update mx script (#1838)

Signed-off-by: Mengni Wang <mengni.wang@intel.com>

Author: mengniwang95

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/mx/run_clm_no_trainer.py

@@ -31,8 +31,9 @@
                     help="For accuracy measurement only.")
 parser.add_argument("--save_accuracy_path", default=None,
                     help="Save accuracy results path.")
-parser.add_argument("--tasks", type=str, default="lambada_openai",
-                    help="tasks list for accuracy validation")
+parser.add_argument("--tasks", nargs="+", default=["lambada_openai"], type=str,
+                    help="tasks list for accuracy validation"
+)
 parser.add_argument("--peft_model_id", type=str, default=None, help="model_name_or_path of peft model")
 
 args = parser.parse_args()
@@ -54,57 +55,41 @@ def get_user_model():
     return user_model, tokenizer
 
 user_model, tokenizer = get_user_model()
-if args.quantize:
-    from neural_compressor.torch.quantization import MXQuantConfig, quantize
-    quant_config = MXQuantConfig(w_dtype=args.w_dtype, act_dtype=args.act_dtype, weight_only=args.woq)
-    user_model = quantize(model=user_model, quant_config=quant_config)
 
+from neural_compressor.torch.quantization import MXQuantConfig, prepare, convert
+quant_config = MXQuantConfig(w_dtype=args.w_dtype, act_dtype=args.act_dtype, weight_only=args.woq)
+user_model = prepare(model=user_model, quant_config=quant_config)
+user_model = convert(model=user_model)
+user_model.eval()
 
-if args.accuracy:
-    user_model.eval()
-    from intel_extension_for_transformers.transformers.llm.evaluation.lm_eval import evaluate, LMEvalParser
-    args = LMEvalParser(
-        model="hf",
-        user_model=user_model,
-        tokenizer=tokenizer,
-        batch_size=args.batch_size,
-        tasks=args.tasks,
-        device="cpu",
-    )
-    results = evaluate(args)
-    dumped = json.dumps(results, indent=2)
-    if args.save_accuracy_path:
-        with open(args.save_accuracy_path, "w") as f:
-            f.write(dumped)
-    for task_name in args.tasks:
-        if task_name == "wikitext":
-            acc = results["results"][task_name]["word_perplexity"]
-        else:
-            acc = results["results"][task_name]["acc"]
-    print("Accuracy: %.5f" % acc)
-    print('Batch size = %d' % args.batch_size)
+from intel_extension_for_transformers.transformers.llm.evaluation.lm_eval import evaluate, LMEvalParser
+eval_args = LMEvalParser(
+    model="hf",
+    user_model=user_model,
+    tokenizer=tokenizer,
+    batch_size=args.batch_size,
+    tasks=','.join(args.tasks),
+    device="cpu",
+)
 
-if args.performance:
-    user_model.eval()
-    from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
-    import time
-    samples = args.iters * args.batch_size
-    start = time.time()
-    results = evaluate(
-        model="hf",
-        tokenizer=tokenizer,
-        user_model=user_model,
-        batch_size=args.batch_size,
-        tasks=args.tasks,
-        limit=samples,
-    )
-    end = time.time()
-    for task_name in args.tasks:
-        if task_name == "wikitext":
-            acc = results["results"][task_name]["word_perplexity"]
-        else:
-            acc = results["results"][task_name]["acc"]
-    print("Accuracy: %.5f" % acc)
-    print('Throughput: %.3f samples/sec' % (samples / (end - start)))
-    print('Latency: %.3f ms' % ((end - start)*1000 / samples))
-    print('Batch size = %d' % args.batch_size)
+results = evaluate(eval_args)
+dumped = json.dumps(results, indent=2)
+if args.save_accuracy_path:
+    with open(args.save_accuracy_path, "w") as f:
+        f.write(dumped)
+
+eval_acc = 0
+for task_name in args.tasks:
+    if task_name == "wikitext":
+        print("Accuracy for %s is: %s" %
+              (task_name, results["results"][task_name]["word_perplexity,none"]))
+        eval_acc += results["results"][task_name]["word_perplexity,none"]
+    else:
+        print("Accuracy for %s is: %s" %
+              (task_name, results["results"][task_name]["acc,none"]))
+        eval_acc += results["results"][task_name]["acc,none"]
+
+if len(args.tasks) != 0:
+    eval_acc /= len(args.tasks)
+print("Accuracy: %.5f" % eval_acc)
+print('Batch size = %d' % args.batch_size)