Delete files src/task/training.py with huggingface_hub

src/task/training.py

@@ -1,103 +0,0 @@
-import torch
-import torch.nn as nn
-
-from src.infra.dataset import (AutoRegressiveDataset, get_batch)
-
-
-@torch.no_grad()
-def evaluate(
-    model,
-    dataset: AutoRegressiveDataset,
-    model_config,
-    training_config,
-):
-    model.eval()
-
-    loss_function = nn.CrossEntropyLoss()
-
-    losses = []
-
-    for _ in range(training_config.eval_batches):
-
-        x, y = get_batch(
-            dataset=dataset,
-            batch_size=training_config.batch_size,
-        )
-
-        logits = model(x)
-
-        loss = loss_function(
-            logits.view(-1, logits.size(-1)),
-            y.view(-1),
-        )
-
-        losses.append(loss.item())
-
-    model.train()
-
-    return sum(losses) / len(losses)
-
-
-
-
-
-
-def train(
-    model,
-    train_dataset: AutoRegressiveDataset,
-    val_dataset: AutoRegressiveDataset,
-    model_config,
-    training_config,
-):
-    loss_function = nn.CrossEntropyLoss()
-
-    optimizer = torch.optim.AdamW(
-        model.parameters(),
-        lr=training_config.learning_rate,
-    )
-
-    model.train()
-
-    for step in range(training_config.num_steps):
-
-        if step % training_config.eval_interval == 0:
-
-            train_loss = evaluate(
-                model=model,
-                dataset=train_dataset,
-                model_config=model_config,
-                training_config=training_config,
-            )
-
-            val_loss = evaluate(
-                model=model,
-                dataset=val_dataset,
-                model_config=model_config,
-                training_config=training_config,
-            )
-
-            print(
-                f"step {step:4d} | "
-                f"train {train_loss:.4f} | "
-                f"val {val_loss:.4f}"
-            )
-
-        x, y = get_batch(
-            dataset=train_dataset,
-            batch_size=training_config.batch_size,
-        )
-
-        logits = model(x)
-
-        loss = loss_function(
-            logits.view(-1, logits.size(-1)),
-            y.view(-1),
-        )
-
-        optimizer.zero_grad()
-
-        loss.backward()
-
-        optimizer.step()
-
-    return model