Update E2E links

Author: newling

docs/Torch-ops-E2E-implementation.md

@@ -9,29 +9,27 @@ https://github.com/llvm/torch-mlir/pull/294
 
 ### 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.
+Add an end-to-end test to the [end-to-end test suite](https://github.com/llvm/torch-mlir/blob/main/docs/adding_an_e2e_test.md)). 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.
+Update [torch_ods_gen.py](https://github.com/llvm/torch-mlir/blob/main/projects/pt1/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 4. Add a shape function to the shape library
+### Step 3. Propagate types
+It’s essential to make sure the new op implements shape and dtype inference. See [abstract_interp_lib](https://github.com/llvm/torch-mlir/blob/main/docs/abstract_interp_lib.md) for information on adding shape and dtype inference.
 
-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
+### Step 4. 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).
+You can find example PRs [here](https://github.com/llvm/torch-mlir/pull/2550) and [here](https://github.com/llvm/torch-mlir/pull/2553).
 
 #### Lower to Linalg
 
@@ -40,7 +38,7 @@ The `Torch` dialect needs to be lowered to [Linalg](https://mlir.llvm.org/docs/D
 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: 
+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)
@@ -49,4 +47,4 @@ You can find an [example PR here](https://github.com/llvm/torch-mlir/pull/294).
 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. 
+5. Rebase on `HEAD` before delivering.