reasoning
Now I understand the hardware. Let me write an optimized kernel for this task.
The operations are:
1. BatchNorm1d on (batch, channels, seq_len) - normalizes along the batch dimension for each channel and position
2. ReLU activation
3. Dilated Conv1d with kernel_size=3, dilation=128
4. Add residual
Key optimizations:
1. Fuse BatchNorm + ReLU
2. Fuse Conv1d with residual add
3. Use efficient memory access for dilated convolution
For BatchNorm, we need to use the running statistics (running_mean, running_var, weight, bias) for inference.
Let me write the kernel:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
# Define CUDA kernel for fused batchnorm + relu + dilated conv + residual add
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <c10/cuda/CUDAException.h>
template <typename scalar_t>
__global__ void fused_bn_relu_conv_residual_kernel(
const scalar_t* __restrict__ input,
const scalar_t* __restrict__ residual,
const scalar_t* __restrict__ weight,
const scalar_t* __restrict__ bias,
const scalar_t* __restrict__ running_mean,
const scalar_t* __restrict__ running_var,
scalar_t* __restrict__ output,
int batch_size, int channels, int seq_len,
int kernel_size, int dilation, int padding,
float eps, float momentum) {
// Each thread handles one output element
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = batch_size * channels * seq_len;
if (idx < total) {
int n = idx / (channels * seq_len);
int c = (idx % (channels * seq_len)) / seq_len;
int s = idx % seq_len;
// Load input value
scalar_t x = input[idx];
// Apply batchnorm (inference mode)
scalar_t mean = running_mean[c];
scalar_t var = running_var[c];
scalar_t gamma = weight[c];
scalar_t beta = bias[c];
scalar_t x_norm = (x - mean) / sqrt(var + eps);
x = gamma * x_norm + beta;
// Apply ReLU
x = max(scalar_t(0), x);
// Now apply dilated convolution
// For kernel_size=3, dilation=d
// We need to compute: sum over k of input[b, c_out, s + (k - 1)*d] * weight[c_out, c_in, k]
// Actually wait - the convolution is between channels, not within channels
// Conv1d has shape (out_channels, in_channels, kernel_size)
// Since in_channels = out_channels = channels, we have:
// output[b, c_out, s] = sum_{c_in, k} input[b, c_in, s + (k-1)*d - padding] * weight[c_out, c_in, k]
// This is more complex... each output element requires a reduction across all input channels
// Let me re-think...
}
}
"""
Actually, the convolution is the tricky part. For a Conv1d, the output at position (n, c_out, s) is:
output[n, c_out, s] = sum_{c_in=0}^{channels-1} sum_{k=0}^{kernel_size-1} input[n, c_in, s + (k - padding) + k*dilation] * weight[c_out, c_in, k]
With dilation=128, kernel_size=3, padding=128:
- k=0: offset = -128 + 0 = -128
- k=1: offset = -128 + 128 = 0
- k=2: offset = -128 + 256 = 128
So the kernel accesses s-128, s, s+128 for each input channel.
This is quite complex to fuse efficiently. Let me think about a better approach...
One approach is to:
1. First apply batchnorm + ReLU in a fused kernel
2. Then apply dilated convolution
3. Finally add residual
But even better, we can:
1. Apply batchnorm + ReLU fused
2. Apply dilated conv with residual add fused
Actually, let me reconsider. The memory access pattern for dilated convolution with dilation=128 is very sparse - we're reading every 128th element in the sequence dimension. This is going to be very inefficient for memory access.
However, for small kernel_size=3, we can potentially use shared memory to cache the input patches and then compute the convolution more efficiently.
Let me think about a tiling approach:
- For each output block, we need to load a corresponding input block that is larger by dilation*(kernel_size-1) in the sequence dimension
- With dilation=128 and kernel_size=3, the input block needs to be larger by 256 elements on each side
Actually, given the constraints, let me start with a straightforward implementation and then optimize:
1. Fused BatchNorm + ReLU kernel
2. Fused Dilated Conv + Residual Add kernel
Let me write this:
```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>
#include <c10/cuda/CUDAException.h>
// Fused BatchNorm + ReLU kernel
__global__ void fused_bn_relu_kernel(
const float* __restrict__ input,
const float* __restrict__ running_mean,
const float* __restrict__ running_var,
const float* __restrict__ weight,
const float* __restrict__ bias,
float* __restrict__ output,
int batch_size, int channels, int seq_len,
float eps) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = batch_size * channels * seq_len;
if (idx < total) {
int c = (idx / seq_len) % channels;
float x = input[idx];
float mean = running_mean[c];
float var = running_var[c];
float gamma = weight[c];
float beta = bias[c];
float x_norm = (x - mean) * rsqrtf(var + eps);
x = fmaxf(0.0f, gamma * x_norm + beta);
output[idx] = x;
}
}
// Dilated Conv1d + Residual Add kernel
// Input: (batch, channels, seq_len)
// Weight: (channels, channels, kernel_size)
// Output: (batch, channels, seq_len)
__global__ void dilated_conv_residual_kernel(
const float* __restrict__ input,
const float* __restrict__ residual,
const float* __restrict__ weight,
const float* __restrict__ bias,
float* __restrict__ output,
int batch_size, int channels, int seq_len,
int kernel_size, int dilation, int padding) {
int n = blockIdx.z;
int c_out = blockIdx.y;
int s = blockIdx.x * blockDim.x + threadIdx.x;
if (s >= seq_len) return;
float sum = bias[c_out];
for (int c_in = 0; c_in < channels; c_in++) {
for (int k = 0; k < kernel_size; k++) {
int s_in = s + k * dilation - padding;
if (s_in >= 0 && s_in < seq_len) {
float in_val = input[n * channels * seq_len + c_in * seq_len + s_in];
float w = weight[c_out * channels * kernel_size + c_in * kernel_size + k];
sum += in_val * w;
}
}
}
// Add residual
sum += residual[n * channels * seq_len + c_out * seq_len + s];
output[n * channels * seq_len + c_out * seq_len + s] = sum;
}
torch::Tensor forward_cuda(
torch::Tensor input,
torch::Tensor residual,
torch::Tensor conv_weight,
torch::Tensor conv_bias,
torch::Tensor bn_mean,
torch::Tensor bn_var,
torch::Tensor bn_weight,
torch::Tensor bn_bias,
int kernel_size, int dilation, int padding,
float eps) {
int batch_size = input.size(0);
int channels = input.size(1);
int seq_len = input.size(2);
auto output = torch::empty_like(input);
auto temp = torch::empty_like(input);
int total = batch_size * channels * seq_len;
int threads = 256;
int blocks = (total + threads - 1) / threads;
// Apply fused BatchNorm + ReLU
fused_bn_relu_kernel<<<blocks, threads>>>(
input.data_ptr<float>(),
bn_mean.data_ptr<float>(),
bn_var.data_ptr<float>(),
bn_weight.data_ptr<float>(),
bn_bias.data_ptr<float>(),
temp.data_ptr<float>(),
batch_size, channels, seq_len,
eps
);
// Apply Dilated Conv + Residual Add
dim3 grid((seq_len + threads - 1) / threads, channels, batch_size);
dilated_conv_residual_kernel<<<grid, threads>>>(
temp.data_ptr<float>(),
residual.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, padding
);
return output;
}
"""
cpp_source = """
torch::Tensor forward_cuda(
torch::Tensor input,
torch::Tensor residual,
torch::Tensor conv_weight,
torch::Tensor conv_bias,
torch::Tensor bn_mean,
torch::Tensor bn_var,
torch::Tensor bn_weight,
torch::Tensor bn_bias,
int kernel_size, int dilation, int padding,
float eps);
"""
# Load the extension
module = load_inline(
name='fused_kernel',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['forward_cuda'],
extra_cuda_cflags=['-O3'],
verbose=True
)
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.channels = channels
self.kernel_size = kernel_size
self.dilation = dilation
self.padding = dilation
def forward(self, x):
residual = x
# Use custom CUDA kernel
output = module.forward_cuda(
x,
residual,
self.conv.weight,
self.conv.bias,
self.norm.running_mean,
self.norm.running_var,
self.norm.weight,
self.norm.bias,
self.kernel_size,
self.dilation,
self.padding,
self.norm.eps
)
return output
```
Wait, I need to be careful. The Conv1d weight shape in PyTorch is (out_channels, in_channels, kernel_size). Let me verify the indexing.
Also, I should optimize the convolution kernel. The current approach has each thread iterating over all input channels, which is O(channels) memory accesses. For channels=256, this is significant.
Let me optimize using shared memory or better memory access patterns...
Actually, let me first get a working version and then optimize. Let me compile and check correctness first.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Fused BatchNorm + ReLU kernel\n__global__ void fused_bn_relu_kernel(\n const float* __restrict__ input,\n const float* __restrict__ running_mean,\n const float* __restrict__ running_var,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ output,\n int batch_size, int channels, int seq_len,\n float eps) {\n \n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = batch_size * channels * seq_len;\n \n if (idx < total) {\n int c = (idx / seq_len) % channels;\n \n float x = input[idx];\n float mean = running_mean[c];\n float var = running_var[c];\n float gamma = weight[c];\n float beta = bias[c];\n \n float x_norm = (x - mean) * rsqrtf(var + eps);\n x = fmaxf(0.0f, gamma * x_norm + beta);\n \n output[idx] = x;\n }\n}\n\n// Dilated Conv1d + Residual Add kernel\n// Input: (batch, channels, seq_len)\n// Weight: (channels, channels, kernel_size)\n// Output: (batch, channels, seq_len)\n__global__ void dilated_conv_residual_kernel(\n const float* __restrict__ input,\n const float* __restrict__ residual,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n float* __restrict__ output,\n int batch_size, int channels, int seq_len,\n int kernel_size, int dilation, int padding) {\n \n int n = blockIdx.z;\n int c_out = blockIdx.y;\n int s = blockIdx.x * blockDim.x + threadIdx.x;\n \n if (s >= seq_len) return;\n \n float sum = bias[c_out];\n \n for (int c_in = 0; c_in < channels; c_in++) {\n for (int k = 0; k < kernel_size; k++) {\n int s_in = s + k * dilation - padding;\n if (s_in >= 0 && s_in < seq_len) {\n float in_val = input[n * channels * seq_len + c_in * seq_len + s_in];\n float w = weight[c_out * channels * kernel_size + c_in * kernel_size + k];\n sum += in_val * w;\n }\n }\n }\n \n // Add residual\n sum += residual[n * channels * seq_len + c_out * seq_len + s];\n \n output[n * channels * seq_len + c_out * seq_len + s] = sum;\n}\n\ntorch::Tensor forward_cuda(\n torch::Tensor input,\n torch::Tensor residual,\n torch::Tensor conv_weight,\n torch::Tensor conv_bias,\n torch::Tensor bn_mean,\n torch::Tensor bn_var,\n torch::Tensor bn_weight,\n torch::Tensor bn_bias,\n int kernel_size, int dilation, int padding,\n float eps) {\n \n int batch_size = input.size(0);\n int channels = input.size(1);\n int seq_len = input.size(2);\n \n auto output = torch::empty_like(input);\n auto temp = torch::empty_like(input);\n \n int total = batch_size * channels * seq_len;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n // Apply fused BatchNorm + ReLU\n fused_bn_relu_kernel<<<blocks, threads>>>(\n input.data_ptr<float>(),\n bn_mean.data_ptr<float>(),\n bn_var.data_ptr<float>(),\n bn_weight.data_ptr<float>(),\n bn_bias.data_ptr<float>(),\n temp.data_ptr<float>(),\n batch_size, channels, seq_len,\n eps\n );\n \n // Apply Dilated Conv + Residual Add\n dim3 grid((seq_len + threads - 1) / threads, channels, batch_size);\n dilated_conv_residual_kernel<<<grid, threads>>>(\n temp.data_ptr<float>(),\n residual.data_ptr<float>(),\n conv_weight.data_ptr<float>(),\n conv_bias.data_ptr<float>(),\n output.data_ptr<float>(),\n batch_size, channels, seq_len,\n kernel_size, dilation, padding\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor forward_cuda(\n torch::Tensor input,\n torch::Tensor residual,\n torch::Tensor conv_weight,\n torch::Tensor conv_bias,\n torch::Tensor bn_mean,\n torch::Tensor bn_var,\n torch::Tensor bn_weight,\n torch::Tensor bn_bias,\n int kernel_size, int dilation, int padding,\n float eps);\n\"\"\"\n\n# Load the extension\nfused_module = load_inline(\n name='fused_kernel_v1',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['forward_cuda'],\n extra_cuda_cflags=['-O3'],\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.activation = nn.ReLU()\n self.conv = nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation)\n \n self.channels = channels\n self.kernel_size = kernel_size\n self.dilation = dilation\n self.padding = dilation\n \n def forward(self, x):\n residual = x\n \n # Use custom CUDA kernel\n output = fused_module.forward_cuda(\n x,\n residual,\n self.conv.weight,\n self.conv.bias,\n self.norm.running_mean,\n self.norm.running_var,\n self.norm.weight,\n self.norm.bias,\n self.kernel_size,\n self.dilation,\n self.padding,\n self.norm.eps\n )\n \n return output"
}