Reset max_length to supported_text_length when the latter is smaller?

[xyang2013]

chp06 ipynb notebook extract:

def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):
    model.eval()

    # Prepare inputs to the model
    input_ids = tokenizer.encode(text)
    supported_context_length = model.pos_emb.weight.shape[0]
    # Note: In the book, this was originally written as pos_emb.weight.shape[1] by mistake
    # It didn't break the code but would have caused unnecessary truncation (to 768 instead of 1024)

    # Truncate sequences if they too long
    input_ids = input_ids[:min(max_length, supported_context_length)]

    # Pad sequences to the longest sequence
    input_ids += [pad_token_id] * (max_length - len(input_ids))
    input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension

    # Model inference
    with torch.no_grad():
        logits = model(input_tensor)[:, -1, :]  # Logits of the last output token
    predicted_label = torch.argmax(logits, dim=-1).item()

    # Return the classified result
    return "spam" if predicted_label == 1 else "not spam

Hi, if the supported_context_length is less than max_length, should you set the max_length to supported_context_length to make the padding work?

Thank you.

[rasbt]

That's a good point, and yes, you are right. I added a safeguard like that to the notebook in cell 15 (i.e., the assert part):

CHOOSE_MODEL = "gpt2-small (124M)"
INPUT_PROMPT = "Every effort moves"

BASE_CONFIG = {
    "vocab_size": 50257,     # Vocabulary size
    "context_length": 1024,  # Context length
    "drop_rate": 0.0,        # Dropout rate
    "qkv_bias": True         # Query-key-value bias
}

model_configs = {
    "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
    "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
    "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
    "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
}

BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

assert train_dataset.max_length <= BASE_CONFIG["context_length"], (
    f"Dataset length {train_dataset.max_length} exceeds model's context "
    f"length {BASE_CONFIG['context_length']}. Reinitialize data sets with "
    f"`max_length={BASE_CONFIG['context_length']}`"
)