Support LoRA ChatGLM with Alpaca Dataset (#11580)

* Support LoRA ChatGLM with Alpaca Dataset

* refine

* fix

* add 2-card alpaca

python/llm/example/GPU/LLM-Finetuning/LoRA/chatglm_finetune/README.md

@@ -31,20 +31,30 @@ source /opt/intel/oneapi/setvars.sh
 
 ### 3. LoRA Fine-Tune on ChatGLM3-6B
 
-First, download the dataset: we use `AdvertiseGen` to finetune ChatGLM3-6B in the following, and please now get it from [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1), and unzip it in the current directory. Then, process the dataset with the below script:
+First, as for the dataset, you have two options:
+
+1. `AdvertiseGen`: please now get it from [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1), and unzip it in the current directory. Then, process the dataset with the below script:
 
 ```bash
 python process_advertise_gen_dataset.py
 ```
 
 Then, './AdvertiseGen' will be converted to './AdvertiseGen_fix'. Now, we have prepared the dataset, and are going to start LoRA fine-tuning on ChatGLM3-6B.
 
+2. `Alapca`: We also support [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) that contains generated instructions and demonstrations. It does not require preprocessing, and please directy run the following script.
+
 #### 3.1. Fine-Tune with a Single Arc Card
 
-Start the fine-tuning by:
+1. For `AdvertiseGen`, start the fine-tuning by:
+
+```bash
+bash lora_finetuning_chatglm3_6b_on_advertise_gen_with_1_arc_card.sh
+```
+
+2. For `Alpaca`, start the fine-tuning by:
 
 ```bash
-bash lora_finetuning_on_chatglm3_6b_with_1_arc_card.sh
+bash lora_finetuning_chatglm3_6b_on_alpaca_with_1_arc_card.sh
 ```
 
 Then, you will get output are as below:
@@ -145,6 +155,14 @@ Training completed. Do not forget to share your model on huggingface.co/models =
 
 Start the data-parallel fine-tuning on 2 Intel Arc XPU cards by:
 
+1. `AdvertiseGen` dataset:
+
+```bash
+bash lora_finetuning_chatglm3_6b_on_advertise_gen_with_2_arc_cards.sh
+```
+
+2. `Alpaca` dataset:
+
 ```bash
-bash lora_finetuning_on_chatglm3_6b_with_2_arc_cards.sh
+bash lora_finetuning_chatglm3_6b_on_alpaca_with_2_arc_cards.sh
 ```

python/llm/example/GPU/LLM-Finetuning/LoRA/chatglm_finetune/lora_finetune_chatglm.py

@@ -65,8 +65,15 @@
 )
 from transformers import DataCollatorForSeq2Seq as _DataCollatorForSeq2Seq
 
+from transformers import Trainer
 from transformers import Seq2SeqTrainer as _Seq2SeqTrainer
 
+current_dir = os.path.dirname(os.path.realpath(__file__))
+common_util_path = os.path.join(current_dir, '..', '..')
+import sys
+sys.path.append(common_util_path)
+from common.utils import get_train_val_data, Prompter
+
 ModelType = Union[PreTrainedModel, PeftModelForCausalLM]
 TokenizerType = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
 app = typer.Typer(pretty_exceptions_show_locals=False)
@@ -247,7 +254,7 @@ def _load_datasets(
     return dataset_dct
 
 
-class DataManager(object):
+class AdvertiseGenDataManager(object):
     def __init__(self, data_dir: str, data_config: DataConfig):
         self._num_proc = data_config.num_proc
 
@@ -283,6 +290,52 @@ def get_dataset(
             num_proc=self._num_proc,
         )
 
+class AlpacaDataConfig(object):
+    def __init__(self, tokenizer, prompter, train_on_inputs,
+                 add_eos_token, cutoff_len, val_set_size, seed):
+        self.tokenizer = tokenizer
+        self.prompter = prompter
+        self.train_on_inputs = train_on_inputs
+        self.add_eos_token = add_eos_token
+        self.cutoff_len = cutoff_len
+        self.val_set_size = val_set_size
+        self.seed = seed
+
+
+class AlpacaDataManager(object):
+    def __init__(self, data_dir: str, data_config: AlpacaDataConfig):
+        if data_dir.endswith(".json") or data_dir.endswith(".jsonl"):
+            data = load_dataset("json", data_files=data_dir)
+        else:
+            data = load_dataset(data_dir)
+        self.train_data, self.val_data = get_train_val_data(
+            data,
+            data_config.tokenizer,
+            data_config.prompter,
+            data_config.train_on_inputs,
+            data_config.add_eos_token,
+            data_config.cutoff_len,
+            data_config.val_set_size,
+            seed=data_config.seed)
+        self.train_data = self.train_data.remove_columns(
+            ['output', 'input', 'instruction', 'attention_mask', 'position_ids'])
+        self.val_data = self.val_data.remove_columns(
+            ['output', 'input', 'instruction', 'attention_mask', 'position_ids'])
+
+    def get_dataset(
+            self,
+            split: NamedSplit,
+            process_fn: Callable[[dict[str, Any]], dict[str, Any]],
+            batched: bool = True,
+            remove_orig_columns: bool = True,
+    ) -> Optional[Dataset]:
+        if split == Split.TRAIN:
+            return self.train_data
+        elif split == Split.VALIDATION:
+            return self.val_data
+        else:
+            return None
+
 
 def print_model_size(model: PreTrainedModel):
     print("--> Model")
@@ -484,7 +537,17 @@ def main(
 ):
     ft_config = FinetuningConfig.from_file(config_file)
     tokenizer, model = load_tokenizer_and_model(model_dir, peft_config=ft_config.peft_config)
-    data_manager = DataManager(data_dir, ft_config.data_config)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    if 'AdvertiseGen' in data_dir:
+        data_manager = AdvertiseGenDataManager(data_dir, ft_config.data_config)
+    elif 'alpaca' in data_dir:
+        data_config = AlpacaDataConfig(tokenizer=tokenizer, prompter=Prompter("alpaca"),
+                                       train_on_inputs=True, add_eos_token=False,
+                                       cutoff_len=256, val_set_size=2000, seed=42)
+        data_manager = AlpacaDataManager(data_dir, data_config)
+    else:
+        raise NotImplementedError("Wrong dataset, currently only support AdvertiseGen and Alpaca")
 
     train_dataset = data_manager.get_dataset(
         Split.TRAIN,
@@ -530,38 +593,47 @@ def main(
     # turn model to fp32
     _prepare_model_for_training(model, ft_config.training_args.use_cpu)
 
-    ft_config.training_args.generation_config.pad_token_id = (
-        tokenizer.pad_token_id
-    )
-    ft_config.training_args.generation_config.eos_token_id = [
-        tokenizer.eos_token_id,
-        tokenizer.get_command('<|user|>'),
-        tokenizer.get_command('<|observation|>'),
-    ]
+    if 'AdvertiseGen' in data_dir:
+        ft_config.training_args.generation_config.pad_token_id = (
+            tokenizer.pad_token_id
+        )
+        ft_config.training_args.generation_config.eos_token_id = [
+            tokenizer.eos_token_id,
+            tokenizer.get_command('<|user|>'),
+            tokenizer.get_command('<|observation|>'),
+        ]
     model.gradient_checkpointing_enable()
     model.enable_input_require_grads()
 
-    use_tokenizer = True
-    if ft_config.peft_config is not None:
-        use_tokenizer = False if ft_config.peft_config.peft_type == "LORA" else True
+    if 'AdvertiseGen' in data_dir:
+        use_tokenizer = True
+        if ft_config.peft_config is not None:
+            use_tokenizer = False if ft_config.peft_config.peft_type == "LORA" else True
+    else:
+        use_tokenizer = False
 
     # Add below L544-L546 to enable finetuning on 2 Intel Arc XPU cards on top of oneccl and deepspeed
     if deepspeed_config_file != '':
         ft_config.training_args.ddp_backend = "ccl"
         ft_config.training_args.deepspeed = deepspeed_config_file
 
-    trainer = Seq2SeqTrainer(
+    from transformers import Trainer, TrainingArguments, DataCollatorForSeq2Seq
+
+    BASE_TRAINER = Trainer if 'alpaca' in data_dir else Seq2SeqTrainer
+
+    trainer = BASE_TRAINER(
         model=model,
         args=ft_config.training_args,
         data_collator=DataCollatorForSeq2Seq(
             tokenizer=tokenizer,
-            padding='longest',
             return_tensors='pt',
+            padding=True if 'alpaca' in data_dir else 'longest',
+            pad_to_multiple_of=8 if 'alpaca' in data_dir else None,
         ),
         train_dataset=train_dataset,
         eval_dataset=val_dataset.select(list(range(50))),
         tokenizer=tokenizer if use_tokenizer else None,  # LORA does not need tokenizer
-        compute_metrics=functools.partial(compute_metrics, tokenizer=tokenizer),
+        compute_metrics=functools.partial(compute_metrics, tokenizer=tokenizer) if 'AdvertiseGen' in data_dir else None,
     )
 
     if auto_resume_from_checkpoint.upper() == "" or auto_resume_from_checkpoint is None:

python/llm/example/GPU/LLM-Finetuning/LoRA/chatglm_finetune/lora_finetuning_chatglm3_6b_on_alpaca_with_1_arc_card.sh

@@ -0,0 +1,23 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+export BIGDL_CHECK_DUPLICATE_IMPORT=0
+
+# You can also set the remote model repository to a local model path
+python lora_finetune_chatglm.py \
+       yahma/alpaca-cleaned \
+       THUDM/chatglm3-6b  \
+       ./lora_config.yaml

python/llm/example/GPU/LLM-Finetuning/LoRA/chatglm_finetune/lora_finetuning_chatglm3_6b_on_alpaca_with_2_arc_cards.sh

@@ -0,0 +1,29 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+export MASTER_ADDR=127.0.0.1
+export OMP_NUM_THREADS=6
+export FI_PROVIDER=tcp
+export CCL_ATL_TRANSPORT=ofi
+export BIGDL_CHECK_DUPLICATE_IMPORT=0
+
+# You can also set the remote model repository to a local model path
+mpirun -n 2 \
+    python lora_finetune_chatglm.py \
+        yahma/alpaca-cleaned  \
+        THUDM/chatglm3-6b  \
+        ./lora_config.yaml \
+	./deepspeed_config.json