forked from ccf-ai-infra/GPUCodeForces
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.cpp_extension import load_inline
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class ModelNew(nn.Module):
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def __init__(self):
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super().__init__()
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self._compile_cuda_kernel()
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def _compile_cuda_kernel(self):
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cpp_source = """
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torch::Tensor brelu_cuda(torch::Tensor x);
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"""
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cuda_source = """
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#include <torch/extension.h>
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#include <cuda_runtime.h>
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#include <math.h>
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__device__ __forceinline__ float brelu_op(float x, int index) {
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// Even index (i % 2 == 0): ReLU(x) -> max(0, x)
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if ((index & 1) == 0) {
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return fmaxf(0.0f, x);
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}
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// Odd index (i % 2 != 0): -ReLU(-x) -> min(0, x)
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return fminf(0.0f, x);
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}
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__global__ void brelu_kernel(
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const float* __restrict__ x,
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float* __restrict__ output,
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const int n_elements)
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{
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const int tid = blockIdx.x * blockDim.x + threadIdx.x;
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const int stride = blockDim.x * gridDim.x;
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const int vec_loops = n_elements >> 2;
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const float4* x_vec = reinterpret_cast<const float4*>(x);
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float4* out_vec = reinterpret_cast<float4*>(output);
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for (int i = tid; i < vec_loops; i += stride) {
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const int base_index = i << 2;
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float4 v = __ldg(&x_vec[i]);
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float4 r;
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r.x = brelu_op(v.x, base_index);
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r.y = brelu_op(v.y, base_index + 1);
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r.z = brelu_op(v.z, base_index + 2);
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r.w = brelu_op(v.w, base_index + 3);
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out_vec[i] = r;
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}
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const int tail_start = vec_loops << 2;
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for (int i = tail_start + tid; i < n_elements; i += stride) {
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output[i] = brelu_op(x[i], i);
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}
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}
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torch::Tensor brelu_cuda(torch::Tensor x) {
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auto x_c = x.contiguous();
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const int n_elements = x_c.numel();
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auto output = torch::empty_like(x_c);
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const int threads = 256;
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const int max_blocks = 65535;
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const int blocks = std::min((n_elements + threads * 4 - 1) / (threads * 4), max_blocks);
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brelu_kernel<<<blocks, threads>>>(
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x_c.data_ptr<float>(),
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output.data_ptr<float>(),
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n_elements
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);
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return output;
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}
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"""
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self.op = load_inline(
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name="brelu_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["brelu_cuda"],
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extra_cuda_cflags=["-O3", "--use_fast_math"],
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verbose=False
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)
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def forward(self, x):
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return self.op.brelu_cuda(x) |