This PR removes the need for passing `ragged_size` into the `NestedTensor` constructor. This was an artifact of fake-ification, where sometimes we needed the NT to have a symbolic singleton symint shape for the ragged dimension. The new way of achieving this is to also store mappings between fake / functional tensors -> symbolic symints in the ragged structure registry. Now the `NestedTensor` constructor can just query this registry for the `ragged_size`.
Old: `NestedTensor(values, offsets, *, ragged_size=None, **kwargs)`
New: `NestedTensor(values, offsets, **kwargs)`
This makes it possible to have a `_nested_view_from_values_offsets(values, offsets)` without needing to pass a `ragged_size`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113491
Approved by: https://github.com/ezyang, https://github.com/soulitzer
Summary:
Add split and layer_norm_backward.
Note: It is non trivial to support split_with_sizes backward so adding the split operation to support the use case in the model.
Test Plan: unit tests
Differential Revision: D51052966
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113108
Approved by: https://github.com/soulitzer
This PR:
* Adds support for the `layout` kwarg to `torch.nested.as_nested_tensor()`
* Fixes `torch.nested.nested_tensor()`
* It should accept a list of lists of scalars
* It should not preserve autograd history
* Adds extensive testing for these two functions
Semantics for the two functions follow those of the strided layout:
* `torch.nested.nested_tensor(tensor_list, layout=torch.jagged)`: Creates a new jagged layout NT **with no autograd history**
* `tensor_list` can be a list of Tensors or list of lists of scalars
* `torch.nested.as_nested_tensor(tensor_list, layout=torch.jagged)`: Creates a new jagged layout NT **preserving autograd history of `tensor_list`**
* `tensor_list` must be a list of Tensors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112304
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
This PR has a number of changes that improve subclass support for AOTAutograd/Inductor in general:
- previously if a subclass does extra aliasing between graph outputs/inputs in a way, the partitioner would complain because grad_outputs are the outputs reused as-is. Now we do a view_as(self) to workaround this.
- Use dense -> dense metadata when working with fwd_output_strides during backward. This is important since the stride information comes from inductor which sees the dense to dense graph.
- Inductor requires that the inputs to the compiled backward to match some expected strides computed during compilation. We make sure to make the inner tensors of the subclass contiguous (previously, we only made the subclass itself contiguous)
Changes specific to NestedTensor relevant to compilation:
- Properly handle the case where `__tensor_unflatten__` is passed non-symbolic dense tensors and with meta extracted from fake subclasses.
- Skip var_to_range logic for singleton int
- Skip size hint logic in inductor for singleton int
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110529
Approved by: https://github.com/bdhirsh
In this PR:
- Adds support for strides for jagged tensor (design doc for this coming soon)
- NestedTensor skips automatic dynamic
- Make use of @bdhirsh's subclass fakification logic by adding the __tensor_{un,}flatten__ functions.
- Additional logic for fakification: since existing subclass fakification logic does not handle the case where the outer tensor has an additional dimension. We insert one-off logic to (1) insert an extra SingletonSymInt onto the fakified NestedTensor. (2) make sure we call track_symint on both the sizes on the inner and outer tensor during guard creation.
Remaining things that are weird:
- Still need to skip some logic in meta utils for some reason (I was going to write this up more, but decided not to since we're not able to do this anyway for a immediate reason: we cannot arbitrarily compare singleton ints. For now I'm just following Brian's advise from [here](https://github.com/pytorch/pytorch/pull/109171#discussion_r1328137070) )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109171
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
We want to be able to use SingletonSymNode to represent strides for Jagged layout tensor. The following is for 3D, but easily generalizable to higher dimensions.
Constraints:
- [B, x, D] (where x represents the "variably lengthed dim") can be strided in two ways [x, 1, sum(x)] and [dx, d, 1]. We need two different placeholder values depending on how the jagged tensor is strided.
- When doing operations we need the strides of output tensors to be expressable in terms of the strides and sizes of the inner tensors. Given [B, x, D] @ [D, D'], the output strides is [x * D', D', 1] rather than some opaque [x2, D', 1]. This constraint exists because if I'm tracing, I need a symint to represent the output stride. This symint needs to come from somewhere; I get it in several ways: (1) create a constant, (2) unbacked symint, (3) create a new input using a source, (4) output of an operation on an existing symint. It is clear that (4) is what we want here, which brings us to the design below.
Design:
Given the two constraints, the most straightforward way to implement this is actually to update SingletonSymNode to include some scalar factor, i.e. Morally, SingletonSymNode represents `factor * [s_0, s_1, …, s_n]` This enables us to symbolically compute strides from sizes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110369
Approved by: https://github.com/ezyang
ghstack dependencies: #110044
# Summary
Preivously we disallowd dis-contiguous NTs to passed into to empty_like. This was done out of an abundance of caution, :think:. However it should be safe to create an empty NT for dis-contiguous NTs. Empty like does account for offsets, strides, and sizes in construction of the result and therefore this should be safe.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98383
Approved by: https://github.com/cpuhrsch
This is needed for the HSTU model.
Details:
* ~~NT `chunk` now calls into NT `split_with_sizes` since the latter is more general~~ (removed; they're totally separate)
* Throws for backward
* Only operates over the last dim (`dim=-1`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97446
Approved by: https://github.com/cpuhrsch
# Summary
NestedTensors currenlty don't support non-identical strided addition. When accumulating grad it possible to try and accumulate a grad with different striding then the old var and there is no way to change this in user code. This is a solution.. probs should support strided addition for NT
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97195
Approved by: https://github.com/albanD, https://github.com/cpuhrsch
