cut-cross-entropy

import torch from cut_cross_entropy import cut_cross_entropy if torch.cuda.is_available(): device = torch.device("cuda") print(f"Using CUDA device: {device}") # Example: logits (batch_size, vocab_size), labels (batch_size,) batch_size = 2 vocab_size = 4 # Data often uses float16 for memory efficiency and performance on GPU logits = torch.randn(batch_size, vocab_size, device=device, dtype=torch.float16) labels = torch.randint(0, vocab_size, (batch_size,), device=device, dtype=torch.int64) # Calculate loss loss = cut_cross_entropy(logits, labels) print(f"Calculated loss: {loss.item():.4f}") # Example with num_total_tokens (for distributed/batched scenarios) num_total_tokens = torch.tensor([10], device=device, dtype=torch.int64) loss_with_tokens = cut_cross_entropy(logits, labels, num_total_tokens) print(f"Calculated loss with total tokens: {loss_with_tokens.item():.4f}") else: print("CUDA is not available. This library is designed for NVIDIA GPUs.") print("Please ensure you have a CUDA-enabled GPU and the correct PyTorch installation.")