forked from ccf-ai-infra/GPUCodeForces
Merge pull request 'finish PDELU #37' (#288) from gsd123/GPUCodeForces:gsd37 into main
This commit is contained in:
commit
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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, alpha=1.0, t=1.5):
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super().__init__()
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self.alpha = alpha
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self.t = t
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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 pdelu_cuda(torch::Tensor x, float alpha, float t);
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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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__global__ void pdelu_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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const float alpha,
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const float t)
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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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const float one_minus_t = 1.0f - t;
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const float inv_one_minus_t = 1.0f / one_minus_t;
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for (int i = tid; i < vec_loops; i += stride) {
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float4 v = __ldg(&x_vec[i]);
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float4 r;
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if (v.x > 0.0f) {
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r.x = v.x;
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} else {
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float base = 1.0f + one_minus_t * v.x;
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r.x = alpha * (powf(base, inv_one_minus_t) - 1.0f);
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}
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if (v.y > 0.0f) {
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r.y = v.y;
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} else {
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float base = 1.0f + one_minus_t * v.y;
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r.y = alpha * (powf(base, inv_one_minus_t) - 1.0f);
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}
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if (v.z > 0.0f) {
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r.z = v.z;
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} else {
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float base = 1.0f + one_minus_t * v.z;
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r.z = alpha * (powf(base, inv_one_minus_t) - 1.0f);
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}
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if (v.w > 0.0f) {
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r.w = v.w;
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} else {
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float base = 1.0f + one_minus_t * v.w;
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r.w = alpha * (powf(base, inv_one_minus_t) - 1.0f);
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}
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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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float v = x[i];
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if (v > 0.0f) {
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output[i] = v;
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} else {
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float base = 1.0f + one_minus_t * v;
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output[i] = alpha * (powf(base, inv_one_minus_t) - 1.0f);
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}
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}
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}
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torch::Tensor pdelu_cuda(torch::Tensor x, float alpha, float t) {
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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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pdelu_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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alpha,
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t
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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="pdelu_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["pdelu_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.pdelu_cuda(x, self.alpha, self.t)
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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, alpha=1.0, t=1.5):
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super().__init__()
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self.alpha = alpha
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self.t = t
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.where(
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x > 0,
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x,
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self.alpha * (torch.pow(1 + (1 - self.t) * x, 1 / (1 - self.t)) - 1)
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)
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batch_size = 1024
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feature_dim = 1024
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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 [1.0, 1.5]
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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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CUDA Optimization Strategies:
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Vectorized Memory Access
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Uses float4 for 4-element vector loads/stores
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__ldg() for read-only caching through texture memory
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Bit shifts for division (>> 2, << 2) for efficiency
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PDELU Activation Function
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Piecewise: x if x > 0 else α * ((1 + (1-t)x)^{1/(1-t)} - 1)
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Generalized ELU variant with parameter t
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Requires expensive powf for negative values
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Precomputed Constants
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Precomputes one_minus_t = 1 - t and inv_one_minus_t = 1/(1-t)
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Avoids repeated computation in loop
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Improves arithmetic intensity
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Memory Access
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contiguous() tensors for coalescing
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__restrict__ pointers
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Grid-stride loop for arbitrary sizes
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Performance Optimization
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Compiler flags: -O3, --use_fast_math
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Efficient kernel launch configuration
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Block count limited to 65535
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Conditional branching per element
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Mathematical Efficiency
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Vectorized operations for 4 elements simultaneously
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Uses powf for power function
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Branch prediction friendly (positive/negative split)
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Key Innovation: Vectorized PDELU activation with parameterized exponential decay, optimized with precomputed constants for the power function's base and exponent.
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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, alpha=1.0, t=1.5):
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super().__init__()
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self.alpha = alpha
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self.t = t
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.where(
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x > 0,
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x,
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self.alpha * (torch.pow(1 + (1 - self.t) * x, 1 / (1 - self.t)) - 1)
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)
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batch_size = 1024
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feature_dim = 1024
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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 [1.0, 1.5]
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@ -0,0 +1,77 @@
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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 PDELU_torch import Model, get_inputs, get_init_inputs
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from PDELU_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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