Pypto Case Loss Crossentropy

模式 D：Loss — CrossEntropyLoss

def create_cross_entropy_kernel(batch, num_classes):
    @pypto.frontend.jit(runtime_options=..., debug_options=...)
    def kernel(
        predictions: pypto.Tensor((batch, num_classes), pypto.DT_FP32),
        targets: pypto.Tensor((batch,), pypto.DT_INT64),
    ) -> pypto.Tensor((1,), pypto.DT_FP32):
        output = pypto.tensor([1], pypto.DT_FP32)
        # Phase 1: per-sample softmax + gather
        pypto.set_vec_tile_shapes(1024, 16)
        log_probs = pypto.log(pypto.softmax(predictions, dim=1))
        targets_i32 = pypto.cast(targets, pypto.DT_INT32)
        idx = pypto.unsqueeze(targets_i32, 1)
        picked = pypto.gather(log_probs, dim=1, index=idx)
        neg_picked = pypto.mul(picked, -1.0)
        # Phase 2: batch reduction
        pypto.set_vec_tile_shapes(2048, 8)
        total = pypto.sum(neg_picked, dim=0, keepdim=False)
        output[:] = total / batch
        return output
    return kernel

forward：assert → contiguous → 调 kernel → reshape(1,)

模式要点

• 两段 tile：不同计算阶段用不同 tile 配置
• pypto.cast(targets, DT_INT32) — INT64 输入需转 INT32
• pypto.unsqueeze + pypto.gather — 按 index 取元素
• pypto.mul(x, -1.0) — 取反的标准写法（规则 R2 的应用）
• 标量输出：pypto.tensor([1], ...) + output[:] = scalar
• 逐元素 loss（MSE/Huber 等）更简单：所有输入 reshape(-1) 用 1D kernel