chore: import upstream snapshot with attribution

tutorials/full_finetune_example.py

@@ -0,0 +1,122 @@
+"""
+This script is meant to be the simplest possible starting point for full finetuning a GPT model using lightning fabric with code (not CLI).
+
+- no checkpoints
+- no out dir
+- no precision
+- no resume
+- no train/eval args (or any args in general)
+- no logger (only to terminal)
+- no grad accumulation
+and no other fancy stuff.
+
+To add all the above stuff, you can slowly add them in yourself by looking at the code in litgpt/finetune/full.py or the docs for litgpt/fabric.
+"""
+
+import os
+
+import lightning as L
+import torch
+import torch.nn as nn
+
+from litgpt.data import Alpaca
+from litgpt.model import GPT, Config
+from litgpt.tokenizer import Tokenizer
+from litgpt.utils import num_parameters
+
+# training params/args
+SEED = 1337
+MODEL_NAME = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"  # try also "stabilityai/stablelm-base-alpha-3b"!
+BATCH_SIZE = 4
+LR_WARMUP_STEPS = 100
+MAX_STEPS = 601
+
+
+def validate(model, val_dataloader):
+    model.eval()
+    loss = 0
+    with torch.no_grad():
+        for batch in val_dataloader:
+            input_ids, targets = batch["input_ids"], batch["labels"]
+            logits = model(input_ids)
+            logits = logits.reshape(-1, logits.size(-1))
+            targets = targets.reshape(-1)
+            loss += nn.functional.cross_entropy(logits[..., :-1, :], targets[..., 1:])
+    fabric.print(f"Validation loss: {loss / len(val_dataloader)}")
+
+
+def train(fabric, model, optimizer, scheduler, train_dataloader, val_dataloader):
+    for iter_num, batch in enumerate(train_dataloader):
+        input_ids, targets = batch["input_ids"], batch["labels"]
+
+        # get model preds (logits)
+        logits = model(input_ids)
+        logits = logits.reshape(-1, logits.size(-1))
+
+        # get loss
+        targets = targets.reshape(-1)
+        loss = nn.functional.cross_entropy(logits[..., :-1, :], targets[..., 1:])
+
+        # update weights
+        fabric.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+        scheduler.step()
+
+        # print train loss every 100 steps
+        if iter_num % 100 == 0 or iter_num == 0:
+            fabric.print(f"Train iter {iter_num} -  loss {loss}")
+
+        # validate every 300 steps
+        if iter_num % 300 == 0 or iter_num == 0:
+            validate(model, val_dataloader)
+            model.train()
+        iter_num += 1
+
+        if iter_num >= MAX_STEPS:
+            break
+
+
+def main(fabric):
+    fabric.seed_everything(SEED)
+
+    # setup data, make tokenizer and make dataloaders
+    data = Alpaca()
+    tokenizer = Tokenizer(checkpoint_dir=f"checkpoints/{MODEL_NAME}")
+    data.connect(tokenizer=tokenizer, batch_size=BATCH_SIZE, max_seq_length=1024)
+    data.setup()
+    train_dataloader = data.train_dataloader()
+    val_dataloader = data.val_dataloader()
+    train_dataloader, val_dataloader = fabric.setup_dataloaders(train_dataloader, val_dataloader)
+
+    # print how many steps in an epoch
+    fabric.print(f"Steps in an epoch: {len(train_dataloader)}")
+
+    # setup model
+    config = Config.from_file(f"checkpoints/{MODEL_NAME}/model_config.yaml")
+    model = GPT(config)
+    fabric.print(f"Number of trainable parameters: {num_parameters(model, requires_grad=True):,}")
+    model = fabric.setup(model)
+
+    # setup optimizer
+    optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3, weight_decay=0.02, betas=(0.9, 0.95))
+    optimizer = fabric.setup_optimizers(optimizer)
+
+    # setup lr scheduler
+    scheduler1 = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda step: step / LR_WARMUP_STEPS)
+    scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=(MAX_STEPS - LR_WARMUP_STEPS))
+    scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, [scheduler1, scheduler2], milestones=[LR_WARMUP_STEPS])
+
+    # Start training!!!
+    train(fabric, model, optimizer, scheduler, train_dataloader, val_dataloader)
+
+
+if __name__ == "__main__":
+    # check that the model exists (downloaded to ./checkpoints/)
+    if not os.path.exists(f"checkpoints/{MODEL_NAME}"):
+        print(f"Model {MODEL_NAME} not found. Please download it using `litgpt download --repo {MODEL_NAME}`")
+        exit()
+
+    ### Setup and launch
+    fabric = L.Fabric(devices="auto", strategy="auto")
+    fabric.launch(main)