Add auto-tuner to SD apps (#1291)

apps/stable_diffusion/scripts/tuner.py

@@ -0,0 +1,114 @@
+import os
+from pathlib import Path
+from shark_tuner.codegen_tuner import SharkCodegenTuner
+from shark_tuner.iree_utils import (
+    dump_dispatches,
+    create_context,
+    export_module_to_mlir_file,
+)
+from shark_tuner.model_annotation import model_annotation
+from apps.stable_diffusion.src.utils.stable_args import args
+from apps.stable_diffusion.src.utils.utils import set_init_device_flags
+from apps.stable_diffusion.src.utils.sd_annotation import (
+    get_device_args,
+    load_winograd_configs,
+)
+from apps.stable_diffusion.src.models import SharkifyStableDiffusionModel
+
+
+def load_mlir_module():
+    sd_model = SharkifyStableDiffusionModel(
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        max_len=args.max_length,
+        batch_size=args.batch_size,
+        height=args.height,
+        width=args.width,
+        use_base_vae=args.use_base_vae,
+        use_tuned=False,
+        low_cpu_mem_usage=args.low_cpu_mem_usage,
+        return_mlir=True,
+    )
+
+    if args.annotation_model == "unet":
+        mlir_module = sd_model.unet()
+        model_name = sd_model.model_name["unet"]
+    elif args.annotation_model == "vae":
+        mlir_module = sd_model.vae()
+        model_name = sd_model.model_name["vae"]
+    else:
+        raise ValueError(
+            f"{args.annotation_model} is not supported for tuning."
+        )
+
+    return mlir_module, model_name
+
+
+def main():
+    args.use_tuned = False
+    set_init_device_flags()
+    mlir_module, model_name = load_mlir_module()
+
+    # Get device and device specific arguments
+    device, device_spec_args = get_device_args()
+    device_spec = ""
+    if device_spec_args:
+        device_spec = device_spec_args[-1].split("=")[-1].strip()
+        if device == "vulkan":
+            device_spec = device_spec.split("-")[0]
+
+    # Add winograd annotation for vulkan device
+    winograd_config = (
+        load_winograd_configs()
+        if device == "vulkan" and args.annotation_model in ["unet", "vae"]
+        else ""
+    )
+    with create_context() as ctx:
+        input_module = model_annotation(
+            ctx,
+            input_contents=mlir_module,
+            config_path=winograd_config,
+            search_op="conv",
+            winograd=True,
+        )
+
+    # Dump model dispatches
+    if device == "vulkan" and device_spec == "rdna3":
+        device = "vulkan/RX 7900"
+    generates_dir = Path.home() / "tmp"
+    if not os.path.exists(generates_dir):
+        os.makedirs(generates_dir)
+    dump_mlir = generates_dir / "temp.mlir"
+    dispatch_dir = generates_dir / f"{model_name}_{device_spec}_dispatches"
+    export_module_to_mlir_file(input_module, dump_mlir)
+    dump_dispatches(dump_mlir, device, dispatch_dir, False)
+
+    # Tune each dispatch
+    dtype = "f16" if args.precision == "fp16" else "f32"
+    config_filename = f"{model_name}_{device_spec}_configs.json"
+
+    for f_path in os.listdir(dispatch_dir):
+        if not f_path.endswith(".mlir"):
+            continue
+
+        model_dir = os.path.join(dispatch_dir, f_path)
+
+        tuner = SharkCodegenTuner(
+            model_dir,
+            device,
+            "random",
+            args.num_iters,
+            args.tuned_config_dir,
+            dtype,
+            args.search_op,
+            batch_size=1,
+            config_filename=config_filename,
+            use_dispatch=True,
+        )
+        tuner.tune()
+
+
+if __name__ == "__main__":
+    main()