Updates to opt_causallm example (#1905)

* Updates to opt_causallm example

* Fixup opt_perf_comparison.py

* Use same filenames across opt examples.

tank/examples/opt/opt_causallm.py

@@ -1,3 +1,4 @@
+import argparse
 import os
 import torch
 import numpy as np
@@ -10,21 +11,16 @@
 from shark.shark_importer import import_with_fx
 from transformers import AutoTokenizer, OPTForCausalLM
 
-OPT_MODEL = "opt-1.3b"
-OPT_FS_NAME = "opt-1_3b"
-MAX_SEQUENCE_LENGTH = 128
-MAX_NEW_TOKENS = 60
 
-
-def create_module(model_name, tokenizer, device):
-    opt_base_model = OPTForCausalLM.from_pretrained("facebook/" + model_name)
+def create_module(model_name, tokenizer, device, args):
+    opt_base_model = OPTForCausalLM.from_pretrained(model_name)
     opt_base_model.eval()
     opt_model = OPTForCausalLMModel(opt_base_model)
     encoded_inputs = tokenizer(
         "What is the meaning of life?",
         padding="max_length",
         truncation=True,
-        max_length=MAX_SEQUENCE_LENGTH,
+        max_length=args.max_seq_len,
         return_tensors="pt",
     )
     inputs = (
@@ -33,16 +29,19 @@ def create_module(model_name, tokenizer, device):
     )
     # np.save("model_inputs_0.npy", inputs[0])
     # np.save("model_inputs_1.npy", inputs[1])
+    opt_fs_name = "-".join(
+        "_".join(args.model_name.split("/")[1].split("-")).split(".")
+    )
 
-    mlir_path = f"./{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch.mlir"
+    mlir_path = f"./{opt_fs_name}_causallm_{args.max_seq_len}_torch.mlir"
     if os.path.isfile(mlir_path):
         print(f"Found .mlir from {mlir_path}")
     else:
         (model_mlir, func_name) = import_with_fx(
             model=opt_model,
             inputs=inputs,
             is_f16=False,
-            model_name=OPT_FS_NAME,
+            model_name=opt_fs_name,
             return_str=True,
         )
         with open(mlir_path, "w") as f:
@@ -57,7 +56,7 @@ def create_module(model_name, tokenizer, device):
         is_benchmark=False,
     )
 
-    vmfb_name = f"{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch_{device}"
+    vmfb_name = f"{opt_fs_name}_causallm_{args.max_seq_len}_torch_cpu"
     shark_module.save_module(module_name=vmfb_name, debug=False)
     vmfb_path = vmfb_name + ".vmfb"
     return vmfb_path
@@ -71,11 +70,11 @@ def shouldStop(tokens):
     return False
 
 
-def generate_new_token(shark_model, tokenizer, new_text):
+def generate_new_token(shark_model, tokenizer, new_text, args):
     model_inputs = tokenizer(
         new_text,
         padding="max_length",
-        max_length=MAX_SEQUENCE_LENGTH,
+        max_length=args.max_seq_len,
         truncation=True,
         return_tensors="pt",
     )
@@ -104,28 +103,66 @@ def generate_new_token(shark_model, tokenizer, new_text):
     return ret_dict
 
 
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--max-seq-len", type=int, default=32)
+    parser.add_argument(
+        "--model-name",
+        help="Model name",
+        type=str,
+        choices=[
+            "facebook/opt-125m",
+            "facebook/opt-350m",
+            "facebook/opt-1.3b",
+            "facebook/opt-6.7b",
+        ],
+        default="facebook/opt-1.3b",
+    )
+    parser.add_argument(
+        "--recompile",
+        help="If set, recompiles MLIR -> .vmfb",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+    )
+    parser.add_argument(
+        "--plugin-path",
+        help="path to executable plugin",
+        type=str,
+        default=None,
+    )
+    args = parser.parse_args()
+    print("args={}".format(args))
+    return args
+
+
 if __name__ == "__main__":
-    tokenizer = AutoTokenizer.from_pretrained(
-        "facebook/" + OPT_MODEL, use_fast=False
+    args = parse_args()
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_fast=False)
+    opt_fs_name = "-".join(
+        "_".join(args.model_name.split("/")[1].split("-")).split(".")
     )
-    vmfb_path = (
-        f"./{OPT_FS_NAME}_causallm_{MAX_SEQUENCE_LENGTH}_torch_cpu-task.vmfb"
+    vmfb_path = f"./{opt_fs_name}_causallm_{args.max_seq_len}_torch_cpu.vmfb"
+    if args.plugin_path is not None:
+        rt_flags = [f"--executable_plugin={args.plugin_path}"]
+    else:
+        rt_flags = []
+    opt_shark_module = SharkInference(
+        mlir_module=None, device="cpu-task", rt_flags=rt_flags
     )
-    opt_shark_module = SharkInference(mlir_module=None, device="cpu-task")
     if os.path.isfile(vmfb_path):
         opt_shark_module.load_module(vmfb_path)
     else:
-        vmfb_path = create_module(OPT_MODEL, tokenizer, "cpu-task")
+        vmfb_path = create_module(args.model_name, tokenizer, "cpu-task", args)
         opt_shark_module.load_module(vmfb_path)
     while True:
         try:
             new_text = input("Give me a sentence to complete:")
             new_text_init = new_text
             words_list = []
 
-            for i in range(MAX_NEW_TOKENS):
+            for i in range(args.max_seq_len):
                 generated_token_op = generate_new_token(
-                    opt_shark_module, tokenizer, new_text
+                    opt_shark_module, tokenizer, new_text, args
                 )
                 detok = generated_token_op["detok"]
                 stop_generation = generated_token_op["stop_generation"]

tank/examples/opt/opt_perf_comparison.py

@@ -147,7 +147,7 @@ def load_shark_model(
     plugin_path: str = [],
 ) -> ModelWrapper:
     opt_fs_name = get_opt_fs_name(model_name)
-    vmfb_name = f"{opt_fs_name}_causallm_{max_seq_len}_torch_{DEVICE}_tiled_ukernels.vmfb"
+    vmfb_name = f"{opt_fs_name}_causallm_{max_seq_len}_torch_{DEVICE}.vmfb"
     tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
     if recompile_shark or not os.path.isfile(vmfb_name):
         print(f"vmfb not found. compiling and saving to {vmfb_name}")
@@ -344,7 +344,7 @@ def parse_args():
         default=PLATFORM_SHARK,
     )
     parser.add_argument(
-        "--plugin_path",
+        "--plugin-path",
         help="path to executable plugin",
         type=str,
         default=None,