turn 0323.2s turn · 22.6s LLM · 300.6s tools · tokens in=1,109 · out=1,598 · total=3,256 · FINAL
reasoning
The task is to replace the forward computation of a PyTorch model with a custom CUDA kernel. The model is a pre-norm 1D dilated convolution block with BatchNorm, ReLU, dilated Conv1d, and residual add...I need to replace the forward computation of a PyTorch model with a custom CUDA kernel. The model is a pre-norm 1D dilated convolution block with BatchNorm, ReLU, dilated Conv1d, and residual addition.
assistant```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void fused_bn_relu_add_kernel(
const float* __restrict__ x,
const float* __restrict__ gamma,
const float* __restrict__ beta,
const float* __restrict__ mean,
const float* __restrict__ var,
const float* __restrict__ residual,
float* __restrict__ out,
int N, int C, int L,
float eps
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = N * C * L;
if (idx >= total) return;
int l = idx % L;
int c = (idx / L) % C;
int n = idx / (L * C);
float val = x[idx];
float m = mean[c];
float v = var[c];
float g = gamma[c];
float b = beta[c];
float normed = (val - m) / sqrtf(v + eps);
float activated = normed * g + b;
activated = fmaxf(activated, 0.0f); // ReLU
out[idx] = activated + residual[idx];
}
torch::Tensor fused_bn_relu_add(
torch::Tensor x,
torch::Tensor gamma,
torch::Tensor beta,
torch::Tensor mean,
torch::Tensor var,
torch::Tensor residual,
float eps
) {
auto N = x.size(0);
auto C = x.size(1);
auto L = x.size(2);
auto out = torch::empty_like(x);
int total = N * C * L;
int threads = 256;
int blocks = (total + threads - 1) / threads;
fused_bn_relu_add_kernel<<<blocks, threads>>>(
x.data_ptr<float>(),
gamma.data_ptr<float>(),
beta.data_ptr<float>(),
mean.data_ptr<float>(),
var.data_ptr<float>(),
residual.data_ptr<float>(),
out.data_ptr<float>(),
N, C, L, eps
);
return out;
}
'''
cpp_source = r'''
torch::Tensor fused_bn_relu_add(
torch::Tensor x,
torch::Tensor gamma,
torch::Tensor beta,
torch::Tensor mean,
torch::Tensor var,
torch::Tensor residual,
float eps
);
'''
_fused_ext = load_inline(
name='fused_bn_relu_add_ext',
cpp_sources=[cpp_source],
cuda_sources=[cuda_source],
functions=['fused_bn_relu_add'],
verbose=False
)
class ModelNew(nn.Module):
def __init__(self, channels, kernel_size, dilation):
super(ModelNew, self).__init__()
self.norm = nn.BatchNorm1d(channels)
self.activation = nn.ReLU()
self.conv = nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation)
self.eps = 1e-5
def forward(self, x):
residual = x
# Use custom fused kernel for BN + ReLU + residual add
x = self.norm(x)
x = _fused_ext.fused_bn_relu_add(
x, self.norm.weight, self.norm.bias,
self.norm.running_mean, self.norm.running_var,
residual, self.eps
)
# Apply conv (not fused for simplicity/correctness)
x = self.conv(x)
# Note: residual already added in kernel, but conv output needs no extra add here
# Wait, the model is norm->relu->conv->+residual
# So after fused (which did norm relu +residual), we still need to do conv and add? No:
# Mistake: the fused did norm+relu+add BEFORE conv, wrong order.
# Correct order: norm, relu, conv, +residual
# So redo properly.
# Actually let's fix logic: we can't submit broken.
# Re-implement correctly:
pass # placeholder, will not reach
```
Wait, the code above has a logical error in fusion. I need a correct complete version. Since this is the submission call, here's the corrected full valid code:
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void bn_relu_kernel(\n const float* __restrict__ x,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n const float* __restrict__ mean,\n const float* __restrict__ var,\n float* __restrict__ out,\n int N, int C, int L,\n float eps\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = N * C * L;\n if (idx >= total) return;\n\n int c = (idx / L) % C;\n\n float val = x[idx];\n float m = mean[c];\n float v = var[c];\n float g = gamma[c];\n float b = beta[c];\n\n float normed = (val - m) / sqrtf(v + eps);\n float activated = normed * g + b;\n activated = fmaxf(activated, 0.0f);\n out[idx] = activated;\n}\n\ntorch::Tensor bn_relu_cuda(\n torch::Tensor x,\n torch::Tensor gamma,\n torch::Tensor beta,\n torch::Tensor mean,\n torch::Tensor var,\n float eps\n) {\n auto N = x.size(0);\n auto C = x.size(1);\n auto L = x.size(2);\n auto out = torch::empty_like(x);\n\n int total = N * C * L;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n\n bn_relu_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n gamma.data_ptr<float>(),\n beta.data_ptr<float>(),\n mean.data_ptr<float>(),\n var.data_ptr<float>(),\n out.data_ptr<float>(),\n N, C, L, eps\n );\n\n return out;\n}\n'''\n\ncpp_source = r'''\ntorch::Tensor bn_relu_cuda(\n torch::Tensor x,\n torch::Tensor gamma,\n torch::Tensor beta,\n torch::Tensor mean,\n torch::Tensor var,\n float eps\n);\n'''\n\n_ext = load_inline(\n name='bn_relu_ext',\n cpp_sources=[cpp_source],\n cuda_sources=[cuda_source],\n functions=['bn_relu_cuda'],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, kernel_size, dilation):\n super(ModelNew, self).__init__()\n self.norm = nn.BatchNorm1d(channels)\n self.conv = nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation)\n self.eps = 1e-5\n\n def forward(self, x):\n residual = x\n x = self.norm(x)\n x = _ext.bn_relu_cuda(\n x,\n self.norm.weight,\n self.norm.bias,\n self.norm.running_mean,\n self.norm.running_var,\n self.eps\n )\n x = self.conv(x)\n x = x + residual\n return x\n```"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Syntax error in custom generated code or ModelNew not found