import torch
import torch.nn.functional as F

def fused_linear_ce(X: torch.Tensor, W: torch.Tensor, B: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
    """
    Baseline fused linear cross entropy implementation using PyTorch.
    
    Args:
        X: Input tensor of shape (M, K) - input features (float16)
        W: Weight tensor of shape (K, N) - weight matrix (float16)
        B: Bias tensor of shape (N,) - bias vector (float32)
        targets: Target tensor of shape (M,) - target class indices (int64)
    
    Returns:
        Output tensor of shape (M,) - negative log-likelihood loss per sample (float32)
    """
    logits = (X @ W).float() + B.float()
    return F.cross_entropy(logits, targets, reduction='none')