| import torch |
| import torch.nn.functional as F |
|
|
| def fused_linear_jsd(X: torch.Tensor, W1: torch.Tensor, B1: torch.Tensor, W2: torch.Tensor, B2: torch.Tensor) -> torch.Tensor: |
| """ |
| Baseline fused linear Jensen-Shannon Divergence implementation using PyTorch. |
| |
| Args: |
| X: Input tensor of shape (M, K) - input features (float16) |
| W1: Weight tensor of shape (K, N) - first weight matrix (float16) |
| B1: Bias tensor of shape (N,) - first bias vector (float32) |
| W2: Weight tensor of shape (K, N) - second weight matrix (float16) |
| B2: Bias tensor of shape (N,) - second bias vector (float32) |
| |
| Returns: |
| Output tensor of shape (M,) - Jensen-Shannon Divergence per sample (float32) |
| """ |
| logits1 = (X.float() @ W1.float()) + B1.float() |
| logits2 = (X.float() @ W2.float()) + B2.float() |
| P = torch.softmax(logits1, dim=-1) |
| Q = torch.softmax(logits2, dim=-1) |
| Mmid = 0.5 * (P + Q) |
| eps = 1e-12 |
| jsd = 0.5 * (torch.sum(P * (torch.log(P + eps) - torch.log(Mmid + eps)), dim=-1) + |
| torch.sum(Q * (torch.log(Q + eps) - torch.log(Mmid + eps)), dim=-1)) |
| return jsd |
|
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|
