forked from ccf-ai-infra/GPUCodeForces
79 lines
2.5 KiB
Python
79 lines
2.5 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, alpha: float = 1.0, beta: float = 1.0):
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super().__init__()
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self.alpha = alpha
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self.beta = beta
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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 psmish_cuda(torch::Tensor x, float alpha, float beta);
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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__ double psmish_op_double(double x, double alpha, double beta) {
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double bx = beta * x;
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double gate = tanh(log(1.0 + exp(bx)));
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return alpha * x * gate;
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}
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__global__ void psmish_kernel_double_intermediate(
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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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const float alpha,
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const float beta)
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{
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const int block_start = blockIdx.x * blockDim.x;
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const int block_end = min(block_start + blockDim.x, n_elements);
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double alpha_d = (double)alpha;
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double beta_d = (double)beta;
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for (int i = block_start + threadIdx.x; i < block_end; i += blockDim.x) {
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double val_d = (double)x[i];
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double result_d = psmish_op_double(val_d, alpha_d, beta_d);
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output[i] = (float)result_d;
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}
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}
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torch::Tensor psmish_cuda(torch::Tensor x, float alpha, float beta) {
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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 - 1) / threads, max_blocks);
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psmish_kernel_double_intermediate<<<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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alpha,
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beta
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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="psmish_v3",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["psmish_cuda"],
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extra_cuda_cflags=["-O3"],
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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.psmish_cuda(x, self.alpha, self.beta) |