[tuner] Update README (#532)

Add a warning about the tuner being in early development. Update the
tuner readme and remove obsolete instructions.

README.md

@@ -50,6 +50,11 @@ conversion tools to produce inference-optimized programs.
 The Tuner sub-project assists with tuning program performance by searching for
 optimal parameter configurations to use during model compilation.
 
+> [!WARNING]
+> SHARK Tuner is still in early development. Interested users may want
+> to try it out, but the tuner is not ready for general use yet. Check out
+> [the readme](tuner/README.md) for more details.
+
 ## Support matrix
 
 <!-- TODO: version requirements for Python, ROCm, Linux, etc.  -->

tuner/README.md

@@ -1,5 +1,8 @@
 # IREE dispatch auto-tuning scripts
-`libtuner.py` is the core Python script that provides the fundamental functions for the tuning loop. It imports `candidate_gen.py` for candidate generation. To implement the full tuning loop, `libtuner.py` requires a separate Python script that uses the provided `TuningClient` API from `libtuner.py`.
+`libtuner.py` is the core Python script that provides the fundamental functions
+for the tuning loop. It imports `candidate_gen.py` for candidate generation. To
+implement the full tuning loop, `libtuner.py` requires a separate Python script
+that uses the provided `TuningClient` API from `libtuner.py`.
 
 ## Prerequisites
 [Optional] Using virtual environments:
@@ -22,47 +25,13 @@ Using the IREE's Python bindings:
    - Set environment
       ```shell
       source ../iree-build/.env && export PYTHONPATH
+      export PATH="$(realpath ../iree-build/tools):$PATH"
       ```
-For more information, refer to the [IREE documentation](https://iree.dev/building-from-source/getting-started/#python-bindings)
+For more information, refer to the [IREE
+documentation](https://iree.dev/building-from-source/getting-started/#python-bindings).
 
-### Overall flow
+## Examples
 
-1. Symlink all scripts and mlir/irpa files in your build dir.
-   - Symlink `iree-build-dir/tools` inside `tuning`.
-   - Symlink ML model MLIR and weights based on `unet.sh`.
-
-2. Copy the attention/matmul spec as `config.mlir` in the tuning dir.
-
-3. Temporarily comment out all the existing configs in `config.mlir`.
-   - Example:
-     ```mlir
-     // , @match_mmt_2048x10240x1280 -> @apply_op_config
-     // , @match_mmt_2048x1280x5120 -> @apply_op_config
-     // , @match_mmt_2048x1280x1280 -> @apply_op_config
-     ```
-
-4. Compile a baseline unet
-```shell
-./unet.sh winograd unet.mlir -o unet_baseline.vmfb --iree-hal-dump-executable-files-to=dump-winograd
-```
-
-5. Find the matmul to tune and copy the `*_benchmark.mlir` file to the build dir.
-```shell
-cp dump-winograd/*_141_*benchmark.mlir ./141.mlir
-```
-
-6. Run the tuning script.
-   - Example:
-    ```shell
-    python -m examples.punet 141.mlir --devices=hip://GPU-0,hip://GPU-4 --num-candidates=1024
-    ```
-
-7. Check the winner candidate in `result_summary.log`, find and copy the transform spec.
-
-8. Paste the transform spec into the `config.mlir` and uncomment them.
-
-9. Add the match function to the entry point in `config.mlir`
-   - Example:
-     ```mlir
-     @match_something -> @apply_op_config
-     ```
+Check the `examples` directory for sample tuners implemented with `libtuner`.
+The [`dispatch` example](https://github.com/nod-ai/SHARK-Platform/tree/main/tuner/examples/dispatch)
+should be a good starting point for most users.