LLM: remove CPU english_quotes dataset and update docker example (int…

…el-analytics#10399) * update dataset * update readme * update docker cpu * update xpu docker
hxsz1997 · Mar 18, 2024 · a4d9882 · a4d9882
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diff --git a/docker/llm/README.md b/docker/llm/README.md
@@ -467,7 +467,7 @@ docker build \
 
 ### 2. Prepare Base Model, Data and Container
 
-Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below:
+Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
 
 ```bash
 export BASE_MODE_PATH=your_downloaded_base_model_path
@@ -483,7 +483,7 @@ docker run -itd \
    -e http_proxy=${HTTP_PROXY} \
    -e https_proxy=${HTTPS_PROXY} \
    -v $BASE_MODE_PATH:/model \
-   -v $DATA_PATH:/data/english_quotes \
+   -v $DATA_PATH:/data/alpaca-cleaned \
    --shm-size="16g" \
    intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT
 ```
@@ -524,17 +524,17 @@ bash start-qlora-finetuning-on-xpu.sh
 After minutes, it is expected to get results like:
 
 ```bash
-{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03}
-{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06}
-{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1}
-{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13}
-{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16}
-{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19}
-{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22}
-{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26}
-{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29}
-{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32}
-{'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32}
-100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [03:24<00:00,  1.02s/it]
-TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32})
+{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
+{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
+{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
+{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
+{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
+{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
+{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
+{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
+{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
+{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
+{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
+100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
+TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
 ```
diff --git a/docker/llm/finetune/qlora/cpu/docker/Dockerfile b/docker/llm/finetune/qlora/cpu/docker/Dockerfile
@@ -36,13 +36,16 @@ RUN mkdir -p /bigdl/data && mkdir -p /bigdl/model && \
     pip install datasets transformers==4.35.0 && \
     pip install fire peft==0.5.0 && \
     pip install accelerate==0.23.0 && \
+    pip install bitsandbytes && \
     # install basic dependencies
     apt-get update && apt-get install -y curl wget gpg gpg-agent software-properties-common libunwind8-dev && \
     # get qlora example code
     ln -s /usr/bin/python3 /usr/bin/python && \
     cd /bigdl && \
     git clone https://github.com/intel-analytics/BigDL.git && \
     mv BigDL/python/llm/example/CPU/QLoRA-FineTuning/* . && \
+    mkdir -p /GPU/LLM-Finetuning && \
+    mv BigDL/python/llm/example/GPU/LLM-Finetuning/common /GPU/LLM-Finetuning/common && \
     rm -r BigDL && \
     chown -R mpiuser /bigdl
 

diff --git a/docker/llm/finetune/qlora/cpu/docker/README.md b/docker/llm/finetune/qlora/cpu/docker/README.md
@@ -40,7 +40,7 @@ docker build \
 
 ### 2. Prepare Base Model, Data and Container
 
-Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below:
+Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
 
 ```bash
 export BASE_MODE_PATH=your_downloaded_base_model_path
@@ -54,7 +54,7 @@ docker run -itd \
    -e http_proxy=${HTTP_PROXY} \
    -e https_proxy=${HTTPS_PROXY} \
    -v $BASE_MODE_PATH:/bigdl/model \
-   -v $DATA_PATH:/bigdl/data/english_quotes \
+   -v $DATA_PATH:/bigdl/data/alpaca-cleaned \
    intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
 ```
 
@@ -92,19 +92,19 @@ bash start-qlora-finetuning-on-cpu.sh
 After minutes, it is expected to get results like:
 
 ```bash
-{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03}
-{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06}
-{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1}
-{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13}
-{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16}
-{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19}
-{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22}
-{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26}
-{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29}
-{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32}
-{'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32}
-100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [xx:xx<xx:xx,  xxxs/it]
-TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32})
+{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
+{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
+{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
+{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
+{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
+{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
+{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
+{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
+{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
+{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
+{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
+100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
+TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
 ```
 
 ### 4. Merge the adapter into the original model
@@ -117,38 +117,6 @@ python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --
 
 Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.
 
-### 5. Use BigDL-LLM to verify the fine-tuning effect
-
-Train more steps and try input sentence like `['quote'] -> [?]` to verify. For example, using `“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ` to inference.
-BigDL-LLM llama2 example [link](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2). Update the `LLAMA2_PROMPT_FORMAT = "{prompt}"`.
-
-```bash
-python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt "“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:"  --n-predict 20
-```
-
-#### Sample Output
-
-Base_model output
-
-```log
-Inference time: xxx s
--------------------- Prompt --------------------
-“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
--------------------- Output --------------------
-“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: 💻 Fine-tuning a language model on a powerful device like an Intel CPU
-```
-
-Merged_model output
-
-```log
-Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-Inference time: xxx s
--------------------- Prompt --------------------
-“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
--------------------- Output --------------------
-“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ['bigdl'] ['deep-learning'] ['distributed-computing'] ['intel'] ['optimization'] ['training'] ['training-speed']
-```
-
 ### 4. Start Multi-Porcess Fine-Tuning in One Docker
 
 <img src="https://github.com/Uxito-Ada/BigDL/assets/60865256/f25c43b3-2b24-4476-a0fe-804c0ef3c36c" height="240px"><br>

diff --git a/docker/llm/finetune/qlora/cpu/docker/qlora_finetuning_cpu.py b/docker/llm/finetune/qlora/cpu/docker/qlora_finetuning_cpu.py
diff --git a/docker/llm/finetune/qlora/cpu/docker/start-qlora-finetuning-on-cpu.sh b/docker/llm/finetune/qlora/cpu/docker/start-qlora-finetuning-on-cpu.sh
@@ -11,9 +11,9 @@ then
   MODEL_PARAM="--repo-id-or-model-path ./model"  # otherwise, default to download from HF repo
 fi
 
-if [ -d "./data/english_quotes" ];
+if [ -d "./data/alpaca-cleaned" ];
 then
-  DATA_PARAM="--dataset ./data/english_quotes" # otherwise, default to download from HF dataset
+  DATA_PARAM="--dataset ./data/alpaca-cleaned" # otherwise, default to download from HF dataset
 fi
 
 if [ "$STANDALONE_DOCKER" = "TRUE" ]

diff --git a/docker/llm/finetune/qlora/xpu/docker/Dockerfile b/docker/llm/finetune/qlora/xpu/docker/Dockerfile
@@ -34,6 +34,9 @@ RUN curl -fsSL https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-P
     pip install git+https://github.com/huggingface/transformers.git@${TRANSFORMERS_COMMIT_ID} && \
     pip install peft==0.5.0 datasets accelerate==0.23.0 && \
     pip install bitsandbytes scipy && \
+    git clone https://github.com/intel-analytics/BigDL.git && \
+    mv BigDL/python/llm/example/GPU/LLM-Finetuning/common /common && \
+    rm -r BigDL && \
     wget https://raw.githubusercontent.com/intel-analytics/BigDL/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/simple-example/qlora_finetuning.py
 
 COPY ./start-qlora-finetuning-on-xpu.sh /start-qlora-finetuning-on-xpu.sh
diff --git a/docker/llm/finetune/qlora/xpu/docker/README.md b/docker/llm/finetune/qlora/xpu/docker/README.md
@@ -25,7 +25,7 @@ docker build \
 
 ### 2. Prepare Base Model, Data and Container
 
-Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below:
+Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset, and please download them and start a docker container with files mounted like below:
 
 ```bash
 export BASE_MODE_PATH=your_downloaded_base_model_path
@@ -41,7 +41,7 @@ docker run -itd \
    -e http_proxy=${HTTP_PROXY} \
    -e https_proxy=${HTTPS_PROXY} \
    -v $BASE_MODE_PATH:/model \
-   -v $DATA_PATH:/data/english_quotes \
+   -v $DATA_PATH:/data/alpaca-cleaned \
    --shm-size="16g" \
    intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT
 ```
@@ -82,17 +82,17 @@ bash start-qlora-finetuning-on-xpu.sh
 After minutes, it is expected to get results like:
 
 ```bash
-{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03}
-{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06}
-{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1}
-{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13}
-{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16}
-{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19}
-{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22}
-{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26}
-{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29}
-{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32}
-{'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32}
-100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [03:24<00:00,  1.02s/it]
-TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': 204.4633, 'train_samples_per_second': 3.913, 'train_steps_per_second': 0.978, 'train_loss': 1.5072882556915284, 'epoch': 0.32})
+{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
+{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
+{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
+{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
+{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
+{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
+{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
+{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
+{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
+{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
+{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
+100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
+TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
 ```
diff --git a/docker/llm/finetune/qlora/xpu/docker/start-qlora-finetuning-on-xpu.sh b/docker/llm/finetune/qlora/xpu/docker/start-qlora-finetuning-on-xpu.sh
@@ -9,9 +9,9 @@ then
   MODEL_PARAM="--repo-id-or-model-path ./model"  # otherwise, default to download from HF repo
 fi
 
-if [ -d "./data/english_quotes" ];
+if [ -d "./data/alpaca-cleaned" ];
 then
-  DATA_PARAM="--dataset ./data/english_quotes" # otherwise, default to download from HF dataset
+  DATA_PARAM="--dataset ./data/alpaca-cleaned" # otherwise, default to download from HF dataset
 fi
 
 python qlora_finetuning.py $MODEL_PARAM $DATA_PARAM