Add simple OPT tuning script.

apps/stable_diffusion/src/utils/utils.py

@@ -305,6 +305,11 @@ def set_init_device_flags():
     ):
         args.use_tuned = False
 
+    elif (
+        args.height != args.width and "rdna2" in args.iree_vulkan_target_triple
+    ):
+        args.use_tuned = False
+
     elif base_model_id not in [
         "Linaqruf/anything-v3.0",
         "dreamlike-art/dreamlike-diffusion-1.0",

tank/examples/opt/tune_opt.py

@@ -0,0 +1,88 @@
+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 shark_opt_wrapper import OPTForCausalLMModel
+from transformers import AutoTokenizer, OPTForCausalLM
+from shark.shark_importer import import_with_fx
+
+NUM_ITERS = 400
+MODEL_NAME = "facebook/opt-1.3b"
+MODEL_FNAME = "opt-1_3b-causallm"
+
+def load_mlir_module():
+    hf_model = OPTForCausalLM.from_pretrained(MODEL_NAME)
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+
+    opt_model = OPTForCausalLMModel(hf_model)
+
+    prompt = "What is the meaning of life?"
+    model_inputs = tokenizer(prompt, return_tensors="pt")
+    inputs = (
+        model_inputs["input_ids"],
+        model_inputs["attention_mask"],
+    )
+    
+    (
+        mlir_module,
+        func_name,
+    ) = import_with_fx(
+        model=opt_model,
+        inputs=inputs,
+        is_f16=False,
+        model_name=MODEL_NAME.split("/")[1],
+    )
+    return mlir_module, model_name
+
+
+def main():
+    #mlir_module, model_name = load_mlir_module()
+
+    # Get device and device specific arguments
+    device = "cpu"
+
+    # Dump model dispatches
+    model_name = MODEL_NAME
+    #generates_dir = "."
+    #if not os.path.exists(generates_dir):
+    #    os.makedirs(generates_dir)
+    #dump_mlir = generates_dir / "temp.mlir"
+    dispatch_dir = f"./{MODEL_FNAME}_{device}_dispatches"
+    #export_module_to_mlir_file(mlir_module, dump_mlir)
+    #dump_dispatches(
+    #    dump_mlir,
+    #    device,
+    #    dispatch_dir,
+    #)
+
+    # Tune each dispatch
+    dtype = "f32"
+    config_filename = f"{MODEL_FNAME}_{device}_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",
+            NUM_ITERS,
+            ".",
+            dtype,
+            search_op="all",
+            batch_size=1,
+            config_filename=config_filename,
+            use_dispatch=True,
+        )
+        tuner.tune()
+
+
+if __name__ == "__main__":
+    main()