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


class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()
        self._compile_cuda_kernel()

    def _compile_cuda_kernel(self):
        cpp_source = """
        #include <torch/extension.h>
        torch::Tensor geglu_dynamic_parallel(torch::Tensor input);
        """

        cuda_source = """
        #include <cuda_runtime.h>

        __device__ float gelu_exact(float x) {
            return 0.5f * x * (1.0f + erff(x * 0.7071067811865475f));
        }

        __global__ void geglu_dynamic_kernel(
            const float* __restrict__ input,
            float* __restrict__ output,
            int feature_dim, int total_elements) {

            extern __shared__ float shared_data[];

            int tid = threadIdx.x;
            int bid = blockIdx.x;
            int bdim = blockDim.x;

            // 动态确定每个block处理的元素数量
            int elements_per_block = min(bdim * 4, total_elements - bid * bdim * 4);
            elements_per_block = max(elements_per_block, 0);

            float* gate_shared = shared_data;
            float* act_shared = shared_data + elements_per_block;

            // 协作加载
            for (int i = tid; i < elements_per_block; i += bdim) {
                int global_idx = bid * bdim * 4 + i;
                if (global_idx < total_elements) {
                    int row = global_idx / (feature_dim / 2);
                    int col = global_idx % (feature_dim / 2);

                    gate_shared[i] = input[row * feature_dim + col];
                    act_shared[i] = input[row * feature_dim + col + (feature_dim / 2)];
                }
            }
            __syncthreads();

            // 处理
            for (int i = tid; i < elements_per_block; i += bdim) {
                int global_idx = bid * bdim * 4 + i;
                if (global_idx < total_elements) {
                    float gate_val = gate_shared[i];
                    float act_val = act_shared[i];
                    output[global_idx] = gelu_exact(gate_val) * act_val;
                }
            }
        }

        torch::Tensor geglu_dynamic_parallel(torch::Tensor input) {
            input = input.contiguous();
            auto sizes = input.sizes().vec();
            int feature_dim = sizes.back();
            sizes.back() /= 2;
            auto output = torch::empty(sizes, input.options());

            int total_elements = output.numel();
            int threads = 128;
            int blocks = (total_elements + threads * 4 - 1) / (threads * 4);
            int shared_mem = threads * 4 * 2 * sizeof(float);

            geglu_dynamic_kernel<<<blocks, threads, shared_mem>>>(
                input.data_ptr<float>(), output.data_ptr<float>(),
                feature_dim, total_elements);

            return output;
        }
        """

        self.op = load_inline(
            name="geglu_dynamic",
            cpp_sources=cpp_source,
            cuda_sources=cuda_source,
            functions=["geglu_dynamic_parallel"],
            extra_cuda_cflags=["-O3"],
            verbose=True
        )

    def forward(self, x):
        return self.op.geglu_dynamic_parallel(x)