Add opt_causallm_samples.py. (#1916)

tank/examples/opt/opt_causallm.py

@@ -10,6 +10,7 @@
 from shark.shark_inference import SharkInference
 from shark.shark_importer import import_with_fx
 from transformers import AutoTokenizer, OPTForCausalLM
+from typing import Iterable
 
 
 def create_module(model_name, tokenizer, device, args):
@@ -70,11 +71,11 @@ def shouldStop(tokens):
     return False
 
 
-def generate_new_token(shark_model, tokenizer, new_text, args):
+def generate_new_token(shark_module, tokenizer, new_text, max_seq_len: int):
     model_inputs = tokenizer(
         new_text,
         padding="max_length",
-        max_length=args.max_seq_len,
+        max_length=max_seq_len,
         truncation=True,
         return_tensors="pt",
     )
@@ -83,7 +84,7 @@ def generate_new_token(shark_model, tokenizer, new_text, args):
         model_inputs["attention_mask"],
     )
     sum_attentionmask = torch.sum(model_inputs.attention_mask)
-    output = shark_model("forward", inputs)
+    output = shark_module("forward", inputs)
     output = torch.FloatTensor(output[0])
     next_toks = torch.topk(output, 1)
     stop_generation = False
@@ -135,6 +136,34 @@ def parse_args():
     return args
 
 
+def generate_tokens(
+    opt_shark_module: "SharkInference",
+    tokenizer,
+    input_text: str,
+    max_output_len: int,
+    print_intermediate_results: True,
+) -> Iterable[str]:
+    words_list = []
+    new_text = input_text
+    try:
+        for _ in range(max_output_len):
+            generated_token_op = generate_new_token(
+                opt_shark_module, tokenizer, new_text, max_output_len
+            )
+            detok = generated_token_op["detok"]
+            if generated_token_op["stop_generation"]:
+                break
+            if print_intermediate_results:
+                print(detok, end="", flush=True)
+            words_list.append(detok)
+            if detok == "":
+                break
+            new_text += detok
+    except KeyboardInterrupt as e:
+        print("Exiting token generation.")
+    return words_list
+
+
 if __name__ == "__main__":
     args = parse_args()
     tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_fast=False)
@@ -155,25 +184,7 @@ def parse_args():
         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(args.max_seq_len):
-                generated_token_op = generate_new_token(
-                    opt_shark_module, tokenizer, new_text, args
-                )
-                detok = generated_token_op["detok"]
-                stop_generation = generated_token_op["stop_generation"]
-                if stop_generation:
-                    break
-                print(detok, end="", flush=True)
-                words_list.append(detok)
-                if detok == "":
-                    break
-                new_text = new_text + detok
-
-        except KeyboardInterrupt:
-            print("Exiting program.")
-            break
+        input_text = input("Give me a sentence to complete:")
+        generate_tokens(
+            opt_shark_module, tokenizer, input_text, args.max_seq_len
+        )

tank/examples/opt/opt_causallm_samples.py

@@ -0,0 +1,74 @@
+import argparse
+import os
+
+import opt_causallm
+import opt_util
+
+from shark.shark_inference import SharkInference
+from transformers import AutoTokenizer, OPTForCausalLM
+
+
+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__":
+    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_{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
+    )
+    if os.path.isfile(vmfb_path):
+        opt_shark_module.load_module(vmfb_path)
+    else:
+        vmfb_path = opt_causallm.create_module(
+            args.model_name, tokenizer, "cpu-task", args
+        )
+        opt_shark_module.load_module(vmfb_path)
+
+    for prompt in opt_util.PROMPTS:
+        print("\n\nprompt: {}".format(prompt))
+        response = opt_causallm.generate_tokens(
+            opt_shark_module,
+            tokenizer,
+            prompt,
+            args.max_seq_len,
+            print_intermediate_results=False,
+        )
+        print("reponse: {}".format("".join(response)))

tank/examples/opt/opt_perf_comparison.py

@@ -22,6 +22,7 @@
 import numpy as np
 from typing import Tuple
 
+from opt_util import PROMPTS
 from shark.shark_inference import SharkInference
 from shark.shark_importer import import_with_fx
 from transformers import AutoTokenizer, OPTForCausalLM
@@ -44,19 +45,6 @@
 REPORT_RUN_PHYSICAL_MEMORY_MB = "run_physical_MB"
 REPORT_RUN_VIRTUAL_MEMORY_MB = "run_virtual_MB"
 
-PROMPTS = [
-    "What is the meaning of life?",
-    "Tell me something you don't know.",
-    "What does Xilinx do?",
-    "What is the mass of earth?",
-    "What is a poem?",
-    "What is recursion?",
-    "Tell me a one line joke.",
-    "Who is Gilgamesh?",
-    "Tell me something about cryptocurrency.",
-    "How did it all begin?",
-]
-
 ModelWrapper = collections.namedtuple("ModelWrapper", ["model", "tokenizer"])
 
 

tank/examples/opt/opt_util.py

@@ -0,0 +1,12 @@
+PROMPTS = [
+    "What is the meaning of life?",
+    "Tell me something you don't know.",
+    "What does Xilinx do?",
+    "What is the mass of earth?",
+    "What is a poem?",
+    "What is recursion?",
+    "Tell me a one line joke.",
+    "Who is Gilgamesh?",
+    "Tell me something about cryptocurrency.",
+    "How did it all begin?",
+]