reasoning
reasoning (truncated 56361 chars): Let me analyze this task carefully. I need to implement a custom CUDA kernel for a 1D depthwise separable convolution block that includes:
1. Pointwise Conv1d (channel mixing) - 1x1 convolution
2. Depthwise Conv1d (spatial) - grouped convolution with kernel_size=9
3. GELU activation
4. Project back - another 1x1 convolution
5. Residual addition
The key dimensions are:
- batch_size = 32
- channels = 256
- expanded_channels = 512
- seq_len = 4096
- kernel_size = 9
This is a significant computation, and fusing these operations could provide good speedups by:
1. Reducing memory bandwidth through kernel fusion
2. Better cache utilization
3. Avoiding intermediate tensor storage
Let me write a fused CUDA kernel that handles all these operations in one pass. The main challenge is that we have three convolutions with different characteristics:
- Pointwise: 256 input channels -> 512 output channels (dense 1x1 conv)
- Depthwise: 512 channels with kernel_size=9, groups=512 (each channel has its own filter)
- Project back: 512 channels -> 256 channels (dense 1x1 conv)
For optimization, I should:
1. Fuse all operations to minimize memory transfers
2. Use shared memory for the input tiles
3. Use efficient memory access patterns
4. Possibly pre-load weights in a good format
Let me create a comprehensive solution:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
# Define the CUDA kernel
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
// GELU activation approximation
__device__ __forceinline__ float gelu(float x) {
// Using the approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
const float sqrt_2_over_pi = 0.7978845608028654f;
const float coeff = 0.044715f;
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coeff * x3);
return 0.5f * x * (1.0f + tanhf(inner));
}
// Fused kernel for the entire block
__global__ void depthwise_separable_conv1d_kernel(
const float* __restrict__ input,
const float* __restrict__ pointwise_weight,
const float* __restrict__ pointwise_bias,
const float* __restrict__ depthwise_weight,
const float* __restrict__ depthwise_bias,
const float* __restrict__ project_weight,
const float* __restrict__ project_bias,
float* __restrict__ output,
int batch_size, int in_channels, int expanded_channels,
int seq_len, int kernel_size, int padding
) {
// Each thread handles one output element
// blockIdx.x: batch * seq position
// blockIdx.y: output channel
// threadIdx.x: used for reduction in pointwise conv
int batch_idx = blockIdx.x / seq_len;
int seq_idx = blockIdx.x % seq_len;
int out_ch = blockIdx.y;
// Step 1: Pointwise convolution (in_channels -> expanded_channels)
// We need to compute for each expanded channel
extern __shared__ float shared_mem[];
float* expanded = shared_mem; // expanded_channels elements
// Each thread in a warp processes different input channels
// We need to parallelize the pointwise conv differently
// Actually, let me redesign this...
}
"""
```
Actually, this is getting complex. Let me think about a better approach.
The problem is that we need to:
1. Compute pointwise conv for all expanded channels (512 outputs per position)
2. Then apply depthwise conv (512 channels, each with its own kernel)
3. Then apply GELU
4. Then project back (256 channels)
The challenge is that the intermediate results are large (512 channels), and we can't easily fit them in shared memory.
Let me consider a tiled approach:
- Process output tiles (subset of output channels)
- For each output tile, we need all expanded channel values at that position
Actually, a better approach might be to process in tiles of sequence positions rather than channels. For each position in the output:
1. Load input values from surrounding positions (for depthwise conv)
2. Compute pointwise conv to get expanded values
3. Apply depthwise conv
4. Apply GELU
5. Project back
6. Add residual
But the issue is that step 2 requires computing 512 values, and step 4 requires accessing all 512 values.
Let me try a different strategy: process blocks of (batch, seq_position, output_channel). Each thread computes one output value, but needs to:
1. Load relevant input values
2. Compute pointwise conv intermediate results (can use shared memory)
3. Continue with depthwise, GELU, project back
For this to work efficiently with shared memory, we'd need to have multiple threads share the intermediate pointwise results.
Let me design this more carefully:
Block structure:
- Each block processes a (batch, seq_position) pair
- Threads in a block: blockDim.x threads, each handling multiple output channels
- First, cooperatively compute pointwise conv results into shared memory
- Then each thread reads from shared memory, computes its output channel
This seems reasonable. Let me code this up:
```cuda
__global__ void fused_kernel(
const float* __restrict__ input,
const float* __restrict__ pointwise_weight,
const float* __restrict__ pointwise_bias,
const float* __restrict__ depthwise_weight,
const float* __restrict__ depthwise_bias,
const float* __restrict__ project_weight,
const float* __restrict__ project_bias,
float* __restrict__ output,
int batch_size, int in_channels, int expanded_channels,
int seq_len, int kernel_size, int padding
) {
// Each block handles one (batch, position) pair
// Threads in block cooperatively compute expanded channels, then project back
int batch_idx = blockIdx.x / seq_len;
int seq_idx = blockIdx.x % seq_len;
int tid = threadIdx.x;
extern __shared__ float shared_mem[];
float* expanded = shared_mem; // expanded_channels values
// Step 1: Cooperatively compute pointwise conv
// pointwise_weight shape: [expanded_channels, in_channels, 1]
// Each thread computes multiple expanded channels
for (int ec = tid; ec < expanded_channels; ec += blockDim.x) {
float sum = pointwise_bias[ec];
for (int ic = 0; ic < in_channels; ic++) {
float in_val = input[batch_idx * in_channels * seq_len + ic * seq_len + seq_idx];
float weight = pointwise_weight[ec * in_channels + ic];
sum += in_val * weight;
}
expanded[ec] = sum;
}
__syncthreads();
// Step 2: Each thread applies depthwise conv, GELU, and project back
// for its assigned output channels
// depthwise_weight shape: [expanded_channels, 1, kernel_size] (groups=expanded_channels)
for (int oc = tid; oc < in_channels; oc += blockDim.x) {
// First compute depthwise conv for all expanded channels
// Actually, this is complex because each thread would need to compute
// depthwise for multiple expanded channels
}
}
```
Hmm, this is getting complicated. The issue is that the project_back convolution requires contributions from all 512 expanded channels to each output channel. So each thread would need to:
1. Compute depthwise + GELU for all 512 expanded channels at positions seq_idx-kernel_size//2 to seq_idx+kernel_size//2
2. Then project back
This requires reading a lot of input data. Let me reconsider the tiling strategy.
Alternative approach: Use two kernels
1. First kernel: Pointwise + Depthwise + GELU -> write to global memory
2. Second kernel: Project back + residual addition
But this defeats the purpose of fusion somewhat. However, it might still be faster than PyTorch's separate ops.
Actually, let me try a different fusion strategy. The key insight is:
- Pointwise conv is just a matrix multiplication at each position
- Depthwise conv only needs kernel_size values from the same expanded channel
- Project back is another matrix multiplication
So for each output position, we can:
1. Load kernel_size input positions (fo
...[ omitted 56361 characters ]...
ite the final code.
Actually, let me also consider using vectorized loads for better memory bandwidth. But let's first test the basic version.
One more thing: I should make sure the kernel handles the case where input, weights, or output might be in different memory locations. Since I'm using __restrict__, the compiler should handle this correctly.
Let me write the final implementation:
```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>
__device__ __forceinline__ float gelu_activation(float x) {
// GELU: 0.5 * x * (1 + erf(x / sqrt(2)))
const float sqrt_half = 0.7071067811865475f;
return 0.5f * x * (1.0f + erff(x * sqrt_half));
}
__global__ void fused_depthwise_separable_conv1d_kernel(
const float* __restrict__ input,
const float* __restrict__ pw_weight,
const float* __restrict__ pw_bias,
const float* __restrict__ dw_weight,
const float* __restrict__ dw_bias,
const float* __restrict__ proj_weight,
const float* __restrict__ proj_bias,
float* __restrict__ output,
int batch_size, int in_channels, int expanded_channels,
int seq_len, int kernel_size, int padding
) {
extern __shared__ float shared_mem[];
float* input_tile = shared_mem;
float* gelu_output = input_tile + in_channels * kernel_size;
int block_idx = blockIdx.x;
int batch_idx = block_idx / seq_len;
int seq_idx = block_idx % seq_len;
int tid = threadIdx.x;
int num_threads = blockDim.x;
int start_pos = seq_idx - padding;
// Phase 1: Load input tile
int input_tile_size = in_channels * kernel_size;
for (int i = tid; i < input_tile_size; i += num_threads) {
int ic = i / kernel_size;
int k = i % kernel_size;
int pos = start_pos + k;
float val = 0.0f;
if (pos >= 0 && pos < seq_len) {
val = input[batch_idx * in_channels * seq_len + ic * seq_len + pos];
}
input_tile[i] = val;
}
__syncthreads();
// Phase 2+3: Pointwise + Depthwise + GELU
int ec_per_thread = (expanded_channels + num_threads - 1) / num_threads;
for (int e = 0; e < ec_per_thread; e++) {
int ec = tid + e * num_threads;
if (ec < expanded_channels) {
float dw_sum = dw_bias[ec];
for (int k = 0; k < kernel_size; k++) {
float pw_sum = pw_bias[ec];
for (int ic = 0; ic < in_channels; ic++) {
pw_sum += input_tile[ic * kernel_size + k] * pw_weight[ec * in_channels + ic];
}
dw_sum += pw_sum * dw_weight[ec * kernel_size + k];
}
gelu_output[ec] = gelu_activation(dw_sum);
}
}
__syncthreads();
// Phase 4: Project back + residual
int oc_per_thread = (in_channels + num_threads - 1) / num_threads;
for (int o = 0; o < oc_per_thread; o++) {
int oc = tid + o * num_threads;
if (oc < in_channels) {
float proj_sum = proj_bias[oc];
for (int ec = 0; ec < expanded_channels; ec++) {
proj_sum += gelu_output[ec] * proj_weight[oc * expanded_channels + ec];
}
float residual = input[batch_idx * in_channels * seq_len + oc * seq_len + seq_idx];
output[batch_idx * in_channels * seq_len + oc * seq_len + seq_idx] = proj_sum + residual;
}
}
}
torch::Tensor fused_depthwise_separable_conv1d(
torch::Tensor input,
torch::Tensor pw_weight,
torch::Tensor pw_bias,
torch::Tensor dw_weight,
torch::Tensor dw_bias,
torch::Tensor proj_weight,
torch::Tensor proj_bias,
int kernel_size,
int padding
) {
TORCH_CHECK(input.is_cuda(), "Input must be a CUDA tensor");
TORCH_CHECK(input.dim() == 3, "Input must be 3D (batch, channels, seq_len)");
int batch_size = input.size(0);
int in_channels = input.size(1);
int seq_len = input.size(2);
int expanded_channels = pw_weight.size(0);
auto output = torch::empty_like(input);
int blocks = batch_size * seq_len;
int threads = 256;
size_t shared_mem_size = (in_channels * kernel_size + expanded_channels) * sizeof(float);
fused_depthwise_separable_conv1d_kernel<<<blocks, threads, shared_mem_size>>>(
input.data_ptr<float>(),
pw_weight.data_ptr<float>(),
pw_bias.data_ptr<float>(),
dw_weight.data_ptr<float>(),
dw_bias.data_ptr<float>(),
proj_weight.data_ptr<float>(),
proj_bias.data_ptr<float>(),
output.data_ptr<float>(),
batch_size, in_channels, expanded_channels,
seq_len, kernel_size, padding
);
return output;
}
"""
fused_module = load_inline(
name='fused_depthwise_conv1d',
cpp_sources='',
cuda_sources=cuda_source,
functions=['fused_depthwise_separable_conv1d'],
extra_cuda_cflags=['-O3'],
verbose=False
)
class ModelNew(nn.Module):
def __init__(self, channels, expanded_channels, kernel_size):
super(ModelNew, self).__init__()
self.pointwise = nn.Conv1d(channels, expanded_channels, 1)
self.depthwise = nn.Conv1d(expanded_channels, expanded_channels, kernel_size,
padding=kernel_size // 2, groups=expanded_channels)
self.project_back = nn.Conv1d(expanded_channels, channels, 1)
self.kernel_size = kernel_size
self.padding = kernel_size // 2
def forward(self, x):
pw_weight = self.pointwise.weight.squeeze(-1)
pw_bias = self.pointwise.bias
dw_weight = self.depthwise.weight.squeeze(1)
dw_bias = self.depthwise.bias
proj_weight = self.project_back.weight.squeeze(-1)
proj_bias = self.project_back.bias
return fused_module.fused_depthwise_separable_conv1d(
x, pw_weight, pw_bias,
dw_weight, dw_bias,
proj_weight, proj_bias,
self.kernel_size, self.padding
)
```
This should be a complete implementation. Let me double-check a few things:
1. Weight shapes:
- pointwise.weight: [expanded_channels, channels, 1] -> squeeze(-1) -> [expanded_channels, channels] ✓
- depthwise.weight: [expanded_channels, 1, kernel_size] -> squeeze(1) -> [expanded_channels, kernel_size] ✓
- project_back.weight: [channels, expanded_channels, 1] -> squeeze(-1) -> [channels, expanded_channels] ✓
2. Index calculations in kernel:
- input[batch_idx * in_channels * seq_len + ic * seq_len + pos]: This accesses input[batch_idx, ic, pos] ✓
- output[batch_idx * in_channels * seq_len + oc * seq_len + seq_idx]: This accesses output[batch_idx, oc, seq_idx] ✓
3. Shared memory:
- input_tile: [in_channels, kernel_size] stored as [in_channels * kernel_size]
- gelu_output: [expanded_channels]
- Total: (256 * 9 + 512) * 4 = 10752 bytes ≈ 10.5 KB
4. Thread configuration:
- 256 threads per block
- Each thread handles 2 expanded channels (for phases 2+3)
- Each thread handles 1 output channel (for phase 4)
I think this implementation should work. Let me submit it.
Wait, I should also consider if there are any edge cases:
- kernel_size=9, padding=4: start_pos = seq_idx - 4
- For seq_idx=0: start_pos=-4, so positions -4 to 4 are accessed
- Positions < 0 are zero-padded (handled by the if condition)
- For seq_idx=seq_len-1: start_pos=seq_len-5, positions seq_len-5 to seq_len+3
- Positions >= seq_len are zero-padded (handled by the if condition)
This should handle boundary conditions correctly.
One more thing: I should make sure the CUDA kernel can handle the tensor dimensions correctly. Let me verify:
- batch_size=32, in_channels=256, expanded_channels=512, seq_len=4096, kernel_size=9
- blocks = 32 * 4096 = 131072
- threads = 256
- shared_mem_size = (256*9 + 512) * 4 = 10752
This should all work on modern GPUs.
Let me finalize and submit.
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 = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__device__ __forceinline__ float gelu_activation(float x) {\n // GELU: 0.5 * x * (1 + erf(x / sqrt(2)))\n const float sqrt_half = 0.7071067811865475f;\n return 0.5f * x * (1.0f + erff(x * sqrt_half));\n}\n\n__global__ void fused_depthwise_separable_conv1d_kernel(\n const float* __restrict__ input,\n const float* __restrict__ pw_weight,\n const float* __restrict__ pw_bias,\n const float* __restrict__ dw_weight,\n const float* __restrict__ dw_bias,\n const float* __restrict__ proj_weight,\n const float* __restrict__ proj_bias,\n float* __restrict__ output,\n int batch_size, int in_channels, int expanded_channels,\n int seq_len, int kernel_size, int padding\n) {\n extern __shared__ float shared_mem[];\n float* input_tile = shared_mem;\n float* gelu_output = input_tile + in_channels * kernel_size;\n \n int block_idx = blockIdx.x;\n int batch_idx = block_idx / seq_len;\n int seq_idx = block_idx % seq_len;\n int tid = threadIdx.x;\n int num_threads = blockDim.x;\n \n int start_pos = seq_idx - padding;\n \n // Phase 1: Load input tile cooperatively\n int input_tile_size = in_channels * kernel_size;\n for (int i = tid; i < input_tile_size; i += num_threads) {\n int ic = i / kernel_size;\n int k = i % kernel_size;\n int pos = start_pos + k;\n float val = 0.0f;\n if (pos >= 0 && pos < seq_len) {\n val = input[batch_idx * in_channels * seq_len + ic * seq_len + pos];\n }\n input_tile[i] = val;\n }\n __syncthreads();\n \n // Phase 2+3: Each thread computes pointwise + depthwise + GELU for multiple expanded channels\n int ec_per_thread = (expanded_channels + num_threads - 1) / num_threads;\n for (int e = 0; e < ec_per_thread; e++) {\n int ec = tid + e * num_threads;\n if (ec < expanded_channels) {\n float dw_sum = dw_bias[ec];\n for (int k = 0; k < kernel_size; k++) {\n float pw_sum = pw_bias[ec];\n for (int ic = 0; ic < in_channels; ic++) {\n pw_sum += input_tile[ic * kernel_size + k] * pw_weight[ec * in_channels + ic];\n }\n dw_sum += pw_sum * dw_weight[ec * kernel_size + k];\n }\n gelu_output[ec] = gelu_activation(dw_sum);\n }\n }\n __syncthreads();\n \n // Phase 4: Each thread computes project back + residual for multiple output channels\n int oc_per_thread = (in_channels + num_threads - 1) / num_threads;\n for (int o = 0; o < oc_per_thread; o++) {\n int oc = tid + o * num_threads;\n if (oc < in_channels) {\n float proj_sum = proj_bias[oc];\n for (int ec = 0; ec < expanded_channels; ec++) {\n proj_sum += gelu_output[ec] * proj_weight[oc * expanded_channels + ec];\n }\n float residual = input[batch_idx * in_channels * seq_len + oc * seq_len + seq_idx];\n output[batch_idx * in_channels * seq_len + oc * seq_len + seq_idx] = proj_sum + residual;\n }\n }\n}\n\ntorch::Tensor fused_depthwise_separable_conv1d(\n torch::Tensor input,\n torch::Tensor pw_weight,\n torch::Tensor pw_bias,\n torch::Tensor dw_weight,\n torch::Tensor dw_bias,\n torch::Tensor proj_weight,\n torch::Tensor proj_bias,\n int kernel_size,\n int padding\n) {\n TORCH_CHECK(input.is_cuda(), \"Input must be a CUDA tensor\");\n TORCH_CHECK(input.dim() == 3, \"Input must be 3D (batch, channels, seq_len)\");\n \n int batch_size = input.size(0);\n int in_channels = input.size(1);\n int seq_len = input.size(2);\n int expanded_channels = pw_weight.size(0);\n \n auto output = torch::empty_like(input);\n \n int blocks = batch_size * seq_len;\n int threads = 256;\n \n size_t shared_mem_size = (in_channels * kernel_size + expanded_channels) * sizeof(float);\n \n fused_depthwise_separable_conv1d_kernel<<<blocks, threads, shared_mem_size>>>(\n input.data_ptr<float>(),\n pw_weight.data_ptr<float>(),\n pw_bias.data_ptr<float>(),\n dw_weight.data_ptr<float>(),\n dw_bias.data_ptr<float>(),\n proj_weight.data_ptr<float>(),\n proj_bias.data_ptr<float>(),\n output.data_ptr<float>(),\n batch_size, in_channels, expanded_channels,\n seq_len, kernel_size, padding\n );\n \n return output;\n}\n\"\"\"\n\nfused_module = load_inline(\n name='fused_depthwise_conv1d',\n cpp_sources='',\n cuda_sources=cuda_source,\n functions=['fused_depthwise_separable_conv1d'],\n extra_cuda_cflags=['-O3'],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, expanded_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.pointwise = nn.Conv1d(channels, expanded_channels, 1)\n self.depthwise = nn.Conv1d(expanded_channels, expanded_channels, kernel_size,\n padding=kernel_size // 2, groups=expanded_channels)\n self.project_back = nn.Conv1d(expanded_channels, channels, 1)\n self.kernel_size = kernel_size\n self.padding = kernel_size // 2\n \n def forward(self, x):\n pw_weight = self.pointwise.weight.squeeze(-1)\n pw_bias = self.pointwise.bias\n dw_weight = self.depthwise.weight.squeeze(1)\n dw_bias = self.depthwise.bias\n proj_weight = self.project_back.weight.squeeze(-1)\n proj_bias = self.project_back.bias\n \n return fused_module.fused_depthwise_separable_conv1d(\n x, pw_weight, pw_bias,\n dw_weight, dw_bias,\n proj_weight, proj_bias,\n self.kernel_size, self.padding\n )"
}