torch-mlir/docs/Torch-ops-E2E-implementation.md

## Tutorials
- Linalg.generic op introduction [here](https://www.youtube.com/watch?v=PdQPlPudT90)
- Basic E2E debugging walk-through [here](https://www.youtube.com/watch?v=E5epCJOtrf8&t=1556s)

## Example PR
https://github.com/llvm/torch-mlir/pull/294

## Major steps

### Step 1. Add an end-to-end test to iterate on

Add an end-to-end test to the [end-to-end test suite](https://github.com/llvm/torch-mlir/tree/main/python/torch_mlir_e2e_test/test_suite). Ideally there is an existing file that your op fits into. If not, you can create a new file.

We generally recommend testing by invoking `torch.ops.aten.someop` from Python -- that gives a very precise test for the individual Torch operator you are implementing (calling `torch.ops.aten.someop` from Python always lowers into the MLIR `torch.aten.someop` operation)

The end-to-end test is important to check the correctness of the other steps.

### Step 2. Update ods
Update [torch_ods_gen.py](https://github.com/llvm/torch-mlir/blob/main/python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py) with the new op and run [update_torch_ods.sh](https://github.com/llvm/torch-mlir/blob/main/build_tools/update_torch_ods.sh) to generate the ods. Running `update_torch_ods.sh` would dump all the operators with schema into `JITOperatorRegistryDump.txt`. It’s convenient to look for ops signatures and operands names in this file. 

### Step 3. Propagate dtypes with RefineTypes pass
The RefineTypes pass propagates refined tensor dtypes across the entire program. Each visit function infers the output tensor dtype based on the input. It’s necessary to make sure the new op is handled correctly by this pass. If existing helpers can’t be reused and new code logic is added, unit tests like those in [test/Dialect/Torch/refine-types.mlir](https://github.com/llvm/torch-mlir/blob/main/test/Dialect/Torch/refine-types.mlir) are needed. The unit tests use LLVM’s FileCheck and MLIR provides a script [mlir/utils/generate-test-checks.py](https://github.com/llvm/llvm-project/blob/main/mlir/utils/generate-test-checks.py) to generate [FileCheck](https://llvm.org/docs/CommandGuide/FileCheck.html) statements.

### Step 4. Add a shape function to the shape library

See the documentation in [Adding a Shape Function](https://github.com/llvm/torch-mlir/blob/main/docs/adding_a_shape_function.md).

### Step 5. Torch ops lowering

#### Decompose

If your op can be decomposed into other supported ops, then you can add a pattern into [DecomposeComplexOps](https://github.com/llvm/torch-mlir/blob/8d3ca887df5ac5126fa3fc2ec3546c6322a4d066/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp#L1).

You can find an [example PR here](https://github.com/llvm/torch-mlir/pull/1453).

#### Lower to Linalg

The `Torch` dialect needs to be lowered to [Linalg](https://mlir.llvm.org/docs/Dialects/Linalg/) dialect which can be used as input IR of backends. [Here](https://mlir.llvm.org/docs/Dialects/Linalg/#high-level-description-of-linalg-opsa-namelinalg_opsa) is a high level introduction about Linalg ops and [here](https://www.youtube.com/watch?v=PdQPlPudT90) is a video explaining `linalg.generic` op. The building block is the `linalg.generic` op which consists of indexing maps, iterator types, input/output tensors and a compute payload. You would want to get familiar with the concept of [affine map](https://mlir.llvm.org/docs/Dialects/Affine/#affine-expressions). The `linalg.generic` op anatomy [tutorial](https://www.youtube.com/watch?v=PdQPlPudT90&list=PLHPjgRtRcfTpVGFMrLP2KQyXhvtSQiiai&index=1) covers the basics of `linalg.generic` from a user's perspective.

You can find an [example PR here](https://github.com/llvm/torch-mlir/pull/294).

## Delivering Code
1. The codebase follows the [LLVM’s coding conventions](https://llvm.org/docs/CodingStandards.html).The following items might be the most frequently used rules: 
- [use-early-exits-and-continue-to-simplify-code](https://llvm.org/docs/CodingStandards.html#use-early-exits-and-continue-to-simplify-code)
- [don-t-use-else-after-a-return](https://llvm.org/docs/CodingStandards.html#don-t-use-else-after-a-return)
- [use-auto-type-deduction-to-make-code-more-readable](https://llvm.org/docs/CodingStandards.html#use-auto-type-deduction-to-make-code-more-readable)
- [anonymous-namespaces](https://llvm.org/docs/CodingStandards.html#anonymous-namespaces)
- [avoid-braces-on-simple-single-statement-bodies-of-if-else-loop-statements](https://llvm.org/docs/CodingStandards.html#don-t-use-braces-on-simple-single-statement-bodies-of-if-else-loop-statements)
2. Try to refactor and reuse existing code/helpers when working on RefineTypes and TorchToLinalg lowering for easier maintenance, testing and better readability. Try not to copy & paste existing code.
3. Squash all the commits into one, including the commits addressing review comments.
4. Use `git clang-format HEAD~1` to automatically format your commit.
5. Rebase on `HEAD` before delivering.