support HQQ/EETQ hiyouga#4113

README.md

@@ -48,7 +48,7 @@ Choose your path:
 
 - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
-- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
+- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
 - **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
 - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
@@ -341,7 +341,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
 
-Extra dependencies available: torch, torch_npu, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
+Extra dependencies available: torch, torch-npu, metrics, deepspeed, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, qwen, modelscope, quality
 
 > [!TIP]
 > Use `pip install --no-deps -e .` to resolve package conflicts.

README_zh.md

@@ -48,7 +48,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 - **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
 - **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
-- **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
+- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
 - **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
 - **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
 - **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
@@ -341,7 +341,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
 
-可选的额外依赖项：torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
+可选的额外依赖项：torch、torch-npu、metrics、deepspeed、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、qwen、modelscope、quality
 
 > [!TIP]
 > 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。

setup.py

@@ -39,12 +39,14 @@ def get_requires():
     "metrics": ["nltk", "jieba", "rouge-chinese"],
     "deepspeed": ["deepspeed>=0.10.0"],
     "bitsandbytes": ["bitsandbytes>=0.39.0"],
-    "vllm": ["vllm>=0.4.3"],
-    "galore": ["galore-torch"],
-    "badam": ["badam>=1.2.1"],
+    "hqq": ["hqq"],
+    "eetq": ["eetq"],
     "gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
     "awq": ["autoawq"],
     "aqlm": ["aqlm[gpu]>=1.1.0"],
+    "vllm": ["vllm>=0.4.3"],
+    "galore": ["galore-torch"],
+    "badam": ["badam>=1.2.1"],
     "qwen": ["transformers_stream_generator"],
     "modelscope": ["modelscope"],
     "dev": ["ruff", "pytest"],

src/llamafactory/extras/env.py

@@ -1,4 +1,7 @@
-# Copyright 2024 the LlamaFactory team.
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

src/llamafactory/hparams/model_args.py

@@ -77,6 +77,10 @@ class ModelArguments:
         default=True,
         metadata={"help": "Whether or not to use memory-efficient model loading."},
     )
+    quantization_method: Literal["bitsandbytes", "hqq", "eetq"] = field(
+        default="bitsandbytes",
+        metadata={"help": "Quantization method to use for on-the-fly quantization."},
+    )
     quantization_bit: Optional[int] = field(
         default=None,
         metadata={"help": "The number of bits to quantize the model using bitsandbytes."},
@@ -235,9 +239,6 @@ def __post_init__(self):
         if self.new_special_tokens is not None:  # support multiple special tokens
             self.new_special_tokens = [token.strip() for token in self.new_special_tokens.split(",")]
 
-        assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
-        assert self.export_quantization_bit in [None, 8, 4, 3, 2], "We only accept 2/3/4/8-bit quantization."
-
         if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
             raise ValueError("Quantization dataset is necessary for exporting.")
 

src/llamafactory/model/__init__.py

@@ -14,10 +14,12 @@
 
 from .loader import load_config, load_model, load_tokenizer
 from .model_utils.misc import find_all_linear_modules
+from .model_utils.quantization import QuantizationMethod
 from .model_utils.valuehead import load_valuehead_params
 
 
 __all__ = [
+    "QuantizationMethod",
     "load_config",
     "load_model",
     "load_tokenizer",

src/llamafactory/model/loader.py

@@ -186,11 +186,11 @@ def load_model(
 
     trainable_params, all_param = count_parameters(model)
     if is_trainable:
-        param_stats = "trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
+        param_stats = "trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
             trainable_params, all_param, 100 * trainable_params / all_param
         )
     else:
-        param_stats = "all params: {:d}".format(all_param)
+        param_stats = "all params: {:,}".format(all_param)
 
     logger.info(param_stats)
 

src/llamafactory/model/model_utils/quantization.py

@@ -23,7 +23,7 @@
 
 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 == 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.")
-
-            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_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.")
+
+            # 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
+
+            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.")
+
+            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))

src/llamafactory/webui/chatter.py

@@ -23,7 +23,7 @@
 from ..extras.constants import PEFT_METHODS
 from ..extras.misc import torch_gc
 from ..extras.packages import is_gradio_available
-from .common import get_save_dir
+from .common import QUANTIZATION_BITS, get_save_dir
 from .locales import ALERTS
 
 
@@ -76,11 +76,17 @@ def load_model(self, data) -> Generator[str, None, None]:
             yield error
             return
 
+        if get("top.quantization_bit") in QUANTIZATION_BITS:
+            quantization_bit = int(get("top.quantization_bit"))
+        else:
+            quantization_bit = None
+
         yield ALERTS["info_loading"][lang]
         args = dict(
             model_name_or_path=model_path,
             finetuning_type=finetuning_type,
-            quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
+            quantization_bit=quantization_bit,
+            quantization_method=get("top.quantization_method"),
             template=get("top.template"),
             flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
             use_unsloth=(get("top.booster") == "unsloth"),

src/llamafactory/webui/common.py

@@ -47,6 +47,8 @@
 DEFAULT_DATA_DIR = "data"
 DEFAULT_SAVE_DIR = "saves"
 USER_CONFIG = "user_config.yaml"
+QUANTIZATION_BITS = ["8", "6", "5", "4", "3", "2", "1"]
+GPTQ_BITS = ["8", "4", "3", "2"]
 
 
 def get_save_dir(*paths: str) -> os.PathLike:

src/llamafactory/webui/components/export.py

@@ -18,7 +18,7 @@
 from ...extras.misc import torch_gc
 from ...extras.packages import is_gradio_available
 from ...train.tuner import export_model
-from ..common import get_save_dir
+from ..common import GPTQ_BITS, get_save_dir
 from ..locales import ALERTS
 
 
@@ -32,9 +32,6 @@
     from ..engine import Engine
 
 
-GPTQ_BITS = ["8", "4", "3", "2"]
-
-
 def can_quantize(checkpoint_path: Union[str, List[str]]) -> "gr.Dropdown":
     if isinstance(checkpoint_path, list) and len(checkpoint_path) != 0:
         return gr.Dropdown(value="none", interactive=False)

src/llamafactory/webui/components/top.py

@@ -18,7 +18,7 @@
 from ...extras.constants import METHODS, SUPPORTED_MODELS
 from ...extras.packages import is_gradio_available
 from ..common import get_model_info, list_checkpoints, save_config
-from ..utils import can_quantize
+from ..utils import can_quantize, can_quantize_to
 
 
 if is_gradio_available():
@@ -43,10 +43,11 @@ def create_top() -> Dict[str, "Component"]:
 
     with gr.Accordion(open=False) as advanced_tab:
         with gr.Row():
-            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=2)
-            template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default", scale=2)
-            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=3)
-            booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none", scale=3)
+            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=1)
+            quantization_method = gr.Dropdown(choices=["bitsandbytes", "hqq", "eetq"], value="bitsandbytes", scale=1)
+            template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default", scale=1)
+            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=2)
+            booster = gr.Radio(choices=["auto", "flashattn2", "unsloth"], value="auto", scale=2)
             visual_inputs = gr.Checkbox(scale=1)
 
     model_name.change(get_model_info, [model_name], [model_path, template, visual_inputs], queue=False).then(
@@ -58,6 +59,7 @@ def create_top() -> Dict[str, "Component"]:
         list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
     )
     checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False)
+    quantization_method.change(can_quantize_to, [quantization_method], [quantization_bit], queue=False)
 
     return dict(
         lang=lang,
@@ -67,6 +69,7 @@ def create_top() -> Dict[str, "Component"]:
         checkpoint_path=checkpoint_path,
         advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,
+        quantization_method=quantization_method,
         template=template,
         rope_scaling=rope_scaling,
         booster=booster,

src/llamafactory/webui/locales.py

@@ -85,15 +85,29 @@
     "quantization_bit": {
         "en": {
             "label": "Quantization bit",
-            "info": "Enable 4/8-bit model quantization (QLoRA).",
+            "info": "Enable quantization (QLoRA).",
         },
         "ru": {
             "label": "Уровень квантования",
-            "info": "Включить 4/8-битное квантование модели (QLoRA).",
+            "info": "Включить квантование (QLoRA).",
         },
         "zh": {
             "label": "量化等级",
-            "info": "启用 4/8 比特模型量化（QLoRA）。",
+            "info": "启用量化（QLoRA）。",
+        },
+    },
+    "quantization_method": {
+        "en": {
+            "label": "Quantization method",
+            "info": "Quantization algorithm to use.",
+        },
+        "ru": {
+            "label": "Метод квантования",
+            "info": "Алгоритм квантования, который следует использовать.",
+        },
+        "zh": {
+            "label": "量化方法",
+            "info": "使用的量化算法。",
         },
     },
     "template": {

src/llamafactory/webui/manager.py

@@ -71,6 +71,7 @@ def get_base_elems(self) -> Set["Component"]:
             self._id_to_elem["top.finetuning_type"],
             self._id_to_elem["top.checkpoint_path"],
             self._id_to_elem["top.quantization_bit"],
+            self._id_to_elem["top.quantization_method"],
             self._id_to_elem["top.template"],
             self._id_to_elem["top.rope_scaling"],
             self._id_to_elem["top.booster"],