import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

class ModelNew(nn.Module):
    def __init__(self, lambd=1.0507, alpha=1.67326):
        super().__init__()
        self.lambd = lambd
        self.alpha = alpha
        self._compile_cuda_kernel()

    def _compile_cuda_kernel(self):
        cpp_source = """
        torch::Tensor serlu_cuda(torch::Tensor x, float lambd, float alpha);
        """

        cuda_source = """
        #include <torch/extension.h>
        #include <cuda_runtime.h>
        #include <math.h>

        __global__ void serlu_kernel(
            const float* __restrict__ x,
            float* __restrict__ output,
            const int n_elements,
            const float lambd,
            const float alpha) 
        {
            const int tid = blockIdx.x * blockDim.x + threadIdx.x;
            const int stride = blockDim.x * gridDim.x;

            const int vec_loops = n_elements >> 2;
            const float4* x_vec = reinterpret_cast<const float4*>(x);
            float4* out_vec = reinterpret_cast<float4*>(output);

            const float neg_scale = lambd * alpha;

            for (int i = tid; i < vec_loops; i += stride) {
                float4 v = __ldg(&x_vec[i]);
                float4 r;

                // expm1f(x) 計算 exp(x) - 1，在 x 接近 0 時比 expf(x) - 1 更精確
                r.x = (v.x >= 0.0f) ? (lambd * v.x) : (neg_scale * expm1f(v.x));
                r.y = (v.y >= 0.0f) ? (lambd * v.y) : (neg_scale * expm1f(v.y));
                r.z = (v.z >= 0.0f) ? (lambd * v.z) : (neg_scale * expm1f(v.z));
                r.w = (v.w >= 0.0f) ? (lambd * v.w) : (neg_scale * expm1f(v.w));

                out_vec[i] = r;
            }

            const int tail_start = vec_loops << 2;
            for (int i = tail_start + tid; i < n_elements; i += stride) {
                float v = x[i];
                output[i] = (v >= 0.0f) ? (lambd * v) : (neg_scale * expm1f(v));
            }
        }

        torch::Tensor serlu_cuda(torch::Tensor x, float lambd, float alpha) {
            auto x_c = x.contiguous();
            const int n_elements = x_c.numel();
            auto output = torch::empty_like(x_c);

            const int threads = 256;
            const int max_blocks = 65535;
            const int blocks = std::min((n_elements + threads * 4 - 1) / (threads * 4), max_blocks);

            serlu_kernel<<<blocks, threads>>>(
                x_c.data_ptr<float>(),
                output.data_ptr<float>(),
                n_elements,
                lambd,
                alpha
            );

            return output;
        }
        """

        self.op = load_inline(
            name="serlu_v2",
            cpp_sources=cpp_source,
            cuda_sources=cuda_source,
            functions=["serlu_cuda"],
            extra_cuda_cflags=["-O3", "--use_fast_math"],
            verbose=False
        )

    def forward(self, x):
        return self.op.serlu_cuda(x, self.lambd, self.alpha)