Delete utils.py

utils.py

@@ -1,121 +0,0 @@
-import os
-import torch
-from datetime import datetime
-
-# hyperparameters
-BATCH_SIZE = 32  # how many independent sequences will we process in parallel?
-BLOCK_SIZE = 64  # what is the maximum context length for predictions?
-MAX_ITER = 500  # number of training iterations
-EVAL_INTER = 1
-LEARNING_RATE = 3e-4
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-NUM_HEAD = 6
-NUM_EMBED = NUM_HEAD * 128
-NUM_LAYER = 6
-DROPOUT = 0.2
-
-def encode(text_seq: str, tokenizer: any) -> torch.Tensor:
-    """
-    Function to encode input text using a pre-trained tokenizer and vectorized lookups
-    """
-    # tokenize the input text
-    tokens = tokenizer.tokenize(text_seq)
-    # convert the tokens to their corresponding ids
-    token_indices = tokenizer.convert_tokens_to_ids(tokens)
-    token_indices = torch.tensor(token_indices, dtype=torch.long)
-    return token_indices
-
-
-def decode(enc_sec: torch.Tensor, tokenizer: any) -> str:
-    """
-    Function to decode a sequence of token indices back to a string
-    """
-    # convert the indices to a list
-    enc_sec = enc_sec.tolist()
-    # 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(
-    data: list[str],
-    model: torch.nn.Module,
-    block_size: int,
-    batch_size: int,
-    eval_iters: int = 10,
-):
-    out = {}
-    model.eval()
-    losses = torch.zeros(eval_iters)
-    for k in range(eval_iters):
-        X, Y = get_batch(data=data, block_size=block_size, batch_size=batch_size)
-        logits, loss = model.forward(X, Y)
-        losses[k] = loss.item()
-    out = losses.mean()
-    model.train()
-    return out
-
-
-def load_model_from_checkpoint(
-    model_class: torch.nn.Module,
-    path_to_checkpoint: str = "checkpoints/state_dict_model.pt",
-    **kwargs: dict,
-) -> torch.nn.Module:
-    try:
-        state_dict = torch.load(path_to_checkpoint)
-        print("Successfully loaded model from the checkpoint")
-    except Exception as e:
-        print(f"Error loading the model from the checkpoint. {e}")
-
-    model = model_class(**kwargs)
-    # load the state_dict into the model
-    model.load_state_dict(state_dict)
-    return model
-
-
-def save_model_to_chekpoint(
-    model: torch.nn.Module, path_to_checkpoint: str = "checkpoints", epoch: int = 0
-):
-    # check if path exists, otherwise create it
-    if not os.path.exists(path_to_checkpoint):
-        os.makedirs(path_to_checkpoint)
-
-    # datetime object containing current date and time
-    now = datetime.now()
-    # dd/mm/YY H:M:S
-    dt_string = now.strftime("%d.%m.%Y_%H:%M:%S")
-    checkpoint_name = "checkpoint_epoch-" + str(epoch) + "_" + dt_string + ".pt"
-    full_path = os.path.join(path_to_checkpoint, checkpoint_name)
-    try:
-        torch.save(model.state_dict(), full_path)
-        print("Successfully saved the model to {}".format(full_path))
-    except Exception as e:
-        print(f"Error saving the model to checkpoint. {e}")