Update utils.py

utils.py

@@ -3,18 +3,12 @@ import torch
 from datetime import datetime
 
 # hyperparameters
-BATCH_SIZE = 64  # how many independent sequences will we process in parallel?
-BLOCK_SIZE = 128  # what is the maximum context length for predictions?
-MAX_ITER = 2  # number of training iterations
-EVAL_INTER = 1
-LEARNING_RATE = 1e-5
-EPS = 1e-5
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 NUM_HEAD = 6
 NUM_EMBED = NUM_HEAD * 128
 NUM_LAYER = 8
 DROPOUT = 0.3
-MAX_SEQ_LEN = 2048
+MAX_SEQ_LEN = 4096
 
 def encode(text_seq: str, tokenizer: any) -> torch.Tensor:
     """
@@ -37,52 +31,3 @@ def decode(enc_sec: torch.Tensor, tokenizer: any) -> str:
     # decode the indices to a string
     text = tokenizer.decode(enc_sec)
     return text
-
-
-def get_batch(data: list[str], block_size: int, batch_size: int):
-    """
-    This is a simple function to create batches of data.
-    GPUs allow for parallel processing we can feed multiple chunks at once
-    so that's why we would need batches - how many independant sequences
-    will we process in parallel.
-
-    Parameters:
-    data: list[str]: data to take batch from
-    block_size (int): size of the text that is proccessed at once
-    batch_size (int): number of sequences to process in parallel
-
-    Returns:
-    x, y: a tuple with token sequence and token target
-    """
-
-    ix = torch.randint(len(data) - block_size, (batch_size, ))
-    # we stack batch_size rows of sentences
-    # so x and y are the matrices with rows_num=batch_size
-    # and col_num=block_size
-    x = torch.stack([data[i : i + block_size] for i in ix])
-    # y is x shifted one position right - because we predict
-    # word in y having all the previous words as context
-    y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix])
-    x, y = x.to(DEVICE), y.to(DEVICE)
-    return x, y
-
-
-@torch.no_grad()
-def estimate_loss(
-    val_loader,
-    model: torch.nn.Module,
-    eval_iters: int = 10
-):
-    out = {}
-    model.eval()
-    losses = torch.zeros(eval_iters)
-    k = 0
-    for x, y in val_loader:
-        if k >= eval_iters:
-            break
-        logits, loss = model.forward(x, y)
-        losses[k] = loss.item()
-        k += 1
-    out = losses.mean()
-    model.train()
-    return out