Fixes#145874
This PR takes the approach of updating the logic determining whether multiple shapes broadcast together to handle nested ints specially.
Possible alternative approach: don't update `broadcast_shapes()` + indicate that e.g. `Ne(j0, 1)` should statically evaluate to False. I briefly tried this but it wasn't straightforward. Is it better?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145957
Approved by: https://github.com/bobrenjc93
Co-authored-by: bobrenjc93 <bobren@meta.com>
Disabled by default for now behind `TORCH_CUDNN_SDPA_NESTED_TENSOR_ENABLED=1`
Just wanted to get this out before starting a series of SDPA cleanup PRs---the biggest thing is we don't need the boilerplate around all of the `build_graph_and_tensors*` functions anymore as we can now use the `UID`-style referencing of tensor nodes as was done for the Conv-V8 API backend.
CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141178
Approved by: https://github.com/jbschlosser
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
Disabled by default for now behind `TORCH_CUDNN_SDPA_NESTED_TENSOR_ENABLED=1`
Just wanted to get this out before starting a series of SDPA cleanup PRs---the biggest thing is we don't need the boilerplate around all of the `build_graph_and_tensors*` functions anymore as we can now use the `UID`-style referencing of tensor nodes as was done for the Conv-V8 API backend.
CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141178
Approved by: https://github.com/jbschlosser
Fixes 3 issues:
1. The test wasn't actually testing SDPA: both were checking cuda, and the inputs to SDPA were not transposed.
2. FlopCounterMode has been renamed _FlopCounterMode (and a wrapper named FlopCounterMode has been added)
3. offsets_to_list also needs to ignore the actual offset values if offsets is a meta tensor.
Differential Revision: [D69558785](https://our.internmc.facebook.com/intern/diff/D69558785)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147032
Approved by: https://github.com/jbschlosser
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
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
There are several xfails related to data-dependent errors in torch.compile. This PR sets `torch._dynamo.config.capture_scalar_outputs=True` to avoid these, which tends to exercise unbacked SymInt logic and will require `torch._check()`-related fixes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144588
Approved by: https://github.com/soulitzer
ghstack dependencies: #144586, #144587
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
This PR implements missing backward support for NJT matmul. Notably, for dense tensors, matmul dispatches to bmm. However, due to historical reasons related to NST, NJT handles matmul directly, and thus can't rely on the CompositeImplicit impl of matmul to get the derivative formula.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144587
Approved by: https://github.com/soulitzer
ghstack dependencies: #144586
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
This PR implements the missing `fill.Scalar` support, which works fine for contiguous inputs, but there is still some AOTAutograd debugging required to handle non-contiguous transposed NJTs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144586
Approved by: https://github.com/soulitzer
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
Before this PR, `frexp()` for NJT was handled via the unary pointwise fallback. The op returns a tuple, however, and the fallback doesn't handle that. This PR defines an explicit impl for `frexp()` that wraps both returned `(mantissa, exponent)` as NJTs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144585
Approved by: https://github.com/soulitzer
ghstack dependencies: #144582, #144583, #144584
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
Implements `chunk()` backward on the batch dim, which was left out before. This PR unbinds the components and invokes `copy_()` on these to pass along the appropriate gradients.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144584
Approved by: https://github.com/soulitzer
ghstack dependencies: #144582, #144583
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
`value_selecting_reduction_backward()` is used in the backward for min / max, so this PR implements it for NJT. Notably, this isn't enough for reducing over the ragged dim, since that results in a dense tensor and thus NJT's torch_dispatch will not be called for this op. We need factory function support for nested ints to fix that case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144583
Approved by: https://github.com/soulitzer
ghstack dependencies: #144582
Part of my BE project addressing NJT bugs surfaced via OpInfo tests.
The OpInfo entry for prelu was wrong before this PR; `weight` needs to be passed as well. The op isn't fully implemented yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144582
Approved by: https://github.com/soulitzer
Based on https://github.com/pytorch/pytorch/pull/126376, this PR tries to update all PT callers (e.g., `Tensor.is_pinned()`, `Tensor.pin_memory()`) to not pass `device` argument.
As for `storage/untyped_storage.is_pinned()/pin_memory()`, we keep the `device` argument but passing `device` is discouraged. And if not given, the default `device` is still 'cuda' for BC.
Additionally, based on device-agnostic pin_memory, `pin_memory_device` argument of `torch.utils.data.DataLoader` is discouraged now. For BC, explictly passing this argument is still effective. If not given, the default `device` will be the current accelerator.
Fixes#124908
Relates https://github.com/pytorch/pytorch/pull/126376
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131858
Approved by: https://github.com/albanD
Co-authored-by: albanD <desmaison.alban@gmail.com>
This PR fixes some issues with NJT backward / compile backward tests:
1. `requires_grad` was not being propagated appropriately during `SampleInput` generation, so a LOT of backward cases were untested before (sad times). This PR utilizes a helper function `_clone()` to clone() / detach() NJTs for SampleInputs while preserving `requires_grad` status. Note: the clone() / detach() stuff is for autograd; can't have two SampleInputs as part of the same autograd graph.
2. Per-sample skips weren't -fully- working; the op logic would still be invoked even with a skip. I found this out thanks to `split_with_sizes`, which segfaults during backwards because it tries to use an NST-specific formula. As annoying as it is, I tried a ton of things but ultimately had to split the `subtest_ctx` into that + a `skip_xfail_ctx` to run the subtests within.
* Updated all uses of per-sample skips / xfails: 4 in `test_nestedtensor.py` and 1 in `test_vmap.py`
3. Added the appropriate skips / xfails to get everything passing. There are a shitton of bugs to fix!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143072
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
Allow mutations mutations for subclasses that are non-contiguous.
Changes:
Removing assert in collect_metadata_analysis
Main requested testcase:
Compilation of NJT.index_put()
Adding test in test_nestedtensor.py, that compiles NJT.index_put()
It is decomposed to NJT split,unbind, which needed additional `torch._check`, `torch._check_is_size` for NJT.unbind() and guard_size_oblivious() usage in _meta_registrations and _inductor/lowering.py.
Special case:
If tangent is mutated outside of the graph, it does not participate in backward graph. Autograd in this case will set this tangent to zeros tensor.
We handle it separately in CompiledFunction.backward: not doing any processing for this tangent and broadcast to number of expected subclass unwrapped arguments.
disabling for dynamo 2 tests:
1/ For nested tensor - symbolic shapes issue on nested_tensor index operation that does splits [0, 0, 0] - there is a failure with "pending unbacked symints". This PR does not add more .tolist()/item() ops than it was before.
2/ As we do not fail with exception in collect_metadata_analysis new paths for dynamo started working and it started failing with smth strange that set_ in storage_offset (because of test for views) handling updates storage "cpu" -> "meta"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139630
Approved by: https://github.com/bdhirsh
This PR contains three `unsqueeze()`-related fixes for NJT:
1. Adjusts the output's `_ragged_idx` when `unsqueeze()` inserts a dim before the ragged dim
2. Corrects the unbind reference for `unsqueeze()` after the last input dim. For this case, the dim kwarg canonicalization logic needs to be applied wrt `inp.dim() + 1` to account for `dim=-1` properly
3. Adds ragged dim support to `unsqueeze()`, allowing for e.g. `(B, j1, D) -> (B, 1, j1, D)`. This is okay now after #137125
Note that `unsqueeze()` still doesn't support batch dim operation, and arguably should never support this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141392
Approved by: https://github.com/cpuhrsch
ghstack dependencies: #141500, #140736, #140161
This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info.
Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops").
Testing is added over the following ops:
* `chunk()`
* `narrow()`
* `select()`
* `split()`
* `split_with_sizes()`
* `squeeze()`
* `unflatten()`
* `unsqueeze()`
Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed.
I also slipped in a couple minor fixes (sorry):
1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items)
2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140161
Approved by: https://github.com/Skylion007, https://github.com/cpuhrsch
ghstack dependencies: #141500, #140736
**Background:** It's common to use `scalar_tensor()` in the input to `where()` to convert any scalars present to compatible tensors with matching options, *including layout*. This shows up in various places, notably including derivative formulas ([example](78491d6afc/tools/autograd/derivatives.yaml (L432-L434))). It causes problems for NJTs because they have `layout=torch.jagged` and it never makes sense to create a scalar tensor with this layout. Some of the breakage only seems to happen in CI for reasons I don't fully understand (see the revert of #140736 due to softshrink's derivative formula).
**This PR:**
* Allows non-contiguous NJT inputs to `where()` + adds tests for this
* Handles scalar tensor / dense tensor inputs for `condition` / `other` + adds tests for this
* Uses limited `broadcast_tensors()` / `broadcast_to()` support
* Improves `expand()` to work on non-contig NJTs
* Changes `scalar_tensor()` to use `torch.strided` instead of `torch.jagged` in both eager and torch.compile (i.e. meta registration)
* Changes backward formulas for `sinc`, `pow`, `special.i1`, and `special.i1e` to uses `scalar_tensor()` instead of e.g. `zeros({})`
**Alternative approach:** Update all problematic usages of `scalar_tensor()` to avoid ever passing `layout=torch.jagged`. This is an extensive change and includes `torch.where()` logic, a bunch of derivative formulas, and likely other places not yet discovered.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141500
Approved by: https://github.com/malfet, https://github.com/cpuhrsch, https://github.com/soulitzer
This PR contains three `unsqueeze()`-related fixes for NJT:
1. Adjusts the output's `_ragged_idx` when `unsqueeze()` inserts a dim before the ragged dim
2. Corrects the unbind reference for `unsqueeze()` after the last input dim. For this case, the dim kwarg canonicalization logic needs to be applied wrt `inp.dim() + 1` to account for `dim=-1` properly
3. Adds ragged dim support to `unsqueeze()`, allowing for e.g. `(B, j1, D) -> (B, 1, j1, D)`. This is okay now after #137125
Note that `unsqueeze()` still doesn't support batch dim operation, and arguably should never support this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141392
Approved by: https://github.com/cpuhrsch
ghstack dependencies: #140736, #140161
This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info.
Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops").
Testing is added over the following ops:
* `chunk()`
* `narrow()`
* `select()`
* `split()`
* `split_with_sizes()`
* `squeeze()`
* `unflatten()`
* `unsqueeze()`
Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed.
I also slipped in a couple minor fixes (sorry):
1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items)
2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140161
Approved by: https://github.com/Skylion007, https://github.com/cpuhrsch
ghstack dependencies: #140736
