1. Fix the tt.divisibility format in hints.py. Previously, it was `{((0,), (1,)): [["tt.divisibility", 16]]}`. Now it is `{(0,): [["tt.divisibility", 16]], (1,): [["tt.divisibility", 16]]}`. This was an oversight in the first PR I added. I've verified that we now get `{ tt.divisibility = 16 }` in the generated TTGIR.
2. Update the test_codegen_triton.py test to work with multiple triton versions (and test this divisibility format in the new triton version)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145575
Approved by: https://github.com/SamGinzburg
Fixes#144761
This PR adds NJT impls for those *_like functions that were previously missing:
* `full_like()`
* `rand_like()`
* `randint_like()`
It also fixes a bug in existing *_like functions when a new device is specified. Fix is to also transfer `offsets` / `lengths` to the new device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144889
Approved by: https://github.com/soulitzer
Prior to this patch, the `test_cuda_event_created_outside_of_graph`
is flaky in CI, and that's because we have read and write to the same
`foo` tensor buffer from 2 different streams. This patch eliminates that
by adding a synchronization to wait till read finishes before starting
the write.
Fixes#133837, #133828.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145658
Approved by: https://github.com/yifuwang
Fixes#144976
Using appoach ① `IO[bytes]`, but could also try with a protocol.
## Notes:
- moved `torch.serialization.FILE_LIKE` to `torch.types.FileLike`
- Use `FileLike` annotation where it makes sense
- made sure those functions also support `os.PathLike`
- Replaced `isinstance(x, io.BytesIO)` with `isinstance(x, (io.IOBase, IO))` where appropriate.
- Replaced `BinaryIO` with `IO[bytes]` (the two ABCs are almost identical, the only difference is that `BinaryIO` allows `bytearray` input to `write`, whereas `IO[bytes]` only `bytes`)
- needed to make `torch.serialization._opener` generic to avoid LSP violations.
- skipped `torch/onnx/verification` for now (functions use `BytesIO.getvalue` which is not part of the `IO[bytes]` ABC, but it kind of seems that this is redundant, as e.g. `onnx.load` supports `str | PathLike[str] | IO[bytes]` directly...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144994
Approved by: https://github.com/ezyang, https://github.com/Skylion007
It's better to call `mtl_setArgs` rather than set arguments one by one with the risk of making a typo
Also, all interactions with MTLCommandBuffer must be serialized, which is commonly done using dispatch queues
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145736
Approved by: https://github.com/Skylion007
According to the [APL documentation](https://developer.arm.com/documentation/101004/2404/General-information/Arm-Performance-Libraries-example-programs), libraries ending with _mp are OpenMP multi-threaded libraries.
When a project is compiled with MSVC and the -openmp flag, the vcomp library (Visual C++ implementation of OpenMP) is used for runtime calls.
However, the current APL implementation uses the libomp.dll (LLVM) variant.
As a result, there are unexpected behaviors at runtime.
---
For Example:
```python
import torch
# Create a sparse tensor
# Input (Sparse Tensor):
# [[0, 1],
# [1, 0]]
indices = torch.tensor([[0, 1], [1, 0]])
values = torch.tensor([1, 1], dtype=torch.float32)
size = torch.Size([2, 2])
sparse_tensor = torch.sparse_coo_tensor(indices, values, size)
# Convert sparse tensor to dense tensor
dense_tensor = sparse_tensor.to_dense()
# Expected Output (Dense Tensor):
# [[0, 1],
# [1, 0]]
print("\nDense Tensor:")
print(dense_tensor)
```
However, it prints unexpected outputs such as:
```python
# [[0, 11],
# [10, 0]]
```
The issue arises because the following code does not function as expected at runtime:
https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/ParallelOpenMP.h#L30
```c++
// returns 1 , however since OpenMP is enabled it should return total number of threads
int64_t num_threads = omp_get_num_threads();
```
---
In the runtime, loading multiple OpenMP libraries (in this case `libomp` and `vcomp`) is causing unexpected behaviours.
So, we've changed libraries from `_mp` to non `_mp` versions and we used `vcomp` for OpenMP calls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145215
Approved by: https://github.com/ozanMSFT, https://github.com/malfet
Co-authored-by: Ozan Aydin <148207261+ozanMSFT@users.noreply.github.com>
While working on conda-forge integration, I needed to look at the way the include paths are calculated, and noticed an avoidable duplication between `torch/utils/cpp_extension.py` and `torch/_inductor/cpp_builder.py`. The latter already imports the former anyway, so simply reuse the same function.
Furthermore, remove long-obsolete include-paths. AFAICT, the `/TH` headers have not existed since pytorch 1.11.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145480
Approved by: https://github.com/ezyang
Using Philox4 as PRNG
Test plan (other that CI)
Run
```python
mport torch
from torch._inductor.utils import run_and_get_code
from contextlib import nullcontext
def foo(x):
return x * torch.randn_like(x)
foo_c = torch.compile(foo)
x = torch.ones(100, 100, device="mps")
y = foo_c(x)
print(y.mean().item(), y.std().item())
for i in range(25):
print(y[i].mean(), y[i].std())
```
And observe that printed values are close to 0 and 1
TODO: Better `randint` algorithm for large ranges
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145705
Approved by: https://github.com/dcci, https://github.com/jansel
Pickling GraphModule needs some special handling for wrapping things that normally can't be pickled - but async compile needs to pass them across a wire so we need to be able to serialize it - add some helpers to enable that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141659
Approved by: https://github.com/jamesjwu
#136627 has almost fixed the issue that test binaries' runpath has not been set correctly, with few cases left.
This PR fixes the rest.
The binaries are found by `auditwheel repair` a wheel built with `BUILD_TEST=1`.
@malfet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144305
Approved by: https://github.com/malfet
Fixes https://github.com/pytorch/pytorch/issues/142466.
Remove the `weight.numel() != 0` check to align the behavior with CUDA for `ConvTranspose` when `out_channels=0`. After removing this check, the existing code is already able to give an empty output in such case.
Test plan:
```
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cpu_float32
python -u test/nn/test_convolution.py -k test_ConvTranspose_output_channels_0_cuda_float32
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142859
Approved by: https://github.com/mingfeima, https://github.com/malfet
