support HQQ/EETQ #4113

Former-commit-id: b7cb51ddb394f04fe4646b2c297fc8d918c9979e

src/llamafactory/model/model_utils/quantization.py

@@ -23,7 +23,7 @@ from typing import TYPE_CHECKING, Any, Dict, List
 
 import torch
 from datasets import load_dataset
-from transformers import BitsAndBytesConfig, GPTQConfig
+from transformers import BitsAndBytesConfig, EetqConfig, GPTQConfig, HqqConfig
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
 from transformers.utils.versions import require_version
@@ -59,7 +59,7 @@ class QuantizationMethod(str, Enum):
 
 def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[Dict[str, Any]]:
     r"""
-    Prepares the dataset to perform AutoGPTQ.
+    Prepares the tokenized dataset to perform AutoGPTQ. Do not use tensor output for JSON serialization.
     """
     if os.path.isfile(model_args.export_quantization_dataset):
         data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
@@ -93,7 +93,7 @@ def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "Mod
         word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
         input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
         attention_mask = sample["attention_mask"][:, word_idx : word_idx + maxlen]
-        samples.append({"input_ids": input_ids, "attention_mask": attention_mask})
+        samples.append({"input_ids": input_ids.tolist(), "attention_mask": attention_mask.tolist()})
 
     return samples
 
@@ -105,7 +105,7 @@ def configure_quantization(
     init_kwargs: Dict[str, Any],
 ) -> None:
     r"""
-    Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
+    Priority: PTQ-quantized (train/infer) > AutoGPTQ (export) > On-the-fly quantization (train/infer)
     """
     if getattr(config, "quantization_config", None):  # ptq
         if is_deepspeed_zero3_enabled():
@@ -131,6 +131,9 @@ def configure_quantization(
         logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
 
     elif model_args.export_quantization_bit is not None:  # auto-gptq
+        if model_args.export_quantization_bit not in [8, 4, 3, 2]:
+            raise ValueError("AutoGPTQ only accepts 2/3/4/8-bit quantization.")
+
         require_version("optimum>=1.17.0", "To fix: pip install optimum>=1.17.0")
         require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
         from accelerate.utils import get_max_memory
@@ -146,30 +149,48 @@ def configure_quantization(
         init_kwargs["max_memory"] = get_max_memory()
         logger.info("Quantizing model to {} bit with AutoGPTQ.".format(model_args.export_quantization_bit))
 
-    elif model_args.quantization_bit is not None:  # bnb
-        if model_args.quantization_bit == 8:
-            require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
-            init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+    elif model_args.quantization_bit is not None:  # on-the-fly
+        if model_args.quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
+            if model_args.quantization_bit == 8:
+                require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
+                init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+            elif model_args.quantization_bit == 4:
+                require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
+                init_kwargs["quantization_config"] = BitsAndBytesConfig(
+                    load_in_4bit=True,
+                    bnb_4bit_compute_dtype=model_args.compute_dtype,
+                    bnb_4bit_use_double_quant=model_args.double_quantization,
+                    bnb_4bit_quant_type=model_args.quantization_type,
+                    bnb_4bit_quant_storage=model_args.compute_dtype,  # crucial for fsdp+qlora
+                )
+            else:
+                raise ValueError("Bitsandbytes only accepts 4-bit or 8-bit quantization.")
 
-        elif model_args.quantization_bit == 4:
-            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
-            init_kwargs["quantization_config"] = BitsAndBytesConfig(
-                load_in_4bit=True,
-                bnb_4bit_compute_dtype=model_args.compute_dtype,
-                bnb_4bit_use_double_quant=model_args.double_quantization,
-                bnb_4bit_quant_type=model_args.quantization_type,
-                bnb_4bit_quant_storage=model_args.compute_dtype,  # crucial for fsdp+qlora
-            )
+            # Do not assign device map if:
+            # 1. deepspeed zero3 or fsdp (train)
+            # 2. auto quantization device map (inference)
+            if is_deepspeed_zero3_enabled() or is_fsdp_enabled() or model_args.quantization_device_map == "auto":
+                if model_args.quantization_bit != 4:
+                    raise ValueError("Only 4-bit quantized model can use fsdp+qlora or auto device map.")
 
-        # Do not assign device map if:
-        # 1. deepspeed zero3 or fsdp (train)
-        # 2. auto quantization device map (inference)
-        if is_deepspeed_zero3_enabled() or is_fsdp_enabled() or model_args.quantization_device_map == "auto":
-            if model_args.quantization_bit != 4:
-                raise ValueError("Only 4-bit quantized model can use fsdp+qlora or auto device map.")
+                require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
+            else:
+                init_kwargs["device_map"] = {"": get_current_device()}  # change auto device map for inference
 
-            require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
-        else:
-            init_kwargs["device_map"] = {"": get_current_device()}  # change auto device map for inference
+            logger.info("Quantizing model to {} bit with bitsandbytes.".format(model_args.quantization_bit))
+        elif model_args.quantization_method == QuantizationMethod.HQQ.value:
+            if model_args.quantization_bit not in [8, 6, 5, 4, 3, 2, 1]:
+                raise ValueError("HQQ only accepts 1/2/3/4/5/6/8-bit quantization.")
 
-        logger.info("Quantizing model to {} bit with bitsandbytes.".format(model_args.quantization_bit))
+            require_version("hqq", "To fix: pip install hqq")
+            init_kwargs["quantization_config"] = HqqConfig(
+                nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
+            )  # use ATEN kernel (axis=0) for performance
+            logger.info("Quantizing model to {} bit with HQQ.".format(model_args.quantization_bit))
+        elif model_args.quantization_method == QuantizationMethod.EETQ.value:
+            if model_args.quantization_bit != 8:
+                raise ValueError("EETQ only accepts 8-bit quantization.")
+
+            require_version("eetq", "To fix: pip install eetq")
+            init_kwargs["quantization_config"] = EetqConfig()
+            logger.info("Quantizing model to {} bit with EETQ.".format(model_args.quantization_bit))