Fixes#89034
Updated tensor_to_numpy() function in tensor_numpy.cpp to handle ZeroTensors by throwing an error if force=False and returning an array full of zeros if force=True.
@ngimel, I just saw that you mentioned PyTorch is not too concerned with this issue but I had already worked on it so I figured I would push it anyways and see what you thought. Feel free to close the PR if you think it is not worth merging.
@albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164487
Approved by: https://github.com/izaitsevfb
Low-level PyTorch APIs should be usable/stable enough at this point but we might move the underlying driver API usage a bit from here...
Built on top of @drisspg 's branch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159104
Approved by: https://github.com/ngimel
Co-authored-by: drisspg <drisspguessous@gmail.com>
Fixes some tests that seemed to start flaking out as reported in #163202, due to cuBLASLt workspaces becoming persistent following that change.
It's relatively obvious why the workspaces/allocations corresponding to them should be cleaned up for `test_memory_snapshot_script` but less obvious for `test_memory_plots_free_segment_stack`? Why does not cleaning up workspace prevent `empty_cache` from showing up?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163299
Approved by: https://github.com/albanD
Fixes#163330
I tried to reproduce the bug with my 4-GPU setup (the original issue used 8 GPUs). I created several different test scenarios, trying to trigger the bug by:
- creating two different device meshes
- slicing them in various ways
- checking if get_root_mesh() would get confused
but the bug didn't show up! Everything worked correctly in `2.10`. I found that there was a massive refactoring of the `DeviceMesh` code (PR #163213) that landed on October 2nd. That PR completely rewrote how `DeviceMesh` tracks relationships between parent meshes and submeshes using. It seems like this refactoring fixed the bug! But I added a regression test to make sure it doesn't come back. The test (`test_get_root_mesh_multiple_independent_meshes`) does exactly what the bug report described:
- creates two independent meshes
- slices them both
- verifies that each submesh correctly points back to its real parent
- makes sure submeshes from mesh1 don't incorrectly claim mesh2 as their parent
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164731
Approved by: https://github.com/fduwjj
Python 3.13 added PyObject_GetOptionalAttrString. I'm not 100% certain that it is strictly better than the old approach in all cases, but based on documentation/comments it seems to be meant for this type of use, and it's faster when I profile torchtitan training (which gets to the "check for the `__torch_function__` attr on some object" part of maybe_has_torch_function frequently enough to notice, but wastes a bunch of time generating exceptions that we then suppressed here).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164624
Approved by: https://github.com/Skylion007
This should help stabilize some flaky test behavior where miopen would pick different solutions for different parts of the same test and the test expects bitwise identical results.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164598
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
Summary:
Previously, weight deduplication was done by simply grouping tensors with their untyped storage and saving the first tensor in the group.
A more rigorous approach would be to find a complete tensor that covers the storage and store that tensor. This is particularly important for GPU weights because when saving to raw bytes, we move the weight to CPU first, and if the weight being saved is not a complete one, it will lose the storage information during the copy to CPU.
In this diff, we reuse code in `_package_weights.py` for better weights and constants deduplication in `torch.export.save`.
Test Plan: buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_weight_sharing_gpu
Differential Revision: D83523690
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164196
Approved by: https://github.com/angelayi
Summary: Relax absolute tolerance from 1e-2 to 1e-1 for `test_non_contiguous_input_mm_plus_mm` in `test_max_autotune.py`.
Test Plan: `test_max_autotune.py`
Differential Revision: D83391942
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164022
Approved by: https://github.com/eellison
Summary:
Fix decompose_k test failure (`test_max_autotune_decompose_k `) in `test_max_autotune.py` on B200s by setting `torch._inductor.config` patches for variables `comprehensive_padding` and `shape_padding`. Initial failure was `AssertionError: False is not true : Could not find a split in {3, 9, 2187, 81, 243, 729, 27} in # AOT ID: ['6_forward']`.
Refactor decompose_k test to follow patch semantics when setting all environment variables within a test.
Test Plan:
`test_max_autotune.py`:
```
buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/inductor:max_autotune -c fbcode.nvcc_arch=b200a -c fbcode.enable_gpu_sections=true -c fbcode.platform010_cuda_version=12.8 -c fbcode.re_gpu_tests=False -- test_max_autotune_decompose_k
```
Differential Revision: D83390563
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164021
Approved by: https://github.com/njriasan, https://github.com/mlazos, https://github.com/eellison
Adds [control_deps](https://en.wikipedia.org/wiki/Control_dependency) higher-order operator to enforce explicit scheduling dependencies in FX graphs. This prevents unwanted operation reordering/fusion by giving nodes additional dependencies, which we also respect in inductor by adding weakdeps on the additional dependencies.
This can be generally useful (such as for ordering collectives) but in this case I am using it so that fusions do not interfere with aten planned comm-compute overlap.
There's definitely some similarity with the `with_effects` hop. Talked with @angelayi - when @zou3519 is back we will figure out how we want to consolidate.
The implementation needs to be a subgraph (as opposed to `with_effects`) because inductor relies on `V.graph.current_node`. Changing the signature of the node with `with_effects` breaks this, and additionally, also breaks striding constraints on the wrapped node - see this [TODO](aed66248a0/torch/fx/experimental/proxy_tensor.py (L1246-L1249)). By maintaining the node with its original calling structure in subgraph this all works.
Example transformation:
Before:
```
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1), kwargs = {})
%mm : [num_users=1] = call_function[target=torch.ops.aten.mm.default](args = (%arg1_1, %arg1_1), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 2), kwargs = {})
```
After:
```
add: "f32[256, 256]" = torch.ops.aten.add.Tensor(arg0_1, 1)
mm: "f32[256, 256]" = torch.ops.higher_order.control_deps((add,), subgraph_mm, arg1_1, arg1_1)
mul: "f32[256, 256]" = torch.ops.higher_order.control_deps((mm,), subgraph_mul, add)
```
The mm operation now explicitly depends on add completing first, and mul depends on mm, with original operations preserved in subgraphs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164568
Approved by: https://github.com/ezyang, https://github.com/IvanKobzarev
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
```bash
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
DEBUG
DEBUG Remark: The warnings can be suppressed with "-diag-suppress <warning-number>"
DEBUG
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
DEBUG
DEBUG Remark: The warnings can be suppressed with "-diag-suppress <warning-number>"
DEBUG
DEBUG /data/vllm-community-homes/vllm-user-6/pytorch/aten/src/ATen/cuda/CUDAGraph.h(59): warning #68-D: integer conversion resulted in a change of sign
DEBUG CaptureId_t capture_id_ = -1;
DEBUG ^
```
Cuda won't use 0 as a capture id, so it is safe to initialize with 0, which also matches the initialization in `pytorch/aten/src/ATen/native/cudnn/RNN.cpp:2362`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163898
Approved by: https://github.com/houseroad
