Summary:
This diff is reverting D46920584
D46920584: Make `torch.empty*` deterministic by filling with NaN or max int value (#101849) by generatedunixname499836121 has been identified to be causing the following test or build failures:
Tests affected:
- [torchrec/distributed/composable/tests:test_fsdp - torchrec.distributed.composable.tests.test_fsdp.FullyShardTest: test_composable_checkpoint](https://www.internalfb.com/intern/test/281475062923125/)
Here's the Multisect link:
https://www.internalfb.com/multisect/2341386
Here are the tasks that are relevant to this breakage:
We're generating a revert to back out the changes in this diff, please note the backout may land if someone accepts it.
If you believe this diff has been generated in error you may Commandeer and Abandon it.
Test Plan: NA
Reviewed By: huydhn, osalpekar
Differential Revision: D46997394
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104302
Approved by: https://github.com/osalpekar
Fixes#99259 , drawing to attention that input is optional by putting a variation of the method signature at the top of the file and by modifying the input arguments.
Note that I'm not certain how to get the additional signature at the same level of indentation as the first one, but I think this change does a good job of highlighting the change is optional.
Would be happy to iterate on this if there are any issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99650
Approved by: https://github.com/mikaylagawarecki
torch.empty_permuted is a generalized version of torch.empty(memory_format=...), where you can pass an arbitrary physical layout as a tuple of dims to allow you to setup dense, non-overlapping tensors with non-standard memory format. Check the docblock for a full description of semantics.
The initial motivation for this PR is with guard-less unbacked SymInts. Traditionally, the way we allocate dense tensors with arbitrary layout is with `empty_strided`. However, `empty_strided` does not know that the given strides are actually contiguous, and must test this manually to find out if it is the case. With `empty_permuted`, this is known statically to be the case and helps us skip some 0/1 guards.
However, I also think torch.empty_permuted is a useful API in its own right. It is technically possible to simulate this with an empty and a permute; however, there are some downsides:
* The manual incant is tricky to work out. To allocate an NHWC tensor, the invocation is `torch.empty(N, H, W, C).permute(0, 3, 1, 2)`; the permute call has to take NHWC to NCHW, and is the *inverse* of the permutation people are typically thinking of when they talk about NHWC (0, 2, 3, 1). Instead, torch.empty_permuted lets you say `torch.empty_permuted((N, C, H, W), (0, 2, 3, 1))`, letting you provide the intuitive permutation. It can be literally be read off as NHWC if you assign N=0, C=1, H=2, W=3.
* An empty(requires_grad=True).permute() is no longer a leaf tensor. You can force it to be a leaf with a detach(), but it is more straightforward and less error prone to allow directly allocating a tensor with the correct permutation.
It is also technically possible to simulate this with empty_strided. However, this requires the user to manually compute the contiguous output strides and is bad from a reduction of guards perspective. For what it's worth, this is one of the more common uses of as_strided in the wild, and it would be nice to get rid of it.
A nice enhancement of this feature would be to accept `physical_layout` anywhere `memory_format` is accepted. However, this would be a pretty involved change, so I'm doing the easy thing instead.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95069
Approved by: https://github.com/malfet, https://github.com/ngimel, https://github.com/albanD, https://github.com/dagitses
Follow up from: Quansight-Labs/numpy_pytorch_interop#3
This PR adds support for NumPy scalars for `torch.asarray`.
**Before:** treats the scalar as an object that implements the buffer protocol. Thus, interprets the data as the default data type (`float32`)
```python
>>> torch.asarray(numpy.float64(0.5))
tensor([0.0000, 1.7500])
```
**After:** identifies the NumPy scalar, and does the "right" thing. i.e. creates a 0-dimensional tensor from the NumPy array that doesn't share its memory
```python
>>> torch.asarray(numpy.float64(0.5))
tensor(0.5000, dtype=torch.float64)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90914
Approved by: https://github.com/lezcano, https://github.com/mruberry
Ref #70924
This addresses part 1 of the issue, allowing `torch.squeeze` to be
passed a tuple of dimensions. e.g.
```python
x.squeeze(0).squeeze(0)
```
can now be written
```python
x.squeeze((0, 1))
```
(assuming x has at least 2 dimensions)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89017
Approved by: https://github.com/albanD
This PR is a copy of https://github.com/pytorch/pytorch/pull/90849 that merge was reverted.
The PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
`torch.sparse.check_sparse_tensor_invariants` class provides different ways to enable/disable the invariant checking.
`torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR fixes https://github.com/pytorch/pytorch/issues/90833
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92094
Approved by: https://github.com/cpuhrsch
This PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
- `torch.enable_check_sparse_tensor_invariants` and `torch.is_check_sparse_tensor_invariants_enabled` functions to globally enable/disable the invariant checks and to retrieve the state of the feature, respectively
- `torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR also fixes https://github.com/pytorch/pytorch/issues/90833
# Main issue
*The following content is outdated after merging the PRs in this ghstack but kept for the record.*
The importance of this feature is that when enabling the invariants checks by default, say, via
<details>
```
$ git diff
diff --git a/torch/__init__.py b/torch/__init__.py
index c8543057c7..19a91d0482 100644
--- a/torch/__init__.py
+++ b/torch/__init__.py
@@ -1239,3 +1239,8 @@ if 'TORCH_CUDA_SANITIZER' in os.environ:
# Populate magic methods on SymInt and SymFloat
import torch.fx.experimental.symbolic_shapes
+
+# temporarily enable sparse tensor arguments validation in unsafe
+# constructors:
+
+torch._C._set_check_sparse_tensor_invariants(True)
```
</details>
a massive number of test failures/errors occur in test_sparse_csr.py tests:
```
$ pytest -sv test/test_sparse_csr.py
<snip>
==== 4293 failed, 1557 passed, 237 skipped, 2744 errors in 69.71s (0:01:09) ====
```
that means that we are silently constructing sparse compressed tensors that do not satisfy the sparse tensor invariants. In particular, the following errors are raised:
```
AssertionError: "resize_as_sparse_compressed_tensor_: self and src must have the same layout" does not match "expected values to be a strided and contiguous tensor"
RuntimeError: CUDA error: device-side assert triggered
RuntimeError: `col_indices[..., crow_indices[..., i - 1]:crow_indices[..., i]] for all i = 1, ..., nrows are sorted and distinct along the last dimension values` is not satisfied.
RuntimeError: expected col_indices to be a strided and contiguous tensor
RuntimeError: expected row_indices to be a strided and contiguous tensor
RuntimeError: expected values to be a strided and contiguous tensor
RuntimeError: for_each: failed to synchronize: cudaErrorAssert: device-side assert triggered
RuntimeError: tensor dimensionality must be sum of batch, base, and dense dimensionalities (=0 + 2 + 0) but got 3
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90849
Approved by: https://github.com/amjames, https://github.com/cpuhrsch
Ref #70924
This addresses part 1 of the issue, allowing `torch.squeeze` to be
passed a tuple of dimensions. e.g.
```python
x.squeeze(0).squeeze(0)
```
can now be written
```python
x.squeeze((0, 1))
```
(assuming x has at least 2 dimensions)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89017
Approved by: https://github.com/albanD
Fixes#91107
Added `softmax` docs in
- `pytorch/torch/_tensor_docs.py`
- `pytorch/torch/_torch_docs.py `
- `pytorch/docs/XXX.rst` files. Here XXX represents all those files where I made the change
Although I have added `softmax` in `docs` directory, I was not sure which files/folders required the edits so there could be issues
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91292
Approved by: https://github.com/lezcano
Fixes#91107
Added `softmax` docs in
- `pytorch/torch/_tensor_docs.py`
- `pytorch/torch/_torch_docs.py `
- `pytorch/docs/XXX.rst` files. Here XXX represents all those files where I made the change
Although I have added `softmax` in `docs` directory, I was not sure which files/folders required the edits so there could be issues
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91292
Approved by: https://github.com/lezcano
The documentation of `torch.rand` was missing the `generator` keyword argument in the function signature. However, the argument is explained in the documentation and `torch.rand` accepts that argument.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90071
Approved by: https://github.com/janeyx99
