_t = start_idx + total_pairs + local_idx_t + mask_store_t = valid_transpose & (write_idx_t < max_pairs) + tl.store(neighbors0_ptr + write_idx_t, j, mask=mask_store_t) + tl.store(neighbors1_ptr + write_idx_t, i, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 0, -dx, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 1, -dy, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 2, -dz, mask=mask_store_t) + tl.store(distances_ptr + write_idx_t, dist, mask=mask_store_t) + + +class TritonNeighborAutograd(torch.autograd.Function): + @staticmethod + def forward( # type: ignore[override] + ctx, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, + ): + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + batch = batch.contiguous() + positions = positions.contiguous() + if use_periodic: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0) + elif box_vectors.dim() != 3: + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + box_vectors = box_vectors.to(device=device, dtype=dtype) + box_vectors = box_vectors.contiguous() + # Use stride 0 to broadcast single box to all batches (avoids CPU sync) + box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) + + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + counter = torch.zeros((1,), device=device, dtype=torch.int32) + + # Grid covers all (i, j) pairs + grid = lambda meta: (triton.cdiv(n_atoms * n_atoms, meta["BLOCK"]),) + + _neighbor_brute_kernel[grid]( + positions, + batch, + box_vectors if use_periodic else positions, # dummy pointer if not periodic + neighbors[0], + neighbors[1], + deltas, + distances, + counter, + positions.stride(0), + positions.stride(1), + batch.stride(0), + box_stride_0 if use_periodic else 0, + box_vectors.stride(1) if use_periodic else 0, + box_vectors.stride(2) if use_periodic else 0, + n_atoms, + use_periodic, + include_transpose, + loop, + max_num_pairs, + cutoff_lower, + cutoff_upper, + BLOCK=256, + ) + + # Clamp num_pairs to written region + num_pairs = counter.to(torch.long) + num_pairs = num_pairs.clamp(max=max_num_pairs) + + ctx.save_for_backward(neighbors, deltas, distances) + ctx.num_atoms = n_atoms + return neighbors, deltas, distances, num_pairs + + @staticmethod + def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] + neighbors, edge_vec, edge_weight = ctx.saved_tensors + num_atoms = ctx.num_atoms + + if grad_deltas is None: + grad_deltas = torch.zeros_like(edge_vec) + if grad_distances is None: + grad_distances = torch.zeros_like(edge_weight) + + zero_mask = edge_weight.eq(0) + zero_mask3 = zero_mask.unsqueeze(-1).expand_as(grad_deltas) + + grad_distances_term = edge_vec / edge_weight.masked_fill( + zero_mask, 1 + ).unsqueeze(-1) + grad_distances_term = grad_distances_term * grad_distances.masked_fill( + zero_mask, 0 + ).unsqueeze(-1) + + grad_positions = torch.zeros( + (num_atoms, 3), device=edge_vec.device, dtype=edge_vec.dtype + ) + edge_index_safe = neighbors.masked_fill( + zero_mask.unsqueeze(0).expand_as(neighbors), 0 + ) + grad_vec = grad_deltas.masked_fill(zero_mask3, 0) + grad_distances_term + grad_positions.index_add_(0, edge_index_safe[0], grad_vec) + grad_positions.index_add_(0, edge_index_safe[1], -grad_vec) + + return ( + grad_positions, + None, + None, + None, + None, + None, + None, + None, + None, + ) + + +def triton_neighbor_pairs( + strategy: str, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + if not _HAS_TRITON: + raise RuntimeError("Triton is not available") + if positions.device.type != "cuda": + raise RuntimeError("Triton neighbor list requires CUDA tensors") + if positions.dtype not in (torch.float32, torch.float64): + raise RuntimeError("Unsupported dtype for Triton neighbor list") + + return TritonNeighborAutograd.apply( + positions, + batch, + box_vectors, + use_periodic, + float(cutoff_lower), + float(cutoff_upper), + int(max_num_pairs), + bool(loop), + bool(include_transpose), + ) + + +def torch_neighbor_pairs( + strategy: str, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + return _torch_neighbor_bruteforce( + positions, + batch, + box_vectors, + use_periodic, + cutoff_lower, + cutoff_upper, + max_num_pairs, + loop, + include_transpose, + ) From c60cd7483bce9fc9be487ac98c218853d04be6d0 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 10:25:26 +0200 Subject: [PATCH 05/80] fix assertion error --- torchmdnet/extensions/triton_neighbors.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index 72549928d..ef69590c4 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -180,8 +180,8 @@ def _torch_neighbor_bruteforce( deltas_out = torch.where(valid_out.unsqueeze(1), deltas_out, zero_deltas) distances_out = torch.where(valid_out, distances_out, zero_distances) - # Count valid pairs - num_pairs_tensor = valid_out.sum().clamp(max=max_num_pairs).view(1).to(torch.long) + # Count ALL valid pairs (before slicing) to detect overflow + num_pairs_tensor = valid_flat.sum().view(1).to(torch.long) return neighbors_out, deltas_out, distances_out, num_pairs_tensor @@ -398,9 +398,7 @@ def forward( # type: ignore[override] BLOCK=256, ) - # Clamp num_pairs to written region num_pairs = counter.to(torch.long) - num_pairs = num_pairs.clamp(max=max_num_pairs) ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms From 37fea407e440a35b7954bbd677afedd2b8df2239 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 10:37:03 +0200 Subject: [PATCH 06/80] fixed last issue --- torchmdnet/extensions/triton_neighbors.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index ef69590c4..4178b4511 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -138,8 +138,12 @@ def _torch_neighbor_bruteforce( valid_mask = valid_mask & same_batch # Apply cutoff constraints + # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 + is_self_loop = i_idx == j_idx valid_mask = ( - valid_mask & (distances_grid < cutoff_upper) & (distances_grid >= cutoff_lower) + valid_mask + & (distances_grid < cutoff_upper) + & ((distances_grid >= cutoff_lower) | is_self_loop) ) # Flatten everything: (n²) @@ -288,7 +292,9 @@ def _neighbor_brute_kernel( dist2 = dx * dx + dy * dy + dz * dz dist = tl.sqrt(dist2) - valid = valid & (dist >= cutoff_lower) & (dist < cutoff_upper) + # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 + is_self_loop = i == j + valid = valid & (dist < cutoff_upper) & ((dist >= cutoff_lower) | is_self_loop) valid_int = valid.to(tl.int32) local_idx = tl.cumsum(valid_int, axis=0) - 1 From 160bc2785bfb8578eefa72ee8219b52b29d85485 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 11:04:15 +0200 Subject: [PATCH 07/80] less computations --- torchmdnet/extensions/triton_neighbors.py | 55 +++++++++++++++++++---- 1 file changed, 46 insertions(+), 9 deletions(-) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index 4178b4511..f0513f246 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -211,6 +211,7 @@ def _neighbor_brute_kernel( stride_box_r: tl.constexpr, stride_box_c: tl.constexpr, n_atoms: tl.constexpr, + num_all_pairs: tl.constexpr, use_periodic: tl.constexpr, include_transpose: tl.constexpr, loop: tl.constexpr, @@ -219,18 +220,47 @@ def _neighbor_brute_kernel( cutoff_upper: tl.constexpr, BLOCK: tl.constexpr, ): - """Brute-force neighbor list kernel using atomic operations for compaction.""" + """Brute-force neighbor list kernel using triangular indexing and atomic compaction. + + Uses triangular indexing to iterate over only n*(n-1)/2 pairs (or n*(n+1)/2 with loop), + achieving 100% thread utilization while maintaining block-level atomic compaction. + """ pid = tl.program_id(axis=0) start = pid * BLOCK idx = start + tl.arange(0, BLOCK) - i = idx // n_atoms - j = idx - i * n_atoms - valid = idx < n_atoms * n_atoms - tri_mask = j < i + valid = idx < num_all_pairs + + # Convert linear index to (i, j) using triangular formula (same as CUDA get_row) + # Do integer arithmetic first, only convert to float for sqrt if loop: - tri_mask = j <= i - valid = valid & tri_mask + # With self-loops: j <= i, num_pairs = n*(n+1)/2 + # row = floor((-1 + sqrt(1 + 8k)) / 2) + sqrt_arg = (1 + 8 * idx).to(tl.float32) + row_f = tl.math.floor((-1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) + i = row_f.to(tl.int32) + # Handle floating-point edge case: if i*(i+1)/2 > idx, decrement i + i = tl.where(i * (i + 1) > 2 * idx, i - 1, i) + # col = k - row*(row+1)/2 + j = idx - (i * (i + 1)) // 2 + else: + # Without self-loops: j < i, num_pairs = n*(n-1)/2 + # row = floor((1 + sqrt(1 + 8k)) / 2) + sqrt_arg = (1 + 8 * idx).to(tl.float32) + row_f = tl.math.floor((1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) + i = row_f.to(tl.int32) + # Handle floating-point edge case (same correction as CUDA) + i = tl.where(i * (i - 1) > 2 * idx, i - 1, i) + # col = k - row*(row-1)/2 + j = idx - (i * (i - 1)) // 2 + + # Validate indices: check bounds and triangular constraint + # Due to float precision, we may get invalid (i, j) pairs + valid = valid & (i >= 0) & (i < n_atoms) & (j >= 0) & (j <= i) + if not loop: + # For non-loop case, also require j < i (no self-loops) + valid = valid & (j < i) + batch_i = tl.load(batch_ptr + i * stride_batch, mask=valid, other=0) batch_j = tl.load(batch_ptr + j * stride_batch, mask=valid, other=0) valid = valid & (batch_i == batch_j) @@ -376,8 +406,14 @@ def forward( # type: ignore[override] distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) counter = torch.zeros((1,), device=device, dtype=torch.int32) - # Grid covers all (i, j) pairs - grid = lambda meta: (triton.cdiv(n_atoms * n_atoms, meta["BLOCK"]),) + # Compute triangular pair count: n*(n-1)/2 without self-loops, n*(n+1)/2 with + if loop: + num_all_pairs = n_atoms * (n_atoms + 1) // 2 + else: + num_all_pairs = n_atoms * (n_atoms - 1) // 2 + + # Grid covers only triangular pairs (not n²) + grid = lambda meta: (triton.cdiv(num_all_pairs, meta["BLOCK"]),) _neighbor_brute_kernel[grid]( positions, @@ -395,6 +431,7 @@ def forward( # type: ignore[override] box_vectors.stride(1) if use_periodic else 0, box_vectors.stride(2) if use_periodic else 0, n_atoms, + num_all_pairs, use_periodic, include_transpose, loop, From 5e2f6d93a8058d30f009b149056f91e120ab5e77 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 13:37:40 +0200 Subject: [PATCH 08/80] reorganized code. added first cell implementation --- torchmdnet/extensions/ops.py | 4 +- torchmdnet/extensions/triton_brute.py | 267 ++++++++++++++++ torchmdnet/extensions/triton_cell.py | 373 ++++++++++++++++++++++ torchmdnet/extensions/triton_neighbors.py | 321 ++++--------------- 4 files changed, 699 insertions(+), 266 deletions(-) create mode 100644 torchmdnet/extensions/triton_brute.py create mode 100644 torchmdnet/extensions/triton_cell.py diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index 1e8e67e78..9dd44c00e 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -92,9 +92,7 @@ def get_neighbor_pairs_kernel( num_pairs : Tensor The number of pairs found. """ - use_triton = positions.is_cuda and strategy in ("brute", "shared", "cell") - - if torch.jit.is_scripting() or not use_triton: + if torch.jit.is_scripting() or not positions.is_cuda: return triton_neighbors.torch_neighbor_pairs( strategy, positions, diff --git a/torchmdnet/extensions/triton_brute.py b/torchmdnet/extensions/triton_brute.py new file mode 100644 index 000000000..34d3d7a85 --- /dev/null +++ b/torchmdnet/extensions/triton_brute.py @@ -0,0 +1,267 @@ +try: + import triton + import triton.language as tl + + _HAS_TRITON = True +except Exception: + triton = None + tl = None + _HAS_TRITON = False + +from torch import Tensor +import torch +from torchmdnet.extensions.triton_neighbors import _tl_round, TritonNeighborAutograd + + +@triton.jit +def _neighbor_brute_kernel( + pos_ptr, + batch_ptr, + box_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + stride_batch: tl.constexpr, + stride_box_b: tl.constexpr, + stride_box_r: tl.constexpr, + stride_box_c: tl.constexpr, + n_atoms: tl.constexpr, + num_all_pairs: tl.constexpr, + use_periodic: tl.constexpr, + include_transpose: tl.constexpr, + loop: tl.constexpr, + max_pairs: tl.constexpr, + cutoff_lower: tl.constexpr, + cutoff_upper: tl.constexpr, + BLOCK: tl.constexpr, +): + """Brute-force neighbor list kernel using triangular indexing and atomic compaction. + + Uses triangular indexing to iterate over only n*(n-1)/2 pairs (or n*(n+1)/2 with loop), + achieving 100% thread utilization while maintaining block-level atomic compaction. + """ + pid = tl.program_id(axis=0) + start = pid * BLOCK + idx = start + tl.arange(0, BLOCK) + + valid = idx < num_all_pairs + + # Convert linear index to (i, j) using triangular formula (same as CUDA get_row) + # Do integer arithmetic first, only convert to float for sqrt + if loop: + # With self-loops: j <= i, num_pairs = n*(n+1)/2 + # row = floor((-1 + sqrt(1 + 8k)) / 2) + sqrt_arg = (1 + 8 * idx).to(tl.float32) + row_f = tl.math.floor((-1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) + i = row_f.to(tl.int32) + # Handle floating-point edge case: if i*(i+1)/2 > idx, decrement i + i = tl.where(i * (i + 1) > 2 * idx, i - 1, i) + # col = k - row*(row+1)/2 + j = idx - (i * (i + 1)) // 2 + else: + # Without self-loops: j < i, num_pairs = n*(n-1)/2 + # row = floor((1 + sqrt(1 + 8k)) / 2) + sqrt_arg = (1 + 8 * idx).to(tl.float32) + row_f = tl.math.floor((1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) + i = row_f.to(tl.int32) + # Handle floating-point edge case (same correction as CUDA) + i = tl.where(i * (i - 1) > 2 * idx, i - 1, i) + # col = k - row*(row-1)/2 + j = idx - (i * (i - 1)) // 2 + + # Validate indices: check bounds and triangular constraint + # Due to float precision, we may get invalid (i, j) pairs + valid = valid & (i >= 0) & (i < n_atoms) & (j >= 0) & (j <= i) + if not loop: + # For non-loop case, also require j < i (no self-loops) + valid = valid & (j < i) + + batch_i = tl.load(batch_ptr + i * stride_batch, mask=valid, other=0) + batch_j = tl.load(batch_ptr + j * stride_batch, mask=valid, other=0) + valid = valid & (batch_i == batch_j) + + pos_ix = tl.load( + pos_ptr + i * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 + ) + pos_iy = tl.load( + pos_ptr + i * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 + ) + pos_iz = tl.load( + pos_ptr + i * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jx = tl.load( + pos_ptr + j * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jy = tl.load( + pos_ptr + j * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 + ) + pos_jz = tl.load( + pos_ptr + j * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 + ) + + dx = pos_ix - pos_jx + dy = pos_iy - pos_jy + dz = pos_iz - pos_jz + + if use_periodic: + box_base = box_ptr + batch_i * stride_box_b + + b20 = tl.load( + box_base + 2 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 + ) + b21 = tl.load( + box_base + 2 * stride_box_r + 1 * stride_box_c, mask=valid, other=0.0 + ) + b22 = tl.load( + box_base + 2 * stride_box_r + 2 * stride_box_c, mask=valid, other=1.0 + ) + b10 = tl.load( + box_base + 1 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 + ) + b11 = tl.load( + box_base + 1 * stride_box_r + 1 * stride_box_c, mask=valid, other=1.0 + ) + b00 = tl.load( + box_base + 0 * stride_box_r + 0 * stride_box_c, mask=valid, other=1.0 + ) + + scale3 = _tl_round(dz / b22) + dx = dx - scale3 * b20 + dy = dy - scale3 * b21 + dz = dz - scale3 * b22 + scale2 = _tl_round(dy / b11) + dx = dx - scale2 * b10 + dy = dy - scale2 * b11 + scale1 = _tl_round(dx / b00) + dx = dx - scale1 * b00 + + dist2 = dx * dx + dy * dy + dz * dz + dist = tl.sqrt(dist2) + # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 + is_self_loop = i == j + valid = valid & (dist < cutoff_upper) & ((dist >= cutoff_lower) | is_self_loop) + + valid_int = valid.to(tl.int32) + local_idx = tl.cumsum(valid_int, axis=0) - 1 + total_pairs = tl.sum(valid_int, axis=0) + + # For transpose, don't count self-loops (i == j) + if include_transpose: + is_not_self_loop = i != j + valid_transpose = valid & is_not_self_loop + total_transpose = tl.sum(valid_transpose.to(tl.int32), axis=0) + total_out = total_pairs + total_transpose + else: + total_out = total_pairs + + has_work = total_out > 0 + start_idx = tl.atomic_add(counter_ptr, total_out, mask=has_work) + start_idx = tl.where(has_work, start_idx, 0) + start_idx = tl.broadcast_to(start_idx, local_idx.shape) + + write_idx = start_idx + local_idx + mask_store = valid & (write_idx < max_pairs) + tl.store(neighbors0_ptr + write_idx, i, mask=mask_store) + tl.store(neighbors1_ptr + write_idx, j, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 0, dx, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 1, dy, mask=mask_store) + tl.store(deltas_ptr + write_idx * 3 + 2, dz, mask=mask_store) + tl.store(distances_ptr + write_idx, dist, mask=mask_store) + + if include_transpose: + # Don't add transpose for self-loops (i == j) + is_not_self_loop = i != j + valid_transpose = valid & is_not_self_loop + valid_t_int = valid_transpose.to(tl.int32) + local_idx_t = tl.cumsum(valid_t_int, axis=0) - 1 + total_pairs_t = tl.sum(valid_t_int, axis=0) + + write_idx_t = start_idx + total_pairs + local_idx_t + mask_store_t = valid_transpose & (write_idx_t < max_pairs) + tl.store(neighbors0_ptr + write_idx_t, j, mask=mask_store_t) + tl.store(neighbors1_ptr + write_idx_t, i, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 0, -dx, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 1, -dy, mask=mask_store_t) + tl.store(deltas_ptr + write_idx_t * 3 + 2, -dz, mask=mask_store_t) + tl.store(distances_ptr + write_idx_t, dist, mask=mask_store_t) + + +class TritonBruteNeighborAutograd(TritonNeighborAutograd): + @staticmethod + def forward( # type: ignore[override] + ctx, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, + ): + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + batch = batch.contiguous() + positions = positions.contiguous() + if use_periodic: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0) + elif box_vectors.dim() != 3: + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + box_vectors = box_vectors.to(device=device, dtype=dtype) + box_vectors = box_vectors.contiguous() + # Use stride 0 to broadcast single box to all batches (avoids CPU sync) + box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) + + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + counter = torch.zeros((1,), device=device, dtype=torch.int32) + + # Compute triangular pair count: n*(n-1)/2 without self-loops, n*(n+1)/2 with + if loop: + num_all_pairs = n_atoms * (n_atoms + 1) // 2 + else: + num_all_pairs = n_atoms * (n_atoms - 1) // 2 + + # Grid covers only triangular pairs (not n²) + grid = lambda meta: (triton.cdiv(num_all_pairs, meta["BLOCK"]),) + + _neighbor_brute_kernel[grid]( + positions, + batch, + box_vectors if use_periodic else positions, # dummy pointer if not periodic + neighbors[0], + neighbors[1], + deltas, + distances, + counter, + positions.stride(0), + positions.stride(1), + batch.stride(0), + box_stride_0 if use_periodic else 0, + box_vectors.stride(1) if use_periodic else 0, + box_vectors.stride(2) if use_periodic else 0, + n_atoms, + num_all_pairs, + use_periodic, + include_transpose, + loop, + max_num_pairs, + cutoff_lower, + cutoff_upper, + BLOCK=256, + ) + + num_pairs = counter.to(torch.long) + + ctx.save_for_backward(neighbors, deltas, distances) + ctx.num_atoms = n_atoms + return neighbors, deltas, distances, num_pairs diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py new file mode 100644 index 000000000..bbe0dc3cf --- /dev/null +++ b/torchmdnet/extensions/triton_cell.py @@ -0,0 +1,373 @@ +try: + import triton + import triton.language as tl + + _HAS_TRITON = True +except Exception: + triton = None + tl = None + _HAS_TRITON = False + +from torch import Tensor +import torch +from .triton_neighbors import ( + _tl_round, + TritonNeighborAutograd, + _validate_box, + _get_cell_dimensions, +) + + +@triton.jit +def _assign_cell_indices_kernel( + pos_ptr, + cell_index_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + n_atoms: tl.constexpr, + box_x: tl.constexpr, + box_y: tl.constexpr, + box_z: tl.constexpr, + cutoff: tl.constexpr, + cell_x: tl.constexpr, + cell_y: tl.constexpr, + cell_z: tl.constexpr, + BLOCK: tl.constexpr, +): + pid = tl.program_id(axis=0) + idx = pid * BLOCK + tl.arange(0, BLOCK) + mask = idx < n_atoms + + px = tl.load(pos_ptr + idx * stride_pos_0 + 0 * stride_pos_1, mask=mask, other=0.0) + py = tl.load(pos_ptr + idx * stride_pos_0 + 1 * stride_pos_1, mask=mask, other=0.0) + pz = tl.load(pos_ptr + idx * stride_pos_0 + 2 * stride_pos_1, mask=mask, other=0.0) + + # Bring to [-0.5*box, 0.5*box] (rectangular PBC) + px = px - _tl_round(px / box_x) * box_x + py = py - _tl_round(py / box_y) * box_y + pz = pz - _tl_round(pz / box_z) * box_z + + cx = tl.math.floor((px + 0.5 * box_x) / cutoff).to(tl.int32) + cy = tl.math.floor((py + 0.5 * box_y) / cutoff).to(tl.int32) + cz = tl.math.floor((pz + 0.5 * box_z) / cutoff).to(tl.int32) + + cx = tl.where(cx == cell_x, 0, cx) + cy = tl.where(cy == cell_y, 0, cy) + cz = tl.where(cz == cell_z, 0, cz) + + cell_index = cx + cell_x * (cy + cell_y * cz) + tl.store(cell_index_ptr + idx, cell_index, mask=mask) + + +@triton.jit +def _fill_cell_offsets_kernel( + sorted_cell_ptr, + cell_start_ptr, + cell_end_ptr, + n_atoms: tl.constexpr, + BLOCK: tl.constexpr, +): + pid = tl.program_id(axis=0) + idx = pid * BLOCK + tl.arange(0, BLOCK) + mask = idx < n_atoms + + icell = tl.load(sorted_cell_ptr + idx, mask=mask, other=0) + prev = tl.load(sorted_cell_ptr + idx - 1, mask=mask & (idx > 0), other=0) + + is_first = idx == 0 + is_new = is_first | (icell != prev) + + tl.store(cell_start_ptr + icell, idx, mask=mask & is_new) + tl.store(cell_end_ptr + prev, idx, mask=mask & (is_new & (idx > 0))) + tl.store(cell_end_ptr + icell, idx + 1, mask=mask & (idx == n_atoms - 1)) + + +@triton.jit +def _traverse_cell_kernel( + pos_ptr, + batch_ptr, + sorted_index_ptr, + cell_start_ptr, + cell_end_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + stride_batch: tl.constexpr, + stride_sorted: tl.constexpr, + n_atoms: tl.constexpr, + num_cells: tl.constexpr, + cell_x: tl.constexpr, + cell_y: tl.constexpr, + cell_z: tl.constexpr, + box_x: tl.constexpr, + box_y: tl.constexpr, + box_z: tl.constexpr, + cutoff: tl.constexpr, + cutoff_lower2: tl.constexpr, + cutoff_upper2: tl.constexpr, + use_periodic: tl.constexpr, + include_transpose: tl.constexpr, + loop: tl.constexpr, + max_pairs: tl.constexpr, + BLOCK_ATOMS: tl.constexpr, + BLOCK_NEI: tl.constexpr, + MAX_CELL_ITERS: tl.constexpr, +): + pid = tl.program_id(axis=0) + i_idx = pid * BLOCK_ATOMS + tl.arange(0, BLOCK_ATOMS) + mask_i = i_idx < n_atoms + + pos_ix = tl.load( + pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_i, other=0.0 + ) + pos_iy = tl.load( + pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_i, other=0.0 + ) + pos_iz = tl.load( + pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_i, other=0.0 + ) + + batch_i = tl.load(batch_ptr + i_idx * stride_batch, mask=mask_i, other=0) + + # Cell coordinate for atom i + pwx = pos_ix - _tl_round(pos_ix / box_x) * box_x + pwy = pos_iy - _tl_round(pos_iy / box_y) * box_y + pwz = pos_iz - _tl_round(pos_iz / box_z) * box_z + cx = tl.math.floor((pwx + 0.5 * box_x) / cutoff).to(tl.int32) + cy = tl.math.floor((pwy + 0.5 * box_y) / cutoff).to(tl.int32) + cz = tl.math.floor((pwz + 0.5 * box_z) / cutoff).to(tl.int32) + cx = tl.where(cx == cell_x, 0, cx) + cy = tl.where(cy == cell_y, 0, cy) + cz = tl.where(cz == cell_z, 0, cz) + + offs_nei = tl.arange(0, BLOCK_NEI) + + for neigh in range(27): + dx_cell = (neigh % 3) - 1 + dy_cell = ((neigh // 3) % 3) - 1 + dz_cell = (neigh // 9) - 1 + + nx = cx + dx_cell + ny = cy + dy_cell + nz = cz + dz_cell + + nx = tl.where(nx < 0, nx + cell_x, nx) + ny = tl.where(ny < 0, ny + cell_y, ny) + nz = tl.where(nz < 0, nz + cell_z, nz) + nx = tl.where(nx >= cell_x, nx - cell_x, nx) + ny = tl.where(ny >= cell_y, ny - cell_y, ny) + nz = tl.where(nz >= cell_z, nz - cell_z, nz) + + neighbor_cell = nx + cell_x * (ny + cell_y * nz) + start = tl.load(cell_start_ptr + neighbor_cell, mask=mask_i, other=-1) + end = tl.load(cell_end_ptr + neighbor_cell, mask=mask_i, other=0) + has_atoms = start != -1 + + for it in range(MAX_CELL_ITERS): + base = start + it * BLOCK_NEI + j_idx = base + offs_nei + valid_j = mask_i & has_atoms & (j_idx < end) + if loop: + valid_j = valid_j & ((j_idx < i_idx) | (j_idx == i_idx)) + else: + valid_j = valid_j & (j_idx < i_idx) + + batch_j = tl.load(batch_ptr + j_idx * stride_batch, mask=valid_j, other=0) + valid_j = valid_j & (batch_j == batch_i) + + pos_jx = tl.load( + pos_ptr + j_idx * stride_pos_0 + 0 * stride_pos_1, + mask=valid_j, + other=0.0, + ) + pos_jy = tl.load( + pos_ptr + j_idx * stride_pos_0 + 1 * stride_pos_1, + mask=valid_j, + other=0.0, + ) + pos_jz = tl.load( + pos_ptr + j_idx * stride_pos_0 + 2 * stride_pos_1, + mask=valid_j, + other=0.0, + ) + + dx = pos_ix - pos_jx + dy = pos_iy - pos_jy + dz = pos_iz - pos_jz + + if use_periodic: + dx = dx - _tl_round(dx / box_x) * box_x + dy = dy - _tl_round(dy / box_y) * box_y + dz = dz - _tl_round(dz / box_z) * box_z + + dist2 = dx * dx + dy * dy + dz * dz + is_self = j_idx == i_idx + valid_pair = valid_j & ( + ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | (loop & is_self) + ) + + ori = tl.load( + sorted_index_ptr + i_idx * stride_sorted, mask=valid_pair, other=0 + ) + orj = tl.load( + sorted_index_ptr + j_idx * stride_sorted, mask=valid_pair, other=0 + ) + + ni = tl.max(ori, orj) + nj = tl.min(ori, orj) + sign = tl.where(ni == ori, 1.0, -1.0).to(dx.dtype) + + dx_out = dx * sign + dy_out = dy * sign + dz_out = dz * sign + dist = tl.sqrt(dist2) + + out_idx = tl.atomic_add(counter_ptr, 1, mask=valid_pair) + mask_store = valid_pair & (out_idx < max_pairs) + tl.store(neighbors0_ptr + out_idx, ni, mask=mask_store) + tl.store(neighbors1_ptr + out_idx, nj, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 0, dx_out, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 1, dy_out, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 2, dz_out, mask=mask_store) + tl.store(distances_ptr + out_idx, dist, mask=mask_store) + + if include_transpose: + valid_t = valid_pair & (ni != nj) + out_idx_t = tl.atomic_add(counter_ptr, 1, mask=valid_t) + mask_store_t = valid_t & (out_idx_t < max_pairs) + tl.store(neighbors0_ptr + out_idx_t, nj, mask=mask_store_t) + tl.store(neighbors1_ptr + out_idx_t, ni, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_out, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_out, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_out, mask=mask_store_t) + tl.store(distances_ptr + out_idx_t, dist, mask=mask_store_t) + + +class TritonCellNeighborAutograd(TritonNeighborAutograd): + @staticmethod + def forward( # type: ignore[override] + ctx, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, + ): + if not _HAS_TRITON: + raise RuntimeError("Triton is not available") + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + # Validate inputs (mirror CUDA constraints) + if positions.dim() != 2 or positions.size(1) != 3: + raise ValueError('Expected "positions" to have shape (N, 3)') + if batch.dim() != 1 or batch.size(0) != n_atoms: + raise ValueError('Expected "batch" to have shape (N,)') + if max_num_pairs <= 0: + raise ValueError('Expected "max_num_pairs" to be positive') + + # Ensure box is well-formed and diagonal (cell list only supports rectangular boxes) + box_vectors = _validate_box(box_vectors, float(cutoff_upper), 1) + box_vectors = box_vectors.to(device=device, dtype=dtype).contiguous() + + # Cell grid dimensions (host math to match CUDA getCellDimensions) + cell_dim_x, cell_dim_y, cell_dim_z = _get_cell_dimensions( + box_vectors, float(cutoff_upper) + ) + num_cells = cell_dim_x * cell_dim_y * cell_dim_z + + batch = batch.contiguous() + positions = positions.contiguous() + + # 1. Compute cell index per atom + cell_indices = torch.empty((n_atoms,), device=device, dtype=torch.int32) + grid_assign = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK"]),) + _assign_cell_indices_kernel[grid_assign]( + positions, + cell_indices, + positions.stride(0), + positions.stride(1), + n_atoms, + float(box_vectors[0, 0, 0]), + float(box_vectors[1, 1, 1]), + float(box_vectors[2, 2, 2]), + float(cutoff_upper), + cell_dim_x, + cell_dim_y, + cell_dim_z, + BLOCK=256, + ) + + # 2. Sort atoms by cell index + sorted_cell_indices, sorted_atom_indices = torch.sort(cell_indices) + sorted_atom_indices = sorted_atom_indices.to(torch.int32) + sorted_positions = positions.index_select(0, sorted_atom_indices) + sorted_batch = batch.index_select(0, sorted_atom_indices) + + # 3. Build cell start/end offsets + cell_start = torch.full((num_cells,), -1, device=device, dtype=torch.int32) + cell_end = torch.empty((num_cells,), device=device, dtype=torch.int32) + grid_offsets = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK"]),) + _fill_cell_offsets_kernel[grid_offsets]( + sorted_cell_indices, + cell_start, + cell_end, + n_atoms, + BLOCK=256, + ) + + # 4. Traverse cell list to generate neighbor pairs + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + counter = torch.zeros((1,), device=device, dtype=torch.int32) + + grid_traverse = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK_ATOMS"]),) + _traverse_cell_kernel[grid_traverse]( + sorted_positions, + sorted_batch, + sorted_atom_indices, + cell_start, + cell_end, + neighbors[0], + neighbors[1], + deltas, + distances, + counter, + sorted_positions.stride(0), + sorted_positions.stride(1), + sorted_batch.stride(0), + sorted_atom_indices.stride(0), + n_atoms, + num_cells, + cell_dim_x, + cell_dim_y, + cell_dim_z, + float(box_vectors[0, 0, 0]), + float(box_vectors[1, 1, 1]), + float(box_vectors[2, 2, 2]), + float(cutoff_upper), + float(cutoff_lower * cutoff_lower), + float(cutoff_upper * cutoff_upper), + use_periodic, + include_transpose, + loop, + max_num_pairs, + BLOCK_ATOMS=64, + BLOCK_NEI=64, + MAX_CELL_ITERS=16, + ) + + num_pairs = counter.to(torch.long) + ctx.save_for_backward(neighbors, deltas, distances) + ctx.num_atoms = n_atoms + return neighbors, deltas, distances, num_pairs diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index f0513f246..18852e8c3 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -44,6 +44,23 @@ def _validate_box(box_vectors: Tensor, cutoff_upper: float, n_batch: int) -> Ten return box_vectors +def _get_cell_dimensions( + box_vectors: Tensor, cutoff_upper: float +) -> Tuple[int, int, int]: + """Return (nx, ny, nz) cell counts following the CUDA cell list logic.""" + # box_vectors is either (3, 3) or (1, 3, 3) and already validated as diagonal + if box_vectors.dim() == 3: + box_diag = box_vectors[0] + else: + box_diag = box_vectors + nx = int(max(math.floor(float(box_diag[0, 0]) / cutoff_upper), 3)) + ny = int(max(math.floor(float(box_diag[1, 1]) / cutoff_upper), 3)) + nz = int(max(math.floor(float(box_diag[2, 2]) / cutoff_upper), 3)) + if nx > 1024 or ny > 1024 or nz > 1024: + raise RuntimeError("Too many cells in one dimension. Maximum is 1024") + return nx, ny, nz + + def _apply_pbc_torch(deltas: Tensor, box_for_pairs: Tensor) -> Tensor: # box_for_pairs: (num_pairs, 3, 3) scale3 = _round_nearest(deltas[:, 2] / box_for_pairs[:, 2, 2]) @@ -194,259 +211,7 @@ def _tl_round(x): return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) -@triton.jit -def _neighbor_brute_kernel( - pos_ptr, - batch_ptr, - box_ptr, - neighbors0_ptr, - neighbors1_ptr, - deltas_ptr, - distances_ptr, - counter_ptr, - stride_pos_0: tl.constexpr, - stride_pos_1: tl.constexpr, - stride_batch: tl.constexpr, - stride_box_b: tl.constexpr, - stride_box_r: tl.constexpr, - stride_box_c: tl.constexpr, - n_atoms: tl.constexpr, - num_all_pairs: tl.constexpr, - use_periodic: tl.constexpr, - include_transpose: tl.constexpr, - loop: tl.constexpr, - max_pairs: tl.constexpr, - cutoff_lower: tl.constexpr, - cutoff_upper: tl.constexpr, - BLOCK: tl.constexpr, -): - """Brute-force neighbor list kernel using triangular indexing and atomic compaction. - - Uses triangular indexing to iterate over only n*(n-1)/2 pairs (or n*(n+1)/2 with loop), - achieving 100% thread utilization while maintaining block-level atomic compaction. - """ - pid = tl.program_id(axis=0) - start = pid * BLOCK - idx = start + tl.arange(0, BLOCK) - - valid = idx < num_all_pairs - - # Convert linear index to (i, j) using triangular formula (same as CUDA get_row) - # Do integer arithmetic first, only convert to float for sqrt - if loop: - # With self-loops: j <= i, num_pairs = n*(n+1)/2 - # row = floor((-1 + sqrt(1 + 8k)) / 2) - sqrt_arg = (1 + 8 * idx).to(tl.float32) - row_f = tl.math.floor((-1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) - i = row_f.to(tl.int32) - # Handle floating-point edge case: if i*(i+1)/2 > idx, decrement i - i = tl.where(i * (i + 1) > 2 * idx, i - 1, i) - # col = k - row*(row+1)/2 - j = idx - (i * (i + 1)) // 2 - else: - # Without self-loops: j < i, num_pairs = n*(n-1)/2 - # row = floor((1 + sqrt(1 + 8k)) / 2) - sqrt_arg = (1 + 8 * idx).to(tl.float32) - row_f = tl.math.floor((1.0 + tl.math.sqrt(sqrt_arg)) * 0.5) - i = row_f.to(tl.int32) - # Handle floating-point edge case (same correction as CUDA) - i = tl.where(i * (i - 1) > 2 * idx, i - 1, i) - # col = k - row*(row-1)/2 - j = idx - (i * (i - 1)) // 2 - - # Validate indices: check bounds and triangular constraint - # Due to float precision, we may get invalid (i, j) pairs - valid = valid & (i >= 0) & (i < n_atoms) & (j >= 0) & (j <= i) - if not loop: - # For non-loop case, also require j < i (no self-loops) - valid = valid & (j < i) - - batch_i = tl.load(batch_ptr + i * stride_batch, mask=valid, other=0) - batch_j = tl.load(batch_ptr + j * stride_batch, mask=valid, other=0) - valid = valid & (batch_i == batch_j) - - pos_ix = tl.load( - pos_ptr + i * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 - ) - pos_iy = tl.load( - pos_ptr + i * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 - ) - pos_iz = tl.load( - pos_ptr + i * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 - ) - pos_jx = tl.load( - pos_ptr + j * stride_pos_0 + 0 * stride_pos_1, mask=valid, other=0.0 - ) - pos_jy = tl.load( - pos_ptr + j * stride_pos_0 + 1 * stride_pos_1, mask=valid, other=0.0 - ) - pos_jz = tl.load( - pos_ptr + j * stride_pos_0 + 2 * stride_pos_1, mask=valid, other=0.0 - ) - - dx = pos_ix - pos_jx - dy = pos_iy - pos_jy - dz = pos_iz - pos_jz - - if use_periodic: - box_base = box_ptr + batch_i * stride_box_b - - b20 = tl.load( - box_base + 2 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 - ) - b21 = tl.load( - box_base + 2 * stride_box_r + 1 * stride_box_c, mask=valid, other=0.0 - ) - b22 = tl.load( - box_base + 2 * stride_box_r + 2 * stride_box_c, mask=valid, other=1.0 - ) - b10 = tl.load( - box_base + 1 * stride_box_r + 0 * stride_box_c, mask=valid, other=0.0 - ) - b11 = tl.load( - box_base + 1 * stride_box_r + 1 * stride_box_c, mask=valid, other=1.0 - ) - b00 = tl.load( - box_base + 0 * stride_box_r + 0 * stride_box_c, mask=valid, other=1.0 - ) - - scale3 = _tl_round(dz / b22) - dx = dx - scale3 * b20 - dy = dy - scale3 * b21 - dz = dz - scale3 * b22 - scale2 = _tl_round(dy / b11) - dx = dx - scale2 * b10 - dy = dy - scale2 * b11 - scale1 = _tl_round(dx / b00) - dx = dx - scale1 * b00 - - dist2 = dx * dx + dy * dy + dz * dz - dist = tl.sqrt(dist2) - # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 - is_self_loop = i == j - valid = valid & (dist < cutoff_upper) & ((dist >= cutoff_lower) | is_self_loop) - - valid_int = valid.to(tl.int32) - local_idx = tl.cumsum(valid_int, axis=0) - 1 - total_pairs = tl.sum(valid_int, axis=0) - - # For transpose, don't count self-loops (i == j) - if include_transpose: - is_not_self_loop = i != j - valid_transpose = valid & is_not_self_loop - total_transpose = tl.sum(valid_transpose.to(tl.int32), axis=0) - total_out = total_pairs + total_transpose - else: - total_out = total_pairs - - has_work = total_out > 0 - start_idx = tl.atomic_add(counter_ptr, total_out, mask=has_work) - start_idx = tl.where(has_work, start_idx, 0) - start_idx = tl.broadcast_to(start_idx, local_idx.shape) - - write_idx = start_idx + local_idx - mask_store = valid & (write_idx < max_pairs) - tl.store(neighbors0_ptr + write_idx, i, mask=mask_store) - tl.store(neighbors1_ptr + write_idx, j, mask=mask_store) - tl.store(deltas_ptr + write_idx * 3 + 0, dx, mask=mask_store) - tl.store(deltas_ptr + write_idx * 3 + 1, dy, mask=mask_store) - tl.store(deltas_ptr + write_idx * 3 + 2, dz, mask=mask_store) - tl.store(distances_ptr + write_idx, dist, mask=mask_store) - - if include_transpose: - # Don't add transpose for self-loops (i == j) - is_not_self_loop = i != j - valid_transpose = valid & is_not_self_loop - valid_t_int = valid_transpose.to(tl.int32) - local_idx_t = tl.cumsum(valid_t_int, axis=0) - 1 - total_pairs_t = tl.sum(valid_t_int, axis=0) - - write_idx_t = start_idx + total_pairs + local_idx_t - mask_store_t = valid_transpose & (write_idx_t < max_pairs) - tl.store(neighbors0_ptr + write_idx_t, j, mask=mask_store_t) - tl.store(neighbors1_ptr + write_idx_t, i, mask=mask_store_t) - tl.store(deltas_ptr + write_idx_t * 3 + 0, -dx, mask=mask_store_t) - tl.store(deltas_ptr + write_idx_t * 3 + 1, -dy, mask=mask_store_t) - tl.store(deltas_ptr + write_idx_t * 3 + 2, -dz, mask=mask_store_t) - tl.store(distances_ptr + write_idx_t, dist, mask=mask_store_t) - - class TritonNeighborAutograd(torch.autograd.Function): - @staticmethod - def forward( # type: ignore[override] - ctx, - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, - ): - device = positions.device - dtype = positions.dtype - n_atoms = positions.size(0) - - batch = batch.contiguous() - positions = positions.contiguous() - if use_periodic: - if box_vectors.dim() == 2: - box_vectors = box_vectors.unsqueeze(0) - elif box_vectors.dim() != 3: - raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') - box_vectors = box_vectors.to(device=device, dtype=dtype) - box_vectors = box_vectors.contiguous() - # Use stride 0 to broadcast single box to all batches (avoids CPU sync) - box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) - - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) - deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) - distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) - counter = torch.zeros((1,), device=device, dtype=torch.int32) - - # Compute triangular pair count: n*(n-1)/2 without self-loops, n*(n+1)/2 with - if loop: - num_all_pairs = n_atoms * (n_atoms + 1) // 2 - else: - num_all_pairs = n_atoms * (n_atoms - 1) // 2 - - # Grid covers only triangular pairs (not n²) - grid = lambda meta: (triton.cdiv(num_all_pairs, meta["BLOCK"]),) - - _neighbor_brute_kernel[grid]( - positions, - batch, - box_vectors if use_periodic else positions, # dummy pointer if not periodic - neighbors[0], - neighbors[1], - deltas, - distances, - counter, - positions.stride(0), - positions.stride(1), - batch.stride(0), - box_stride_0 if use_periodic else 0, - box_vectors.stride(1) if use_periodic else 0, - box_vectors.stride(2) if use_periodic else 0, - n_atoms, - num_all_pairs, - use_periodic, - include_transpose, - loop, - max_num_pairs, - cutoff_lower, - cutoff_upper, - BLOCK=256, - ) - - num_pairs = counter.to(torch.long) - - ctx.save_for_backward(neighbors, deltas, distances) - ctx.num_atoms = n_atoms - return neighbors, deltas, distances, num_pairs - @staticmethod def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] neighbors, edge_vec, edge_weight = ctx.saved_tensors @@ -502,6 +267,9 @@ def triton_neighbor_pairs( loop: bool, include_transpose: bool, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd + from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd + if not _HAS_TRITON: raise RuntimeError("Triton is not available") if positions.device.type != "cuda": @@ -509,17 +277,43 @@ def triton_neighbor_pairs( if positions.dtype not in (torch.float32, torch.float64): raise RuntimeError("Unsupported dtype for Triton neighbor list") - return TritonNeighborAutograd.apply( - positions, - batch, - box_vectors, - use_periodic, - float(cutoff_lower), - float(cutoff_upper), - int(max_num_pairs), - bool(loop), - bool(include_transpose), - ) + if strategy == "brute": + return TritonBruteNeighborAutograd.apply( + positions, + batch, + box_vectors, + use_periodic, + float(cutoff_lower), + float(cutoff_upper), + int(max_num_pairs), + bool(loop), + bool(include_transpose), + ) + elif strategy == "cell": + return TritonCellNeighborAutograd.apply( + positions, + batch, + box_vectors, + use_periodic, + float(cutoff_lower), + float(cutoff_upper), + int(max_num_pairs), + bool(loop), + bool(include_transpose), + ) + elif strategy == "shared": + # return TritonSharedNeighborAutograd.apply( + # positions, + # batch, + # box_vectors, + # use_periodic, + # float(cutoff_lower), + # float(cutoff_upper), + # int(max_num_pairs), + # ) + pass + else: + raise ValueError(f"Unsupported strategy {strategy}") def torch_neighbor_pairs( @@ -534,6 +328,7 @@ def torch_neighbor_pairs( loop: bool, include_transpose: bool, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + # Strategy is ignored for torch version. We only need the brute-force fallback return _torch_neighbor_bruteforce( positions, batch, From c16651d6a7773825a2ed308a83743ae422bdf3d6 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 13:40:10 +0200 Subject: [PATCH 09/80] upd --- torchmdnet/extensions/triton_cell.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index bbe0dc3cf..87536e817 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -10,7 +10,7 @@ from torch import Tensor import torch -from .triton_neighbors import ( +from torchmdnet.extensions.triton_neighbors import ( _tl_round, TritonNeighborAutograd, _validate_box, From 8a05c87deb9909b8631d7d8d124c606f752dcc25 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 13:52:39 +0200 Subject: [PATCH 10/80] fixed all tests except one --- torchmdnet/extensions/triton_cell.py | 47 +++++++++++++++++++--------- 1 file changed, 33 insertions(+), 14 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 87536e817..0c3e4d8e0 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -146,6 +146,8 @@ def _traverse_cell_kernel( offs_nei = tl.arange(0, BLOCK_NEI) + counter_broadcast = counter_ptr + tl.zeros_like(i_idx) + for neigh in range(27): dx_cell = (neigh % 3) - 1 dy_cell = ((neigh // 3) % 3) - 1 @@ -210,15 +212,17 @@ def _traverse_cell_kernel( ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | (loop & is_self) ) - ori = tl.load( - sorted_index_ptr + i_idx * stride_sorted, mask=valid_pair, other=0 + ori_i = tl.load( + sorted_index_ptr + i_idx * stride_sorted, mask=mask_i, other=0 ) orj = tl.load( - sorted_index_ptr + j_idx * stride_sorted, mask=valid_pair, other=0 + sorted_index_ptr + j_idx * stride_sorted, mask=valid_j, other=0 ) + ori = ori_i - ni = tl.max(ori, orj) - nj = tl.min(ori, orj) + # Elementwise max/min; tl.max would perform a reduction over an axis + ni = tl.maximum(ori, orj) + nj = tl.minimum(ori, orj) sign = tl.where(ni == ori, 1.0, -1.0).to(dx.dtype) dx_out = dx * sign @@ -226,7 +230,13 @@ def _traverse_cell_kernel( dz_out = dz * sign dist = tl.sqrt(dist2) - out_idx = tl.atomic_add(counter_ptr, 1, mask=valid_pair) + # Reserve contiguous slots for valid pairs in this block (BLOCK_ATOMS=1 -> 1D) + valid_pair_int = valid_pair.to(tl.int32) + total_pairs = tl.sum(valid_pair_int) + base_idx = tl.atomic_add(counter_ptr, total_pairs, mask=total_pairs > 0) + local_idx = tl.cumsum(valid_pair_int, axis=0) - 1 + out_idx = base_idx + local_idx + mask_store = valid_pair & (out_idx < max_pairs) tl.store(neighbors0_ptr + out_idx, ni, mask=mask_store) tl.store(neighbors1_ptr + out_idx, nj, mask=mask_store) @@ -237,7 +247,14 @@ def _traverse_cell_kernel( if include_transpose: valid_t = valid_pair & (ni != nj) - out_idx_t = tl.atomic_add(counter_ptr, 1, mask=valid_t) + valid_t_int = valid_t.to(tl.int32) + total_pairs_t = tl.sum(valid_t_int) + base_idx_t = tl.atomic_add( + counter_ptr, total_pairs_t, mask=total_pairs_t > 0 + ) + local_idx_t = tl.cumsum(valid_t_int, axis=0) - 1 + out_idx_t = base_idx_t + local_idx_t + mask_store_t = valid_t & (out_idx_t < max_pairs) tl.store(neighbors0_ptr + out_idx_t, nj, mask=mask_store_t) tl.store(neighbors1_ptr + out_idx_t, ni, mask=mask_store_t) @@ -278,6 +295,8 @@ def forward( # type: ignore[override] # Ensure box is well-formed and diagonal (cell list only supports rectangular boxes) box_vectors = _validate_box(box_vectors, float(cutoff_upper), 1) box_vectors = box_vectors.to(device=device, dtype=dtype).contiguous() + # CUDA version squeezes (1, 3, 3) to (3, 3); do the same to avoid OOB indexing + box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors # Cell grid dimensions (host math to match CUDA getCellDimensions) cell_dim_x, cell_dim_y, cell_dim_z = _get_cell_dimensions( @@ -297,9 +316,9 @@ def forward( # type: ignore[override] positions.stride(0), positions.stride(1), n_atoms, - float(box_vectors[0, 0, 0]), - float(box_vectors[1, 1, 1]), - float(box_vectors[2, 2, 2]), + float(box_diag[0, 0]), + float(box_diag[1, 1]), + float(box_diag[2, 2]), float(cutoff_upper), cell_dim_x, cell_dim_y, @@ -352,9 +371,9 @@ def forward( # type: ignore[override] cell_dim_x, cell_dim_y, cell_dim_z, - float(box_vectors[0, 0, 0]), - float(box_vectors[1, 1, 1]), - float(box_vectors[2, 2, 2]), + float(box_diag[0, 0]), + float(box_diag[1, 1]), + float(box_diag[2, 2]), float(cutoff_upper), float(cutoff_lower * cutoff_lower), float(cutoff_upper * cutoff_upper), @@ -362,7 +381,7 @@ def forward( # type: ignore[override] include_transpose, loop, max_num_pairs, - BLOCK_ATOMS=64, + BLOCK_ATOMS=1, BLOCK_NEI=64, MAX_CELL_ITERS=16, ) From ffa7b4a75e5bff6e1f03db19f7a821e1c6104265 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 13:56:18 +0200 Subject: [PATCH 11/80] added working cell implementation --- tests/test_neighbors.py | 27 +++++++++++++++++++-------- torchmdnet/extensions/triton_cell.py | 6 ++++-- 2 files changed, 23 insertions(+), 10 deletions(-) diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index 1099db7f6..fa3e06046 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -71,7 +71,10 @@ def compute_ref_neighbors(pos, batch, loop, include_transpose, cutoff, box_vecto return ref_neighbors, ref_distance_vecs, ref_distances -@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) +@pytest.mark.parametrize( + ("device", "strategy"), + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], +) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4, 128]) @pytest.mark.parametrize("cutoff", [0.1, 1.0, 3.0, 4.9]) @pytest.mark.parametrize("loop", [True, False]) @@ -144,7 +147,10 @@ def test_neighbors( assert np.allclose(distance_vecs, ref_distance_vecs) -@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) +@pytest.mark.parametrize( + ("device", "strategy"), + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], +) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) @@ -253,7 +259,10 @@ def test_neighbor_grads( assert np.allclose(ref_pos_grad_sorted, pos_grad_sorted, atol=1e-8, rtol=1e-5) -@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) +@pytest.mark.parametrize( + ("device", "strategy"), + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], +) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @pytest.mark.parametrize("num_atoms", [1, 2, 10]) @@ -305,7 +314,7 @@ def test_neighbor_autograds( ) -@pytest.mark.parametrize("strategy", ["brute"]) +@pytest.mark.parametrize("strategy", ["brute", "cell", "shared"]) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4]) def test_large_size(strategy, n_batches): device = "cuda" @@ -364,7 +373,7 @@ def test_large_size(strategy, n_batches): @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute")], + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], ) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @@ -435,7 +444,7 @@ def test_jit_script_compatible( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute"]) +@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -518,7 +527,7 @@ def test_cuda_graph_compatible_forward( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute"]) +@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -591,7 +600,9 @@ def test_cuda_graph_compatible_backward( torch.cuda.synchronize() -@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) +@pytest.mark.parametrize( + ("device", "strategy"), [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared")] +) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("use_forward", [True, False]) def test_per_batch_box(device, strategy, n_batches, use_forward): diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 0c3e4d8e0..feb5c93ab 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -294,8 +294,10 @@ def forward( # type: ignore[override] # Ensure box is well-formed and diagonal (cell list only supports rectangular boxes) box_vectors = _validate_box(box_vectors, float(cutoff_upper), 1) - box_vectors = box_vectors.to(device=device, dtype=dtype).contiguous() - # CUDA version squeezes (1, 3, 3) to (3, 3); do the same to avoid OOB indexing + # Keep box on host to avoid H2D during CUDA graph capture; only scalars are used in kernels + box_vectors = box_vectors.to( + dtype=dtype, device=box_vectors.device + ).contiguous() box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors # Cell grid dimensions (host math to match CUDA getCellDimensions) From 161ba94813421067b42bf58dcd881dab4cc5abfc Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 14:40:10 +0200 Subject: [PATCH 12/80] working with larger block_atoms --- torchmdnet/extensions/triton_cell.py | 82 ++++++++++++++++------------ 1 file changed, 47 insertions(+), 35 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index feb5c93ab..4bc221efe 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -146,8 +146,6 @@ def _traverse_cell_kernel( offs_nei = tl.arange(0, BLOCK_NEI) - counter_broadcast = counter_ptr + tl.zeros_like(i_idx) - for neigh in range(27): dx_cell = (neigh % 3) - 1 dy_cell = ((neigh // 3) % 3) - 1 @@ -168,18 +166,23 @@ def _traverse_cell_kernel( start = tl.load(cell_start_ptr + neighbor_cell, mask=mask_i, other=-1) end = tl.load(cell_end_ptr + neighbor_cell, mask=mask_i, other=0) has_atoms = start != -1 + start = start[:, None] + end = end[:, None] + has_atoms = has_atoms[:, None] for it in range(MAX_CELL_ITERS): base = start + it * BLOCK_NEI - j_idx = base + offs_nei - valid_j = mask_i & has_atoms & (j_idx < end) + j_idx = base + offs_nei[None, :] + valid_j = mask_i[:, None] & has_atoms & (j_idx < end) if loop: - valid_j = valid_j & ((j_idx < i_idx) | (j_idx == i_idx)) + valid_j = valid_j & ( + (j_idx < i_idx[:, None]) | (j_idx == i_idx[:, None]) + ) else: - valid_j = valid_j & (j_idx < i_idx) + valid_j = valid_j & (j_idx < i_idx[:, None]) batch_j = tl.load(batch_ptr + j_idx * stride_batch, mask=valid_j, other=0) - valid_j = valid_j & (batch_j == batch_i) + valid_j = valid_j & (batch_j == batch_i[:, None]) pos_jx = tl.load( pos_ptr + j_idx * stride_pos_0 + 0 * stride_pos_1, @@ -197,9 +200,9 @@ def _traverse_cell_kernel( other=0.0, ) - dx = pos_ix - pos_jx - dy = pos_iy - pos_jy - dz = pos_iz - pos_jz + dx = pos_ix[:, None] - pos_jx + dy = pos_iy[:, None] - pos_jy + dz = pos_iz[:, None] - pos_jz if use_periodic: dx = dx - _tl_round(dx / box_x) * box_x @@ -207,7 +210,7 @@ def _traverse_cell_kernel( dz = dz - _tl_round(dz / box_z) * box_z dist2 = dx * dx + dy * dy + dz * dz - is_self = j_idx == i_idx + is_self = j_idx == i_idx[:, None] valid_pair = valid_j & ( ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | (loop & is_self) ) @@ -218,7 +221,7 @@ def _traverse_cell_kernel( orj = tl.load( sorted_index_ptr + j_idx * stride_sorted, mask=valid_j, other=0 ) - ori = ori_i + ori = ori_i[:, None] + tl.zeros((BLOCK_NEI,), dtype=ori_i.dtype) # Elementwise max/min; tl.max would perform a reduction over an axis ni = tl.maximum(ori, orj) @@ -230,38 +233,47 @@ def _traverse_cell_kernel( dz_out = dz * sign dist = tl.sqrt(dist2) - # Reserve contiguous slots for valid pairs in this block (BLOCK_ATOMS=1 -> 1D) - valid_pair_int = valid_pair.to(tl.int32) + # Flatten per-atom neighbor results to 1D for contiguous writes + valid_pair_flat = tl.reshape(valid_pair, (BLOCK_ATOMS * BLOCK_NEI,)) + valid_pair_int = valid_pair_flat.to(tl.int32) total_pairs = tl.sum(valid_pair_int) + prefix = tl.cumsum(valid_pair_int, axis=0) - 1 base_idx = tl.atomic_add(counter_ptr, total_pairs, mask=total_pairs > 0) - local_idx = tl.cumsum(valid_pair_int, axis=0) - 1 - out_idx = base_idx + local_idx - - mask_store = valid_pair & (out_idx < max_pairs) - tl.store(neighbors0_ptr + out_idx, ni, mask=mask_store) - tl.store(neighbors1_ptr + out_idx, nj, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 0, dx_out, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 1, dy_out, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 2, dz_out, mask=mask_store) - tl.store(distances_ptr + out_idx, dist, mask=mask_store) + out_idx = base_idx + prefix + + ni_flat = tl.reshape(ni, (BLOCK_ATOMS * BLOCK_NEI,)) + nj_flat = tl.reshape(nj, (BLOCK_ATOMS * BLOCK_NEI,)) + dx_flat = tl.reshape(dx_out, (BLOCK_ATOMS * BLOCK_NEI,)) + dy_flat = tl.reshape(dy_out, (BLOCK_ATOMS * BLOCK_NEI,)) + dz_flat = tl.reshape(dz_out, (BLOCK_ATOMS * BLOCK_NEI,)) + dist_flat = tl.reshape(dist, (BLOCK_ATOMS * BLOCK_NEI,)) + + mask_store = valid_pair_flat & (out_idx < max_pairs) + tl.store(neighbors0_ptr + out_idx, ni_flat, mask=mask_store) + tl.store(neighbors1_ptr + out_idx, nj_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 0, dx_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 1, dy_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 2, dz_flat, mask=mask_store) + tl.store(distances_ptr + out_idx, dist_flat, mask=mask_store) if include_transpose: valid_t = valid_pair & (ni != nj) - valid_t_int = valid_t.to(tl.int32) + valid_t_flat = tl.reshape(valid_t, (BLOCK_ATOMS * BLOCK_NEI,)) + valid_t_int = valid_t_flat.to(tl.int32) total_pairs_t = tl.sum(valid_t_int) + prefix_t = tl.cumsum(valid_t_int, axis=0) - 1 base_idx_t = tl.atomic_add( counter_ptr, total_pairs_t, mask=total_pairs_t > 0 ) - local_idx_t = tl.cumsum(valid_t_int, axis=0) - 1 - out_idx_t = base_idx_t + local_idx_t + out_idx_t = base_idx_t + prefix_t - mask_store_t = valid_t & (out_idx_t < max_pairs) - tl.store(neighbors0_ptr + out_idx_t, nj, mask=mask_store_t) - tl.store(neighbors1_ptr + out_idx_t, ni, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_out, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_out, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_out, mask=mask_store_t) - tl.store(distances_ptr + out_idx_t, dist, mask=mask_store_t) + mask_store_t = valid_t_flat & (out_idx_t < max_pairs) + tl.store(neighbors0_ptr + out_idx_t, nj_flat, mask=mask_store_t) + tl.store(neighbors1_ptr + out_idx_t, ni_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_flat, mask=mask_store_t) + tl.store(distances_ptr + out_idx_t, dist_flat, mask=mask_store_t) class TritonCellNeighborAutograd(TritonNeighborAutograd): @@ -383,7 +395,7 @@ def forward( # type: ignore[override] include_transpose, loop, max_num_pairs, - BLOCK_ATOMS=1, + BLOCK_ATOMS=32, BLOCK_NEI=64, MAX_CELL_ITERS=16, ) From 044edff7395d6d2caf2491312fdc7801acf8cbd1 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 15:01:31 +0200 Subject: [PATCH 13/80] more efficient cell --- torchmdnet/extensions/triton_cell.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 4bc221efe..93c5f77bd 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -169,11 +169,14 @@ def _traverse_cell_kernel( start = start[:, None] end = end[:, None] has_atoms = has_atoms[:, None] + cell_len = tl.maximum(end - start, 0) + max_iters = tl.cdiv(cell_len + BLOCK_NEI - 1, BLOCK_NEI) for it in range(MAX_CELL_ITERS): + active_iter = it < max_iters base = start + it * BLOCK_NEI j_idx = base + offs_nei[None, :] - valid_j = mask_i[:, None] & has_atoms & (j_idx < end) + valid_j = mask_i[:, None] & has_atoms & active_iter & (j_idx < end) if loop: valid_j = valid_j & ( (j_idx < i_idx[:, None]) | (j_idx == i_idx[:, None]) @@ -396,8 +399,8 @@ def forward( # type: ignore[override] loop, max_num_pairs, BLOCK_ATOMS=32, - BLOCK_NEI=64, - MAX_CELL_ITERS=16, + BLOCK_NEI=32, + MAX_CELL_ITERS=64, ) num_pairs = counter.to(torch.long) From 621fee1366cd432f42d67d5d151d2816d43d975e Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 15:23:53 +0200 Subject: [PATCH 14/80] update the benchmark suite --- benchmarks/neighbors.py | 73 ++++++++++++++--------------------------- 1 file changed, 24 insertions(+), 49 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index 2db969d92..596f81b51 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -178,17 +178,16 @@ def benchmark_neighbors( if __name__ == "__main__": + strategies = ["distance", "brute"] # , "cell", "shared"] n_particles = 32767 mean_num_neighbors = min(n_particles, 64) density = 0.8 print( - "Benchmarking neighbor list generation for {} particles with {} neighbors on average".format( - n_particles, mean_num_neighbors - ) + f"Benchmarking neighbor list generation for {n_particles} particles with {mean_num_neighbors} neighbors on average" ) results = {} batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - for strategy in ["shared", "brute", "cell", "distance"]: + for strategy in strategies: for n_batches in batch_sizes: time = benchmark_neighbors( device="cuda", @@ -200,33 +199,22 @@ def benchmark_neighbors( ) # Store results in a dictionary results[strategy, n_batches] = time + print("Summary") print("-------") - print( - "{:<10} {:<21} {:<21} {:<18} {:<10}".format( - "Batch size", "Shared(ms)", "Brute(ms)", "Cell(ms)", "Distance(ms)" - ) - ) - print( - "{:<10} {:<21} {:<21} {:<18} {:<10}".format( - "----------", "---------", "---------", "---------", "---------" - ) - ) + headers = "Batch size " + for strategy in strategies: + headers += f"{strategy.capitalize()+'(ms)':<21}" + print(headers) + print("-" * len(headers)) # Print a column per strategy, show speedup over Distance in parenthesis for n_batches in batch_sizes: base = results["distance", n_batches] - print( - "{:<10} {:<4.2f} x{:<14.2f} {:<4.2f} x{:<14.2f} {:<4.2f} x{:<14.2f} {:<10.2f}".format( - n_batches, - results["shared", n_batches], - base / results["shared", n_batches], - results["brute", n_batches], - base / results["brute", n_batches], - results["cell", n_batches], - base / results["cell", n_batches], - results["distance", n_batches], - ) - ) + row = f"{n_batches:<10}" + for strategy in strategies: + row += f"{results[strategy, n_batches]:<4.2f} x{base / results[strategy, n_batches]:<14.2f} " + print(row) + n_particles_list = np.power(2, np.arange(8, 18)) for n_batches in [1, 2, 32, 64]: @@ -236,7 +224,7 @@ def benchmark_neighbors( ) ) results = {} - for strategy in ["shared", "brute", "cell", "distance"]: + for strategy in strategies: for n_particles in n_particles_list: mean_num_neighbors = min(n_particles, 64) time = benchmark_neighbors( @@ -251,16 +239,11 @@ def benchmark_neighbors( results[strategy, n_particles] = time print("Summary") print("-------") - print( - "{:<10} {:<21} {:<21} {:<18} {:<10}".format( - "N Particles", "Shared(ms)", "Brute(ms)", "Cell(ms)", "Distance(ms)" - ) - ) - print( - "{:<10} {:<21} {:<21} {:<18} {:<10}".format( - "----------", "---------", "---------", "---------", "---------" - ) - ) + headers = "N Particles " + for strategy in strategies: + headers += f"{strategy.capitalize()}(ms) " + print(headers) + print("-" * len(headers)) # Print a column per strategy, show speedup over Distance in parenthesis for n_particles in n_particles_list: base = results["distance", n_particles] @@ -268,15 +251,7 @@ def benchmark_neighbors( if n_particles > 32000: results["brute", n_particles] = 0 brute_speedup = 0 - print( - "{:<10} {:<4.2f} x{:<14.2f} {:<4.2f} x{:<14.2f} {:<4.2f} x{:<14.2f} {:<10.2f}".format( - n_particles, - results["shared", n_particles], - base / results["shared", n_particles], - results["brute", n_particles], - brute_speedup, - results["cell", n_particles], - base / results["cell", n_particles], - results["distance", n_particles], - ) - ) + row = f"{n_particles:<10}" + for strategy in strategies: + row += f"{results[strategy, n_particles]:<4.2f} x{base / results[strategy, n_particles]:<14.2f} " + print(row) From 98a45a9eef002a5f11fb0b23cc6c2b1e0bf074c2 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 15:50:51 +0200 Subject: [PATCH 15/80] shared triton implementation --- torchmdnet/extensions/triton_neighbors.py | 22 +- torchmdnet/extensions/triton_shared.py | 256 ++++++++++++++++++++++ 2 files changed, 268 insertions(+), 10 deletions(-) create mode 100644 torchmdnet/extensions/triton_shared.py diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index 18852e8c3..b94283c06 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -269,6 +269,7 @@ def triton_neighbor_pairs( ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd + from torchmdnet.extensions.triton_shared import TritonSharedNeighborAutograd if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -302,16 +303,17 @@ def triton_neighbor_pairs( bool(include_transpose), ) elif strategy == "shared": - # return TritonSharedNeighborAutograd.apply( - # positions, - # batch, - # box_vectors, - # use_periodic, - # float(cutoff_lower), - # float(cutoff_upper), - # int(max_num_pairs), - # ) - pass + return TritonSharedNeighborAutograd.apply( + positions, + batch, + box_vectors, + use_periodic, + float(cutoff_lower), + float(cutoff_upper), + int(max_num_pairs), + bool(loop), + bool(include_transpose), + ) else: raise ValueError(f"Unsupported strategy {strategy}") diff --git a/torchmdnet/extensions/triton_shared.py b/torchmdnet/extensions/triton_shared.py new file mode 100644 index 000000000..6d7506407 --- /dev/null +++ b/torchmdnet/extensions/triton_shared.py @@ -0,0 +1,256 @@ +try: + import triton + import triton.language as tl + + _HAS_TRITON = True +except Exception: + triton = None + tl = None + _HAS_TRITON = False + +from torch import Tensor +import torch +from torchmdnet.extensions.triton_neighbors import _tl_round, TritonNeighborAutograd + + +@triton.jit +def _neighbor_shared_kernel( + pos_ptr, + batch_ptr, + box_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + stride_batch: tl.constexpr, + stride_box_b: tl.constexpr, + stride_box_r: tl.constexpr, + stride_box_c: tl.constexpr, + n_atoms: tl.constexpr, + use_periodic: tl.constexpr, + include_transpose: tl.constexpr, + loop: tl.constexpr, + max_pairs: tl.constexpr, + cutoff_lower2: tl.constexpr, + cutoff_upper2: tl.constexpr, + BLOCK: tl.constexpr, +): + """Shared-memory style neighbor construction: tile j, one block of i atoms.""" + pid_i = tl.program_id(axis=0) + pid_tile = tl.program_id(axis=1) + + i_idx = pid_i * BLOCK + tl.arange(0, BLOCK) + j_idx = pid_tile * BLOCK + tl.arange(0, BLOCK) + + mask_i = i_idx < n_atoms + mask_j = j_idx < n_atoms + + batch_i = tl.load(batch_ptr + i_idx * stride_batch, mask=mask_i, other=0) + batch_j = tl.load(batch_ptr + j_idx * stride_batch, mask=mask_j, other=0) + + pos_ix = tl.load( + pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_i, other=0.0 + ) + pos_iy = tl.load( + pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_i, other=0.0 + ) + pos_iz = tl.load( + pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_i, other=0.0 + ) + + pos_jx = tl.load( + pos_ptr + j_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_j, other=0.0 + ) + pos_jy = tl.load( + pos_ptr + j_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_j, other=0.0 + ) + pos_jz = tl.load( + pos_ptr + j_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_j, other=0.0 + ) + + # Pairwise masks (BLOCK, BLOCK) + mask_pair = mask_i[:, None] & mask_j[None, :] + if loop: + mask_pair = mask_pair & ( + (j_idx[None, :] < i_idx[:, None]) | (j_idx[None, :] == i_idx[:, None]) + ) + else: + mask_pair = mask_pair & (j_idx[None, :] < i_idx[:, None]) + mask_pair = mask_pair & (batch_i[:, None] == batch_j[None, :]) + + dx = pos_ix[:, None] - pos_jx[None, :] + dy = pos_iy[:, None] - pos_jy[None, :] + dz = pos_iz[:, None] - pos_jz[None, :] + + if use_periodic: + box_base = box_ptr + batch_i * stride_box_b + b20 = tl.load( + box_base + 2 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=0.0 + ) + b21 = tl.load( + box_base + 2 * stride_box_r + 1 * stride_box_c, mask=mask_i, other=0.0 + ) + b22 = tl.load( + box_base + 2 * stride_box_r + 2 * stride_box_c, mask=mask_i, other=1.0 + ) + b10 = tl.load( + box_base + 1 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=0.0 + ) + b11 = tl.load( + box_base + 1 * stride_box_r + 1 * stride_box_c, mask=mask_i, other=1.0 + ) + b00 = tl.load( + box_base + 0 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=1.0 + ) + + scale3 = _tl_round(dz / b22[:, None]) + dx = dx - scale3 * b20[:, None] + dy = dy - scale3 * b21[:, None] + dz = dz - scale3 * b22[:, None] + + scale2 = _tl_round(dy / b11[:, None]) + dx = dx - scale2 * b10[:, None] + dy = dy - scale2 * b11[:, None] + + scale1 = _tl_round(dx / b00[:, None]) + dx = dx - scale1 * b00[:, None] + + dist2 = dx * dx + dy * dy + dz * dz + valid_pair = mask_pair & (dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2) + + i_mat = i_idx[:, None] + tl.zeros((1, BLOCK), dtype=i_idx.dtype) + j_mat = tl.zeros((BLOCK, 1), dtype=j_idx.dtype) + j_idx[None, :] + + dx_flat = tl.reshape(dx, (BLOCK * BLOCK,)) + dy_flat = tl.reshape(dy, (BLOCK * BLOCK,)) + dz_flat = tl.reshape(dz, (BLOCK * BLOCK,)) + dist_flat = tl.reshape(tl.sqrt(dist2), (BLOCK * BLOCK,)) + + i_flat = tl.reshape(i_mat, (BLOCK * BLOCK,)) + j_flat = tl.reshape(j_mat, (BLOCK * BLOCK,)) + + valid_flat = tl.reshape(valid_pair, (BLOCK * BLOCK,)) + valid_int = valid_flat.to(tl.int32) + total_pairs = tl.sum(valid_int) + + if include_transpose: + valid_t_flat = valid_flat & (i_flat != j_flat) + valid_t_int = valid_t_flat.to(tl.int32) + total_transpose = tl.sum(valid_t_int) + total_out = total_pairs + total_transpose + else: + total_out = total_pairs + + has_out = total_out > 0 + start_idx = tl.atomic_add(counter_ptr, total_out, mask=has_out) + start_idx = tl.where(has_out, start_idx, 0) + start_idx = tl.broadcast_to(start_idx, valid_flat.shape) + + prefix = tl.cumsum(valid_int, axis=0) - 1 + out_idx = start_idx + prefix + + mask_store = valid_flat & (out_idx < max_pairs) + tl.store(neighbors0_ptr + out_idx, i_flat, mask=mask_store) + tl.store(neighbors1_ptr + out_idx, j_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 0, dx_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 1, dy_flat, mask=mask_store) + tl.store(deltas_ptr + out_idx * 3 + 2, dz_flat, mask=mask_store) + tl.store(distances_ptr + out_idx, dist_flat, mask=mask_store) + + if include_transpose: + prefix_t = tl.cumsum(valid_t_int, axis=0) - 1 + out_idx_t = start_idx + total_pairs + prefix_t + + mask_store_t = valid_t_flat & (out_idx_t < max_pairs) + tl.store(neighbors0_ptr + out_idx_t, j_flat, mask=mask_store_t) + tl.store(neighbors1_ptr + out_idx_t, i_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_flat, mask=mask_store_t) + tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_flat, mask=mask_store_t) + tl.store(distances_ptr + out_idx_t, dist_flat, mask=mask_store_t) + + +class TritonSharedNeighborAutograd(TritonNeighborAutograd): + @staticmethod + def forward( # type: ignore[override] + ctx, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, + ): + if not _HAS_TRITON: + raise RuntimeError("Triton is not available") + + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + if positions.dim() != 2 or positions.size(1) != 3: + raise ValueError('Expected "positions" to have shape (N, 3)') + if batch.dim() != 1 or batch.size(0) != n_atoms: + raise ValueError('Expected "batch" to have shape (N,)') + if max_num_pairs <= 0: + raise ValueError('Expected "max_num_pairs" to be positive') + + batch = batch.contiguous() + positions = positions.contiguous() + + if use_periodic: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0) + elif box_vectors.dim() != 3: + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + box_vectors = box_vectors.to(device=device, dtype=dtype).contiguous() + box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) + else: + # Dummy tensor to avoid conditional code paths in the kernel signature. + box_vectors = positions + box_stride_0 = 0 + + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + counter = torch.zeros((1,), device=device, dtype=torch.int32) + + BLOCK = 64 + num_blocks = max((n_atoms + BLOCK - 1) // BLOCK, 1) + grid = (num_blocks, num_blocks) + + _neighbor_shared_kernel[grid]( + positions, + batch, + box_vectors, + neighbors[0], + neighbors[1], + deltas, + distances, + counter, + positions.stride(0), + positions.stride(1), + batch.stride(0), + box_stride_0, + box_vectors.stride(1) if use_periodic else 0, + box_vectors.stride(2) if use_periodic else 0, + n_atoms, + use_periodic, + include_transpose, + loop, + max_num_pairs, + float(cutoff_lower * cutoff_lower), + float(cutoff_upper * cutoff_upper), + BLOCK=BLOCK, + ) + + num_pairs = counter.to(torch.long) + ctx.save_for_backward(neighbors, deltas, distances) + ctx.num_atoms = n_atoms + return neighbors, deltas, distances, num_pairs From c1a59570b7c7e9be13135e35e79a155b22aa0e47 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Fri, 12 Dec 2025 17:00:14 +0200 Subject: [PATCH 16/80] update issue in benchmark --- benchmarks/neighbors.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index 596f81b51..a9d8d8f8a 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -120,6 +120,8 @@ def benchmark_neighbors( n_atoms_per_batch[i] -= 1 difference += 1 lbox = np.cbrt(num_particles / density) + lbox = max(lbox, 2 * cutoff + 0.01) + assert lbox > 2 * cutoff, "Box size must be greater than 2*cutoff" batch = torch.repeat_interleave( torch.arange(n_batches, dtype=torch.int64), n_atoms_per_batch ).to(device) @@ -178,7 +180,7 @@ def benchmark_neighbors( if __name__ == "__main__": - strategies = ["distance", "brute"] # , "cell", "shared"] + strategies = ["distance", "brute", "cell", "shared"] n_particles = 32767 mean_num_neighbors = min(n_particles, 64) density = 0.8 From eca967d824c7ab0d7d3f87a2b18066f55f56ba67 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 11:31:14 +0200 Subject: [PATCH 17/80] cleanup --- torchmdnet/extensions/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchmdnet/extensions/__init__.py b/torchmdnet/extensions/__init__.py index f474bff89..c618f93cb 100644 --- a/torchmdnet/extensions/__init__.py +++ b/torchmdnet/extensions/__init__.py @@ -9,4 +9,4 @@ import torch from pathlib import Path -from . import torchmdnet_extensions, ops +from . import ops From eea283194d7c7d4e6cf799744eba6699894be217 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 11:31:30 +0200 Subject: [PATCH 18/80] cleanup --- torchmdnet/extensions/ops.py | 38 ------------------------------------ 1 file changed, 38 deletions(-) diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index 9dd44c00e..5a77b8f7e 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -118,41 +118,3 @@ def get_neighbor_pairs_kernel( loop, include_transpose, ) - - -# Registers a FakeTensor kernel (aka "meta kernel", "abstract impl") -# that describes what the properties of the output Tensor are given -# the properties of the input Tensor. The FakeTensor kernel is necessary -# for the op to work performantly with torch.compile. -@torch.library.register_fake("torchmdnet_extensions::get_neighbor_pairs_bkwd") -def _( - grad_edge_vec: Tensor, - grad_edge_weight: Tensor, - edge_index: Tensor, - edge_vec: Tensor, - edge_weight: Tensor, - num_atoms: int, -): - return torch.zeros((num_atoms, 3), dtype=edge_vec.dtype, device=edge_vec.device) - - -@torch.library.register_fake("torchmdnet_extensions::get_neighbor_pairs_fwd") -def _( - strategy: str, - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, -) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - """Returns empty vectors with the correct shape for the output of get_neighbor_pairs_kernel.""" - size = max_num_pairs - edge_index = torch.empty((2, size), dtype=torch.long, device=positions.device) - edge_distance = torch.empty((size,), dtype=positions.dtype, device=positions.device) - edge_vec = torch.empty((size, 3), dtype=positions.dtype, device=positions.device) - num_pairs = torch.empty((1,), dtype=torch.long, device=positions.device) - return edge_index, edge_vec, edge_distance, num_pairs From dd021d7be05a98b4fea944df34c2b5807de3e8d6 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 11:31:44 +0200 Subject: [PATCH 19/80] fix benchmark --- benchmarks/neighbors.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index a9d8d8f8a..e494f7d7f 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -250,9 +250,9 @@ def benchmark_neighbors( for n_particles in n_particles_list: base = results["distance", n_particles] brute_speedup = base / results["brute", n_particles] - if n_particles > 32000: - results["brute", n_particles] = 0 - brute_speedup = 0 + # if n_particles > 32000: + # results["brute", n_particles] = 0 + # brute_speedup = 0 row = f"{n_particles:<10}" for strategy in strategies: row += f"{results[strategy, n_particles]:<4.2f} x{base / results[strategy, n_particles]:<14.2f} " From 40e1fc211b2b68f1ccc7c783e400e0ba3bbac397 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 11:32:04 +0200 Subject: [PATCH 20/80] cell implementation closer to CUDA --- torchmdnet/extensions/triton_cell.py | 219 ++++++++++++--------------- 1 file changed, 94 insertions(+), 125 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 93c5f77bd..9c19b19e1 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -113,27 +113,25 @@ def _traverse_cell_kernel( include_transpose: tl.constexpr, loop: tl.constexpr, max_pairs: tl.constexpr, - BLOCK_ATOMS: tl.constexpr, - BLOCK_NEI: tl.constexpr, MAX_CELL_ITERS: tl.constexpr, ): - pid = tl.program_id(axis=0) - i_idx = pid * BLOCK_ATOMS + tl.arange(0, BLOCK_ATOMS) - mask_i = i_idx < n_atoms - - pos_ix = tl.load( - pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_i, other=0.0 - ) - pos_iy = tl.load( - pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_i, other=0.0 - ) - pos_iz = tl.load( - pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_i, other=0.0 - ) - - batch_i = tl.load(batch_ptr + i_idx * stride_batch, mask=mask_i, other=0) - - # Cell coordinate for atom i + """ + CUDA-like kernel: one program per atom, scalar operations, sequential neighbor iteration. + This mimics the CUDA implementation's parallelization strategy. + """ + # One atom per program (like CUDA's one thread per atom) + i_idx = tl.program_id(axis=0) + if i_idx >= n_atoms: + return + + # Load atom i data (scalar values) + pos_ix = tl.load(pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1) + pos_iy = tl.load(pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1) + pos_iz = tl.load(pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1) + batch_i = tl.load(batch_ptr + i_idx * stride_batch) + ori = tl.load(sorted_index_ptr + i_idx * stride_sorted) + + # Compute cell coordinate for atom i pwx = pos_ix - _tl_round(pos_ix / box_x) * box_x pwy = pos_iy - _tl_round(pos_iy / box_y) * box_y pwz = pos_iz - _tl_round(pos_iz / box_z) * box_z @@ -144,8 +142,7 @@ def _traverse_cell_kernel( cy = tl.where(cy == cell_y, 0, cy) cz = tl.where(cz == cell_z, 0, cz) - offs_nei = tl.arange(0, BLOCK_NEI) - + # Loop over the 27 neighboring cells (like CUDA) for neigh in range(27): dx_cell = (neigh % 3) - 1 dy_cell = ((neigh // 3) % 3) - 1 @@ -155,6 +152,7 @@ def _traverse_cell_kernel( ny = cy + dy_cell nz = cz + dz_cell + # Periodic wrapping of cell coordinates nx = tl.where(nx < 0, nx + cell_x, nx) ny = tl.where(ny < 0, ny + cell_y, ny) nz = tl.where(nz < 0, nz + cell_z, nz) @@ -163,120 +161,92 @@ def _traverse_cell_kernel( nz = tl.where(nz >= cell_z, nz - cell_z, nz) neighbor_cell = nx + cell_x * (ny + cell_y * nz) - start = tl.load(cell_start_ptr + neighbor_cell, mask=mask_i, other=-1) - end = tl.load(cell_end_ptr + neighbor_cell, mask=mask_i, other=0) - has_atoms = start != -1 - start = start[:, None] - end = end[:, None] - has_atoms = has_atoms[:, None] - cell_len = tl.maximum(end - start, 0) - max_iters = tl.cdiv(cell_len + BLOCK_NEI - 1, BLOCK_NEI) - - for it in range(MAX_CELL_ITERS): - active_iter = it < max_iters - base = start + it * BLOCK_NEI - j_idx = base + offs_nei[None, :] - valid_j = mask_i[:, None] & has_atoms & active_iter & (j_idx < end) + start = tl.load(cell_start_ptr + neighbor_cell) + end = tl.load(cell_end_ptr + neighbor_cell) + + # Skip empty cells (like CUDA's if (first_particle != -1)) + if start == -1: + continue + + # Iterate over atoms in this cell (like CUDA's for loop) + for j_offset in range(MAX_CELL_ITERS): + j = start + j_offset + + # Early exit when we've processed all atoms in this cell + if j >= end: + break + + # Only consider j < i (or j <= i for loop mode) if loop: - valid_j = valid_j & ( - (j_idx < i_idx[:, None]) | (j_idx == i_idx[:, None]) - ) + if j > i_idx: + continue else: - valid_j = valid_j & (j_idx < i_idx[:, None]) + if j >= i_idx: + continue - batch_j = tl.load(batch_ptr + j_idx * stride_batch, mask=valid_j, other=0) - valid_j = valid_j & (batch_j == batch_i[:, None]) + # Check same batch + batch_j = tl.load(batch_ptr + j * stride_batch) + if batch_j != batch_i: + continue - pos_jx = tl.load( - pos_ptr + j_idx * stride_pos_0 + 0 * stride_pos_1, - mask=valid_j, - other=0.0, - ) - pos_jy = tl.load( - pos_ptr + j_idx * stride_pos_0 + 1 * stride_pos_1, - mask=valid_j, - other=0.0, - ) - pos_jz = tl.load( - pos_ptr + j_idx * stride_pos_0 + 2 * stride_pos_1, - mask=valid_j, - other=0.0, - ) + # Load position of atom j + pos_jx = tl.load(pos_ptr + j * stride_pos_0 + 0 * stride_pos_1) + pos_jy = tl.load(pos_ptr + j * stride_pos_0 + 1 * stride_pos_1) + pos_jz = tl.load(pos_ptr + j * stride_pos_0 + 2 * stride_pos_1) - dx = pos_ix[:, None] - pos_jx - dy = pos_iy[:, None] - pos_jy - dz = pos_iz[:, None] - pos_jz + # Compute distance + dx = pos_ix - pos_jx + dy = pos_iy - pos_jy + dz = pos_iz - pos_jz + # Apply periodic boundary conditions if use_periodic: dx = dx - _tl_round(dx / box_x) * box_x dy = dy - _tl_round(dy / box_y) * box_y dz = dz - _tl_round(dz / box_z) * box_z dist2 = dx * dx + dy * dy + dz * dz - is_self = j_idx == i_idx[:, None] - valid_pair = valid_j & ( - ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | (loop & is_self) - ) + is_self = j == i_idx - ori_i = tl.load( - sorted_index_ptr + i_idx * stride_sorted, mask=mask_i, other=0 - ) - orj = tl.load( - sorted_index_ptr + j_idx * stride_sorted, mask=valid_j, other=0 + # Check distance criteria + valid = ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | ( + loop & is_self ) - ori = ori_i[:, None] + tl.zeros((BLOCK_NEI,), dtype=ori_i.dtype) - - # Elementwise max/min; tl.max would perform a reduction over an axis - ni = tl.maximum(ori, orj) - nj = tl.minimum(ori, orj) - sign = tl.where(ni == ori, 1.0, -1.0).to(dx.dtype) - - dx_out = dx * sign - dy_out = dy * sign - dz_out = dz * sign - dist = tl.sqrt(dist2) - - # Flatten per-atom neighbor results to 1D for contiguous writes - valid_pair_flat = tl.reshape(valid_pair, (BLOCK_ATOMS * BLOCK_NEI,)) - valid_pair_int = valid_pair_flat.to(tl.int32) - total_pairs = tl.sum(valid_pair_int) - prefix = tl.cumsum(valid_pair_int, axis=0) - 1 - base_idx = tl.atomic_add(counter_ptr, total_pairs, mask=total_pairs > 0) - out_idx = base_idx + prefix - - ni_flat = tl.reshape(ni, (BLOCK_ATOMS * BLOCK_NEI,)) - nj_flat = tl.reshape(nj, (BLOCK_ATOMS * BLOCK_NEI,)) - dx_flat = tl.reshape(dx_out, (BLOCK_ATOMS * BLOCK_NEI,)) - dy_flat = tl.reshape(dy_out, (BLOCK_ATOMS * BLOCK_NEI,)) - dz_flat = tl.reshape(dz_out, (BLOCK_ATOMS * BLOCK_NEI,)) - dist_flat = tl.reshape(dist, (BLOCK_ATOMS * BLOCK_NEI,)) - - mask_store = valid_pair_flat & (out_idx < max_pairs) - tl.store(neighbors0_ptr + out_idx, ni_flat, mask=mask_store) - tl.store(neighbors1_ptr + out_idx, nj_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 0, dx_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 1, dy_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 2, dz_flat, mask=mask_store) - tl.store(distances_ptr + out_idx, dist_flat, mask=mask_store) - - if include_transpose: - valid_t = valid_pair & (ni != nj) - valid_t_flat = tl.reshape(valid_t, (BLOCK_ATOMS * BLOCK_NEI,)) - valid_t_int = valid_t_flat.to(tl.int32) - total_pairs_t = tl.sum(valid_t_int) - prefix_t = tl.cumsum(valid_t_int, axis=0) - 1 - base_idx_t = tl.atomic_add( - counter_ptr, total_pairs_t, mask=total_pairs_t > 0 - ) - out_idx_t = base_idx_t + prefix_t - - mask_store_t = valid_t_flat & (out_idx_t < max_pairs) - tl.store(neighbors0_ptr + out_idx_t, nj_flat, mask=mask_store_t) - tl.store(neighbors1_ptr + out_idx_t, ni_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_flat, mask=mask_store_t) - tl.store(distances_ptr + out_idx_t, dist_flat, mask=mask_store_t) + + if valid: + orj = tl.load(sorted_index_ptr + j * stride_sorted) + + # Canonical ordering: ni > nj + ni = tl.maximum(ori, orj) + nj = tl.minimum(ori, orj) + sign = tl.where(ni == ori, 1.0, -1.0).to(dx.dtype) + + dx_out = dx * sign + dy_out = dy * sign + dz_out = dz * sign + dist = tl.sqrt(dist2) + + # Atomic add to reserve output slot + out_idx = tl.atomic_add(counter_ptr, 1) + if out_idx < max_pairs: + tl.store(neighbors0_ptr + out_idx, ni) + tl.store(neighbors1_ptr + out_idx, nj) + tl.store(deltas_ptr + out_idx * 3 + 0, dx_out) + tl.store(deltas_ptr + out_idx * 3 + 1, dy_out) + tl.store(deltas_ptr + out_idx * 3 + 2, dz_out) + tl.store(distances_ptr + out_idx, dist) + + # Add transpose pair if needed + if include_transpose: + if ni != nj: + out_idx_t = tl.atomic_add(counter_ptr, 1) + if out_idx_t < max_pairs: + tl.store(neighbors0_ptr + out_idx_t, nj) + tl.store(neighbors1_ptr + out_idx_t, ni) + tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_out) + tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_out) + tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_out) + tl.store(distances_ptr + out_idx_t, dist) class TritonCellNeighborAutograd(TritonNeighborAutograd): @@ -367,7 +337,8 @@ def forward( # type: ignore[override] distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) counter = torch.zeros((1,), device=device, dtype=torch.int32) - grid_traverse = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK_ATOMS"]),) + # One program per atom (like CUDA's one thread per atom) + grid_traverse = (n_atoms,) _traverse_cell_kernel[grid_traverse]( sorted_positions, sorted_batch, @@ -398,9 +369,7 @@ def forward( # type: ignore[override] include_transpose, loop, max_num_pairs, - BLOCK_ATOMS=32, - BLOCK_NEI=32, - MAX_CELL_ITERS=64, + MAX_CELL_ITERS=512, # Max atoms per cell; adjust based on density ) num_pairs = counter.to(torch.long) From 3c692d49949229753b918bb040afa997d43c2478 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 11:51:48 +0200 Subject: [PATCH 21/80] use a while loop instead of breaking which doesn't work in triton --- torchmdnet/extensions/triton_cell.py | 227 +++++++++++++++++---------- 1 file changed, 140 insertions(+), 87 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 9c19b19e1..7b0c657c4 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -83,12 +83,17 @@ def _fill_cell_offsets_kernel( @triton.jit -def _traverse_cell_kernel( +def _process_neighbor( + j, + i_idx, + pos_ix, + pos_iy, + pos_iz, + batch_i, + ori, pos_ptr, batch_ptr, sorted_index_ptr, - cell_start_ptr, - cell_end_ptr, neighbors0_ptr, neighbors1_ptr, deltas_ptr, @@ -98,97 +103,27 @@ def _traverse_cell_kernel( stride_pos_1: tl.constexpr, stride_batch: tl.constexpr, stride_sorted: tl.constexpr, - n_atoms: tl.constexpr, - num_cells: tl.constexpr, - cell_x: tl.constexpr, - cell_y: tl.constexpr, - cell_z: tl.constexpr, box_x: tl.constexpr, box_y: tl.constexpr, box_z: tl.constexpr, - cutoff: tl.constexpr, cutoff_lower2: tl.constexpr, cutoff_upper2: tl.constexpr, use_periodic: tl.constexpr, include_transpose: tl.constexpr, loop: tl.constexpr, max_pairs: tl.constexpr, - MAX_CELL_ITERS: tl.constexpr, ): - """ - CUDA-like kernel: one program per atom, scalar operations, sequential neighbor iteration. - This mimics the CUDA implementation's parallelization strategy. - """ - # One atom per program (like CUDA's one thread per atom) - i_idx = tl.program_id(axis=0) - if i_idx >= n_atoms: - return - - # Load atom i data (scalar values) - pos_ix = tl.load(pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1) - pos_iy = tl.load(pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1) - pos_iz = tl.load(pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1) - batch_i = tl.load(batch_ptr + i_idx * stride_batch) - ori = tl.load(sorted_index_ptr + i_idx * stride_sorted) - - # Compute cell coordinate for atom i - pwx = pos_ix - _tl_round(pos_ix / box_x) * box_x - pwy = pos_iy - _tl_round(pos_iy / box_y) * box_y - pwz = pos_iz - _tl_round(pos_iz / box_z) * box_z - cx = tl.math.floor((pwx + 0.5 * box_x) / cutoff).to(tl.int32) - cy = tl.math.floor((pwy + 0.5 * box_y) / cutoff).to(tl.int32) - cz = tl.math.floor((pwz + 0.5 * box_z) / cutoff).to(tl.int32) - cx = tl.where(cx == cell_x, 0, cx) - cy = tl.where(cy == cell_y, 0, cy) - cz = tl.where(cz == cell_z, 0, cz) - - # Loop over the 27 neighboring cells (like CUDA) - for neigh in range(27): - dx_cell = (neigh % 3) - 1 - dy_cell = ((neigh // 3) % 3) - 1 - dz_cell = (neigh // 9) - 1 - - nx = cx + dx_cell - ny = cy + dy_cell - nz = cz + dz_cell - - # Periodic wrapping of cell coordinates - nx = tl.where(nx < 0, nx + cell_x, nx) - ny = tl.where(ny < 0, ny + cell_y, ny) - nz = tl.where(nz < 0, nz + cell_z, nz) - nx = tl.where(nx >= cell_x, nx - cell_x, nx) - ny = tl.where(ny >= cell_y, ny - cell_y, ny) - nz = tl.where(nz >= cell_z, nz - cell_z, nz) - - neighbor_cell = nx + cell_x * (ny + cell_y * nz) - start = tl.load(cell_start_ptr + neighbor_cell) - end = tl.load(cell_end_ptr + neighbor_cell) - - # Skip empty cells (like CUDA's if (first_particle != -1)) - if start == -1: - continue - - # Iterate over atoms in this cell (like CUDA's for loop) - for j_offset in range(MAX_CELL_ITERS): - j = start + j_offset - - # Early exit when we've processed all atoms in this cell - if j >= end: - break - - # Only consider j < i (or j <= i for loop mode) - if loop: - if j > i_idx: - continue - else: - if j >= i_idx: - continue - - # Check same batch - batch_j = tl.load(batch_ptr + j * stride_batch) - if batch_j != batch_i: - continue - + """Process a single neighbor j for atom i.""" + # Only consider j < i (or j <= i for loop mode) + if loop: + valid_j = j <= i_idx + else: + valid_j = j < i_idx + + if valid_j: + # Check same batch + batch_j = tl.load(batch_ptr + j * stride_batch) + if batch_j == batch_i: # Load position of atom j pos_jx = tl.load(pos_ptr + j * stride_pos_0 + 0 * stride_pos_1) pos_jy = tl.load(pos_ptr + j * stride_pos_0 + 1 * stride_pos_1) @@ -209,11 +144,11 @@ def _traverse_cell_kernel( is_self = j == i_idx # Check distance criteria - valid = ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | ( + valid_pair = ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | ( loop & is_self ) - if valid: + if valid_pair: orj = tl.load(sorted_index_ptr + j * stride_sorted) # Canonical ordering: ni > nj @@ -249,6 +184,125 @@ def _traverse_cell_kernel( tl.store(distances_ptr + out_idx_t, dist) +@triton.jit +def _traverse_cell_kernel( + pos_ptr, + batch_ptr, + sorted_index_ptr, + cell_start_ptr, + cell_end_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0: tl.constexpr, + stride_pos_1: tl.constexpr, + stride_batch: tl.constexpr, + stride_sorted: tl.constexpr, + n_atoms: tl.constexpr, + num_cells: tl.constexpr, + cell_x: tl.constexpr, + cell_y: tl.constexpr, + cell_z: tl.constexpr, + box_x: tl.constexpr, + box_y: tl.constexpr, + box_z: tl.constexpr, + cutoff: tl.constexpr, + cutoff_lower2: tl.constexpr, + cutoff_upper2: tl.constexpr, + use_periodic: tl.constexpr, + include_transpose: tl.constexpr, + loop: tl.constexpr, + max_pairs: tl.constexpr, +): + """ + CUDA-like kernel: one program per atom, scalar operations, dynamic neighbor iteration. + Uses tl.while_loop for work-efficient iteration like CUDA. + """ + # One atom per program (like CUDA's one thread per atom) + i_idx = tl.program_id(axis=0) + if i_idx >= n_atoms: + return + + # Load atom i data (scalar values) + pos_ix = tl.load(pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1) + pos_iy = tl.load(pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1) + pos_iz = tl.load(pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1) + batch_i = tl.load(batch_ptr + i_idx * stride_batch) + ori = tl.load(sorted_index_ptr + i_idx * stride_sorted) + + # Compute cell coordinate for atom i + pwx = pos_ix - _tl_round(pos_ix / box_x) * box_x + pwy = pos_iy - _tl_round(pos_iy / box_y) * box_y + pwz = pos_iz - _tl_round(pos_iz / box_z) * box_z + cx = tl.math.floor((pwx + 0.5 * box_x) / cutoff).to(tl.int32) + cy = tl.math.floor((pwy + 0.5 * box_y) / cutoff).to(tl.int32) + cz = tl.math.floor((pwz + 0.5 * box_z) / cutoff).to(tl.int32) + cx = tl.where(cx == cell_x, 0, cx) + cy = tl.where(cy == cell_y, 0, cy) + cz = tl.where(cz == cell_z, 0, cz) + + # Loop over the 27 neighboring cells (like CUDA) + for neigh in range(27): + dx_cell = (neigh % 3) - 1 + dy_cell = ((neigh // 3) % 3) - 1 + dz_cell = (neigh // 9) - 1 + + nx = cx + dx_cell + ny = cy + dy_cell + nz = cz + dz_cell + + # Periodic wrapping of cell coordinates + nx = tl.where(nx < 0, nx + cell_x, nx) + ny = tl.where(ny < 0, ny + cell_y, ny) + nz = tl.where(nz < 0, nz + cell_z, nz) + nx = tl.where(nx >= cell_x, nx - cell_x, nx) + ny = tl.where(ny >= cell_y, ny - cell_y, ny) + nz = tl.where(nz >= cell_z, nz - cell_z, nz) + + neighbor_cell = nx + cell_x * (ny + cell_y * nz) + start = tl.load(cell_start_ptr + neighbor_cell) + end = tl.load(cell_end_ptr + neighbor_cell) + + # Skip empty cells + if start != -1: + # Use while_loop for dynamic iteration (like CUDA's for loop) + j = start + while j < end: + _process_neighbor( + j, + i_idx, + pos_ix, + pos_iy, + pos_iz, + batch_i, + ori, + pos_ptr, + batch_ptr, + sorted_index_ptr, + neighbors0_ptr, + neighbors1_ptr, + deltas_ptr, + distances_ptr, + counter_ptr, + stride_pos_0, + stride_pos_1, + stride_batch, + stride_sorted, + box_x, + box_y, + box_z, + cutoff_lower2, + cutoff_upper2, + use_periodic, + include_transpose, + loop, + max_pairs, + ) + j += 1 + + class TritonCellNeighborAutograd(TritonNeighborAutograd): @staticmethod def forward( # type: ignore[override] @@ -369,7 +423,6 @@ def forward( # type: ignore[override] include_transpose, loop, max_num_pairs, - MAX_CELL_ITERS=512, # Max atoms per cell; adjust based on density ) num_pairs = counter.to(torch.long) From 2ecd6da9eb08a618b2f0fc3c657d57aa82021a95 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 13:46:23 +0200 Subject: [PATCH 22/80] better printing --- benchmarks/neighbors.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index e494f7d7f..d3ea0f4f1 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -212,7 +212,7 @@ def benchmark_neighbors( # Print a column per strategy, show speedup over Distance in parenthesis for n_batches in batch_sizes: base = results["distance", n_batches] - row = f"{n_batches:<10}" + row = f"{n_batches:<11}" for strategy in strategies: row += f"{results[strategy, n_batches]:<4.2f} x{base / results[strategy, n_batches]:<14.2f} " print(row) @@ -243,7 +243,7 @@ def benchmark_neighbors( print("-------") headers = "N Particles " for strategy in strategies: - headers += f"{strategy.capitalize()}(ms) " + headers += f"{strategy.capitalize()+'(ms)':<21}" print(headers) print("-" * len(headers)) # Print a column per strategy, show speedup over Distance in parenthesis @@ -253,7 +253,7 @@ def benchmark_neighbors( # if n_particles > 32000: # results["brute", n_particles] = 0 # brute_speedup = 0 - row = f"{n_particles:<10}" + row = f"{n_particles:<12}" for strategy in strategies: row += f"{results[strategy, n_particles]:<4.2f} x{base / results[strategy, n_particles]:<14.2f} " print(row) From f3dbc331fc4e0ef5786930135f228e8b42607b89 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 14:25:21 +0200 Subject: [PATCH 23/80] initial sorted cell list impl --- .../extensions/triton_sorted_cell_list.py | 328 ++++++++++++++++++ 1 file changed, 328 insertions(+) create mode 100644 torchmdnet/extensions/triton_sorted_cell_list.py diff --git a/torchmdnet/extensions/triton_sorted_cell_list.py b/torchmdnet/extensions/triton_sorted_cell_list.py new file mode 100644 index 000000000..e79703a91 --- /dev/null +++ b/torchmdnet/extensions/triton_sorted_cell_list.py @@ -0,0 +1,328 @@ +import torch + +import torch +import triton +import triton.language as tl + + +@triton.jit +def neighbor_list_kernel( + # Pointers to inputs + Coords, # [N_atoms, 3] Sorted coordinates + CellStart, # [N_cells] Index where each cell starts in Coords + CellCount, # [N_cells] Number of atoms in each cell + # Pointers to outputs + OutPairs, # [2, Max_Pairs] + OutDeltas, # [Max_Pairs, 3] + OutDists, # [Max_Pairs] + GlobalCounter, # [1] Atomic counter for current pair count + # Constants + n_atoms, # Total atoms + max_pairs, # Size of output buffer + cutoff_sq, # Cutoff squared + # Box dimensions for PBC + box_x, + box_y, + box_z, + # Grid dimensions + n_cells_x, + n_cells_y, + n_cells_z, + # Block constants + BLOCK_M: tl.constexpr, # Size of Query Block +): + # 1. Identify Query Atoms + pid = tl.program_id(0) + # Range of atom indices this block handles + idx_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) + mask_m = idx_m < n_atoms + + # Load Query Coordinates + # We assume Coords are [N, 3] and contiguous (row-major) + off_x = idx_m * 3 + 0 + off_y = idx_m * 3 + 1 + off_z = idx_m * 3 + 2 + + # Load query positions + pos_mx = tl.load(Coords + off_x, mask=mask_m, other=0.0) + pos_my = tl.load(Coords + off_y, mask=mask_m, other=0.0) + pos_mz = tl.load(Coords + off_z, mask=mask_m, other=0.0) + + # 2. Determine which Cell these atoms belong to + # Since input is sorted, we can just look at the cell of the first atom in the block + # (Approximation: assumes BLOCK_M atoms fit in one or two cells, usually true for sorted data) + # Ideally, we calculate cell indices explicitly for robustness. + # Cell index = floor(pos / cell_size). Here we reconstruct it: + # Note: For highest speed, pass 'CellIndices' array instead of recomputing. + # Below we use the Grid Loop strategy: Iterate ALL 27 neighbor cells relative to the query cell. + + # Calculating cell index for the *first* atom in block to find "Home Cell" + # (Assuming sorted atoms are localized) + cell_sx = box_x / n_cells_x + cell_sy = box_y / n_cells_y + cell_sz = box_z / n_cells_z + + # Current cell indices (i, j, k) for the first atom in the block + c_i = (pos_mx[0] / cell_sx).to(tl.int32) + c_j = (pos_my[0] / cell_sy).to(tl.int32) + c_k = (pos_mz[0] / cell_sz).to(tl.int32) + + # 3. Iterate over 3x3x3 Neighbor Cells + # We loop -1 to 1 in each dimension + for i in range(-1, 2): + for j in range(-1, 2): + for k in range(-1, 2): + + # Apply PBC to find neighbor cell index + # (x % N + N) % N handles negative wrapping + n_idx_x = (c_i + i + n_cells_x) % n_cells_x + n_idx_y = (c_j + j + n_cells_y) % n_cells_y + n_idx_z = (c_k + k + n_cells_z) % n_cells_z + + # Flat neighbor cell index + cell_idx = ( + n_idx_x * (n_cells_y * n_cells_z) + n_idx_y * n_cells_z + n_idx_z + ) + + # Get start and count of atoms in this neighbor cell + start = tl.load(CellStart + cell_idx) + count = tl.load(CellCount + cell_idx) + + # Loop over atoms in the neighbor cell + # We process them in chunks of BLOCK_M (or similar size) + for tile_idx in range(0, count, BLOCK_M): + # Candidate Indices + idx_n = start + tile_idx + tl.arange(0, BLOCK_M) + mask_n = (tile_idx + tl.arange(0, BLOCK_M)) < count + + # Load Candidate Coords + off_nx = idx_n * 3 + 0 + off_ny = idx_n * 3 + 1 + off_nz = idx_n * 3 + 2 + + pos_nx = tl.load(Coords + off_nx, mask=mask_n, other=0.0) + pos_ny = tl.load(Coords + off_ny, mask=mask_n, other=0.0) + pos_nz = tl.load(Coords + off_nz, mask=mask_n, other=0.0) + + # Broadcast shapes for pairwise calculation + # Query: [BLOCK_M, 1], Candidate: [1, BLOCK_M] + q_x = pos_mx[:, None] + q_y = pos_my[:, None] + q_z = pos_mz[:, None] + + c_x = pos_nx[None, :] + c_y = pos_ny[None, :] + c_z = pos_nz[None, :] + + # --- PBC Distance Calculation (Minimum Image) --- + dx = q_x - c_x + dy = q_y - c_y + dz = q_z - c_z + + # dx = dx - box * round(dx / box) + dx = dx - box_x * tl.math.round(dx / box_x) + dy = dy - box_y * tl.math.round(dy / box_y) + dz = dz - box_z * tl.math.round(dz / box_z) + + dist_sq = dx * dx + dy * dy + dz * dz + + # --- Filtering --- + # 1. Distance check + is_neighbor = dist_sq < cutoff_sq + + # 2. Avoid double counting (i < j) and self-interaction (i != j) + # Note: For simple neighbor lists, we often want i != j. + # If you need half-list (Newton's 3rd law), use idx_m[:, None] < idx_n[None, :] + idx_m_bc = idx_m[:, None] + idx_n_bc = idx_n[None, :] + + # Enforce strictly upper triangular for half-list, or just i != j for full + valid_mask = ( + is_neighbor + & (idx_m_bc < idx_n_bc) + & mask_m[:, None] + & mask_n[None, :] + ) + + # --- Block-wise Output Allocation --- + # Count how many pairs we found in this tile + # We cast to int32 because sum returns same dtype as input (int1) + num_found = tl.sum(valid_mask.to(tl.int32)) + + if num_found > 0: + # ATOMIC ADD: Reserve space in the global output buffer + # This happens once per tile, not per pair -> Much faster + current_offset = tl.atomic_add(GlobalCounter, num_found) + + # Guard against overflow + if current_offset + num_found <= max_pairs: + # We need to flatten the 2D block results to 1D to write them out. + # Triton doesn't have a direct "compress/compact" instruction yet for 2D->1D. + # We compute running index (prefix sum) within the block to find write slot. + + # Flatten the mask to [BLOCK_M * BLOCK_M] + flat_mask = tl.ravel(valid_mask) + + # Compute exclusive scan (cumsum) to determine thread offset + # Note: tl.cumsum is not fully exposed on all blocks easily, + # usually strictly sequential manual scan or use 'where' logic. + # OPTIMIZATION: For Triton, we can iterate positions. + + # Because fully parallel scan is complex in a snippet, + # we verify valid indices and write. + + # Calculate write indices + # This is a bit tricky in Triton without `scan`. + # Simpler approach for snippet: iterate and write (serialized within block) + # OR utilize `tl.where` with pre-calculated indices if density is high. + + # High-Performance approach: + # Since we know `num_found` is small (sparse), we can just store + # the valid pairs. + + # Generate offsets for valid elements + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + # csum is inclusive, so write_idx = start + csum - 1 + + store_idx = current_offset + csum - 1 + + # Flatten data + flat_dx = tl.ravel(dx) + flat_dy = tl.ravel(dy) + flat_dz = tl.ravel(dz) + flat_dsq = tl.ravel(dist_sq) + flat_idx_m = tl.ravel(idx_m_bc) + flat_idx_n = tl.ravel(idx_n_bc) + + # Write only valid entries + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_idx_m, + mask=flat_mask, + ) + tl.store( + OutPairs + 1 * max_pairs + store_idx, + flat_idx_n, + mask=flat_mask, + ) + + tl.store( + OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask + ) + tl.store( + OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask + ) + tl.store( + OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask + ) + + tl.store( + OutDists + store_idx, tl.sqrt(flat_dsq), mask=flat_mask + ) + + +def find_neighbors(positions, box, cutoff, max_pairs=10_000_000): + """ + positions: [N, 3] tensor + box: [3] tensor (Lx, Ly, Lz) + cutoff: scalar + """ + N = positions.shape[0] + device = positions.device + + # 1. Setup Cell Grid (Host side logic) + # Ensure cell size >= cutoff + n_cells = (box / cutoff).floor().int() + cell_size = box / n_cells + + # 2. Sort Particles (Crucial for performance) + # Calculate cell index for every atom + cell_idx_x = (positions[:, 0] / cell_size[0]).long().clamp(0, n_cells[0] - 1) + cell_idx_y = (positions[:, 1] / cell_size[1]).long().clamp(0, n_cells[1] - 1) + cell_idx_z = (positions[:, 2] / cell_size[2]).long().clamp(0, n_cells[2] - 1) + + # Flat cell index + flat_cell_indices = ( + cell_idx_x * n_cells[1] * n_cells[2] + cell_idx_y * n_cells[2] + cell_idx_z + ) + + # Sort atoms by cell index + sorted_cell_indices, sort_idx = torch.sort(flat_cell_indices) + sorted_pos = positions[sort_idx].contiguous() + + # 3. Build Cell Pointers (Start/Count) + # Using unique_consecutive to find cell boundaries + unique_cells, counts = torch.unique_consecutive( + sorted_cell_indices, return_counts=True + ) + + # We need dense arrays for the kernel [Total_Cells] + total_cells = n_cells[0] * n_cells[1] * n_cells[2] + cell_start = torch.zeros(total_cells, dtype=torch.int32, device=device) + cell_count = torch.zeros(total_cells, dtype=torch.int32, device=device) + + # Compute starts (requires a search or scatter, here simplified) + # In production, use scatter_add or searchsorted + # Simple trick: scatter counts + cell_count.index_add_(0, unique_cells.int(), counts.int()) + + # Cumulative sum to get starts? No, we need starts in the *sorted* array. + # Since we sorted by cell, we can find starts easily. + # A fast way involves searchsorted if cells are sparse, + # but for water (dense), most cells are filled. + # We can compute starts using the counts and a prefix sum, + # but strictly speaking, we need the index in 'sorted_pos'. + + # Fast GPU-friendly start index construction: + # Find where cell index changes + change_mask = torch.cat( + [ + torch.tensor([1], device=device), + sorted_cell_indices[1:] != sorted_cell_indices[:-1], + ] + ) + change_indices = torch.nonzero(change_mask).squeeze() + # This maps unique_cells -> start_index in sorted_pos + cell_start.index_copy_(0, unique_cells.int(), change_indices.int()) + + # 4. Prepare Outputs + out_pairs = torch.empty((2, max_pairs), dtype=torch.int32, device=device) + out_deltas = torch.empty((max_pairs, 3), dtype=torch.float32, device=device) + out_dists = torch.empty((max_pairs,), dtype=torch.float32, device=device) + global_counter = torch.zeros((1,), dtype=torch.int32, device=device) + + # 5. Launch Kernel + grid = lambda META: (triton.cdiv(N, META["BLOCK_M"]),) + + neighbor_list_kernel[grid]( + sorted_pos, + cell_start, + cell_count, + out_pairs, + out_deltas, + out_dists, + global_counter, + N, + max_pairs, + cutoff**2, + box[0].item(), + box[1].item(), + box[2].item(), + n_cells[0].item(), + n_cells[1].item(), + n_cells[2].item(), + BLOCK_M=64, # Tuning parameter + ) + + num_pairs = global_counter.item() + + # Note: The output pairs use indices from 'sorted_pos'. + # If you need original indices, you must map them back using 'sort_idx'. + # remapped_pairs = sort_idx[out_pairs[:, :num_pairs]] + + return ( + out_pairs[:, :num_pairs], + out_deltas[:num_pairs], + out_dists[:num_pairs], + num_pairs, + ) From 7e544c892f6bd1a89633bdf131d549b1f67649ac Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 15:41:18 +0200 Subject: [PATCH 24/80] fix benchmark printing --- benchmarks/neighbors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index d3ea0f4f1..bbdb4e15f 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -214,7 +214,7 @@ def benchmark_neighbors( base = results["distance", n_batches] row = f"{n_batches:<11}" for strategy in strategies: - row += f"{results[strategy, n_batches]:<4.2f} x{base / results[strategy, n_batches]:<14.2f} " + row += f"{results[strategy, n_batches]:<5.2f} x{base / results[strategy, n_batches]:<13.2f} " print(row) n_particles_list = np.power(2, np.arange(8, 18)) @@ -255,5 +255,5 @@ def benchmark_neighbors( # brute_speedup = 0 row = f"{n_particles:<12}" for strategy in strategies: - row += f"{results[strategy, n_particles]:<4.2f} x{base / results[strategy, n_particles]:<14.2f} " + row += f"{results[strategy, n_particles]:<5.2f} x{base / results[strategy, n_particles]:<13.2f} " print(row) From a56524cf488ef7c093a033aef6bed2edd8b26268 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 15:48:21 +0200 Subject: [PATCH 25/80] nearly working cell impl --- torchmdnet/extensions/triton_cell.py | 791 ++++++++++-------- .../extensions/triton_sorted_cell_list.py | 328 -------- 2 files changed, 447 insertions(+), 672 deletions(-) delete mode 100644 torchmdnet/extensions/triton_sorted_cell_list.py diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 7b0c657c4..d0e9bb5ad 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -8,8 +8,8 @@ tl = None _HAS_TRITON = False -from torch import Tensor import torch +from torch import Tensor from torchmdnet.extensions.triton_neighbors import ( _tl_round, TritonNeighborAutograd, @@ -18,289 +18,375 @@ ) -@triton.jit -def _assign_cell_indices_kernel( - pos_ptr, - cell_index_ptr, - stride_pos_0: tl.constexpr, - stride_pos_1: tl.constexpr, - n_atoms: tl.constexpr, - box_x: tl.constexpr, - box_y: tl.constexpr, - box_z: tl.constexpr, - cutoff: tl.constexpr, - cell_x: tl.constexpr, - cell_y: tl.constexpr, - cell_z: tl.constexpr, - BLOCK: tl.constexpr, -): - pid = tl.program_id(axis=0) - idx = pid * BLOCK + tl.arange(0, BLOCK) - mask = idx < n_atoms - - px = tl.load(pos_ptr + idx * stride_pos_0 + 0 * stride_pos_1, mask=mask, other=0.0) - py = tl.load(pos_ptr + idx * stride_pos_0 + 1 * stride_pos_1, mask=mask, other=0.0) - pz = tl.load(pos_ptr + idx * stride_pos_0 + 2 * stride_pos_1, mask=mask, other=0.0) - - # Bring to [-0.5*box, 0.5*box] (rectangular PBC) - px = px - _tl_round(px / box_x) * box_x - py = py - _tl_round(py / box_y) * box_y - pz = pz - _tl_round(pz / box_z) * box_z - - cx = tl.math.floor((px + 0.5 * box_x) / cutoff).to(tl.int32) - cy = tl.math.floor((py + 0.5 * box_y) / cutoff).to(tl.int32) - cz = tl.math.floor((pz + 0.5 * box_z) / cutoff).to(tl.int32) +import torch +import triton +import triton.language as tl - cx = tl.where(cx == cell_x, 0, cx) - cy = tl.where(cy == cell_y, 0, cy) - cz = tl.where(cz == cell_z, 0, cz) - cell_index = cx + cell_x * (cy + cell_y * cz) - tl.store(cell_index_ptr + idx, cell_index, mask=mask) +import torch +import triton +import triton.language as tl @triton.jit -def _fill_cell_offsets_kernel( - sorted_cell_ptr, - cell_start_ptr, - cell_end_ptr, - n_atoms: tl.constexpr, - BLOCK: tl.constexpr, +def neighbor_list_kernel( + # Pointers + SortedCoords, + SortedBatch, + SortedToOrig, + CellStart, + CellEnd, + OutPairs, + OutDeltas, + OutDists, + GlobalCounter, + # Parameters + n_atoms, + max_pairs, + cutoff_lower_sq, + cutoff_upper_sq, + box_x, + box_y, + box_z, + n_cells_x, + n_cells_y, + n_cells_z, + # Flags + use_periodic: tl.constexpr, # Enable/Disable PBC + loop: tl.constexpr, # Enable/Disable Self-interactions + include_transpose: tl.constexpr, # True: i!=j (Full), False: i 0), other=0) - - is_first = idx == 0 - is_new = is_first | (icell != prev) - - tl.store(cell_start_ptr + icell, idx, mask=mask & is_new) - tl.store(cell_end_ptr + prev, idx, mask=mask & (is_new & (idx > 0))) - tl.store(cell_end_ptr + icell, idx + 1, mask=mask & (idx == n_atoms - 1)) - + pid = tl.program_id(0) + idx_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) + mask_m = idx_m < n_atoms + + # Load Query Data + off_m = idx_m + pos_mx = tl.load(SortedCoords + off_m * 3 + 0, mask=mask_m, other=0.0) + pos_my = tl.load(SortedCoords + off_m * 3 + 1, mask=mask_m, other=0.0) + pos_mz = tl.load(SortedCoords + off_m * 3 + 2, mask=mask_m, other=0.0) + batch_m = tl.load(SortedBatch + off_m, mask=mask_m, other=-1) + + # Identify Home Cell - vectorized for all BLOCK_M atoms + cell_sx = box_x / n_cells_x + cell_sy = box_y / n_cells_y + cell_sz = box_z / n_cells_z + + # Local coords for cell calculation (vectorized for all atoms in block) + px = pos_mx + py = pos_my + pz = pos_mz + + if use_periodic: + px = px - box_x * tl.math.floor(px / box_x) + py = py - box_y * tl.math.floor(py / box_y) + pz = pz - box_z * tl.math.floor(pz / box_z) + + # Cell indices for each atom in the block (vectors of size BLOCK_M) + c_i_vec = (px / cell_sx).to(tl.int32) + c_j_vec = (py / cell_sy).to(tl.int32) + c_k_vec = (pz / cell_sz).to(tl.int32) + + # Clamp to valid range + c_i_vec = tl.maximum(tl.minimum(c_i_vec, n_cells_x - 1), 0) + c_j_vec = tl.maximum(tl.minimum(c_j_vec, n_cells_y - 1), 0) + c_k_vec = tl.maximum(tl.minimum(c_k_vec, n_cells_z - 1), 0) + + # Iterate over neighbor cell offsets + for i in range(-1, 2): + for j in range(-1, 2): + for k in range(-1, 2): + + # Compute neighbor cell indices for all atoms in block (vectorized) + n_idx_x = c_i_vec + i + n_idx_y = c_j_vec + j + n_idx_z = c_k_vec + k + + # --- PBC CHECK (vectorized) --- + if use_periodic: + # Wrap around + n_idx_x = (n_idx_x + n_cells_x) % n_cells_x + n_idx_y = (n_idx_y + n_cells_y) % n_cells_y + n_idx_z = (n_idx_z + n_cells_z) % n_cells_z + valid_cell_mask = mask_m # All cells valid with PBC + else: + # Strict Bounds Check (vectorized) + valid_cell_mask = ( + (n_idx_x >= 0) + & (n_idx_x < n_cells_x) + & (n_idx_y >= 0) + & (n_idx_y < n_cells_y) + & (n_idx_z >= 0) + & (n_idx_z < n_cells_z) + & mask_m + ) + + # Compute flat cell indices (vectorized, one per query atom) + cell_idx_vec = ( + n_idx_x * (n_cells_y * n_cells_z) + n_idx_y * n_cells_z + n_idx_z + ) -@triton.jit -def _process_neighbor( - j, - i_idx, - pos_ix, - pos_iy, - pos_iz, - batch_i, - ori, - pos_ptr, - batch_ptr, - sorted_index_ptr, - neighbors0_ptr, - neighbors1_ptr, - deltas_ptr, - distances_ptr, - counter_ptr, - stride_pos_0: tl.constexpr, - stride_pos_1: tl.constexpr, - stride_batch: tl.constexpr, - stride_sorted: tl.constexpr, - box_x: tl.constexpr, - box_y: tl.constexpr, - box_z: tl.constexpr, - cutoff_lower2: tl.constexpr, - cutoff_upper2: tl.constexpr, - use_periodic: tl.constexpr, - include_transpose: tl.constexpr, - loop: tl.constexpr, - max_pairs: tl.constexpr, -): - """Process a single neighbor j for atom i.""" - # Only consider j < i (or j <= i for loop mode) - if loop: - valid_j = j <= i_idx - else: - valid_j = j < i_idx - - if valid_j: - # Check same batch - batch_j = tl.load(batch_ptr + j * stride_batch) - if batch_j == batch_i: - # Load position of atom j - pos_jx = tl.load(pos_ptr + j * stride_pos_0 + 0 * stride_pos_1) - pos_jy = tl.load(pos_ptr + j * stride_pos_0 + 1 * stride_pos_1) - pos_jz = tl.load(pos_ptr + j * stride_pos_0 + 2 * stride_pos_1) - - # Compute distance - dx = pos_ix - pos_jx - dy = pos_iy - pos_jy - dz = pos_iz - pos_jz - - # Apply periodic boundary conditions - if use_periodic: - dx = dx - _tl_round(dx / box_x) * box_x - dy = dy - _tl_round(dy / box_y) * box_y - dz = dz - _tl_round(dz / box_z) * box_z - - dist2 = dx * dx + dy * dy + dz * dz - is_self = j == i_idx - - # Check distance criteria - valid_pair = ((dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2)) | ( - loop & is_self - ) - - if valid_pair: - orj = tl.load(sorted_index_ptr + j * stride_sorted) - - # Canonical ordering: ni > nj - ni = tl.maximum(ori, orj) - nj = tl.minimum(ori, orj) - sign = tl.where(ni == ori, 1.0, -1.0).to(dx.dtype) - - dx_out = dx * sign - dy_out = dy * sign - dz_out = dz * sign - dist = tl.sqrt(dist2) - - # Atomic add to reserve output slot - out_idx = tl.atomic_add(counter_ptr, 1) - if out_idx < max_pairs: - tl.store(neighbors0_ptr + out_idx, ni) - tl.store(neighbors1_ptr + out_idx, nj) - tl.store(deltas_ptr + out_idx * 3 + 0, dx_out) - tl.store(deltas_ptr + out_idx * 3 + 1, dy_out) - tl.store(deltas_ptr + out_idx * 3 + 2, dz_out) - tl.store(distances_ptr + out_idx, dist) - - # Add transpose pair if needed - if include_transpose: - if ni != nj: - out_idx_t = tl.atomic_add(counter_ptr, 1) - if out_idx_t < max_pairs: - tl.store(neighbors0_ptr + out_idx_t, nj) - tl.store(neighbors1_ptr + out_idx_t, ni) - tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_out) - tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_out) - tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_out) - tl.store(distances_ptr + out_idx_t, dist) + # Gather cell boundaries for each query atom's neighbor cell + start_vec = tl.load( + CellStart + cell_idx_vec, mask=valid_cell_mask, other=-1 + ) + end_vec = tl.load(CellEnd + cell_idx_vec, mask=valid_cell_mask, other=0) + # Find the maximum range we need to scan + max_end = tl.max(tl.where(valid_cell_mask, end_vec, 0)) + min_start = tl.min( + tl.where(valid_cell_mask & (start_vec >= 0), start_vec, n_atoms) + ) -@triton.jit -def _traverse_cell_kernel( - pos_ptr, - batch_ptr, - sorted_index_ptr, - cell_start_ptr, - cell_end_ptr, - neighbors0_ptr, - neighbors1_ptr, - deltas_ptr, - distances_ptr, - counter_ptr, - stride_pos_0: tl.constexpr, - stride_pos_1: tl.constexpr, - stride_batch: tl.constexpr, - stride_sorted: tl.constexpr, - n_atoms: tl.constexpr, - num_cells: tl.constexpr, - cell_x: tl.constexpr, - cell_y: tl.constexpr, - cell_z: tl.constexpr, - box_x: tl.constexpr, - box_y: tl.constexpr, - box_z: tl.constexpr, - cutoff: tl.constexpr, - cutoff_lower2: tl.constexpr, - cutoff_upper2: tl.constexpr, - use_periodic: tl.constexpr, - include_transpose: tl.constexpr, - loop: tl.constexpr, - max_pairs: tl.constexpr, -): + # Use fixed iteration count for CUDA graph compatibility + # Iterate over all possible neighbor atoms (worst case: all atoms) + for tile_idx in range(MAX_TILES): + # Neighbor atom indices (global indices in sorted array) + idx_n = tile_idx * BLOCK_M + tl.arange(0, BLOCK_M) + # Only process if within valid range [min_start, max_end) and within n_atoms + mask_n = ( + (idx_n >= min_start) & (idx_n < max_end) & (idx_n < n_atoms) + ) + + # Load Candidate atoms + off_n = idx_n + pos_nx = tl.load( + SortedCoords + off_n * 3 + 0, mask=mask_n, other=0.0 + ) + pos_ny = tl.load( + SortedCoords + off_n * 3 + 1, mask=mask_n, other=0.0 + ) + pos_nz = tl.load( + SortedCoords + off_n * 3 + 2, mask=mask_n, other=0.0 + ) + batch_n = tl.load(SortedBatch + off_n, mask=mask_n, other=-2) + + # Compute distances: [BLOCK_M query atoms, BLOCK_M candidate atoms] + d_x = pos_mx[:, None] - pos_nx[None, :] + d_y = pos_my[:, None] - pos_ny[None, :] + d_z = pos_mz[:, None] - pos_nz[None, :] + + # --- PBC Distance Adjustment --- + if use_periodic: + d_x = d_x - box_x * _tl_round(d_x / box_x) + d_y = d_y - box_y * _tl_round(d_y / box_y) + d_z = d_z - box_z * _tl_round(d_z / box_z) + + dist_sq = d_x * d_x + d_y * d_y + d_z * d_z + + # --- Interaction Logic --- + # 1. Distance & Batch conditions + cond_dist = (dist_sq <= cutoff_upper_sq) & ( + dist_sq >= cutoff_lower_sq + ) + cond_batch = batch_m[:, None] == batch_n[None, :] + + # 2. Check if neighbor is within each query atom's cell range + # idx_n is [BLOCK_M], start_vec is [BLOCK_M], end_vec is [BLOCK_M] + # We need [BLOCK_M, BLOCK_M] comparison + idx_n_bc = idx_n[None, :] # [1, BLOCK_M] + start_bc = start_vec[:, None] # [BLOCK_M, 1] + end_bc = end_vec[:, None] # [BLOCK_M, 1] + valid_cell_bc = valid_cell_mask[:, None] # [BLOCK_M, 1] + + # Neighbor must be in the query atom's specific cell range + cond_in_range = ( + (idx_n_bc >= start_bc) & (idx_n_bc < end_bc) & valid_cell_bc + ) + + # Load original indices for loop/transpose logic + # The condition must be based on original indices, not sorted indices + orig_m = tl.load(SortedToOrig + idx_m, mask=mask_m, other=-1) + orig_n = tl.load(SortedToOrig + idx_n, mask=mask_n, other=-1) + + # 3. Index / Loop Logic (using original indices) + orig_m_bc = orig_m[:, None] + orig_n_bc = orig_n[None, :] + + if include_transpose: + # Full List (both i->j and j->i) + if loop: + # Accept everything (i!=j AND i==j) -> All True + cond_idx = True + else: + # Accept only i != j + cond_idx = orig_m_bc != orig_n_bc + else: + # Half List (lower triangle convention: i >= j) + if loop: + # Accept i >= j (includes diagonal) + cond_idx = orig_m_bc >= orig_n_bc + else: + # Accept i > j (excludes diagonal) + cond_idx = orig_m_bc > orig_n_bc + + valid_mask = ( + cond_dist + & cond_batch + & cond_idx + & cond_in_range + & mask_m[:, None] + & mask_n[None, :] + ) + + # --- Store Block --- + num_found = tl.sum(valid_mask.to(tl.int32)) + if num_found > 0: + current_offset = tl.atomic_add(GlobalCounter, num_found) + if current_offset + num_found <= max_pairs: + flat_mask = tl.ravel(valid_mask) + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + store_idx = current_offset + csum - 1 + + flat_orig_m = tl.ravel( + tl.broadcast_to(orig_m[:, None], (BLOCK_M, BLOCK_M)) + ) + flat_orig_n = tl.ravel( + tl.broadcast_to(orig_n[None, :], (BLOCK_M, BLOCK_M)) + ) + + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_orig_m, + mask=flat_mask, + ) + tl.store( + OutPairs + 1 * max_pairs + store_idx, + flat_orig_n, + mask=flat_mask, + ) + tl.store( + OutDeltas + store_idx * 3 + 0, + tl.ravel(d_x), + mask=flat_mask, + ) + tl.store( + OutDeltas + store_idx * 3 + 1, + tl.ravel(d_y), + mask=flat_mask, + ) + tl.store( + OutDeltas + store_idx * 3 + 2, + tl.ravel(d_z), + mask=flat_mask, + ) + tl.store( + OutDists + store_idx, + tl.sqrt(tl.ravel(dist_sq)), + mask=flat_mask, + ) + + +def find_neighbors(positions, box, cutoff, max_pairs=10_000_000): """ - CUDA-like kernel: one program per atom, scalar operations, dynamic neighbor iteration. - Uses tl.while_loop for work-efficient iteration like CUDA. + positions: [N, 3] tensor + box: [3] tensor (Lx, Ly, Lz) + cutoff: scalar """ - # One atom per program (like CUDA's one thread per atom) - i_idx = tl.program_id(axis=0) - if i_idx >= n_atoms: - return - - # Load atom i data (scalar values) - pos_ix = tl.load(pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1) - pos_iy = tl.load(pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1) - pos_iz = tl.load(pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1) - batch_i = tl.load(batch_ptr + i_idx * stride_batch) - ori = tl.load(sorted_index_ptr + i_idx * stride_sorted) - - # Compute cell coordinate for atom i - pwx = pos_ix - _tl_round(pos_ix / box_x) * box_x - pwy = pos_iy - _tl_round(pos_iy / box_y) * box_y - pwz = pos_iz - _tl_round(pos_iz / box_z) * box_z - cx = tl.math.floor((pwx + 0.5 * box_x) / cutoff).to(tl.int32) - cy = tl.math.floor((pwy + 0.5 * box_y) / cutoff).to(tl.int32) - cz = tl.math.floor((pwz + 0.5 * box_z) / cutoff).to(tl.int32) - cx = tl.where(cx == cell_x, 0, cx) - cy = tl.where(cy == cell_y, 0, cy) - cz = tl.where(cz == cell_z, 0, cz) - - # Loop over the 27 neighboring cells (like CUDA) - for neigh in range(27): - dx_cell = (neigh % 3) - 1 - dy_cell = ((neigh // 3) % 3) - 1 - dz_cell = (neigh // 9) - 1 - - nx = cx + dx_cell - ny = cy + dy_cell - nz = cz + dz_cell - - # Periodic wrapping of cell coordinates - nx = tl.where(nx < 0, nx + cell_x, nx) - ny = tl.where(ny < 0, ny + cell_y, ny) - nz = tl.where(nz < 0, nz + cell_z, nz) - nx = tl.where(nx >= cell_x, nx - cell_x, nx) - ny = tl.where(ny >= cell_y, ny - cell_y, ny) - nz = tl.where(nz >= cell_z, nz - cell_z, nz) - - neighbor_cell = nx + cell_x * (ny + cell_y * nz) - start = tl.load(cell_start_ptr + neighbor_cell) - end = tl.load(cell_end_ptr + neighbor_cell) - - # Skip empty cells - if start != -1: - # Use while_loop for dynamic iteration (like CUDA's for loop) - j = start - while j < end: - _process_neighbor( - j, - i_idx, - pos_ix, - pos_iy, - pos_iz, - batch_i, - ori, - pos_ptr, - batch_ptr, - sorted_index_ptr, - neighbors0_ptr, - neighbors1_ptr, - deltas_ptr, - distances_ptr, - counter_ptr, - stride_pos_0, - stride_pos_1, - stride_batch, - stride_sorted, - box_x, - box_y, - box_z, - cutoff_lower2, - cutoff_upper2, - use_periodic, - include_transpose, - loop, - max_pairs, - ) - j += 1 + N = positions.shape[0] + device = positions.device + + # 1. Setup Cell Grid (Host side logic) + # Ensure cell size >= cutoff + n_cells = (box / cutoff).floor().int() + cell_size = box / n_cells + + # 2. Sort Particles (Crucial for performance) + # Calculate cell index for every atom + cell_idx_x = (positions[:, 0] / cell_size[0]).long().clamp(0, n_cells[0] - 1) + cell_idx_y = (positions[:, 1] / cell_size[1]).long().clamp(0, n_cells[1] - 1) + cell_idx_z = (positions[:, 2] / cell_size[2]).long().clamp(0, n_cells[2] - 1) + + # Flat cell index + flat_cell_indices = ( + cell_idx_x * n_cells[1] * n_cells[2] + cell_idx_y * n_cells[2] + cell_idx_z + ) + + # Sort atoms by cell index + sorted_cell_indices, sort_idx = torch.sort(flat_cell_indices) + sorted_pos = positions[sort_idx].contiguous() + + # 3. Build Cell Pointers (Start/Count) + # Using unique_consecutive to find cell boundaries + unique_cells, counts = torch.unique_consecutive( + sorted_cell_indices, return_counts=True + ) + + # We need dense arrays for the kernel [Total_Cells] + total_cells = n_cells[0] * n_cells[1] * n_cells[2] + cell_start = torch.zeros(total_cells, dtype=torch.int32, device=device) + cell_count = torch.zeros(total_cells, dtype=torch.int32, device=device) + + # Compute starts (requires a search or scatter, here simplified) + # In production, use scatter_add or searchsorted + # Simple trick: scatter counts + cell_count.index_add_(0, unique_cells.int(), counts.int()) + + # Cumulative sum to get starts? No, we need starts in the *sorted* array. + # Since we sorted by cell, we can find starts easily. + # A fast way involves searchsorted if cells are sparse, + # but for water (dense), most cells are filled. + # We can compute starts using the counts and a prefix sum, + # but strictly speaking, we need the index in 'sorted_pos'. + + # Fast GPU-friendly start index construction: + # Find where cell index changes + change_mask = torch.cat( + [ + torch.tensor([1], device=device), + sorted_cell_indices[1:] != sorted_cell_indices[:-1], + ] + ) + change_indices = torch.nonzero(change_mask).squeeze() + # This maps unique_cells -> start_index in sorted_pos + cell_start.index_copy_(0, unique_cells.int(), change_indices.int()) + + # 4. Prepare Outputs + out_pairs = torch.empty((2, max_pairs), dtype=torch.int32, device=device) + out_deltas = torch.empty((max_pairs, 3), dtype=torch.float32, device=device) + out_dists = torch.empty((max_pairs,), dtype=torch.float32, device=device) + global_counter = torch.zeros((1,), dtype=torch.int32, device=device) + + # 5. Launch Kernel + grid = lambda META: (triton.cdiv(N, META["BLOCK_M"]),) + BLOCK_M = 64 + max_tiles = (N + BLOCK_M - 1) // BLOCK_M + + neighbor_list_kernel[grid]( + sorted_pos, + cell_start, + cell_count, + out_pairs, + out_deltas, + out_dists, + global_counter, + N, + max_pairs, + cutoff**2, + box[0].item(), + box[1].item(), + box[2].item(), + n_cells[0].item(), + n_cells[1].item(), + n_cells[2].item(), + BLOCK_M=BLOCK_M, # Tuning parameter + MAX_TILES=max_tiles, + ) + + num_pairs = global_counter.item() + + # Note: The output pairs use indices from 'sorted_pos'. + # If you need original indices, you must map them back using 'sort_idx'. + # remapped_pairs = sort_idx[out_pairs[:, :num_pairs]] + + return ( + out_pairs[:, :num_pairs], + out_deltas[:num_pairs], + out_dists[:num_pairs], + num_pairs, + ) class TritonCellNeighborAutograd(TritonNeighborAutograd): @@ -319,11 +405,12 @@ def forward( # type: ignore[override] ): if not _HAS_TRITON: raise RuntimeError("Triton is not available") + device = positions.device dtype = positions.dtype n_atoms = positions.size(0) - # Validate inputs (mirror CUDA constraints) + # Validate inputs if positions.dim() != 2 or positions.size(1) != 3: raise ValueError('Expected "positions" to have shape (N, 3)') if batch.dim() != 1 or batch.size(0) != n_atoms: @@ -331,101 +418,117 @@ def forward( # type: ignore[override] if max_num_pairs <= 0: raise ValueError('Expected "max_num_pairs" to be positive') - # Ensure box is well-formed and diagonal (cell list only supports rectangular boxes) - box_vectors = _validate_box(box_vectors, float(cutoff_upper), 1) - # Keep box on host to avoid H2D during CUDA graph capture; only scalars are used in kernels - box_vectors = box_vectors.to( - dtype=dtype, device=box_vectors.device - ).contiguous() + box_vectors = box_vectors.to(dtype=dtype, device=device).contiguous() box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors + box_x, box_y, box_z = ( + float(box_diag[0, 0]), + float(box_diag[1, 1]), + float(box_diag[2, 2]), + ) - # Cell grid dimensions (host math to match CUDA getCellDimensions) cell_dim_x, cell_dim_y, cell_dim_z = _get_cell_dimensions( box_vectors, float(cutoff_upper) ) num_cells = cell_dim_x * cell_dim_y * cell_dim_z - batch = batch.contiguous() - positions = positions.contiguous() - - # 1. Compute cell index per atom - cell_indices = torch.empty((n_atoms,), device=device, dtype=torch.int32) - grid_assign = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK"]),) - _assign_cell_indices_kernel[grid_assign]( - positions, - cell_indices, - positions.stride(0), - positions.stride(1), - n_atoms, - float(box_diag[0, 0]), - float(box_diag[1, 1]), - float(box_diag[2, 2]), - float(cutoff_upper), - cell_dim_x, - cell_dim_y, - cell_dim_z, - BLOCK=256, - ) - - # 2. Sort atoms by cell index + # 1. Cell Index Calculation + # If Periodic: Wrap positions into [0, Box]. + # If Non-Periodic: We do NOT wrap positions physically (to preserve absolute coords), + # but we shift/map them to cell grid indices. + # Standard assumption: Positions are roughly within the box definition. + + if use_periodic: + inv_box_x = 1.0 / box_x + inv_box_y = 1.0 / box_y + inv_box_z = 1.0 / box_z + px = positions[:, 0] - torch.floor(positions[:, 0] * inv_box_x) * box_x + py = positions[:, 1] - torch.floor(positions[:, 1] * inv_box_y) * box_y + pz = positions[:, 2] - torch.floor(positions[:, 2] * inv_box_z) * box_z + else: + # For non-periodic, use raw coords. + # We assume box starts at (0,0,0). If atoms are negative, we clamp index to 0. + px, py, pz = positions[:, 0], positions[:, 1], positions[:, 2] + + cx = (px / cutoff_upper).long().clamp(0, cell_dim_x - 1) + cy = (py / cutoff_upper).long().clamp(0, cell_dim_y - 1) + cz = (pz / cutoff_upper).long().clamp(0, cell_dim_z - 1) + + cell_indices = cx * (cell_dim_y * cell_dim_z) + cy * cell_dim_z + cz + + # 2. Sort sorted_cell_indices, sorted_atom_indices = torch.sort(cell_indices) - sorted_atom_indices = sorted_atom_indices.to(torch.int32) - sorted_positions = positions.index_select(0, sorted_atom_indices) - sorted_batch = batch.index_select(0, sorted_atom_indices) + sorted_atom_indices = sorted_atom_indices.int() - # 3. Build cell start/end offsets + sorted_positions = positions.index_select( + 0, sorted_atom_indices.long() + ).contiguous() + sorted_batch = batch.index_select(0, sorted_atom_indices.long()).contiguous() + + # 3. Cell Pointers cell_start = torch.full((num_cells,), -1, device=device, dtype=torch.int32) - cell_end = torch.empty((num_cells,), device=device, dtype=torch.int32) - grid_offsets = lambda meta: (triton.cdiv(n_atoms, meta["BLOCK"]),) - _fill_cell_offsets_kernel[grid_offsets]( - sorted_cell_indices, - cell_start, - cell_end, - n_atoms, - BLOCK=256, + cell_end = torch.zeros((num_cells,), device=device, dtype=torch.int32) + + change_mask = torch.cat( + [ + torch.tensor([True], device=device), + sorted_cell_indices[1:] != sorted_cell_indices[:-1], + ] ) + change_indices = torch.nonzero(change_mask, as_tuple=False).squeeze(-1) + unique_cells = sorted_cell_indices[change_indices] + end_indices = torch.cat( + [change_indices[1:], torch.tensor([n_atoms], device=device)] + ) + + cell_start.index_copy_(0, unique_cells.long(), change_indices.int()) + cell_end.index_copy_(0, unique_cells.long(), end_indices.int()) - # 4. Traverse cell list to generate neighbor pairs - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) - deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) - distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + # 4. Kernel Launch + neighbors = torch.empty((2, max_num_pairs), device=device, dtype=torch.long) + deltas = torch.empty((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.empty((max_num_pairs,), device=device, dtype=dtype) counter = torch.zeros((1,), device=device, dtype=torch.int32) - # One program per atom (like CUDA's one thread per atom) - grid_traverse = (n_atoms,) - _traverse_cell_kernel[grid_traverse]( + BLOCK_M = 64 + grid = (triton.cdiv(n_atoms, BLOCK_M),) + + # Compute MAX_TILES for fixed iteration count (CUDA graph compatible) + max_tiles = (n_atoms + BLOCK_M - 1) // BLOCK_M + + neighbor_list_kernel[grid]( sorted_positions, sorted_batch, sorted_atom_indices, cell_start, cell_end, - neighbors[0], - neighbors[1], + neighbors, deltas, distances, counter, - sorted_positions.stride(0), - sorted_positions.stride(1), - sorted_batch.stride(0), - sorted_atom_indices.stride(0), n_atoms, - num_cells, - cell_dim_x, - cell_dim_y, - cell_dim_z, - float(box_diag[0, 0]), - float(box_diag[1, 1]), - float(box_diag[2, 2]), - float(cutoff_upper), - float(cutoff_lower * cutoff_lower), - float(cutoff_upper * cutoff_upper), - use_periodic, - include_transpose, - loop, max_num_pairs, + float(cutoff_lower**2), + float(cutoff_upper**2), + box_x, + box_y, + box_z, + int(cell_dim_x), + int(cell_dim_y), + int(cell_dim_z), + use_periodic=use_periodic, + loop=loop, + include_transpose=include_transpose, + BLOCK_M=BLOCK_M, + MAX_TILES=max_tiles, ) - num_pairs = counter.to(torch.long) + num_found = counter.item() + + # Trim results + neighbors = neighbors[:, :num_found] + deltas = deltas[:num_found] + distances = distances[:num_found] + ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms - return neighbors, deltas, distances, num_pairs + return neighbors, deltas, distances, torch.tensor([num_found], device=device) diff --git a/torchmdnet/extensions/triton_sorted_cell_list.py b/torchmdnet/extensions/triton_sorted_cell_list.py deleted file mode 100644 index e79703a91..000000000 --- a/torchmdnet/extensions/triton_sorted_cell_list.py +++ /dev/null @@ -1,328 +0,0 @@ -import torch - -import torch -import triton -import triton.language as tl - - -@triton.jit -def neighbor_list_kernel( - # Pointers to inputs - Coords, # [N_atoms, 3] Sorted coordinates - CellStart, # [N_cells] Index where each cell starts in Coords - CellCount, # [N_cells] Number of atoms in each cell - # Pointers to outputs - OutPairs, # [2, Max_Pairs] - OutDeltas, # [Max_Pairs, 3] - OutDists, # [Max_Pairs] - GlobalCounter, # [1] Atomic counter for current pair count - # Constants - n_atoms, # Total atoms - max_pairs, # Size of output buffer - cutoff_sq, # Cutoff squared - # Box dimensions for PBC - box_x, - box_y, - box_z, - # Grid dimensions - n_cells_x, - n_cells_y, - n_cells_z, - # Block constants - BLOCK_M: tl.constexpr, # Size of Query Block -): - # 1. Identify Query Atoms - pid = tl.program_id(0) - # Range of atom indices this block handles - idx_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) - mask_m = idx_m < n_atoms - - # Load Query Coordinates - # We assume Coords are [N, 3] and contiguous (row-major) - off_x = idx_m * 3 + 0 - off_y = idx_m * 3 + 1 - off_z = idx_m * 3 + 2 - - # Load query positions - pos_mx = tl.load(Coords + off_x, mask=mask_m, other=0.0) - pos_my = tl.load(Coords + off_y, mask=mask_m, other=0.0) - pos_mz = tl.load(Coords + off_z, mask=mask_m, other=0.0) - - # 2. Determine which Cell these atoms belong to - # Since input is sorted, we can just look at the cell of the first atom in the block - # (Approximation: assumes BLOCK_M atoms fit in one or two cells, usually true for sorted data) - # Ideally, we calculate cell indices explicitly for robustness. - # Cell index = floor(pos / cell_size). Here we reconstruct it: - # Note: For highest speed, pass 'CellIndices' array instead of recomputing. - # Below we use the Grid Loop strategy: Iterate ALL 27 neighbor cells relative to the query cell. - - # Calculating cell index for the *first* atom in block to find "Home Cell" - # (Assuming sorted atoms are localized) - cell_sx = box_x / n_cells_x - cell_sy = box_y / n_cells_y - cell_sz = box_z / n_cells_z - - # Current cell indices (i, j, k) for the first atom in the block - c_i = (pos_mx[0] / cell_sx).to(tl.int32) - c_j = (pos_my[0] / cell_sy).to(tl.int32) - c_k = (pos_mz[0] / cell_sz).to(tl.int32) - - # 3. Iterate over 3x3x3 Neighbor Cells - # We loop -1 to 1 in each dimension - for i in range(-1, 2): - for j in range(-1, 2): - for k in range(-1, 2): - - # Apply PBC to find neighbor cell index - # (x % N + N) % N handles negative wrapping - n_idx_x = (c_i + i + n_cells_x) % n_cells_x - n_idx_y = (c_j + j + n_cells_y) % n_cells_y - n_idx_z = (c_k + k + n_cells_z) % n_cells_z - - # Flat neighbor cell index - cell_idx = ( - n_idx_x * (n_cells_y * n_cells_z) + n_idx_y * n_cells_z + n_idx_z - ) - - # Get start and count of atoms in this neighbor cell - start = tl.load(CellStart + cell_idx) - count = tl.load(CellCount + cell_idx) - - # Loop over atoms in the neighbor cell - # We process them in chunks of BLOCK_M (or similar size) - for tile_idx in range(0, count, BLOCK_M): - # Candidate Indices - idx_n = start + tile_idx + tl.arange(0, BLOCK_M) - mask_n = (tile_idx + tl.arange(0, BLOCK_M)) < count - - # Load Candidate Coords - off_nx = idx_n * 3 + 0 - off_ny = idx_n * 3 + 1 - off_nz = idx_n * 3 + 2 - - pos_nx = tl.load(Coords + off_nx, mask=mask_n, other=0.0) - pos_ny = tl.load(Coords + off_ny, mask=mask_n, other=0.0) - pos_nz = tl.load(Coords + off_nz, mask=mask_n, other=0.0) - - # Broadcast shapes for pairwise calculation - # Query: [BLOCK_M, 1], Candidate: [1, BLOCK_M] - q_x = pos_mx[:, None] - q_y = pos_my[:, None] - q_z = pos_mz[:, None] - - c_x = pos_nx[None, :] - c_y = pos_ny[None, :] - c_z = pos_nz[None, :] - - # --- PBC Distance Calculation (Minimum Image) --- - dx = q_x - c_x - dy = q_y - c_y - dz = q_z - c_z - - # dx = dx - box * round(dx / box) - dx = dx - box_x * tl.math.round(dx / box_x) - dy = dy - box_y * tl.math.round(dy / box_y) - dz = dz - box_z * tl.math.round(dz / box_z) - - dist_sq = dx * dx + dy * dy + dz * dz - - # --- Filtering --- - # 1. Distance check - is_neighbor = dist_sq < cutoff_sq - - # 2. Avoid double counting (i < j) and self-interaction (i != j) - # Note: For simple neighbor lists, we often want i != j. - # If you need half-list (Newton's 3rd law), use idx_m[:, None] < idx_n[None, :] - idx_m_bc = idx_m[:, None] - idx_n_bc = idx_n[None, :] - - # Enforce strictly upper triangular for half-list, or just i != j for full - valid_mask = ( - is_neighbor - & (idx_m_bc < idx_n_bc) - & mask_m[:, None] - & mask_n[None, :] - ) - - # --- Block-wise Output Allocation --- - # Count how many pairs we found in this tile - # We cast to int32 because sum returns same dtype as input (int1) - num_found = tl.sum(valid_mask.to(tl.int32)) - - if num_found > 0: - # ATOMIC ADD: Reserve space in the global output buffer - # This happens once per tile, not per pair -> Much faster - current_offset = tl.atomic_add(GlobalCounter, num_found) - - # Guard against overflow - if current_offset + num_found <= max_pairs: - # We need to flatten the 2D block results to 1D to write them out. - # Triton doesn't have a direct "compress/compact" instruction yet for 2D->1D. - # We compute running index (prefix sum) within the block to find write slot. - - # Flatten the mask to [BLOCK_M * BLOCK_M] - flat_mask = tl.ravel(valid_mask) - - # Compute exclusive scan (cumsum) to determine thread offset - # Note: tl.cumsum is not fully exposed on all blocks easily, - # usually strictly sequential manual scan or use 'where' logic. - # OPTIMIZATION: For Triton, we can iterate positions. - - # Because fully parallel scan is complex in a snippet, - # we verify valid indices and write. - - # Calculate write indices - # This is a bit tricky in Triton without `scan`. - # Simpler approach for snippet: iterate and write (serialized within block) - # OR utilize `tl.where` with pre-calculated indices if density is high. - - # High-Performance approach: - # Since we know `num_found` is small (sparse), we can just store - # the valid pairs. - - # Generate offsets for valid elements - csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) - # csum is inclusive, so write_idx = start + csum - 1 - - store_idx = current_offset + csum - 1 - - # Flatten data - flat_dx = tl.ravel(dx) - flat_dy = tl.ravel(dy) - flat_dz = tl.ravel(dz) - flat_dsq = tl.ravel(dist_sq) - flat_idx_m = tl.ravel(idx_m_bc) - flat_idx_n = tl.ravel(idx_n_bc) - - # Write only valid entries - tl.store( - OutPairs + 0 * max_pairs + store_idx, - flat_idx_m, - mask=flat_mask, - ) - tl.store( - OutPairs + 1 * max_pairs + store_idx, - flat_idx_n, - mask=flat_mask, - ) - - tl.store( - OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask - ) - tl.store( - OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask - ) - tl.store( - OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask - ) - - tl.store( - OutDists + store_idx, tl.sqrt(flat_dsq), mask=flat_mask - ) - - -def find_neighbors(positions, box, cutoff, max_pairs=10_000_000): - """ - positions: [N, 3] tensor - box: [3] tensor (Lx, Ly, Lz) - cutoff: scalar - """ - N = positions.shape[0] - device = positions.device - - # 1. Setup Cell Grid (Host side logic) - # Ensure cell size >= cutoff - n_cells = (box / cutoff).floor().int() - cell_size = box / n_cells - - # 2. Sort Particles (Crucial for performance) - # Calculate cell index for every atom - cell_idx_x = (positions[:, 0] / cell_size[0]).long().clamp(0, n_cells[0] - 1) - cell_idx_y = (positions[:, 1] / cell_size[1]).long().clamp(0, n_cells[1] - 1) - cell_idx_z = (positions[:, 2] / cell_size[2]).long().clamp(0, n_cells[2] - 1) - - # Flat cell index - flat_cell_indices = ( - cell_idx_x * n_cells[1] * n_cells[2] + cell_idx_y * n_cells[2] + cell_idx_z - ) - - # Sort atoms by cell index - sorted_cell_indices, sort_idx = torch.sort(flat_cell_indices) - sorted_pos = positions[sort_idx].contiguous() - - # 3. Build Cell Pointers (Start/Count) - # Using unique_consecutive to find cell boundaries - unique_cells, counts = torch.unique_consecutive( - sorted_cell_indices, return_counts=True - ) - - # We need dense arrays for the kernel [Total_Cells] - total_cells = n_cells[0] * n_cells[1] * n_cells[2] - cell_start = torch.zeros(total_cells, dtype=torch.int32, device=device) - cell_count = torch.zeros(total_cells, dtype=torch.int32, device=device) - - # Compute starts (requires a search or scatter, here simplified) - # In production, use scatter_add or searchsorted - # Simple trick: scatter counts - cell_count.index_add_(0, unique_cells.int(), counts.int()) - - # Cumulative sum to get starts? No, we need starts in the *sorted* array. - # Since we sorted by cell, we can find starts easily. - # A fast way involves searchsorted if cells are sparse, - # but for water (dense), most cells are filled. - # We can compute starts using the counts and a prefix sum, - # but strictly speaking, we need the index in 'sorted_pos'. - - # Fast GPU-friendly start index construction: - # Find where cell index changes - change_mask = torch.cat( - [ - torch.tensor([1], device=device), - sorted_cell_indices[1:] != sorted_cell_indices[:-1], - ] - ) - change_indices = torch.nonzero(change_mask).squeeze() - # This maps unique_cells -> start_index in sorted_pos - cell_start.index_copy_(0, unique_cells.int(), change_indices.int()) - - # 4. Prepare Outputs - out_pairs = torch.empty((2, max_pairs), dtype=torch.int32, device=device) - out_deltas = torch.empty((max_pairs, 3), dtype=torch.float32, device=device) - out_dists = torch.empty((max_pairs,), dtype=torch.float32, device=device) - global_counter = torch.zeros((1,), dtype=torch.int32, device=device) - - # 5. Launch Kernel - grid = lambda META: (triton.cdiv(N, META["BLOCK_M"]),) - - neighbor_list_kernel[grid]( - sorted_pos, - cell_start, - cell_count, - out_pairs, - out_deltas, - out_dists, - global_counter, - N, - max_pairs, - cutoff**2, - box[0].item(), - box[1].item(), - box[2].item(), - n_cells[0].item(), - n_cells[1].item(), - n_cells[2].item(), - BLOCK_M=64, # Tuning parameter - ) - - num_pairs = global_counter.item() - - # Note: The output pairs use indices from 'sorted_pos'. - # If you need original indices, you must map them back using 'sort_idx'. - # remapped_pairs = sort_idx[out_pairs[:, :num_pairs]] - - return ( - out_pairs[:, :num_pairs], - out_deltas[:num_pairs], - out_dists[:num_pairs], - num_pairs, - ) From a4b4564b631acd2195eee7acd50fe08d91f5aa58 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 16:20:57 +0200 Subject: [PATCH 26/80] wip --- torchmdnet/extensions/triton_cell.py | 93 ++++++++++++++-------------- 1 file changed, 47 insertions(+), 46 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index d0e9bb5ad..bc2acc0b2 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -1,3 +1,5 @@ +import math + try: import triton import triton.language as tl @@ -18,16 +20,6 @@ ) -import torch -import triton -import triton.language as tl - - -import torch -import triton -import triton.language as tl - - @triton.jit def neighbor_list_kernel( # Pointers @@ -75,14 +67,16 @@ def neighbor_list_kernel( cell_sz = box_z / n_cells_z # Local coords for cell calculation (vectorized for all atoms in block) - px = pos_mx - py = pos_my - pz = pos_mz - + # Match CUDA implementation: for periodic, wrap to [0, box); for non-periodic, shift by 0.5*box if use_periodic: - px = px - box_x * tl.math.floor(px / box_x) - py = py - box_y * tl.math.floor(py / box_y) - pz = pz - box_z * tl.math.floor(pz / box_z) + px = pos_mx - box_x * tl.math.floor(pos_mx / box_x) + py = pos_my - box_y * tl.math.floor(pos_my / box_y) + pz = pos_mz - box_z * tl.math.floor(pos_mz / box_z) + else: + # For non-periodic, shift by half box (like CUDA impl: (p + 0.5*box) / cutoff) + px = pos_mx + 0.5 * box_x + py = pos_my + 0.5 * box_y + pz = pos_mz + 0.5 * box_z # Cell indices for each atom in the block (vectors of size BLOCK_M) c_i_vec = (px / cell_sx).to(tl.int32) @@ -418,7 +412,12 @@ def forward( # type: ignore[override] if max_num_pairs <= 0: raise ValueError('Expected "max_num_pairs" to be positive') - box_vectors = box_vectors.to(dtype=dtype, device=device).contiguous() + # Handle box_vectors - following CUDA cell implementation approach: + # The box must be provided and valid. For non-periodic, the box defines the cell grid bounds. + # Move to correct device/dtype only if needed (avoid CPU sync during graph capture) + if box_vectors.device != device or box_vectors.dtype != dtype: + box_vectors = box_vectors.to(dtype=dtype, device=device) + box_vectors = box_vectors.contiguous() box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors box_x, box_y, box_z = ( float(box_diag[0, 0]), @@ -426,17 +425,16 @@ def forward( # type: ignore[override] float(box_diag[2, 2]), ) + # Compute cell dimensions - match CUDA implementation limits cell_dim_x, cell_dim_y, cell_dim_z = _get_cell_dimensions( box_vectors, float(cutoff_upper) ) num_cells = cell_dim_x * cell_dim_y * cell_dim_z # 1. Cell Index Calculation - # If Periodic: Wrap positions into [0, Box]. - # If Non-Periodic: We do NOT wrap positions physically (to preserve absolute coords), - # but we shift/map them to cell grid indices. - # Standard assumption: Positions are roughly within the box definition. - + # For periodic: wrap positions to [0, box) using PBC + # For non-periodic: shift positions by half box (matching CUDA cell implementation) + # The CUDA implementation uses: (p + 0.5 * box_size) / cutoff for cell index if use_periodic: inv_box_x = 1.0 / box_x inv_box_y = 1.0 / box_y @@ -445,9 +443,11 @@ def forward( # type: ignore[override] py = positions[:, 1] - torch.floor(positions[:, 1] * inv_box_y) * box_y pz = positions[:, 2] - torch.floor(positions[:, 2] * inv_box_z) * box_z else: - # For non-periodic, use raw coords. - # We assume box starts at (0,0,0). If atoms are negative, we clamp index to 0. - px, py, pz = positions[:, 0], positions[:, 1], positions[:, 2] + # For non-periodic, shift positions by half box (like CUDA impl) + # This centers the cell grid around the origin + px = positions[:, 0] + 0.5 * box_x + py = positions[:, 1] + 0.5 * box_y + pz = positions[:, 2] + 0.5 * box_z cx = (px / cutoff_upper).long().clamp(0, cell_dim_x - 1) cy = (py / cutoff_upper).long().clamp(0, cell_dim_y - 1) @@ -464,24 +464,29 @@ def forward( # type: ignore[override] ).contiguous() sorted_batch = batch.index_select(0, sorted_atom_indices.long()).contiguous() - # 3. Cell Pointers - cell_start = torch.full((num_cells,), -1, device=device, dtype=torch.int32) + # 3. Cell Pointers (CUDA graph compatible using scatter_reduce) + # Initialize cell_start with n_atoms (will use scatter_reduce 'amin') + # Initialize cell_end with 0 (will use scatter_reduce 'amax') + cell_start = torch.full((num_cells,), n_atoms, device=device, dtype=torch.int32) cell_end = torch.zeros((num_cells,), device=device, dtype=torch.int32) - change_mask = torch.cat( - [ - torch.tensor([True], device=device), - sorted_cell_indices[1:] != sorted_cell_indices[:-1], - ] + # Compute cell boundaries using scatter_reduce (fixed output size, CUDA graph compatible) + atom_indices = torch.arange(n_atoms, device=device, dtype=torch.int32) + sorted_cell_indices_long = sorted_cell_indices.long() + # cell_start[cell] = min index of atoms in that cell + cell_start.scatter_reduce_( + 0, sorted_cell_indices_long, atom_indices, reduce="amin", include_self=True ) - change_indices = torch.nonzero(change_mask, as_tuple=False).squeeze(-1) - unique_cells = sorted_cell_indices[change_indices] - end_indices = torch.cat( - [change_indices[1:], torch.tensor([n_atoms], device=device)] + # cell_end[cell] = max index + 1 of atoms in that cell + end_indices = atom_indices + 1 + cell_end.scatter_reduce_( + 0, sorted_cell_indices_long, end_indices, reduce="amax", include_self=True ) - cell_start.index_copy_(0, unique_cells.long(), change_indices.int()) - cell_end.index_copy_(0, unique_cells.long(), end_indices.int()) + # Mark empty cells with -1 for cell_start (cells where cell_end remained 0) + # Use in-place operation to avoid tensor creation during CUDA graph capture + empty_cell_mask = cell_end == 0 + cell_start.masked_fill_(empty_cell_mask, -1) # 4. Kernel Launch neighbors = torch.empty((2, max_num_pairs), device=device, dtype=torch.long) @@ -522,13 +527,9 @@ def forward( # type: ignore[override] MAX_TILES=max_tiles, ) - num_found = counter.item() - - # Trim results - neighbors = neighbors[:, :num_found] - deltas = deltas[:num_found] - distances = distances[:num_found] + # Return counter as tensor to avoid GPU->CPU sync (CUDA graph compatible) + num_pairs = counter.to(torch.long) ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms - return neighbors, deltas, distances, torch.tensor([num_found], device=device) + return neighbors, deltas, distances, num_pairs From ade0732a1ca527615817bad80b8d0128bc4041b3 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Mon, 15 Dec 2025 16:22:14 +0200 Subject: [PATCH 27/80] wip --- torchmdnet/extensions/triton_cell.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index bc2acc0b2..2aec8067e 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -489,9 +489,10 @@ def forward( # type: ignore[override] cell_start.masked_fill_(empty_cell_mask, -1) # 4. Kernel Launch - neighbors = torch.empty((2, max_num_pairs), device=device, dtype=torch.long) - deltas = torch.empty((max_num_pairs, 3), device=device, dtype=dtype) - distances = torch.empty((max_num_pairs,), device=device, dtype=dtype) + # Initialize neighbors to -1 so unused slots are properly marked + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) counter = torch.zeros((1,), device=device, dtype=torch.int32) BLOCK_M = 64 From 96bdd98df112b56e726d86df142ca209c90d2228 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 12:41:26 +0200 Subject: [PATCH 28/80] different cell impl --- torchmdnet/extensions/ops.py | 32 +----- .../extensions/torchmdnet_extensions.cpp | 73 -------------- torchmdnet/extensions/triton_cell.py | 99 ++++++++++++------- torchmdnet/extensions/triton_neighbors.py | 23 +---- torchmdnet/models/output_modules.py | 3 +- torchmdnet/models/utils.py | 24 ++++- 6 files changed, 96 insertions(+), 158 deletions(-) delete mode 100644 torchmdnet/extensions/torchmdnet_extensions.cpp diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index 5a77b8f7e..d10a58762 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -10,36 +10,11 @@ import torch from torch import Tensor from typing import Tuple -from torch.library import register_fake from . import triton_neighbors -__all__ = ["is_current_stream_capturing", "get_neighbor_pairs_kernel"] - - -def is_current_stream_capturing(): - """Returns True if the current CUDA stream is capturing. - - Returns False if CUDA is not available or the current stream is not capturing. - - This utility is required because the builtin torch function that does this is not scriptable. - """ - _is_current_stream_capturing = ( - torch.ops.torchmdnet_extensions.is_current_stream_capturing - ) - return _is_current_stream_capturing() - - -@torch.jit.ignore -def _stream_is_capturing_runtime() -> bool: - try: - return torch.cuda.is_current_stream_capturing() - except Exception: - try: - return is_current_stream_capturing() - except Exception: - return False +__all__ = ["get_neighbor_pairs_kernel"] def get_neighbor_pairs_kernel( @@ -53,6 +28,7 @@ def get_neighbor_pairs_kernel( max_num_pairs: int, loop: bool, include_transpose: bool, + num_cells: int, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: """Computes the neighbor pairs for a given set of atomic positions. The list is generated as a list of pairs (i,j) without any enforced ordering. @@ -80,7 +56,8 @@ def get_neighbor_pairs_kernel( Whether to include self-interactions. include_transpose : bool Whether to include the transpose of the neighbor list (pair i,j and pair j,i). - + num_cells : int + The number of cells in the grid if using the cell strategy. Returns ------- neighbors : Tensor @@ -117,4 +94,5 @@ def get_neighbor_pairs_kernel( max_num_pairs, loop, include_transpose, + num_cells, ) diff --git a/torchmdnet/extensions/torchmdnet_extensions.cpp b/torchmdnet/extensions/torchmdnet_extensions.cpp deleted file mode 100644 index 992a3b178..000000000 --- a/torchmdnet/extensions/torchmdnet_extensions.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * Raul P. Pelaez 2023. Torch extensions to the torchmdnet library. - * You can expose functions to python here which will be compatible with TorchScript. - * Add your exports to the TORCH_LIBRARY macro below, see __init__.py to see how to access them from python. - * The WITH_CUDA macro will be defined when compiling with CUDA support. - */ - - -#include -#include -#include -#include - -#if defined(WITH_CUDA) -#include -#include -#endif - - -extern "C" { - /* Creates a dummy empty torchmdnet_extensions module that can be imported from Python. - The import from Python will load the .so consisting of this file - in this extension, so that the TORCH_LIBRARY static initializers - below are run. */ - PyObject* PyInit_torchmdnet_extensions(void) - { - static struct PyModuleDef module_def = { - PyModuleDef_HEAD_INIT, - "torchmdnet_extensions", /* name of module */ - NULL, /* module documentation, may be NULL */ - -1, /* size of per-interpreter state of the module, - or -1 if the module keeps state in global variables. */ - NULL, /* methods */ - }; - return PyModule_Create(&module_def); - } -} - -/* @brief Returns true if the current torch CUDA stream is capturing. - * This function is required because the one available in torch is not compatible with TorchScript. - * @return True if the current torch CUDA stream is capturing. - */ -bool is_current_stream_capturing() { -#if defined(WITH_CUDA) - auto current_stream = at::cuda::getCurrentCUDAStream().stream(); - cudaStreamCaptureStatus capture_status; - cudaError_t err = cudaStreamGetCaptureInfo(current_stream, &capture_status, nullptr); - if (err != cudaSuccess) { - throw std::runtime_error(cudaGetErrorString(err)); - } - return capture_status == cudaStreamCaptureStatus::cudaStreamCaptureStatusActive; -#else - return false; -#endif -} - -TORCH_LIBRARY(torchmdnet_extensions, m) { - m.def("is_current_stream_capturing", is_current_stream_capturing); - m.def("get_neighbor_pairs(str strategy, Tensor positions, Tensor batch, Tensor box_vectors, " - "bool use_periodic, Scalar cutoff_lower, Scalar cutoff_upper, Scalar max_num_pairs, bool " - "loop, bool include_transpose) -> (Tensor neighbors, Tensor distances, Tensor " - "distance_vecs, Tensor num_pairs)"); - //The individual fwd and bkwd functions must be exposed in order to register their meta implementations python side. - m.def("get_neighbor_pairs_fwd(str strategy, Tensor positions, Tensor batch, Tensor box_vectors, " - "bool use_periodic, Scalar cutoff_lower, Scalar cutoff_upper, Scalar max_num_pairs, bool " - "loop, bool include_transpose) -> (Tensor neighbors, Tensor distances, Tensor " - "distance_vecs, Tensor num_pairs)"); - m.def("get_neighbor_pairs_bkwd(Tensor grad_edge_vec, Tensor grad_edge_weight, Tensor edge_index, " - "Tensor edge_vec, Tensor edge_weight, int num_atoms) -> Tensor"); -} diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 2aec8067e..1b895379e 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -1,5 +1,3 @@ -import math - try: import triton import triton.language as tl @@ -16,7 +14,6 @@ _tl_round, TritonNeighborAutograd, _validate_box, - _get_cell_dimensions, ) @@ -32,17 +29,13 @@ def neighbor_list_kernel( OutDeltas, OutDists, GlobalCounter, + BoxSizes, # Pointer to [box_x, box_y, box_z] + CellDims, # Pointer to [n_cells_x, n_cells_y, n_cells_z] # Parameters n_atoms, max_pairs, cutoff_lower_sq, cutoff_upper_sq, - box_x, - box_y, - box_z, - n_cells_x, - n_cells_y, - n_cells_z, # Flags use_periodic: tl.constexpr, # Enable/Disable PBC loop: tl.constexpr, # Enable/Disable Self-interactions @@ -54,6 +47,16 @@ def neighbor_list_kernel( idx_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) mask_m = idx_m < n_atoms + # Load box sizes (CUDA graph compatible - no CPU sync) + box_x = tl.load(BoxSizes + 0) + box_y = tl.load(BoxSizes + 1) + box_z = tl.load(BoxSizes + 2) + + # Load cell dimensions (CUDA graph compatible - no CPU sync) + n_cells_x = tl.load(CellDims + 0) + n_cells_y = tl.load(CellDims + 1) + n_cells_z = tl.load(CellDims + 2) + # Load Query Data off_m = idx_m pos_mx = tl.load(SortedCoords + off_m * 3 + 0, mask=mask_m, other=0.0) @@ -383,6 +386,18 @@ def find_neighbors(positions, box, cutoff, max_pairs=10_000_000): ) +def _get_cell_dimensions( + box_x: torch.float32, + box_y: torch.float32, + box_z: torch.float32, + cutoff_upper: torch.float32, +) -> int: + nx = torch.floor(box_x / cutoff_upper).clamp(min=3).long() + ny = torch.floor(box_y / cutoff_upper).clamp(min=3).long() + nz = torch.floor(box_z / cutoff_upper).clamp(min=3).long() + return torch.stack([nx, ny, nz]) + + class TritonCellNeighborAutograd(TritonNeighborAutograd): @staticmethod def forward( # type: ignore[override] @@ -391,11 +406,12 @@ def forward( # type: ignore[override] batch: Tensor, box_vectors: Tensor, use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, + cutoff_lower: torch.float32, + cutoff_upper: torch.float32, max_num_pairs: int, loop: bool, include_transpose: bool, + num_cells: int, ): if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -415,21 +431,19 @@ def forward( # type: ignore[override] # Handle box_vectors - following CUDA cell implementation approach: # The box must be provided and valid. For non-periodic, the box defines the cell grid bounds. # Move to correct device/dtype only if needed (avoid CPU sync during graph capture) - if box_vectors.device != device or box_vectors.dtype != dtype: - box_vectors = box_vectors.to(dtype=dtype, device=device) + # if box_vectors.device != device or box_vectors.dtype != dtype: + # box_vectors = box_vectors.to(dtype=dtype, device=device) box_vectors = box_vectors.contiguous() box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors - box_x, box_y, box_z = ( - float(box_diag[0, 0]), - float(box_diag[1, 1]), - float(box_diag[2, 2]), - ) + # Keep as tensors to avoid GPU->CPU sync during CUDA graph capture + box_x = box_diag[0, 0] + box_y = box_diag[1, 1] + box_z = box_diag[2, 2] + # Create a contiguous tensor for kernel (CUDA graph compatible) + box_sizes = torch.stack([box_x, box_y, box_z]).contiguous() - # Compute cell dimensions - match CUDA implementation limits - cell_dim_x, cell_dim_y, cell_dim_z = _get_cell_dimensions( - box_vectors, float(cutoff_upper) - ) - num_cells = cell_dim_x * cell_dim_y * cell_dim_z + # Compute dimensions using torch operations (stays on GPU) + cell_dims = _get_cell_dimensions(box_x, box_y, box_z, cutoff_upper) # 1. Cell Index Calculation # For periodic: wrap positions to [0, box) using PBC @@ -449,11 +463,22 @@ def forward( # type: ignore[override] py = positions[:, 1] + 0.5 * box_y pz = positions[:, 2] + 0.5 * box_z - cx = (px / cutoff_upper).long().clamp(0, cell_dim_x - 1) - cy = (py / cutoff_upper).long().clamp(0, cell_dim_y - 1) - cz = (pz / cutoff_upper).long().clamp(0, cell_dim_z - 1) + # Compute cell indices and clamp using broadcasting to avoid scalar extraction + # Stack positions for vectorized operation + p_stacked = torch.stack([px, py, pz], dim=1) # (n_atoms, 3) + cell_coords = (p_stacked / cutoff_upper).long() # (n_atoms, 3) - cell_indices = cx * (cell_dim_y * cell_dim_z) + cy * cell_dim_z + cz + # Clamp using broadcasting: cell_dims is shape (3,), cell_coords is (n_atoms, 3) + cell_coords = torch.clamp( + cell_coords, min=torch.zeros(3, device=device), max=cell_dims - 1 + ) + + # Extract individual coordinates + cx = cell_coords[:, 0] + cy = cell_coords[:, 1] + cz = cell_coords[:, 2] + + cell_indices = cx * (cell_dims[1] * cell_dims[2]) + cy * cell_dims[2] + cz # 2. Sort sorted_cell_indices, sorted_atom_indices = torch.sort(cell_indices) @@ -467,6 +492,7 @@ def forward( # type: ignore[override] # 3. Cell Pointers (CUDA graph compatible using scatter_reduce) # Initialize cell_start with n_atoms (will use scatter_reduce 'amin') # Initialize cell_end with 0 (will use scatter_reduce 'amax') + # num_cells_t is a tensor, PyTorch will extract the value for buffer size cell_start = torch.full((num_cells,), n_atoms, device=device, dtype=torch.int32) cell_end = torch.zeros((num_cells,), device=device, dtype=torch.int32) @@ -511,16 +537,12 @@ def forward( # type: ignore[override] deltas, distances, counter, + box_sizes, + cell_dims, n_atoms, max_num_pairs, - float(cutoff_lower**2), - float(cutoff_upper**2), - box_x, - box_y, - box_z, - int(cell_dim_x), - int(cell_dim_y), - int(cell_dim_z), + cutoff_lower**2, + cutoff_upper**2, use_periodic=use_periodic, loop=loop, include_transpose=include_transpose, @@ -534,3 +556,10 @@ def forward( # type: ignore[override] ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms return neighbors, deltas, distances, num_pairs + + @staticmethod + def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] + bwd = TritonNeighborAutograd.backward( + ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs + ) + return *bwd, None diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index b94283c06..bca2842ce 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -44,23 +44,6 @@ def _validate_box(box_vectors: Tensor, cutoff_upper: float, n_batch: int) -> Ten return box_vectors -def _get_cell_dimensions( - box_vectors: Tensor, cutoff_upper: float -) -> Tuple[int, int, int]: - """Return (nx, ny, nz) cell counts following the CUDA cell list logic.""" - # box_vectors is either (3, 3) or (1, 3, 3) and already validated as diagonal - if box_vectors.dim() == 3: - box_diag = box_vectors[0] - else: - box_diag = box_vectors - nx = int(max(math.floor(float(box_diag[0, 0]) / cutoff_upper), 3)) - ny = int(max(math.floor(float(box_diag[1, 1]) / cutoff_upper), 3)) - nz = int(max(math.floor(float(box_diag[2, 2]) / cutoff_upper), 3)) - if nx > 1024 or ny > 1024 or nz > 1024: - raise RuntimeError("Too many cells in one dimension. Maximum is 1024") - return nx, ny, nz - - def _apply_pbc_torch(deltas: Tensor, box_for_pairs: Tensor) -> Tensor: # box_for_pairs: (num_pairs, 3, 3) scale3 = _round_nearest(deltas[:, 2] / box_for_pairs[:, 2, 2]) @@ -266,6 +249,7 @@ def triton_neighbor_pairs( max_num_pairs: int, loop: bool, include_transpose: bool, + num_cells: int, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd @@ -296,11 +280,12 @@ def triton_neighbor_pairs( batch, box_vectors, use_periodic, - float(cutoff_lower), - float(cutoff_upper), + cutoff_lower, + cutoff_upper, int(max_num_pairs), bool(loop), bool(include_transpose), + num_cells, ) elif strategy == "shared": return TritonSharedNeighborAutograd.apply( diff --git a/torchmdnet/models/output_modules.py b/torchmdnet/models/output_modules.py index a2537a0e6..5bcee7d08 100644 --- a/torchmdnet/models/output_modules.py +++ b/torchmdnet/models/output_modules.py @@ -13,7 +13,6 @@ MLP, ) from torchmdnet.utils import atomic_masses -from torchmdnet.extensions.ops import is_current_stream_capturing from warnings import warn __all__ = ["Scalar", "DipoleMoment", "ElectronicSpatialExtent"] @@ -40,7 +39,7 @@ def pre_reduce(self, x, v, z, pos, batch): return def reduce(self, x, batch): - is_capturing = x.is_cuda and is_current_stream_capturing() + is_capturing = x.is_cuda and torch.cuda.is_current_stream_capturing() if not x.is_cuda or not is_capturing: self.dim_size = int(batch.max().item() + 1) if is_capturing: diff --git a/torchmdnet/models/utils.py b/torchmdnet/models/utils.py index 7b0452a9e..c1c579233 100644 --- a/torchmdnet/models/utils.py +++ b/torchmdnet/models/utils.py @@ -207,6 +207,7 @@ def __init__( self.include_transpose = include_transpose self.resize_to_fit = resize_to_fit self.use_periodic = True + self.num_cells = 0 if self.box is None: self.use_periodic = False self.box = torch.empty((0, 0)) @@ -217,7 +218,18 @@ def __init__( [[lbox, 0, 0], [0, lbox, 0], [0, 0, lbox]], device="cpu" ) if self.strategy == "cell": - self.box = self.box.cpu() + from torchmdnet.extensions.triton_cell import _get_cell_dimensions + + cell_dims = _get_cell_dimensions( + self.box[0, 0], self.box[1, 1], self.box[2, 2], cutoff_upper + ) + self.num_cells = int(cell_dims.prod()) + if self.num_cells > 1024**3: + raise RuntimeError( + f"Too many cells: {self.num_cells}. Maximum is 1024^3. " + f"Reduce box size or increase cutoff." + ) + self.check_errors = check_errors self.long_edge_index = long_edge_index @@ -255,9 +267,16 @@ def forward( use_periodic = self.use_periodic if not use_periodic: use_periodic = box is not None + using_default_box = box is None box = self.box if box is None else box assert box is not None, "Box must be provided" - box = box.to(pos.dtype) + # Move box to correct device/dtype. Cache result if using default box + # to avoid CPU->GPU transfer during CUDA graph capture. + if box.device != pos.device or box.dtype != pos.dtype: + box = box.to(dtype=pos.dtype, device=pos.device) + if using_default_box: + self.box = box + max_pairs: int = self.max_num_pairs if self.max_num_pairs < 0: max_pairs = -self.max_num_pairs * pos.shape[0] @@ -274,6 +293,7 @@ def forward( include_transpose=self.include_transpose, box_vectors=box, use_periodic=use_periodic, + num_cells=self.num_cells, ) if self.check_errors: assert ( From c14981a8a092fe61431dbec28a54249e98c5f5fc Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 13:26:38 +0200 Subject: [PATCH 29/80] one more cell implementation --- torchmdnet/extensions/triton_cell.py | 110 ----- torchmdnet/extensions/triton_cell_v2.py | 498 ++++++++++++++++++++++ torchmdnet/extensions/triton_neighbors.py | 3 +- 3 files changed, 500 insertions(+), 111 deletions(-) create mode 100644 torchmdnet/extensions/triton_cell_v2.py diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 1b895379e..f129b93be 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -276,116 +276,6 @@ def neighbor_list_kernel( ) -def find_neighbors(positions, box, cutoff, max_pairs=10_000_000): - """ - positions: [N, 3] tensor - box: [3] tensor (Lx, Ly, Lz) - cutoff: scalar - """ - N = positions.shape[0] - device = positions.device - - # 1. Setup Cell Grid (Host side logic) - # Ensure cell size >= cutoff - n_cells = (box / cutoff).floor().int() - cell_size = box / n_cells - - # 2. Sort Particles (Crucial for performance) - # Calculate cell index for every atom - cell_idx_x = (positions[:, 0] / cell_size[0]).long().clamp(0, n_cells[0] - 1) - cell_idx_y = (positions[:, 1] / cell_size[1]).long().clamp(0, n_cells[1] - 1) - cell_idx_z = (positions[:, 2] / cell_size[2]).long().clamp(0, n_cells[2] - 1) - - # Flat cell index - flat_cell_indices = ( - cell_idx_x * n_cells[1] * n_cells[2] + cell_idx_y * n_cells[2] + cell_idx_z - ) - - # Sort atoms by cell index - sorted_cell_indices, sort_idx = torch.sort(flat_cell_indices) - sorted_pos = positions[sort_idx].contiguous() - - # 3. Build Cell Pointers (Start/Count) - # Using unique_consecutive to find cell boundaries - unique_cells, counts = torch.unique_consecutive( - sorted_cell_indices, return_counts=True - ) - - # We need dense arrays for the kernel [Total_Cells] - total_cells = n_cells[0] * n_cells[1] * n_cells[2] - cell_start = torch.zeros(total_cells, dtype=torch.int32, device=device) - cell_count = torch.zeros(total_cells, dtype=torch.int32, device=device) - - # Compute starts (requires a search or scatter, here simplified) - # In production, use scatter_add or searchsorted - # Simple trick: scatter counts - cell_count.index_add_(0, unique_cells.int(), counts.int()) - - # Cumulative sum to get starts? No, we need starts in the *sorted* array. - # Since we sorted by cell, we can find starts easily. - # A fast way involves searchsorted if cells are sparse, - # but for water (dense), most cells are filled. - # We can compute starts using the counts and a prefix sum, - # but strictly speaking, we need the index in 'sorted_pos'. - - # Fast GPU-friendly start index construction: - # Find where cell index changes - change_mask = torch.cat( - [ - torch.tensor([1], device=device), - sorted_cell_indices[1:] != sorted_cell_indices[:-1], - ] - ) - change_indices = torch.nonzero(change_mask).squeeze() - # This maps unique_cells -> start_index in sorted_pos - cell_start.index_copy_(0, unique_cells.int(), change_indices.int()) - - # 4. Prepare Outputs - out_pairs = torch.empty((2, max_pairs), dtype=torch.int32, device=device) - out_deltas = torch.empty((max_pairs, 3), dtype=torch.float32, device=device) - out_dists = torch.empty((max_pairs,), dtype=torch.float32, device=device) - global_counter = torch.zeros((1,), dtype=torch.int32, device=device) - - # 5. Launch Kernel - grid = lambda META: (triton.cdiv(N, META["BLOCK_M"]),) - BLOCK_M = 64 - max_tiles = (N + BLOCK_M - 1) // BLOCK_M - - neighbor_list_kernel[grid]( - sorted_pos, - cell_start, - cell_count, - out_pairs, - out_deltas, - out_dists, - global_counter, - N, - max_pairs, - cutoff**2, - box[0].item(), - box[1].item(), - box[2].item(), - n_cells[0].item(), - n_cells[1].item(), - n_cells[2].item(), - BLOCK_M=BLOCK_M, # Tuning parameter - MAX_TILES=max_tiles, - ) - - num_pairs = global_counter.item() - - # Note: The output pairs use indices from 'sorted_pos'. - # If you need original indices, you must map them back using 'sort_idx'. - # remapped_pairs = sort_idx[out_pairs[:, :num_pairs]] - - return ( - out_pairs[:, :num_pairs], - out_deltas[:num_pairs], - out_dists[:num_pairs], - num_pairs, - ) - - def _get_cell_dimensions( box_x: torch.float32, box_y: torch.float32, diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py new file mode 100644 index 000000000..b1b2c7488 --- /dev/null +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -0,0 +1,498 @@ +""" +Efficient cell list neighbor list implementation for Triton. + +Key design: +- One program per cell (not per atom) +- Fixed-size padded cell buffers for CUDA graph compatibility +- O(n) complexity: each cell examines only 27 neighboring cells +""" + +try: + import triton + import triton.language as tl + + _HAS_TRITON = True +except Exception: + triton = None + tl = None + _HAS_TRITON = False + +import torch +from torch import Tensor +from typing import Tuple + +from torchmdnet.extensions.triton_neighbors import TritonNeighborAutograd +from torchmdnet.extensions.triton_cell import _get_cell_dimensions + + +@triton.jit +def _tl_round(x): + return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) + + +@triton.jit +def cell_neighbor_kernel( + # Cell data structure + CellAtoms, # [num_cells, MAX_ATOMS_PER_CELL] - atom indices in each cell + CellCounts, # [num_cells] - number of atoms in each cell + Positions, # [n_atoms, 3] - positions (original order) + Batch, # [n_atoms] - batch indices + # Box parameters + BoxSizes, # [3] - box dimensions + CellDims, # [3] - number of cells in each dimension (int32) + # Output + OutPairs, # [2, max_pairs] + OutDeltas, # [max_pairs, 3] + OutDists, # [max_pairs] + GlobalCounter, # [1] + # Scalar parameters + max_pairs, + cutoff_lower_sq, + cutoff_upper_sq, + # Flags + use_periodic: tl.constexpr, + loop: tl.constexpr, + include_transpose: tl.constexpr, + # Block sizes + MAX_ATOMS_PER_CELL: tl.constexpr, +): + """ + Each program processes one cell (the "home" cell). + It finds all pairs where the first atom is in the home cell + and the second atom is in one of the 27 neighboring cells (including home). + + To avoid double-counting, we use the convention: + - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom + - For full list (include_transpose=True): emit both directions + """ + home_cell_id = tl.program_id(0) + + # Load box and cell dimensions + box_x = tl.load(BoxSizes + 0) + box_y = tl.load(BoxSizes + 1) + box_z = tl.load(BoxSizes + 2) + + num_cells_x = tl.load(CellDims + 0) + num_cells_y = tl.load(CellDims + 1) + num_cells_z = tl.load(CellDims + 2) + + # Decompose home cell ID into 3D coordinates + cells_yz = num_cells_y * num_cells_z + home_cx = home_cell_id // cells_yz + home_cy = (home_cell_id % cells_yz) // num_cells_z + home_cz = home_cell_id % num_cells_z + + # Load home cell data + home_count = tl.load(CellCounts + home_cell_id) + home_idx = tl.arange(0, MAX_ATOMS_PER_CELL) + home_mask = home_idx < home_count + + # Load atom indices for home cell + home_atoms = tl.load( + CellAtoms + home_cell_id * MAX_ATOMS_PER_CELL + home_idx, + mask=home_mask, + other=0, + ) + + # Load positions for home atoms (gather) + home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) + home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) + home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) + home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) + + # Loop over 27 neighbor cells + # Use tl.range() for runtime loop (not unrolled at compile time) + for neighbor_offset in tl.range(0, 27): + # Decompose neighbor_offset into di, dj, dk (each in {-1, 0, 1}) + di = (neighbor_offset % 3) - 1 + dj = ((neighbor_offset // 3) % 3) - 1 + dk = (neighbor_offset // 9) - 1 + + # Compute neighbor cell coordinates + ni = home_cx + di + nj = home_cy + dj + nk = home_cz + dk + + # Handle boundary conditions + if use_periodic: + # Wrap around with PBC + ni = (ni + num_cells_x) % num_cells_x + nj = (nj + num_cells_y) % num_cells_y + nk = (nk + num_cells_z) % num_cells_z + cell_valid = True + else: + # Check bounds for non-periodic + cell_valid = ( + (ni >= 0) + & (ni < num_cells_x) + & (nj >= 0) + & (nj < num_cells_y) + & (nk >= 0) + & (nk < num_cells_z) + ) + + # Skip invalid cells (non-periodic boundary) + # Note: We can't early-continue in Triton, so we use masking + neighbor_cell_id = ni * cells_yz + nj * num_cells_z + nk + + # Load neighbor cell data + neighbor_count = tl.load(CellCounts + neighbor_cell_id) + # Apply cell_valid to count (if cell invalid, treat as empty) + neighbor_count = tl.where(cell_valid, neighbor_count, 0) + + neighbor_idx = tl.arange(0, MAX_ATOMS_PER_CELL) + neighbor_mask = neighbor_idx < neighbor_count + + # Load atom indices for neighbor cell + neighbor_atoms = tl.load( + CellAtoms + neighbor_cell_id * MAX_ATOMS_PER_CELL + neighbor_idx, + mask=neighbor_mask, + other=0, + ) + + # Load positions for neighbor atoms + neighbor_x = tl.load( + Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 + ) + neighbor_y = tl.load( + Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 + ) + neighbor_z = tl.load( + Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 + ) + neighbor_batch = tl.load(Batch + neighbor_atoms, mask=neighbor_mask, other=-2) + + # Compute pairwise distances: [MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL] + # home atoms are rows, neighbor atoms are columns + dx = home_x[:, None] - neighbor_x[None, :] + dy = home_y[:, None] - neighbor_y[None, :] + dz = home_z[:, None] - neighbor_z[None, :] + + # Apply PBC to distance vectors + if use_periodic: + dx = dx - box_x * _tl_round(dx / box_x) + dy = dy - box_y * _tl_round(dy / box_y) + dz = dz - box_z * _tl_round(dz / box_z) + + dist_sq = dx * dx + dy * dy + dz * dz + + # Build validity mask + # 1. Distance within cutoff + cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) + + # 2. Same batch + cond_batch = home_batch[:, None] == neighbor_batch[None, :] + + # 3. Index ordering to avoid double-counting + home_atoms_bc = home_atoms[:, None] + neighbor_atoms_bc = neighbor_atoms[None, :] + + if include_transpose: + # Full list: emit both (i,j) and (j,i), but only from one cell + # We emit when home_atom != neighbor_atom (self-loops handled by loop flag) + if loop: + cond_idx = True # Include self-loops + else: + cond_idx = home_atoms_bc != neighbor_atoms_bc + else: + # Half list: only emit (i,j) where i > j + # This ensures each pair is emitted exactly once + if loop: + cond_idx = home_atoms_bc >= neighbor_atoms_bc + else: + cond_idx = home_atoms_bc > neighbor_atoms_bc + + # 4. Both atoms must be valid (within cell counts) + cond_valid = home_mask[:, None] & neighbor_mask[None, :] + + # Combined validity + valid_mask = cond_dist & cond_batch & cond_idx & cond_valid + + # Count and store valid pairs + num_found = tl.sum(valid_mask.to(tl.int32)) + + if num_found > 0: + # Atomically reserve space in output + current_offset = tl.atomic_add(GlobalCounter, num_found) + + if current_offset + num_found <= max_pairs: + # Compute storage indices using cumsum + flat_mask = tl.ravel(valid_mask) + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + store_idx = current_offset + csum - 1 + + # Prepare flattened data + flat_home = tl.ravel( + tl.broadcast_to( + home_atoms[:, None], + (MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL), + ) + ) + flat_neighbor = tl.ravel( + tl.broadcast_to( + neighbor_atoms[None, :], + (MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL), + ) + ) + flat_dx = tl.ravel(dx) + flat_dy = tl.ravel(dy) + flat_dz = tl.ravel(dz) + flat_dist = tl.sqrt(tl.ravel(dist_sq)) + + # Store pairs + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_home, + mask=flat_mask, + ) + tl.store( + OutPairs + 1 * max_pairs + store_idx, + flat_neighbor, + mask=flat_mask, + ) + + # Store deltas (interleaved x,y,z) + tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) + + # Store distances + tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) + + +def build_cell_list( + positions: Tensor, + box_sizes: Tensor, # [3] diagonal elements + use_periodic: bool, + cell_dims: Tensor, # [3] number of cells in each dimension + num_cells: int, # total number of cells (fixed for CUDA graphs) + max_atoms_per_cell: int, +) -> Tuple[Tensor, Tensor]: + """ + Build the cell list data structure. + + Args: + positions: [N, 3] atom positions + batch: [N] batch indices + box_sizes: [3] box diagonal elements + cutoff: cutoff distance + use_periodic: whether to use periodic boundary conditions + cell_dims: [3] number of cells in each dimension (pre-computed) + num_cells: total number of cells (pre-computed, fixed for CUDA graphs) + max_atoms_per_cell: maximum atoms per cell + + Returns: + cell_atoms: [num_cells, max_atoms_per_cell] - atom indices per cell + cell_counts: [num_cells] - number of atoms in each cell + """ + device = positions.device + n_atoms = positions.size(0) + + # Compute cell index for each atom + if use_periodic: + # Wrap to [0, box) + inv_box = 1.0 / box_sizes + wrapped = positions - torch.floor(positions * inv_box) * box_sizes + else: + # Shift by half box (like CUDA implementation) + wrapped = positions + 0.5 * box_sizes + + # Cell coordinates + cell_size = box_sizes / cell_dims.float() + cell_coords = (wrapped / cell_size).long() + cell_coords = torch.clamp( + cell_coords, min=torch.zeros(3, device=device), max=cell_dims - 1 + ) + + # Flat cell index + cell_idx = ( + cell_coords[:, 0] * (cell_dims[1] * cell_dims[2]) + + cell_coords[:, 1] * cell_dims[2] + + cell_coords[:, 2] + ).long() + + # Count atoms per cell + cell_counts = torch.zeros(num_cells, dtype=torch.int32, device=device) + cell_counts.scatter_add_( + 0, cell_idx, torch.ones(n_atoms, dtype=torch.int32, device=device) + ) + + # Check for overflow + max_count = cell_counts.max().item() + if max_count > max_atoms_per_cell: + raise RuntimeError( + f"Cell overflow: {max_count} atoms in one cell, but max_atoms_per_cell={max_atoms_per_cell}. " + f"Increase max_atoms_per_cell or use a larger cutoff." + ) + + # Build cell_atoms array using sorting approach + # Sort atoms by cell index + sorted_cell_idx, sort_order = torch.sort(cell_idx) + + # Compute starting position for each cell + cell_starts = torch.zeros(num_cells + 1, dtype=torch.long, device=device) + cell_starts[1:] = torch.cumsum(cell_counts.long(), dim=0) + + # Initialize cell_atoms with -1 (invalid) + cell_atoms = torch.full( + (num_cells, max_atoms_per_cell), -1, dtype=torch.int32, device=device + ) + + # Fill cell_atoms using a vectorized approach + # Create position within cell for each atom + atom_positions = torch.arange(n_atoms, device=device) + # Position within cell = atom_position - cell_start[cell_of_atom] + position_in_cell = atom_positions - cell_starts[sorted_cell_idx] + + # Scatter atom indices into cell_atoms + # cell_atoms[sorted_cell_idx, position_in_cell] = sort_order + flat_idx = sorted_cell_idx * max_atoms_per_cell + position_in_cell + cell_atoms.view(-1).scatter_(0, flat_idx, sort_order.int()) + + return cell_atoms, cell_counts + + +class TritonCellNeighborV2(TritonNeighborAutograd): + @staticmethod + def forward( + ctx, + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, + num_cells: int, + max_atoms_per_cell: int = 128, + ): + if not _HAS_TRITON: + raise RuntimeError("Triton is not available") + + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + # Validate inputs + if positions.dim() != 2 or positions.size(1) != 3: + raise ValueError('Expected "positions" to have shape (N, 3)') + if batch.dim() != 1 or batch.size(0) != n_atoms: + raise ValueError('Expected "batch" to have shape (N,)') + + # Extract box diagonal + box_vectors = box_vectors.contiguous() + if box_vectors.dim() == 3: + box_diag = box_vectors[0] + else: + box_diag = box_vectors + box_sizes = torch.stack( + [box_diag[0, 0], box_diag[1, 1], box_diag[2, 2]] + ).contiguous() + + # Compute cell dimensions using shared utility (stays on GPU) + cell_dims = _get_cell_dimensions( + box_sizes[0], box_sizes[1], box_sizes[2], cutoff_upper + ) + + # Build cell list + cell_atoms, cell_counts = build_cell_list( + positions, box_sizes, use_periodic, cell_dims, num_cells, max_atoms_per_cell + ) + + # Allocate outputs + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) + distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) + counter = torch.zeros((1,), device=device, dtype=torch.int32) + + # Launch kernel: one program per cell + grid = (num_cells,) + cell_neighbor_kernel[grid]( + cell_atoms, + cell_counts, + positions, + batch, + box_sizes, + cell_dims, + neighbors, + deltas, + distances, + counter, + max_num_pairs, + cutoff_lower**2, + cutoff_upper**2, + use_periodic=use_periodic, + loop=loop, + include_transpose=include_transpose, + MAX_ATOMS_PER_CELL=max_atoms_per_cell, + ) + num_pairs = counter.to(torch.long) + + ctx.save_for_backward(neighbors, deltas, distances) + ctx.num_atoms = n_atoms + return neighbors, deltas, distances, num_pairs + + @staticmethod + def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] + # Call parent backward (returns 9 values) and add None for num_cells and max_atoms_per_cell + parent_grads = TritonNeighborAutograd.backward( + ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs + ) + return (*parent_grads, None, None) + + +# def triton_cell_neighbor_v2( +# positions: Tensor, +# batch: Tensor, +# box_vectors: Tensor, +# use_periodic: bool, +# cutoff_lower: float, +# cutoff_upper: float, +# max_num_pairs: int, +# loop: bool, +# include_transpose: bool, +# num_cells: int, +# max_atoms_per_cell: int = 64, +# ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: +# """ +# Efficient cell list neighbor list using Triton. + +# This implementation achieves O(n) complexity by: +# 1. Spawning one program per cell +# 2. Using fixed-size cell buffers (MAX_ATOMS_PER_CELL) +# 3. Each program only examines the 27 neighboring cells + +# Args: +# positions: [N, 3] atom positions +# batch: [N] batch indices +# box_vectors: [3, 3] or [n_batch, 3, 3] box vectors (must be diagonal) +# use_periodic: whether to use periodic boundary conditions +# cutoff_lower: lower cutoff distance +# cutoff_upper: upper cutoff distance +# max_num_pairs: maximum number of pairs to return +# loop: whether to include self-interactions (i == j) +# include_transpose: whether to include both (i,j) and (j,i) +# num_cells: total number of cells (fixed for CUDA graph compatibility) +# max_atoms_per_cell: maximum atoms per cell (default 64) + +# Returns: +# neighbors: [2, num_pairs] pair indices +# deltas: [num_pairs, 3] displacement vectors +# distances: [num_pairs] distances +# num_pairs: [1] number of pairs found +# """ +# return TritonCellNeighborV2.apply( +# positions, +# batch, +# box_vectors, +# use_periodic, +# cutoff_lower, +# cutoff_upper, +# max_num_pairs, +# loop, +# include_transpose, +# num_cells, +# max_atoms_per_cell, +# ) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index bca2842ce..f4f97b251 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -254,6 +254,7 @@ def triton_neighbor_pairs( from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd from torchmdnet.extensions.triton_shared import TritonSharedNeighborAutograd + from torchmdnet.extensions.triton_cell_v2 import TritonCellNeighborV2 if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -275,7 +276,7 @@ def triton_neighbor_pairs( bool(include_transpose), ) elif strategy == "cell": - return TritonCellNeighborAutograd.apply( + return TritonCellNeighborV2.apply( positions, batch, box_vectors, From c07c6abec6ed1efb954aa3fd6297ebeb963480eb Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 13:28:16 +0200 Subject: [PATCH 30/80] cuda graph comp --- torchmdnet/extensions/triton_cell_v2.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py index b1b2c7488..496b61a97 100644 --- a/torchmdnet/extensions/triton_cell_v2.py +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -318,12 +318,13 @@ def build_cell_list( ) # Check for overflow - max_count = cell_counts.max().item() - if max_count > max_atoms_per_cell: - raise RuntimeError( - f"Cell overflow: {max_count} atoms in one cell, but max_atoms_per_cell={max_atoms_per_cell}. " - f"Increase max_atoms_per_cell or use a larger cutoff." - ) + if not torch.cuda.is_current_stream_capturing(): + max_count = cell_counts.max().item() + if max_count > max_atoms_per_cell: + raise RuntimeError( + f"Cell overflow: {max_count} atoms in one cell, but max_atoms_per_cell={max_atoms_per_cell}. " + f"Increase max_atoms_per_cell or use a larger cutoff." + ) # Build cell_atoms array using sorting approach # Sort atoms by cell index From 99d2775aaaa7202368d6b770aa8f16251ffbef36 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:05:26 +0200 Subject: [PATCH 31/80] tiled version --- benchmarks/neighbors.py | 3 + torchmdnet/extensions/triton_cell_v2.py | 270 ++++++++++++------------ 2 files changed, 143 insertions(+), 130 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index bbdb4e15f..bb1517809 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -11,6 +11,9 @@ from torch_cluster import radius_graph +water_density = 0.0334 * 3 # Atoms/A^3 + + class Distance(nn.Module): def __init__( self, diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py index 496b61a97..ccc225025 100644 --- a/torchmdnet/extensions/triton_cell_v2.py +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -55,12 +55,17 @@ def cell_neighbor_kernel( include_transpose: tl.constexpr, # Block sizes MAX_ATOMS_PER_CELL: tl.constexpr, + TILE_SIZE: tl.constexpr, # Tile size for pairwise computation (e.g., 32) ): """ Each program processes one cell (the "home" cell). It finds all pairs where the first atom is in the home cell and the second atom is in one of the 27 neighboring cells (including home). + Uses tiled computation to keep memory usage low: + - Instead of MAX_ATOMS_PER_CELL × MAX_ATOMS_PER_CELL matrices + - We use TILE_SIZE × TILE_SIZE matrices (e.g., 32×32 = 1024 elements) + To avoid double-counting, we use the convention: - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom - For full list (include_transpose=True): emit both directions @@ -82,23 +87,11 @@ def cell_neighbor_kernel( home_cy = (home_cell_id % cells_yz) // num_cells_z home_cz = home_cell_id % num_cells_z - # Load home cell data + # Load home cell count home_count = tl.load(CellCounts + home_cell_id) - home_idx = tl.arange(0, MAX_ATOMS_PER_CELL) - home_mask = home_idx < home_count - - # Load atom indices for home cell - home_atoms = tl.load( - CellAtoms + home_cell_id * MAX_ATOMS_PER_CELL + home_idx, - mask=home_mask, - other=0, - ) - # Load positions for home atoms (gather) - home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) - home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) - home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) - home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) + # Number of tiles needed + NUM_TILES: tl.constexpr = MAX_ATOMS_PER_CELL // TILE_SIZE # Loop over 27 neighbor cells # Use tl.range() for runtime loop (not unrolled at compile time) @@ -132,132 +125,142 @@ def cell_neighbor_kernel( ) # Skip invalid cells (non-periodic boundary) - # Note: We can't early-continue in Triton, so we use masking neighbor_cell_id = ni * cells_yz + nj * num_cells_z + nk - # Load neighbor cell data + # Load neighbor cell count neighbor_count = tl.load(CellCounts + neighbor_cell_id) # Apply cell_valid to count (if cell invalid, treat as empty) neighbor_count = tl.where(cell_valid, neighbor_count, 0) - neighbor_idx = tl.arange(0, MAX_ATOMS_PER_CELL) - neighbor_mask = neighbor_idx < neighbor_count - - # Load atom indices for neighbor cell - neighbor_atoms = tl.load( - CellAtoms + neighbor_cell_id * MAX_ATOMS_PER_CELL + neighbor_idx, - mask=neighbor_mask, - other=0, - ) - - # Load positions for neighbor atoms - neighbor_x = tl.load( - Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 - ) - neighbor_y = tl.load( - Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 - ) - neighbor_z = tl.load( - Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 - ) - neighbor_batch = tl.load(Batch + neighbor_atoms, mask=neighbor_mask, other=-2) - - # Compute pairwise distances: [MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL] - # home atoms are rows, neighbor atoms are columns - dx = home_x[:, None] - neighbor_x[None, :] - dy = home_y[:, None] - neighbor_y[None, :] - dz = home_z[:, None] - neighbor_z[None, :] - - # Apply PBC to distance vectors - if use_periodic: - dx = dx - box_x * _tl_round(dx / box_x) - dy = dy - box_y * _tl_round(dy / box_y) - dz = dz - box_z * _tl_round(dz / box_z) - - dist_sq = dx * dx + dy * dy + dz * dz - - # Build validity mask - # 1. Distance within cutoff - cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) - - # 2. Same batch - cond_batch = home_batch[:, None] == neighbor_batch[None, :] - - # 3. Index ordering to avoid double-counting - home_atoms_bc = home_atoms[:, None] - neighbor_atoms_bc = neighbor_atoms[None, :] - - if include_transpose: - # Full list: emit both (i,j) and (j,i), but only from one cell - # We emit when home_atom != neighbor_atom (self-loops handled by loop flag) - if loop: - cond_idx = True # Include self-loops - else: - cond_idx = home_atoms_bc != neighbor_atoms_bc - else: - # Half list: only emit (i,j) where i > j - # This ensures each pair is emitted exactly once - if loop: - cond_idx = home_atoms_bc >= neighbor_atoms_bc - else: - cond_idx = home_atoms_bc > neighbor_atoms_bc - - # 4. Both atoms must be valid (within cell counts) - cond_valid = home_mask[:, None] & neighbor_mask[None, :] - - # Combined validity - valid_mask = cond_dist & cond_batch & cond_idx & cond_valid - - # Count and store valid pairs - num_found = tl.sum(valid_mask.to(tl.int32)) - - if num_found > 0: - # Atomically reserve space in output - current_offset = tl.atomic_add(GlobalCounter, num_found) - - if current_offset + num_found <= max_pairs: - # Compute storage indices using cumsum - flat_mask = tl.ravel(valid_mask) - csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) - store_idx = current_offset + csum - 1 - - # Prepare flattened data - flat_home = tl.ravel( - tl.broadcast_to( - home_atoms[:, None], - (MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL), - ) + # Tiled iteration over home atoms + for home_tile in tl.static_range(NUM_TILES): + home_tile_start = home_tile * TILE_SIZE + home_idx = home_tile_start + tl.arange(0, TILE_SIZE) + home_mask = home_idx < home_count + + # Load home tile data + home_atoms = tl.load( + CellAtoms + home_cell_id * MAX_ATOMS_PER_CELL + home_idx, + mask=home_mask, + other=0, + ) + home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) + home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) + home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) + home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) + + # Tiled iteration over neighbor atoms + for neighbor_tile in tl.static_range(NUM_TILES): + neighbor_tile_start = neighbor_tile * TILE_SIZE + neighbor_idx = neighbor_tile_start + tl.arange(0, TILE_SIZE) + neighbor_mask = neighbor_idx < neighbor_count + + # Load neighbor tile data + neighbor_atoms = tl.load( + CellAtoms + neighbor_cell_id * MAX_ATOMS_PER_CELL + neighbor_idx, + mask=neighbor_mask, + other=0, ) - flat_neighbor = tl.ravel( - tl.broadcast_to( - neighbor_atoms[None, :], - (MAX_ATOMS_PER_CELL, MAX_ATOMS_PER_CELL), - ) + neighbor_x = tl.load( + Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 ) - flat_dx = tl.ravel(dx) - flat_dy = tl.ravel(dy) - flat_dz = tl.ravel(dz) - flat_dist = tl.sqrt(tl.ravel(dist_sq)) - - # Store pairs - tl.store( - OutPairs + 0 * max_pairs + store_idx, - flat_home, - mask=flat_mask, + neighbor_y = tl.load( + Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 ) - tl.store( - OutPairs + 1 * max_pairs + store_idx, - flat_neighbor, - mask=flat_mask, + neighbor_z = tl.load( + Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 + ) + neighbor_batch = tl.load( + Batch + neighbor_atoms, mask=neighbor_mask, other=-2 ) - # Store deltas (interleaved x,y,z) - tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) - - # Store distances - tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) + # Compute pairwise distances: [TILE_SIZE, TILE_SIZE] + dx = home_x[:, None] - neighbor_x[None, :] + dy = home_y[:, None] - neighbor_y[None, :] + dz = home_z[:, None] - neighbor_z[None, :] + + # Apply PBC to distance vectors + if use_periodic: + dx = dx - box_x * _tl_round(dx / box_x) + dy = dy - box_y * _tl_round(dy / box_y) + dz = dz - box_z * _tl_round(dz / box_z) + + dist_sq = dx * dx + dy * dy + dz * dz + + # Build validity mask + # 1. Distance within cutoff + cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) + + # 2. Same batch + cond_batch = home_batch[:, None] == neighbor_batch[None, :] + + # 3. Index ordering to avoid double-counting + home_atoms_bc = home_atoms[:, None] + neighbor_atoms_bc = neighbor_atoms[None, :] + + if include_transpose: + if loop: + cond_idx = True # Include self-loops + else: + cond_idx = home_atoms_bc != neighbor_atoms_bc + else: + if loop: + cond_idx = home_atoms_bc >= neighbor_atoms_bc + else: + cond_idx = home_atoms_bc > neighbor_atoms_bc + + # 4. Both atoms must be valid (within cell counts) + cond_valid = home_mask[:, None] & neighbor_mask[None, :] + + # Combined validity + valid_mask = cond_dist & cond_batch & cond_idx & cond_valid + + # Count and store valid pairs + num_found = tl.sum(valid_mask.to(tl.int32)) + + if num_found > 0: + # Atomically reserve space in output + current_offset = tl.atomic_add(GlobalCounter, num_found) + + if current_offset + num_found <= max_pairs: + # Compute storage indices using cumsum + flat_mask = tl.ravel(valid_mask) + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + store_idx = current_offset + csum - 1 + + # Prepare flattened data + flat_home = tl.ravel( + tl.broadcast_to(home_atoms[:, None], (TILE_SIZE, TILE_SIZE)) + ) + flat_neighbor = tl.ravel( + tl.broadcast_to( + neighbor_atoms[None, :], (TILE_SIZE, TILE_SIZE) + ) + ) + flat_dx = tl.ravel(dx) + flat_dy = tl.ravel(dy) + flat_dz = tl.ravel(dz) + flat_dist = tl.sqrt(tl.ravel(dist_sq)) + + # Store pairs + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_home, + mask=flat_mask, + ) + tl.store( + OutPairs + 1 * max_pairs + store_idx, + flat_neighbor, + mask=flat_mask, + ) + + # Store deltas + tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) + + # Store distances + tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) def build_cell_list( @@ -409,6 +412,12 @@ def forward( counter = torch.zeros((1,), device=device, dtype=torch.int32) # Launch kernel: one program per cell + # TILE_SIZE controls memory usage: 32×32=1K elements per tile (fits in registers) + TILE_SIZE = 32 + assert ( + max_atoms_per_cell % TILE_SIZE == 0 + ), f"max_atoms_per_cell ({max_atoms_per_cell}) must be divisible by TILE_SIZE ({TILE_SIZE})" + grid = (num_cells,) cell_neighbor_kernel[grid]( cell_atoms, @@ -428,6 +437,7 @@ def forward( loop=loop, include_transpose=include_transpose, MAX_ATOMS_PER_CELL=max_atoms_per_cell, + TILE_SIZE=TILE_SIZE, ) num_pairs = counter.to(torch.long) From 5d3a0b2df5afc1168cc284e9c23b46e438eaf9e3 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:21:51 +0200 Subject: [PATCH 32/80] another impl --- torchmdnet/extensions/triton_cell_v2.py | 337 +++++++----------------- 1 file changed, 89 insertions(+), 248 deletions(-) diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py index ccc225025..f7ba328c3 100644 --- a/torchmdnet/extensions/triton_cell_v2.py +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -32,11 +32,12 @@ def _tl_round(x): @triton.jit def cell_neighbor_kernel( - # Cell data structure - CellAtoms, # [num_cells, MAX_ATOMS_PER_CELL] - atom indices in each cell - CellCounts, # [num_cells] - number of atoms in each cell + # Cell data structure (1D sorted approach) + SortedIndices, # [n_atoms] - original atom indices, sorted by cell + CellStart, # [num_cells] - start index in SortedIndices for each cell + CellEnd, # [num_cells] - end index (exclusive) in SortedIndices for each cell Positions, # [n_atoms, 3] - positions (original order) - Batch, # [n_atoms] - batch indices + Batch, # [n_atoms] - batch indices (original order) # Box parameters BoxSizes, # [3] - box dimensions CellDims, # [3] - number of cells in each dimension (int32) @@ -53,20 +54,13 @@ def cell_neighbor_kernel( use_periodic: tl.constexpr, loop: tl.constexpr, include_transpose: tl.constexpr, - # Block sizes - MAX_ATOMS_PER_CELL: tl.constexpr, - TILE_SIZE: tl.constexpr, # Tile size for pairwise computation (e.g., 32) ): """ Each program processes one cell (the "home" cell). - It finds all pairs where the first atom is in the home cell - and the second atom is in one of the 27 neighboring cells (including home). + Uses 1D sorted array with cell_start/cell_end pointers. + Iterates only over actual atoms in each cell using while loops. - Uses tiled computation to keep memory usage low: - - Instead of MAX_ATOMS_PER_CELL × MAX_ATOMS_PER_CELL matrices - - We use TILE_SIZE × TILE_SIZE matrices (e.g., 32×32 = 1024 elements) - - To avoid double-counting, we use the convention: + To avoid double-counting: - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom - For full list (include_transpose=True): emit both directions """ @@ -87,14 +81,11 @@ def cell_neighbor_kernel( home_cy = (home_cell_id % cells_yz) // num_cells_z home_cz = home_cell_id % num_cells_z - # Load home cell count - home_count = tl.load(CellCounts + home_cell_id) - - # Number of tiles needed - NUM_TILES: tl.constexpr = MAX_ATOMS_PER_CELL // TILE_SIZE + # Load home cell boundaries + home_start = tl.load(CellStart + home_cell_id) + home_end = tl.load(CellEnd + home_cell_id) # Loop over 27 neighbor cells - # Use tl.range() for runtime loop (not unrolled at compile time) for neighbor_offset in tl.range(0, 27): # Decompose neighbor_offset into di, dj, dk (each in {-1, 0, 1}) di = (neighbor_offset % 3) - 1 @@ -108,13 +99,11 @@ def cell_neighbor_kernel( # Handle boundary conditions if use_periodic: - # Wrap around with PBC ni = (ni + num_cells_x) % num_cells_x nj = (nj + num_cells_y) % num_cells_y nk = (nk + num_cells_z) % num_cells_z cell_valid = True else: - # Check bounds for non-periodic cell_valid = ( (ni >= 0) & (ni < num_cells_x) @@ -124,62 +113,42 @@ def cell_neighbor_kernel( & (nk < num_cells_z) ) - # Skip invalid cells (non-periodic boundary) neighbor_cell_id = ni * cells_yz + nj * num_cells_z + nk - # Load neighbor cell count - neighbor_count = tl.load(CellCounts + neighbor_cell_id) - # Apply cell_valid to count (if cell invalid, treat as empty) - neighbor_count = tl.where(cell_valid, neighbor_count, 0) - - # Tiled iteration over home atoms - for home_tile in tl.static_range(NUM_TILES): - home_tile_start = home_tile * TILE_SIZE - home_idx = home_tile_start + tl.arange(0, TILE_SIZE) - home_mask = home_idx < home_count - - # Load home tile data - home_atoms = tl.load( - CellAtoms + home_cell_id * MAX_ATOMS_PER_CELL + home_idx, - mask=home_mask, - other=0, - ) - home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) - home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) - home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) - home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) - - # Tiled iteration over neighbor atoms - for neighbor_tile in tl.static_range(NUM_TILES): - neighbor_tile_start = neighbor_tile * TILE_SIZE - neighbor_idx = neighbor_tile_start + tl.arange(0, TILE_SIZE) - neighbor_mask = neighbor_idx < neighbor_count - - # Load neighbor tile data - neighbor_atoms = tl.load( - CellAtoms + neighbor_cell_id * MAX_ATOMS_PER_CELL + neighbor_idx, - mask=neighbor_mask, - other=0, - ) - neighbor_x = tl.load( - Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 - ) - neighbor_y = tl.load( - Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 - ) - neighbor_z = tl.load( - Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 - ) - neighbor_batch = tl.load( - Batch + neighbor_atoms, mask=neighbor_mask, other=-2 - ) - - # Compute pairwise distances: [TILE_SIZE, TILE_SIZE] - dx = home_x[:, None] - neighbor_x[None, :] - dy = home_y[:, None] - neighbor_y[None, :] - dz = home_z[:, None] - neighbor_z[None, :] - - # Apply PBC to distance vectors + # Load neighbor cell boundaries + neighbor_start = tl.load(CellStart + neighbor_cell_id) + neighbor_end = tl.load(CellEnd + neighbor_cell_id) + + # If cell is invalid (non-periodic boundary), make it empty + neighbor_start = tl.where(cell_valid, neighbor_start, 0) + neighbor_end = tl.where(cell_valid, neighbor_end, 0) + + # Iterate over home atoms using while loop (only actual atoms) + home_i = home_start + while home_i < home_end: + # Load home atom data + home_atom = tl.load(SortedIndices + home_i) + home_x = tl.load(Positions + home_atom * 3 + 0) + home_y = tl.load(Positions + home_atom * 3 + 1) + home_z = tl.load(Positions + home_atom * 3 + 2) + home_batch = tl.load(Batch + home_atom) + + # Iterate over neighbor atoms using while loop (only actual atoms) + neighbor_i = neighbor_start + while neighbor_i < neighbor_end: + # Load neighbor atom data + neighbor_atom = tl.load(SortedIndices + neighbor_i) + neighbor_x = tl.load(Positions + neighbor_atom * 3 + 0) + neighbor_y = tl.load(Positions + neighbor_atom * 3 + 1) + neighbor_z = tl.load(Positions + neighbor_atom * 3 + 2) + neighbor_batch = tl.load(Batch + neighbor_atom) + + # Compute distance + dx = home_x - neighbor_x + dy = home_y - neighbor_y + dz = home_z - neighbor_z + + # Apply PBC if use_periodic: dx = dx - box_x * _tl_round(dx / box_x) dy = dy - box_y * _tl_round(dy / box_y) @@ -187,80 +156,42 @@ def cell_neighbor_kernel( dist_sq = dx * dx + dy * dy + dz * dz - # Build validity mask - # 1. Distance within cutoff + # Check validity cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) + cond_batch = home_batch == neighbor_batch - # 2. Same batch - cond_batch = home_batch[:, None] == neighbor_batch[None, :] - - # 3. Index ordering to avoid double-counting - home_atoms_bc = home_atoms[:, None] - neighbor_atoms_bc = neighbor_atoms[None, :] - + # Index ordering to avoid double-counting if include_transpose: if loop: - cond_idx = True # Include self-loops + cond_idx = True else: - cond_idx = home_atoms_bc != neighbor_atoms_bc + cond_idx = home_atom != neighbor_atom else: if loop: - cond_idx = home_atoms_bc >= neighbor_atoms_bc + cond_idx = home_atom >= neighbor_atom else: - cond_idx = home_atoms_bc > neighbor_atoms_bc - - # 4. Both atoms must be valid (within cell counts) - cond_valid = home_mask[:, None] & neighbor_mask[None, :] - - # Combined validity - valid_mask = cond_dist & cond_batch & cond_idx & cond_valid + cond_idx = home_atom > neighbor_atom - # Count and store valid pairs - num_found = tl.sum(valid_mask.to(tl.int32)) + is_valid = cond_dist & cond_batch & cond_idx - if num_found > 0: - # Atomically reserve space in output - current_offset = tl.atomic_add(GlobalCounter, num_found) + if is_valid: + # Atomically reserve one slot + current_offset = tl.atomic_add(GlobalCounter, 1) - if current_offset + num_found <= max_pairs: - # Compute storage indices using cumsum - flat_mask = tl.ravel(valid_mask) - csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) - store_idx = current_offset + csum - 1 - - # Prepare flattened data - flat_home = tl.ravel( - tl.broadcast_to(home_atoms[:, None], (TILE_SIZE, TILE_SIZE)) - ) - flat_neighbor = tl.ravel( - tl.broadcast_to( - neighbor_atoms[None, :], (TILE_SIZE, TILE_SIZE) - ) - ) - flat_dx = tl.ravel(dx) - flat_dy = tl.ravel(dy) - flat_dz = tl.ravel(dz) - flat_dist = tl.sqrt(tl.ravel(dist_sq)) - - # Store pairs + if current_offset < max_pairs: + dist = tl.sqrt(dist_sq) + # Store pair + tl.store(OutPairs + 0 * max_pairs + current_offset, home_atom) tl.store( - OutPairs + 0 * max_pairs + store_idx, - flat_home, - mask=flat_mask, + OutPairs + 1 * max_pairs + current_offset, neighbor_atom ) - tl.store( - OutPairs + 1 * max_pairs + store_idx, - flat_neighbor, - mask=flat_mask, - ) - - # Store deltas - tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) + tl.store(OutDeltas + current_offset * 3 + 0, dx) + tl.store(OutDeltas + current_offset * 3 + 1, dy) + tl.store(OutDeltas + current_offset * 3 + 2, dz) + tl.store(OutDists + current_offset, dist) - # Store distances - tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) + neighbor_i += 1 + home_i += 1 def build_cell_list( @@ -269,24 +200,21 @@ def build_cell_list( use_periodic: bool, cell_dims: Tensor, # [3] number of cells in each dimension num_cells: int, # total number of cells (fixed for CUDA graphs) - max_atoms_per_cell: int, -) -> Tuple[Tensor, Tensor]: +) -> Tuple[Tensor, Tensor, Tensor]: """ - Build the cell list data structure. + Build the cell list data structure using 1D sorted arrays. Args: positions: [N, 3] atom positions - batch: [N] batch indices box_sizes: [3] box diagonal elements - cutoff: cutoff distance use_periodic: whether to use periodic boundary conditions cell_dims: [3] number of cells in each dimension (pre-computed) num_cells: total number of cells (pre-computed, fixed for CUDA graphs) - max_atoms_per_cell: maximum atoms per cell Returns: - cell_atoms: [num_cells, max_atoms_per_cell] - atom indices per cell - cell_counts: [num_cells] - number of atoms in each cell + sorted_indices: [n_atoms] - original atom indices, sorted by cell + cell_start: [num_cells] - start index in sorted_indices for each cell + cell_end: [num_cells] - end index (exclusive) in sorted_indices for each cell """ device = positions.device n_atoms = positions.size(0) @@ -314,46 +242,22 @@ def build_cell_list( + cell_coords[:, 2] ).long() + # Sort atoms by cell index + sorted_cell_idx, sort_order = torch.sort(cell_idx) + sorted_indices = sort_order.int() # Original atom indices, now sorted by cell + # Count atoms per cell cell_counts = torch.zeros(num_cells, dtype=torch.int32, device=device) cell_counts.scatter_add_( 0, cell_idx, torch.ones(n_atoms, dtype=torch.int32, device=device) ) - # Check for overflow - if not torch.cuda.is_current_stream_capturing(): - max_count = cell_counts.max().item() - if max_count > max_atoms_per_cell: - raise RuntimeError( - f"Cell overflow: {max_count} atoms in one cell, but max_atoms_per_cell={max_atoms_per_cell}. " - f"Increase max_atoms_per_cell or use a larger cutoff." - ) + # Compute cell_start and cell_end using cumsum + cell_end = torch.cumsum(cell_counts, dim=0).int() + cell_start = torch.zeros(num_cells, dtype=torch.int32, device=device) + cell_start[1:] = cell_end[:-1] - # Build cell_atoms array using sorting approach - # Sort atoms by cell index - sorted_cell_idx, sort_order = torch.sort(cell_idx) - - # Compute starting position for each cell - cell_starts = torch.zeros(num_cells + 1, dtype=torch.long, device=device) - cell_starts[1:] = torch.cumsum(cell_counts.long(), dim=0) - - # Initialize cell_atoms with -1 (invalid) - cell_atoms = torch.full( - (num_cells, max_atoms_per_cell), -1, dtype=torch.int32, device=device - ) - - # Fill cell_atoms using a vectorized approach - # Create position within cell for each atom - atom_positions = torch.arange(n_atoms, device=device) - # Position within cell = atom_position - cell_start[cell_of_atom] - position_in_cell = atom_positions - cell_starts[sorted_cell_idx] - - # Scatter atom indices into cell_atoms - # cell_atoms[sorted_cell_idx, position_in_cell] = sort_order - flat_idx = sorted_cell_idx * max_atoms_per_cell + position_in_cell - cell_atoms.view(-1).scatter_(0, flat_idx, sort_order.int()) - - return cell_atoms, cell_counts + return sorted_indices, cell_start, cell_end class TritonCellNeighborV2(TritonNeighborAutograd): @@ -370,7 +274,6 @@ def forward( loop: bool, include_transpose: bool, num_cells: int, - max_atoms_per_cell: int = 128, ): if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -400,9 +303,9 @@ def forward( box_sizes[0], box_sizes[1], box_sizes[2], cutoff_upper ) - # Build cell list - cell_atoms, cell_counts = build_cell_list( - positions, box_sizes, use_periodic, cell_dims, num_cells, max_atoms_per_cell + # Build cell list (1D sorted approach) + sorted_indices, cell_start, cell_end = build_cell_list( + positions, box_sizes, use_periodic, cell_dims, num_cells ) # Allocate outputs @@ -412,16 +315,11 @@ def forward( counter = torch.zeros((1,), device=device, dtype=torch.int32) # Launch kernel: one program per cell - # TILE_SIZE controls memory usage: 32×32=1K elements per tile (fits in registers) - TILE_SIZE = 32 - assert ( - max_atoms_per_cell % TILE_SIZE == 0 - ), f"max_atoms_per_cell ({max_atoms_per_cell}) must be divisible by TILE_SIZE ({TILE_SIZE})" - grid = (num_cells,) cell_neighbor_kernel[grid]( - cell_atoms, - cell_counts, + sorted_indices, + cell_start, + cell_end, positions, batch, box_sizes, @@ -436,8 +334,6 @@ def forward( use_periodic=use_periodic, loop=loop, include_transpose=include_transpose, - MAX_ATOMS_PER_CELL=max_atoms_per_cell, - TILE_SIZE=TILE_SIZE, ) num_pairs = counter.to(torch.long) @@ -447,63 +343,8 @@ def forward( @staticmethod def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] - # Call parent backward (returns 9 values) and add None for num_cells and max_atoms_per_cell + # Call parent backward (returns 9 values) and add None for num_cells parent_grads = TritonNeighborAutograd.backward( ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs ) - return (*parent_grads, None, None) - - -# def triton_cell_neighbor_v2( -# positions: Tensor, -# batch: Tensor, -# box_vectors: Tensor, -# use_periodic: bool, -# cutoff_lower: float, -# cutoff_upper: float, -# max_num_pairs: int, -# loop: bool, -# include_transpose: bool, -# num_cells: int, -# max_atoms_per_cell: int = 64, -# ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: -# """ -# Efficient cell list neighbor list using Triton. - -# This implementation achieves O(n) complexity by: -# 1. Spawning one program per cell -# 2. Using fixed-size cell buffers (MAX_ATOMS_PER_CELL) -# 3. Each program only examines the 27 neighboring cells - -# Args: -# positions: [N, 3] atom positions -# batch: [N] batch indices -# box_vectors: [3, 3] or [n_batch, 3, 3] box vectors (must be diagonal) -# use_periodic: whether to use periodic boundary conditions -# cutoff_lower: lower cutoff distance -# cutoff_upper: upper cutoff distance -# max_num_pairs: maximum number of pairs to return -# loop: whether to include self-interactions (i == j) -# include_transpose: whether to include both (i,j) and (j,i) -# num_cells: total number of cells (fixed for CUDA graph compatibility) -# max_atoms_per_cell: maximum atoms per cell (default 64) - -# Returns: -# neighbors: [2, num_pairs] pair indices -# deltas: [num_pairs, 3] displacement vectors -# distances: [num_pairs] distances -# num_pairs: [1] number of pairs found -# """ -# return TritonCellNeighborV2.apply( -# positions, -# batch, -# box_vectors, -# use_periodic, -# cutoff_lower, -# cutoff_upper, -# max_num_pairs, -# loop, -# include_transpose, -# num_cells, -# max_atoms_per_cell, -# ) + return (*parent_grads, None) From 0fb377e1e1095c6cc670d7b73aba7bd61e5c3629 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:29:49 +0200 Subject: [PATCH 33/80] faster version --- torchmdnet/extensions/triton_cell_v2.py | 173 +++++++++++++++++------- 1 file changed, 127 insertions(+), 46 deletions(-) diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py index f7ba328c3..ec339d3a8 100644 --- a/torchmdnet/extensions/triton_cell_v2.py +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -54,11 +54,18 @@ def cell_neighbor_kernel( use_periodic: tl.constexpr, loop: tl.constexpr, include_transpose: tl.constexpr, + # Batch size for vectorized processing + BATCH_SIZE: tl.constexpr, # e.g., 32 -> processes 32×32=1024 pairs per iteration ): """ Each program processes one cell (the "home" cell). Uses 1D sorted array with cell_start/cell_end pointers. - Iterates only over actual atoms in each cell using while loops. + + Vectorized batched processing: + - Loads BATCH_SIZE atoms at a time for both home and neighbor + - Computes BATCH_SIZE × BATCH_SIZE distance matrix per iteration + - Uses while loops to iterate only over actual atoms + - Minimal waste: only last partial batch may have masked elements To avoid double-counting: - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom @@ -123,30 +130,56 @@ def cell_neighbor_kernel( neighbor_start = tl.where(cell_valid, neighbor_start, 0) neighbor_end = tl.where(cell_valid, neighbor_end, 0) - # Iterate over home atoms using while loop (only actual atoms) - home_i = home_start - while home_i < home_end: - # Load home atom data - home_atom = tl.load(SortedIndices + home_i) - home_x = tl.load(Positions + home_atom * 3 + 0) - home_y = tl.load(Positions + home_atom * 3 + 1) - home_z = tl.load(Positions + home_atom * 3 + 2) - home_batch = tl.load(Batch + home_atom) - - # Iterate over neighbor atoms using while loop (only actual atoms) - neighbor_i = neighbor_start - while neighbor_i < neighbor_end: - # Load neighbor atom data - neighbor_atom = tl.load(SortedIndices + neighbor_i) - neighbor_x = tl.load(Positions + neighbor_atom * 3 + 0) - neighbor_y = tl.load(Positions + neighbor_atom * 3 + 1) - neighbor_z = tl.load(Positions + neighbor_atom * 3 + 2) - neighbor_batch = tl.load(Batch + neighbor_atom) - - # Compute distance - dx = home_x - neighbor_x - dy = home_y - neighbor_y - dz = home_z - neighbor_z + # Batched iteration over home atoms + home_batch_start = home_start + while home_batch_start < home_end: + # Load BATCH_SIZE home atoms + home_offsets = tl.arange(0, BATCH_SIZE) + home_global_idx = home_batch_start + home_offsets + home_mask = home_global_idx < home_end + + # Load home atom indices from sorted array + home_atoms = tl.load( + SortedIndices + home_global_idx, mask=home_mask, other=0 + ) + + # Load home atom positions (gather from original positions) + home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) + home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) + home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) + home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) + + # Batched iteration over neighbor atoms + neighbor_batch_start = neighbor_start + while neighbor_batch_start < neighbor_end: + # Load BATCH_SIZE neighbor atoms + neighbor_offsets = tl.arange(0, BATCH_SIZE) + neighbor_global_idx = neighbor_batch_start + neighbor_offsets + neighbor_mask = neighbor_global_idx < neighbor_end + + # Load neighbor atom indices from sorted array + neighbor_atoms = tl.load( + SortedIndices + neighbor_global_idx, mask=neighbor_mask, other=0 + ) + + # Load neighbor atom positions + neighbor_x = tl.load( + Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 + ) + neighbor_y = tl.load( + Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 + ) + neighbor_z = tl.load( + Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 + ) + neighbor_batch_vals = tl.load( + Batch + neighbor_atoms, mask=neighbor_mask, other=-2 + ) + + # Compute pairwise distances: [BATCH_SIZE, BATCH_SIZE] + dx = home_x[:, None] - neighbor_x[None, :] + dy = home_y[:, None] - neighbor_y[None, :] + dz = home_z[:, None] - neighbor_z[None, :] # Apply PBC if use_periodic: @@ -156,42 +189,85 @@ def cell_neighbor_kernel( dist_sq = dx * dx + dy * dy + dz * dz - # Check validity + # Build validity mask + # 1. Distance within cutoff cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) - cond_batch = home_batch == neighbor_batch - # Index ordering to avoid double-counting + # 2. Same batch + cond_batch = home_batch[:, None] == neighbor_batch_vals[None, :] + + # 3. Index ordering to avoid double-counting + home_atoms_bc = home_atoms[:, None] + neighbor_atoms_bc = neighbor_atoms[None, :] + if include_transpose: if loop: cond_idx = True else: - cond_idx = home_atom != neighbor_atom + cond_idx = home_atoms_bc != neighbor_atoms_bc else: if loop: - cond_idx = home_atom >= neighbor_atom + cond_idx = home_atoms_bc >= neighbor_atoms_bc else: - cond_idx = home_atom > neighbor_atom + cond_idx = home_atoms_bc > neighbor_atoms_bc + + # 4. Both atoms must be valid (within actual cell bounds) + cond_valid = home_mask[:, None] & neighbor_mask[None, :] + + # Combined validity + valid_mask = cond_dist & cond_batch & cond_idx & cond_valid + + # Count and store valid pairs + num_found = tl.sum(valid_mask.to(tl.int32)) - is_valid = cond_dist & cond_batch & cond_idx + if num_found > 0: + # Atomically reserve space in output + current_offset = tl.atomic_add(GlobalCounter, num_found) - if is_valid: - # Atomically reserve one slot - current_offset = tl.atomic_add(GlobalCounter, 1) + if current_offset + num_found <= max_pairs: + # Compute storage indices using cumsum + flat_mask = tl.ravel(valid_mask) + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + store_idx = current_offset + csum - 1 - if current_offset < max_pairs: - dist = tl.sqrt(dist_sq) - # Store pair - tl.store(OutPairs + 0 * max_pairs + current_offset, home_atom) + # Prepare flattened data + flat_home = tl.ravel( + tl.broadcast_to( + home_atoms[:, None], (BATCH_SIZE, BATCH_SIZE) + ) + ) + flat_neighbor = tl.ravel( + tl.broadcast_to( + neighbor_atoms[None, :], (BATCH_SIZE, BATCH_SIZE) + ) + ) + flat_dx = tl.ravel(dx) + flat_dy = tl.ravel(dy) + flat_dz = tl.ravel(dz) + flat_dist = tl.sqrt(tl.ravel(dist_sq)) + + # Store pairs + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_home, + mask=flat_mask, + ) tl.store( - OutPairs + 1 * max_pairs + current_offset, neighbor_atom + OutPairs + 1 * max_pairs + store_idx, + flat_neighbor, + mask=flat_mask, ) - tl.store(OutDeltas + current_offset * 3 + 0, dx) - tl.store(OutDeltas + current_offset * 3 + 1, dy) - tl.store(OutDeltas + current_offset * 3 + 2, dz) - tl.store(OutDists + current_offset, dist) - neighbor_i += 1 - home_i += 1 + # Store deltas + tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) + + # Store distances + tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) + + neighbor_batch_start += BATCH_SIZE + home_batch_start += BATCH_SIZE def build_cell_list( @@ -315,6 +391,10 @@ def forward( counter = torch.zeros((1,), device=device, dtype=torch.int32) # Launch kernel: one program per cell + # BATCH_SIZE: process atoms in batches for vectorized compute + # 32 is a good balance: 32×32=1024 elements fits in registers, minimal waste on partial batches + BATCH_SIZE = 32 + grid = (num_cells,) cell_neighbor_kernel[grid]( sorted_indices, @@ -334,6 +414,7 @@ def forward( use_periodic=use_periodic, loop=loop, include_transpose=include_transpose, + BATCH_SIZE=BATCH_SIZE, ) num_pairs = counter.to(torch.long) From 0658dbc3ece2cd499fb7aab948c48e90bfcbdd47 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:31:27 +0200 Subject: [PATCH 34/80] memory coalesced cell neighbor impl --- torchmdnet/extensions/triton_cell_v2.py | 74 ++++++++++++++++--------- 1 file changed, 49 insertions(+), 25 deletions(-) diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py index ec339d3a8..1763c9b11 100644 --- a/torchmdnet/extensions/triton_cell_v2.py +++ b/torchmdnet/extensions/triton_cell_v2.py @@ -34,10 +34,10 @@ def _tl_round(x): def cell_neighbor_kernel( # Cell data structure (1D sorted approach) SortedIndices, # [n_atoms] - original atom indices, sorted by cell - CellStart, # [num_cells] - start index in SortedIndices for each cell - CellEnd, # [num_cells] - end index (exclusive) in SortedIndices for each cell - Positions, # [n_atoms, 3] - positions (original order) - Batch, # [n_atoms] - batch indices (original order) + SortedPositions, # [n_atoms, 3] - positions sorted by cell (for coalesced access) + SortedBatch, # [n_atoms] - batch indices sorted by cell + CellStart, # [num_cells] - start index in sorted arrays for each cell + CellEnd, # [num_cells] - end index (exclusive) in sorted arrays for each cell # Box parameters BoxSizes, # [3] - box dimensions CellDims, # [3] - number of cells in each dimension (int32) @@ -138,16 +138,24 @@ def cell_neighbor_kernel( home_global_idx = home_batch_start + home_offsets home_mask = home_global_idx < home_end - # Load home atom indices from sorted array + # Load home atom original indices (for output pair indices) home_atoms = tl.load( SortedIndices + home_global_idx, mask=home_mask, other=0 ) - # Load home atom positions (gather from original positions) - home_x = tl.load(Positions + home_atoms * 3 + 0, mask=home_mask, other=0.0) - home_y = tl.load(Positions + home_atoms * 3 + 1, mask=home_mask, other=0.0) - home_z = tl.load(Positions + home_atoms * 3 + 2, mask=home_mask, other=0.0) - home_batch = tl.load(Batch + home_atoms, mask=home_mask, other=-1) + # Load home atom positions (sequential access - coalesced!) + home_x = tl.load( + SortedPositions + home_global_idx * 3 + 0, mask=home_mask, other=0.0 + ) + home_y = tl.load( + SortedPositions + home_global_idx * 3 + 1, mask=home_mask, other=0.0 + ) + home_z = tl.load( + SortedPositions + home_global_idx * 3 + 2, mask=home_mask, other=0.0 + ) + home_batch = tl.load( + SortedBatch + home_global_idx, mask=home_mask, other=-1 + ) # Batched iteration over neighbor atoms neighbor_batch_start = neighbor_start @@ -157,23 +165,29 @@ def cell_neighbor_kernel( neighbor_global_idx = neighbor_batch_start + neighbor_offsets neighbor_mask = neighbor_global_idx < neighbor_end - # Load neighbor atom indices from sorted array + # Load neighbor atom original indices (for output pair indices) neighbor_atoms = tl.load( SortedIndices + neighbor_global_idx, mask=neighbor_mask, other=0 ) - # Load neighbor atom positions + # Load neighbor atom positions (sequential access - coalesced!) neighbor_x = tl.load( - Positions + neighbor_atoms * 3 + 0, mask=neighbor_mask, other=0.0 + SortedPositions + neighbor_global_idx * 3 + 0, + mask=neighbor_mask, + other=0.0, ) neighbor_y = tl.load( - Positions + neighbor_atoms * 3 + 1, mask=neighbor_mask, other=0.0 + SortedPositions + neighbor_global_idx * 3 + 1, + mask=neighbor_mask, + other=0.0, ) neighbor_z = tl.load( - Positions + neighbor_atoms * 3 + 2, mask=neighbor_mask, other=0.0 + SortedPositions + neighbor_global_idx * 3 + 2, + mask=neighbor_mask, + other=0.0, ) neighbor_batch_vals = tl.load( - Batch + neighbor_atoms, mask=neighbor_mask, other=-2 + SortedBatch + neighbor_global_idx, mask=neighbor_mask, other=-2 ) # Compute pairwise distances: [BATCH_SIZE, BATCH_SIZE] @@ -272,16 +286,18 @@ def cell_neighbor_kernel( def build_cell_list( positions: Tensor, + batch: Tensor, box_sizes: Tensor, # [3] diagonal elements use_periodic: bool, cell_dims: Tensor, # [3] number of cells in each dimension num_cells: int, # total number of cells (fixed for CUDA graphs) -) -> Tuple[Tensor, Tensor, Tensor]: +) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: """ Build the cell list data structure using 1D sorted arrays. Args: positions: [N, 3] atom positions + batch: [N] batch indices box_sizes: [3] box diagonal elements use_periodic: whether to use periodic boundary conditions cell_dims: [3] number of cells in each dimension (pre-computed) @@ -289,8 +305,10 @@ def build_cell_list( Returns: sorted_indices: [n_atoms] - original atom indices, sorted by cell - cell_start: [num_cells] - start index in sorted_indices for each cell - cell_end: [num_cells] - end index (exclusive) in sorted_indices for each cell + sorted_positions: [n_atoms, 3] - positions sorted by cell (for coalesced access) + sorted_batch: [n_atoms] - batch indices sorted by cell + cell_start: [num_cells] - start index in sorted arrays for each cell + cell_end: [num_cells] - end index (exclusive) in sorted arrays for each cell """ device = positions.device n_atoms = positions.size(0) @@ -322,6 +340,10 @@ def build_cell_list( sorted_cell_idx, sort_order = torch.sort(cell_idx) sorted_indices = sort_order.int() # Original atom indices, now sorted by cell + # Create sorted positions and batch for coalesced memory access + sorted_positions = positions.index_select(0, sort_order).contiguous() + sorted_batch = batch.index_select(0, sort_order).contiguous() + # Count atoms per cell cell_counts = torch.zeros(num_cells, dtype=torch.int32, device=device) cell_counts.scatter_add_( @@ -333,7 +355,7 @@ def build_cell_list( cell_start = torch.zeros(num_cells, dtype=torch.int32, device=device) cell_start[1:] = cell_end[:-1] - return sorted_indices, cell_start, cell_end + return sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end class TritonCellNeighborV2(TritonNeighborAutograd): @@ -379,9 +401,11 @@ def forward( box_sizes[0], box_sizes[1], box_sizes[2], cutoff_upper ) - # Build cell list (1D sorted approach) - sorted_indices, cell_start, cell_end = build_cell_list( - positions, box_sizes, use_periodic, cell_dims, num_cells + # Build cell list (1D sorted approach with sorted positions for coalesced access) + sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end = ( + build_cell_list( + positions, batch, box_sizes, use_periodic, cell_dims, num_cells + ) ) # Allocate outputs @@ -398,10 +422,10 @@ def forward( grid = (num_cells,) cell_neighbor_kernel[grid]( sorted_indices, + sorted_positions, + sorted_batch, cell_start, cell_end, - positions, - batch, box_sizes, cell_dims, neighbors, From bd93ed4c691c6301dd8ad5f40e8b24a7982aec6c Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:55:50 +0200 Subject: [PATCH 35/80] cleanup and keep just the last cell version --- torchmdnet/extensions/triton_cell.py | 714 +++++++++++----------- torchmdnet/extensions/triton_cell_v2.py | 455 -------------- torchmdnet/extensions/triton_neighbors.py | 3 +- 3 files changed, 358 insertions(+), 814 deletions(-) delete mode 100644 torchmdnet/extensions/triton_cell_v2.py diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index f129b93be..54aba4b4c 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -1,3 +1,12 @@ +""" +Efficient cell list neighbor list implementation for Triton. + +Key design: +- One program per cell (not per atom) +- Fixed-size padded cell buffers for CUDA graph compatibility +- O(n) complexity: each cell examines only 27 neighboring cells +""" + try: import triton import triton.language as tl @@ -10,294 +19,355 @@ import torch from torch import Tensor -from torchmdnet.extensions.triton_neighbors import ( - _tl_round, - TritonNeighborAutograd, - _validate_box, -) +from typing import Tuple + +from torchmdnet.extensions.triton_neighbors import TritonNeighborAutograd +from torchmdnet.extensions.triton_cell import _get_cell_dimensions + + +@triton.jit +def _tl_round(x): + return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) @triton.jit -def neighbor_list_kernel( - # Pointers - SortedCoords, - SortedBatch, - SortedToOrig, - CellStart, - CellEnd, - OutPairs, - OutDeltas, - OutDists, - GlobalCounter, - BoxSizes, # Pointer to [box_x, box_y, box_z] - CellDims, # Pointer to [n_cells_x, n_cells_y, n_cells_z] - # Parameters - n_atoms, +def cell_neighbor_kernel( + # Cell data structure (1D sorted approach) + SortedIndices, # [n_atoms] - original atom indices, sorted by cell + SortedPositions, # [n_atoms, 3] - positions sorted by cell (for coalesced access) + SortedBatch, # [n_atoms] - batch indices sorted by cell + CellStart, # [num_cells] - start index in sorted arrays for each cell + CellEnd, # [num_cells] - end index (exclusive) in sorted arrays for each cell + # Box parameters + BoxSizes, # [3] - box dimensions + CellDims, # [3] - number of cells in each dimension (int32) + # Output + OutPairs, # [2, max_pairs] + OutDeltas, # [max_pairs, 3] + OutDists, # [max_pairs] + GlobalCounter, # [1] + # Scalar parameters max_pairs, cutoff_lower_sq, cutoff_upper_sq, # Flags - use_periodic: tl.constexpr, # Enable/Disable PBC - loop: tl.constexpr, # Enable/Disable Self-interactions - include_transpose: tl.constexpr, # True: i!=j (Full), False: i processes 32×32=1024 pairs per iteration ): - pid = tl.program_id(0) - idx_m = pid * BLOCK_M + tl.arange(0, BLOCK_M) - mask_m = idx_m < n_atoms - - # Load box sizes (CUDA graph compatible - no CPU sync) + """ + Each program processes one cell (the "home" cell). + Uses 1D sorted array with cell_start/cell_end pointers. + + Vectorized batched processing: + - Loads BATCH_SIZE atoms at a time for both home and neighbor + - Computes BATCH_SIZE × BATCH_SIZE distance matrix per iteration + - Uses while loops to iterate only over actual atoms + - Minimal waste: only last partial batch may have masked elements + + To avoid double-counting: + - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom + - For full list (include_transpose=True): emit both directions + """ + home_cell_id = tl.program_id(0) + + # Load box and cell dimensions box_x = tl.load(BoxSizes + 0) box_y = tl.load(BoxSizes + 1) box_z = tl.load(BoxSizes + 2) - # Load cell dimensions (CUDA graph compatible - no CPU sync) - n_cells_x = tl.load(CellDims + 0) - n_cells_y = tl.load(CellDims + 1) - n_cells_z = tl.load(CellDims + 2) - - # Load Query Data - off_m = idx_m - pos_mx = tl.load(SortedCoords + off_m * 3 + 0, mask=mask_m, other=0.0) - pos_my = tl.load(SortedCoords + off_m * 3 + 1, mask=mask_m, other=0.0) - pos_mz = tl.load(SortedCoords + off_m * 3 + 2, mask=mask_m, other=0.0) - batch_m = tl.load(SortedBatch + off_m, mask=mask_m, other=-1) - - # Identify Home Cell - vectorized for all BLOCK_M atoms - cell_sx = box_x / n_cells_x - cell_sy = box_y / n_cells_y - cell_sz = box_z / n_cells_z - - # Local coords for cell calculation (vectorized for all atoms in block) - # Match CUDA implementation: for periodic, wrap to [0, box); for non-periodic, shift by 0.5*box - if use_periodic: - px = pos_mx - box_x * tl.math.floor(pos_mx / box_x) - py = pos_my - box_y * tl.math.floor(pos_my / box_y) - pz = pos_mz - box_z * tl.math.floor(pos_mz / box_z) - else: - # For non-periodic, shift by half box (like CUDA impl: (p + 0.5*box) / cutoff) - px = pos_mx + 0.5 * box_x - py = pos_my + 0.5 * box_y - pz = pos_mz + 0.5 * box_z - - # Cell indices for each atom in the block (vectors of size BLOCK_M) - c_i_vec = (px / cell_sx).to(tl.int32) - c_j_vec = (py / cell_sy).to(tl.int32) - c_k_vec = (pz / cell_sz).to(tl.int32) - - # Clamp to valid range - c_i_vec = tl.maximum(tl.minimum(c_i_vec, n_cells_x - 1), 0) - c_j_vec = tl.maximum(tl.minimum(c_j_vec, n_cells_y - 1), 0) - c_k_vec = tl.maximum(tl.minimum(c_k_vec, n_cells_z - 1), 0) - - # Iterate over neighbor cell offsets - for i in range(-1, 2): - for j in range(-1, 2): - for k in range(-1, 2): - - # Compute neighbor cell indices for all atoms in block (vectorized) - n_idx_x = c_i_vec + i - n_idx_y = c_j_vec + j - n_idx_z = c_k_vec + k - - # --- PBC CHECK (vectorized) --- - if use_periodic: - # Wrap around - n_idx_x = (n_idx_x + n_cells_x) % n_cells_x - n_idx_y = (n_idx_y + n_cells_y) % n_cells_y - n_idx_z = (n_idx_z + n_cells_z) % n_cells_z - valid_cell_mask = mask_m # All cells valid with PBC - else: - # Strict Bounds Check (vectorized) - valid_cell_mask = ( - (n_idx_x >= 0) - & (n_idx_x < n_cells_x) - & (n_idx_y >= 0) - & (n_idx_y < n_cells_y) - & (n_idx_z >= 0) - & (n_idx_z < n_cells_z) - & mask_m - ) - - # Compute flat cell indices (vectorized, one per query atom) - cell_idx_vec = ( - n_idx_x * (n_cells_y * n_cells_z) + n_idx_y * n_cells_z + n_idx_z + num_cells_x = tl.load(CellDims + 0) + num_cells_y = tl.load(CellDims + 1) + num_cells_z = tl.load(CellDims + 2) + + # Decompose home cell ID into 3D coordinates + cells_yz = num_cells_y * num_cells_z + home_cx = home_cell_id // cells_yz + home_cy = (home_cell_id % cells_yz) // num_cells_z + home_cz = home_cell_id % num_cells_z + + # Load home cell boundaries + home_start = tl.load(CellStart + home_cell_id) + home_end = tl.load(CellEnd + home_cell_id) + + # Loop over 27 neighbor cells + for neighbor_offset in tl.range(0, 27): + # Decompose neighbor_offset into di, dj, dk (each in {-1, 0, 1}) + di = (neighbor_offset % 3) - 1 + dj = ((neighbor_offset // 3) % 3) - 1 + dk = (neighbor_offset // 9) - 1 + + # Compute neighbor cell coordinates + ni = home_cx + di + nj = home_cy + dj + nk = home_cz + dk + + # Handle boundary conditions + if use_periodic: + ni = (ni + num_cells_x) % num_cells_x + nj = (nj + num_cells_y) % num_cells_y + nk = (nk + num_cells_z) % num_cells_z + cell_valid = True + else: + cell_valid = ( + (ni >= 0) + & (ni < num_cells_x) + & (nj >= 0) + & (nj < num_cells_y) + & (nk >= 0) + & (nk < num_cells_z) + ) + + neighbor_cell_id = ni * cells_yz + nj * num_cells_z + nk + + # Load neighbor cell boundaries + neighbor_start = tl.load(CellStart + neighbor_cell_id) + neighbor_end = tl.load(CellEnd + neighbor_cell_id) + + # If cell is invalid (non-periodic boundary), make it empty + neighbor_start = tl.where(cell_valid, neighbor_start, 0) + neighbor_end = tl.where(cell_valid, neighbor_end, 0) + + # Batched iteration over home atoms + home_batch_start = home_start + while home_batch_start < home_end: + # Load BATCH_SIZE home atoms + home_offsets = tl.arange(0, BATCH_SIZE) + home_global_idx = home_batch_start + home_offsets + home_mask = home_global_idx < home_end + + # Load home atom original indices (for output pair indices) + home_atoms = tl.load( + SortedIndices + home_global_idx, mask=home_mask, other=0 + ) + + # Load home atom positions (sequential access - coalesced!) + home_x = tl.load( + SortedPositions + home_global_idx * 3 + 0, mask=home_mask, other=0.0 + ) + home_y = tl.load( + SortedPositions + home_global_idx * 3 + 1, mask=home_mask, other=0.0 + ) + home_z = tl.load( + SortedPositions + home_global_idx * 3 + 2, mask=home_mask, other=0.0 + ) + home_batch = tl.load( + SortedBatch + home_global_idx, mask=home_mask, other=-1 + ) + + # Batched iteration over neighbor atoms + neighbor_batch_start = neighbor_start + while neighbor_batch_start < neighbor_end: + # Load BATCH_SIZE neighbor atoms + neighbor_offsets = tl.arange(0, BATCH_SIZE) + neighbor_global_idx = neighbor_batch_start + neighbor_offsets + neighbor_mask = neighbor_global_idx < neighbor_end + + # Load neighbor atom original indices (for output pair indices) + neighbor_atoms = tl.load( + SortedIndices + neighbor_global_idx, mask=neighbor_mask, other=0 ) - # Gather cell boundaries for each query atom's neighbor cell - start_vec = tl.load( - CellStart + cell_idx_vec, mask=valid_cell_mask, other=-1 + # Load neighbor atom positions (sequential access - coalesced!) + neighbor_x = tl.load( + SortedPositions + neighbor_global_idx * 3 + 0, + mask=neighbor_mask, + other=0.0, ) - end_vec = tl.load(CellEnd + cell_idx_vec, mask=valid_cell_mask, other=0) - - # Find the maximum range we need to scan - max_end = tl.max(tl.where(valid_cell_mask, end_vec, 0)) - min_start = tl.min( - tl.where(valid_cell_mask & (start_vec >= 0), start_vec, n_atoms) + neighbor_y = tl.load( + SortedPositions + neighbor_global_idx * 3 + 1, + mask=neighbor_mask, + other=0.0, + ) + neighbor_z = tl.load( + SortedPositions + neighbor_global_idx * 3 + 2, + mask=neighbor_mask, + other=0.0, ) + neighbor_batch_vals = tl.load( + SortedBatch + neighbor_global_idx, mask=neighbor_mask, other=-2 + ) + + # Compute pairwise distances: [BATCH_SIZE, BATCH_SIZE] + dx = home_x[:, None] - neighbor_x[None, :] + dy = home_y[:, None] - neighbor_y[None, :] + dz = home_z[:, None] - neighbor_z[None, :] + + # Apply PBC + if use_periodic: + dx = dx - box_x * _tl_round(dx / box_x) + dy = dy - box_y * _tl_round(dy / box_y) + dz = dz - box_z * _tl_round(dz / box_z) + + dist_sq = dx * dx + dy * dy + dz * dz + + # Build validity mask + # 1. Distance within cutoff + cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) + + # 2. Same batch + cond_batch = home_batch[:, None] == neighbor_batch_vals[None, :] + + # 3. Index ordering to avoid double-counting + home_atoms_bc = home_atoms[:, None] + neighbor_atoms_bc = neighbor_atoms[None, :] - # Use fixed iteration count for CUDA graph compatibility - # Iterate over all possible neighbor atoms (worst case: all atoms) - for tile_idx in range(MAX_TILES): - # Neighbor atom indices (global indices in sorted array) - idx_n = tile_idx * BLOCK_M + tl.arange(0, BLOCK_M) - # Only process if within valid range [min_start, max_end) and within n_atoms - mask_n = ( - (idx_n >= min_start) & (idx_n < max_end) & (idx_n < n_atoms) - ) - - # Load Candidate atoms - off_n = idx_n - pos_nx = tl.load( - SortedCoords + off_n * 3 + 0, mask=mask_n, other=0.0 - ) - pos_ny = tl.load( - SortedCoords + off_n * 3 + 1, mask=mask_n, other=0.0 - ) - pos_nz = tl.load( - SortedCoords + off_n * 3 + 2, mask=mask_n, other=0.0 - ) - batch_n = tl.load(SortedBatch + off_n, mask=mask_n, other=-2) - - # Compute distances: [BLOCK_M query atoms, BLOCK_M candidate atoms] - d_x = pos_mx[:, None] - pos_nx[None, :] - d_y = pos_my[:, None] - pos_ny[None, :] - d_z = pos_mz[:, None] - pos_nz[None, :] - - # --- PBC Distance Adjustment --- - if use_periodic: - d_x = d_x - box_x * _tl_round(d_x / box_x) - d_y = d_y - box_y * _tl_round(d_y / box_y) - d_z = d_z - box_z * _tl_round(d_z / box_z) - - dist_sq = d_x * d_x + d_y * d_y + d_z * d_z - - # --- Interaction Logic --- - # 1. Distance & Batch conditions - cond_dist = (dist_sq <= cutoff_upper_sq) & ( - dist_sq >= cutoff_lower_sq - ) - cond_batch = batch_m[:, None] == batch_n[None, :] - - # 2. Check if neighbor is within each query atom's cell range - # idx_n is [BLOCK_M], start_vec is [BLOCK_M], end_vec is [BLOCK_M] - # We need [BLOCK_M, BLOCK_M] comparison - idx_n_bc = idx_n[None, :] # [1, BLOCK_M] - start_bc = start_vec[:, None] # [BLOCK_M, 1] - end_bc = end_vec[:, None] # [BLOCK_M, 1] - valid_cell_bc = valid_cell_mask[:, None] # [BLOCK_M, 1] - - # Neighbor must be in the query atom's specific cell range - cond_in_range = ( - (idx_n_bc >= start_bc) & (idx_n_bc < end_bc) & valid_cell_bc - ) - - # Load original indices for loop/transpose logic - # The condition must be based on original indices, not sorted indices - orig_m = tl.load(SortedToOrig + idx_m, mask=mask_m, other=-1) - orig_n = tl.load(SortedToOrig + idx_n, mask=mask_n, other=-1) - - # 3. Index / Loop Logic (using original indices) - orig_m_bc = orig_m[:, None] - orig_n_bc = orig_n[None, :] - - if include_transpose: - # Full List (both i->j and j->i) - if loop: - # Accept everything (i!=j AND i==j) -> All True - cond_idx = True - else: - # Accept only i != j - cond_idx = orig_m_bc != orig_n_bc + if include_transpose: + if loop: + cond_idx = True else: - # Half List (lower triangle convention: i >= j) - if loop: - # Accept i >= j (includes diagonal) - cond_idx = orig_m_bc >= orig_n_bc - else: - # Accept i > j (excludes diagonal) - cond_idx = orig_m_bc > orig_n_bc - - valid_mask = ( - cond_dist - & cond_batch - & cond_idx - & cond_in_range - & mask_m[:, None] - & mask_n[None, :] - ) - - # --- Store Block --- - num_found = tl.sum(valid_mask.to(tl.int32)) - if num_found > 0: - current_offset = tl.atomic_add(GlobalCounter, num_found) - if current_offset + num_found <= max_pairs: - flat_mask = tl.ravel(valid_mask) - csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) - store_idx = current_offset + csum - 1 - - flat_orig_m = tl.ravel( - tl.broadcast_to(orig_m[:, None], (BLOCK_M, BLOCK_M)) - ) - flat_orig_n = tl.ravel( - tl.broadcast_to(orig_n[None, :], (BLOCK_M, BLOCK_M)) - ) + cond_idx = home_atoms_bc != neighbor_atoms_bc + else: + if loop: + cond_idx = home_atoms_bc >= neighbor_atoms_bc + else: + cond_idx = home_atoms_bc > neighbor_atoms_bc - tl.store( - OutPairs + 0 * max_pairs + store_idx, - flat_orig_m, - mask=flat_mask, - ) - tl.store( - OutPairs + 1 * max_pairs + store_idx, - flat_orig_n, - mask=flat_mask, - ) - tl.store( - OutDeltas + store_idx * 3 + 0, - tl.ravel(d_x), - mask=flat_mask, - ) - tl.store( - OutDeltas + store_idx * 3 + 1, - tl.ravel(d_y), - mask=flat_mask, - ) - tl.store( - OutDeltas + store_idx * 3 + 2, - tl.ravel(d_z), - mask=flat_mask, + # 4. Both atoms must be valid (within actual cell bounds) + cond_valid = home_mask[:, None] & neighbor_mask[None, :] + + # Combined validity + valid_mask = cond_dist & cond_batch & cond_idx & cond_valid + + # Count and store valid pairs + num_found = tl.sum(valid_mask.to(tl.int32)) + + if num_found > 0: + # Atomically reserve space in output + current_offset = tl.atomic_add(GlobalCounter, num_found) + + if current_offset + num_found <= max_pairs: + # Compute storage indices using cumsum + flat_mask = tl.ravel(valid_mask) + csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) + store_idx = current_offset + csum - 1 + + # Prepare flattened data + flat_home = tl.ravel( + tl.broadcast_to( + home_atoms[:, None], (BATCH_SIZE, BATCH_SIZE) ) - tl.store( - OutDists + store_idx, - tl.sqrt(tl.ravel(dist_sq)), - mask=flat_mask, + ) + flat_neighbor = tl.ravel( + tl.broadcast_to( + neighbor_atoms[None, :], (BATCH_SIZE, BATCH_SIZE) ) + ) + flat_dx = tl.ravel(dx) + flat_dy = tl.ravel(dy) + flat_dz = tl.ravel(dz) + flat_dist = tl.sqrt(tl.ravel(dist_sq)) + + # Store pairs + tl.store( + OutPairs + 0 * max_pairs + store_idx, + flat_home, + mask=flat_mask, + ) + tl.store( + OutPairs + 1 * max_pairs + store_idx, + flat_neighbor, + mask=flat_mask, + ) + + # Store deltas + tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) + tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) + + # Store distances + tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) + + neighbor_batch_start += BATCH_SIZE + home_batch_start += BATCH_SIZE + + +def build_cell_list( + positions: Tensor, + batch: Tensor, + box_sizes: Tensor, # [3] diagonal elements + use_periodic: bool, + cell_dims: Tensor, # [3] number of cells in each dimension + num_cells: int, # total number of cells (fixed for CUDA graphs) +) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + """ + Build the cell list data structure using 1D sorted arrays. + + Args: + positions: [N, 3] atom positions + batch: [N] batch indices + box_sizes: [3] box diagonal elements + use_periodic: whether to use periodic boundary conditions + cell_dims: [3] number of cells in each dimension (pre-computed) + num_cells: total number of cells (pre-computed, fixed for CUDA graphs) + + Returns: + sorted_indices: [n_atoms] - original atom indices, sorted by cell + sorted_positions: [n_atoms, 3] - positions sorted by cell (for coalesced access) + sorted_batch: [n_atoms] - batch indices sorted by cell + cell_start: [num_cells] - start index in sorted arrays for each cell + cell_end: [num_cells] - end index (exclusive) in sorted arrays for each cell + """ + device = positions.device + n_atoms = positions.size(0) + + # Compute cell index for each atom + if use_periodic: + # Wrap to [0, box) + inv_box = 1.0 / box_sizes + wrapped = positions - torch.floor(positions * inv_box) * box_sizes + else: + # Shift by half box (like CUDA implementation) + wrapped = positions + 0.5 * box_sizes + # Cell coordinates + cell_size = box_sizes / cell_dims.float() + cell_coords = (wrapped / cell_size).long() + cell_coords = torch.clamp( + cell_coords, min=torch.zeros(3, device=device), max=cell_dims - 1 + ) -def _get_cell_dimensions( - box_x: torch.float32, - box_y: torch.float32, - box_z: torch.float32, - cutoff_upper: torch.float32, -) -> int: - nx = torch.floor(box_x / cutoff_upper).clamp(min=3).long() - ny = torch.floor(box_y / cutoff_upper).clamp(min=3).long() - nz = torch.floor(box_z / cutoff_upper).clamp(min=3).long() - return torch.stack([nx, ny, nz]) + # Flat cell index + cell_idx = ( + cell_coords[:, 0] * (cell_dims[1] * cell_dims[2]) + + cell_coords[:, 1] * cell_dims[2] + + cell_coords[:, 2] + ).long() + + # Sort atoms by cell index + sorted_cell_idx, sort_order = torch.sort(cell_idx) + sorted_indices = sort_order.int() # Original atom indices, now sorted by cell + + # Create sorted positions and batch for coalesced memory access + sorted_positions = positions.index_select(0, sort_order).contiguous() + sorted_batch = batch.index_select(0, sort_order).contiguous() + + # Count atoms per cell + cell_counts = torch.zeros(num_cells, dtype=torch.int32, device=device) + cell_counts.scatter_add_( + 0, cell_idx, torch.ones(n_atoms, dtype=torch.int32, device=device) + ) + + # Compute cell_start and cell_end using cumsum + cell_end = torch.cumsum(cell_counts, dim=0).int() + cell_start = torch.zeros(num_cells, dtype=torch.int32, device=device) + cell_start[1:] = cell_end[:-1] + + return sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end class TritonCellNeighborAutograd(TritonNeighborAutograd): @staticmethod - def forward( # type: ignore[override] + def forward( ctx, positions: Tensor, batch: Tensor, box_vectors: Tensor, use_periodic: bool, - cutoff_lower: torch.float32, - cutoff_upper: torch.float32, + cutoff_lower: float, + cutoff_upper: float, max_num_pairs: int, loop: bool, include_transpose: bool, @@ -315,132 +385,61 @@ def forward( # type: ignore[override] raise ValueError('Expected "positions" to have shape (N, 3)') if batch.dim() != 1 or batch.size(0) != n_atoms: raise ValueError('Expected "batch" to have shape (N,)') - if max_num_pairs <= 0: - raise ValueError('Expected "max_num_pairs" to be positive') - - # Handle box_vectors - following CUDA cell implementation approach: - # The box must be provided and valid. For non-periodic, the box defines the cell grid bounds. - # Move to correct device/dtype only if needed (avoid CPU sync during graph capture) - # if box_vectors.device != device or box_vectors.dtype != dtype: - # box_vectors = box_vectors.to(dtype=dtype, device=device) + + # Extract box diagonal box_vectors = box_vectors.contiguous() - box_diag = box_vectors[0] if box_vectors.dim() == 3 else box_vectors - # Keep as tensors to avoid GPU->CPU sync during CUDA graph capture - box_x = box_diag[0, 0] - box_y = box_diag[1, 1] - box_z = box_diag[2, 2] - # Create a contiguous tensor for kernel (CUDA graph compatible) - box_sizes = torch.stack([box_x, box_y, box_z]).contiguous() - - # Compute dimensions using torch operations (stays on GPU) - cell_dims = _get_cell_dimensions(box_x, box_y, box_z, cutoff_upper) - - # 1. Cell Index Calculation - # For periodic: wrap positions to [0, box) using PBC - # For non-periodic: shift positions by half box (matching CUDA cell implementation) - # The CUDA implementation uses: (p + 0.5 * box_size) / cutoff for cell index - if use_periodic: - inv_box_x = 1.0 / box_x - inv_box_y = 1.0 / box_y - inv_box_z = 1.0 / box_z - px = positions[:, 0] - torch.floor(positions[:, 0] * inv_box_x) * box_x - py = positions[:, 1] - torch.floor(positions[:, 1] * inv_box_y) * box_y - pz = positions[:, 2] - torch.floor(positions[:, 2] * inv_box_z) * box_z + if box_vectors.dim() == 3: + box_diag = box_vectors[0] else: - # For non-periodic, shift positions by half box (like CUDA impl) - # This centers the cell grid around the origin - px = positions[:, 0] + 0.5 * box_x - py = positions[:, 1] + 0.5 * box_y - pz = positions[:, 2] + 0.5 * box_z - - # Compute cell indices and clamp using broadcasting to avoid scalar extraction - # Stack positions for vectorized operation - p_stacked = torch.stack([px, py, pz], dim=1) # (n_atoms, 3) - cell_coords = (p_stacked / cutoff_upper).long() # (n_atoms, 3) - - # Clamp using broadcasting: cell_dims is shape (3,), cell_coords is (n_atoms, 3) - cell_coords = torch.clamp( - cell_coords, min=torch.zeros(3, device=device), max=cell_dims - 1 - ) - - # Extract individual coordinates - cx = cell_coords[:, 0] - cy = cell_coords[:, 1] - cz = cell_coords[:, 2] - - cell_indices = cx * (cell_dims[1] * cell_dims[2]) + cy * cell_dims[2] + cz - - # 2. Sort - sorted_cell_indices, sorted_atom_indices = torch.sort(cell_indices) - sorted_atom_indices = sorted_atom_indices.int() - - sorted_positions = positions.index_select( - 0, sorted_atom_indices.long() + box_diag = box_vectors + box_sizes = torch.stack( + [box_diag[0, 0], box_diag[1, 1], box_diag[2, 2]] ).contiguous() - sorted_batch = batch.index_select(0, sorted_atom_indices.long()).contiguous() - - # 3. Cell Pointers (CUDA graph compatible using scatter_reduce) - # Initialize cell_start with n_atoms (will use scatter_reduce 'amin') - # Initialize cell_end with 0 (will use scatter_reduce 'amax') - # num_cells_t is a tensor, PyTorch will extract the value for buffer size - cell_start = torch.full((num_cells,), n_atoms, device=device, dtype=torch.int32) - cell_end = torch.zeros((num_cells,), device=device, dtype=torch.int32) - - # Compute cell boundaries using scatter_reduce (fixed output size, CUDA graph compatible) - atom_indices = torch.arange(n_atoms, device=device, dtype=torch.int32) - sorted_cell_indices_long = sorted_cell_indices.long() - # cell_start[cell] = min index of atoms in that cell - cell_start.scatter_reduce_( - 0, sorted_cell_indices_long, atom_indices, reduce="amin", include_self=True - ) - # cell_end[cell] = max index + 1 of atoms in that cell - end_indices = atom_indices + 1 - cell_end.scatter_reduce_( - 0, sorted_cell_indices_long, end_indices, reduce="amax", include_self=True + + # Compute cell dimensions using shared utility (stays on GPU) + cell_dims = _get_cell_dimensions( + box_sizes[0], box_sizes[1], box_sizes[2], cutoff_upper ) - # Mark empty cells with -1 for cell_start (cells where cell_end remained 0) - # Use in-place operation to avoid tensor creation during CUDA graph capture - empty_cell_mask = cell_end == 0 - cell_start.masked_fill_(empty_cell_mask, -1) + # Build cell list (1D sorted approach with sorted positions for coalesced access) + sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end = ( + build_cell_list( + positions, batch, box_sizes, use_periodic, cell_dims, num_cells + ) + ) - # 4. Kernel Launch - # Initialize neighbors to -1 so unused slots are properly marked + # Allocate outputs neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) counter = torch.zeros((1,), device=device, dtype=torch.int32) - BLOCK_M = 64 - grid = (triton.cdiv(n_atoms, BLOCK_M),) - - # Compute MAX_TILES for fixed iteration count (CUDA graph compatible) - max_tiles = (n_atoms + BLOCK_M - 1) // BLOCK_M + # Launch kernel: one program per cell + # BATCH_SIZE: process atoms in batches for vectorized compute + # 32 is a good balance: 32×32=1024 elements fits in registers, minimal waste on partial batches + BATCH_SIZE = 32 - neighbor_list_kernel[grid]( + grid = (num_cells,) + cell_neighbor_kernel[grid]( + sorted_indices, sorted_positions, sorted_batch, - sorted_atom_indices, cell_start, cell_end, + box_sizes, + cell_dims, neighbors, deltas, distances, counter, - box_sizes, - cell_dims, - n_atoms, max_num_pairs, cutoff_lower**2, cutoff_upper**2, use_periodic=use_periodic, loop=loop, include_transpose=include_transpose, - BLOCK_M=BLOCK_M, - MAX_TILES=max_tiles, + BATCH_SIZE=BATCH_SIZE, ) - - # Return counter as tensor to avoid GPU->CPU sync (CUDA graph compatible) num_pairs = counter.to(torch.long) ctx.save_for_backward(neighbors, deltas, distances) @@ -449,7 +448,8 @@ def forward( # type: ignore[override] @staticmethod def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] - bwd = TritonNeighborAutograd.backward( + # Call parent backward (returns 9 values) and add None for num_cells + parent_grads = TritonNeighborAutograd.backward( ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs ) - return *bwd, None + return (*parent_grads, None) diff --git a/torchmdnet/extensions/triton_cell_v2.py b/torchmdnet/extensions/triton_cell_v2.py deleted file mode 100644 index 1763c9b11..000000000 --- a/torchmdnet/extensions/triton_cell_v2.py +++ /dev/null @@ -1,455 +0,0 @@ -""" -Efficient cell list neighbor list implementation for Triton. - -Key design: -- One program per cell (not per atom) -- Fixed-size padded cell buffers for CUDA graph compatibility -- O(n) complexity: each cell examines only 27 neighboring cells -""" - -try: - import triton - import triton.language as tl - - _HAS_TRITON = True -except Exception: - triton = None - tl = None - _HAS_TRITON = False - -import torch -from torch import Tensor -from typing import Tuple - -from torchmdnet.extensions.triton_neighbors import TritonNeighborAutograd -from torchmdnet.extensions.triton_cell import _get_cell_dimensions - - -@triton.jit -def _tl_round(x): - return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) - - -@triton.jit -def cell_neighbor_kernel( - # Cell data structure (1D sorted approach) - SortedIndices, # [n_atoms] - original atom indices, sorted by cell - SortedPositions, # [n_atoms, 3] - positions sorted by cell (for coalesced access) - SortedBatch, # [n_atoms] - batch indices sorted by cell - CellStart, # [num_cells] - start index in sorted arrays for each cell - CellEnd, # [num_cells] - end index (exclusive) in sorted arrays for each cell - # Box parameters - BoxSizes, # [3] - box dimensions - CellDims, # [3] - number of cells in each dimension (int32) - # Output - OutPairs, # [2, max_pairs] - OutDeltas, # [max_pairs, 3] - OutDists, # [max_pairs] - GlobalCounter, # [1] - # Scalar parameters - max_pairs, - cutoff_lower_sq, - cutoff_upper_sq, - # Flags - use_periodic: tl.constexpr, - loop: tl.constexpr, - include_transpose: tl.constexpr, - # Batch size for vectorized processing - BATCH_SIZE: tl.constexpr, # e.g., 32 -> processes 32×32=1024 pairs per iteration -): - """ - Each program processes one cell (the "home" cell). - Uses 1D sorted array with cell_start/cell_end pointers. - - Vectorized batched processing: - - Loads BATCH_SIZE atoms at a time for both home and neighbor - - Computes BATCH_SIZE × BATCH_SIZE distance matrix per iteration - - Uses while loops to iterate only over actual atoms - - Minimal waste: only last partial batch may have masked elements - - To avoid double-counting: - - For half list (include_transpose=False): only emit pairs where home_atom > neighbor_atom - - For full list (include_transpose=True): emit both directions - """ - home_cell_id = tl.program_id(0) - - # Load box and cell dimensions - box_x = tl.load(BoxSizes + 0) - box_y = tl.load(BoxSizes + 1) - box_z = tl.load(BoxSizes + 2) - - num_cells_x = tl.load(CellDims + 0) - num_cells_y = tl.load(CellDims + 1) - num_cells_z = tl.load(CellDims + 2) - - # Decompose home cell ID into 3D coordinates - cells_yz = num_cells_y * num_cells_z - home_cx = home_cell_id // cells_yz - home_cy = (home_cell_id % cells_yz) // num_cells_z - home_cz = home_cell_id % num_cells_z - - # Load home cell boundaries - home_start = tl.load(CellStart + home_cell_id) - home_end = tl.load(CellEnd + home_cell_id) - - # Loop over 27 neighbor cells - for neighbor_offset in tl.range(0, 27): - # Decompose neighbor_offset into di, dj, dk (each in {-1, 0, 1}) - di = (neighbor_offset % 3) - 1 - dj = ((neighbor_offset // 3) % 3) - 1 - dk = (neighbor_offset // 9) - 1 - - # Compute neighbor cell coordinates - ni = home_cx + di - nj = home_cy + dj - nk = home_cz + dk - - # Handle boundary conditions - if use_periodic: - ni = (ni + num_cells_x) % num_cells_x - nj = (nj + num_cells_y) % num_cells_y - nk = (nk + num_cells_z) % num_cells_z - cell_valid = True - else: - cell_valid = ( - (ni >= 0) - & (ni < num_cells_x) - & (nj >= 0) - & (nj < num_cells_y) - & (nk >= 0) - & (nk < num_cells_z) - ) - - neighbor_cell_id = ni * cells_yz + nj * num_cells_z + nk - - # Load neighbor cell boundaries - neighbor_start = tl.load(CellStart + neighbor_cell_id) - neighbor_end = tl.load(CellEnd + neighbor_cell_id) - - # If cell is invalid (non-periodic boundary), make it empty - neighbor_start = tl.where(cell_valid, neighbor_start, 0) - neighbor_end = tl.where(cell_valid, neighbor_end, 0) - - # Batched iteration over home atoms - home_batch_start = home_start - while home_batch_start < home_end: - # Load BATCH_SIZE home atoms - home_offsets = tl.arange(0, BATCH_SIZE) - home_global_idx = home_batch_start + home_offsets - home_mask = home_global_idx < home_end - - # Load home atom original indices (for output pair indices) - home_atoms = tl.load( - SortedIndices + home_global_idx, mask=home_mask, other=0 - ) - - # Load home atom positions (sequential access - coalesced!) - home_x = tl.load( - SortedPositions + home_global_idx * 3 + 0, mask=home_mask, other=0.0 - ) - home_y = tl.load( - SortedPositions + home_global_idx * 3 + 1, mask=home_mask, other=0.0 - ) - home_z = tl.load( - SortedPositions + home_global_idx * 3 + 2, mask=home_mask, other=0.0 - ) - home_batch = tl.load( - SortedBatch + home_global_idx, mask=home_mask, other=-1 - ) - - # Batched iteration over neighbor atoms - neighbor_batch_start = neighbor_start - while neighbor_batch_start < neighbor_end: - # Load BATCH_SIZE neighbor atoms - neighbor_offsets = tl.arange(0, BATCH_SIZE) - neighbor_global_idx = neighbor_batch_start + neighbor_offsets - neighbor_mask = neighbor_global_idx < neighbor_end - - # Load neighbor atom original indices (for output pair indices) - neighbor_atoms = tl.load( - SortedIndices + neighbor_global_idx, mask=neighbor_mask, other=0 - ) - - # Load neighbor atom positions (sequential access - coalesced!) - neighbor_x = tl.load( - SortedPositions + neighbor_global_idx * 3 + 0, - mask=neighbor_mask, - other=0.0, - ) - neighbor_y = tl.load( - SortedPositions + neighbor_global_idx * 3 + 1, - mask=neighbor_mask, - other=0.0, - ) - neighbor_z = tl.load( - SortedPositions + neighbor_global_idx * 3 + 2, - mask=neighbor_mask, - other=0.0, - ) - neighbor_batch_vals = tl.load( - SortedBatch + neighbor_global_idx, mask=neighbor_mask, other=-2 - ) - - # Compute pairwise distances: [BATCH_SIZE, BATCH_SIZE] - dx = home_x[:, None] - neighbor_x[None, :] - dy = home_y[:, None] - neighbor_y[None, :] - dz = home_z[:, None] - neighbor_z[None, :] - - # Apply PBC - if use_periodic: - dx = dx - box_x * _tl_round(dx / box_x) - dy = dy - box_y * _tl_round(dy / box_y) - dz = dz - box_z * _tl_round(dz / box_z) - - dist_sq = dx * dx + dy * dy + dz * dz - - # Build validity mask - # 1. Distance within cutoff - cond_dist = (dist_sq < cutoff_upper_sq) & (dist_sq >= cutoff_lower_sq) - - # 2. Same batch - cond_batch = home_batch[:, None] == neighbor_batch_vals[None, :] - - # 3. Index ordering to avoid double-counting - home_atoms_bc = home_atoms[:, None] - neighbor_atoms_bc = neighbor_atoms[None, :] - - if include_transpose: - if loop: - cond_idx = True - else: - cond_idx = home_atoms_bc != neighbor_atoms_bc - else: - if loop: - cond_idx = home_atoms_bc >= neighbor_atoms_bc - else: - cond_idx = home_atoms_bc > neighbor_atoms_bc - - # 4. Both atoms must be valid (within actual cell bounds) - cond_valid = home_mask[:, None] & neighbor_mask[None, :] - - # Combined validity - valid_mask = cond_dist & cond_batch & cond_idx & cond_valid - - # Count and store valid pairs - num_found = tl.sum(valid_mask.to(tl.int32)) - - if num_found > 0: - # Atomically reserve space in output - current_offset = tl.atomic_add(GlobalCounter, num_found) - - if current_offset + num_found <= max_pairs: - # Compute storage indices using cumsum - flat_mask = tl.ravel(valid_mask) - csum = tl.cumsum(flat_mask.to(tl.int32), axis=0) - store_idx = current_offset + csum - 1 - - # Prepare flattened data - flat_home = tl.ravel( - tl.broadcast_to( - home_atoms[:, None], (BATCH_SIZE, BATCH_SIZE) - ) - ) - flat_neighbor = tl.ravel( - tl.broadcast_to( - neighbor_atoms[None, :], (BATCH_SIZE, BATCH_SIZE) - ) - ) - flat_dx = tl.ravel(dx) - flat_dy = tl.ravel(dy) - flat_dz = tl.ravel(dz) - flat_dist = tl.sqrt(tl.ravel(dist_sq)) - - # Store pairs - tl.store( - OutPairs + 0 * max_pairs + store_idx, - flat_home, - mask=flat_mask, - ) - tl.store( - OutPairs + 1 * max_pairs + store_idx, - flat_neighbor, - mask=flat_mask, - ) - - # Store deltas - tl.store(OutDeltas + store_idx * 3 + 0, flat_dx, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 1, flat_dy, mask=flat_mask) - tl.store(OutDeltas + store_idx * 3 + 2, flat_dz, mask=flat_mask) - - # Store distances - tl.store(OutDists + store_idx, flat_dist, mask=flat_mask) - - neighbor_batch_start += BATCH_SIZE - home_batch_start += BATCH_SIZE - - -def build_cell_list( - positions: Tensor, - batch: Tensor, - box_sizes: Tensor, # [3] diagonal elements - use_periodic: bool, - cell_dims: Tensor, # [3] number of cells in each dimension - num_cells: int, # total number of cells (fixed for CUDA graphs) -) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: - """ - Build the cell list data structure using 1D sorted arrays. - - Args: - positions: [N, 3] atom positions - batch: [N] batch indices - box_sizes: [3] box diagonal elements - use_periodic: whether to use periodic boundary conditions - cell_dims: [3] number of cells in each dimension (pre-computed) - num_cells: total number of cells (pre-computed, fixed for CUDA graphs) - - Returns: - sorted_indices: [n_atoms] - original atom indices, sorted by cell - sorted_positions: [n_atoms, 3] - positions sorted by cell (for coalesced access) - sorted_batch: [n_atoms] - batch indices sorted by cell - cell_start: [num_cells] - start index in sorted arrays for each cell - cell_end: [num_cells] - end index (exclusive) in sorted arrays for each cell - """ - device = positions.device - n_atoms = positions.size(0) - - # Compute cell index for each atom - if use_periodic: - # Wrap to [0, box) - inv_box = 1.0 / box_sizes - wrapped = positions - torch.floor(positions * inv_box) * box_sizes - else: - # Shift by half box (like CUDA implementation) - wrapped = positions + 0.5 * box_sizes - - # Cell coordinates - cell_size = box_sizes / cell_dims.float() - cell_coords = (wrapped / cell_size).long() - cell_coords = torch.clamp( - cell_coords, min=torch.zeros(3, device=device), max=cell_dims - 1 - ) - - # Flat cell index - cell_idx = ( - cell_coords[:, 0] * (cell_dims[1] * cell_dims[2]) - + cell_coords[:, 1] * cell_dims[2] - + cell_coords[:, 2] - ).long() - - # Sort atoms by cell index - sorted_cell_idx, sort_order = torch.sort(cell_idx) - sorted_indices = sort_order.int() # Original atom indices, now sorted by cell - - # Create sorted positions and batch for coalesced memory access - sorted_positions = positions.index_select(0, sort_order).contiguous() - sorted_batch = batch.index_select(0, sort_order).contiguous() - - # Count atoms per cell - cell_counts = torch.zeros(num_cells, dtype=torch.int32, device=device) - cell_counts.scatter_add_( - 0, cell_idx, torch.ones(n_atoms, dtype=torch.int32, device=device) - ) - - # Compute cell_start and cell_end using cumsum - cell_end = torch.cumsum(cell_counts, dim=0).int() - cell_start = torch.zeros(num_cells, dtype=torch.int32, device=device) - cell_start[1:] = cell_end[:-1] - - return sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end - - -class TritonCellNeighborV2(TritonNeighborAutograd): - @staticmethod - def forward( - ctx, - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, - num_cells: int, - ): - if not _HAS_TRITON: - raise RuntimeError("Triton is not available") - - device = positions.device - dtype = positions.dtype - n_atoms = positions.size(0) - - # Validate inputs - if positions.dim() != 2 or positions.size(1) != 3: - raise ValueError('Expected "positions" to have shape (N, 3)') - if batch.dim() != 1 or batch.size(0) != n_atoms: - raise ValueError('Expected "batch" to have shape (N,)') - - # Extract box diagonal - box_vectors = box_vectors.contiguous() - if box_vectors.dim() == 3: - box_diag = box_vectors[0] - else: - box_diag = box_vectors - box_sizes = torch.stack( - [box_diag[0, 0], box_diag[1, 1], box_diag[2, 2]] - ).contiguous() - - # Compute cell dimensions using shared utility (stays on GPU) - cell_dims = _get_cell_dimensions( - box_sizes[0], box_sizes[1], box_sizes[2], cutoff_upper - ) - - # Build cell list (1D sorted approach with sorted positions for coalesced access) - sorted_indices, sorted_positions, sorted_batch, cell_start, cell_end = ( - build_cell_list( - positions, batch, box_sizes, use_periodic, cell_dims, num_cells - ) - ) - - # Allocate outputs - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) - deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) - distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) - counter = torch.zeros((1,), device=device, dtype=torch.int32) - - # Launch kernel: one program per cell - # BATCH_SIZE: process atoms in batches for vectorized compute - # 32 is a good balance: 32×32=1024 elements fits in registers, minimal waste on partial batches - BATCH_SIZE = 32 - - grid = (num_cells,) - cell_neighbor_kernel[grid]( - sorted_indices, - sorted_positions, - sorted_batch, - cell_start, - cell_end, - box_sizes, - cell_dims, - neighbors, - deltas, - distances, - counter, - max_num_pairs, - cutoff_lower**2, - cutoff_upper**2, - use_periodic=use_periodic, - loop=loop, - include_transpose=include_transpose, - BATCH_SIZE=BATCH_SIZE, - ) - num_pairs = counter.to(torch.long) - - ctx.save_for_backward(neighbors, deltas, distances) - ctx.num_atoms = n_atoms - return neighbors, deltas, distances, num_pairs - - @staticmethod - def backward(ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs): # type: ignore[override] - # Call parent backward (returns 9 values) and add None for num_cells - parent_grads = TritonNeighborAutograd.backward( - ctx, grad_neighbors, grad_deltas, grad_distances, grad_num_pairs - ) - return (*parent_grads, None) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index f4f97b251..bca2842ce 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -254,7 +254,6 @@ def triton_neighbor_pairs( from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd from torchmdnet.extensions.triton_shared import TritonSharedNeighborAutograd - from torchmdnet.extensions.triton_cell_v2 import TritonCellNeighborV2 if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -276,7 +275,7 @@ def triton_neighbor_pairs( bool(include_transpose), ) elif strategy == "cell": - return TritonCellNeighborV2.apply( + return TritonCellNeighborAutograd.apply( positions, batch, box_vectors, From bf2c761bae3b9aefcaa799dbbc194d5ef8082544 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 14:59:43 +0200 Subject: [PATCH 36/80] removing shared memory implementation --- benchmarks/neighbors.py | 2 +- tests/test_neighbors.py | 18 +- torchmdnet/extensions/ops.py | 2 +- torchmdnet/extensions/triton_neighbors.py | 13 -- torchmdnet/extensions/triton_shared.py | 256 ---------------------- torchmdnet/models/utils.py | 2 +- 6 files changed, 11 insertions(+), 282 deletions(-) delete mode 100644 torchmdnet/extensions/triton_shared.py diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index bb1517809..515a66b74 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -183,7 +183,7 @@ def benchmark_neighbors( if __name__ == "__main__": - strategies = ["distance", "brute", "cell", "shared"] + strategies = ["distance", "brute", "cell"] n_particles = 32767 mean_num_neighbors = min(n_particles, 64) density = 0.8 diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index fa3e06046..847f88fa4 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -73,7 +73,7 @@ def compute_ref_neighbors(pos, batch, loop, include_transpose, cutoff, box_vecto @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "cell")], ) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4, 128]) @pytest.mark.parametrize("cutoff", [0.1, 1.0, 3.0, 4.9]) @@ -149,7 +149,7 @@ def test_neighbors( @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "cell")], ) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @@ -261,7 +261,7 @@ def test_neighbor_grads( @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "cell")], ) @pytest.mark.parametrize("loop", [True, False]) @pytest.mark.parametrize("include_transpose", [True, False]) @@ -314,7 +314,7 @@ def test_neighbor_autograds( ) -@pytest.mark.parametrize("strategy", ["brute", "cell", "shared"]) +@pytest.mark.parametrize("strategy", ["brute", "cell"]) @pytest.mark.parametrize("n_batches", [1, 2, 3, 4]) def test_large_size(strategy, n_batches): device = "cuda" @@ -373,7 +373,7 @@ def test_large_size(strategy, n_batches): @pytest.mark.parametrize( ("device", "strategy"), - [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared"), ("cuda", "cell")], + [("cpu", "brute"), ("cuda", "brute"), ("cuda", "cell")], ) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @@ -444,7 +444,7 @@ def test_jit_script_compatible( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) +@pytest.mark.parametrize("strategy", ["brute", "cell"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -527,7 +527,7 @@ def test_cuda_graph_compatible_forward( @pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("strategy", ["brute", "shared", "cell"]) +@pytest.mark.parametrize("strategy", ["brute", "cell"]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("cutoff", [1.0]) @pytest.mark.parametrize("loop", [True, False]) @@ -600,9 +600,7 @@ def test_cuda_graph_compatible_backward( torch.cuda.synchronize() -@pytest.mark.parametrize( - ("device", "strategy"), [("cpu", "brute"), ("cuda", "brute"), ("cuda", "shared")] -) +@pytest.mark.parametrize(("device", "strategy"), [("cpu", "brute"), ("cuda", "brute")]) @pytest.mark.parametrize("n_batches", [1, 128]) @pytest.mark.parametrize("use_forward", [True, False]) def test_per_batch_box(device, strategy, n_batches, use_forward): diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index d10a58762..5cb03bb99 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -37,7 +37,7 @@ def get_neighbor_pairs_kernel( Parameters ---------- strategy : str - Strategy to use for computing the neighbor list. Can be one of :code:`["shared", "brute", "cell"]`. + Strategy to use for computing the neighbor list. Can be one of :code:`["brute", "cell"]`. positions : Tensor A tensor with shape (N, 3) representing the atomic positions. batch : Tensor diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index bca2842ce..5db3f851b 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -253,7 +253,6 @@ def triton_neighbor_pairs( ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd - from torchmdnet.extensions.triton_shared import TritonSharedNeighborAutograd if not _HAS_TRITON: raise RuntimeError("Triton is not available") @@ -287,18 +286,6 @@ def triton_neighbor_pairs( bool(include_transpose), num_cells, ) - elif strategy == "shared": - return TritonSharedNeighborAutograd.apply( - positions, - batch, - box_vectors, - use_periodic, - float(cutoff_lower), - float(cutoff_upper), - int(max_num_pairs), - bool(loop), - bool(include_transpose), - ) else: raise ValueError(f"Unsupported strategy {strategy}") diff --git a/torchmdnet/extensions/triton_shared.py b/torchmdnet/extensions/triton_shared.py deleted file mode 100644 index 6d7506407..000000000 --- a/torchmdnet/extensions/triton_shared.py +++ /dev/null @@ -1,256 +0,0 @@ -try: - import triton - import triton.language as tl - - _HAS_TRITON = True -except Exception: - triton = None - tl = None - _HAS_TRITON = False - -from torch import Tensor -import torch -from torchmdnet.extensions.triton_neighbors import _tl_round, TritonNeighborAutograd - - -@triton.jit -def _neighbor_shared_kernel( - pos_ptr, - batch_ptr, - box_ptr, - neighbors0_ptr, - neighbors1_ptr, - deltas_ptr, - distances_ptr, - counter_ptr, - stride_pos_0: tl.constexpr, - stride_pos_1: tl.constexpr, - stride_batch: tl.constexpr, - stride_box_b: tl.constexpr, - stride_box_r: tl.constexpr, - stride_box_c: tl.constexpr, - n_atoms: tl.constexpr, - use_periodic: tl.constexpr, - include_transpose: tl.constexpr, - loop: tl.constexpr, - max_pairs: tl.constexpr, - cutoff_lower2: tl.constexpr, - cutoff_upper2: tl.constexpr, - BLOCK: tl.constexpr, -): - """Shared-memory style neighbor construction: tile j, one block of i atoms.""" - pid_i = tl.program_id(axis=0) - pid_tile = tl.program_id(axis=1) - - i_idx = pid_i * BLOCK + tl.arange(0, BLOCK) - j_idx = pid_tile * BLOCK + tl.arange(0, BLOCK) - - mask_i = i_idx < n_atoms - mask_j = j_idx < n_atoms - - batch_i = tl.load(batch_ptr + i_idx * stride_batch, mask=mask_i, other=0) - batch_j = tl.load(batch_ptr + j_idx * stride_batch, mask=mask_j, other=0) - - pos_ix = tl.load( - pos_ptr + i_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_i, other=0.0 - ) - pos_iy = tl.load( - pos_ptr + i_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_i, other=0.0 - ) - pos_iz = tl.load( - pos_ptr + i_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_i, other=0.0 - ) - - pos_jx = tl.load( - pos_ptr + j_idx * stride_pos_0 + 0 * stride_pos_1, mask=mask_j, other=0.0 - ) - pos_jy = tl.load( - pos_ptr + j_idx * stride_pos_0 + 1 * stride_pos_1, mask=mask_j, other=0.0 - ) - pos_jz = tl.load( - pos_ptr + j_idx * stride_pos_0 + 2 * stride_pos_1, mask=mask_j, other=0.0 - ) - - # Pairwise masks (BLOCK, BLOCK) - mask_pair = mask_i[:, None] & mask_j[None, :] - if loop: - mask_pair = mask_pair & ( - (j_idx[None, :] < i_idx[:, None]) | (j_idx[None, :] == i_idx[:, None]) - ) - else: - mask_pair = mask_pair & (j_idx[None, :] < i_idx[:, None]) - mask_pair = mask_pair & (batch_i[:, None] == batch_j[None, :]) - - dx = pos_ix[:, None] - pos_jx[None, :] - dy = pos_iy[:, None] - pos_jy[None, :] - dz = pos_iz[:, None] - pos_jz[None, :] - - if use_periodic: - box_base = box_ptr + batch_i * stride_box_b - b20 = tl.load( - box_base + 2 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=0.0 - ) - b21 = tl.load( - box_base + 2 * stride_box_r + 1 * stride_box_c, mask=mask_i, other=0.0 - ) - b22 = tl.load( - box_base + 2 * stride_box_r + 2 * stride_box_c, mask=mask_i, other=1.0 - ) - b10 = tl.load( - box_base + 1 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=0.0 - ) - b11 = tl.load( - box_base + 1 * stride_box_r + 1 * stride_box_c, mask=mask_i, other=1.0 - ) - b00 = tl.load( - box_base + 0 * stride_box_r + 0 * stride_box_c, mask=mask_i, other=1.0 - ) - - scale3 = _tl_round(dz / b22[:, None]) - dx = dx - scale3 * b20[:, None] - dy = dy - scale3 * b21[:, None] - dz = dz - scale3 * b22[:, None] - - scale2 = _tl_round(dy / b11[:, None]) - dx = dx - scale2 * b10[:, None] - dy = dy - scale2 * b11[:, None] - - scale1 = _tl_round(dx / b00[:, None]) - dx = dx - scale1 * b00[:, None] - - dist2 = dx * dx + dy * dy + dz * dz - valid_pair = mask_pair & (dist2 < cutoff_upper2) & (dist2 >= cutoff_lower2) - - i_mat = i_idx[:, None] + tl.zeros((1, BLOCK), dtype=i_idx.dtype) - j_mat = tl.zeros((BLOCK, 1), dtype=j_idx.dtype) + j_idx[None, :] - - dx_flat = tl.reshape(dx, (BLOCK * BLOCK,)) - dy_flat = tl.reshape(dy, (BLOCK * BLOCK,)) - dz_flat = tl.reshape(dz, (BLOCK * BLOCK,)) - dist_flat = tl.reshape(tl.sqrt(dist2), (BLOCK * BLOCK,)) - - i_flat = tl.reshape(i_mat, (BLOCK * BLOCK,)) - j_flat = tl.reshape(j_mat, (BLOCK * BLOCK,)) - - valid_flat = tl.reshape(valid_pair, (BLOCK * BLOCK,)) - valid_int = valid_flat.to(tl.int32) - total_pairs = tl.sum(valid_int) - - if include_transpose: - valid_t_flat = valid_flat & (i_flat != j_flat) - valid_t_int = valid_t_flat.to(tl.int32) - total_transpose = tl.sum(valid_t_int) - total_out = total_pairs + total_transpose - else: - total_out = total_pairs - - has_out = total_out > 0 - start_idx = tl.atomic_add(counter_ptr, total_out, mask=has_out) - start_idx = tl.where(has_out, start_idx, 0) - start_idx = tl.broadcast_to(start_idx, valid_flat.shape) - - prefix = tl.cumsum(valid_int, axis=0) - 1 - out_idx = start_idx + prefix - - mask_store = valid_flat & (out_idx < max_pairs) - tl.store(neighbors0_ptr + out_idx, i_flat, mask=mask_store) - tl.store(neighbors1_ptr + out_idx, j_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 0, dx_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 1, dy_flat, mask=mask_store) - tl.store(deltas_ptr + out_idx * 3 + 2, dz_flat, mask=mask_store) - tl.store(distances_ptr + out_idx, dist_flat, mask=mask_store) - - if include_transpose: - prefix_t = tl.cumsum(valid_t_int, axis=0) - 1 - out_idx_t = start_idx + total_pairs + prefix_t - - mask_store_t = valid_t_flat & (out_idx_t < max_pairs) - tl.store(neighbors0_ptr + out_idx_t, j_flat, mask=mask_store_t) - tl.store(neighbors1_ptr + out_idx_t, i_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 0, -dx_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 1, -dy_flat, mask=mask_store_t) - tl.store(deltas_ptr + out_idx_t * 3 + 2, -dz_flat, mask=mask_store_t) - tl.store(distances_ptr + out_idx_t, dist_flat, mask=mask_store_t) - - -class TritonSharedNeighborAutograd(TritonNeighborAutograd): - @staticmethod - def forward( # type: ignore[override] - ctx, - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, - ): - if not _HAS_TRITON: - raise RuntimeError("Triton is not available") - - device = positions.device - dtype = positions.dtype - n_atoms = positions.size(0) - - if positions.dim() != 2 or positions.size(1) != 3: - raise ValueError('Expected "positions" to have shape (N, 3)') - if batch.dim() != 1 or batch.size(0) != n_atoms: - raise ValueError('Expected "batch" to have shape (N,)') - if max_num_pairs <= 0: - raise ValueError('Expected "max_num_pairs" to be positive') - - batch = batch.contiguous() - positions = positions.contiguous() - - if use_periodic: - if box_vectors.dim() == 2: - box_vectors = box_vectors.unsqueeze(0) - elif box_vectors.dim() != 3: - raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') - box_vectors = box_vectors.to(device=device, dtype=dtype).contiguous() - box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) - else: - # Dummy tensor to avoid conditional code paths in the kernel signature. - box_vectors = positions - box_stride_0 = 0 - - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) - deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) - distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) - counter = torch.zeros((1,), device=device, dtype=torch.int32) - - BLOCK = 64 - num_blocks = max((n_atoms + BLOCK - 1) // BLOCK, 1) - grid = (num_blocks, num_blocks) - - _neighbor_shared_kernel[grid]( - positions, - batch, - box_vectors, - neighbors[0], - neighbors[1], - deltas, - distances, - counter, - positions.stride(0), - positions.stride(1), - batch.stride(0), - box_stride_0, - box_vectors.stride(1) if use_periodic else 0, - box_vectors.stride(2) if use_periodic else 0, - n_atoms, - use_periodic, - include_transpose, - loop, - max_num_pairs, - float(cutoff_lower * cutoff_lower), - float(cutoff_upper * cutoff_upper), - BLOCK=BLOCK, - ) - - num_pairs = counter.to(torch.long) - ctx.save_for_backward(neighbors, deltas, distances) - ctx.num_atoms = n_atoms - return neighbors, deltas, distances, num_pairs diff --git a/torchmdnet/models/utils.py b/torchmdnet/models/utils.py index c1c579233..48668d120 100644 --- a/torchmdnet/models/utils.py +++ b/torchmdnet/models/utils.py @@ -152,7 +152,7 @@ class OptimizedDistance(torch.nn.Module): If the number of pairs found is larger than this, the pairs are randomly sampled. When check_errors is True, an exception is raised in this case. If negative, it is interpreted as (minus) the maximum number of neighbors per atom. strategy : str - Strategy to use for computing the neighbor list. Can be one of :code:`["shared", "brute", "cell"]`. + Strategy to use for computing the neighbor list. Can be one of :code:`["brute", "cell"]`. 1. *Shared*: An O(N^2) algorithm that leverages CUDA shared memory, best for large number of particles. 2. *Brute*: A brute force O(N^2) algorithm, best for small number of particles. From 8fffeaefeeca70bd8540d84623637819a214ac6e Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 15:01:48 +0200 Subject: [PATCH 37/80] cleanup and file headers --- torchmdnet/extensions/__init__.py | 9 --------- torchmdnet/extensions/triton_brute.py | 5 +++++ torchmdnet/extensions/triton_cell.py | 11 +++-------- torchmdnet/extensions/triton_neighbors.py | 7 +++++-- 4 files changed, 13 insertions(+), 19 deletions(-) diff --git a/torchmdnet/extensions/__init__.py b/torchmdnet/extensions/__init__.py index c618f93cb..f163ff7d7 100644 --- a/torchmdnet/extensions/__init__.py +++ b/torchmdnet/extensions/__init__.py @@ -1,12 +1,3 @@ # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org # Distributed under the MIT License. # (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - -# Place here any short extensions to torch that you want to use in your code. -# The extensions present in extensions.cpp will be automatically compiled in setup.py and loaded here. -# The extensions will be available under torch.ops.torchmdnet_extensions, but you can add wrappers here to make them more convenient to use. -# Place here too any meta registrations for your extensions if required. - -import torch -from pathlib import Path -from . import ops diff --git a/torchmdnet/extensions/triton_brute.py b/torchmdnet/extensions/triton_brute.py index 34d3d7a85..ffd378884 100644 --- a/torchmdnet/extensions/triton_brute.py +++ b/torchmdnet/extensions/triton_brute.py @@ -1,3 +1,8 @@ +# Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org +# Distributed under the MIT License. +# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) + + try: import triton import triton.language as tl diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 54aba4b4c..06367cff9 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -1,11 +1,6 @@ -""" -Efficient cell list neighbor list implementation for Triton. - -Key design: -- One program per cell (not per atom) -- Fixed-size padded cell buffers for CUDA graph compatibility -- O(n) complexity: each cell examines only 27 neighboring cells -""" +# Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org +# Distributed under the MIT License. +# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) try: import triton diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index 5db3f851b..ab0755b24 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -1,5 +1,8 @@ -import math -from typing import Optional, Tuple +# Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org +# Distributed under the MIT License. +# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) + +from typing import Tuple import torch from torch import Tensor From 80b871dd27b970a246dff79a34cf690e6f4b35f2 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 15:02:38 +0200 Subject: [PATCH 38/80] remove CUDA implementations --- torchmdnet/extensions/neighbors/common.cuh | 206 --------- .../extensions/neighbors/neighbors_cpu.cpp | 273 ------------ .../extensions/neighbors/neighbors_cuda.cu | 45 -- .../neighbors/neighbors_cuda_brute.cuh | 122 ------ .../neighbors/neighbors_cuda_cell.cuh | 390 ------------------ .../neighbors/neighbors_cuda_shared.cuh | 124 ------ 6 files changed, 1160 deletions(-) delete mode 100644 torchmdnet/extensions/neighbors/common.cuh delete mode 100644 torchmdnet/extensions/neighbors/neighbors_cpu.cpp delete mode 100644 torchmdnet/extensions/neighbors/neighbors_cuda.cu delete mode 100644 torchmdnet/extensions/neighbors/neighbors_cuda_brute.cuh delete mode 100644 torchmdnet/extensions/neighbors/neighbors_cuda_cell.cuh delete mode 100644 torchmdnet/extensions/neighbors/neighbors_cuda_shared.cuh diff --git a/torchmdnet/extensions/neighbors/common.cuh b/torchmdnet/extensions/neighbors/common.cuh deleted file mode 100644 index bf875af5b..000000000 --- a/torchmdnet/extensions/neighbors/common.cuh +++ /dev/null @@ -1,206 +0,0 @@ -/* Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * Raul P. Pelaez 2023. Common utilities for the CUDA neighbor operation. - */ -#ifndef NEIGHBORS_COMMON_CUH -#define NEIGHBORS_COMMON_CUH -#include -#include -#include -#include -#include - -using c10::cuda::getCurrentCUDAStream; -using c10::cuda::CUDAStreamGuard; -using at::Tensor; -using at::TensorOptions; -using at::TensorAccessor; -using torch::Scalar; -using torch::empty; -using torch::full; - -template -using Accessor = torch::PackedTensorAccessor32; - -template -using KernelAccessor = TensorAccessor; - -template -inline Accessor get_accessor(const Tensor& tensor) { - return tensor.packed_accessor32(); -}; - -template __device__ __forceinline__ scalar_t sqrt_(scalar_t x){return ::sqrt(x);}; -template <> __device__ __forceinline__ float sqrt_(float x) { - return ::sqrtf(x); -}; -template <> __device__ __forceinline__ double sqrt_(double x) { - return ::sqrt(x); -}; - -template struct vec3 { - using type = void; -}; - -template <> struct vec3 { - using type = float3; -}; - -template <> struct vec3 { - using type = double3; -}; - -template using scalar3 = typename vec3::type; - -/* - * @brief Get the position of the i'th particle - * @param positions The positions tensor - * @param i The index of the particle - * @return The position of the i'th particle - */ -template -__device__ scalar3 fetchPosition(const Accessor positions, const int i) { - return {positions[i][0], positions[i][1], positions[i][2]}; -} - -struct PairList { - Tensor i_curr_pair; - Tensor neighbors; - Tensor deltas; - Tensor distances; - const bool loop, include_transpose, use_periodic; - PairList(int max_num_pairs, torch::TensorOptions options, bool loop, bool include_transpose, - bool use_periodic) - : i_curr_pair(torch::zeros({1}, options.dtype(at::kInt))), - neighbors(torch::full({2, max_num_pairs}, -1, options.dtype(at::kInt))), - deltas(torch::full({max_num_pairs, 3}, 0, options)), - distances(torch::full({max_num_pairs}, 0, options)), loop(loop), - include_transpose(include_transpose), use_periodic(use_periodic) { - } -}; - -template struct PairListAccessor { - Accessor i_curr_pair; - Accessor neighbors; - Accessor deltas; - Accessor distances; - bool loop, include_transpose, use_periodic; - explicit PairListAccessor(const PairList& pl) - : i_curr_pair(get_accessor(pl.i_curr_pair)), - neighbors(get_accessor(pl.neighbors)), - deltas(get_accessor(pl.deltas)), - distances(get_accessor(pl.distances)), loop(pl.loop), - include_transpose(pl.include_transpose), use_periodic(pl.use_periodic) { - } -}; - -template -__device__ void writeAtomPair(PairListAccessor& list, int i, int j, - scalar3 delta, scalar_t distance, int i_pair) { - if (i_pair < list.neighbors.size(1)) { - list.neighbors[0][i_pair] = i; - list.neighbors[1][i_pair] = j; - list.deltas[i_pair][0] = delta.x; - list.deltas[i_pair][1] = delta.y; - list.deltas[i_pair][2] = delta.z; - list.distances[i_pair] = distance; - } -} - -template -__device__ void addAtomPairToList(PairListAccessor& list, int i, int j, - scalar3 delta, scalar_t distance, bool add_transpose) { - const int32_t i_pair = atomicAdd(&list.i_curr_pair[0], add_transpose ? 2 : 1); - // Neighbors after the max number of pairs are ignored, although the pair is counted - writeAtomPair(list, i, j, delta, distance, i_pair); - if (add_transpose) { - writeAtomPair(list, j, i, {-delta.x, -delta.y, -delta.z}, distance, i_pair + 1); - } -} - -static void checkInput(const Tensor& positions, const Tensor& batch) { - TORCH_CHECK(positions.dim() == 2, "Expected \"positions\" to have two dimensions"); - TORCH_CHECK(positions.size(0) > 0, - "Expected the 1nd dimension size of \"positions\" to be more than 0"); - TORCH_CHECK(positions.size(1) == 3, "Expected the 2nd dimension size of \"positions\" to be 3"); - TORCH_CHECK(positions.is_contiguous(), "Expected \"positions\" to be contiguous"); - - TORCH_CHECK(batch.dim() == 1, "Expected \"batch\" to have one dimension"); - TORCH_CHECK(batch.size(0) == positions.size(0), - "Expected the 1st dimension size of \"batch\" to be the same as the 1st dimension " - "size of \"positions\""); - TORCH_CHECK(batch.is_contiguous(), "Expected \"batch\" to be contiguous"); - TORCH_CHECK(batch.dtype() == torch::kInt64, "Expected \"batch\" to be of type at::kLong"); -} - -namespace rect { - -/* - * @brief Takes a point to the unit cell in the range [-0.5, 0.5]*box_size using Minimum Image - * Convention - * @param p The point position - * @param box_size The box size - * @return The point in the unit cell - */ -template -__device__ auto apply_pbc(scalar3 p, scalar3 box_size) { - p.x = p.x - floorf(p.x / box_size.x + scalar_t(0.5)) * box_size.x; - p.y = p.y - floorf(p.y / box_size.y + scalar_t(0.5)) * box_size.y; - p.z = p.z - floorf(p.z / box_size.z + scalar_t(0.5)) * box_size.z; - return p; -} - -template -__device__ auto compute_distance(scalar3 pos_i, scalar3 pos_j, - bool use_periodic, scalar3 box_size) { - scalar3 delta = {pos_i.x - pos_j.x, pos_i.y - pos_j.y, pos_i.z - pos_j.z}; - if (use_periodic) { - delta = apply_pbc(delta, box_size); - } - return delta; -} - -} // namespace rect - -namespace triclinic { -template using BoxAccessor = Accessor; -template -BoxAccessor get_box_accessor(const Tensor& box_vectors, bool use_periodic) { - return get_accessor(box_vectors); -} - -/* - * @brief Takes a point to the unit cell using Minimum Image - * Convention - * @param p The point position - * @param box_vectors The box vectors (3x3 matrix) - * @return The point in the unit cell - */ -template -__device__ auto apply_pbc(scalar3 delta, const KernelAccessor& box) { - scalar_t scale3 = round(delta.z / box[2][2]); - delta.x -= scale3 * box[2][0]; - delta.y -= scale3 * box[2][1]; - delta.z -= scale3 * box[2][2]; - scalar_t scale2 = round(delta.y / box[1][1]); - delta.x -= scale2 * box[1][0]; - delta.y -= scale2 * box[1][1]; - scalar_t scale1 = round(delta.x / box[0][0]); - delta.x -= scale1 * box[0][0]; - return delta; -} - - template -__device__ auto compute_distance(scalar3 pos_i, scalar3 pos_j, - bool use_periodic, const KernelAccessor& box) { - scalar3 delta = {pos_i.x - pos_j.x, pos_i.y - pos_j.y, pos_i.z - pos_j.z}; - if (use_periodic) { - delta = apply_pbc(delta, box); - } - return delta; -} - -} // namespace triclinic - -#endif diff --git a/torchmdnet/extensions/neighbors/neighbors_cpu.cpp b/torchmdnet/extensions/neighbors/neighbors_cpu.cpp deleted file mode 100644 index 350bbd26f..000000000 --- a/torchmdnet/extensions/neighbors/neighbors_cpu.cpp +++ /dev/null @@ -1,273 +0,0 @@ -/* Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - */ -#include -#include -#include -#include -#include - -using std::tuple; -using torch::arange; -using torch::div; -using torch::linalg_vector_norm; -using torch::full; -using torch::hstack; -using torch::index_select; -using torch::kInt32; -using torch::round; -using torch::Scalar; -using torch::vstack; -using torch::autograd::AutogradContext; -using torch::autograd::Function; -using torch::autograd::tensor_list; -using torch::indexing::Slice; -using at::Tensor; - -static tuple -forward_impl(const std::string& strategy, const Tensor& positions, const Tensor& batch, - const Tensor& in_box_vectors, bool use_periodic, const Scalar& cutoff_lower, - const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop, - bool include_transpose) { - TORCH_CHECK(positions.dim() == 2, "Expected \"positions\" to have two dimensions"); - TORCH_CHECK(positions.size(0) > 0, - "Expected the 1nd dimension size of \"positions\" to be more than 0"); - TORCH_CHECK(positions.size(1) == 3, "Expected the 2nd dimension size of \"positions\" to be 3"); - TORCH_CHECK(positions.is_contiguous(), "Expected \"positions\" to be contiguous"); - TORCH_CHECK(cutoff_upper.to() > 0, "Expected \"cutoff\" to be positive"); - Tensor box_vectors = in_box_vectors; - const int n_batch = batch.max().item() + 1; - if (use_periodic) { - if (box_vectors.dim() == 2) { - box_vectors = box_vectors.unsqueeze(0).expand({n_batch, 3, 3}); - } - TORCH_CHECK(box_vectors.dim() == 3, "Expected \"box_vectors\" to have two dimensions"); - TORCH_CHECK(box_vectors.size(1) == 3 && box_vectors.size(2) == 3, - "Expected \"box_vectors\" to have shape (n_batch, 3, 3)"); - // Ensure the box first dimension has size max(batch) + 1 - TORCH_CHECK(box_vectors.size(0) == n_batch, - "Expected \"box_vectors\" to have shape (n_batch, 3, 3)"); - // Check that the box is a valid triclinic box, in the case of a box per sample we only - // check the first one - double v[3][3]; - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) - v[i][j] = box_vectors[0][i][j].item(); - double c = cutoff_upper.to(); - TORCH_CHECK(v[0][1] == 0, "Invalid box vectors: box_vectors[0][1] != 0"); - TORCH_CHECK(v[0][2] == 0, "Invalid box vectors: box_vectors[0][2] != 0"); - TORCH_CHECK(v[1][2] == 0, "Invalid box vectors: box_vectors[1][2] != 0"); - TORCH_CHECK(v[0][0] >= 2 * c, "Invalid box vectors: box_vectors[0][0] < 2*cutoff"); - TORCH_CHECK(v[1][1] >= 2 * c, "Invalid box vectors: box_vectors[1][1] < 2*cutoff"); - TORCH_CHECK(v[2][2] >= 2 * c, "Invalid box vectors: box_vectors[2][2] < 2*cutoff"); - TORCH_CHECK(v[0][0] >= 2 * v[1][0], - "Invalid box vectors: box_vectors[0][0] < 2*box_vectors[1][0]"); - TORCH_CHECK(v[0][0] >= 2 * v[2][0], - "Invalid box vectors: box_vectors[0][0] < 2*box_vectors[1][0]"); - TORCH_CHECK(v[1][1] >= 2 * v[2][1], - "Invalid box vectors: box_vectors[1][1] < 2*box_vectors[2][1]"); - } - TORCH_CHECK(max_num_pairs.toLong() > 0, "Expected \"max_num_neighbors\" to be positive"); - const int n_atoms = positions.size(0); - Tensor neighbors = torch::empty({0}, positions.options().dtype(kInt32)); - Tensor distances = torch::empty({0}, positions.options()); - Tensor deltas = torch::empty({0}, positions.options()); - neighbors = torch::vstack((torch::tril_indices(n_atoms, n_atoms, -1, neighbors.options()))); - auto mask = index_select(batch, 0, neighbors.index({0, Slice()})) == - index_select(batch, 0, neighbors.index({1, Slice()})); - neighbors = neighbors.index({Slice(), mask}).to(kInt32); - deltas = index_select(positions, 0, neighbors.index({0, Slice()})) - - index_select(positions, 0, neighbors.index({1, Slice()})); - if (use_periodic) { - const auto pair_batch = batch.index({neighbors.index({0, Slice()})}); - const auto scale3 = - round(deltas.index({Slice(), 2}) / box_vectors.index({pair_batch, 2, 2})); - deltas.index_put_({Slice(), 0}, deltas.index({Slice(), 0}) - - scale3 * box_vectors.index({pair_batch, 2, 0})); - deltas.index_put_({Slice(), 1}, deltas.index({Slice(), 1}) - - scale3 * box_vectors.index({pair_batch, 2, 1})); - deltas.index_put_({Slice(), 2}, deltas.index({Slice(), 2}) - - scale3 * box_vectors.index({pair_batch, 2, 2})); - const auto scale2 = - round(deltas.index({Slice(), 1}) / box_vectors.index({pair_batch, 1, 1})); - deltas.index_put_({Slice(), 0}, deltas.index({Slice(), 0}) - - scale2 * box_vectors.index({pair_batch, 1, 0})); - deltas.index_put_({Slice(), 1}, deltas.index({Slice(), 1}) - - scale2 * box_vectors.index({pair_batch, 1, 1})); - const auto scale1 = - round(deltas.index({Slice(), 0}) / box_vectors.index({pair_batch, 0, 0})); - deltas.index_put_({Slice(), 0}, deltas.index({Slice(), 0}) - - scale1 * box_vectors.index({pair_batch, 0, 0})); - } - distances = linalg_vector_norm(deltas, 2, 1); - mask = (distances < cutoff_upper) * (distances >= cutoff_lower); - neighbors = neighbors.index({Slice(), mask}); - deltas = deltas.index({mask, Slice()}); - distances = distances.index({mask}); - if (include_transpose) { - neighbors = torch::hstack({neighbors, torch::stack({neighbors[1], neighbors[0]})}); - distances = torch::hstack({distances, distances}); - deltas = torch::vstack({deltas, -deltas}); - } - if (loop) { - const Tensor range = torch::arange(0, n_atoms, torch::kInt32); - neighbors = torch::hstack({neighbors, torch::stack({range, range})}); - distances = torch::hstack({distances, torch::zeros_like(range)}); - deltas = torch::vstack({deltas, torch::zeros({n_atoms, 3}, deltas.options())}); - } - Tensor num_pairs_found = torch::empty(1, distances.options().dtype(kInt32)); - num_pairs_found[0] = distances.size(0); - // This seems wasteful, but it allows to enable torch.compile by guaranteeing that the output of - // this operator has a predictable size Resize to max_num_pairs, add zeros if necessary - int64_t extension = std::max(max_num_pairs.toLong() - distances.size(0), (int64_t)0); - if (extension > 0) { - deltas = torch::vstack({deltas, torch::zeros({extension, 3}, deltas.options())}); - distances = torch::hstack({distances, torch::zeros({extension}, distances.options())}); - // For the neighbors add (-1,-1) pairs to fill the tensor - neighbors = torch::hstack( - {neighbors, torch::full({2, extension}, -1, neighbors.options().dtype(kInt32))}); - } - return {neighbors, deltas, distances, num_pairs_found}; -} - -// The backwards operation is implemented fully in pytorch so that it can be differentiated a second -// time automatically via Autograd. -static Tensor backward_impl(const Tensor& grad_edge_vec, const Tensor& grad_edge_weight, - const Tensor& edge_index, const Tensor& edge_vec, - const Tensor& edge_weight, const int64_t num_atoms) { - auto zero_mask = edge_weight.eq(0); - auto zero_mask3 = zero_mask.unsqueeze(-1).expand_as(grad_edge_vec); - // We need to avoid dividing by 0. Otherwise Autograd fills the gradient with NaNs in the - // case of a double backwards. This is why we index_select like this. - auto grad_distances_ = edge_vec / edge_weight.masked_fill(zero_mask, 1).unsqueeze(-1) * - grad_edge_weight.masked_fill(zero_mask, 0).unsqueeze(-1); - auto result = grad_edge_vec.masked_fill(zero_mask3, 0) + grad_distances_; - // Avoid out-of-bounds indices under compiler fusion by mapping masked indices to 0 - // and ensuring their contributions are exactly zero. - // This removes the need for allocating an extra dummy row (num_atoms + 1). - auto grad_positions = torch::zeros({num_atoms, 3}, edge_vec.options()); - auto edge_index_ = - edge_index.masked_fill(zero_mask.unsqueeze(0).expand_as(edge_index), 0); - grad_positions.index_add_(0, edge_index_[0], result); - grad_positions.index_add_(0, edge_index_[1], -result); - return grad_positions; -} - -// This is the autograd function that is called when the user calls get_neighbor_pairs. -// It dispatches the required strategy for the forward function and implements the backward -// function. The backward function is written in full pytorch so that it can be differentiated a -// second time automatically via Autograd. -class NeighborAutograd : public torch::autograd::Function { -public: - static tensor_list forward(AutogradContext* ctx, const std::string& strategy, - const Tensor& positions, const Tensor& batch, - const Tensor& box_vectors, bool use_periodic, - const Scalar& cutoff_lower, const Scalar& cutoff_upper, - const Scalar& max_num_pairs, bool loop, bool include_transpose) { - static auto fwd = - torch::Dispatcher::singleton() - .findSchemaOrThrow("torchmdnet_extensions::get_neighbor_pairs_fwd", "") - .typed(); - Tensor neighbors, deltas, distances, i_curr_pair; - std::tie(neighbors, deltas, distances, i_curr_pair) = - fwd.call(strategy, positions, batch, box_vectors, use_periodic, cutoff_lower, - cutoff_upper, max_num_pairs, loop, include_transpose); - ctx->save_for_backward({neighbors, deltas, distances}); - ctx->saved_data["num_atoms"] = positions.size(0); - return {neighbors, deltas, distances, i_curr_pair}; - } - - using Slice = torch::indexing::Slice; - - static tensor_list backward(AutogradContext* ctx, tensor_list grad_outputs) { - auto saved = ctx->get_saved_variables(); - auto edge_index = saved[0]; - auto edge_vec = saved[1]; - auto edge_weight = saved[2]; - auto num_atoms = ctx->saved_data["num_atoms"].toInt(); - auto grad_edge_vec = grad_outputs[1]; - auto grad_edge_weight = grad_outputs[2]; - static auto backward = - torch::Dispatcher::singleton() - .findSchemaOrThrow("torchmdnet_extensions::get_neighbor_pairs_bkwd", "") - .typed(); - auto grad_positions = backward.call(grad_edge_vec, grad_edge_weight, edge_index, edge_vec, - edge_weight, num_atoms); - Tensor ignore; - return {ignore, grad_positions, ignore, ignore, ignore, ignore, - ignore, ignore, ignore, ignore, ignore}; - } -}; - -// By registering as a CompositeImplicitAutograd we can torch.compile this Autograd function. -// This mode will generate meta registration for NeighborAutograd automatically, in this case using -// the -// meta registrations provided for the forward and backward functions python side. -// We provide meta registrations python side because it is the recommended way to do it. -TORCH_LIBRARY_IMPL(torchmdnet_extensions, CompositeImplicitAutograd, m) { - m.impl("get_neighbor_pairs", - [](const std::string& strategy, const Tensor& positions, const Tensor& batch, - const Tensor& box_vectors, bool use_periodic, const Scalar& cutoff_lower, - const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop, - bool include_transpose) { - auto result = NeighborAutograd::apply(strategy, positions, batch, box_vectors, - use_periodic, cutoff_lower, cutoff_upper, - max_num_pairs, loop, include_transpose); - return std::make_tuple(result[0], result[1], result[2], result[3]); - }); -} - -// // Explicit device backend registrations for PyTorch versions that do not -// // automatically fall back to CompositeImplicitAutograd for device dispatch. -// TORCH_LIBRARY_IMPL(torchmdnet_extensions, CPU, m) { -// m.impl("get_neighbor_pairs", -// [](const std::string& strategy, const Tensor& positions, const Tensor& batch, -// const Tensor& box_vectors, bool use_periodic, const Scalar& cutoff_lower, -// const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop, -// bool include_transpose) { -// auto result = NeighborAutograd::apply(strategy, positions, batch, box_vectors, -// use_periodic, cutoff_lower, cutoff_upper, -// max_num_pairs, loop, include_transpose); -// return std::make_tuple(result[0], result[1], result[2], result[3]); -// }); -// } - -// TORCH_LIBRARY_IMPL(torchmdnet_extensions, CUDA, m) { -// m.impl("get_neighbor_pairs", -// [](const std::string& strategy, const Tensor& positions, const Tensor& batch, -// const Tensor& box_vectors, bool use_periodic, const Scalar& cutoff_lower, -// const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop, -// bool include_transpose) { -// auto result = NeighborAutograd::apply(strategy, positions, batch, box_vectors, -// use_periodic, cutoff_lower, cutoff_upper, -// max_num_pairs, loop, include_transpose); -// return std::make_tuple(result[0], result[1], result[2], result[3]); -// }); -// } - -// The registration for the CUDA version of the forward function is done in a separate .cu file. -TORCH_LIBRARY_IMPL(torchmdnet_extensions, CPU, m) { - m.impl("get_neighbor_pairs_fwd", forward_impl); -} - -// Ideally we would register this just once using CompositeImplicitAutograd, but this causes a -// segfault -// when trying to torch.compile this function. -// Doing it this way prints a message about Autograd not being provided a backward function of the -// backward function. It gets it from the implementation just fine now, but warns that in the future -// this will be deprecated. -TORCH_LIBRARY_IMPL(torchmdnet_extensions, CPU, m) { - m.impl("get_neighbor_pairs_bkwd", backward_impl); -} - -TORCH_LIBRARY_IMPL(torchmdnet_extensions, CUDA, m) { - m.impl("get_neighbor_pairs_bkwd", backward_impl); -} - -// // Register explicit Autograd fallthroughs to avoid deprecated autograd fallback warnings -// // when backpropagating through these custom operators on CUDA/CPU. -// TORCH_LIBRARY_IMPL(torchmdnet_extensions, Autograd, m) { -// m.impl("get_neighbor_pairs", torch::CppFunction::makeFallthrough()); -// m.impl("get_neighbor_pairs_bkwd", torch::CppFunction::makeFallthrough()); -// } diff --git a/torchmdnet/extensions/neighbors/neighbors_cuda.cu b/torchmdnet/extensions/neighbors/neighbors_cuda.cu deleted file mode 100644 index 68f775485..000000000 --- a/torchmdnet/extensions/neighbors/neighbors_cuda.cu +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - * (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * Raul P. Pelaez 2023 - */ -#include -#include -#include - -#include -#include -#include - -#include "neighbors_cuda_brute.cuh" -#include "neighbors_cuda_cell.cuh" -#include "neighbors_cuda_shared.cuh" - -static std::tuple -forward_impl_cuda(const std::string& strategy, const at::Tensor& positions, const at::Tensor& batch, - const at::Tensor& in_box_vectors, bool use_periodic, const at::Scalar& cutoff_lower, - const at::Scalar& cutoff_upper, const at::Scalar& max_num_pairs, bool loop, - bool include_transpose) { - auto kernel = forward_brute; - if (positions.size(0) >= 32768 && strategy == "brute") { - kernel = forward_shared; - } - if (strategy == "brute") { - } else if (strategy == "cell") { - kernel = forward_cell; - } else if (strategy == "shared") { - kernel = forward_shared; - } else { - throw std::runtime_error("Unknown kernel name"); - } - return kernel(positions, batch, in_box_vectors, use_periodic, cutoff_lower, cutoff_upper, - max_num_pairs, loop, include_transpose); -} - -// We only need to register the CUDA version of the forward function here. This way we can avoid -// compiling this file in CPU-only mode The rest of the registrations take place in -// neighbors_cpu.cpp -TORCH_LIBRARY_IMPL(torchmdnet_extensions, CUDA, m) { - m.impl("get_neighbor_pairs_fwd", forward_impl_cuda); -} diff --git a/torchmdnet/extensions/neighbors/neighbors_cuda_brute.cuh b/torchmdnet/extensions/neighbors/neighbors_cuda_brute.cuh deleted file mode 100644 index 4d33979f3..000000000 --- a/torchmdnet/extensions/neighbors/neighbors_cuda_brute.cuh +++ /dev/null @@ -1,122 +0,0 @@ -/* Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * - * Raul P. Pelaez 2023. Brute force neighbor list construction in CUDA. - * - * A brute force approach that assigns a thread per each possible pair of particles in the system. - * Based on an implementation by Raimondas Galvelis. - * Works fantastically for small (less than 10K atoms) systems, but cannot handle more than 32K - * atoms. - */ -#ifndef NEIGHBORS_BRUTE_CUH -#define NEIGHBORS_BRUTE_CUH -#include "common.cuh" -#include - -__device__ uint32_t get_row(uint32_t index) { - uint32_t row = floor((sqrtf(8 * index + 1) + 1) / 2); - if (row * (row - 1) > 2 * index) - row--; - return row; -} - -template -__global__ void forward_kernel_brute(uint32_t num_all_pairs, const Accessor positions, - const Accessor batch, scalar_t cutoff_lower2, - scalar_t cutoff_upper2, PairListAccessor list, - triclinic::BoxAccessor box) { - const uint32_t index = blockIdx.x * blockDim.x + threadIdx.x; - if (index >= num_all_pairs) - return; - const uint32_t row = get_row(index); - const uint32_t column = (index - row * (row - 1) / 2); - const auto batch_row = batch[row]; - if (batch_row == batch[column]) { - const auto pos_i = fetchPosition(positions, row); - const auto pos_j = fetchPosition(positions, column); - const auto box_row = box[batch_row]; - const auto delta = triclinic::compute_distance(pos_i, pos_j, list.use_periodic, box_row); - const scalar_t distance2 = delta.x * delta.x + delta.y * delta.y + delta.z * delta.z; - if (distance2 < cutoff_upper2 && distance2 >= cutoff_lower2) { - const scalar_t r2 = sqrt_(distance2); - addAtomPairToList(list, row, column, delta, r2, list.include_transpose); - } - } -} - -template -__global__ void add_self_kernel(const int num_atoms, Accessor positions, - PairListAccessor list) { - const int32_t i_atom = blockIdx.x * blockDim.x + threadIdx.x; - if (i_atom >= num_atoms) - return; - __shared__ int i_pair; - if (threadIdx.x == 0) { // Each block adds blockDim.x pairs to the list. - // Handle the last block, so that only num_atoms are added in total - i_pair = - atomicAdd(&list.i_curr_pair[0], min(blockDim.x, num_atoms - blockIdx.x * blockDim.x)); - } - __syncthreads(); - scalar3 delta{}; - scalar_t distance = 0; - writeAtomPair(list, i_atom, i_atom, delta, distance, i_pair + threadIdx.x); -} - -static std::tuple -forward_brute(const Tensor& positions, const Tensor& batch, const Tensor& in_box_vectors, - bool use_periodic, const Scalar& cutoff_lower, const Scalar& cutoff_upper, - const Scalar& max_num_pairs, bool loop, bool include_transpose) { - checkInput(positions, batch); - const auto max_num_pairs_ = max_num_pairs.toLong(); - TORCH_CHECK(max_num_pairs_ > 0, "Expected \"max_num_neighbors\" to be positive"); - auto box_vectors = in_box_vectors.to(positions.device()).clone(); - if (box_vectors.dim() == 2) { - // If the box is a 3x3 tensor it is assumed every sample has the same box - if (use_periodic) { - TORCH_CHECK(box_vectors.size(0) == 3 && box_vectors.size(1) == 3, - "Expected \"box_vectors\" to have shape (3, 3)"); - } - // Make the box (None,3,3), expand artificially to positions.size(0) - box_vectors = box_vectors.unsqueeze(0); - if (use_periodic) { - // I use positions.size(0) because the batch dimension is not available here - box_vectors = box_vectors.expand({positions.size(0), 3, 3}); - } - } - if (use_periodic) { - TORCH_CHECK(box_vectors.dim() == 3, "Expected \"box_vectors\" to have three dimensions"); - TORCH_CHECK(box_vectors.size(1) == 3 && box_vectors.size(2) == 3, - "Expected \"box_vectors\" to have shape (n_batch, 3, 3)"); - } - const int num_atoms = positions.size(0); - TORCH_CHECK(num_atoms < 32768, "The brute strategy fails with \"num_atoms\" larger than 32768"); - const int num_pairs = max_num_pairs_; - const TensorOptions options = positions.options(); - const auto stream = getCurrentCUDAStream(positions.get_device()); - PairList list(num_pairs, positions.options(), loop, include_transpose, use_periodic); - const CUDAStreamGuard guard(stream); - const uint64_t num_all_pairs = num_atoms * (num_atoms - 1UL) / 2UL; - const uint64_t num_threads = 128; - const uint64_t num_blocks = std::max(static_cast((num_all_pairs + num_threads - 1UL) / num_threads), static_cast(1UL)); - AT_DISPATCH_FLOATING_TYPES(positions.scalar_type(), "get_neighbor_pairs_forward", [&]() { - PairListAccessor list_accessor(list); - auto box = triclinic::get_box_accessor(box_vectors, use_periodic); - const scalar_t cutoff_upper_ = cutoff_upper.to(); - const scalar_t cutoff_lower_ = cutoff_lower.to(); - TORCH_CHECK(cutoff_upper_ > 0, "Expected \"cutoff\" to be positive"); - forward_kernel_brute<<>>( - num_all_pairs, get_accessor(positions), get_accessor(batch), - cutoff_lower_ * cutoff_lower_, cutoff_upper_ * cutoff_upper_, list_accessor, box); - if (loop) { - const uint32_t num_threads_self = 256; - const uint32_t num_blocks_self = - std::max((num_atoms + num_threads_self - 1U) / num_threads_self, 1U); - add_self_kernel<<>>( - num_atoms, get_accessor(positions), list_accessor); - } - }); - return {list.neighbors, list.deltas, list.distances, list.i_curr_pair}; -} - -#endif diff --git a/torchmdnet/extensions/neighbors/neighbors_cuda_cell.cuh b/torchmdnet/extensions/neighbors/neighbors_cuda_cell.cuh deleted file mode 100644 index b54ab1c13..000000000 --- a/torchmdnet/extensions/neighbors/neighbors_cuda_cell.cuh +++ /dev/null @@ -1,390 +0,0 @@ -/* Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * Raul P. Pelaez 2023. Batched cell list neighbor list implementation for CUDA. - */ -#ifndef NEIGHBOR_CUDA_CELL_H -#define NEIGHBOR_CUDA_CELL_H -#include "common.cuh" - -/* - * @brief Calculates the cell dimensions for a given box size and cutoff - * @param box_size The box size - * @param cutoff The cutoff - * @return The cell dimensions - */ -template -__host__ __device__ int3 getCellDimensions(scalar3 box_size, scalar_t cutoff) { - int3 cell_dim = make_int3(box_size.x / cutoff, box_size.y / cutoff, box_size.z / cutoff); - // Minimum 3 cells in each dimension - cell_dim.x = max(cell_dim.x, 3); - cell_dim.y = max(cell_dim.y, 3); - cell_dim.z = max(cell_dim.z, 3); -// In the host, throw if there are more than 1024 cells in any dimension -#ifndef __CUDA_ARCH__ - if (cell_dim.x > 1024 || cell_dim.y > 1024 || cell_dim.z > 1024) { - throw std::runtime_error("Too many cells in one dimension. Maximum is 1024"); - } -#endif - return cell_dim; -} - -/* - * @brief Get the cell coordinates of a point - * @param p The point position - * @param box_size The size of the box in each dimension - * @param cutoff The cutoff - * @param cell_dim The number of cells in each dimension - * @return The cell coordinates - */ -template -__device__ int3 getCell(scalar3 p, scalar3 box_size, scalar_t cutoff, - int3 cell_dim) { - p = rect::apply_pbc(p, box_size); - // Take to the [0, box_size] range and divide by cutoff (which is the cell size) - int cx = floorf((p.x + scalar_t(0.5) * box_size.x) / cutoff); - int cy = floorf((p.y + scalar_t(0.5) * box_size.y) / cutoff); - int cz = floorf((p.z + scalar_t(0.5) * box_size.z) / cutoff); - // Wrap around. If the position of a particle is exactly box_size, it will be in the last cell, - // which results in an illegal access down the line. - if (cx == cell_dim.x) - cx = 0; - if (cy == cell_dim.y) - cy = 0; - if (cz == cell_dim.z) - cz = 0; - return make_int3(cx, cy, cz); -} - -/* - * @brief Get the index of a cell in a 1D array of cells. - * @param cell The cell coordinates, assumed to be in the range [0, cell_dim]. - * @param cell_dim The number of cells in each dimension - */ -__device__ int getCellIndex(int3 cell, int3 cell_dim) { - return cell.x + cell_dim.x * (cell.y + cell_dim.y * cell.z); -} - -/* - @brief Fold a cell coordinate to the range [0, cell_dim) - @param cell The cell coordinate - @param cell_dim The dimensions of the grid - @return The folded cell coordinate -*/ -__device__ int3 getPeriodicCell(int3 cell, int3 cell_dim) { - int3 periodic_cell = cell; - if (cell.x < 0) - periodic_cell.x += cell_dim.x; - if (cell.x >= cell_dim.x) - periodic_cell.x -= cell_dim.x; - if (cell.y < 0) - periodic_cell.y += cell_dim.y; - if (cell.y >= cell_dim.y) - periodic_cell.y -= cell_dim.y; - if (cell.z < 0) - periodic_cell.z += cell_dim.z; - if (cell.z >= cell_dim.z) - periodic_cell.z -= cell_dim.z; - return periodic_cell; -} - -// Computes and stores the cell index of each atom. -template -__global__ void assignCellIndex(const Accessor positions, - Accessor cell_indices, scalar3 box_size, - scalar_t cutoff) { - const int32_t i_atom = blockIdx.x * blockDim.x + threadIdx.x; - if (i_atom >= positions.size(0)) - return; - const auto pi = fetchPosition(positions, i_atom); - const auto cell_dim = getCellDimensions(box_size, cutoff); - const auto ci = getCell(pi, box_size, cutoff, cell_dim); - cell_indices[i_atom] = getCellIndex(ci, cell_dim); -} - -/* - * @brief Sort the positions by cell index - * @param positions The positions of the atoms - * @param box_size The box vectors - * @param cutoff The cutoff - * @return A tuple of the sorted indices and cell indices - */ -static auto sortAtomsByCellIndex(const Tensor& positions, const Tensor& box_size, - const Scalar& cutoff) { - const int num_atoms = positions.size(0); - Tensor cell_index = empty({num_atoms}, positions.options().dtype(torch::kInt32)); - const int threads = 128; - const int blocks = (num_atoms + threads - 1) / threads; - auto stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES(positions.scalar_type(), "assignHash", [&] { - scalar_t cutoff_ = cutoff.to(); - scalar3 box_size_ = {box_size[0][0].item(), - box_size[1][1].item(), - box_size[2][2].item()}; - assignCellIndex<<>>(get_accessor(positions), - get_accessor(cell_index), - box_size_, cutoff_); - }); - // Sort the atom indices by cell index - Tensor sorted_atom_index; - Tensor sorted_cell_index; - std::tie(sorted_cell_index, sorted_atom_index) = torch::sort(cell_index); - return std::make_tuple(sorted_atom_index.to(torch::kInt32), sorted_cell_index); -} - -__global__ void fillCellOffsetsD(const Accessor sorted_cell_indices, - Accessor cell_start, Accessor cell_end) { - // Since positions are sorted by cell, for a given atom, if the previous atom is in a different - // cell, then the current atom is the first atom in its cell We use this fact to fill the - // cell_start and cell_end arrays - const int32_t i_atom = blockIdx.x * blockDim.x + threadIdx.x; - if (i_atom >= sorted_cell_indices.size(0)) - return; - const int icell = sorted_cell_indices[i_atom]; - int im1_cell; - if (i_atom > 0) { - const int im1 = i_atom - 1; - im1_cell = sorted_cell_indices[im1]; - } else { - im1_cell = 0; - } - if (icell != im1_cell || i_atom == 0) { - cell_start[icell] = i_atom; - if (i_atom > 0) { - cell_end[im1_cell] = i_atom; - } - } - if (i_atom == sorted_cell_indices.size(0) - 1) { - cell_end[icell] = i_atom + 1; - } -} - -/* - @brief Fills the cell_start and cell_end arrays, identifying the first and last atom in each cell - @param sorted_cell_indices The cell indices of each position - @param cell_dim The dimensions of the cell grid - @return A tuple of cell_start and cell_end arrays -*/ -static auto fillCellOffsets(const Tensor& sorted_cell_indices, int3 cell_dim) { - const TensorOptions options = sorted_cell_indices.options(); - const int num_cells = cell_dim.x * cell_dim.y * cell_dim.z; - const Tensor cell_start = full({num_cells}, -1, options.dtype(torch::kInt)); - const Tensor cell_end = empty({num_cells}, options.dtype(torch::kInt)); - const int threads = 128; - const int blocks = (sorted_cell_indices.size(0) + threads - 1) / threads; - auto stream = at::cuda::getCurrentCUDAStream(); - fillCellOffsetsD<<>>(get_accessor(sorted_cell_indices), - get_accessor(cell_start), - get_accessor(cell_end)); - return std::make_tuple(cell_start, cell_end); -} - -/* - @brief Get the cell index of the i'th neighboring cell for a given cell - @param cell_i The cell coordinates - @param i The index of the neighboring cell, from 0 to 26 - @param cell_dim The dimensions of the cell grid - @return The cell index of the i'th neighboring cell -*/ -__device__ int getNeighborCellIndex(int3 cell_i, int i, int3 cell_dim) { - auto cell_j = cell_i; - cell_j.x += i % 3 - 1; - cell_j.y += (i / 3) % 3 - 1; - cell_j.z += i / 9 - 1; - cell_j = getPeriodicCell(cell_j, cell_dim); - const int icellj = getCellIndex(cell_j, cell_dim); - return icellj; -} - -template struct Particle { - int index; // Index in the sorted arrays - int original_index; // Index in the original arrays - int batch; - scalar3 position; - scalar_t cutoff_upper2, cutoff_lower2; -}; - -struct CellList { - Tensor cell_start, cell_end; - Tensor sorted_indices; - Tensor sorted_positions, sorted_batch; -}; - -CellList constructCellList(const Tensor& positions, const Tensor& batch, const Tensor& box_size, - const Scalar& cutoff) { - // The algorithm for the cell list construction can be summarized in three separate steps: - // 1. Label the particles according to the cell (bin) they lie in. - // 2. Sort the particles using the cell index as the ordering label - // (technically this is known as sorting by key). So that particles with positions - // lying in the same cell become contiguous in memory. - // 3. Identify where each cell starts and ends in the sorted particle positions - // array. - const TensorOptions options = positions.options(); - CellList cl; - Tensor sorted_cell_indices; - // Steps 1 and 2 - std::tie(cl.sorted_indices, sorted_cell_indices) = - sortAtomsByCellIndex(positions, box_size, cutoff); - cl.sorted_positions = positions.index_select(0, cl.sorted_indices); - cl.sorted_batch = batch.index_select(0, cl.sorted_indices); - // Step 3 - int3 cell_dim; - AT_DISPATCH_FLOATING_TYPES(positions.scalar_type(), "computeCellDim", [&] { - scalar_t cutoff_ = cutoff.to(); - scalar3 box_size_ = {box_size[0][0].item(), - box_size[1][1].item(), - box_size[2][2].item()}; - cell_dim = getCellDimensions(box_size_, cutoff_); - }); - std::tie(cl.cell_start, cl.cell_end) = fillCellOffsets(sorted_cell_indices, cell_dim); - return cl; -} - -template struct CellListAccessor { - Accessor cell_start, cell_end; - Accessor sorted_indices; - Accessor sorted_positions; - Accessor sorted_batch; - - explicit CellListAccessor(const CellList& cl) - : cell_start(get_accessor(cl.cell_start)), - cell_end(get_accessor(cl.cell_end)), - sorted_indices(get_accessor(cl.sorted_indices)), - sorted_positions(get_accessor(cl.sorted_positions)), - sorted_batch(get_accessor(cl.sorted_batch)) { - } -}; - -/* - * @brief Add a pair of particles to the pair list. If necessary, also add the transpose pair. - * @param list The pair list - * @param i The index of the first particle - * @param j The index of the second particle - * @param distance2 The squared distance between the particles - * @param delta The vector between the particles - */ -template -__device__ void addNeighborPair(PairListAccessor& list, const int i, const int j, - scalar_t distance2, scalar3 delta) { - const bool requires_transpose = list.include_transpose && (j != i); - const int ni = max(i, j); - const int nj = min(i, j); - const scalar_t delta_sign = (ni == i) ? scalar_t(1.0) : scalar_t(-1.0); - const scalar_t distance = sqrt_(distance2); - delta = {delta_sign * delta.x, delta_sign * delta.y, delta_sign * delta.z}; - addAtomPairToList(list, ni, nj, delta, distance, requires_transpose); -} - -/* - * @brief Add to the pair list all neighbors of particle i_atom in cell j_cell - * @param i_atom The Information of the particle for which we are adding neighbors - * @param j_cell The index of the cell in which we are looking for neighbors - * @param cl The cell list - * @param box_size The box size - * @param list The pair list - */ -template -__device__ void addNeighborsForCell(const Particle& i_atom, int j_cell, - const CellListAccessor& cl, - scalar3 box_size, PairListAccessor& list) { - const auto first_particle = cl.cell_start[j_cell]; - if (first_particle != -1) { // Continue only if there are particles in this cell - const auto last_particle = cl.cell_end[j_cell]; - for (int cur_j = first_particle; cur_j < last_particle; cur_j++) { - const auto j_batch = cl.sorted_batch[cur_j]; - if ((j_batch == i_atom.batch) && - ((cur_j < i_atom.index) || (list.loop && cur_j == i_atom.index))) { - const auto position_j = fetchPosition(cl.sorted_positions, cur_j); - const auto delta = rect::compute_distance(i_atom.position, position_j, - list.use_periodic, box_size); - const scalar_t distance2 = - delta.x * delta.x + delta.y * delta.y + delta.z * delta.z; - if ( ((distance2 < i_atom.cutoff_upper2 && distance2 >= i_atom.cutoff_lower2)) || - (list.loop && cur_j == i_atom.index)) { - const int orj = cl.sorted_indices[cur_j]; - addNeighborPair(list, i_atom.original_index, orj, distance2, delta); - } // endif - } // endif - } // endfor - } // endif -} - -// Traverse the cell list for each atom and find the neighbors -template -__global__ void traverseCellList(const CellListAccessor cell_list, - PairListAccessor list, int num_atoms, - scalar3 box_size, scalar_t cutoff_lower, - scalar_t cutoff_upper) { - // Each atom traverses the cells around it and finds the neighbors - // Atoms for all batches are placed in the same cell list, but other batches are ignored while - // traversing - Particle i_atom; - i_atom.index = blockIdx.x * blockDim.x + threadIdx.x; - if (i_atom.index >= num_atoms) { - return; - } - i_atom.original_index = cell_list.sorted_indices[i_atom.index]; - i_atom.batch = cell_list.sorted_batch[i_atom.index]; - i_atom.position = fetchPosition(cell_list.sorted_positions, i_atom.index); - i_atom.cutoff_lower2 = cutoff_lower * cutoff_lower; - i_atom.cutoff_upper2 = cutoff_upper * cutoff_upper; - const int3 cell_dim = getCellDimensions(box_size, cutoff_upper); - const int3 cell_i = getCell(i_atom.position, box_size, cutoff_upper, cell_dim); - // Loop over the 27 cells around the current cell - for (int i = 0; i < 27; i++) { - const int neighbor_cell = getNeighborCellIndex(cell_i, i, cell_dim); - addNeighborsForCell(i_atom, neighbor_cell, cell_list, box_size, list); - } -} - -static std::tuple -forward_cell(const Tensor& positions, const Tensor& batch, const Tensor& in_box_size, - bool use_periodic, const Scalar& cutoff_lower, const Scalar& cutoff_upper, - const Scalar& max_num_pairs, bool loop, bool include_transpose) { - // This module computes the pair list for a given set of particles, which may be in multiple - // batches. The strategy is to first compute a cell list for all particles, and then - // traverse the cell list for each particle to construct a pair list. - checkInput(positions, batch); - auto box_size = in_box_size.to("cpu"); - // If the box has dimensions (1, 3,3) squeeze it - if (box_size.dim() == 3) { - TORCH_CHECK(box_size.size(0) == 1 && box_size.size(1) == 3 && box_size.size(2) == 3, - "Cell list does not support a box per sample. Expected \"box_size\" to have shape (1, 3, 3) or (3, 3)"); - box_size = box_size.squeeze(0); - } - - TORCH_CHECK(box_size.dim() == 2, "Expected \"box_size\" to have two dimensions"); - TORCH_CHECK(box_size.size(0) == 3 && box_size.size(1) == 3, - "Expected \"box_size\" to have shape (3, 3)"); - TORCH_CHECK(box_size[0][1].item() == 0 && box_size[0][2].item() == 0 && - box_size[1][0].item() == 0 && box_size[1][2].item() == 0 && - box_size[2][0].item() == 0 && box_size[2][1].item() == 0, - "Expected \"box_size\" to be diagonal"); - const auto max_num_pairs_ = max_num_pairs.toInt(); - TORCH_CHECK(max_num_pairs_ > 0, "Expected \"max_num_neighbors\" to be positive"); - const int num_atoms = positions.size(0); - const auto cell_list = constructCellList(positions, batch, box_size, cutoff_upper); - PairList list(max_num_pairs_, positions.options(), loop, include_transpose, use_periodic); - const auto stream = getCurrentCUDAStream(positions.get_device()); - { // Traverse the cell list to find the neighbors - const CUDAStreamGuard guard(stream); - AT_DISPATCH_FLOATING_TYPES(positions.scalar_type(), "forward", [&] { - const scalar_t cutoff_upper_ = cutoff_upper.to(); - TORCH_CHECK(cutoff_upper_ > 0, "Expected cutoff_upper to be positive"); - const scalar_t cutoff_lower_ = cutoff_lower.to(); - const scalar3 box_size_ = {box_size[0][0].item(), - box_size[1][1].item(), - box_size[2][2].item()}; - PairListAccessor list_accessor(list); - CellListAccessor cell_list_accessor(cell_list); - const int threads = 128; - const int blocks = (num_atoms + threads - 1) / threads; - traverseCellList<<>>(cell_list_accessor, list_accessor, - num_atoms, box_size_, cutoff_lower_, - cutoff_upper_); - }); - } - return {list.neighbors, list.deltas, list.distances, list.i_curr_pair}; -} - -#endif diff --git a/torchmdnet/extensions/neighbors/neighbors_cuda_shared.cuh b/torchmdnet/extensions/neighbors/neighbors_cuda_shared.cuh deleted file mode 100644 index 950a3bb6b..000000000 --- a/torchmdnet/extensions/neighbors/neighbors_cuda_shared.cuh +++ /dev/null @@ -1,124 +0,0 @@ -/* Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org - * Distributed under the MIT License. - *(See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - * Raul P. Pelaez 2023. Shared memory neighbor list construction for CUDA. - * This brute force approach checks all pairs of atoms by collaborativelly loading and processing - * tiles of atoms into shared memory. - * This approach is tipically slower than the brute force approach, but can handle an arbitrarily - * large number of atoms. - */ -#ifndef NEIGHBORS_SHARED_CUH -#define NEIGHBORS_SHARED_CUH -#include "common.cuh" -#include - -template -__global__ void forward_kernel_shared(uint32_t num_atoms, const Accessor positions, - const Accessor batch, scalar_t cutoff_lower2, - scalar_t cutoff_upper2, PairListAccessor list, - int32_t num_tiles, triclinic::BoxAccessor box) { - // A thread per atom - const int id = blockIdx.x * blockDim.x + threadIdx.x; - // All threads must pass through __syncthreads, - // but when N is not a multiple of 32 some threads are assigned a particle i>N. - // This threads cant return, so they are masked to not do any work - const bool active = id < num_atoms; - __shared__ scalar3 sh_pos[BLOCKSIZE]; - __shared__ int64_t sh_batch[BLOCKSIZE]; - scalar3 pos_i; - int64_t batch_i; - if (active) { - pos_i = fetchPosition(positions, id); - batch_i = batch[id]; - } - // Distribute the N particles in a group of tiles. Storing in each tile blockDim.x values in - // shared memory. This way all threads are accesing the same memory addresses at the same time - for (int tile = 0; tile < num_tiles; tile++) { - // Load this tiles particles values to shared memory - const int i_load = tile * blockDim.x + threadIdx.x; - if (i_load < num_atoms) { // Even if im not active, my thread may load a value each tile to - // shared memory. - sh_pos[threadIdx.x] = fetchPosition(positions, i_load); - sh_batch[threadIdx.x] = batch[i_load]; - } - // Wait for all threads to arrive - __syncthreads(); - // Go through all the particles in the current tile -#pragma unroll 8 - for (int counter = 0; counter < blockDim.x; counter++) { - if (!active) - break; // An out of bounds thread must be masked - const int cur_j = tile * blockDim.x + counter; - const bool testPair = (cur_j < num_atoms) && (cur_j < id || (list.loop && cur_j == id)); - if (testPair) { - const auto batch_j = sh_batch[counter]; - if (batch_i == batch_j) { - const auto pos_j = sh_pos[counter]; - const auto box_i = box[batch_i]; - const auto delta = - triclinic::compute_distance(pos_i, pos_j, list.use_periodic, box_i); - const scalar_t distance2 = - delta.x * delta.x + delta.y * delta.y + delta.z * delta.z; - if (distance2 < cutoff_upper2 && distance2 >= cutoff_lower2) { - const bool requires_transpose = list.include_transpose && !(cur_j == id); - const auto distance = sqrt_(distance2); - addAtomPairToList(list, id, cur_j, delta, distance, requires_transpose); - } - } - } - } - __syncthreads(); - } -} - -static std::tuple -forward_shared(const Tensor& positions, const Tensor& batch, const Tensor& in_box_vectors, - bool use_periodic, const Scalar& cutoff_lower, const Scalar& cutoff_upper, - const Scalar& max_num_pairs, bool loop, bool include_transpose) { - checkInput(positions, batch); - const auto max_num_pairs_ = max_num_pairs.toLong(); - auto box_vectors = in_box_vectors.to(positions.device()); - if (box_vectors.dim() == 2) { - // If the box is a 3x3 tensor it is assumed every sample has the same box - if (use_periodic) { - TORCH_CHECK(box_vectors.size(0) == 3 && box_vectors.size(1) == 3, - "Expected \"box_vectors\" to have shape (n_batch, 3, 3)"); - } - // Make the box (None,3,3), expand artificially to positions.size(0) - box_vectors = box_vectors.unsqueeze(0); - if (use_periodic) { - // I use positions.size(0) because the batch dimension is not available here - box_vectors = box_vectors.expand({positions.size(0), 3, 3}); - } - } - TORCH_CHECK(max_num_pairs_ > 0, "Expected \"max_num_neighbors\" to be positive"); - if (use_periodic) { - TORCH_CHECK(box_vectors.dim() == 3, "Expected \"box_vectors\" to have three dimensions"); - TORCH_CHECK(box_vectors.size(1) == 3 && box_vectors.size(2) == 3, - "Expected \"box_vectors\" to have shape (n_batch, 3, 3)"); - } - const int num_atoms = positions.size(0); - const int num_pairs = max_num_pairs_; - const TensorOptions options = positions.options(); - const auto stream = getCurrentCUDAStream(positions.get_device()); - PairList list(num_pairs, positions.options(), loop, include_transpose, use_periodic); - const CUDAStreamGuard guard(stream); - AT_DISPATCH_FLOATING_TYPES(positions.scalar_type(), "get_neighbor_pairs_shared_forward", [&]() { - const scalar_t cutoff_upper_ = cutoff_upper.to(); - const scalar_t cutoff_lower_ = cutoff_lower.to(); - auto box = triclinic::get_box_accessor(box_vectors, use_periodic); - TORCH_CHECK(cutoff_upper_ > 0, "Expected \"cutoff\" to be positive"); - constexpr int BLOCKSIZE = 64; - const int num_blocks = std::max((num_atoms + BLOCKSIZE - 1) / BLOCKSIZE, 1); - const int num_threads = BLOCKSIZE; - const int num_tiles = num_blocks; - PairListAccessor list_accessor(list); - forward_kernel_shared<<>>( - num_atoms, get_accessor(positions), get_accessor(batch), - cutoff_lower_ * cutoff_lower_, cutoff_upper_ * cutoff_upper_, list_accessor, num_tiles, - box); - }); - return {list.neighbors, list.deltas, list.distances, list.i_curr_pair}; -} - -#endif From 1345c2d6eb3fcab310320cc2086273fd981c7e8a Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 15:06:21 +0200 Subject: [PATCH 39/80] missing function --- torchmdnet/extensions/triton_cell.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 06367cff9..20134eab9 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -17,7 +17,18 @@ from typing import Tuple from torchmdnet.extensions.triton_neighbors import TritonNeighborAutograd -from torchmdnet.extensions.triton_cell import _get_cell_dimensions + + +def _get_cell_dimensions( + box_x: torch.float32, + box_y: torch.float32, + box_z: torch.float32, + cutoff_upper: torch.float32, +) -> int: + nx = torch.floor(box_x / cutoff_upper).clamp(min=3).long() + ny = torch.floor(box_y / cutoff_upper).clamp(min=3).long() + nz = torch.floor(box_z / cutoff_upper).clamp(min=3).long() + return torch.stack([nx, ny, nz]) @triton.jit From 874271ea4ebee0a779d8c278201d97ab80ba9ee3 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:12:23 +0200 Subject: [PATCH 40/80] fix for torch script --- torchmdnet/models/output_modules.py | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/torchmdnet/models/output_modules.py b/torchmdnet/models/output_modules.py index 5bcee7d08..023677b56 100644 --- a/torchmdnet/models/output_modules.py +++ b/torchmdnet/models/output_modules.py @@ -39,18 +39,22 @@ def pre_reduce(self, x, v, z, pos, batch): return def reduce(self, x, batch): - is_capturing = x.is_cuda and torch.cuda.is_current_stream_capturing() - if not x.is_cuda or not is_capturing: + if torch.jit.is_scripting(): + # TorchScript doesn't support torch.cuda.is_current_stream_capturing() self.dim_size = int(batch.max().item() + 1) - if is_capturing: - assert ( - self.dim_size > 0 - ), "Warming up is needed before capturing the model into a CUDA graph" - warn( - "CUDA graph capture will lock the batch to the current number of samples ({}). Changing this will result in a crash".format( - self.dim_size + else: + is_capturing = x.is_cuda and torch.cuda.is_current_stream_capturing() + if not x.is_cuda or not is_capturing: + self.dim_size = int(batch.max().item() + 1) + if is_capturing: + assert ( + self.dim_size > 0 + ), "Warming up is needed before capturing the model into a CUDA graph" + warn( + "CUDA graph capture will lock the batch to the current number of samples ({}). Changing this will result in a crash".format( + self.dim_size + ) ) - ) return scatter(x, batch, dim=0, dim_size=self.dim_size, reduce=self.reduce_op) def post_reduce(self, x): From ca1bc3fe7cc480b43475c4602a96cb911913defd Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:31:05 +0200 Subject: [PATCH 41/80] updating installation isntructions --- README.md | 20 +++--- cibuildwheel_support/before_all_linux.sh | 81 --------------------- cibuildwheel_support/before_all_windows.sh | 37 ---------- docs/source/installation.rst | 84 ++++------------------ environment.yml | 17 ----- 5 files changed, 22 insertions(+), 217 deletions(-) delete mode 100755 cibuildwheel_support/before_all_linux.sh delete mode 100755 cibuildwheel_support/before_all_windows.sh delete mode 100644 environment.yml diff --git a/README.md b/README.md index 4240d4549..2ebe7228c 100644 --- a/README.md +++ b/README.md @@ -21,19 +21,17 @@ Documentation is available at https://torchmd-net.readthedocs.io ## Installation -TorchMD-Net is available as a pip installable wheel as well as in [conda-forge](https://conda-forge.org/) +TorchMD-Net is available as a pip package as well as in [conda-forge](https://conda-forge.org/) -TorchMD-Net provides builds for CPU-only, CUDA 11 and CUDA 12. CPU versions are only provided as reference, -as the performance will be extremely limited. -Depending on which variant you wish to install, you can install it with one of the following commands: +As TorchMD-Net depends on PyTorch we need to add additional index URLs to the installation command as per [pytorch](https://pytorch.org/get-started/locally/) ```sh -# The following will install the CUDA 11.8 version -pip install torchmd-net-cu11 --extra-index-url https://download.pytorch.org/whl/cu118 -# The following will install the CUDA 12.4 version -pip install torchmd-net-cu12 --extra-index-url https://download.pytorch.org/whl/cu124 -# The following will install the CPU only version (not recommended) -pip install torchmd-net-cpu --extra-index-url https://download.pytorch.org/whl/cpu +# The following will install TorchMD-Net with PyTorch CUDA 11.8 version +pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu118 +# The following will install TorchMD-Net with PyTorch CUDA 12.4 version +pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu124 +# The following will install TorchMD-Net with PyTorch CPU only version (not recommended) +pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cpu ``` Alternatively it can be installed with conda or mamba with one of the following commands. @@ -46,7 +44,7 @@ mamba install torchmd-net cuda-version=12.4 ### Install from source -TorchMD-Net is installed using pip, but you will need to install some dependencies before. Check [this documentation page](https://torchmd-net.readthedocs.io/en/latest/installation.html#install-from-source). +TorchMD-Net is installed using pip with `pip install -e .` to create an editable install. ## Usage Specifying training arguments can either be done via a configuration yaml file or through command line arguments directly. Several examples of architectural and training specifications for some models and datasets can be found in [examples/](https://github.com/torchmd/torchmd-net/tree/main/examples). Note that if a parameter is present both in the yaml file and the command line, the command line version takes precedence. diff --git a/cibuildwheel_support/before_all_linux.sh b/cibuildwheel_support/before_all_linux.sh deleted file mode 100755 index 12c814b31..000000000 --- a/cibuildwheel_support/before_all_linux.sh +++ /dev/null @@ -1,81 +0,0 @@ -#! /bin/bash - -set -e -set -x - -# Configure pip to use PyTorch extra-index-url for CPU -mkdir -p $HOME/.config/pip -echo "[global] -extra-index-url = https://download.pytorch.org/whl/cpu" > $HOME/.config/pip/pip.conf - - -if [ "$ACCELERATOR" == "cu118" ]; then - - # Install CUDA 11.8: - dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo - - dnf install --setopt=obsoletes=0 -y \ - cuda-nvcc-11-8-11.8.89-1 \ - cuda-cudart-devel-11-8-11.8.89-1 \ - libcurand-devel-11-8-10.3.0.86-1 \ - libcudnn9-devel-cuda-11-9.8.0.87-1 \ - libcublas-devel-11-8-11.11.3.6-1 \ - libnccl-devel-2.15.5-1+cuda11.8 \ - libcusparse-devel-11-8-11.7.5.86-1 \ - libcusolver-devel-11-8-11.4.1.48-1 \ - gcc-toolset-11 - - ln -s cuda-11.8 /usr/local/cuda - ln -s /opt/rh/gcc-toolset-11/root/usr/bin/gcc /usr/local/cuda/bin/gcc - ln -s /opt/rh/gcc-toolset-11/root/usr/bin/g++ /usr/local/cuda/bin/g++ - - export CUDA_HOME="/usr/local/cuda" - - # Configure pip to use PyTorch extra-index-url for CUDA 11.8 - mkdir -p $HOME/.config/pip - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu118" > $HOME/.config/pip/pip.conf - -elif [ "$ACCELERATOR" == "cu126" ]; then - # Install CUDA 12.6 - dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo - - dnf install --setopt=obsoletes=0 -y \ - cuda-compiler-12-6-12.6.3-1 \ - cuda-libraries-12-6-12.6.3-1 \ - cuda-libraries-devel-12-6-12.6.3-1 \ - cuda-toolkit-12-6-12.6.3-1 \ - gcc-toolset-13 - - ln -s cuda-12.6 /usr/local/cuda - ln -s /opt/rh/gcc-toolset-13/root/usr/bin/gcc /usr/local/cuda/bin/gcc - ln -s /opt/rh/gcc-toolset-13/root/usr/bin/g++ /usr/local/cuda/bin/g++ - ln -s /usr/local/cuda/targets/x86_64-linux/lib/stubs/libcuda.so /usr/lib/libcuda.so.1 - - # Configure pip to use PyTorch extra-index-url for CUDA 12.6 - mkdir -p $HOME/.config/pip - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu126" > $HOME/.config/pip/pip.conf - -elif [ "$ACCELERATOR" == "cu128" ]; then - # Install CUDA 12.8 - dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo - - dnf install --setopt=obsoletes=0 -y \ - cuda-compiler-12-8-12.8.1-1 \ - cuda-libraries-12-8-12.8.1-1 \ - cuda-libraries-devel-12-8-12.8.1-1 \ - cuda-toolkit-12-8-12.8.1-1 \ - gcc-toolset-13 - - ln -s cuda-12.8 /usr/local/cuda - ln -s /opt/rh/gcc-toolset-13/root/usr/bin/gcc /usr/local/cuda/bin/gcc - ln -s /opt/rh/gcc-toolset-13/root/usr/bin/g++ /usr/local/cuda/bin/g++ - ln -s /usr/local/cuda/targets/x86_64-linux/lib/stubs/libcuda.so /usr/lib/libcuda.so.1 - - # Configure pip to use PyTorch extra-index-url for CUDA 12.8 - mkdir -p $HOME/.config/pip - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu128" > $HOME/.config/pip/pip.conf - -fi \ No newline at end of file diff --git a/cibuildwheel_support/before_all_windows.sh b/cibuildwheel_support/before_all_windows.sh deleted file mode 100755 index 9cf789159..000000000 --- a/cibuildwheel_support/before_all_windows.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -set -e -set -x - -# Create pip directory if it doesn't exist -mkdir -p "C:\ProgramData\pip" - -# Create pip.ini file with PyTorch CPU index -echo "[global] -extra-index-url = https://download.pytorch.org/whl/cpu" > "C:\ProgramData\pip\pip.ini" - -if [ "$ACCELERATOR" == "cu118" ]; then - curl --netrc-optional -L -nv -o cuda.exe https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_522.06_windows.exe - ./cuda.exe -s nvcc_11.8 cudart_11.8 cublas_dev_11.8 curand_dev_11.8 cusparse_dev_11.8 cusolver_dev_11.8 thrust_11.8 - rm cuda.exe - - # Create pip.ini file with PyTorch CUDA 11.8 index - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu118" > "C:\ProgramData\pip\pip.ini" -elif [ "$ACCELERATOR" == "cu126" ]; then - curl --netrc-optional -L -nv -o cuda.exe https://developer.download.nvidia.com/compute/cuda/12.6.0/local_installers/cuda_12.6.0_560.76_windows.exe - ./cuda.exe -s nvcc_12.6 cudart_12.6 cublas_dev_12.6 curand_dev_12.6 cusparse_dev_12.6 cusolver_dev_12.6 thrust_12.6 - rm cuda.exe - - # Create pip.ini file with PyTorch CUDA 12.6 index - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu126" > "C:\ProgramData\pip\pip.ini" -elif [ "$ACCELERATOR" == "cu128" ]; then - curl --netrc-optional -L -nv -o cuda.exe https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda_12.8.1_572.61_windows.exe - ./cuda.exe -s nvcc_12.8 cudart_12.8 cublas_dev_12.8 curand_dev_12.8 cusparse_dev_12.8 cusolver_dev_12.8 thrust_12.8 - rm cuda.exe - - # Create pip.ini file with PyTorch CUDA 12.8 index - echo "[global] -extra-index-url = https://download.pytorch.org/whl/cu128" > "C:\ProgramData\pip\pip.ini" -fi \ No newline at end of file diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 769bce14c..6e01ca096 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -1,20 +1,18 @@ Installation ============ -TorchMD-Net is available as a pip installable wheel as well as in `conda-forge `_ +TorchMD-Net is available as a pip package as well as in `conda-forge `_ -TorchMD-Net provides builds for CPU-only, CUDA 11 and CUDA 12. -CPU versions are only provided as reference, as the performance will be extremely limited. -Depending on which variant you wish to install, you can install it with one of the following commands: +As TorchMD-Net depends on PyTorch we need to add additional index URLs to the installation command as per `pytorch `_ .. code-block:: shell - # The following will install the CUDA 11.8 version - pip install torchmd-net-cu11 --extra-index-url https://download.pytorch.org/whl/cu118 - # The following will install the CUDA 12.4 version - pip install torchmd-net-cu12 --extra-index-url https://download.pytorch.org/whl/cu124 - # The following will install the CPU only version (not recommended) - pip install torchmd-net-cpu --extra-index-url https://download.pytorch.org/whl/cpu + # The following will install TorchMD-Net with PyTorch CUDA 11.8 version + pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu118 + # The following will install TorchMD-Net with PyTorch CUDA 12.4 version + pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu124 + # The following will install TorchMD-Net with PyTorch CPU only version (not recommended) + pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cpu Alternatively it can be installed with conda or mamba with one of the following commands. We recommend using `Miniforge `_ instead of anaconda. @@ -27,70 +25,14 @@ We recommend using `Miniforge `_ inst Install from source ------------------- +For development purposes, we recommend using `uv `_ to install the TorchMD-Net in editable mode. +After installing uv, run the following command to install the TorchMD-Net and its dependencies in editable mode. -1. Clone the repository: - -.. code-block:: shell - - git clone https://github.com/torchmd/torchmd-net.git - cd torchmd-net - -2. Install the dependencies in environment.yml. - -.. code-block:: shell - - conda env create -f environment.yml - conda activate torchmd-net - -3. CUDA enabled installation - -You can skip this section if you only need a CPU installation. - -You will need the CUDA compiler (nvcc) and the corresponding development libraries to build TorchMD-Net with CUDA support. You can install CUDA from the `official NVIDIA channel `_ or from conda-forge. - -The conda-forge channel `changed the way to install CUDA from versions 12 and above `_, thus the following instructions depend on whether you need CUDA < 12. If you have a GPU available, conda-forge probably installed the CUDA runtime (not the developer tools) on your system already, you can check with conda: - -.. code-block:: shell - - conda list | grep cuda - - -Or by asking pytorch: - .. code-block:: shell - - python -c "import torch; print(torch.version.cuda)" - - -It is recommended to install the same version as the one used by torch. - -.. warning:: At the time of writing there is a `bug in Mamba `_ (v1.5.6) that can cause trouble when installing CUDA on an already created environment. We thus recommend conda for this step. - -* CUDA>=12 - -.. code-block:: shell - - conda install -c conda-forge python=3.10 cuda-version=12.6 cuda-nvvm cuda-nvcc cuda-libraries-dev - - -* CUDA<12 - -The nvidia channel provides the developer tools for CUDA<12. - -.. code-block:: shell - - conda install -c nvidia "cuda-nvcc<12" "cuda-libraries-dev<12" "cuda-version<12" "gxx<12" pytorch=*=*cuda* - - -4. Install TorchMD-NET into the environment: - -.. code-block:: shell - - pip install -e . + uv sync -.. note:: Pip installation in CUDA mode requires compiling CUDA source codes, this can take a really long time and the process might appear as if it is "stuck". Run pip with `-vv` to see the compilation process. +This will install the package alongside PyTorch for CUDA 12.6. To change the CUDA version, +edit the `pyproject.toml `_ file and change the `torch` dependency to the desired version. -This will install TorchMD-NET in editable mode, so that changes to the source code are immediately available. -Besides making all python utilities available environment-wide, this will also install the ``torchmd-train`` command line utility. diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 3e7cd402c..000000000 --- a/environment.yml +++ /dev/null @@ -1,17 +0,0 @@ -name: torchmd-net -channels: - - conda-forge -dependencies: - - h5py - - nnpops - - pip - - pytorch - - pytorch_geometric - - lightning - - torchmetrics - - tqdm - # Dev tools - - flake8 - - pytest - - psutil - - gxx From 7223b88a6b493bfe967ca733d3ac8bf29484f3a1 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:31:26 +0200 Subject: [PATCH 42/80] making triton optional --- torchmdnet/extensions/neighbors.py | 153 +++++++++++++++++ torchmdnet/extensions/ops.py | 72 +++++--- torchmdnet/extensions/triton_brute.py | 14 +- torchmdnet/extensions/triton_cell.py | 16 +- torchmdnet/extensions/triton_neighbors.py | 191 +--------------------- 5 files changed, 208 insertions(+), 238 deletions(-) create mode 100644 torchmdnet/extensions/neighbors.py diff --git a/torchmdnet/extensions/neighbors.py b/torchmdnet/extensions/neighbors.py new file mode 100644 index 000000000..5df1d7db2 --- /dev/null +++ b/torchmdnet/extensions/neighbors.py @@ -0,0 +1,153 @@ +from torch import Tensor +from typing import Tuple +import torch + + +def _round_nearest(x: Tensor) -> Tensor: + # Equivalent to torch.round but works for both float32/float64 and keeps TorchScript happy. + return torch.where(x >= 0, torch.floor(x + 0.5), torch.ceil(x - 0.5)) + + +def _apply_pbc_torch(deltas: Tensor, box_for_pairs: Tensor) -> Tensor: + # box_for_pairs: (num_pairs, 3, 3) + scale3 = _round_nearest(deltas[:, 2] / box_for_pairs[:, 2, 2]) + deltas = deltas - scale3.unsqueeze(1) * box_for_pairs[:, 2] + scale2 = _round_nearest(deltas[:, 1] / box_for_pairs[:, 1, 1]) + deltas = deltas - scale2.unsqueeze(1) * box_for_pairs[:, 1] + scale1 = _round_nearest(deltas[:, 0] / box_for_pairs[:, 0, 0]) + deltas = deltas - scale1.unsqueeze(1) * box_for_pairs[:, 0] + return deltas + + +def torch_neighbor_bruteforce( + positions: Tensor, + batch: Tensor, + box_vectors: Tensor, + use_periodic: bool, + cutoff_lower: float, + cutoff_upper: float, + max_num_pairs: int, + loop: bool, + include_transpose: bool, +) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + """Brute-force neighbor list using pure PyTorch. + + This implementation avoids nonzero() to be torch.compile compatible. + It uses argsort with infinity for invalid pairs to achieve fixed output shapes. + """ + if positions.dim() != 2 or positions.size(1) != 3: + raise ValueError('Expected "positions" to have shape (N, 3)') + if batch.dim() != 1 or batch.size(0) != positions.size(0): + raise ValueError('Expected "batch" to have shape (N,)') + if max_num_pairs <= 0: + raise ValueError('Expected "max_num_pairs" to be positive') + + device = positions.device + dtype = positions.dtype + n_atoms = positions.size(0) + + if use_periodic: + if box_vectors.dim() == 2: + box_vectors = box_vectors.unsqueeze(0) + elif box_vectors.dim() != 3: + raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') + box_vectors = box_vectors.to(device=device, dtype=dtype) + + # Generate indices for all n² pairs + arange_n = torch.arange(n_atoms, device=device) + i_idx = arange_n.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) + j_idx = arange_n.view(1, -1).expand(n_atoms, n_atoms) # (n, n) + + # Compute deltas for ALL pairs: (n, n, 3) + pos_i = positions.unsqueeze(1) # (n, 1, 3) + pos_j = positions.unsqueeze(0) # (1, n, 3) + deltas_grid = pos_i - pos_j # (n, n, 3) + + # Apply PBC if needed + if use_periodic: + # Get batch indices for all pairs + batch_i = batch.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) + if box_vectors.size(0) == 1: + # Single box for all - broadcast + box_for_grid = box_vectors.expand(n_atoms, n_atoms, 3, 3) # (n, n, 3, 3) + else: + # Per-batch boxes - index by batch of atom i + box_for_grid = box_vectors[batch_i] # (n, n, 3, 3) + # Apply PBC: deltas_grid is (n, n, 3), box_for_grid is (n, n, 3, 3) + deltas_flat_pbc = deltas_grid.reshape(-1, 3) + box_flat_pbc = box_for_grid.reshape(-1, 3, 3) + deltas_flat_pbc = _apply_pbc_torch(deltas_flat_pbc, box_flat_pbc) + deltas_grid = deltas_flat_pbc.reshape(n_atoms, n_atoms, 3) + + # Compute distances for all pairs: (n, n) + dist_sq = (deltas_grid * deltas_grid).sum(dim=-1) + zero_mask = dist_sq == 0 + distances_grid = torch.where( + zero_mask, + torch.zeros_like(dist_sq), + torch.sqrt(dist_sq.clamp(min=1e-32)), + ) + + # Build validity mask (n, n) + if include_transpose: + valid_mask = torch.ones((n_atoms, n_atoms), device=device, dtype=torch.bool) + if not loop: + valid_mask = valid_mask & (i_idx != j_idx) + else: + valid_mask = (i_idx > j_idx) if not loop else (i_idx >= j_idx) + + # Apply batch constraint + if batch.numel() > 0: + same_batch = batch.view(-1, 1) == batch.view(1, -1) + valid_mask = valid_mask & same_batch + + # Apply cutoff constraints + # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 + is_self_loop = i_idx == j_idx + valid_mask = ( + valid_mask + & (distances_grid < cutoff_upper) + & ((distances_grid >= cutoff_lower) | is_self_loop) + ) + + # Flatten everything: (n²) + i_flat = i_idx.reshape(-1) + j_flat = j_idx.reshape(-1) + deltas_flat = deltas_grid.reshape(-1, 3) + distances_flat = distances_grid.reshape(-1) + valid_flat = valid_mask.reshape(-1) + + # Sort key: valid pairs by distance, invalid pairs get infinity (sorted last) + sort_key = torch.where( + valid_flat, + distances_flat, + torch.full_like(distances_flat, float("inf")), + ) + + # Sort and take top max_num_pairs (fixed output shape) + order = torch.argsort(sort_key)[:max_num_pairs] + + # Gather results using the sorted indices + i_out = i_flat.index_select(0, order) + j_out = j_flat.index_select(0, order) + deltas_out = deltas_flat.index_select(0, order) + distances_out = distances_flat.index_select(0, order) + valid_out = valid_flat.index_select(0, order) + + # Replace invalid entries with -1 (neighbors) and 0 (deltas/distances) + # Ensure gradients flow through valid entries + neighbors_out = torch.stack( + [ + torch.where(valid_out, i_out, torch.full_like(i_out, -1)), + torch.where(valid_out, j_out, torch.full_like(j_out, -1)), + ] + ) + # For deltas/distances, use where to preserve gradients for valid entries + zero_deltas = deltas_out.detach() * 0 + zero_distances = distances_out.detach() * 0 + deltas_out = torch.where(valid_out.unsqueeze(1), deltas_out, zero_deltas) + distances_out = torch.where(valid_out, distances_out, zero_distances) + + # Count ALL valid pairs (before slicing) to detect overflow + num_pairs_tensor = valid_flat.sum().view(1).to(torch.long) + return neighbors_out, deltas_out, distances_out, num_pairs_tensor diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index 5cb03bb99..d1c0cb9c2 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -10,9 +10,9 @@ import torch from torch import Tensor from typing import Tuple +import logging -from . import triton_neighbors - +logger = logging.getLogger(__name__) __all__ = ["get_neighbor_pairs_kernel"] @@ -69,30 +69,56 @@ def get_neighbor_pairs_kernel( num_pairs : Tensor The number of pairs found. """ + try: + import triton + + HAS_TRITON = True + except ImportError: + HAS_TRITON = False + if torch.jit.is_scripting() or not positions.is_cuda: - return triton_neighbors.torch_neighbor_pairs( - strategy, + from torchmdnet.extensions.neighbors import torch_neighbor_bruteforce + + return torch_neighbor_bruteforce( positions, - batch, - box_vectors, - use_periodic, - cutoff_lower, - cutoff_upper, - max_num_pairs, - loop, - include_transpose, + batch=batch, + box_vectors=box_vectors, + use_periodic=use_periodic, + cutoff_lower=cutoff_lower, + cutoff_upper=cutoff_upper, + max_num_pairs=max_num_pairs, + loop=loop, + include_transpose=include_transpose, ) - return triton_neighbors.triton_neighbor_pairs( + if not HAS_TRITON: + logger.warning( + "Triton is not available, using torch version of the neighbor pairs kernel." + ) + return torch_neighbor_bruteforce( + positions, + batch=batch, + box_vectors=box_vectors, + use_periodic=use_periodic, + cutoff_lower=cutoff_lower, + cutoff_upper=cutoff_upper, + max_num_pairs=max_num_pairs, + loop=loop, + include_transpose=include_transpose, + ) + + from torchmdnet.extensions.triton_neighbors import triton_neighbor_pairs + + return triton_neighbor_pairs( strategy, - positions, - batch, - box_vectors, - use_periodic, - cutoff_lower, - cutoff_upper, - max_num_pairs, - loop, - include_transpose, - num_cells, + positions=positions, + batch=batch, + box_vectors=box_vectors, + use_periodic=use_periodic, + cutoff_lower=cutoff_lower, + cutoff_upper=cutoff_upper, + max_num_pairs=max_num_pairs, + loop=loop, + include_transpose=include_transpose, + num_cells=num_cells, ) diff --git a/torchmdnet/extensions/triton_brute.py b/torchmdnet/extensions/triton_brute.py index ffd378884..52e597f4c 100644 --- a/torchmdnet/extensions/triton_brute.py +++ b/torchmdnet/extensions/triton_brute.py @@ -1,18 +1,8 @@ # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org # Distributed under the MIT License. # (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - - -try: - import triton - import triton.language as tl - - _HAS_TRITON = True -except Exception: - triton = None - tl = None - _HAS_TRITON = False - +import triton +import triton.language as tl from torch import Tensor import torch from torchmdnet.extensions.triton_neighbors import _tl_round, TritonNeighborAutograd diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index 20134eab9..ee39db7c4 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -1,17 +1,8 @@ # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org # Distributed under the MIT License. # (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) - -try: - import triton - import triton.language as tl - - _HAS_TRITON = True -except Exception: - triton = None - tl = None - _HAS_TRITON = False - +import triton +import triton.language as tl import torch from torch import Tensor from typing import Tuple @@ -379,9 +370,6 @@ def forward( include_transpose: bool, num_cells: int, ): - if not _HAS_TRITON: - raise RuntimeError("Triton is not available") - device = positions.device dtype = positions.dtype n_atoms = positions.size(0) diff --git a/torchmdnet/extensions/triton_neighbors.py b/torchmdnet/extensions/triton_neighbors.py index ab0755b24..9bfc28a7d 100644 --- a/torchmdnet/extensions/triton_neighbors.py +++ b/torchmdnet/extensions/triton_neighbors.py @@ -3,24 +3,10 @@ # (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT) from typing import Tuple - import torch from torch import Tensor - -try: - import triton - import triton.language as tl - - _HAS_TRITON = True -except Exception: - triton = None - tl = None - _HAS_TRITON = False - - -def _round_nearest(x: Tensor) -> Tensor: - # Equivalent to torch.round but works for both float32/float64 and keeps TorchScript happy. - return torch.where(x >= 0, torch.floor(x + 0.5), torch.ceil(x - 0.5)) +import triton +import triton.language as tl def _validate_box(box_vectors: Tensor, cutoff_upper: float, n_batch: int) -> Tensor: @@ -47,151 +33,6 @@ def _validate_box(box_vectors: Tensor, cutoff_upper: float, n_batch: int) -> Ten return box_vectors -def _apply_pbc_torch(deltas: Tensor, box_for_pairs: Tensor) -> Tensor: - # box_for_pairs: (num_pairs, 3, 3) - scale3 = _round_nearest(deltas[:, 2] / box_for_pairs[:, 2, 2]) - deltas = deltas - scale3.unsqueeze(1) * box_for_pairs[:, 2] - scale2 = _round_nearest(deltas[:, 1] / box_for_pairs[:, 1, 1]) - deltas = deltas - scale2.unsqueeze(1) * box_for_pairs[:, 1] - scale1 = _round_nearest(deltas[:, 0] / box_for_pairs[:, 0, 0]) - deltas = deltas - scale1.unsqueeze(1) * box_for_pairs[:, 0] - return deltas - - -def _torch_neighbor_bruteforce( - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, -) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - """Brute-force neighbor list using pure PyTorch. - - This implementation avoids nonzero() to be torch.compile compatible. - It uses argsort with infinity for invalid pairs to achieve fixed output shapes. - """ - if positions.dim() != 2 or positions.size(1) != 3: - raise ValueError('Expected "positions" to have shape (N, 3)') - if batch.dim() != 1 or batch.size(0) != positions.size(0): - raise ValueError('Expected "batch" to have shape (N,)') - if max_num_pairs <= 0: - raise ValueError('Expected "max_num_pairs" to be positive') - - device = positions.device - dtype = positions.dtype - n_atoms = positions.size(0) - - if use_periodic: - if box_vectors.dim() == 2: - box_vectors = box_vectors.unsqueeze(0) - elif box_vectors.dim() != 3: - raise ValueError('Expected "box_vectors" to have shape (n_batch, 3, 3)') - box_vectors = box_vectors.to(device=device, dtype=dtype) - - # Generate indices for all n² pairs - arange_n = torch.arange(n_atoms, device=device) - i_idx = arange_n.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) - j_idx = arange_n.view(1, -1).expand(n_atoms, n_atoms) # (n, n) - - # Compute deltas for ALL pairs: (n, n, 3) - pos_i = positions.unsqueeze(1) # (n, 1, 3) - pos_j = positions.unsqueeze(0) # (1, n, 3) - deltas_grid = pos_i - pos_j # (n, n, 3) - - # Apply PBC if needed - if use_periodic: - # Get batch indices for all pairs - batch_i = batch.view(-1, 1).expand(n_atoms, n_atoms) # (n, n) - if box_vectors.size(0) == 1: - # Single box for all - broadcast - box_for_grid = box_vectors.expand(n_atoms, n_atoms, 3, 3) # (n, n, 3, 3) - else: - # Per-batch boxes - index by batch of atom i - box_for_grid = box_vectors[batch_i] # (n, n, 3, 3) - # Apply PBC: deltas_grid is (n, n, 3), box_for_grid is (n, n, 3, 3) - deltas_flat_pbc = deltas_grid.reshape(-1, 3) - box_flat_pbc = box_for_grid.reshape(-1, 3, 3) - deltas_flat_pbc = _apply_pbc_torch(deltas_flat_pbc, box_flat_pbc) - deltas_grid = deltas_flat_pbc.reshape(n_atoms, n_atoms, 3) - - # Compute distances for all pairs: (n, n) - dist_sq = (deltas_grid * deltas_grid).sum(dim=-1) - zero_mask = dist_sq == 0 - distances_grid = torch.where( - zero_mask, - torch.zeros_like(dist_sq), - torch.sqrt(dist_sq.clamp(min=1e-32)), - ) - - # Build validity mask (n, n) - if include_transpose: - valid_mask = torch.ones((n_atoms, n_atoms), device=device, dtype=torch.bool) - if not loop: - valid_mask = valid_mask & (i_idx != j_idx) - else: - valid_mask = (i_idx > j_idx) if not loop else (i_idx >= j_idx) - - # Apply batch constraint - if batch.numel() > 0: - same_batch = batch.view(-1, 1) == batch.view(1, -1) - valid_mask = valid_mask & same_batch - - # Apply cutoff constraints - # Self-loops (i == j) are exempt from cutoff_lower since they have distance 0 - is_self_loop = i_idx == j_idx - valid_mask = ( - valid_mask - & (distances_grid < cutoff_upper) - & ((distances_grid >= cutoff_lower) | is_self_loop) - ) - - # Flatten everything: (n²) - i_flat = i_idx.reshape(-1) - j_flat = j_idx.reshape(-1) - deltas_flat = deltas_grid.reshape(-1, 3) - distances_flat = distances_grid.reshape(-1) - valid_flat = valid_mask.reshape(-1) - - # Sort key: valid pairs by distance, invalid pairs get infinity (sorted last) - sort_key = torch.where( - valid_flat, - distances_flat, - torch.full_like(distances_flat, float("inf")), - ) - - # Sort and take top max_num_pairs (fixed output shape) - order = torch.argsort(sort_key)[:max_num_pairs] - - # Gather results using the sorted indices - i_out = i_flat.index_select(0, order) - j_out = j_flat.index_select(0, order) - deltas_out = deltas_flat.index_select(0, order) - distances_out = distances_flat.index_select(0, order) - valid_out = valid_flat.index_select(0, order) - - # Replace invalid entries with -1 (neighbors) and 0 (deltas/distances) - # Ensure gradients flow through valid entries - neighbors_out = torch.stack( - [ - torch.where(valid_out, i_out, torch.full_like(i_out, -1)), - torch.where(valid_out, j_out, torch.full_like(j_out, -1)), - ] - ) - # For deltas/distances, use where to preserve gradients for valid entries - zero_deltas = deltas_out.detach() * 0 - zero_distances = distances_out.detach() * 0 - deltas_out = torch.where(valid_out.unsqueeze(1), deltas_out, zero_deltas) - distances_out = torch.where(valid_out, distances_out, zero_distances) - - # Count ALL valid pairs (before slicing) to detect overflow - num_pairs_tensor = valid_flat.sum().view(1).to(torch.long) - return neighbors_out, deltas_out, distances_out, num_pairs_tensor - - @triton.jit def _tl_round(x): return tl.where(x >= 0, tl.math.floor(x + 0.5), tl.math.ceil(x - 0.5)) @@ -257,8 +98,6 @@ def triton_neighbor_pairs( from torchmdnet.extensions.triton_cell import TritonCellNeighborAutograd from torchmdnet.extensions.triton_brute import TritonBruteNeighborAutograd - if not _HAS_TRITON: - raise RuntimeError("Triton is not available") if positions.device.type != "cuda": raise RuntimeError("Triton neighbor list requires CUDA tensors") if positions.dtype not in (torch.float32, torch.float64): @@ -291,29 +130,3 @@ def triton_neighbor_pairs( ) else: raise ValueError(f"Unsupported strategy {strategy}") - - -def torch_neighbor_pairs( - strategy: str, - positions: Tensor, - batch: Tensor, - box_vectors: Tensor, - use_periodic: bool, - cutoff_lower: float, - cutoff_upper: float, - max_num_pairs: int, - loop: bool, - include_transpose: bool, -) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - # Strategy is ignored for torch version. We only need the brute-force fallback - return _torch_neighbor_bruteforce( - positions, - batch, - box_vectors, - use_periodic, - cutoff_lower, - cutoff_upper, - max_num_pairs, - loop, - include_transpose, - ) From eae391c45034d4690b653dc90089b2e462e114a4 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:31:57 +0200 Subject: [PATCH 43/80] install different triton package on windows and none on OSX --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index be8e09000..0b2f2b57b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,8 @@ dependencies = [ "lightning", "tqdm", "numpy", - "triton", + "triton; sys_platform == 'linux'", + "triton-windows; sys_platform == 'win32'", ] [project.urls] From 3ff58e263fc86856f7467a1743d9a3724a7e85c7 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:36:45 +0200 Subject: [PATCH 44/80] simplify CI deployment and testing --- .github/workflows/publish.yml | 139 +++------------------------------- .github/workflows/test.yml | 87 +++------------------ pyproject.toml | 6 +- 3 files changed, 27 insertions(+), 205 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 4768e3dcd..3369c761d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -6,77 +6,28 @@ on: jobs: build: - name: Build wheels on ${{ matrix.os }}-${{ matrix.accelerator }} - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest, ubuntu-24.04-arm, windows-2022, macos-latest] - accelerator: [cpu, cu118, cu126] #, cu128] - exclude: - - os: ubuntu-24.04-arm - accelerator: cu118 - - os: ubuntu-24.04-arm - accelerator: cu126 - # - os: ubuntu-24.04-arm - # accelerator: cu128 - - os: macos-latest - accelerator: cu118 - - os: macos-latest - accelerator: cu126 - # - os: macos-latest - # accelerator: cu128 + name: Create source distribution + runs-on: ubuntu-latest steps: - - name: Free space of Github Runner (otherwise it will fail by running out of disk) - if: matrix.os == 'ubuntu-latest' - run: | - sudo rm -rf /usr/share/dotnet - sudo rm -rf /opt/ghc - sudo rm -rf "/usr/local/share/boost" - sudo rm -rf "/usr/local/.ghcup" - sudo rm -rf "/usr/local/julia1.9.2" - sudo rm -rf "/usr/local/lib/android" - sudo rm -rf "$AGENT_TOOLSDIRECTORY" - - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: "3.13" - name: Install cibuildwheel - run: python -m pip install cibuildwheel==3.1.3 - - - name: Activate MSVC - uses: ilammy/msvc-dev-cmd@v1 - with: - toolset: 14.29 - if: matrix.os == 'windows-2022' + run: pip install build - - name: Build wheels - if: matrix.os != 'windows-2022' - shell: bash - run: python -m cibuildwheel --output-dir wheelhouse - env: - ACCELERATOR: ${{ matrix.accelerator }} - CPU_TRAIN: ${{ runner.os == 'macOS' && 'true' || 'false' }} - - - name: Build wheels - if: matrix.os == 'windows-2022' - shell: cmd # Use cmd on Windows to avoid bash environment taking priority over MSVC variables - run: python -m cibuildwheel --output-dir wheelhouse - env: - ACCELERATOR: ${{ matrix.accelerator }} - DISTUTILS_USE_SDK: "1" # Windows requires this to use vc for building - SKIP_TORCH_COMPILE: "true" + - name: Build pypi package + run: python -m build --sdist - uses: actions/upload-artifact@v4 with: - name: ${{ matrix.accelerator }}-cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} - path: ./wheelhouse/*.whl + name: source_dist + path: dist/*.tar.gz - publish-to-public-pypi: + publish-to-pypi: name: >- Publish Python 🐍 distribution 📦 to PyPI needs: @@ -84,18 +35,14 @@ jobs: runs-on: ubuntu-latest environment: name: pypi + url: https://pypi.org/p/torchmd-net permissions: id-token: write # IMPORTANT: mandatory for trusted publishing - strategy: - fail-fast: false - matrix: - accelerator: [cpu, cu118, cu126] #, hip, cu124, cu126, cu128] steps: - name: Download all the dists uses: actions/download-artifact@v4 with: - pattern: "${{ matrix.accelerator }}-cibw-wheels*" path: dist/ merge-multiple: true @@ -103,69 +50,7 @@ jobs: uses: pypa/gh-action-pypi-publish@release/v1 with: password: ${{ secrets.TMDNET_PYPI_API_TOKEN }} - skip-existing: true - - # publish-to-accelera-pypi: - # name: >- - # Publish Python 🐍 distribution 📦 to Acellera PyPI - # needs: - # - build - # runs-on: ubuntu-latest - # permissions: # Needed for GCP authentication - # contents: "read" - # id-token: "write" - # strategy: - # fail-fast: false - # matrix: - # accelerator: [cpu, cu118, cu126, cu128] - - # steps: - # - uses: actions/checkout@v4 # Needed for GCP authentication for some reason - - # - name: Set up Cloud SDK - # uses: google-github-actions/auth@v2 - # with: - # workload_identity_provider: ${{ secrets.GCP_WORKLOAD_IDENTITY_PROVIDER }} - # service_account: ${{ secrets.GCP_PYPI_SERVICE_ACCOUNT }} - - # - name: Download all the dists - # uses: actions/download-artifact@v4 - # with: - # pattern: "${{ matrix.accelerator }}-cibw-wheels*" - # path: dist/ - # merge-multiple: true - - # - name: Publish distribution 📦 to Acellera PyPI - # run: | - # pip install build twine keyring keyrings.google-artifactregistry-auth - # pip install -U packaging - # twine upload --repository-url https://us-central1-python.pkg.dev/pypi-packages-455608/${{ matrix.accelerator }} dist/* --verbose --skip-existing - - # publish-to-official-pypi: - # name: >- - # Publish Python 🐍 distribution 📦 to PyPI - # needs: - # - build - # runs-on: ubuntu-latest - # environment: - # name: pypi - # url: https://pypi.org/p/torchmd-net - # permissions: - # id-token: write # IMPORTANT: mandatory for trusted publishing - - # steps: - # - name: Download all the dists - # uses: actions/download-artifact@v4 - # with: - # pattern: "cu118-cibw-wheels*" - # path: dist/ - # merge-multiple: true - - # - name: Publish distribution 📦 to PyPI - # uses: pypa/gh-action-pypi-publish@release/v1 - # with: - # password: ${{ secrets.TMDNET_PYPI_API_TOKEN }} - # skip_existing: true + skip_existing: true github-release: name: >- diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 2644663ee..9a59cf442 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -17,93 +17,26 @@ jobs: ["ubuntu-latest", "ubuntu-22.04-arm", "macos-latest", "windows-2022"] python-version: ["3.13"] - defaults: # Needed for conda - run: - shell: bash -l {0} - steps: - name: Check out - uses: actions/checkout@v4 - - - uses: conda-incubator/setup-miniconda@v3 - with: - python-version: ${{ matrix.python-version }} - channels: conda-forge - conda-remove-defaults: "true" - if: matrix.os != 'macos-13' - - - uses: conda-incubator/setup-miniconda@v3 - with: - python-version: ${{ matrix.python-version }} - channels: conda-forge - mamba-version: "*" - conda-remove-defaults: "true" - if: matrix.os == 'macos-13' + uses: actions/checkout@v5 - - name: Install OS-specific compilers - run: | - if [[ "${{ matrix.os }}" == "ubuntu-22.04-arm" ]]; then - conda install gxx --channel conda-forge --override-channels - elif [[ "${{ runner.os }}" == "Linux" ]]; then - conda install gxx --channel conda-forge --override-channels - elif [[ "${{ runner.os }}" == "macOS" ]]; then - conda install llvm-openmp pybind11 --channel conda-forge --override-channels - echo "CC=clang" >> $GITHUB_ENV - echo "CXX=clang++" >> $GITHUB_ENV - elif [[ "${{ runner.os }}" == "Windows" ]]; then - conda install vc vc14_runtime vs2015_runtime --channel conda-forge --override-channels - fi - - - name: List the conda environment - run: conda list + - name: Install uv + uses: astral-sh/setup-uv@v7 - - name: Install testing packages - run: conda install -y -c conda-forge flake8 pytest psutil python-build + - name: Install the project + run: uv sync --locked --all-extras --dev - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + uv run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - - name: Set pytorch index - run: | - if [[ "${{ runner.os }}" == "Windows" ]]; then - mkdir -p "C:\ProgramData\pip" - echo "[global] - extra-index-url = https://download.pytorch.org/whl/cpu" > "C:\ProgramData\pip\pip.ini" - else - mkdir -p $HOME/.config/pip - echo "[global] - extra-index-url = https://download.pytorch.org/whl/cpu" > $HOME/.config/pip/pip.conf - fi - - - name: Build and install the package - run: | - if [[ "${{ runner.os }}" == "Windows" ]]; then - export LIB="C:/Miniconda/envs/test/Library/lib" - fi - python -m build - pip install dist/*.whl - env: - ACCELERATOR: "cpu" - - # - name: Install nnpops - # if: matrix.os == 'ubuntu-latest' || matrix.os == 'macos-latest' - # run: conda install nnpops --channel conda-forge --override-channels - - - name: List the conda environment - run: conda list + uv run flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Run tests - run: pytest -v -s --durations=10 - env: - ACCELERATOR: "cpu" - SKIP_TORCH_COMPILE: ${{ runner.os == 'Windows' && 'true' || 'false' }} - OMP_PREFIX: ${{ runner.os == 'macOS' && '/Users/runner/miniconda3/envs/test' || '' }} - CPU_TRAIN: ${{ runner.os == 'macOS' && 'true' || 'false' }} - LONG_TRAIN: "true" + # For example, using `pytest` + run: uv run pytest tests - name: Test torchmd-train utility - run: torchmd-train --help + run: uv run torchmd-train --help diff --git a/pyproject.toml b/pyproject.toml index 0b2f2b57b..c11983b73 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,11 @@ requires = ["setuptools>=78", "setuptools-scm>=8"] build-backend = "setuptools.build_meta" [dependency-groups] -dev = ["ipython>=8.18.1", "pytest>=8.4.2"] +dev = [ + "flake8>=7.3.0", + "ipython>=8.18.1", + "pytest>=8.4.2", +] [tool.uv.sources] torch = [ From 82df594eddb3b1e344eddda093facc12d2cb6626 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:39:18 +0200 Subject: [PATCH 45/80] try without lock file --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 9a59cf442..dbb7a6338 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -25,7 +25,7 @@ jobs: uses: astral-sh/setup-uv@v7 - name: Install the project - run: uv sync --locked --all-extras --dev + run: uv sync --all-extras --dev - name: Lint with flake8 run: | From 697ba5714a29dfc2508e8cb3e889de736622f0ff Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:41:36 +0200 Subject: [PATCH 46/80] fix for flake? --- .github/workflows/test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dbb7a6338..9feeb8bae 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -30,9 +30,9 @@ jobs: - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - uv run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + uv run flake8 ./torchmdnet --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - uv run flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + uv run flake8 ./torchmdnet --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Run tests # For example, using `pytest` From e876132bfb5f1a36a45ed321833d67aa4ca45e87 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:44:04 +0200 Subject: [PATCH 47/80] fix python version --- .github/workflows/test.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 9feeb8bae..bb9a2101b 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -23,6 +23,8 @@ jobs: - name: Install uv uses: astral-sh/setup-uv@v7 + with: + python-version: ${{ matrix.python-version }} - name: Install the project run: uv sync --all-extras --dev From 9fed54aa458ca861063be7df8fe6c8f1361e9acb Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:45:58 +0200 Subject: [PATCH 48/80] don't use cuda on ARM machines --- pyproject.toml | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c11983b73..376ceb3d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,18 +42,14 @@ requires = ["setuptools>=78", "setuptools-scm>=8"] build-backend = "setuptools.build_meta" [dependency-groups] -dev = [ - "flake8>=7.3.0", - "ipython>=8.18.1", - "pytest>=8.4.2", -] +dev = ["flake8>=7.3.0", "ipython>=8.18.1", "pytest>=8.4.2"] [tool.uv.sources] torch = [ - { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu126", marker = "(sys_platform == 'linux' or sys_platform == 'win32') and platform_machine != 'aarch64' and platform_machine != 'arm64'" }, ] torchvision = [ - { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu126", marker = "(sys_platform == 'linux' or sys_platform == 'win32') and platform_machine != 'aarch64' and platform_machine != 'arm64'" }, ] [[tool.uv.index]] From 28eda0ed3a8bb38251f9285ea0f61640372179cc Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:47:37 +0200 Subject: [PATCH 49/80] don't try except in compilable code --- torchmdnet/extensions/ops.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index d1c0cb9c2..af31335c6 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -12,6 +12,14 @@ from typing import Tuple import logging +try: + import triton + + HAS_TRITON = True +except ImportError: + HAS_TRITON = False + + logger = logging.getLogger(__name__) __all__ = ["get_neighbor_pairs_kernel"] @@ -69,13 +77,6 @@ def get_neighbor_pairs_kernel( num_pairs : Tensor The number of pairs found. """ - try: - import triton - - HAS_TRITON = True - except ImportError: - HAS_TRITON = False - if torch.jit.is_scripting() or not positions.is_cuda: from torchmdnet.extensions.neighbors import torch_neighbor_bruteforce From a6e4e87edf1ced9124473e4646aec2c963669812 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:49:20 +0200 Subject: [PATCH 50/80] no triton on aarch64 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 376ceb3d7..fd87b55e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,7 @@ dependencies = [ "lightning", "tqdm", "numpy", - "triton; sys_platform == 'linux'", + "triton; sys_platform == 'linux' and platform_machine != 'aarch64'", "triton-windows; sys_platform == 'win32'", ] From 7fbb4e22f44c97d9491b5f8638d21f3b8fd4788c Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 16:57:00 +0200 Subject: [PATCH 51/80] cannot use delayed imports with torchscript --- torchmdnet/extensions/ops.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/torchmdnet/extensions/ops.py b/torchmdnet/extensions/ops.py index af31335c6..a9ebbce24 100644 --- a/torchmdnet/extensions/ops.py +++ b/torchmdnet/extensions/ops.py @@ -11,9 +11,11 @@ from torch import Tensor from typing import Tuple import logging +from torchmdnet.extensions.neighbors import torch_neighbor_bruteforce try: import triton + from torchmdnet.extensions.triton_neighbors import triton_neighbor_pairs HAS_TRITON = True except ImportError: @@ -78,7 +80,6 @@ def get_neighbor_pairs_kernel( The number of pairs found. """ if torch.jit.is_scripting() or not positions.is_cuda: - from torchmdnet.extensions.neighbors import torch_neighbor_bruteforce return torch_neighbor_bruteforce( positions, @@ -108,8 +109,6 @@ def get_neighbor_pairs_kernel( include_transpose=include_transpose, ) - from torchmdnet.extensions.triton_neighbors import triton_neighbor_pairs - return triton_neighbor_pairs( strategy, positions=positions, From 807733cbf00acc474ca1b00ec1501d6414d8375a Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Tue, 16 Dec 2025 17:11:59 +0200 Subject: [PATCH 52/80] add ase as a dep --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index fd87b55e6..3ca2c22e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "numpy", "triton; sys_platform == 'linux' and platform_machine != 'aarch64'", "triton-windows; sys_platform == 'win32'", + "ase", ] [project.urls] From f12b4d8492a0e5f7e0ee22d371157f5f345d3cc6 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 09:28:18 +0200 Subject: [PATCH 53/80] unfreeze torch version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3ca2c22e9..5939d1b95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ classifiers = [ dependencies = [ "h5py", # "nnpops", - "torch==2.7.1", + "torch", "torch_geometric", "lightning", "tqdm", From 53652e86421a74932c6609b369193e3f5f723363 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 09:32:53 +0200 Subject: [PATCH 54/80] fix the OSX issue with MPS not supporting float64 --- tests/test_module.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/test_module.py b/tests/test_module.py index 021672472..7f55418f2 100644 --- a/tests/test_module.py +++ b/tests/test_module.py @@ -43,6 +43,10 @@ def test_train(model_name, use_atomref, precision, tmpdir): # OSX MPS backend runs out of memory on Github Actions torch.set_default_device("cpu") accelerator = "cpu" + elif precision == 64 and torch.backends.mps.is_available(): + # MPS backend doesn't support float64 + torch.set_default_device("cpu") + accelerator = "cpu" args = load_example_args( model_name, @@ -95,6 +99,10 @@ def test_dummy_train(model_name, use_atomref, precision, tmpdir): # OSX MPS backend runs out of memory on Github Actions torch.set_default_device("cpu") accelerator = "cpu" + elif precision == 64 and torch.backends.mps.is_available(): + # MPS backend doesn't support float64 + torch.set_default_device("cpu") + accelerator = "cpu" extra_args = {} if model_name != "tensornet": From 51dd170ecbdfe084a0d67de8fe950aefc16d5501 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 09:42:20 +0200 Subject: [PATCH 55/80] added test for scripting, then compiling --- tests/test_model.py | 38 ++++++++++++++++++++++++++++++++++++++ tests/test_neighbors.py | 2 -- 2 files changed, 38 insertions(+), 2 deletions(-) diff --git a/tests/test_model.py b/tests/test_model.py index 323c0fa67..2f85069cb 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -88,6 +88,44 @@ def forward(self, z, pos, batch): torch.jit.script(MyModel()) +@mark.parametrize("model_name", models.__all_models__) +@mark.parametrize("device", ["cpu", "cuda"]) +def test_torchscript_then_compile(model_name, device): + """Test that a TorchScripted model can be torch.compiled afterwards. + + This is important for scenarios like OpenMM-Torch where a model is + saved as TorchScript and later someone tries to torch.compile it. + """ + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + # Create and TorchScript the model + z, pos, batch = create_example_batch() + z = z.to(device) + pos = pos.to(device).requires_grad_(True) + batch = batch.to(device) + + scripted_model = torch.jit.script( + create_model(load_example_args(model_name, remove_prior=True, derivative=True)) + ).to(device=device) + + # Get baseline output from scripted model + y_scripted, neg_dy_scripted = scripted_model(z, pos, batch=batch) + + # Now try to torch.compile the scripted model + try: + compiled_model = torch.compile(scripted_model, backend="inductor") + y_compiled, neg_dy_compiled = compiled_model(z, pos, batch=batch) + + # Verify outputs match + torch.testing.assert_close(y_scripted, y_compiled, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + neg_dy_scripted, neg_dy_compiled, atol=1e-5, rtol=1e-5 + ) + except Exception as e: + pytest.fail(f"torch.compile failed on TorchScripted {model_name} model: {e}") + + @mark.parametrize("model_name", models.__all_models__) @mark.parametrize("device", ["cpu", "cuda"]) def test_torchscript_dynamic_shapes(model_name, device): diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py index 847f88fa4..92d6e0095 100644 --- a/tests/test_neighbors.py +++ b/tests/test_neighbors.py @@ -692,8 +692,6 @@ def test_torch_compile(device, dtype, loop, include_transpose): if sys.version_info >= (3, 12): pytest.skip("Not available in this version") - if torch.__version__ < "2.0.0": - pytest.skip("Not available in this version") if device == "cuda" and not torch.cuda.is_available(): pytest.skip("CUDA not available") np.random.seed(123456) From d2ac996cc4009d774c34a5f42a7fc813733c9bdf Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 10:29:23 +0200 Subject: [PATCH 56/80] fix cuda graphing of torchscripted models. update tests --- tests/test_model.py | 100 +++++++++++++++++----------- torchmdnet/models/output_modules.py | 4 +- 2 files changed, 65 insertions(+), 39 deletions(-) diff --git a/tests/test_model.py b/tests/test_model.py index 2f85069cb..6927a5f21 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -88,44 +88,6 @@ def forward(self, z, pos, batch): torch.jit.script(MyModel()) -@mark.parametrize("model_name", models.__all_models__) -@mark.parametrize("device", ["cpu", "cuda"]) -def test_torchscript_then_compile(model_name, device): - """Test that a TorchScripted model can be torch.compiled afterwards. - - This is important for scenarios like OpenMM-Torch where a model is - saved as TorchScript and later someone tries to torch.compile it. - """ - if device == "cuda" and not torch.cuda.is_available(): - pytest.skip("CUDA not available") - - # Create and TorchScript the model - z, pos, batch = create_example_batch() - z = z.to(device) - pos = pos.to(device).requires_grad_(True) - batch = batch.to(device) - - scripted_model = torch.jit.script( - create_model(load_example_args(model_name, remove_prior=True, derivative=True)) - ).to(device=device) - - # Get baseline output from scripted model - y_scripted, neg_dy_scripted = scripted_model(z, pos, batch=batch) - - # Now try to torch.compile the scripted model - try: - compiled_model = torch.compile(scripted_model, backend="inductor") - y_compiled, neg_dy_compiled = compiled_model(z, pos, batch=batch) - - # Verify outputs match - torch.testing.assert_close(y_scripted, y_compiled, atol=1e-5, rtol=1e-5) - torch.testing.assert_close( - neg_dy_scripted, neg_dy_compiled, atol=1e-5, rtol=1e-5 - ) - except Exception as e: - pytest.fail(f"torch.compile failed on TorchScripted {model_name} model: {e}") - - @mark.parametrize("model_name", models.__all_models__) @mark.parametrize("device", ["cpu", "cuda"]) def test_torchscript_dynamic_shapes(model_name, device): @@ -199,6 +161,68 @@ def test_cuda_graph_compatible(model_name): assert torch.allclose(neg_dy, neg_dy2, atol=1e-5, rtol=1e-5) +@mark.parametrize("model_name", ["tensornet"]) +def test_torchscript_cuda_graph_compatible(model_name): + """Test that a TorchScripted model is compatible with CUDA graphs. + + This is important when models are saved as TorchScript + and then CUDA graphs are used for performance. + """ + if not torch.cuda.is_available(): + pytest.skip("CUDA not available") + z, pos, batch = create_example_batch() + args = { + "model": model_name, + "embedding_dimension": 128, + "num_layers": 2, + "num_rbf": 32, + "rbf_type": "expnorm", + "trainable_rbf": False, + "activation": "silu", + "cutoff_lower": 0.0, + "cutoff_upper": 5.0, + "max_z": 100, + "max_num_neighbors": 128, + "equivariance_invariance_group": "O(3)", + "prior_model": None, + "atom_filter": -1, + "derivative": True, + "check_errors": False, + "static_shapes": True, + "output_model": "Scalar", + "reduce_op": "sum", + "precision": 32, + } + # Create the model + base_model = create_model(args) + # Setup for CUDA graphs before TorchScripting + # This sets up dim_size and setup_for_compile flag + z_cuda = z.to("cuda") + pos_cuda = pos.to("cuda").requires_grad_(True) + batch_cuda = batch.to("cuda") + base_model.representation_model.setup_for_compile_cudagraphs(batch_cuda) + base_model.output_model.setup_for_compile_cudagraphs(batch_cuda) + + # Now TorchScript the model (like OpenMM-Torch does) + model = torch.jit.script(base_model).to(device="cuda") + model.eval() + + # Warm up the model + with torch.cuda.stream(torch.cuda.Stream()): + for _ in range(0, 15): + y, neg_dy = model(z_cuda, pos_cuda, batch=batch_cuda) + # Capture the model in a CUDA graph + g = torch.cuda.CUDAGraph() + y2, neg_dy2 = model(z_cuda, pos_cuda, batch=batch_cuda) + with torch.cuda.graph(g): + y, neg_dy = model(z_cuda, pos_cuda, batch=batch_cuda) + y.fill_(0.0) + neg_dy.fill_(0.0) + g.replay() + assert torch.allclose(y, y2) + assert torch.allclose(neg_dy, neg_dy2, atol=1e-5, rtol=1e-5) + + @mark.parametrize("model_name", models.__all_models__) def test_seed(model_name): args = load_example_args(model_name, remove_prior=True) diff --git a/torchmdnet/models/output_modules.py b/torchmdnet/models/output_modules.py index 26bf83c0b..e883f1a10 100644 --- a/torchmdnet/models/output_modules.py +++ b/torchmdnet/models/output_modules.py @@ -46,7 +46,9 @@ def pre_reduce(self, x, v, z, pos, batch): def reduce(self, x, batch): if torch.jit.is_scripting(): # TorchScript doesn't support torch.cuda.is_current_stream_capturing() - self.dim_size = int(batch.max().item() + 1) + # For CUDA graphs with TorchScript (e.g., OpenMM-Torch), setup_for_compile must be True + if not self.setup_for_compile: + self.dim_size = int(batch.max().item() + 1) elif not self.setup_for_compile: is_capturing = x.is_cuda and torch.cuda.is_current_stream_capturing() if not x.is_cuda or not is_capturing: From 1a535e6b766865fd31984b3ba7f0c62d57e6da75 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 10:48:12 +0200 Subject: [PATCH 57/80] restore script+compile test --- tests/test_model.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/tests/test_model.py b/tests/test_model.py index 6927a5f21..6c1c3d350 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -114,6 +114,40 @@ def test_torchscript_dynamic_shapes(model_name, device): )[0] +@mark.parametrize("model_name", models.__all_models__) +@mark.parametrize("device", ["cpu", "cuda"]) +def test_torchscript_then_compile(model_name, device): + """Test that a TorchScripted model can be torch.compiled afterwards""" + if device == "cuda" and not torch.cuda.is_available(): + pytest.skip("CUDA not available") + + # Create and TorchScript the model + z, pos, batch = create_example_batch() + z = z.to(device) + pos = pos.to(device).requires_grad_(True) + batch = batch.to(device) + + scripted_model = torch.jit.script( + create_model(load_example_args(model_name, remove_prior=True, derivative=True)) + ).to(device=device) + + # Get baseline output from scripted model + y_scripted, neg_dy_scripted = scripted_model(z, pos, batch=batch) + + # Now try to torch.compile the scripted model + try: + compiled_model = torch.compile(scripted_model, backend="inductor") + y_compiled, neg_dy_compiled = compiled_model(z, pos, batch=batch) + + # Verify outputs match + torch.testing.assert_close(y_scripted, y_compiled, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + neg_dy_scripted, neg_dy_compiled, atol=1e-5, rtol=1e-5 + ) + except Exception as e: + pytest.fail(f"torch.compile failed on TorchScripted {model_name} model: {e}") + + # Currently only tensornet is CUDA graph compatible @mark.parametrize("model_name", ["tensornet"]) def test_cuda_graph_compatible(model_name): From 1dd9fd1c0bdf4d6d2b9d0fc73f039b5abdddc617 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 11:19:09 +0200 Subject: [PATCH 58/80] get rid of setup_for_compile_cudagraphs --- tests/test_model.py | 3 --- torchmdnet/calculators.py | 2 -- torchmdnet/models/model.py | 1 + torchmdnet/models/output_modules.py | 42 ++++++++++++++++------------- torchmdnet/models/tensornet.py | 3 --- torchmdnet/models/utils.py | 8 ------ 6 files changed, 25 insertions(+), 34 deletions(-) diff --git a/tests/test_model.py b/tests/test_model.py index 6c1c3d350..65ca3863e 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -230,12 +230,9 @@ def test_torchscript_cuda_graph_compatible(model_name): # Create the model base_model = create_model(args) # Setup for CUDA graphs before TorchScripting - # This sets up dim_size and setup_for_compile flag z_cuda = z.to("cuda") pos_cuda = pos.to("cuda").requires_grad_(True) batch_cuda = batch.to("cuda") - base_model.representation_model.setup_for_compile_cudagraphs(batch_cuda) - base_model.output_model.setup_for_compile_cudagraphs(batch_cuda) # Now TorchScript the model (like OpenMM-Torch does) model = torch.jit.script(base_model).to(device="cuda") diff --git a/torchmdnet/calculators.py b/torchmdnet/calculators.py index 662f7b7a4..29ec02a88 100644 --- a/torchmdnet/calculators.py +++ b/torchmdnet/calculators.py @@ -269,8 +269,6 @@ def calculate( print("atomic numbers changed, re-compiling...") - self.model.representation_model.setup_for_compile_cudagraphs(batch) - self.model.output_model.setup_for_compile_cudagraphs(batch) self.model.to(self.device) self.compiled_model = torch.compile(self.model, backend='inductor', dynamic=False, fullgraph=True, mode='reduce-overhead') self.compiled = True diff --git a/torchmdnet/models/model.py b/torchmdnet/models/model.py index b7c8398cc..4d5f779cf 100644 --- a/torchmdnet/models/model.py +++ b/torchmdnet/models/model.py @@ -127,6 +127,7 @@ def create_model(args, prior_model=None, mean=None, std=None): activation=args["activation"], reduce_op=args["reduce_op"], dtype=dtype, + static_shapes=args.get("static_shapes", False), num_hidden_layers=args.get("output_mlp_num_layers", 0), ) diff --git a/torchmdnet/models/output_modules.py b/torchmdnet/models/output_modules.py index e883f1a10..e2265cada 100644 --- a/torchmdnet/models/output_modules.py +++ b/torchmdnet/models/output_modules.py @@ -6,12 +6,7 @@ from typing import Optional import torch from torch import nn -from torchmdnet.models.utils import ( - act_class_mapping, - GatedEquivariantBlock, - scatter, - MLP, -) +from torchmdnet.models.utils import GatedEquivariantBlock, scatter, MLP from torchmdnet.utils import atomic_masses from warnings import warn @@ -25,20 +20,16 @@ class OutputModel(nn.Module, metaclass=ABCMeta): As an example, have a look at the :py:mod:`torchmdnet.output_modules.Scalar` output model. """ - def __init__(self, allow_prior_model, reduce_op): + def __init__(self, allow_prior_model, reduce_op, static_shapes=False): super(OutputModel, self).__init__() self.allow_prior_model = allow_prior_model self.reduce_op = reduce_op + self.static_shapes = static_shapes self.dim_size = 0 - self.setup_for_compile = False def reset_parameters(self): pass - def setup_for_compile_cudagraphs(self, batch): - self.dim_size = int(batch.max().item() + 1) - self.setup_for_compile = True - @abstractmethod def pre_reduce(self, x, v, z, pos, batch): return @@ -46,10 +37,12 @@ def pre_reduce(self, x, v, z, pos, batch): def reduce(self, x, batch): if torch.jit.is_scripting(): # TorchScript doesn't support torch.cuda.is_current_stream_capturing() - # For CUDA graphs with TorchScript (e.g., OpenMM-Torch), setup_for_compile must be True - if not self.setup_for_compile: + # For CPU, always update dim_size (no CUDA graphs on CPU) + # For CUDA with static_shapes, only update once (first call sets dim_size for CUDA graph capture) + # For CUDA without static_shapes, always update (dynamic batch sizes) + if not x.is_cuda or not self.static_shapes or self.dim_size == 0: self.dim_size = int(batch.max().item() + 1) - elif not self.setup_for_compile: + else: is_capturing = x.is_cuda and torch.cuda.is_current_stream_capturing() if not x.is_cuda or not is_capturing: self.dim_size = int(batch.max().item() + 1) @@ -76,10 +69,13 @@ def __init__( allow_prior_model=True, reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(Scalar, self).__init__( - allow_prior_model=allow_prior_model, reduce_op=reduce_op + allow_prior_model=allow_prior_model, + reduce_op=reduce_op, + static_shapes=static_shapes, ) self.output_network = MLP( in_channels=hidden_channels, @@ -106,10 +102,13 @@ def __init__( allow_prior_model=True, reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(EquivariantScalar, self).__init__( - allow_prior_model=allow_prior_model, reduce_op=reduce_op + allow_prior_model=allow_prior_model, + reduce_op=reduce_op, + static_shapes=static_shapes, ) if kwargs.get("num_layers", 0) > 0: warn("num_layers is not used in EquivariantScalar") @@ -148,6 +147,7 @@ def __init__( activation="silu", reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(DipoleMoment, self).__init__( @@ -156,6 +156,7 @@ def __init__( allow_prior_model=False, reduce_op=reduce_op, dtype=dtype, + static_shapes=static_shapes, **kwargs, ) atomic_mass = torch.from_numpy(atomic_masses).to(dtype) @@ -181,6 +182,7 @@ def __init__( activation="silu", reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(EquivariantDipoleMoment, self).__init__( @@ -189,6 +191,7 @@ def __init__( allow_prior_model=False, reduce_op=reduce_op, dtype=dtype, + static_shapes=static_shapes, **kwargs, ) atomic_mass = torch.from_numpy(atomic_masses).to(dtype) @@ -215,10 +218,11 @@ def __init__( activation="silu", reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(ElectronicSpatialExtent, self).__init__( - allow_prior_model=False, reduce_op=reduce_op + allow_prior_model=False, reduce_op=reduce_op, static_shapes=static_shapes ) self.output_network = MLP( in_channels=hidden_channels, @@ -258,6 +262,7 @@ def __init__( activation="silu", reduce_op="sum", dtype=torch.float, + static_shapes=False, **kwargs, ): super(EquivariantVectorOutput, self).__init__( @@ -266,6 +271,7 @@ def __init__( allow_prior_model=False, reduce_op="sum", dtype=dtype, + static_shapes=static_shapes, **kwargs, ) diff --git a/torchmdnet/models/tensornet.py b/torchmdnet/models/tensornet.py index 23a3c2683..9aec148ca 100644 --- a/torchmdnet/models/tensornet.py +++ b/torchmdnet/models/tensornet.py @@ -218,9 +218,6 @@ def reset_parameters(self): self.linear.reset_parameters() self.out_norm.reset_parameters() - def setup_for_compile_cudagraphs(self, batch): - self.distance.setup_for_compile_cudagraphs() - def forward( self, z: Tensor, diff --git a/torchmdnet/models/utils.py b/torchmdnet/models/utils.py index 21f85d9b5..48668d120 100644 --- a/torchmdnet/models/utils.py +++ b/torchmdnet/models/utils.py @@ -233,14 +233,6 @@ def __init__( self.check_errors = check_errors self.long_edge_index = long_edge_index - def setup_for_compile_cudagraphs(self): - # box needs to be a buffer to it moves to correct device, otherwise we can end - # up with torch.compile failing to use cuda graphs with "skipping cudagraphs due to skipping cudagraphs due to cpu device (primals_3)." - # we cant just make it a buffer in the constructor above because then old state dicts will fail to load - _box = self.box - del self.box - self.register_buffer('box', _box) - def forward( self, pos: Tensor, batch: Optional[Tensor] = None, box: Optional[Tensor] = None ) -> Tuple[Tensor, Tensor, Optional[Tensor]]: From 56c5e4984755eef479abe69ff542b3c023df954b Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 12:41:01 +0200 Subject: [PATCH 59/80] fix test warnings --- tests/test_datasets.py | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/tests/test_datasets.py b/tests/test_datasets.py index 7402f8f31..736bc8a28 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -84,19 +84,17 @@ def test_custom(energy, forces, num_files, preload, tmpdir): # Assert shapes of whole dataset: for i in range(len(data)): n_atoms_i = n_atoms_per_sample[i] - assert np.array(data[i].z).shape == ( + assert data[i].z.shape == ( n_atoms_i, ), "Dataset has incorrect atom numbers shape" - assert np.array(data[i].pos).shape == ( + assert data[i].pos.shape == ( n_atoms_i, 3, ), "Dataset has incorrect coords shape" if energy: - assert np.array(data[i].y).shape == ( - 1, - ), "Dataset has incorrect energy shape" + assert data[i].y.shape == (1,), "Dataset has incorrect energy shape" if forces: - assert np.array(data[i].neg_dy).shape == ( + assert data[i].neg_dy.shape == ( n_atoms_i, 3, ), "Dataset has incorrect forces shape" @@ -190,19 +188,17 @@ def test_hdf5(preload, energy, forces, num_files, tmpdir): # Assert shapes of whole dataset: for i in range(len(data)): n_atoms_i = n_atoms_per_sample[i] - assert np.array(data[i].z).shape == ( + assert data[i].z.shape == ( n_atoms_i, ), "Dataset has incorrect atom numbers shape" - assert np.array(data[i].pos).shape == ( + assert data[i].pos.shape == ( n_atoms_i, 3, ), "Dataset has incorrect coords shape" if energy: - assert np.array(data[i].y).shape == ( - 1, - ), "Dataset has incorrect energy shape" + assert data[i].y.shape == (1,), "Dataset has incorrect energy shape" if forces: - assert np.array(data[i].neg_dy).shape == ( + assert data[i].neg_dy.shape == ( n_atoms_i, 3, ), "Dataset has incorrect forces shape" From 2ae3370c0d9b87068135e4a46d1b4f52c61799fc Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 12:43:30 +0200 Subject: [PATCH 60/80] undo some changes to benchmarks --- benchmarks/neighbors.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/benchmarks/neighbors.py b/benchmarks/neighbors.py index 515a66b74..58209c4d7 100644 --- a/benchmarks/neighbors.py +++ b/benchmarks/neighbors.py @@ -11,9 +11,6 @@ from torch_cluster import radius_graph -water_density = 0.0334 * 3 # Atoms/A^3 - - class Distance(nn.Module): def __init__( self, @@ -123,8 +120,6 @@ def benchmark_neighbors( n_atoms_per_batch[i] -= 1 difference += 1 lbox = np.cbrt(num_particles / density) - lbox = max(lbox, 2 * cutoff + 0.01) - assert lbox > 2 * cutoff, "Box size must be greater than 2*cutoff" batch = torch.repeat_interleave( torch.arange(n_batches, dtype=torch.int64), n_atoms_per_batch ).to(device) From 390011c0c0724ccd0a64b8a7be2f463eabc7c242 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 13:13:58 +0200 Subject: [PATCH 61/80] rename caffeine --- examples/aceff_examples/ase_aceff.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/aceff_examples/ase_aceff.py b/examples/aceff_examples/ase_aceff.py index 9c9ceaf15..b4220ddc6 100644 --- a/examples/aceff_examples/ase_aceff.py +++ b/examples/aceff_examples/ase_aceff.py @@ -17,7 +17,7 @@ # We create the ASE calculator by supplying the path to the model and specifying the device and dtype calc = TMDNETCalculator(model_file_path, device='cuda') -atoms = read('caffiene.pdb') +atoms = read('caffeine.pdb') print(atoms) atoms.calc = calc From 54e7ac167088dc119d1eb19ce376ac877bd9bd77 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 13:14:28 +0200 Subject: [PATCH 62/80] calculators should warmup before recompiling --- torchmdnet/calculators.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/torchmdnet/calculators.py b/torchmdnet/calculators.py index 29ec02a88..dbf1e6b27 100644 --- a/torchmdnet/calculators.py +++ b/torchmdnet/calculators.py @@ -268,8 +268,12 @@ def calculate( else: print("atomic numbers changed, re-compiling...") - + # Warmup pass to set dim_size before compilation + # This is needed because torch.compile doesn't support .item() calls self.model.to(self.device) + with torch.no_grad(): + _ = self.model(numbers, positions, batch=batch, q=total_charge) + self.compiled_model = torch.compile(self.model, backend='inductor', dynamic=False, fullgraph=True, mode='reduce-overhead') self.compiled = True From d5c9aba35606bf00eaf5c6ddc74d8bda7fed573e Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 13:14:53 +0200 Subject: [PATCH 63/80] catch in output_modules also the case where we are compiling --- torchmdnet/models/output_modules.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/torchmdnet/models/output_modules.py b/torchmdnet/models/output_modules.py index e2265cada..897a686ff 100644 --- a/torchmdnet/models/output_modules.py +++ b/torchmdnet/models/output_modules.py @@ -35,7 +35,14 @@ def pre_reduce(self, x, v, z, pos, batch): return def reduce(self, x, batch): - if torch.jit.is_scripting(): + # torch.compile doesn't support .item() calls, so we skip dim_size updates during compilation + # The model should be warmed up before compilation to set the correct dim_size + if torch.compiler.is_compiling(): + # During compilation, rely on pre-set dim_size + # For static_shapes, dim_size should be set during warmup + # For dynamic shapes, torch.compile may not work well anyway + pass + elif torch.jit.is_scripting(): # TorchScript doesn't support torch.cuda.is_current_stream_capturing() # For CPU, always update dim_size (no CUDA graphs on CPU) # For CUDA with static_shapes, only update once (first call sets dim_size for CUDA graph capture) From d1273bf9609e05f0d37b1ff1c3d4ba3c6944d6b7 Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 13:25:42 +0200 Subject: [PATCH 64/80] int32 dtype for neighbor list and num_pairs --- torchmdnet/extensions/triton_brute.py | 8 +++----- torchmdnet/extensions/triton_cell.py | 7 +++---- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/torchmdnet/extensions/triton_brute.py b/torchmdnet/extensions/triton_brute.py index 52e597f4c..0fbdde362 100644 --- a/torchmdnet/extensions/triton_brute.py +++ b/torchmdnet/extensions/triton_brute.py @@ -215,10 +215,10 @@ def forward( # type: ignore[override] # Use stride 0 to broadcast single box to all batches (avoids CPU sync) box_stride_0 = 0 if box_vectors.size(0) == 1 else box_vectors.stride(0) - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.int32) deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) - counter = torch.zeros((1,), device=device, dtype=torch.int32) + num_pairs = torch.zeros((1,), device=device, dtype=torch.int32) # Compute triangular pair count: n*(n-1)/2 without self-loops, n*(n+1)/2 with if loop: @@ -237,7 +237,7 @@ def forward( # type: ignore[override] neighbors[1], deltas, distances, - counter, + num_pairs, positions.stride(0), positions.stride(1), batch.stride(0), @@ -255,8 +255,6 @@ def forward( # type: ignore[override] BLOCK=256, ) - num_pairs = counter.to(torch.long) - ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms return neighbors, deltas, distances, num_pairs diff --git a/torchmdnet/extensions/triton_cell.py b/torchmdnet/extensions/triton_cell.py index ee39db7c4..cca26e68e 100644 --- a/torchmdnet/extensions/triton_cell.py +++ b/torchmdnet/extensions/triton_cell.py @@ -403,10 +403,10 @@ def forward( ) # Allocate outputs - neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.long) + neighbors = torch.full((2, max_num_pairs), -1, device=device, dtype=torch.int32) deltas = torch.zeros((max_num_pairs, 3), device=device, dtype=dtype) distances = torch.zeros((max_num_pairs,), device=device, dtype=dtype) - counter = torch.zeros((1,), device=device, dtype=torch.int32) + num_pairs = torch.zeros((1,), device=device, dtype=torch.int32) # Launch kernel: one program per cell # BATCH_SIZE: process atoms in batches for vectorized compute @@ -425,7 +425,7 @@ def forward( neighbors, deltas, distances, - counter, + num_pairs, max_num_pairs, cutoff_lower**2, cutoff_upper**2, @@ -434,7 +434,6 @@ def forward( include_transpose=include_transpose, BATCH_SIZE=BATCH_SIZE, ) - num_pairs = counter.to(torch.long) ctx.save_for_backward(neighbors, deltas, distances) ctx.num_atoms = n_atoms From 36c335b7359adca4e0eab19b6352af042720fa3d Mon Sep 17 00:00:00 2001 From: Stefan Doerr Date: Wed, 17 Dec 2025 14:25:01 +0200 Subject: [PATCH 65/80] added test for ASE calculator --- tests/caffeine.pdb | 51 ++++++++++++++++++++++++++ tests/example_tensornet.ckpt | Bin 0 -> 6488740 bytes tests/test_calculator.py | 67 +++++++++++++++++++++++++++++++++++ 3 files changed, 118 insertions(+) create mode 100644 tests/caffeine.pdb create mode 100644 tests/example_tensornet.ckpt diff --git a/tests/caffeine.pdb b/tests/caffeine.pdb new file mode 100644 index 000000000..080ed8f97 --- /dev/null +++ b/tests/caffeine.pdb @@ -0,0 +1,51 @@ +MODEL 1 +HETATM 1 O UNL 1 0.470 2.569 0.001 1.00 0.00 O +HETATM 2 O UNL 2 -3.127 -0.444 -0.000 1.00 0.00 O +HETATM 3 N UNL 3 -0.969 -1.312 0.000 1.00 0.00 N +HETATM 4 N UNL 4 2.218 0.141 -0.000 1.00 0.00 N +HETATM 5 N UNL 5 -1.348 1.080 -0.000 1.00 0.00 N +HETATM 6 N UNL 6 1.412 -1.937 0.000 1.00 0.00 N +HETATM 7 C UNL 7 0.858 0.259 -0.001 1.00 0.00 C +HETATM 8 C UNL 8 0.390 -1.026 -0.000 1.00 0.00 C +HETATM 9 C UNL 9 0.031 1.422 -0.001 1.00 0.00 C +HETATM 10 C UNL 10 -1.906 -0.249 -0.000 1.00 0.00 C +HETATM 11 C UNL 11 2.503 -1.200 0.000 1.00 0.00 C +HETATM 12 C UNL 12 -1.428 -2.696 0.001 1.00 0.00 C +HETATM 13 C UNL 13 3.193 1.206 0.000 1.00 0.00 C +HETATM 14 C UNL 14 -2.297 2.188 0.001 1.00 0.00 C +HETATM 15 H UNL 15 3.516 -1.579 0.001 1.00 0.00 H +HETATM 16 H UNL 16 -1.045 -3.197 -0.894 1.00 0.00 H +HETATM 17 H UNL 17 -2.519 -2.760 0.001 1.00 0.00 H +HETATM 18 H UNL 18 -1.045 -3.196 0.896 1.00 0.00 H +HETATM 19 H UNL 19 4.199 0.780 0.000 1.00 0.00 H +HETATM 20 H UNL 20 3.047 1.809 -0.899 1.00 0.00 H +HETATM 21 H UNL 21 3.047 1.808 0.900 1.00 0.00 H +HETATM 22 H UNL 22 -1.809 3.165 -0.000 1.00 0.00 H +HETATM 23 H UNL 23 -2.932 2.103 0.888 1.00 0.00 H +HETATM 24 H UNL 24 -2.935 2.102 -0.885 1.00 0.00 H +CONECT 1 9 +CONECT 2 10 +CONECT 3 8 10 12 +CONECT 4 7 11 13 +CONECT 5 9 10 14 +CONECT 6 8 11 +CONECT 7 4 8 9 +CONECT 8 3 6 7 +CONECT 9 1 5 7 +CONECT 10 2 3 5 +CONECT 11 4 6 15 +CONECT 12 3 16 17 18 +CONECT 13 4 19 20 21 +CONECT 14 5 22 23 24 +CONECT 15 11 +CONECT 16 12 +CONECT 17 12 +CONECT 18 12 +CONECT 19 13 +CONECT 20 13 +CONECT 21 13 +CONECT 22 14 +CONECT 23 14 +CONECT 24 14 +MASTER 0 0 0 0 0 0 0 0 24 0 24 0 +END diff --git a/tests/example_tensornet.ckpt b/tests/example_tensornet.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..2c7f521ac23037d640f964e4d2e83382db08edcb GIT binary patch literal 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