llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
2,222 Commits
47 Branches
1195 Tags
91 MiB
Commit Graph

61 Commits (20ea1c9e9159483cd14ca8141c4968845a23dea8)

Author SHA1 Message Date
Henry Tu de5b380143 Bert example and relevant shape inference functions (#831) 2022-07-30 09:40:02 -04:00
Henry Tu 406d1e7538 Use JIT GraphExecutor for execution in example backend (#830) 
* Update LazyShapeInference header

* Use JIT GraphExecutor for execution in example backend
 2022-07-30 09:40:02 -04:00
Jae Hoon (Antonio) Kim 1bde00c73d Fix LTC Decoupling (#815) 
* Initial changes

* Fix up native functions

* Further fix decoupling

* Remove unnecessary ops

* Formatting and copyright banners:

* Add pytorch submodule
 2022-07-30 09:40:02 -04:00
Henry Tu cca9fe126e Enable support for LTC Input/Output Mapping (#764) 
* Save InputOutputAliases to TorchMlirComputation

* Implement GetResultShape for TorchMlirLoweringContext

* Use optional return type for GetResultShape

* Remove support for aten::detach

With this op enabled, tensors were being copied, which resulted in incorrect aliasing.

* Add newline before printing I/O alias mapping

* Changed printout to use "Input param" as label instead of "Input"

* Remote shape inference function for aten::detach

* Moved implementation of SetUpAlias to MlirLoweringContext

As part of this change, TorchMlirComputation has been moved to the end of mlir_lowering_context.h so that it can access some new structs in TorchMlirLoweringContext

* Use updated PyTorch API

* Remove GetResultShape

Complements this upstream PyTorch PR: pytorch/pytorch#75828

This PR adds support for mapping input and output tensors which alias each other. (e.g. maps input weight tensor in parameter to the same tensor in output after a training iteration)

MLIR: 
func @graph(%arg0: !torch.vtensor<[1,5],f32>, %arg1: !torch.vtensor<[1],si64>, ..., %arg6: !torch.vtensor<[10,5],f32>, %arg7: !torch.vtensor<[10],f32>, ...) {
  ...
  return %arg0, %arg1, %17, %23, ... : !torch.vtensor<[1,5],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[10,5],f32>, !torch.vtensor<[10],f32>, ...
}

Input/Output Alias Mapping: 
Output: 0 -> Input: 0
Output: 1 -> Input: 1
Output: 2 -> Input: 6
Output: 3 -> Input: 7
The aten::detach op has also been disabled in this PR to fix the issue of tensors not aliasing properly due to copying.
 2022-07-30 09:40:02 -04:00
Antonio Kim 615ff1d31c Generate MLIR with shape information via LTC frontend (#742) 2022-07-30 09:40:02 -04:00
Henry Tu a605fe279c Add example Torch MLIR LTC Backend (#725) 2022-07-30 09:40:02 -04:00
Henry Tu 3e9b1cbd36 Added JIT to MLIR lowering (#724) 
* Added JIT to MLIR lowering

Lowering to JIT is performed in a way similar to how it's done in the TS LTC backend. After a jit::Graph is constructed, it gets converted to a jit::Function, which is fed into the existing utility to generate an MlirModule in torch-mlir.

* Renamed `csrc/backend` to `csrc/base_lazy_backend`
 2022-07-30 09:40:02 -04:00
Jae Hoon (Antonio) Kim 65cf1465ef Fix Torch-MLIR LTC Backend based off latest PyTorch master (#723) 
* Changes as a result of the LTC TS backend decoupling

* Fix bugs in BackendImpl and codegen

* Fix based on latest PyTorch master
 2022-07-30 09:40:02 -04:00
Jae Hoon (Antonio) Kim c3b20e444c Got LTC working until compile (#689) 2022-07-30 09:40:02 -04:00
Jae Hoon (Antonio) Kim 58338f79a1 Torch-MLIR LTC Backend Lowering Codegen (#621) 
* Codegen and build LTC lowering

* Add LazyShapeInference header
 2022-07-30 09:40:02 -04:00
Jae Hoon (Antonio) Kim 2f22e2ef40 Add initial LTC backend (#610) 
* Add initial LTC backend skeleton

* Disable CI build and move TorchMLIRPyTorch.cmake
 2022-07-30 09:40:02 -04:00