forked from ccf-ai-infra/GPUCodeForces
finish ELUGLU#46
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04c0492b9d
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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):
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super().__init__()
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self.alpha = alpha
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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 eluglu_cuda(torch::Tensor input, float alpha);
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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 elu_f(float x, float alpha) {
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return (x > 0.0f) ? x : alpha * (expf(x) - 1.0f);
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}
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__global__ void eluglu_vec4_kernel(
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const float4* __restrict__ x,
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float4* __restrict__ y,
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int vec_dim_out,
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int n_vec_out,
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float alpha)
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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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for (int i = idx; i < n_vec_out; i += stride) {
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int row = i / vec_dim_out;
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int col = i % vec_dim_out;
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int gate_idx = row * (2 * vec_dim_out) + col;
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int act_idx = gate_idx + vec_dim_out;
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float4 g = x[gate_idx];
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float4 a = x[act_idx];
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float4 out;
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out.x = elu_f(g.x, alpha) * a.x;
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out.y = elu_f(g.y, alpha) * a.y;
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out.z = elu_f(g.z, alpha) * a.z;
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out.w = elu_f(g.w, alpha) * a.w;
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y[i] = out;
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}
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}
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torch::Tensor eluglu_cuda(torch::Tensor input, float alpha) {
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auto x_c = input.contiguous();
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int last_dim = x_c.size(-1);
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TORCH_CHECK(last_dim % 8 == 0, "Feature dim must be divisible by 8 for float4 optimization");
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auto out_sizes = x_c.sizes().vec();
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out_sizes.back() /= 2;
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auto output = torch::empty(out_sizes, x_c.options());
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int numel_out = output.numel();
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int n_vec_out = numel_out / 4;
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int vec_dim_out = out_sizes.back() / 4;
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int threads = 256;
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int blocks = (n_vec_out + threads - 1) / threads;
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if (blocks > 65535) blocks = 65535;
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if (blocks == 0) blocks = 1;
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eluglu_vec4_kernel<<<blocks, threads>>>(
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reinterpret_cast<const float4*>(x_c.data_ptr<float>()),
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reinterpret_cast<float4*>(output.data_ptr<float>()),
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vec_dim_out,
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n_vec_out,
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alpha
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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="eluglu_opt_vec4",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["eluglu_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.eluglu_cuda(x, self.alpha)
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@ -2,23 +2,21 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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def __init__(self, alpha=1.0):
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super().__init__()
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self.alpha = alpha
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x * x + x
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gate, act = x.chunk(2, dim=-1)
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return F.elu(gate, alpha=self.alpha) * act
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batch_size = 128
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feature_dim = 512
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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 []
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return [1.0]
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@ -1,85 +0,0 @@
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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._compile_cuda_kernel()
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def _compile_cuda_kernel(self):
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cpp_source = """
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torch::Tensor squ_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 squ_op(float x) {
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return x * x + x;
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}
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__global__ void squ_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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float4 v = x_vec[i];
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float4 r;
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r.x = squ_op(v.x);
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r.y = squ_op(v.y);
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r.z = squ_op(v.z);
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r.w = squ_op(v.w);
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out_vec[i] = r;
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}
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// 处理尾部剩余的不能被 4 整除的元素
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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] = squ_op(x[i]);
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}
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}
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torch::Tensor squ_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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squ_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="squ_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["squ_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.squ_cuda(x)
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@ -2,15 +2,47 @@ You write custom CUDA kernels to replace the pytorch operators in the given GeGL
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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 a custom activation function (SQU - x² + x) with the following optimizations:
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Vectorization: Uses float4memory operations to process 4 elements per thread, significantly increasing memory throughput by leveraging vector loads/stores.
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Memory Coalescing: Accesses contiguous memory blocks through vector operations, optimizing GPU memory bandwidth utilization and reducing memory transactions.
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Grid-Stride Loop: Handles arbitrary-sized tensors efficiently by having threads process multiple elements with strided indexing, ensuring good GPU utilization.
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Tail Processing: Separately handles non-multiple-of-4 elements after vectorized operations to ensure complete data processing.
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Fast Math Optimization: Uses --use_fast_mathcompiler flag for optimized mathematical operations with relaxed precision requirements.
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Occupancy Optimization: Configures 256 threads per block and dynamically calculates grid size based on vectorized element count (threads × 4) to maximize GPU occupancy.
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Compiler Optimizations: Enabled with -O3flag for aggressive performance optimization of the generated code.
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Inlined Device Function: The core mathematical operation is marked with __forceinline__to eliminate function call overhead within the kernel.
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This CUDA kernel implements optimized ELU Gated Linear Unit (GLU) 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 element-wise computation without temporary storage
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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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ELU GLU: elu(gate, alpha) * activation
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Configurable alpha parameter for ELU
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Fast math compilation flags for optimized expf()
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Branching ELU: x > 0 ? x : alpha * (exp(x) - 1)
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Work Distribution:
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Each thread processes 4 elements via float4
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Automatic indexing for gate and activation components
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Direct multiplication of ELU-activated gate with activation
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The implementation provides maximum throughput through vectorization and fast math optimizations, requiring input feature dimension to be divisible by 8 for optimal performance with configurable ELU alpha parameter.
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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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@ -18,23 +50,21 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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def __init__(self, alpha=1.0):
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super().__init__()
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self.alpha = alpha
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x * x + x
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gate, act = x.chunk(2, dim=-1)
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return F.elu(gate, alpha=self.alpha) * act
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batch_size = 128
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feature_dim = 512
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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 []
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return [1.0]
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@ -4,8 +4,8 @@
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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 ShiftedQuadraticUnit_torch import Model, get_inputs, get_init_inputs
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from ShiftedQuadraticUnit_cuda import ModelNew
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from ELUGLU_torch import Model, get_inputs, get_init_inputs
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from ELUGLU_cuda import ModelNew
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def run_benchmark():
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