forked from ccf-ai-infra/GPUCodeForces
Merge pull request 'finish RationalFUnctionApproximator #52' (#601) from uucoco/GPUCodeForces:uucoco52 into main
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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.a0 = nn.Parameter(torch.tensor(0.0))
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self.a1 = nn.Parameter(torch.tensor(1.0))
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self.a2 = nn.Parameter(torch.tensor(0.0))
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self.b1 = nn.Parameter(torch.tensor(0.0))
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self.b2 = nn.Parameter(torch.tensor(0.0))
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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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#include <torch/extension.h>
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torch::Tensor pau_cuda(torch::Tensor x, float a0, float a1, float a2, float b1, float b2);
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"""
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cuda_source = """
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#include <cuda_runtime.h>
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__device__ __forceinline__ float compute_pau(float x, double a0, double a1, double a2, double b1, double b2) {
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double val = (double)x;
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double val_sq = val * val;
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double abs_val = (val >= 0.0) ? val : -val;
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double num = a0 + a1 * val + a2 * val_sq;
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double den = 1.0 + b1 * abs_val + b2 * val_sq;
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return (float)(num / den);
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}
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__global__ void pau_kernel_vec4(
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const float* __restrict__ x,
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float* __restrict__ y,
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int total_vecs,
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double a0, double a1, double a2, double b1, double b2)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int stride = gridDim.x * blockDim.x;
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const float4* x_vec = reinterpret_cast<const float4*>(x);
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float4* y_vec = reinterpret_cast<float4*>(y);
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for (int i = idx; i < total_vecs; i += stride) {
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float4 v = x_vec[i];
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float4 out;
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out.x = compute_pau(v.x, a0, a1, a2, b1, b2);
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out.y = compute_pau(v.y, a0, a1, a2, b1, b2);
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out.z = compute_pau(v.z, a0, a1, a2, b1, b2);
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out.w = compute_pau(v.w, a0, a1, a2, b1, b2);
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y_vec[i] = out;
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}
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}
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torch::Tensor pau_cuda(torch::Tensor x, float a0, float a1, float a2, float b1, float b2) {
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auto x_c = x.contiguous();
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auto output = torch::empty_like(x_c);
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int total_elements = x_c.numel();
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if (total_elements % 4 != 0) {
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// Fallback logic or assertion for non-aligned sizes could go here
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// For this benchmark assuming aligned
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}
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int total_vecs = total_elements / 4;
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int threads = 256;
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int blocks = (total_vecs + threads - 1) / threads;
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if (blocks > 65535) blocks = 65535;
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double d_a0 = (double)a0;
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double d_a1 = (double)a1;
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double d_a2 = (double)a2;
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double d_b1 = (double)fabsf(b1);
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double d_b2 = (double)fabsf(b2);
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pau_kernel_vec4<<<blocks, threads>>>(
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x_c.data_ptr<float>(),
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output.data_ptr<float>(),
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total_vecs,
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d_a0, d_a1, d_a2, d_b1, d_b2
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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="pau_opt_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["pau_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.pau_cuda(x,
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self.a0.item(), self.a1.item(), self.a2.item(),
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self.b1.item(), self.b2.item())
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import torch
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import torch.nn as nn
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.a0 = nn.Parameter(torch.tensor(0.0))
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self.a1 = nn.Parameter(torch.tensor(1.0))
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self.a2 = nn.Parameter(torch.tensor(0.0))
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self.b1 = nn.Parameter(torch.tensor(0.0))
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self.b2 = nn.Parameter(torch.tensor(0.0))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_sq = x * x
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abs_x = torch.abs(x)
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num = self.a0 + self.a1 * x + self.a2 * x_sq
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den = 1.0 + torch.abs(self.b1) * abs_x + torch.abs(self.b2) * x_sq
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return num / den
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batch_size = 1024
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feature_dim = 4096
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def get_inputs():
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x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
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return [x]
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def get_init_inputs():
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return []
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You write custom CUDA kernels to replace the pytorch operators in the given GeGLU architecture to get speedups.
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You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
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This CUDA kernel implements optimized Pade Activation Unit (PAU) with:
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Memory Optimization:
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Vectorized memory access using float4 for 4x bandwidth
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Contiguous tensor inputs for coalesced memory access
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Direct computation without temporary storage
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Numerical Precision:
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Double precision for rational function computation
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Absolute value for denominator stability
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Parameter conversion to double for accuracy
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Parallelization Strategy:
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Grid-stride loop for efficient workload distribution
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256 threads per block optimal configuration
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Automatic grid size calculation with 65535 block limit
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Computational Optimization:
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Inline rational function: (a0 + a1*x + a2*x²) / (1 + b1*|x| + b2*x²)
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Efficient computation reuse: val_sq = val * val
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Branchless absolute value calculation
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Work Distribution:
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Each thread processes 4 elements via float4
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Independent PAU computation per element
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No shared memory needed (pure element-wise)
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The implementation balances numerical accuracy with performance through double precision computation and vectorized memory access.
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Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
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import torch
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import torch.nn as nn
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.a0 = nn.Parameter(torch.tensor(0.0))
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self.a1 = nn.Parameter(torch.tensor(1.0))
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self.a2 = nn.Parameter(torch.tensor(0.0))
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self.b1 = nn.Parameter(torch.tensor(0.0))
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self.b2 = nn.Parameter(torch.tensor(0.0))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_sq = x * x
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abs_x = torch.abs(x)
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num = self.a0 + self.a1 * x + self.a2 * x_sq
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den = 1.0 + torch.abs(self.b1) * abs_x + torch.abs(self.b2) * x_sq
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return num / den
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batch_size = 1024
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feature_dim = 4096
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def get_inputs():
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x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
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return [x]
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def get_init_inputs():
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return []
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###########################################################
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# 性能和精度验证程序
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###########################################################
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import torch
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import torch.nn as nn
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import time
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from RationalFunctionApproximator_torch import Model, get_inputs, get_init_inputs
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from RationalFunctionApproximator_cuda import ModelNew
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def run_benchmark():
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# 检查 CUDA 是否可用
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if not torch.cuda.is_available():
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print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
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return
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else:
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device = torch.device("cuda")
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# 初始化模型
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init_inputs = get_init_inputs()
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init_inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
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]
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inputs = get_inputs()
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inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
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]
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torch_model = Model(*init_inputs).cuda()
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cuda_model = ModelNew(*init_inputs).cuda()
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torch_model.eval()
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cuda_model.eval()
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print("-------------------- 精度对齐验证 --------------------")
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with torch.no_grad():
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output_torch = torch_model(*inputs)
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output_cuda = cuda_model(*inputs)
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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03)
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if precision_flag:
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print("✅ 精度对齐：两个模型的输出结果非常接近。")
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else:
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print("❌ 精度不一致！")
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print("\n-------------------- 性能加速比测试 --------------------")
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num_iterations = 100
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# PyTorch 模型计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = torch_model(*inputs)
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torch.cuda.synchronize()
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torch_time = (time.time() - start_time) / num_iterations
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# 自定义 CUDA 内核计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = cuda_model(*inputs)
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torch.cuda.synchronize()
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cuda_time = (time.time() - start_time) / num_iterations
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print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
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speedup = 0
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if cuda_time > 0:
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speedup = torch_time / cuda_time
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print(f"加速比 (Speedup): {speedup:.2f}x")
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else:
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print("CUDA 内核执行时间为0，无法计算加速比。")
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return precision_flag, speedup
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if __name__ == "__main__":
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precision_flag, speedup = run_benchmark()
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