reasoning
Now I understand the task. I need to write a custom CUDA kernel that fuses:
1. Depthwise 1D convolution with dilation
2. SiLU activation
3. Pointwise 1x1 convolution
Let me write the kernel code. The key considerations:
- Input shape: (B, C_in, T) = (16, 128, 2048)
- Depthwise conv: groups=in_channels, kernel_size=5, dilation=2
- SiLU: x * sigmoid(x)
- Pointwise conv: in_channels -> out_channels
For the depthwise convolution with dilation=2 and kernel_size=5:
- Effective kernel span = (5-1)*2 + 1 = 9
- Padding = (5-1)*2 // 2 = 4
Each output element of the depthwise conv depends on 9 input elements per channel.
For the pointwise convolution:
- Each output element is a dot product of in_channels (128) weights with 128 input values (one per channel at the same time position)
This is a good fusion opportunity since we can:
1. Compute depthwise conv output for a position
2. Apply SiLU immediately
3. Accumulate into pointwise output buffer
Let me design the kernel:
Strategy:
- Use shared memory to cache input for the depthwise convolution
- Each thread block handles a portion of the sequence
- For pointwise conv, we need to reduce across channels, which could use shared memory
Actually, let me think about this more carefully. The data flow is:
- Input: (B, C_in=128, T=2048)
- After depthwise: (B, C_in=128, T=2048)
- After SiLU: same
- After pointwise: (B, C_out=128, T=2048)
One approach:
1. Compute depthwise conv + SiLU for all channels at one time position
2. Then immediately contribute to all output channels at that time position
But the pointwise conv requires data from all C_in channels to compute one C_out output. So we need to:
1. Compute depthwise + SiLU for all C_in channels at several time positions
2. Store in shared memory
3. Then parallelize across output channels for pointwise conv
Let me design a kernel where:
- Each thread block handles a tile of time positions (let's say 32 or 64)
- Thread blocks also handle tiles of output channels
- Use shared memory to store the intermediate depthwise+SiLU results
Here's my plan:
- Block dimensions: (32 threads for time, 4 threads for out_channels) or similar
- Grid: (batch, ceil(T/32), ceil(C_out/4))
- Each block loads depthwise weights and pointwise weights
- Shared memory for intermediate results
Let me start coding:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
// SiLU activation: x * sigmoid(x)
__device__ __forceinline__ float silu(float x) {
return x * (1.0f / (1.0f + expf(-x)));
}
// Kernel for fused depthwise conv + SiLU + pointwise conv
__global__ void fused_conv1d_kernel(
const float* __restrict__ input,
const float* __restrict__ dw_weight,
const float* __restrict__ pw_weight,
float* __restrict__ output,
int B, int C_in, int C_out, int T,
int kernel_size, int dilation, int padding
) {
// Block configuration
// blockIdx.x: batch index
// blockIdx.y: time tile index
// blockIdx.z: output channel tile index
int b = blockIdx.x;
int t_tile = blockIdx.y;
int oc_tile = blockIdx.z;
int t_start = t_tile * blockDim.x;
int oc_start = oc_tile * blockDim.y;
int t_local = threadIdx.x;
int oc_local = threadIdx.y;
int t = t_start + t_local;
int oc = oc_start + oc_local;
// Shared memory for intermediate depthwise+SiLU results
extern __shared__ float shared_mem[];
float* dw_silu_result = shared_mem; // [blockDim.y][blockDim.x] -> [C_in_tile][T_tile]
// ... continue
}
"""
# Load the extension
fused_conv = load_inline(
name='fused_conv1d',
cpp_sources='',
cuda_sources=cuda_source,
functions=['fused_conv1d_kernel'],
extra_cuda_cflags=['-O3'],
verbose=True
)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.dilation = dilation
self.padding = ((kernel_size - 1) * dilation) // 2
# Create weight tensors (initialized like Conv1d)
self.dw_weight = nn.Parameter(torch.empty(in_channels, 1, kernel_size))
self.pw_weight = nn.Parameter(torch.empty(out_channels, in_channels, 1))
nn.init.kaiming_uniform_(self.dw_weight, a=5)
nn.init.kaiming_uniform_(self.pw_weight, a=5)
def forward(self, x):
# ... call kernel
pass
```
Actually, I realize the weights need to come from somewhere. Looking at the original Model, it has:
- self.dw = nn.Conv1d(...) which has its own weights
- self.pw = nn.Conv1d(...) which has its own weights
I need to initialize ModelNew with the same weights. But wait, looking at the reference, ModelNew just delegates to Model. I should create the weights myself and use the same initialization as Conv1d.
Let me think about a simpler approach first - just make sure correctness works, then optimize.
For simplicity, let me write a kernel that:
1. Each thread computes one output element (b, oc, t)
2. For each thread, iterate over:
- kernel_size positions for depthwise conv
- C_in channels for pointwise conv
This won't be optimal but will establish correctness.
Let me write a cleaner version:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <cuda.h>
#include <cuda_runtime.h>
__device__ __forceinline__ float silu(float x) {
return x * (1.0f / (1.0f + __expf(-x)));
}
__global__ void fused_conv1d_silu_pw_kernel(
const float* __restrict__ input,
const float* __restrict__ dw_weight,
const float* __restrict__ pw_weight,
float* __restrict__ output,
const int B, const int C_in, const int C_out, const int T,
const int kernel_size, const int dilation, const int padding
) {
int b = blockIdx.x;
int oc = blockIdx.y * blockDim.y + threadIdx.y;
int t = blockIdx.z * blockDim.x + threadIdx.x;
if (oc >= C_out || t >= T) return;
// Step 1: Compute depthwise conv + SiLU for all input channels
// We need to compute this for all C_in channels at position t
// Then use them for pointwise conv
extern __shared__ float shared_dw[];
// Layout: [C_in][1] per thread - but we need all C_in values
// Actually, let's just compute directly without shared memory first
// Each thread computes one (b, oc, t) output
// Needs to accumulate over C_in channels
float out_val = 0.0f;
for (int ic = 0; ic < C_in; ic++) {
// Compute depthwise conv for channel ic at position t
float dw_sum = 0.0f;
for (int k = 0; k < kernel_size; k++) {
int t_in = t - padding + k * dilation;
if (t_in >= 0 && t_in < T) {
dw_sum += input[b * C_in * T + ic * T + t_in] *
dw_weight[ic * kernel_size + k];
}
}
// Apply SiLU
float silu_val = silu(dw_sum);
// Accumulate into pointwise conv output
out_val += silu_val * pw_weight[oc * C_in + ic];
}
output[b * C_out * T + oc * T + t] = out_val;
}
"""
fused_conv = load_inline(
name='fused_conv1d',
cpp_sources='',
cuda_sources=cuda_source,
functions=['fused_conv1d_silu_pw_kernel'],
extra_cuda_cflags=['-O3'],
)
class ModelNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.dilation = dilation
self.padding = ((kernel_size - 1) * dilation) // 2
# Depthwise weight shape: (in_channels, 1, kernel_size) but we flatten
self.dw_weight = nn.Parameter(torch.empty(in_channels, kernel_size))
self.pw_weight = nn.Parameter(torch.empty(out_channels, in_channels))
# Kaiming initialization like Conv1d
nn.init.kaiming_uniform_(self.dw_weight, a=5)
nn.init.kaiming_uniform_(self.pw_weight, a=5)
def forward(self, x):
B, C_in, T = x.shape
C_out = self.out_channels
output = torch.empty(B, C_out, T, device=x.device, dtype=x.dtype)
# Block: (32, 4) for (T, C_out)
dimBlock = (32, 4)
grid_x = B
grid_y = (C_out + 3) // 4
grid_z = (T + 31) // 32
fused_conv.fused_conv1d_silu_pw_kernel(
grid=(grid_x, grid_y, grid_z),
block=dimBlock,
args=[
x.data_ptr(),
self.dw_weight.data_ptr(),
self.pw_weight.data_ptr(),
output.data_ptr(),
B, C_in, C_out, T,
self.kernel_size, self.dilation, self.padding
],
shared_mem=0
)
return output
```
Wait, I need to check the weight layouts. In PyTorch Conv1d:
- For depthwise conv with groups=in_channels: weight shape is (in_channels, 1, kernel_size)
- For pointwise conv: weight shape is (out_channels, in_channels, 1)
So:
- dw_weight[ic, 0, k] -> I can flatten to dw_weight[ic * kernel_size + k]
- pw_weight[oc, ic, 0] -> I can flatten to pw_weight[oc * in_channels + ic]
That looks correct.
Let me also make sure the grid dimensions are correct. The problem is I'm using grid_y for C_out and grid_z for T, but in the kernel I'm accessing:
- oc = blockIdx.y * blockDim.y + threadIdx.y
- t = blockIdx.z * blockDim.x + threadIdx.x
So grid_y should be (C_out + blockDim.y - 1) // blockDim.y and grid_z should be (T + blockDim.x - 1) // blockDim.x.
Let me refine the code and test it.
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 <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float silu(float x) {\n return x * (1.0f / (1.0f + __expf(-x)));\n}\n\n// Each thread computes one output element (b, oc, t)\n// Loops over C_in channels and kernel_size taps\n__global__ void fused_conv1d_silu_pw_kernel(\n const float* __restrict__ input,\n const float* __restrict__ dw_weight,\n const float* __restrict__ pw_weight,\n float* __restrict__ output,\n const int B, const int C_in, const int C_out, const int T,\n const int kernel_size, const int dilation, const int padding\n) {\n int b = blockIdx.x;\n int oc = blockIdx.y * blockDim.y + threadIdx.y;\n int t = blockIdx.z * blockDim.x + threadIdx.x;\n \n if (oc >= C_out || t >= T) return;\n \n float out_val = 0.0f;\n \n for (int ic = 0; ic < C_in; ic++) {\n // Compute depthwise conv for channel ic at position t\n float dw_sum = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t - padding + k * dilation;\n if (t_in >= 0 && t_in < T) {\n dw_sum += input[b * C_in * T + ic * T + t_in] * \n dw_weight[ic * kernel_size + k];\n }\n }\n \n // Apply SiLU and accumulate for pointwise\n float silu_val = silu(dw_sum);\n out_val += silu_val * pw_weight[oc * C_in + ic];\n }\n \n output[b * C_out * T + oc * T + t] = out_val;\n}\n\"\"\"\n\nfused_ext = load_inline(\n name='fused_conv1d_v1',\n cpp_sources='',\n cuda_sources=cuda_source,\n functions=['fused_conv1d_silu_pw_kernel'],\n extra_cuda_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, kernel_size: int, dilation: int = 1):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.kernel_size = kernel_size\n self.dilation = dilation\n self.padding = ((kernel_size - 1) * dilation) // 2\n \n # Depthwise weight: (in_channels, kernel_size) - flattened from (in_channels, 1, kernel_size)\n self.dw_weight = nn.Parameter(torch.empty(in_channels, kernel_size))\n # Pointwise weight: (out_channels, in_channels) - flattened from (out_channels, in_channels, 1)\n self.pw_weight = nn.Parameter(torch.empty(out_channels, in_channels))\n \n # Match Conv1d kaiming uniform initialization\n nn.init.kaiming_uniform_(self.dw_weight, a=5)\n nn.init.kaiming_uniform_(self.pw_weight, a=5)\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C_in, T = x.shape\n C_out = self.out_channels\n \n output = torch.empty(B, C_out, T, device=x.device, dtype=x.dtype)\n \n # Block: (T_tile=32, C_out_tile=4)\n BLOCK_T = 32\n BLOCK_C = 4\n \n grid = (B, (C_out + BLOCK_C - 1) // BLOCK_C, (T + BLOCK_T - 1) // BLOCK_T)\n block = (BLOCK_T, BLOCK_C)\n \n fused_ext.fused_conv1d_silu_pw_kernel(\n grid=grid,\n block=block,\n args=[\n x.data_ptr(),\n self.dw_weight.data_ptr(),\n self.pw_weight.data_ptr(),\n output.data_ptr(),\n B, C_in, C_out, T,\n self.kernel_size, self.dilation, self.padding\n ]\n )\n \n return output"
}