Unify the 'iters' and 'calib_iters' in AutoRound config

intel_extension_for_transformers/transformers/llm/quantization/utils.py

@@ -532,7 +532,7 @@ def default_calib_func(model):
                 "autoround_args": {
                     "n_samples": config.nsamples,
                     "seqlen": config.calib_len,
-                    "iters": config.iters,
+                    "iters": config.calib_iters,
                     "scale_dtype": config.scale_dtype,
                     "enable_quanted_input": not config.disable_quanted_input,
                     "lr": config.lr,

intel_extension_for_transformers/transformers/utils/config.py

@@ -1065,7 +1065,7 @@ def __init__(
         minmax_lr: float = None,
         disable_quanted_input: bool = False,
         nsamples: int = 512,
-        iters: int = 200,
+        iters: int = None,
         use_ggml: bool = False,
         use_neural_speed: bool = False,
         llm_int8_skip_modules=None,
@@ -1091,7 +1091,6 @@ def __init__(
         self.lr = lr
         self.minmax_lr = minmax_lr
         self.disable_quanted_input = disable_quanted_input
-        self.iters = iters
         self.llm_int8_skip_modules = (
             llm_int8_skip_modules if llm_int8_skip_modules else []
         )
@@ -1101,7 +1100,14 @@ def __init__(
         self.calib_dataloader = kwargs.get("calib_dataloader", None)
         self.calib_len = kwargs.get("calib_len", 2048)
         self.calib_func = kwargs.get("calib_func", None)
-        self.calib_iters = kwargs.get("calib_iters", 100)
+        calib_iters = kwargs.get("calib_iters", None)
+        if iters is not None:
+            self.calib_iters = iters
+            if calib_iters is not None:
+                logger.info("cannot be set simultaneously for 'iters' and 'calib_iters', "
+                            "we will use 'iters' as calibration iterations!")
+        else:
+            self.calib_iters = 200 if calib_iters is None else calib_iters
         self.scheme = "sym" if self.sym else "asym"
         if isinstance(compute_dtype, torch.dtype):
             self.compute_dtype = convert_dtype_torch2str(compute_dtype)