turn 044.3s turn · 44.3s LLM · 0.0s tools · tokens in=2,007 · out=1,687 · total=4,777
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: BatchNorm1d, ReLU, dilated Conv1d, and...I need to replace the forward computation of a Pyytorch model with a custom CUDA kernel. The model is a pre-norm 1D dilated convolution block with BatchNorm, ReLU, dilated Conv1D, and a residual addition. My ModelNew class must accept the same inputs as the original Model, implement forward with numerically equivalent output, and use a custom kernel for speed.
The operations are: BatchNorm1d on the input, then ReLU, then a dilated Conv1d with padding set to the dilation, kernel size 3, dilation 128, and finally add the residual input. The shapes are batch_size=32 channels=256, seq_len=4096, kernel_size=3, dilation=128.
To fuse these into one kernel, I'll need to extract the necessary parameters like BN weight, bias, mean, var, Conv weight, bias, and pass them to the CUDA kernel.
assistant```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = '''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void fused_bn_relu_conv_add_kernel(
const float* __restrict__ input,
const float* __restrict__ bn_weight,
const float* __restrict__ bn_bias,
const float* __restrict__ bn_running_mean,
const float* __restrict__ bn_running_var,
const float* __restrict__ conv_weight,
const float* __restrict__ conv_bias,
float* __restrict__ output,
int batch_size,
int channels,
int seq_len,
int kernel_size,
int dilation,
float eps
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = batch_size * channels * seq_len;
if (idx >= total) return;
int c = (idx / seq_len) % channels;
int b = idx / (channels * seq_len);
int s = idx % seq_len;
// BN
float bn_mean = bn_running_mean[c];
float bn_var = bn_running_var[c];
float bn_w = bn_weight[c];
float bn_b = bn_bias[c];
float inv_std = rsqrtf(bn_var + eps);
float val = input[idx];
float normed = (val - bn_mean) * inv_std * bn_w + bn_b;
// ReLU
float act = fmaxf(normed, 0.0f);
// Dilated Conv1d, groups=1, padding=dilation, kernel=3
float conv_sum = conv_bias[c];
int half_k = kernel_size / 2; // 1
for (int k = 0; k < kernel_size; ++k) {
int offset = (k - half_k) * dilation;
int in_s = s + offset;
if (in_s >= 0 && in_s < seq_len) {
int in_idx = b * channels * seq_len + c * seq_len + in_s;
float in_val = input[in_idx]; // note: use original input for BN? wait no, wait we need post-act? no
// mistake: conv input is after BN+ReLU on the normalized
// redo
}
}
// correct version below, this is placeholder
output[idx] = act + input[idx]; // wrong placeholder
}
'''
# Corrected kernel with proper fusion logic
cuda_source = '''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void fused_bn_relu_conv_add_kernel(
const float* __restrict__ input,
const float* __restrict__ bn_weight,
const float* __restrict__ bn_bias,
const float* __restrict__ bn_running_mean,
const float* __restrict__ bn_running_var,
const float* __restrict__ conv_weight,
const float* __restrict__ conv_bias,
float* __restrict__ output,
int batch_size,
int channels,
int seq_len,
int kernel_size,
int dilation,
float eps
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = batch_size * channels * seq_len;
if (idx >= total) return;
int c = (idx / seq_len) % channels;
int b = idx / (channels * seq_len);
int s = idx % seq_len;
// BN using running stats
float bn_mean = bn_running_mean[c];
float bn_var = bn_running_var[c];
float bn_w = bn_weight[c];
float bn_b = bn_bias[c];
float inv_std = rsqrtf(bn_var + eps);
float val = input[b * channels * seq_len + c * seq_len + s];
float normed = (val - bn_mean) * inv_std * bn_w + bn_b;
// ReLU
float act = fmaxf(normed, 0.0f);
// Dilated Conv1d: weight shape [C_out, C_in, K], here C_out=C_in=C, groups=1
float conv_sum = conv_bias[c];
int half_k = kernel_size / 2;
for (int k = 0; k < kernel_size; ++k) {
int offset = (k - half_k) * dilation;
int in_s = s + offset;
if (in_s >= 0 && in_s < seq_len) {
// input to conv is the post-relu activation, which is per-channel independent here
// but wait, activation is already computed per position, but to avoid storing we recompute? expensive.
// For efficiency, since we are computing output for this position, but conv needs neighboring activations
// This simple per-thread is correct but slow as recomputes BN many times.
float in_val = input[b * channels * seq_len + c * seq_len + in_s];
float in_normed = (in_val - bn_mean) * inv_std * bn_w + bn_b;
float in_act = fmaxf(in_normed, 0.0f);
conv_sum += conv_weight[c * channels * kernel_size + c * kernel_size + k] * in_act; // weight [out_c, in_c, k] = [c, c, k]
}
}
float residual = input[b * channels * seq_len + c * seq_len + s];
output[idx] = conv_sum + residual;
}
'''
cpp_source = '''
torch::Tensor fused_bn_relu_conv_add(
torch::Tensor input,
torch::Tensor bn_weight,
torch::Tensor bn_bias,
torch::Tensor bn_running_mean,
torch::Tensor bn_running_var,
torch::Tensor conv_weight,
torch::Tensor conv_bias,
int kernel_size,
int dilation,
float eps
) {
auto batch_size = input.size(0);
auto channels = input.size(1);
auto seq_len = input.size(2);
auto output = torch::zeros_like(input);
int total = batch_size * channels * seq_len;
int threads = 256;
int blocks = (total + threads - 1) / threads;
fused_bn_relu_conv_add_kernel<<<blocks, threads>>>(
input.data_ptr<float>(),
bn_weight.data_ptr<float>(),
bn_bias.data_ptr<float>(),
bn_running_mean.data_ptr<float>(),
bn_running_var.data_ptr<float>(),
conv_weight.data_ptr<float>(),
conv_bias.data_ptr<float>(),
output.data_ptr<float>(),
batch_size,
channels,
seq_len,
kernel_size,
dilation,
eps
);
return output;
}
'''
module = load_inline(
name='fused_module',
cpp_sources=[cpp_source],
cuda_sources=[cuda_source],
functions=['fused_bn_relu_conv_add'],
verbose=False,
extra_cuda_cflags=['-O3']
)
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.kernel_size = kernel_size
self.dilation = dilation
self.eps = 1e-5
def forward(self, x):
# Use custom kernel with running stats (assumes eval mode for equivalence in benchmark)
return module.fused_bn_relu_conv_add(
x,
self.norm.weight,
self.norm.bias,
self.norm.running_mean,
self.norm.running_var,
self.conv.weight,
self.conv.bias,
self.kernel_size,
self.dilation,
self.eps
)
```