llama factory llama2-13b and mistral-7b pipeline (#93)

* added llama-factory under llm_rl

* added sft training bash

* added datasets from llama-factory; will delete later

* finished llama-2-13b train and inference

* fixed minor errors

* changed config

* added deepspeed config

* added more training config to train bash

* adding fix for wandb tags and distributed ranks

* added fastchat data to replicate training for 2k

* tyring to replicate fastchat as close as possible

* before merging

* changed finetue scripts for better performance

* added new data

* example bash

* example bash for mistral

llm_rl/finetune-llama-2-13b.sh

@@ -1,46 +1,47 @@
 deepspeed src/train_bash.py \
     --stage sft \
     --model_name_or_path meta-llama/Llama-2-13b-hf \
-    --dataset sotopia_easy_sft \
+    --dataset fastchat-sft \
     --dataset_dir ./data/ \
-    --val_size 0.1 \
     --cutoff_len 4096 \
     --template llama2-sotopia \
     --wandb_project "llama-factory-sft" \
     --wandb_tags "['llama-2-13b-hf']" \
     --use_fast_tokenizer False \
     --do_train \
     --num_train_epochs 15.0 \
-    --per_device_train_batch_size 8 \
-    --gradient_accumulation_steps 8 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 32 \
     --finetuning_type lora \
     --lora_target q_proj,v_proj \
-    --lora_rank 8 \
-    --lora_alpha 16 \
-    --lora_dropout 0.05 \
+    --qlora_compute_dtype bf16 \
     --learning_rate 5e-5 \
     --lr_scheduler_type cosine \
     --weight_decay 0. \
     --warmup_ratio 0.03 \
     --quantization_bit 4 \
     --quantization_type nf4 \
-    --double_quantization \
+    --double_quantization True \
     --flash_attn True \
     --gradient_checkpointing True \
-    --bf16 \
+    --bf16 True \
     --cache_dir ./model_cache \
     --overwrite_cache \
     --output_dir ./llama2-13b-sft_cache \
     --overwrite_output_dir \
     --logging_steps 1 \
-    --evaluation_strategy "steps" \
-    --per_device_eval_batch_size 32 \
-    --eval_accumulation_steps 32 \
     --save_strategy "epoch" \
     --save_total_limit 5 \
     --use_auth_token True \
     --wandb_token "99caa13ec9552adf0e92e5c30021307ce3cf7fa4" \
     --hf_auth_token "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" \
     --deepspeed ./deepspeed_config_s2.json
 
-    # --dataset alpaca_gpt4_en \
+    # --dataset alpaca_gpt4_en \
+    # --val_size 0.1 \
+    # --evaluation_strategy "steps" \
+    # --per_device_eval_batch_size 32 \
+    # --eval_accumulation_steps 32 \
+    # --lora_rank 8 \
+    # --lora_alpha 16 \
+    # --lora_dropout 0.05 \

llm_rl/finetune-mistral-7b.sh

@@ -1,18 +1,17 @@
 deepspeed src/train_bash.py \
     --stage sft \
     --model_name_or_path mistralai/Mistral-7B-v0.1 \
-    --dataset dummy_convs \
+    --dataset sotopia_no_slide_no_filter_format_sft \
     --dataset_dir ./data/ \
-    --val_size 0.1 \
     --cutoff_len 4096 \
     --template llama2-sotopia \
     --wandb_project "llama-factory-sft" \
     --wandb_tags "['mistral-7b']" \
     --use_fast_tokenizer False \
     --do_train \
     --num_train_epochs 15.0 \
-    --per_device_train_batch_size 8 \
-    --gradient_accumulation_steps 8 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 32 \
     --finetuning_type lora \
     --lora_target q_proj,v_proj \
     --learning_rate 5e-5 \
@@ -21,19 +20,15 @@ deepspeed src/train_bash.py \
     --warmup_ratio 0.03 \
     --quantization_bit 4 \
     --quantization_type nf4 \
-    --double_quantization \
+    --double_quantization True \
     --flash_attn True \
     --gradient_checkpointing True \
-    --bf16 \
-    --tf32 True \
+    --bf16 True \
     --cache_dir ./model_cache \
     --overwrite_cache \
     --output_dir ./mistral-7b-sft_cache \
     --overwrite_output_dir \
     --logging_steps 1 \
-    --evaluation_strategy "steps" \
-    --per_device_eval_batch_size 32 \
-    --eval_accumulation_steps 32 \
     --save_strategy "epoch" \
     --save_total_limit 5 \
     --use_auth_token True \
@@ -42,6 +37,10 @@ deepspeed src/train_bash.py \
     --deepspeed ./deepspeed_config_s2.json
 
     # --dataset alpaca_gpt4_en \
+    # --val_size 0.1 \
+    # --evaluation_strategy "steps" \
+    # --per_device_eval_batch_size 32 \
+    # --eval_accumulation_steps 32 \
     # --lora_rank 8 \
     # --lora_alpha 16 \
     # --lora_dropout 0.05 \

llm_rl/preprocess/create_sft_data.py

@@ -22,6 +22,6 @@ def join_json_files(directory_path):
                 joined_data.append(new_data)
     return joined_data
 
-joined_data = join_json_files("./GPT4-4_Redis_Easy_No_Slide/")
-with open("../data/GPT4-4_Redis_Easy_No_Slide.json", "w") as f:
+joined_data = join_json_files("./GPT4-4_Redis_Easy_No_Slide_No_Filter_Format/")
+with open("../data/GPT4-4_Redis_Easy_No_Slide_No_Filter_Format.json", "w") as f:
     json.dump(joined_data, f)

llm_rl/requirements.txt

@@ -1,3 +1,5 @@
+packaging
+wheel
 torch>=1.13.1
 transformers>=4.31.0,<4.35.0
 datasets>=2.12.0
@@ -17,7 +19,6 @@ uvicorn
 pydantic
 fastapi
 sse-starlette
-packaging
 matplotlib
 py-cpuinfo
 deepspeed

llm_rl/src/llmtuner/hparams/finetuning_args.py

@@ -52,6 +52,10 @@ class FinetuningArguments:
                   Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \
                   LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
     )
+    lora_bias: Optional[str] = field(
+        default="none",
+        metadata={"help": "The lora_bias option from bitsandbytes."}
+    )
     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."}

llm_rl/src/llmtuner/hparams/model_args.py

@@ -1,3 +1,4 @@
+from torch import float16, bfloat16, float32
 from typing import Literal, Optional
 from dataclasses import dataclass, field
 
@@ -74,9 +75,12 @@ class ModelArguments:
         default=None,
         metadata={"help": "Path to the directory to save the exported model."}
     )
+    qlora_compute_dtype: Optional[str] = field(
+        default="fp32",
+        metadata={"help": "The compute_dtype option from bitsandbytes."}
+    )
 
     def __post_init__(self):
-        self.compute_dtype = None
         self.model_max_length = None
 
         if self.split_special_tokens and self.use_fast_tokenizer:
@@ -91,3 +95,10 @@ def __post_init__(self):
         if self.use_auth_token == True and self.hf_auth_token is not None:
             from huggingface_hub.hf_api import HfFolder # lazy load
             HfFolder.save_token(self.hf_auth_token)
+
+        if self.qlora_compute_dtype == "bf16":
+            self.compute_dtype = bfloat16
+        elif self.qlora_compute_dtype == "fp16":
+            self.compute_dtype = float16
+        else:
+            self.compute_dtype = float32

llm_rl/src/llmtuner/tuner/core/adapter.py

@@ -89,6 +89,7 @@ def init_adapter(
                 lora_alpha=finetuning_args.lora_alpha,
                 lora_dropout=finetuning_args.lora_dropout,
                 target_modules=target_modules,
+                bias=finetuning_args.lora_bias,
                 modules_to_save=finetuning_args.additional_target
             )
             model = get_peft_model(model, lora_config)

llm_rl/src/llmtuner/tuner/core/loader.py

@@ -74,6 +74,7 @@ def load_model_and_tokenizer(
         padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
         **config_kwargs
     )
+    tokenizer.pad_token = tokenizer.unk_token
 
     if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None:
         model_to_load = model_args.checkpoint_dir[0]

llm_rl/src/llmtuner/tuner/sft/workflow.py

@@ -31,6 +31,8 @@ def run_sft(
 
     if training_args.gradient_checkpointing:
         model.enable_input_require_grads()
+        model.gradient_checkpointing_enable()
+        model.config.use_cache = False
 
     if training_args.predict_with_generate:
         tokenizer.padding_side = "left" # use left-padding in generation