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fix #3694

This commit is contained in:
hiyouga 2024-05-16 00:35:28 +08:00
parent 44cfa9a1cd
commit 2a67ab3925
7 changed files with 133 additions and 77 deletions
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4
examples/extras/llama_pro/llama3_freeze_sft.yaml
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@ -5,8 +5,8 @@ model_name_or_path: models/llama3-8b-instruct-pro
stage: sft
do_train: true
finetuning_type: freeze
name_module_trainable: all
num_layer_trainable: 8
freeze_trainable_layers: 8
freeze_trainable_modules: all
use_llama_pro: true
# dataset

5
scripts/llama_pro.py
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@ -1,5 +1,5 @@
# coding=utf-8
# Performs block expansion for LLaMA, Mistral or Qwen1.5 models.
# Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
# Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
# Inspired by: https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
@ -106,8 +106,7 @@ def block_expansion(
print("Fine-tune this model with:")
print(" --model_name_or_path {} \\".format(output_dir))
print(" --finetuning_type freeze \\")
print(" --name_module_trainable all \\")
print(" --num_layer_trainable {} \\".format(num_expand))
print(" --freeze_trainable_layers {} \\".format(num_expand))
print(" --use_llama_pro")

110
src/llmtuner/hparams/finetuning_args.py
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@ -1,5 +1,4 @@
import json
from dataclasses import asdict, dataclass, field
from dataclasses import dataclass, field
from typing import Literal, Optional
@ -9,22 +8,40 @@ class FreezeArguments:
Arguments pertaining to the freeze (partial-parameter) training.
"""
name_module_trainable: str = field(
default="all",
freeze_trainable_layers: int = field(
default=2,
metadata={
"help": """Name of trainable modules for partial-parameter (freeze) fine-tuning. \
Use commas to separate multiple modules. \
Use "all" to specify all the available modules. \
LLaMA choices: ["mlp", "self_attn"], \
BLOOM & Falcon & ChatGLM choices: ["mlp", "self_attention"], \
Qwen choices: ["mlp", "attn"], \
InternLM2 choices: ["feed_forward", "attention"], \
Others choices: the same as LLaMA."""
"help": (
"The number of trainable layers for freeze (partial-parameter) fine-tuning. "
"Positive numbers mean the last n layers are set as trainable, "
"negative numbers mean the first n layers are set as trainable."
)
},
)
num_layer_trainable: int = field(
default=2,
metadata={"help": "The number of trainable layers for partial-parameter (freeze) fine-tuning."},
freeze_trainable_modules: str = field(
default="all",
metadata={
"help": (
"Name(s) of trainable modules for freeze (partial-parameter) fine-tuning. "
"Use commas to separate multiple modules. "
"Use `all` to specify all the available modules. "
"LLaMA choices: [`mlp`, `self_attn`], "
"BLOOM & Falcon & ChatGLM choices: [`mlp`, `self_attention`], "
"Qwen choices: [`mlp`, `attn`], "
"InternLM2 choices: [`feed_forward`, `attention`], "
"Others choices: the same as LLaMA."
)
},
)
freeze_extra_modules: Optional[str] = field(
default=None,
metadata={
"help": (
"Name(s) of modules apart from hidden layers to be set as trainable "
"for freeze (partial-parameter) fine-tuning. "
"Use commas to separate multiple modules."
)
},
)
@ -37,7 +54,11 @@ class LoraArguments:
additional_target: Optional[str] = field(
default=None,
metadata={
"help": "Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint."
"help": (
"Name(s) of modules apart from LoRA layers to be set as trainable "
"and saved in the final checkpoint. "
"Use commas to separate multiple modules."
)
},
)
lora_alpha: Optional[int] = field(
@ -55,15 +76,17 @@ class LoraArguments:
lora_target: str = field(
default="all",
metadata={
"help": """Name(s) of target modules to apply LoRA. \
Use commas to separate multiple modules. \
Use "all" to specify all the linear modules. \
LLaMA choices: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], \
BLOOM & Falcon & ChatGLM choices: ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"], \
Baichuan choices: ["W_pack", "o_proj", "gate_proj", "up_proj", "down_proj"], \
Qwen choices: ["c_attn", "attn.c_proj", "w1", "w2", "mlp.c_proj"], \
InternLM2 choices: ["wqkv", "wo", "w1", "w2", "w3"], \
Others choices: the same as LLaMA."""
"help": (
"Name(s) of target modules to apply LoRA. "
"Use commas to separate multiple modules. "
"Use `all` to specify all the linear modules. "
"LLaMA choices: [`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`], "
"BLOOM & Falcon & ChatGLM choices: [`query_key_value`, `dense`, `dense_h_to_4h`, `dense_4h_to_h`], "
"Baichuan choices: [`W_pack`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`], "
"Qwen choices: [`c_attn`, `attn.c_proj`, `w1`, `w2`, `mlp.c_proj`], "
"InternLM2 choices: [`wqkv`, `wo`, `w1`, `w2`, `w3`], "
"Others choices: the same as LLaMA."
)
},
)
loraplus_lr_ratio: Optional[float] = field(
@ -177,8 +200,10 @@ class GaloreArguments:
galore_target: str = field(
default="all",
metadata={
"help": """Name(s) of modules to apply GaLore. Use commas to separate multiple modules. \
Use "all" to specify all the linear modules."""
"help": (
"Name(s) of modules to apply GaLore. Use commas to separate multiple modules. "
"Use `all` to specify all the linear modules."
)
},
)
galore_rank: int = field(
@ -238,16 +263,20 @@ class BAdamArgument:
badam_mask_mode: Literal["adjacent", "scatter"] = field(
default="adjacent",
metadata={
"help": """The mode of the mask for BAdam optimizer. \
`adjacent` means that the trainable parameters are adjacent to each other, \
`scatter` means that trainable parameters are randomly choosed from the weight."""
"help": (
"The mode of the mask for BAdam optimizer. "
"`adjacent` means that the trainable parameters are adjacent to each other, "
"`scatter` means that trainable parameters are randomly choosed from the weight."
)
},
)
badam_verbose: int = field(
default=0,
metadata={
"help": """The verbosity level of BAdam optimizer. \
0 for no print, 1 for print the block prefix, 2 for print trainable parameters"""
"help": (
"The verbosity level of BAdam optimizer. "
"0 for no print, 1 for print the block prefix, 2 for print trainable parameters."
)
},
)
@ -285,7 +314,8 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
return [item.strip() for item in arg.split(",")]
return arg
self.name_module_trainable = split_arg(self.name_module_trainable)
self.freeze_trainable_modules = split_arg(self.freeze_trainable_modules)
self.freeze_extra_modules = split_arg(self.freeze_extra_modules)
self.lora_alpha = self.lora_alpha or self.lora_rank * 2
self.lora_target = split_arg(self.lora_target)
self.additional_target = split_arg(self.additional_target)
@ -315,17 +345,3 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
if self.loraplus_lr_ratio is not None and self.finetuning_type != "lora":
raise ValueError("`loraplus_lr_ratio` is only valid for the LoRA training.")
def save_to_json(self, json_path: str):
r"""Saves the content of this instance in JSON format inside `json_path`."""
json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
with open(json_path, "w", encoding="utf-8") as f:
f.write(json_string)
@classmethod
def load_from_json(cls, json_path: str):
r"""Creates an instance from the content of `json_path`."""
with open(json_path, "r", encoding="utf-8") as f:
text = f.read()
return cls(**json.loads(text))

42
src/llmtuner/model/adapter.py
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@ -1,3 +1,4 @@
import re
from typing import TYPE_CHECKING
import torch
@ -68,37 +69,52 @@ def init_adapter(
raise ValueError("Current model does not support freeze tuning.")
if finetuning_args.use_llama_pro:
if num_layers % finetuning_args.num_layer_trainable != 0:
if num_layers % finetuning_args.freeze_trainable_layers != 0:
raise ValueError(
"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(
num_layers, finetuning_args.num_layer_trainable
num_layers, finetuning_args.freeze_trainable_layers
)
)
stride = num_layers // finetuning_args.num_layer_trainable
stride = num_layers // finetuning_args.freeze_trainable_layers
trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
elif finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = range(num_layers - finetuning_args.num_layer_trainable, num_layers)
elif finetuning_args.freeze_trainable_layers > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = range(max(0, num_layers - finetuning_args.freeze_trainable_layers), num_layers)
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = range(-finetuning_args.num_layer_trainable)
trainable_layer_ids = range(min(-finetuning_args.freeze_trainable_layers, num_layers))
freeze_modules = {"all"}
for name, _ in model.named_modules():
hidden_modules = set()
non_hidden_modules = set()
for name, _ in model.named_parameters():
if ".0." in name:
freeze_modules.add(name.split(".0.")[-1].split(".")[0])
hidden_modules.add(name.split(".0.")[-1].split(".")[0])
elif ".1." in name: # MoD starts from layer 1
freeze_modules.add(name.split(".1.")[-1].split(".")[0])
hidden_modules.add(name.split(".1.")[-1].split(".")[0])
if re.search(r"\.\d+\.", name) is None:
non_hidden_modules.add(name.split(".")[-2])
trainable_layers = []
for module_name in finetuning_args.name_module_trainable:
if module_name not in freeze_modules:
for module_name in finetuning_args.freeze_trainable_modules:
if module_name != "all" and module_name not in hidden_modules:
raise ValueError(
"Module {} is not found, please choose from {}".format(module_name, ", ".join(freeze_modules))
"Module {} is not found, please choose from {}".format(module_name, ", ".join(hidden_modules))
)
for idx in trainable_layer_ids:
trainable_layers.append(".{:d}.{}".format(idx, module_name if module_name != "all" else ""))
if finetuning_args.freeze_extra_modules:
for module_name in finetuning_args.freeze_extra_modules:
if module_name not in non_hidden_modules:
raise ValueError(
"Module {} is not found, please choose from {}".format(
module_name, ", ".join(non_hidden_modules)
)
)
trainable_layers.append(module_name)
for name, param in model.named_parameters():
if any(trainable_layer in name for trainable_layer in trainable_layers):
if cast_trainable_params_to_fp32:

12
src/llmtuner/webui/components/train.py
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@ -124,13 +124,17 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
with gr.Accordion(open=False) as freeze_tab:
with gr.Row():
num_layer_trainable = gr.Slider(minimum=1, maximum=128, value=2, step=1)
name_module_trainable = gr.Textbox(value="all")
freeze_trainable_layers = gr.Slider(minimum=-128, maximum=128, value=2, step=1)
freeze_trainable_modules = gr.Textbox(value="all")
freeze_extra_modules = gr.Textbox()
input_elems.update({num_layer_trainable, name_module_trainable})
input_elems.update({freeze_trainable_layers, freeze_trainable_modules, freeze_extra_modules})
elem_dict.update(
dict(
freeze_tab=freeze_tab, num_layer_trainable=num_layer_trainable, name_module_trainable=name_module_trainable
freeze_tab=freeze_tab,
freeze_trainable_layers=freeze_trainable_layers,
freeze_trainable_modules=freeze_trainable_modules,
freeze_extra_modules=freeze_extra_modules,
)
)

32
src/llmtuner/webui/locales.py
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@ -572,24 +572,24 @@ LOCALES = {
"label": "部分参数微调设置",
},
},
"num_layer_trainable": {
"freeze_trainable_layers": {
"en": {
"label": "Trainable layers",
"info": "The number of trainable layers.",
"info": "Number of the last(+)/first(-) hidden layers to be set as trainable.",
},
"ru": {
"label": "Обучаемые слои",
"info": "Количество обучаемых слоев.",
"info": "Количество последних (+)/первых (-) скрытых слоев, которые будут установлены как обучаемые.",
},
"zh": {
"label": "可训练层数",
"info": "可训练模型层的数量。",
"info": "最末尾（+）/最前端（-）可训练隐藏层的数量。",
},
},
"name_module_trainable": {
"freeze_trainable_modules": {
"en": {
"label": "Trainable modules",
"info": "The name of trainable modules. Use commas to separate multiple modules.",
"info": "Name(s) of trainable modules. Use commas to separate multiple modules.",
},
"ru": {
"label": "Обучаемые модули",
@ -600,6 +600,26 @@ LOCALES = {
"info": "可训练模块的名称。使用英文逗号分隔多个名称。",
},
},
"freeze_extra_modules": {
"en": {
"label": "Extra modules (optional)",
"info": (
"Name(s) of modules apart from hidden layers to be set as trainable. "
"Use commas to separate multiple modules."
),
},
"ru": {
"label": "Дополнительные модули (опционально)",
"info": (
"Имена модулей, кроме скрытых слоев, которые следует установить в качестве обучаемых. "
"Используйте запятые для разделения нескольких модулей."
),
},
"zh": {
"label": "额外模块（非必填）",
"info": "除隐藏层以外的可训练模块名称。使用英文逗号分隔多个名称。",
},
},
"lora_tab": {
"en": {
"label": "LoRA configurations",

5
src/llmtuner/webui/runner.py
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@ -146,8 +146,9 @@ class Runner:
)
if args["finetuning_type"] == "freeze":
args["num_layer_trainable"] = get("train.num_layer_trainable")
args["name_module_trainable"] = get("train.name_module_trainable")
args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
args["freeze_trainable_modules"] = get("train.freeze_trainable_modules")
args["freeze_extra_modules"] = get("train.freeze_extra_modules") or None
elif args["finetuning_type"] == "lora":
args["lora_rank"] = get("train.lora_rank")
args["lora_alpha"] = get("train.lora_alpha")