[torch-mlir] remove trailing whitespace from md documentation

docs/add_ops.md

@@ -1,17 +1,17 @@
 # How to Add Ops to Torch-Mlir
 
-Collected links and contacts for how to add ops to torch-mlir. 
+Collected links and contacts for how to add ops to torch-mlir.
 
 
 <details>
 <summary>Turbine Camp: Start Here</summary>
-This document was previously known as `turbine-camp.md` to Nod.ai. "Turbine Camp" is part of Nod.ai's onboarding process. Welcome to turbine camp. This document originated at Nod.ai as a part of onboardding process, where new nod-ai folks learn about the architecture of our work by adding support for 2 ops to torch-mlir. I decided to put this into torch mlir because a lot of this is about torch-mlir. 
+This document was previously known as `turbine-camp.md` to Nod.ai. "Turbine Camp" is part of Nod.ai's onboarding process. Welcome to turbine camp. This document originated at Nod.ai as a part of onboardding process, where new nod-ai folks learn about the architecture of our work by adding support for 2 ops to torch-mlir. I decided to put this into torch mlir because a lot of this is about torch-mlir.
 
 Written & maintained by @renxida
 
 Guides by other folks that were used during the creation of this document:
 - [Chi Liu](https://gist.github.com/AmosLewis/dd31ab37517977b1c499d06495b4adc2)
-- [Sunsoon](https://docs.google.com/document/d/1H79DwW_wnVzUU81EogwY5ueXgnl-QzKet1p2lnqPar4/edit?pli=1) 
+- [Sunsoon](https://docs.google.com/document/d/1H79DwW_wnVzUU81EogwY5ueXgnl-QzKet1p2lnqPar4/edit?pli=1)
 
 ## Before you begin...
 

docs/adding_abstract_interpretation_functions.md

@@ -4,7 +4,7 @@
 
 As part of adding support for a Torch operator in Torch-MLIR, it is usually
 necessary to define a shape and dtype function so that the compiler can infer
-the shapes and dtypes of result tensors for the operator. We use the 
+the shapes and dtypes of result tensors for the operator. We use the
 [abstract interpretation library](abstract_interp_lib.md) for this process.
 
 ## Step-by-step guide
@@ -19,7 +19,7 @@ We will use the example of adding support for the `torch.aten.tanh` op.
    file is the "rosetta stone" that allows translating between
    e.g. `torch.aten.tanh`, `AtenTanhOp`, and the shape and dtype
    function signatures are:
-   
+
    - `def aten〇tanh〡shape(self: List[int]) -> List[int]:`
    - `def aten〇tanh〡dtype(self_rank_dtype: Tuple[int, int]) -> int:`
 
@@ -39,10 +39,10 @@ We will use the example of adding support for the `torch.aten.tanh` op.
    But in general, you will need to write the function and test it
    (see the comments about "Shape, dtype, and decomposition function
    testing infrastructure" in `testing_framework.py`). New shape
-   functions should be added upstream following the example of [this PR](https://github.com/pytorch/pytorch/pull/76889), 
-   though it can be useful to iterate locally in `abstract_interp_lib_gen.py` 
+   functions should be added upstream following the example of [this PR](https://github.com/pytorch/pytorch/pull/76889),
+   though it can be useful to iterate locally in `abstract_interp_lib_gen.py`
    first.
-   
+
    Similarly, dtype functions should ideally just be a call to the helper
    `promote_dtypes` defined in `library_generator.py`. However, some ops will
    require some extra logic to calculate the right result types. While dtypes

docs/architecture.md

@@ -442,5 +442,5 @@ characteristics.
 
 ### Presentations and Talks
 
-* 2021-10-07: MLIR ODM: Introduction to Torch-MLIR. ([recording](https://www.youtube.com/watch?v=QbNkex-gizs) and [slides](https://docs.google.com/presentation/d/1ZhzfE4EK6XV7AdQTYicrsE_OYjkER_yiB0vBeszRfzY/edit#slide=id.gf56404f79c_1_55)) 
-* 2022-08-20: Overview of Torch-MLIR passes. ([recording](https://www.youtube.com/watch?v=ZpwlVxsD9_U) and [slides](https://drive.google.com/file/d/1ZSlk1HGttRuVhJSxtP6spWt_hxClit2T/view)) 
+* 2021-10-07: MLIR ODM: Introduction to Torch-MLIR. ([recording](https://www.youtube.com/watch?v=QbNkex-gizs) and [slides](https://docs.google.com/presentation/d/1ZhzfE4EK6XV7AdQTYicrsE_OYjkER_yiB0vBeszRfzY/edit#slide=id.gf56404f79c_1_55))
+* 2022-08-20: Overview of Torch-MLIR passes. ([recording](https://www.youtube.com/watch?v=ZpwlVxsD9_U) and [slides](https://drive.google.com/file/d/1ZSlk1HGttRuVhJSxtP6spWt_hxClit2T/view))

docs/importers/onnx_importer.md

@@ -11,8 +11,8 @@ for the reference importer which complies with the rules below.
 With the exception of certain special or complicated ONNX operators, most
 are relatively straight-forward to map, following this general procedure:
 
-* Plan the ops you wish to support by consulting the 
-  [ONNX operator database](https://onnx.ai/onnx/operators/). 
+* Plan the ops you wish to support by consulting the
+  [ONNX operator database](https://onnx.ai/onnx/operators/).
   * This database has detailed diffs wrt different support versions but
     at the level of detail we operate, most version diffs are inconsequential
     and just require a bit more pattern support.
@@ -24,7 +24,7 @@ are relatively straight-forward to map, following this general procedure:
   corresponding with the alphabetic sort of the op and add a conversion.
 * Generate successful test cases:
   * All `onnx_importer.py` tests are dumped to the test temp dir (success
-    or failure). This is typically located under 
+    or failure). This is typically located under
     `tools/torch-mlir/test/python/onnx_importer/Output`. The `.mlir` files
     under there should provide good variants to drive lit test coverage of
     conversion.
@@ -34,25 +34,25 @@ are relatively straight-forward to map, following this general procedure:
   * There are often many variants of tests for checking conformance of
     different historic ONNX encodings, but these are often not load bearing
     at the MLIR level.
-  * Pick a handful of test cases and add them to 
+  * Pick a handful of test cases and add them to
     `test/Conversion/TorchOnnxToTorch/simple_ops_x_to_y.mlir` corresponding to
     an alphabetic breakdown. At this time, ignore tests that are not exercising
     useful differences in the pattern implementations.
-    * (Optionally) Use `torch-mlir-opt` to validate the outputs of the new op. 
-      First, build the project using 
+    * (Optionally) Use `torch-mlir-opt` to validate the outputs of the new op.
+      First, build the project using
       `cmake --build build --target tools/torch-mlir/all`. This will generate
       the conversion binary, `torch-mlir-opt`. Then call `torch-mlir-opt` with
       the MLIR pass `convert-torch-onnx-to-torch`:
       ```
       build/bin/torch-mlir-opt -convert-torch-onnx-to-torch \
       -split-input-file [DESIRED_ONNX_FILE].mlir
-      ``` 
+      ```
 * Generate failure test cases:
   * Some ops have forms that do not (easily) map to torch-mlir. If you leave
     an op under-implemented, add a failing test case to
     `test/Conversion/TorchOnnxToTorch/unsupported_simple_ops.mlir`.
-* Optional but recommended: Use your test case files to fuzz against the 
-  torch-mlir backend of your choice by running a backend conversion pipeline 
+* Optional but recommended: Use your test case files to fuzz against the
+  torch-mlir backend of your choice by running a backend conversion pipeline
   and fixing any crashes/issues.
 * Send a patch with your changes.
 
@@ -115,7 +115,7 @@ not yet implemented.
 The `IsolatedFromAbove` parent of the ops can contain the following
 metadata:
 
-* `torch.onnx_meta.ir_version`: 64bit `IntegerAttr` corresponding to 
+* `torch.onnx_meta.ir_version`: 64bit `IntegerAttr` corresponding to
   `ModelProto.ir_version`.
 * `torch.onnx_meta.producer_name`: `StringAttr` corresponding to
   `ModelProto.producer_name`.
@@ -135,7 +135,7 @@ are only minor variations of an op. Major variations should use
 
 ### Special op forms
 
-Certain ONNX operators map to different structural components of 
+Certain ONNX operators map to different structural components of
 torch-mlir's representation:
 
 * `ConstantOfShape`: Mapped to `torch.vtensor.literal` with

docs/ltc_backend.md

@@ -103,7 +103,7 @@ At some point, the tensors will be synced in order to execute the computation --
 >>> torch._lazy.mark_step()
 ```
 
-This triggers a call to `LazyGraphExecutor::SyncLiveTensorsGraph` somewhere in the guts of LTC, which collects all the `TorchMlirNode`s (technically `torch::lazy::Node`s at this point) from the current trace and 
+This triggers a call to `LazyGraphExecutor::SyncLiveTensorsGraph` somewhere in the guts of LTC, which collects all the `TorchMlirNode`s (technically `torch::lazy::Node`s at this point) from the current trace and
 creates an instance of `TorchMlirLoweringContext`. Here, the `TorchMlirNode`s are lowered to JIT via `mlir_node_lowering.cpp` and inserted into a `jit::Graph`.
 
 Next, `TorchMlirLoweringContext::Build` is executed and the final `jit::Graph` is sent to `torch_mlir::importJitFunctionAsFuncOp` to generate MLIR using the existing infrastructure from Torch-MLIR.
@@ -121,7 +121,7 @@ Finally, the compiled computation is sent to `TorchMlirBackendImpl::ExecuteCompu
 
 ## Implementing a custom backend
 
-A reference implementation of a custom backend is available [here](../python/torch_mlir/csrc/reference_lazy_backend/). 
+A reference implementation of a custom backend is available [here](../python/torch_mlir/csrc/reference_lazy_backend/).
 All the work involved with generating MLIR is handled in the base LTC backend, so vendors only need to worry about implementing `Compile`, `ExecuteComputation`, and some other minor methods to interface with the device.
 
 A pybind is needed to invoke C++ code to register the autogen PyTorch kernels and the custom backend itself.

docs/ltc_examples.md

@@ -33,18 +33,18 @@ Received 1 arguments, and returned 2 results during ExecuteCompile!
 
 Results: tensor([[0.7616, 0.9640, 0.9951, 0.9993, 0.9999]], device='lazy:0')
 
-JIT Graph: 
+JIT Graph:
 graph(%p0 : Float(1, 5)):
   %1 : Float(1, 5) = aten::tanh(%p0)
   return (%p0, %1)
 
-MLIR: 
+MLIR:
 func.func @graph(%arg0: !torch.vtensor<[1,5],f32>) -> (!torch.vtensor<[1,5],f32>, !torch.vtensor<[1,5],f32>) {
   %0 = torch.aten.tanh %arg0 : !torch.vtensor<[1,5],f32> -> !torch.vtensor<[1,5],f32>
   return %arg0, %0 : !torch.vtensor<[1,5],f32>, !torch.vtensor<[1,5],f32>
 }
 
-Input/Output Alias Mapping: 
+Input/Output Alias Mapping:
 Output: 0 -> Input param: 0
 
 In Mark Step: true