release v0.5.3

Former-commit-id: f6bc89581b3cd129448da2defc23848de6f494ed

src/llmtuner/model/patcher.py

@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedTokenizer
     from trl import AutoModelForCausalLMWithValueHead
 
-    from ..hparams import FinetuningArguments, ModelArguments
+    from ..hparams import ModelArguments
 
 
 logger = get_logger(__name__)
@@ -157,7 +157,7 @@ def _configure_quantization(
     config_kwargs: Dict[str, Any],
 ) -> None:
     r"""
-    Priority: GPTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
+    Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
     """
     if getattr(config, "quantization_config", None):  # gptq
         if is_deepspeed_zero3_enabled():
@@ -167,7 +167,15 @@ def _configure_quantization(
         quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
         if quantization_config.get("quant_method", None) == "gptq" and quantization_config.get("bits", -1) == 4:
             quantization_config["use_exllama"] = False  # disable exllama
-        logger.info("Loading {}-bit GPTQ-quantized model.".format(quantization_config.get("bits", -1)))
+
+        if quantization_config.get("quant_method", None) == "aqlm":
+            quantization_config["bits"] = 2
+
+        logger.info(
+            "Loading {}-bit {}-quantized model.".format(
+                quantization_config.get("bits", "?"), quantization_config.get("quant_method", None)
+            )
+        )
 
     elif model_args.export_quantization_bit is not None:  # auto-gptq
         require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
@@ -253,7 +261,6 @@ def patch_config(
     config: "PretrainedConfig",
     tokenizer: "PreTrainedTokenizer",
     model_args: "ModelArguments",
-    finetuning_args: "FinetuningArguments",
     config_kwargs: Dict[str, Any],
     is_trainable: bool,
 ) -> None:
@@ -274,9 +281,6 @@ def patch_config(
 
     _configure_quantization(config, tokenizer, model_args, config_kwargs)
 
-    if finetuning_args.use_dora:
-        config_kwargs["device_map"] = {"": get_current_device()}
-
 
 def patch_model(
     model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments", is_trainable: bool