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modelling/ft.py

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+import sys
+import logging
+
+import datasets
+from datasets import load_dataset
+from peft import LoraConfig
+import torch
+import transformers
+from trl import SFTTrainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
+
+training_config = {
+    "bf16": True,
+    "do_eval": False,
+    "learning_rate": 5.0e-06,
+    "log_level": "info",
+    "logging_steps": 20,
+    "logging_strategy": "steps",
+    "lr_scheduler_type": "cosine",
+    "num_train_epochs": 1,
+    "max_steps": -1,
+    "output_dir": "./instruct_chk_dir",
+    "overwrite_output_dir": True,
+    "per_device_eval_batch_size": 4,
+    "per_device_train_batch_size": 4,
+    "remove_unused_columns": True,
+    "save_steps": 100,
+    "save_total_limit": 1,
+    "seed": 0,
+    "gradient_checkpointing": True,
+    "gradient_checkpointing_kwargs":{"use_reentrant": False},
+    "gradient_accumulation_steps": 1,
+    "warmup_ratio": 0.2,
+    }
+
+peft_config = {
+    "r": 16,
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "bias": "none",
+    "task_type": "CAUSAL_LM",
+    "target_modules": "all-linear",
+    "modules_to_save": None,
+}
+
+config = {
+    "max_len": 4096,
+}
+
+train_conf = TrainingArguments(**training_config)
+peft_conf = LoraConfig(**peft_config)
+
+
+# Model Init
+checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"
+model_kwargs = dict(
+    use_cache=False,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",  # loading the model with flash-attenstion support
+    torch_dtype=torch.bfloat16,
+    #device_map=None
+    device_map="sequential"
+)
+model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
+tokenizer.model_max_length = config['max_len']
+tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
+tokenizer.padding_side = 'right'
+
+dataset_id = "BAAI/Infinity-Instruct"
+raw_dataset = load_dataset(dataset_id, "0625", split="train")
+dataset = raw_dataset.select(range(10000))
+
+
+# Preproc dataset
+def preproc(example, tokenizer):
+    convo = example['conversations']
+    for i, dic in enumerate(convo):
+        dic['role'] = dic.pop('from')
+        dic['content'] = dic.pop('value')
+        if dic['role'] == 'gpt':
+            dic['role'] = 'assistant'
+        elif dic['role'] == 'human':
+            dic['role'] = 'user'
+
+    example['text'] = tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=False)
+    return example
+
+column_names = list(dataset.features)
+train_dataset = dataset.map(
+    preproc,
+    fn_kwargs={"tokenizer": tokenizer},
+    num_proc=10,
+    remove_columns=column_names
+)
+
+# eval_dataset = dataset[9000:]
+# eval_dataset = eval_dataset.map(
+#     preproc,
+#     fn_kwargs={"tokenizer": tokenizer},
+#     num_proc=10,
+#     remove_columns=column_names
+# )
+
+
+# Train Model
+trainer = SFTTrainer(
+    model=model,
+    args=train_conf,
+    peft_config=peft_conf,
+    train_dataset=train_dataset,
+    #eval_dataset=eval_dataset,
+    max_seq_length=config['max_len'],
+    dataset_text_field="text",
+    tokenizer=tokenizer,
+    packing=True
+)
+train_result = trainer.train()
+metrics = train_result.metrics
+trainer.log_metrics("train", metrics)
+trainer.save_metrics("train", metrics)
+trainer.save_state()
+
+
+# Eval Model
+tokenizer.padding_side = 'left'
+metrics = trainer.evaluate()
+metrics["eval_samples"] = len(processed_test_dataset)
+trainer.log_metrics("eval", metrics)
+trainer.save_metrics("eval", metrics)
+
+
+# Save model
+trainer.save_model(train_conf.output_dir)
+
+# def apply_chat_template(
+#     example,
+#     tokenizer,
+# ):
+#     messages = example["messages"]
+#     example["text"] = tokenizer.apply_chat_template(
+#         messages, tokenize=False, add_generation_prompt=False)
+#     return example
+
+# raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
+# train_dataset = raw_dataset["train_sft"].select(range(10000))
+# test_dataset = raw_dataset["test_sft"].select(range(1000))
+# column_names = list(train_dataset.features)
+
+# processed_train_dataset = train_dataset.map(
+#     apply_chat_template,
+#     fn_kwargs={"tokenizer": tokenizer},
+#     num_proc=10,
+#     remove_columns=column_names,
+#     desc="Applying chat template to train_sft",
+# )
+
+# processed_test_dataset = test_dataset.map(
+#     apply_chat_template,
+#     fn_kwargs={"tokenizer": tokenizer},
+#     num_proc=10,
+#     remove_columns=column_names,
+#     desc="Applying chat template to test_sft",
+# )