revise training script

experiments/ablations/continued_pretrain.py

@@ -2,82 +2,89 @@
 import torch
 from unsloth import add_new_tokens
 from typing import Optional, List
-from transformers import TrainingArguments
 from unsloth import is_bfloat16_supported
 from unsloth import UnslothTrainer, UnslothTrainingArguments
-import fire
 import wandb
 from datasets import load_dataset
+import fire
 
-
-def load_model(rank: int = 128, train_embeddings: bool = True, add_special_tokens: Optional[List[str]]=None):
-    max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
-    dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
-    load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+def load_model(
+    rank: int = 128,
+    train_embeddings: bool = True,
+    add_special_tokens: Optional[List[str]] = None,
+):
+    max_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!
+    dtype = None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+    load_in_4bit = True  # Use 4bit quantization to reduce memory usage. Can be False.
 
     model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name = "unsloth/llama-3-8b-bnb-4bit",
-        max_seq_length = max_seq_length,
-        dtype = dtype,
-        load_in_4bit = load_in_4bit,
+        model_name="unsloth/llama-3-8b-bnb-4bit",
+        max_seq_length=max_seq_length,
+        dtype=dtype,
+        load_in_4bit=load_in_4bit,
     )
 
-    add_new_tokens(model, tokenizer, new_tokens = add_special_tokens)
+    add_new_tokens(model, tokenizer, new_tokens=add_special_tokens)
 
-    target_modules =  ["q_proj", "k_proj", "v_proj", "o_proj",
-                    "gate_proj", "up_proj", "down_proj"]
+    target_modules = [
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ]
 
     if train_embeddings:
-        target_modules +=    ["embed_tokens", "lm_head"]
+        target_modules += ["embed_tokens", "lm_head"]
     model = FastLanguageModel.get_peft_model(
         model,
-        r = rank, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
-        target_modules = target_modules,
-        lora_alpha = rank/4,
-        lora_dropout = 0, # Supports any, but = 0 is optimized
-        bias = "none",    # Supports any, but = "none" is optimized
+        r=rank,  # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+        target_modules=target_modules,
+        lora_alpha=rank / 4,
+        lora_dropout=0,  # Supports any, but = 0 is optimized
+        bias="none",  # Supports any, but = "none" is optimized
         # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
-        use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
-        random_state = 3407,
-        use_rslora = True,   # We support rank stabilized LoRA
-        loftq_config = None, # And LoftQ
+        use_gradient_checkpointing="unsloth",  # True or "unsloth" for very long context
+        random_state=3407,
+        use_rslora=True,  # We support rank stabilized LoRA
+        loftq_config=None,  # And LoftQ
     )
 
     return model, tokenizer
 
 
-def train(model, tokenizer, dataset, run_name: str, batch_size:int =64, max_seq_length = 2048):
-    wandb.init(
-        project="chemnlp-ablations",
-        name=run_name
-    )
+def train(
+    model, tokenizer, dataset, run_name: str, batch_size: int = 64, max_seq_length=2048
+):
+    wandb.init(project="chemnlp-ablations", name=run_name)
     trainer = UnslothTrainer(
-        model = model,
-        tokenizer = tokenizer,
-        train_dataset = dataset,
-        dataset_text_field = "text",
-        max_seq_length = max_seq_length,
-        dataset_num_proc = 2,
-
-        args = UnslothTrainingArguments(
-            per_device_train_batch_size = batch_size,
-            gradient_accumulation_steps = 1,
-            warmup_ratio = 0.1,
-            num_train_epochs = 1,
-            learning_rate = 5e-5,
-            embedding_learning_rate = 1e-5,
-            fp16 = not is_bfloat16_supported(),
-            bf16 = is_bfloat16_supported(),
-            logging_steps = 1,
-            optim = "adamw_8bit",
-            weight_decay = 0.01,
-            lr_scheduler_type = "linear",
-            seed = 3407,
-            output_dir = f"outputs_{run_name}",
+        model=model,
+        tokenizer=tokenizer,
+        train_dataset=dataset,
+        dataset_text_field="text",
+        max_seq_length=max_seq_length,
+        dataset_num_proc=2,
+        args=UnslothTrainingArguments(
+            per_device_train_batch_size=batch_size,
+            gradient_accumulation_steps=1,
+            warmup_ratio=0.1,
+            num_train_epochs=1,
+            learning_rate=5e-5,
+            embedding_learning_rate=1e-5,
+            fp16=not is_bfloat16_supported(),
+            bf16=is_bfloat16_supported(),
+            logging_steps=1,
+            optim="adamw_8bit",
+            weight_decay=0.01,
+            lr_scheduler_type="linear",
+            seed=3407,
+            output_dir=f"outputs_{run_name}",
         ),
     )
 
-    #@title Show current memory stats
+    # @title Show current memory stats
     gpu_stats = torch.cuda.get_device_properties(0)
     start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
     max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
@@ -86,28 +93,38 @@ def train(model, tokenizer, dataset, run_name: str, batch_size:int =64, max_seq_
 
     trainer_stats = trainer.train()
 
-    model.save_pretrained(f"lora_model_{run_name}") # Local saving
+    model.save_pretrained(f"lora_model_{run_name}")  # Local saving
     tokenizer.save_pretrained(f"lora_model_{run_name}")
 
 
 def create_dataset(tokenizer, datasets):
-    EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
+    EOS_TOKEN = tokenizer.eos_token  # Must add EOS_TOKEN
+
     def formatting_prompts_func(examples):
         outputs = []
-        for t in  examples['text']:
+        for t in examples["text"]:
             outputs.append(t + EOS_TOKEN)
-        return { "text" : outputs, }
+        return {
+            "text": outputs,
+        }
 
     dataset = load_dataset("json", data_files=datasets)
     dataset = dataset["train"]
 
-    dataset = dataset.map(formatting_prompts_func, batched = True)
+    dataset = dataset.map(formatting_prompts_func, batched=True)
 
     return dataset
 
-if __name__ == "__main__":
-    model, tokenizer = load_model(train_embeddings=True, add_special_tokens=None)
 
-    dataset = create_dataset(tokenizer, ["data/chemnlp_train.json", "data/chemnlp_val.json"])
+def run(data_files: List[str], train_embeddings: bool, run_name: str, batch_size: int, add_special_tokens: Optional[List[str]]=None)
+    model, tokenizer = load_model(train_embeddings=train_embeddings, add_special_tokens=add_special_tokens )
 
-    train(model, tokenizer, dataset, "lora_128", batch_size=64)
+    dataset = create_dataset(
+        tokenizer, data_files
+    )
+
+    train(model, tokenizer, dataset, run_name, batch_size=batch_size)
+
+
+if __name__ == "__main__":
+    fire.Fire(run)

pyproject.toml

@@ -13,21 +13,21 @@ dynamic = ["version"]
 [project.optional-dependencies]
 dev = ["pre-commit", "pytest"]
 dataset_creation = [
-    "PyTDC",
-    "rdkit",
-    "ruamel.yaml",
-    "selfies",
-    "deepsmiles",
-    "pubchempy",
-    "bioc",
-    "pylatexenc",
-    "canonicalize_psmiles@git+https://github.com/Ramprasad-Group/canonicalize_psmiles.git",
-    "rxn-chem-utils",
-    "backoff",
-    "givemeconformer",
-    "chembl_webresource_client",
-    "dask",
-    "pandarallel",
+  "PyTDC",
+  "rdkit",
+  "ruamel.yaml",
+  "selfies",
+  "deepsmiles",
+  "pubchempy",
+  "bioc",
+  "pylatexenc",
+  "canonicalize_psmiles@git+https://github.com/Ramprasad-Group/canonicalize_psmiles.git",
+  "rxn-chem-utils",
+  "backoff",
+  "givemeconformer",
+  "chembl_webresource_client",
+  "dask",
+  "pandarallel"
 ]
 
 [project.scripts]

src/chemnlp/data/utils.py

@@ -9,12 +9,13 @@
 
 
 from pathlib import Path
-import fire
+
 
 def get_all_datasets(root_dir):
     return [d.name for d in Path(root_dir).iterdir() if d.is_dir()]
 
-def concatenate_jsonl_files(root_dir, output_file, datasets=None, file_type='train'):
+
+def concatenate_jsonl_files(root_dir, output_file, datasets=None, file_type="train"):
     root_dir = Path(root_dir)
 
     if datasets is None:
@@ -25,28 +26,28 @@ def concatenate_jsonl_files(root_dir, output_file, datasets=None, file_type='tra
     print(f"Processing datasets: {', '.join(datasets)}")
     print(f"File type: {file_type}.jsonl")
 
-    with open(output_file, 'w') as outfile:
+    with open(output_file, "w") as outfile:
         for dataset in datasets:
             dataset_path = root_dir / dataset
             if not dataset_path.is_dir():
                 print(f"Warning: Dataset '{dataset}' not found. Skipping.")
                 continue
 
-            for chunk_dir in dataset_path.glob('chunk_*'):
-                for template_dir in chunk_dir.glob('template_*'):
-                    jsonl_file = template_dir / f'{file_type}.jsonl'
+            for chunk_dir in dataset_path.glob("chunk_*"):
+                for template_dir in chunk_dir.glob("template_*"):
+                    jsonl_file = template_dir / f"{file_type}.jsonl"
                     if jsonl_file.is_file():
-                        with open(jsonl_file, 'r') as infile:
+                        with open(jsonl_file, "r") as infile:
                             for line in infile:
                                 outfile.write(line)
 
     print(f"Concatenated {file_type}.jsonl files have been saved to {output_file}")
 
+
 def concatenate_jsonl_files_cli():
     fire.Fire(concatenate_jsonl_files)
 
 
-
 def add_random_split_column(df):
     # Calculate the number of rows for each split
     n_rows = len(df)