[code] refactor

.gitignore

@@ -1,2 +1,5 @@
+__pycache__
+.ipynb_checkpoints
+exports
 lightning_logs
 .env

generate.py

@@ -1,34 +1,10 @@
 import argparse
-import os
+from typing import List
-from typing import List, Union
 
 import torch
-from transformers import (
+from transformers import PreTrainedModel, PreTrainedTokenizer
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    PreTrainedModel,
-    PreTrainedTokenizer,
-)
 
-
+from utils import load_model, load_tokenizer
-def load_model(model_name_or_path: Union[str, os.PathLike]) -> PreTrainedModel:
-    try:
-        model = AutoModelForCausalLM.from_pretrained(
-            model_name_or_path, trust_remote_code=True
-        )
-    except ValueError:
-        model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
-    return model
-
-
-def load_tokenizer(model_name_or_path: Union[str, os.PathLike]) -> PreTrainedTokenizer:
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_name_or_path, padding_side='left', trust_remote_code=True
-    )
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token = tokenizer.eos_token
-    return tokenizer
 
 
 def eval_prompts(

lit_module.py

@@ -0,0 +1,81 @@
+from functools import cache
+from typing import Dict, Optional
+
+import pytorch_lightning as pl
+import torch
+import torchmetrics
+
+from utils import init_model
+
+
+class LitModule(pl.LightningModule):
+    def __init__(self, model_name: str, use_tril_attention_mask: str = False):
+        super().__init__()
+        self.save_hyperparameters()
+        self.llm = self.register_core_module(init_model(model_name))
+        self.use_tril_attention_mask = use_tril_attention_mask
+        self.metric_loss = torchmetrics.MeanMetric()
+        self.metric_accuracy = torchmetrics.Accuracy(
+            task='multiclass',
+            num_classes=self.llm.config.vocab_size,
+        )
+
+    @cache
+    def get_batch_tril_matrix(
+        self, block_size: int, batch_size: Optional[int] = None
+    ) -> torch.Tensor:
+        matrix = torch.ones(block_size, block_size).tril()
+        if batch_size is not None:
+            matrix = matrix.repeat(batch_size, 1, 1)
+        return matrix
+
+    def register_core_module(self, module: torch.nn.Module) -> torch.nn.Module:
+        object.__setattr__(self, '__core_module__', module)
+        return module
+
+    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx):
+        batch_size, block_size = batch['input_ids'].shape
+        if self.use_tril_attention_mask:
+            batch['attention_mask'] = self.get_batch_tril_matrix(
+                block_size, batch_size=batch_size
+            ).to(self.device)
+        outputs = self.llm(**batch, return_dict=True)
+        loss = outputs.loss
+
+        self.log('train_loss', loss, rank_zero_only=True)
+
+        return loss
+
+    def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx):
+        outputs = self.llm(**batch, return_dict=True)
+        loss = outputs.loss
+        logits = outputs.logits[..., :-1, :]
+        labels = batch['labels'][..., 1:]
+
+        self.metric_loss.update(loss)
+
+        label_mask = labels != -100
+        self.metric_accuracy.update(logits[label_mask], labels[label_mask])
+
+    def on_validation_epoch_end(self) -> None:
+        self.log('val_loss', self.metric_loss, rank_zero_only=True)
+        self.log('accuracy', self.metric_accuracy, rank_zero_only=True)
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(self.trainer.model.parameters(), lr=0.0001)
+        return optimizer
+
+    def configure_callbacks(self):
+        checkpoint_callback = pl.callbacks.ModelCheckpoint(
+            monitor='accuracy',
+            mode='max',
+            filename='{epoch:02d}-{accuracy:.4f}',
+        )
+        early_stop_callback = pl.callbacks.EarlyStopping(
+            monitor='accuracy',
+            min_delta=0.001,
+            patience=3,
+            mode='max',
+            stopping_threshold=1,
+        )
+        return [checkpoint_callback, early_stop_callback]

lit_train.py

@@ -1,45 +1,21 @@
 import argparse
-import os
+from functools import partial
-from functools import cache, partial
 from itertools import chain
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict, Tuple
 
 import datasets
 import pytorch_lightning as pl
 import torch
-import torchmetrics
 from torch.utils.data import ConcatDataset, DataLoader
 from transformers import (
-    AutoConfig,
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoTokenizer,
     BatchEncoding,
     DefaultDataCollator,
-    PreTrainedModel,
     PreTrainedTokenizer,
     set_seed,
 )
 
-
+from lit_module import LitModule
-def init_model(model_name: Union[str, os.PathLike]) -> PreTrainedModel:
+from utils import load_tokenizer
-    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
-    try:
-        model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
-    except ValueError:
-        model = AutoModel.from_config(config, trust_remote_code=True)
-    return model
-
-
-def load_tokenizer(
-    tokenizer_name_or_path: Union[str, os.PathLike]
-) -> PreTrainedTokenizer:
-    tokenizer = AutoTokenizer.from_pretrained(
-        tokenizer_name_or_path, padding_side='left', trust_remote_code=True
-    )
-    if tokenizer.pad_token_id is None:
-        tokenizer.pad_token = tokenizer.eos_token
-    return tokenizer
 
 
 def split_raw_dataset(
@@ -169,79 +145,6 @@ def parse_args():
     return args
 
 
-class LitModule(pl.LightningModule):
-    def __init__(self, model_name: str, use_tril_attention_mask: str = False):
-        super().__init__()
-        self.save_hyperparameters()
-        self.llm = self.register_core_module(init_model(model_name))
-        self.use_tril_attention_mask = use_tril_attention_mask
-        self.metric_loss = torchmetrics.MeanMetric()
-        self.metric_accuracy = torchmetrics.Accuracy(
-            task='multiclass',
-            num_classes=self.llm.config.vocab_size,
-        )
-
-    @cache
-    def get_batch_tril_matrix(
-        self, block_size: int, batch_size: Optional[int] = None
-    ) -> torch.Tensor:
-        matrix = torch.ones(block_size, block_size).tril()
-        if batch_size is not None:
-            matrix = matrix.repeat(batch_size, 1, 1)
-        return matrix
-
-    def register_core_module(self, module: torch.nn.Module) -> torch.nn.Module:
-        object.__setattr__(self, '__core_module__', module)
-        return module
-
-    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx):
-        batch_size, block_size = batch['input_ids'].shape
-        if self.use_tril_attention_mask:
-            batch['attention_mask'] = self.get_batch_tril_matrix(
-                block_size, batch_size=batch_size
-            ).to(self.device)
-        outputs = self.llm(**batch, return_dict=True)
-        loss = outputs.loss
-
-        self.log('train_loss', loss, rank_zero_only=True)
-
-        return loss
-
-    def validation_step(self, batch: Dict[str, torch.Tensor], batch_idx):
-        outputs = self.llm(**batch, return_dict=True)
-        loss = outputs.loss
-        logits = outputs.logits[..., :-1, :]
-        labels = batch['labels'][..., 1:]
-
-        self.metric_loss.update(loss)
-
-        label_mask = labels != -100
-        self.metric_accuracy.update(logits[label_mask], labels[label_mask])
-
-    def on_validation_epoch_end(self) -> None:
-        self.log('val_loss', self.metric_loss, rank_zero_only=True)
-        self.log('accuracy', self.metric_accuracy, rank_zero_only=True)
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.AdamW(self.trainer.model.parameters(), lr=0.0001)
-        return optimizer
-
-    def configure_callbacks(self):
-        checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            monitor='accuracy',
-            mode='max',
-            filename='{epoch:02d}-{accuracy:.4f}',
-        )
-        early_stop_callback = pl.callbacks.EarlyStopping(
-            monitor='accuracy',
-            min_delta=0.001,
-            patience=3,
-            mode='max',
-            stopping_threshold=1,
-        )
-        return [checkpoint_callback, early_stop_callback]
-
-
 if __name__ == '__main__':
     args = parse_args()
 

utils.py

@@ -0,0 +1,41 @@
+import os
+from typing import Union
+
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+)
+
+
+def init_model(model_name: Union[str, os.PathLike]) -> PreTrainedModel:
+    config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    try:
+        model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
+    except ValueError:
+        model = AutoModel.from_config(config, trust_remote_code=True)
+    return model
+
+
+def load_model(model_name_or_path: Union[str, os.PathLike]) -> PreTrainedModel:
+    try:
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name_or_path, trust_remote_code=True
+        )
+    except ValueError:
+        model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
+    return model
+
+
+def load_tokenizer(
+    tokenizer_name_or_path: Union[str, os.PathLike]
+) -> PreTrainedTokenizer:
+    tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_name_or_path, padding_side='left', trust_remote_code=True
+    )
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    return tokenizer