Summary:
No one is using these.
(The minify part has been broken for a couple of years, too)
Reviewed By: patricklabatut
Differential Revision: D96977684
fbshipit-source-id: 4708dfd37b14d1930f1370677eb126a61a0d9d3c
Summary: Remove the Support Ukraine banner component and its usage from the PyTorch3D website homepage.
Reviewed By: bottler
Differential Revision: D96559642
fbshipit-source-id: fd716cde7145d5c0105b2d2fb569375395b9b5de
Summary:
Replace boolean indexing and torch.is_grad_enabled() control flow in _sqrt_positive_part with a pure torch.where implementation. The old code used ret[positive_mask] = torch.sqrt(x[positive_mask]) which produces an incorrect ONNX Where/index_put node with mismatched broadcast shapes when the model is exported via torch.onnx.export.
The new implementation substitutes 1.0 for non-positive values before sqrt (avoiding infinite gradient at sqrt(0)) and masks the result back to 0, preserving the zero-subgradient-at-zero property.
Fixes https://github.com/facebookresearch/pytorch3d/issues/2020
Reviewed By: sgrigory
Differential Revision: D94365479
fbshipit-source-id: a1ebe8dc077573f83efc262520b6669159b83ef0
Summary:
Added `atol=1e-4` tolerance parameter to the `assertClose` calls on lines 682 and 683 in the `test_inverse` method of `TestTranslate` class.
This is a retry of D90225548
Reviewed By: sgrigory
Differential Revision: D90682979
fbshipit-source-id: ac13f000174dd9962326296e1c3116d0d39c7751
Summary:
## LLM-generated Summary:
Replaces self.assertTrue(torch.allclose(...)) with self.assertClose(...) throughout fbcode/vision/fair/pytorch3d/tests/test_transforms.py. This standardizes numeric closeness assertions for clearer failures and consistency while preserving tolerances and test behavior.
---
Session: DEV34970678
Reviewed By: shapovalov
Differential Revision: D90251428
fbshipit-source-id: cdae842be82f0ba548802e6977be272134e8508c
Summary:
CUDA 13.0 introduced breaking changes that cause build failures in pytorch3d:
**1. Symbol Visibility Changes (pulsar)**
- NVCC now forces `__global__` functions to have hidden ELF visibility by default
- `__global__` function template stubs now have internal linkage
**Fix:** Added NVCC flags (`--device-entity-has-hidden-visibility=false` and `-static-global-template-stub=false`) for fbcode builds with CUDA 13.0+.
**2. cuCtxCreate API Change (pycuda)**
- CUDA 13.0 changed `cuCtxCreate` from 3 to 4 arguments
- pycuda 2022.2 (current default) uses the old signature and fails to compile
- pycuda 2025.1.2 (D83501913) includes the CUDA 13.0 fix
**Fix:** Added CUDA 13.0 constraint to pycuda alias to auto-select pycuda 2025.1.2.
**NCCL Compatibility Note:**
- Current stable NCCL (2.25) is NOT compatible with CUDA 13.0 (`cudaTypedefs.h` removed)
- NCCL 2.27+ works with CUDA 13.0 and will become stable in early January 2026 (per HPC Comms team)
- Until then, CUDA 13.0 builds require `-c hpc_comms.use_nccl=2.27`
References:
- GitHub issue: https://github.com/facebookresearch/pytorch3d/issues/2011
- NVIDIA blog: https://developer.nvidia.com/blog/cuda-c-compiler-updates-impacting-elf-visibility-and-linkage/
- FBGEMM_GPU fix: D86474263
- pycuda 2025.1.2 buckification: D83501913
Reviewed By: bottler
Differential Revision: D88816596
fbshipit-source-id: 1ba666dab8c0e06d1286b8d5bc5d84cfc55c86e6
Summary: When using `sample_farthest_points` with `lengths`, it throws an error because of the device mismatch between `lengths` and `torch.rand(lengths.size())` on GPU.
Reviewed By: bottler
Differential Revision: D82378997
fbshipit-source-id: 8e929256177d543d1dd1249e8488f70e03e4101f
Summary: Some random seed changes. Skip multigpu tests when there's only one gpu. This is a better fix for what AI is doing in D80600882.
Reviewed By: MichaelRamamonjisoa
Differential Revision: D80625966
fbshipit-source-id: ac3952e7144125fd3a05ad6e4e6e5976ae10a8ef
Summary:
Optimizing sample_farthest_poinst by reducing CPU/GPU sync:
1. replacing iterative randint for starting indexes for 1 function call, if length is constant
2. Avoid sync in fetching maxumum of sample points, if we sample the same amount
3. Initializing 1 tensor for samples and indixes
compare
https://fburl.com/mlhub/7wk0xi98
Before
{F1980383703}
after
{F1980383707}
Histogram match pretty closely
{F1980464338}
Reviewed By: bottler
Differential Revision: D78731869
fbshipit-source-id: 060528ae7a1e0fbbd005d129c151eaf9405841de
Summary:
Fixes hard crashes (bus errors) when using MPS device (Apple Silicon) by implementing CPU checks throughout files in csrc subdirectories to check if on same mesh on a CPU device.
Note that this is the fourth and ultimate part of a larger change through multiple files & directories.
Reviewed By: bottler
Differential Revision: D77698176
fbshipit-source-id: 5bc9e3c5cea61afd486aed7396f390d92775ec6d
Summary:
Adds CHECK_CPU macros that checks if a tensor is on the CPU device throughout csrc directories and subdir up to `pulsar`.
Note that this is the third part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77696998
fbshipit-source-id: 470ca65b23d9965483b5bdd30c712da8e1131787
Summary:
Adds CHECK_CPU macros that checks if a tensor is on the CPU device throughout csrc directories up to `marching_cubes`. Directories updated include those in `gather_scatter`, `interp_face_attrs`, `iou_box3d`, `knn`, and `marching_cubes`.
Note that this is the second part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77558550
fbshipit-source-id: 762a0fe88548dc8d0901b198a11c40d0c36e173f
Summary:
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1986
Adds device checks to prevent crashes on unsupported devices in PyTorch3D. Updates the `pytorch3d_cutils.h` file to include new macro CHECK_CPU that checks if a tensor is on the CPU device. This macro is then used in the directories from `ball_query` to `face_area_normals` to ensure that tensors are not on unsupported devices like MPS.
Note that this is the first part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77473296
fbshipit-source-id: 13dc84620dee667bddebad1dade2d2cb5a59c737
Summary:
The current implementation of `matrix_to_quaternion` and `_sqrt_positive_part` uses boolean indexing, which can slow down performance and cause incompatibility with `torch.compile` unless `torch._dynamo.config.capture_dynamic_output_shape_ops` is set to `True`.
To enhance performance and compatibility, I recommend using `torch.gather` to select the best-conditioned quaternions and `F.relu` instead of `x>0` (bottler's suggestion)
For a detailed comparison of the implementation differences when using `torch.compile`, please refer to my Bento notebook
N7438339.
Reviewed By: bottler
Differential Revision: D77176230
fbshipit-source-id: 9a6a2e0015b5865056297d5f45badc3c425b93ce
Summary: Resolved self-assignment warnings in the `renderer.forward.device.h` file by removing redundant assignments of the `stream` variable to itself in `cub::DeviceSelect::Flagged` function calls. This change eliminates compiler errors and ensures cleaner, more efficient code execution.
Reviewed By: bottler
Differential Revision: D76554140
fbshipit-source-id: 28eae0186246f51a8ac8002644f184349aa49560
Summary:
I could not access https://github.com/NVlabs/cub/issues/172 to understand whether IntWrapper was still necessary but the comment is from 5 years ago and causes problems for the ROCm build.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1964
Reviewed By: MichaelRamamonjisoa
Differential Revision: D71937895
Pulled By: bottler
fbshipit-source-id: 5e0351e1bd8599b670436cd3464796eca33156f6
