add fine-tuning code with lora support

finetune/README.md

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+## How to Fine-tune DeepSeek-Coder-V2 
+
+We provide script `finetune_deepseekcoder.py` for users to finetune our models on downstream tasks.
+
+The script supports the training with [DeepSpeed](https://github.com/microsoft/DeepSpeed). You need install required packages by:
+
+```bash
+pip install flash-attn --no-build-isolation
+pip install -r requirements.txt
+```
+
+Please follow [Sample Dataset Format](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) to prepare your training data.
+
+You can download the sample dataset from [HuggingFace](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) by:
+
+```bash
+wget https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1/resolve/main/EvolInstruct-Code-80k.json
+```
+
+Each line is a json-serialized string with two required fields `instruction` and `output`.
+
+After data preparation, you can use the sample shell script to finetune `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`. Remember to specify `DATA_PATH`, `OUTPUT_PATH`.
+And please choose appropriate hyper-parameters(e.g., `learning_rate`, `per_device_train_batch_size`) according to your scenario. For devices supported by flash_attention, you can refer [here](https://github.com/Dao-AILab/flash-attention).
+For this configuration, zero_stage needs to be set to 3.
+
+```bash
+DATA_PATH="<your_data_path>"
+OUTPUT_PATH="<your_output_path>"
+MODEL_PATH="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
+
+deepspeed finetune_deepseekcoder.py \
+    --model_name_or_path $MODEL_PATH \
+    --data_path $DATA_PATH \
+    --output_dir $OUTPUT_PATH \
+    --num_train_epochs 3 \
+    --model_max_length 1024 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 100 \
+    --learning_rate 2e-5 \
+    --warmup_steps 10 \
+    --logging_steps 1 \
+    --lr_scheduler_type "cosine" \
+    --gradient_checkpointing True \
+    --report_to "tensorboard" \
+    --deepspeed configs/ds_config_zero3.json \
+    --bf16 True \
+    --use_lora False
+```
+
+You can also finetune the model with 4/8-bits qlora, feel free to try it. For this configuration, it is possible to run on a single A100 80G GPU, and adjustments can be made according to your resources.
+
+```bash
+DATA_PATH="<your_data_path>"
+OUTPUT_PATH="<your_output_path>"
+MODEL_PATH="<your_model_path>"
+
+deepspeed finetune.py \
+    --model_name_or_path $MODEL_PATH \
+    --data_path $DATA_PATH \
+    --output_dir $OUTPUT_PATH \
+    --num_train_epochs 3 \
+    --model_max_length 1024 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 100 \
+    --learning_rate 2e-5 \
+    --warmup_steps 10 \
+    --logging_steps 1 \
+    --lr_scheduler_type "cosine" \
+    --gradient_checkpointing True \
+    --report_to "tensorboard" \
+    --deepspeed configs/ds_config_zero2_no_offload.json \
+    --bf16 True \
+    --use_lora True \
+    --bits 4 \
+    --max_grad_norm 0.3 \
+    --double_quant \
+    --lora_r 64 \
+    --lora_alpha 16 \
+    --quant_type nf4 \
+```

finetune/configs/ds_config_zero2_no_offload.json

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+{
+    "bf16": {
+        "enabled": true
+    },
+
+    "zero_optimization": {
+        "stage": 2,
+        "allgather_partitions": true,
+        "allgather_bucket_size": 1e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": 1e8,
+        "contiguous_gradients": true
+    },
+
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "steps_per_print": 2000,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}

finetune/configs/ds_config_zero3.json

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+{
+    "bf16": {
+        "enabled": "auto"
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "betas": "auto",
+            "eps": "auto",
+            "weight_decay": "auto"
+        }
+    },
+
+    "scheduler": {
+        "type": "WarmupLR",
+        "params": {
+            "warmup_min_lr": "auto",
+            "warmup_max_lr": "auto",
+            "warmup_num_steps": "auto"
+        }
+    },
+
+    "zero_optimization": {
+        "stage": 3,
+        "offload_optimizer": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "offload_param": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": "auto",
+        "stage3_prefetch_bucket_size": "auto",
+        "stage3_param_persistence_threshold": "auto",
+        "stage3_max_live_parameters": 1e9,
+        "stage3_max_reuse_distance": 1e9,
+        "stage3_gather_16bit_weights_on_model_save": true
+    },
+
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "steps_per_print": 20,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}