Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36611
Currently Buf represents underlying storage but it didn't have dtype.
That resulted in specifying dtypes in different places and there was no
mechanism to enforce its consistency: e.g. one could've created a kFloat
expression and use a kInt buffer to store its result. Now we're
centralizing where the logic regarding the storage is located and we can
start enforcing semantics rules.
Follow-ups: we can merge Buffer and BufHandle classes as the former is
now a mere wrapper over the latter.
Test Plan: Imported from OSS
Differential Revision: D21027356
Pulled By: ZolotukhinM
fbshipit-source-id: c06aa2c4077fdcde3bb4ca622d324aece79b5a9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35800
This PR includes the following changes:
* Introduce a new `Expr` type `Buf`: it plays a similar to `Var` role, but also has dimensions.
* Use the new `Buf` class in `Store` and `Load` instead of `Var` for specifying where to store to or load from. `Buf` contains the dimensions info of the buffer we're loading/storing to and hence we are able to keep N-d indexes without flattening them into a 1-d index ([x,y] vs [x+y*W]).
* Flattening of the indexes is now a separate pass that is executed in `LoopNest::prepareForCodegen` - backends still expect indexes to be flattened, and this PR preserves that.
* `Tensor` now contains a `Buf` instead of `Var`, and thus Tensor now has the dimensions info (previously it was a property of a `Function`, not a `Tensor`). This brings us closer to Tensor being a combination of Buffer + Function, where Buffer specifies iteration domain and the Function defines a computation.
TODOs:
* Consider merging `Buffer` with `Buf` or `BufHandle`. It seems that we don't need all of them.
* Harden the logic of how we create buffers in fuser pass. Currently it seems that sometimes we don't set dimensions.
* Use `Buf` in `Allocate` and `Free`.
* Make it clearer that `Function` doesn't "own" dimensions info and that dimensions are a property of a Tensor, not a Function.
Differential Revision: D20789005
Test Plan: Imported from OSS
Reviewed By: zheng-xq
Pulled By: ZolotukhinM
fbshipit-source-id: e04188d1d297f195f1c46669c614557d6bb6cde4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34842
This PR (hopefully the last one of such kind) is merging changes from a
side branch where tensor expessions based fuser work has been done so
far. This PR is is a squashed version of changes in the side branch,
which is available here: https://github.com/bertmaher/pytorch
Differential Revision: D20478208
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: 21556e009f1fd88099944732edba72ac40e9b9c0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34224
Our development has been happening on a side branch `pytorch_fusion` in
`bertmaher/pytorch` fork. This PR moves changes to the core classes
representing expressions and transformations on them.
At this moment, the tensor expressions are only used in tests.
Subsequent PRs add LLVM and CUDA codegen for tensor expressions and
implement fuser on top of these.
This PR is huge as it is a squashed version of changes in the side
branch. It is not practical to pull changes one by one from the branch,
so here is the squashed version. If you're interested in seeing the
history of changes, please refer to https://github.com/bertmaher/pytorch
Differential Revision: D20251835
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: 1a871acc09cf3c6f7fb4af40d408cdbb82dc7dab
