correct
7_Conv1d_FFT.py
started
2026-05-11T18:44:34Z
timingkernelbench.timing
kernel mean / median / std
1.00 / — / 0.00 μs
ref mean / median / std
1.28 / — / 0.00 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
61.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
14172.70 GB/s
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Performs 1D convolution via FFT for long learnable filters. Computes circular convolution by
zero-padding to avoid wraparound, performing FFT on both input and filter, multiplying in
frequency domain, and inverse FFT. This is the core computational primitive in Hyena, H3,
S4, and other SSM-based architectures for subquadratic sequence mixing.
Args:
channels (int): Number of independent channels (each has its own filter).
seq_len (int): Length of input sequences.
"""
def __init__(self, channels: int, seq_len: int):
super(Model, self).__init__()
self.channels = channels
self.seq_len = seq_len
self.fft_size = 2 * seq_len
self.filter = nn.Parameter(torch.randn(channels, seq_len))
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, C, L = x.shape
x_padded = torch.nn.functional.pad(x, (0, self.seq_len))
k_padded = torch.nn.functional.pad(self.filter, (0, self.seq_len))
x_f = torch.fft.rfft(x_padded, dim=-1)
k_f = torch.fft.rfft(k_padded, dim=-1)
out_f = x_f * k_f.unsqueeze(0)
out = torch.fft.irfft(out_f, n=self.fft_size, dim=-1)
return out[..., :self.seq_len]
batch_size = 16
channels = 256
seq_len = 8192
def get_inputs():
return [torch.randn(popcorn_pri.jitter_int(batch_size), channels, seq_len)]
def get_init_inputs():
return [channels, seq_len]turn 0324.2s turn · 4.4s LLM · 319.8s tools · tokens in=2,178 · out=323 · total=2,501
reasoning
Let me understand the task:
1. I need to implement a custom CUDA kernel that replaces the PyTorch model's forward computation
2. The model does 1D convolution via FFT with learnable filters
3. The computation flow is:
- Pad input x of shape (B, C, L) to length 2*L (zero padding)
- Pad filter of shape (C, L) to length 2*L
- Compute RFFT of both padded tensors
- Multiply in frequency domain
- Compute IRFFT
- Return first L elements
Let me think about how to optimize this:
1. cuFFT is highly optimized for FFT operations on GPU
2. The key operations are:
- Padding (can be optimized by avoiding explicit padding and using cuFFT's built-in mechanisms or custom kernels)
- RFFT (use cuFFT)
- Complex multiplication in frequency domain
- IRFFT (use cuFFT)
- Slicing (just pointer arithmetic)
The sizes are:
- B = 16 (batch size)
- C = 256 (channels)
- L = 8192 (sequence length)
- FFT size = 16384
So we have 16 * 256 = 4096 FFTs of size 16384 each.
Let me first get GPU specs to understand the hardware:
assistantI'll analyze this task and create an optimized CUDA kernel for FFT-based convolution. Let me start by understanding the GPU specs and then develop the solution.
call → get_gpu_specs
callget_gpu_specs
{}
call → ert_roofline
callert_roofline
{}
result ← get_gpu_specs (ok)
okget_gpu_specs
GPU specs for NVIDIA H100 80GB HBM3:
total memory (runtime): 79.1 GB
spec entry: H100
GPU Architecture: Hopper
GPU Memory: 80GB
Memory Bandwidth: 3.35 TB/s
FP64 TFLOPS: 34
FP64 Tensor Core TFLOPS: 67
FP32 TFLOPS: 67
TF32 Tensor Core TFLOPS: 989 with sparsity
BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity
FP16 Tensor Core TFLOPS: 1979 with sparsity
FP8 Tensor Core TFLOPS: 3958 with sparsity
INT8 Tensor Core TOPS: 3958 with sparsity
Register File Size: 64K 32-bit registers per SM
Maximum number of registers per thread: 255
Maximum number of thread blocks per SM: 32
Shared memory capacity per SM: 228 KB
Maximum shared memory per thread block: 227 KB
result ← ert_roofline (ok)
okert_roofline
ert_roofline PASSED: empirical roofline benchmarks complete.
=== Empirical Roofline Model ===
Device: NVIDIA H100 80GB HBM3
--- Measured Bandwidth ---
L1 13.6 GB/s (working set: 32 KB)
L2 927.6 GB/s (working set: 2048 KB)
DRAM 2577.3 GB/s (working set: 262144 KB)
--- Measured Compute Throughput ---
FP32 51.85 TFLOPS
FP16 (tensor core) 758.77 TFLOPS
Ridge point (FP32): 20.12 FLOPs/byte
Kernels with arithmetic intensity below the ridge point are
memory-bound; above it they are compute-bound.
turn 1 · 1 tool call(s) · 223.5s turn · 25.2s LLM · tokens in=2,642 · out=2,409 · total=5,051 · 10,253 chars (click to expand)
turn 1223.5s turn · 25.2s LLM · 198.3s tools · tokens in=2,642 · out=2,409 · total=5,051
reasoning
Now let me understand the operation better:
- Input: (B, C, L) where B=16, C=256, L=8192
- Filter: (C, L)
- FFT size: 2*L = 16384
- Each channel has its own filter
The key computational operations:
1. Padding input and filter to fft_size (16384)
2. RFFT on padded input: (B, C, fft_size) -> (B, C, fft_size/2+1) complex
3. RFFT on padded filter: (C, fft_size) -> (C, fft_size/2+1) complex
4. Complex multiplication: (B, C, fft_size/2+1)
5. IRFFT: (B, C, fft_size) -> (B, C, fft_size/2+1)
6. Slice: (B, C, seq_len)
The FFT is the main cost here. The data size:
- Input: 16 * 256 * 8192 * 4 = 134 MB
- After padding: 16 * 256 * 16384 * 4 = 268 MB (for input alone)
- Filter: 256 * 8192 * 4 = 8 MB
cuFFT is highly optimized for this. The key is to:
1. Plan the FFT once (in constructor or first forward)
2. Use batched FFT operations
3. Minimize memory allocations/moves
Let me write an initial version using cuFFT:
assistantNow I understand the problem. This is FFT-based circular convolution where the main operations are:
1. Zero-pad input (B, C, L) and filter (C, L) to size 2L
2. RFFT both
3. Complex multiply
4. IRFFT and slice to L
The FFT operations are the dominant cost. Let me create an optimized CUDA kernel using cuFFT:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\n# CUDA source for FFT-based convolution\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cufft.h>\n#include <cuda_runtime.h>\n#include <vector>\n#include <mutex>\n#include <unordered_map>\n\n#define CUFFT_CHECK(call) \\\\\n do { \\\\\n cufftResult err = call; \\\\\n if (err != CUFFT_SUCCESS) { \\\\\n throw std::runtime_error(\"cuFFT error: \" + std::to_string(err)); \\\\\n } \\\\\n } while(0)\n\n// Thread-safe FFT plan cache\nstruct FFTPlanCache {\n std::mutex mutex;\n std::unordered_map<size_t, cufftHandle> plans;\n \n cufftHandle get_or_create(int fft_size, int batch_size, int datatype) {\n size_t key = (size_t)fft_size * 1000000 + batch_size;\n \n std::lock_guard<std::mutex> lock(mutex);\n auto it = plans.find(key);\n if (it != plans.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n // Use C2C for complex-to-complex FFTs\n // For RFFT we'd need R2C, but let's use C2C for simplicity\n int n[1] = {fft_size};\n int idist = fft_size;\n int odist = fft_size;\n int istride = 1;\n int ostride = 1;\n int inembed[1] = {fft_size};\n int onembed[1] = {fft_size};\n \n // Use 64-bit handle\n CUFFT_CHECK(cufftPlanMany(&plan, 1, n, \n inembed, istride, idist,\n onembed, ostride, odist,\n datatype, batch_size));\n \n plans[key] = plan;\n return plan;\n }\n};\n\nstatic FFTPlanCache plan_cache;\n\n// Kernel to pad input with zeros\n__global__ void pad_input_kernel(\n const float* __restrict__ input,\n float* __restrict__ output,\n int batch, int channels, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = batch * channels * fft_size;\n \n if (idx < total) {\n int fft_idx = idx % fft_size;\n idx /= fft_size;\n int c = idx % channels;\n idx /= channels;\n int b = idx;\n \n int in_idx = b * channels * seq_len + c * seq_len + fft_idx;\n \n if (fft_idx < seq_len) {\n output[b * channels * fft_size + c * fft_size + fft_idx] = input[in_idx];\n } else {\n output[b * channels * fft_size + c * fft_size + fft_idx] = 0.0f;\n }\n }\n}\n\n// Kernel to pad filter with zeros\n__global__ void pad_filter_kernel(\n const float* __restrict__ filter,\n float* __restrict__ output,\n int channels, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = channels * fft_size;\n \n if (idx < total) {\n int fft_idx = idx % fft_size;\n int c = idx / fft_size;\n \n if (fft_idx < seq_len) {\n output[c * fft_size + fft_idx] = filter[c * seq_len + fft_idx];\n } else {\n output[c * fft_size + fft_idx] = 0.0f;\n }\n }\n}\n\n// Complex multiplication kernel\n__global__ void complex_mul_kernel(\n const cufftComplex* __restrict__ x_f,\n const cufftComplex* __restrict__ k_f,\n cufftComplex* __restrict__ out_f,\n int batch, int channels, int fft_size\n) {\n // For RFFT, output size is fft_size/2 + 1\n int half_fft = fft_size / 2 + 1;\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = batch * channels * half_fft;\n \n if (idx < total) {\n int j = idx % half_fft;\n idx /= half_fft;\n int c = idx % channels;\n idx /= channels;\n int b = idx;\n \n int flat_idx = b * channels * half_fft + c * half_fft + j;\n int k_idx = c * half_fft + j;\n \n cufftComplex x_val = x_f[flat_idx];\n cufftComplex k_val = k_f[k_idx];\n \n // Complex multiplication: (a + bi)(c + di) = (ac - bd) + (ad + bc)i\n cufftComplex result;\n result.x = x_val.x * k_val.x - x_val.y * k_val.y;\n result.y = x_val.x * k_val.y + x_val.y * k_val.x;\n \n out_f[flat_idx] = result;\n }\n}\n\n// Slice output kernel\n__global__ void slice_output_kernel(\n const float* __restrict__ input,\n float* __restrict__ output,\n int batch, int channels, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = batch * channels * seq_len;\n \n if (idx < total) {\n int j = idx % seq_len;\n idx /= seq_len;\n int c = idx % channels;\n idx /= channels;\n int b = idx;\n \n output[b * channels * seq_len + c * seq_len + j] = \n input[b * channels * fft_size + c * fft_size + j];\n }\n}\n\ntorch::Tensor fft_conv_forward(\n torch::Tensor x,\n torch::Tensor filter,\n int seq_len,\n int fft_size\n) {\n int batch = x.size(0);\n int channels = x.size(1);\n \n // Allocate padded buffers\n auto x_padded = torch::empty({batch, channels, fft_size}, \n x.options().dtype(torch::kFloat32));\n auto k_padded = torch::empty({channels, fft_size}, \n filter.options().dtype(torch::kFloat32));\n \n // Pad input\n int threads = 256;\n int blocks = (batch * channels * fft_size + threads - 1) / threads;\n pad_input_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n x_padded.data_ptr<float>(),\n batch, channels, seq_len, fft_size\n );\n \n // Pad filter\n blocks = (channels * fft_size + threads - 1) / threads;\n pad_filter_kernel<<<blocks, threads>>>(\n filter.data_ptr<float>(),\n k_padded.data_ptr<float>(),\n channels, seq_len, fft_size\n );\n \n // Perform RFFT on input\n // cuFFT R2C produces fft_size/2 + 1 complex values\n int half_fft = fft_size / 2 + 1;\n \n auto x_f = torch::empty({batch, channels, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n auto k_f = torch::empty({channels, half_fft}, \n filter.options().dtype(torch::kComplexFloat));\n \n // Use torch's FFT which is already optimized\n auto x_f_torch = torch::fft_rfft(x_padded, fft_size, -1);\n auto k_f_torch = torch::fft_rfft(k_padded, fft_size, -1);\n \n // Complex multiplication\n auto out_f_torch = x_f_torch * k_f_torch.unsqueeze(0);\n \n // IRFFT\n auto out_padded = torch::fft_irfft(out_f_torch, fft_size, -1);\n \n // Slice to seq_len\n return out_padded.slice(-1, 0, seq_len).contiguous();\n}\n\nstd::vector<torch::Tensor> fft_conv_forward_complex(\n torch::Tensor x,\n torch::Tensor filter\n) {\n int batch = x.size(0);\n int channels = x.size(1);\n int seq_len = x.size(2);\n int fft_size = 2 * seq_len;\n \n auto result = fft_conv_forward(x, filter, seq_len, fft_size);\n return {result};\n}\n\"\"\"\n\n# Load the extension\ntry:\n fft_conv_ext = load_inline(\n name='fft_conv_ext',\n cpp_sources='',\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_complex'],\n extra_cuda_cflags=['-O3', '-lcufft'],\n verbose=True,\n with_cuda=True\n )\nexcept Exception as e:\n print(f\"Failed to load extension: {e}\")\n raise\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_complex(x, self.filter)[0]"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
Failed to load extension: Error building extension 'fft_conv_ext'
turn 2141.5s turn · 7.0s LLM · 134.4s tools · tokens in=4,750 · out=528 · total=5,278
reasoning
The compilation failed. The issue is likely that I need to be more careful with the CUDA extension. Let me simplify and use PyTorch's built-in FFT operations which are already CUDA-optimized. The key optimization would be to avoid unnecessary memory allocations and copies.
Actually, the reference implementation already uses torch.fft.rfft and torch.fft.irfft which are CUDA-optimized. The real question is whether we can do better by:
1. Fusing operations to reduce kernel launch overhead
2. Reducing memory allocation overhead
3. Pre-planning FFTs
Let me try a simpler approach first - just optimize the Python-level operations to reduce overhead.
assistantLet me try a simpler approach, using PyTorch's built-in FFT but optimizing memory management:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\n\nclass ModelNew(nn.Module):\n \"\"\"\n Optimized FFT-based 1D convolution.\n \"\"\"\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n # Pre-allocate padded filter (only need to do this once per forward)\n # since filter is a parameter that can change, we compute it on the fly\n # but use efficient padding\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n \n # Efficient padding using set_ with storage offset or constant_pad_nd\n # Use torch.nn.functional.pad which is optimized\n x_padded = torch.nn.functional.pad(x, (0, L))\n k_padded = torch.nn.functional.pad(self.filter, (0, L))\n \n # RFFT - this is the main compute, already CUDA optimized\n x_f = torch.fft.rfft(x_padded, dim=-1)\n k_f = torch.fft.rfft(k_padded, dim=-1)\n \n # Complex multiplication with broadcasting\n # k_f is (C, L_fft) and x_f is (B, C, L_fft)\n # unsqueeze to broadcast properly\n out_f = x_f * k_f.unsqueeze(0)\n \n # IRFFT back to time domain\n out = torch.fft.irfft(out_f, n=self.fft_size, dim=-1)\n \n # Slice to original length\n return out[..., :self.seq_len]"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 3152.8s turn · 3.5s LLM · 149.3s tools · tokens in=5,160 · out=293 · total=5,453
reasoning
The kernel compiled. But wait, I just submitted the original reference code! Let me check correctness first, and then I need to actually create a custom CUDA kernel to speed it up.
assistantLet me check correctness of this first, then I'll create a truly optimized custom CUDA kernel:
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\n\nclass ModelNew(nn.Module):\n \"\"\"\n Optimized FFT-based 1D convolution.\n \"\"\"\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n x_padded = torch.nn.functional.pad(x, (0, L))\n k_padded = torch.nn.functional.pad(self.filter, (0, L))\n x_f = torch.fft.rfft(x_padded, dim=-1)\n k_f = torch.fft.rfft(k_padded, dim=-1)\n out_f = x_f * k_f.unsqueeze(0)\n out = torch.fft.irfft(out_f, n=self.fft_size, dim=-1)\n return out[..., :self.seq_len]"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 4205.9s turn · 3.1s LLM · 202.9s tools · tokens in=5,473 · out=253 · total=5,726
reasoning
Good, the simple implementation is correct. Now let me profile it to see where the time is spent:
assistantLet me profile this baseline to understand the bottleneck:
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n x_padded = torch.nn.functional.pad(x, (0, L))\n k_padded = torch.nn.functional.pad(self.filter, (0, L))\n x_f = torch.fft.rfft(x_padded, dim=-1)\n k_f = torch.fft.rfft(k_padded, dim=-1)\n out_f = x_f * k_f.unsqueeze(0)\n out = torch.fft.irfft(out_f, n=self.fft_size, dim=-1)\n return out[..., :self.seq_len]"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::pad 16119.0μs (19%) calls=1
#2: aten::constant_pad_nd 16065.0μs (19%) calls=1
#3: aten::copy_ 7455.0μs (9%) calls=1
#4: aten::fill_ 7179.0μs (9%) calls=1
#5: aten::mul 5546.0μs (7%) calls=1
... +56 more kernels
--- Timing ---
GPU time: 1051.71 μs
--- Memory Bandwidth ---
DRAM BW: 18495.3/3350 GB/s (100.0%)
read=9418.7 write=9076.7 GB/s
Hit rates: L2=544.8%
--- Compute Throughput ---
Dominant pipe: FP32 util=129.2%
Pipe util: FMA=129.2%, Tensor=4.8%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: COMPUTE-BOUND
--- Occupancy ---
Achieved: 573.0%
Eligible warps/cycle: 6.01
Launch: regs/thread=394, smem/blk=110592B, block=1536, grid=413968
--- Memory Access Quality ---
Global ld coalescing: 54.40 sectors/req (54.4x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 34862.3% ####################
mio_throttle 10045.4% ####################
short_scoreboard 8501.3% ####################
--- Optimization Hints ---
-> 34862% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=54.4 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=54.4x (ideal=1.0) -> 54x wasted DRAM BW. Make per-warp accesses contiguous.
-> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.
turn 5 · 1 tool call(s) · 329.1s turn · 25.0s LLM · tokens in=6,267 · out=2,581 · total=8,848 · 11,324 chars (click to expand)
turn 5329.1s turn · 25.0s LLM · 304.0s tools · tokens in=6,267 · out=2,581 · total=8,848
reasoning
The profiling shows:
1. The GPU time is about 1052 μs
2. It's actually classified as compute-bound with high FP32 utilization (129.2%)
3. There's significant global memory latency (long_scoreboard stalls)
4. The main time is spent in padding operations (pad, constant_pad_nd, copy_, fill_) totaling about 46% of GPU time
The issue is that padding is done inefficiently. Let me create a custom CUDA kernel that:
1. Fuses the padding with the FFT preparation
2. Uses cuFFT for the FFT operations
3. Fuses operations where possible
Let me create a proper custom CUDA kernel using cuFFT:
assistantI see the profiling shows the padding operations are taking significant time. Let me create a properly optimized CUDA kernel that uses cuFFT and fuses operations:
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 <cufft.h>\n#include <cufftXt.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\n// Thread-safe plan cache\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n// Fused pad + R2C FFT kernel for input\n__global__ void padAndR2CInputKernel(\n const float* __restrict__ input,\n float* __restrict__ padded_real,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n int c = bc % C;\n int b = bc / C;\n \n float val;\n if (t < seq_len) {\n val = input[b * C * seq_len + c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n// Fused pad + R2C FFT kernel for filter\n__global__ void padAndR2CFilterKernel(\n const float* __restrict__ filter,\n float* __restrict__ padded_real,\n int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n \n float val;\n if (t < seq_len) {\n val = filter[c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n// Complex multiplication kernel (broadcast filter over batch)\n__global__ void complexMulKernel(\n const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n \n if (idx < total) {\n int t = idx % half_fft;\n int bc = idx / half_fft;\n int c = bc % C;\n int b = bc / C;\n \n int x_idx = idx;\n int k_idx = c * half_fft + t;\n \n cufftComplex x = x_freq[x_idx];\n cufftComplex k = k_freq[k_idx];\n \n // Complex multiplication\n out_freq[x_idx].x = x.x * k.x - x.y * k.y;\n out_freq[x_idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n// Slice output kernel\n__global__ void sliceOutputKernel(\n const float* __restrict__ padded_out,\n float* __restrict__ output,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n \n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n \n output[idx] = padded_out[bc * fft_size + t];\n }\n}\n\n// Main forward function\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n) {\n TORCH_CHECK(x.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(filter.is_cuda(), \"Filter must be CUDA tensor\");\n TORCH_CHECK(x.scalar_type() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(filter.scalar_type() == torch::kFloat32, \"Filter must be float32\");\n \n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n \n // Allocate padded buffers (real)\n torch::Tensor x_padded = torch::empty({B, C, n}, x.options());\n torch::Tensor k_padded = torch::empty({C, n}, filter.options());\n \n // Allocate frequency domain buffers\n torch::Tensor x_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor k_freq = torch::empty({C, half_fft}, \n filter.options().dtype(torch::kComplexFloat));\n torch::Tensor out_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor out_padded = torch::empty({B, C, n}, x.options());\n \n int threads = 256;\n \n // Pad input\n int blocks = (B * C * n + threads - 1) / threads;\n padAndR2CInputKernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n x_padded.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n // Pad filter\n blocks = (C * n + threads - 1) / threads;\n padAndR2CFilterKernel<<<blocks, threads>>>(\n filter.data_ptr<float>(),\n k_padded.data_ptr<float>(),\n C, seq_len, n\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n // R2C FFT for input (batch = B * C)\n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, B * C);\n CUFFT_CHECK(cufftExecR2C(plan, \n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n // R2C FFT for filter (batch = C)\n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, C);\n CUFFT_CHECK(cufftExecR2C(plan,\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n // Complex multiplication\n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n // C2R IFFT\n {\n cufftHandle plan = g_planCache.getOrCreateC2R(n, B * C);\n CUFFT_CHECK(cufftExecC2R(plan,\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())\n ));\n }\n \n // Slice output and normalize\n torch::Tensor output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceOutputKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(),\n output.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n // Normalize by FFT size (cuFFT doesn't normalize)\n output.div_(fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n);\n\"\"\"\n\n# Load extension\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 6 · 1 tool call(s) · 248.1s turn · 19.0s LLM · tokens in=8,720 · out=2,419 · total=11,139 · 10,630 chars (click to expand)
turn 6248.1s turn · 19.0s LLM · 229.1s tools · tokens in=8,720 · out=2,419 · total=11,139
reasoning
Good, let me check correctness now:
call → run_correctness
callrun_correctness
{
"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 <cufft.h>\n#include <cufftXt.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\n// Thread-safe plan cache\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n// Fused pad + R2C FFT kernel for input\n__global__ void padAndR2CInputKernel(\n const float* __restrict__ input,\n float* __restrict__ padded_real,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n int c = bc % C;\n int b = bc / C;\n \n float val;\n if (t < seq_len) {\n val = input[b * C * seq_len + c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n// Fused pad + R2C FFT kernel for filter\n__global__ void padAndR2CFilterKernel(\n const float* __restrict__ filter,\n float* __restrict__ padded_real,\n int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n \n float val;\n if (t < seq_len) {\n val = filter[c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n// Complex multiplication kernel (broadcast filter over batch)\n__global__ void complexMulKernel(\n const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n \n if (idx < total) {\n int t = idx % half_fft;\n int bc = idx / half_fft;\n int c = bc % C;\n int b = bc / C;\n \n int x_idx = idx;\n int k_idx = c * half_fft + t;\n \n cufftComplex x = x_freq[x_idx];\n cufftComplex k = k_freq[k_idx];\n \n // Complex multiplication\n out_freq[x_idx].x = x.x * k.x - x.y * k.y;\n out_freq[x_idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n// Slice output kernel\n__global__ void sliceOutputKernel(\n const float* __restrict__ padded_out,\n float* __restrict__ output,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n \n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n \n output[idx] = padded_out[bc * fft_size + t];\n }\n}\n\n// Main forward function\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n) {\n TORCH_CHECK(x.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(filter.is_cuda(), \"Filter must be CUDA tensor\");\n TORCH_CHECK(x.scalar_type() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(filter.scalar_type() == torch::kFloat32, \"Filter must be float32\");\n \n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n \n // Allocate padded buffers (real)\n torch::Tensor x_padded = torch::empty({B, C, n}, x.options());\n torch::Tensor k_padded = torch::empty({C, n}, filter.options());\n \n // Allocate frequency domain buffers\n torch::Tensor x_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor k_freq = torch::empty({C, half_fft}, \n filter.options().dtype(torch::kComplexFloat));\n torch::Tensor out_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor out_padded = torch::empty({B, C, n}, x.options());\n \n int threads = 256;\n \n // Pad input\n int blocks = (B * C * n + threads - 1) / threads;\n padAndR2CInputKernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n x_padded.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n // Pad filter\n blocks = (C * n + threads - 1) / threads;\n padAndR2CFilterKernel<<<blocks, threads>>>(\n filter.data_ptr<float>(),\n k_padded.data_ptr<float>(),\n C, seq_len, n\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n // R2C FFT for input (batch = B * C)\n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, B * C);\n CUFFT_CHECK(cufftExecR2C(plan, \n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n // R2C FFT for filter (batch = C)\n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, C);\n CUFFT_CHECK(cufftExecR2C(plan,\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n // Complex multiplication\n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n // C2R IFFT\n {\n cufftHandle plan = g_planCache.getOrCreateC2R(n, B * C);\n CUFFT_CHECK(cufftExecC2R(plan,\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())\n ));\n }\n \n // Slice output and normalize\n torch::Tensor output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceOutputKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(),\n output.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n // Normalize by FFT size (cuFFT doesn't normalize)\n output.div_(fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n);\n\"\"\"\n\n# Load extension\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 7 · 1 tool call(s) · 240.8s turn · 19.2s LLM · tokens in=11,188 · out=2,295 · total=13,483 · 11,977 chars (click to expand)
turn 7240.8s turn · 19.2s LLM · 221.6s tools · tokens in=11,188 · out=2,295 · total=13,483
reasoning
The correctness passed. Now let me profile this kernel to see performance:
assistantCorrectness passed. Let me profile the custom CUDA kernel:
call → profile_kernel
callprofile_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 <cufft.h>\n#include <cufftXt.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\n// Thread-safe plan cache\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n \n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) {\n return it->second;\n }\n \n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padAndR2CInputKernel(\n const float* __restrict__ input,\n float* __restrict__ padded_real,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n int c = bc % C;\n int b = bc / C;\n \n float val;\n if (t < seq_len) {\n val = input[b * C * seq_len + c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n__global__ void padAndR2CFilterKernel(\n const float* __restrict__ filter,\n float* __restrict__ padded_real,\n int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = C * fft_size;\n \n if (idx < total) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n \n float val;\n if (t < seq_len) {\n val = filter[c * seq_len + t];\n } else {\n val = 0.0f;\n }\n padded_real[idx] = val;\n }\n}\n\n__global__ void complexMulKernel(\n const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n \n if (idx < total) {\n int t = idx % half_fft;\n int bc = idx / half_fft;\n int c = bc % C;\n int b = bc / C;\n \n int x_idx = idx;\n int k_idx = c * half_fft + t;\n \n cufftComplex x = x_freq[x_idx];\n cufftComplex k = k_freq[k_idx];\n \n out_freq[x_idx].x = x.x * k.x - x.y * k.y;\n out_freq[x_idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceOutputKernel(\n const float* __restrict__ padded_out,\n float* __restrict__ output,\n int B, int C, int seq_len, int fft_size\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n \n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n \n output[idx] = padded_out[bc * fft_size + t];\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n) {\n TORCH_CHECK(x.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(filter.is_cuda(), \"Filter must be CUDA tensor\");\n TORCH_CHECK(x.scalar_type() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(filter.scalar_type() == torch::kFloat32, \"Filter must be float32\");\n \n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n \n torch::Tensor x_padded = torch::empty({B, C, n}, x.options());\n torch::Tensor k_padded = torch::empty({C, n}, filter.options());\n \n torch::Tensor x_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor k_freq = torch::empty({C, half_fft}, \n filter.options().dtype(torch::kComplexFloat));\n torch::Tensor out_freq = torch::empty({B, C, half_fft}, \n x.options().dtype(torch::kComplexFloat));\n torch::Tensor out_padded = torch::empty({B, C, n}, x.options());\n \n int threads = 256;\n \n int blocks = (B * C * n + threads - 1) / threads;\n padAndR2CInputKernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n x_padded.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n blocks = (C * n + threads - 1) / threads;\n padAndR2CFilterKernel<<<blocks, threads>>>(\n filter.data_ptr<float>(),\n k_padded.data_ptr<float>(),\n C, seq_len, n\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, B * C);\n CUFFT_CHECK(cufftExecR2C(plan, \n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n {\n cufftHandle plan = g_planCache.getOrCreateR2C(n, C);\n CUFFT_CHECK(cufftExecR2C(plan,\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())\n ));\n }\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft\n );\n \n CUDA_CHECK(cudaGetLastError());\n \n {\n cufftHandle plan = g_planCache.getOrCreateC2R(n, B * C);\n CUFFT_CHECK(cufftExecC2R(plan,\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())\n ));\n }\n \n torch::Tensor output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceOutputKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(),\n output.data_ptr<float>(),\n B, C, seq_len, n\n );\n \n output.div_(fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n int64_t seq_len,\n int64_t fft_size\n);\n\"\"\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::div_ 9032.0μs (39%) calls=1
#2: aten::div_ 9025.0μs (39%) calls=1
#3: aten::empty 1259.0μs (5%) calls=1
#4: aten::empty 1140.0μs (5%) calls=1
#5: aten::empty 1128.0μs (5%) calls=1
... +4 more kernels
--- Timing ---
GPU time: 1068.00 μs [↑16.3μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 17033.5/3350 GB/s (100.0%)
read=9765.3 write=7268.2 GB/s
Hit rates: L2=422.8%
--- Compute Throughput ---
Dominant pipe: tensor-core util=7.3% [↓121.9% REGRESSED]
Pipe util: FMA=166.3%, Tensor=7.3%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 459.5% [↓113.5% REGRESSED]
Eligible warps/cycle: 7.66
Launch: regs/thread=372, smem/blk=109568B, block=1920, grid=581904
--- Memory Access Quality ---
Global ld coalescing: 58.40 sectors/req (58.4x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 31406.9% ####################
short_scoreboard 2683.4% ####################
mio_throttle 1305.9% ####################
--- Optimization Hints ---
-> 31407% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=58.4 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=58.4x (ideal=1.0) -> 58x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 1051.71→1068.00μs (↑16.29) REGRESSED
Compute%: 129.17→7.25% (↓121.92) REGRESSED
Occupancy: 573.05→459.53% (↓113.52) REGRESSED
Eligible warps/cyc: 6.01→7.66 (↑1.65) ok
Ld sectors/req: 54.40→58.40 (↑4.00) REGRESSED
L2 hit%: 544.78→422.76% (↓122.02) REGRESSED
turn 8 · 1 tool call(s) · 332.8s turn · 17.9s LLM · tokens in=14,182 · out=2,087 · total=16,269 · 9,278 chars (click to expand)
turn 8332.8s turn · 17.9s LLM · 314.9s tools · tokens in=14,182 · out=2,087 · total=16,269
reasoning
The profiling shows that the `aten::div_` operation (normalization) is taking most of the time - about 80% of the GPU time. The cuFFT itself is fast but the normalization is slow because PyTorch is handling it as a separate operation.
Let me fuse the normalization into the slice kernel to avoid the separate division operation.
assistantI see the issue - the `div_` operation is taking 80% of the time. Let me fuse the normalization into the slice kernel:
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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n// Fused slice + normalization kernel\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter,\n int64_t seq_len, int64_t fft_size) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n auto options = x.options();\n auto x_padded = torch::empty({B, C, n}, options);\n auto k_padded = torch::empty({C, n}, filter.options());\n auto x_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto k_freq = torch::empty({C, half_fft}, filter.options().dtype(torch::kComplexFloat));\n auto out_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto out_padded = torch::empty({B, C, n}, options);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded.data_ptr<float>(), B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded.data_ptr<float>(), C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())));\n \n auto output = torch::empty({B, C, seq_len}, options);\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(), output.data_ptr<float>(),\n B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 9 · 1 tool call(s) · 278.8s turn · 16.1s LLM · tokens in=16,211 · out=1,989 · total=18,200 · 8,867 chars (click to expand)
turn 9278.8s turn · 16.1s LLM · 262.7s tools · tokens in=16,211 · out=1,989 · total=18,200
reasoning
Good, let me check correctness and then profile.
call → run_correctness
callrun_correctness
{
"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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter,\n int64_t seq_len, int64_t fft_size) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n auto options = x.options();\n auto x_padded = torch::empty({B, C, n}, options);\n auto k_padded = torch::empty({C, n}, filter.options());\n auto x_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto k_freq = torch::empty({C, half_fft}, filter.options().dtype(torch::kComplexFloat));\n auto out_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto out_padded = torch::empty({B, C, n}, options);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded.data_ptr<float>(), B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded.data_ptr<float>(), C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())));\n \n auto output = torch::empty({B, C, seq_len}, options);\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(), output.data_ptr<float>(),\n B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 10 · 1 tool call(s) · 371.8s turn · 17.0s LLM · tokens in=18,247 · out=1,990 · total=20,237 · 10,601 chars (click to expand)
turn 10371.8s turn · 17.0s LLM · 354.9s tools · tokens in=18,247 · out=1,990 · total=20,237
reasoning
Good, correctness still passes. Let me profile this version.
call → profile_kernel
callprofile_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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter,\n int64_t seq_len, int64_t fft_size) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n auto options = x.options();\n auto x_padded = torch::empty({B, C, n}, options);\n auto k_padded = torch::empty({C, n}, filter.options());\n auto x_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto k_freq = torch::empty({C, half_fft}, filter.options().dtype(torch::kComplexFloat));\n auto out_freq = torch::empty({B, C, half_fft}, options.dtype(torch::kComplexFloat));\n auto out_padded = torch::empty({B, C, n}, options);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded.data_ptr<float>(), B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded.data_ptr<float>(), C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>())));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded.data_ptr<float>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>())));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(k_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq.data_ptr<c10::complex<float>>()),\n reinterpret_cast<cufftReal*>(out_padded.data_ptr<float>())));\n \n auto output = torch::empty({B, C, seq_len}, options);\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded.data_ptr<float>(), output.data_ptr<float>(),\n B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, self.seq_len, self.fft_size)"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 1248.0μs (24%) calls=1
#2: aten::empty 1114.0μs (22%) calls=1
#3: aten::empty 1102.0μs (21%) calls=1
#4: aten::empty 1004.0μs (19%) calls=1
#5: aten::empty 466.0μs (9%) calls=1
... +2 more kernels
--- Timing ---
GPU time: 977.95 μs [↓90.0μs ok]
--- Memory Bandwidth ---
DRAM BW: 14249.6/3350 GB/s (100.0%)
read=8246.2 write=6003.4 GB/s
Hit rates: L2=372.7%
--- Compute Throughput ---
Dominant pipe: tensor-core util=7.3% [↑0.1% ok]
Pipe util: FMA=165.4%, Tensor=7.3%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 374.8% [↓84.8% REGRESSED]
Eligible warps/cycle: 7.67
Launch: regs/thread=340, smem/blk=108544B, block=1792, grid=549136
--- Memory Access Quality ---
Global ld coalescing: 42.40 sectors/req (42.4x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 8262.8% ####################
short_scoreboard 863.6% ####################
mio_throttle 513.4% ####################
--- Optimization Hints ---
-> 8263% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=42.4 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=42.4x (ideal=1.0) -> 42x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 1068.00→977.95μs (↓90.05) ok
Compute%: 7.25→7.31% (↑0.06) ok
Occupancy: 459.53→374.76% (↓84.77) REGRESSED
Eligible warps/cyc: 7.66→7.67 (↑0.01) ok
Ld sectors/req: 58.40→42.40 (↓16.00) ok
L2 hit%: 422.76→372.68% (↓50.08) REGRESSED
turn 11 · 1 tool call(s) · 277.4s turn · 24.6s LLM · tokens in=20,925 · out=2,813 · total=23,738 · 12,087 chars (click to expand)
turn 11277.4s turn · 24.6s LLM · 252.8s tools · tokens in=20,925 · out=2,813 · total=23,738
reasoning
Good progress! GPU time dropped from 1068μs to 978μs. The `aten::empty` calls are taking most of the time (memory allocation overhead).
The main issue is still the `aten::empty` overhead. Let me try using pre-allocated buffers stored in the model to reduce allocation overhead, and also optimize the kernel access patterns.
Actually, looking at the profile, most of the time is spent in `aten::empty` which suggests that cuFFT kernels are very fast and the overhead is from PyTorch's tensor allocation. Let me try a different approach - use a single contiguous buffer for all intermediate results and reduce allocations.
assistantThe GPU time improved to 978μs. The `aten::empty` calls are taking most of the time. Let me optimize memory allocation by using pre-allocated buffers:
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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n// Vectorized load/store for better memory throughput\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n const int vec_size = 4;\n int idx = (blockIdx.x * blockDim.x + threadIdx.x) * vec_size;\n int total = B * C * fft_size;\n \n if (idx + vec_size <= total) {\n int t0 = idx % fft_size;\n int bc = idx / fft_size;\n int c = bc % C;\n int b = bc / C;\n \n float4 vals;\n \n #pragma unroll\n for (int i = 0; i < vec_size; i++) {\n int t = t0 + i;\n int bc_i = bc;\n if (t >= fft_size) {\n bc_i = (idx + i) / fft_size;\n t = (idx + i) % fft_size;\n }\n \n if (t < seq_len) {\n int c_i = bc_i % C;\n int b_i = bc_i / C;\n reinterpret_cast<float*>(&vals)[i] = input[b_i * C * seq_len + c_i * seq_len + t];\n } else {\n reinterpret_cast<float*>(&vals)[i] = 0.0f;\n }\n }\n \n *reinterpret_cast<float4*>(padded + idx) = vals;\n } else {\n // Handle remainder\n for (int i = 0; idx + i < total; i++) {\n int cur_idx = idx + i;\n int t = cur_idx % fft_size;\n int bc = cur_idx / fft_size;\n \n if (t < seq_len) {\n int c = bc % C;\n int b = bc / C;\n padded[cur_idx] = input[b * C * seq_len + c * seq_len + t];\n } else {\n padded[cur_idx] = 0.0f;\n }\n }\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\n// Fused version using provided buffer\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n torch::Tensor workspace,\n int64_t seq_len,\n int64_t fft_size\n) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n // Layout in workspace:\n // [0, B*C*n) -> x_padded (real)\n // [B*C*n, B*C*n + C*n) -> k_padded (real) \n // [B*C*n + C*n, ...) -> freq domain (complex)\n \n float* x_padded = workspace.data_ptr<float>();\n float* k_padded = x_padded + B * C * n;\n c10::complex<float>* x_freq = reinterpret_cast<c10::complex<float>*>(k_padded + C * n);\n c10::complex<float>* k_freq = x_freq + B * C * half_fft;\n c10::complex<float>* out_freq = k_freq + C * half_fft;\n float* out_padded = reinterpret_cast<float*>(out_freq + B * C * half_fft);\n \n const int threads = 256;\n int blocks;\n \n // Pad input\n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded, B, C, seq_len, n);\n \n // Pad filter\n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded, C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n // R2C FFT for input\n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded),\n reinterpret_cast<cufftComplex*>(x_freq)));\n \n // R2C FFT for filter\n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded),\n reinterpret_cast<cufftComplex*>(k_freq)));\n \n // Complex multiplication\n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq),\n reinterpret_cast<cufftComplex*>(k_freq),\n reinterpret_cast<cufftComplex*>(out_freq),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n // C2R IFFT\n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq),\n reinterpret_cast<cufftReal*>(out_padded)));\n \n // Output tensor (need to allocate)\n auto output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded, output.data_ptr<float>(), B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n torch::Tensor workspace,\n int64_t seq_len,\n int64_t fft_size\n);\n\"\"\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n # Pre-allocate workspace for typical batch size\n # Will be re-allocated if batch size changes\n self._workspace = None\n self._workspace_batch = None\n \n def _get_workspace(self, B: int, device, dtype):\n n = self.fft_size\n C = self.channels\n half_fft = n // 2 + 1\n \n # Calculate workspace size\n # x_padded: B*C*n (float)\n # k_padded: C*n (float)\n # x_freq: B*C*half_fft (complex)\n # k_freq: C*half_fft (complex)\n # out_freq: B*C*half_fft (complex)\n # out_padded: B*C*n (float)\n size_floats = B * C * n + C * n + B * C * n # real arrays\n size_complex = (B * C * half_fft + C * half_fft + B * C * half_fft) * 2 # complex = 2 floats\n total_size = size_floats + size_complex\n \n if self._workspace is None or self._workspace_batch != B:\n self._workspace = torch.empty(total_size, dtype=dtype, device=device)\n self._workspace_batch = B\n \n return self._workspace\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n workspace = self._get_workspace(B, x.device, x.dtype)\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, workspace, self.seq_len, self.fft_size)"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 12 · 1 tool call(s) · 342.3s turn · 17.5s LLM · tokens in=23,648 · out=2,161 · total=25,809 · 9,463 chars (click to expand)
turn 12342.3s turn · 17.5s LLM · 324.8s tools · tokens in=23,648 · out=2,161 · total=25,809
reasoning
The user is suggesting I should submit my kernel if I have a correct one and no specific next optimization to try. Let me check if my current kernel is correct by running the correctness test, and then submit it.
Actually, let me first verify correctness of the workspace-based version I just compiled, then submit if it passes.
call → run_correctness
callrun_correctness
{
"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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n torch::Tensor workspace,\n int64_t seq_len,\n int64_t fft_size\n) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n float* x_padded = workspace.data_ptr<float>();\n float* k_padded = x_padded + B * C * n;\n c10::complex<float>* x_freq = reinterpret_cast<c10::complex<float>*>(k_padded + C * n);\n c10::complex<float>* k_freq = x_freq + B * C * half_fft;\n c10::complex<float>* out_freq = k_freq + C * half_fft;\n float* out_padded = reinterpret_cast<float*>(out_freq + B * C * half_fft);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded, B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded, C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded),\n reinterpret_cast<cufftComplex*>(x_freq)));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded),\n reinterpret_cast<cufftComplex*>(k_freq)));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq),\n reinterpret_cast<cufftComplex*>(k_freq),\n reinterpret_cast<cufftComplex*>(out_freq),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq),\n reinterpret_cast<cufftReal*>(out_padded)));\n \n auto output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded, output.data_ptr<float>(), B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, torch::Tensor workspace, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n self._workspace = None\n self._workspace_batch = None\n \n def _get_workspace(self, B: int, device, dtype):\n n = self.fft_size\n C = self.channels\n half_fft = n // 2 + 1\n size_floats = B * C * n + C * n + B * C * n\n size_complex = (B * C * half_fft + C * half_fft + B * C * half_fft) * 2\n total_size = size_floats + size_complex\n \n if self._workspace is None or self._workspace_batch != B:\n self._workspace = torch.empty(total_size, dtype=dtype, device=device)\n self._workspace_batch = B\n return self._workspace\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n workspace = self._get_workspace(B, x.device, x.dtype)\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, workspace, self.seq_len, self.fft_size)"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 13 · 1 tool call(s) · 385.2s turn · 17.5s LLM · tokens in=25,833 · out=2,112 · total=27,945 · 10,693 chars (click to expand)
turn 13385.2s turn · 17.5s LLM · 367.7s tools · tokens in=25,833 · out=2,112 · total=27,945
reasoning
The kernel is correct. Let me profile it to see the performance improvement before submitting.
call → profile_kernel
callprofile_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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n torch::Tensor workspace,\n int64_t seq_len,\n int64_t fft_size\n) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n float* x_padded = workspace.data_ptr<float>();\n float* k_padded = x_padded + B * C * n;\n c10::complex<float>* x_freq = reinterpret_cast<c10::complex<float>*>(k_padded + C * n);\n c10::complex<float>* k_freq = x_freq + B * C * half_fft;\n c10::complex<float>* out_freq = k_freq + C * half_fft;\n float* out_padded = reinterpret_cast<float*>(out_freq + B * C * half_fft);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded, B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded, C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded),\n reinterpret_cast<cufftComplex*>(x_freq)));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded),\n reinterpret_cast<cufftComplex*>(k_freq)));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq),\n reinterpret_cast<cufftComplex*>(k_freq),\n reinterpret_cast<cufftComplex*>(out_freq),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq),\n reinterpret_cast<cufftReal*>(out_padded)));\n \n auto output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded, output.data_ptr<float>(), B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, torch::Tensor workspace, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n self._workspace = None\n self._workspace_batch = None\n \n def _get_workspace(self, B: int, device, dtype):\n n = self.fft_size\n C = self.channels\n half_fft = n // 2 + 1\n size_floats = B * C * n + C * n + B * C * n\n size_complex = (B * C * half_fft + C * half_fft + B * C * half_fft) * 2\n total_size = size_floats + size_complex\n \n if self._workspace is None or self._workspace_batch != B:\n self._workspace = torch.empty(total_size, dtype=dtype, device=device)\n self._workspace_batch = B\n return self._workspace\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n workspace = self._get_workspace(B, x.device, x.dtype)\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, workspace, self.seq_len, self.fft_size)"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 4782.0μs (91%) calls=1
#2: aten::empty 475.0μs (9%) calls=1
--- Timing ---
GPU time: 976.67 μs [↓1.3μs ok]
--- Memory Bandwidth ---
DRAM BW: 14222.9/3350 GB/s (100.0%)
read=8259.3 write=5963.5 GB/s
Hit rates: L2=372.6%
--- Compute Throughput ---
Dominant pipe: tensor-core util=7.3% [↑0.0% ok]
Pipe util: FMA=165.8%, Tensor=7.3%
--- Roofline Analysis ---
Ridge point (tensor-core): 295.22 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 375.5% [↑0.7% ok]
Eligible warps/cycle: 7.68
Launch: regs/thread=340, smem/blk=108544B, block=1792, grid=549136
--- Memory Access Quality ---
Global ld coalescing: 42.40 sectors/req (42.4x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 8290.1% ####################
short_scoreboard 859.7% ####################
mio_throttle 505.6% ####################
--- Optimization Hints ---
-> 8290% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=42.4 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=42.4x (ideal=1.0) -> 42x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 977.95→976.67μs (↓1.28) ok
Compute%: 7.31→7.33% (↑0.02) ok
Occupancy: 374.76→375.50% (↑0.74) ok
L2 hit%: 372.68→372.59% (↓0.09) REGRESSED
turn 14 · FINAL · 1 tool call(s) · 372.4s turn · 18.6s LLM · tokens in=28,594 · out=2,131 · total=30,725 · 9,476 chars (click to expand)
turn 14372.4s turn · 18.6s LLM · 353.8s tools · tokens in=28,594 · out=2,131 · total=30,725 · FINAL
reasoning
I have a working kernel that passes correctness tests and has shown improvement over the baseline (from 1051μs to 976μs). Let me submit it now.
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 <cufft.h>\n#include <cuda_runtime.h>\n#include <complex>\n#include <mutex>\n#include <map>\n\n#define CUDA_CHECK(call) \\\n do { \\\n cudaError_t err = call; \\\n if (err != cudaSuccess) { \\\n throw std::runtime_error(std::string(\"CUDA error: \") + cudaGetErrorString(err)); \\\n } \\\n } while(0)\n\n#define CUFFT_CHECK(call) \\\n do { \\\n cufftResult err = call; \\\n if (err != CUFFT_SUCCESS) { \\\n throw std::runtime_error(std::string(\"cuFFT error: \") + std::to_string(static_cast<int>(err))); \\\n } \\\n } while(0)\n\nclass FFTPlanCache {\npublic:\n ~FFTPlanCache() {\n std::lock_guard<std::mutex> lock(mutex_);\n for (auto& pair : plans_) {\n cufftDestroy(pair.second);\n }\n }\n \n cufftHandle getOrCreateR2C(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));\n plans_[key] = plan;\n return plan;\n }\n \n cufftHandle getOrCreateC2R(int n, int batch) {\n size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);\n std::lock_guard<std::mutex> lock(mutex_);\n auto it = plans_.find(key);\n if (it != plans_.end()) return it->second;\n cufftHandle plan;\n CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));\n plans_[key] = plan;\n return plan;\n }\n \nprivate:\n std::mutex mutex_;\n std::map<size_t, cufftHandle> plans_;\n};\n\nstatic FFTPlanCache g_planCache;\n\n__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,\n int B, int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * fft_size;\n if (idx < total) {\n int t = idx % fft_size;\n int bc = idx / fft_size;\n padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,\n int C, int seq_len, int fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n if (idx < C * fft_size) {\n int t = idx % fft_size;\n int c = idx / fft_size;\n padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;\n }\n}\n\n__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,\n const cufftComplex* __restrict__ k_freq,\n cufftComplex* __restrict__ out_freq,\n int B, int C, int half_fft) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * half_fft;\n if (idx < total) {\n int t = idx % half_fft;\n int c = (idx / half_fft) % C;\n cufftComplex x = x_freq[idx];\n cufftComplex k = k_freq[c * half_fft + t];\n out_freq[idx].x = x.x * k.x - x.y * k.y;\n out_freq[idx].y = x.x * k.y + x.y * k.x;\n }\n}\n\n__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,\n int B, int C, int seq_len, int fft_size, float inv_fft_size) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * seq_len;\n if (idx < total) {\n int t = idx % seq_len;\n int bc = idx / seq_len;\n output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;\n }\n}\n\ntorch::Tensor fft_conv_forward_cuda(\n torch::Tensor x,\n torch::Tensor filter,\n torch::Tensor workspace,\n int64_t seq_len,\n int64_t fft_size\n) {\n int B = x.size(0);\n int C = x.size(1);\n int n = static_cast<int>(fft_size);\n int half_fft = n / 2 + 1;\n float inv_fft_size = 1.0f / static_cast<float>(n);\n \n float* x_padded = workspace.data_ptr<float>();\n float* k_padded = x_padded + B * C * n;\n c10::complex<float>* x_freq = reinterpret_cast<c10::complex<float>*>(k_padded + C * n);\n c10::complex<float>* k_freq = x_freq + B * C * half_fft;\n c10::complex<float>* out_freq = k_freq + C * half_fft;\n float* out_padded = reinterpret_cast<float*>(out_freq + B * C * half_fft);\n \n const int threads = 256;\n int blocks;\n \n blocks = (B * C * n + threads - 1) / threads;\n padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded, B, C, seq_len, n);\n \n blocks = (C * n + threads - 1) / threads;\n padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded, C, seq_len, n);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),\n reinterpret_cast<cufftReal*>(x_padded),\n reinterpret_cast<cufftComplex*>(x_freq)));\n \n CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),\n reinterpret_cast<cufftReal*>(k_padded),\n reinterpret_cast<cufftComplex*>(k_freq)));\n \n blocks = (B * C * half_fft + threads - 1) / threads;\n complexMulKernel<<<blocks, threads>>>(\n reinterpret_cast<cufftComplex*>(x_freq),\n reinterpret_cast<cufftComplex*>(k_freq),\n reinterpret_cast<cufftComplex*>(out_freq),\n B, C, half_fft);\n \n CUDA_CHECK(cudaGetLastError());\n \n CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),\n reinterpret_cast<cufftComplex*>(out_freq),\n reinterpret_cast<cufftReal*>(out_padded)));\n \n auto output = torch::empty({B, C, seq_len}, x.options());\n blocks = (B * C * seq_len + threads - 1) / threads;\n sliceAndNormalizeKernel<<<blocks, threads>>>(\n out_padded, output.data_ptr<float>(), B, C, seq_len, n, inv_fft_size);\n \n CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, torch::Tensor workspace, int64_t seq_len, int64_t fft_size);\"\n\nfft_conv_ext = load_inline(\n name='fft_conv_cuda',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n extra_ldflags=['-lcufft'],\n verbose=True,\n with_cuda=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels: int, seq_len: int):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.fft_size = 2 * seq_len\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n self._workspace = None\n self._workspace_batch = None\n \n def _get_workspace(self, B: int, device, dtype):\n n = self.fft_size\n C = self.channels\n half_fft = n // 2 + 1\n size_floats = B * C * n + C * n + B * C * n\n size_complex = (B * C * half_fft + C * half_fft + B * C * half_fft) * 2\n total_size = size_floats + size_complex\n \n if self._workspace is None or self._workspace_batch != B:\n self._workspace = torch.empty(total_size, dtype=dtype, device=device)\n self._workspace_batch = B\n return self._workspace\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n workspace = self._get_workspace(B, x.device, x.dtype)\n return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, workspace, self.seq_len, self.fft_size)"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (3 / 3) correctness trials all passed.
Kernel runtime: 1.00 μs
Runtime stats: mean=1.00μs median=?μs std=0.00μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 1361.06 MB peak (ref: 2801.19 MB, ratio: 0.4859x)
Kernel launches: 1 (ref: 61, fusion ratio: 61.0)
SOL score: 1.0
final submitted kernel (216 lines, 7,640 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <torch/extension.h>
#include <cufft.h>
#include <cuda_runtime.h>
#include <complex>
#include <mutex>
#include <map>
#define CUDA_CHECK(call) \
do { \
cudaError_t err = call; \
if (err != cudaSuccess) { \
throw std::runtime_error(std::string("CUDA error: ") + cudaGetErrorString(err)); \
} \
} while(0)
#define CUFFT_CHECK(call) \
do { \
cufftResult err = call; \
if (err != CUFFT_SUCCESS) { \
throw std::runtime_error(std::string("cuFFT error: ") + std::to_string(static_cast<int>(err))); \
} \
} while(0)
class FFTPlanCache {
public:
~FFTPlanCache() {
std::lock_guard<std::mutex> lock(mutex_);
for (auto& pair : plans_) {
cufftDestroy(pair.second);
}
}
cufftHandle getOrCreateR2C(int n, int batch) {
size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch);
std::lock_guard<std::mutex> lock(mutex_);
auto it = plans_.find(key);
if (it != plans_.end()) return it->second;
cufftHandle plan;
CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_R2C, batch));
plans_[key] = plan;
return plan;
}
cufftHandle getOrCreateC2R(int n, int batch) {
size_t key = (static_cast<size_t>(n) << 20) | static_cast<size_t>(batch) | (1ULL << 40);
std::lock_guard<std::mutex> lock(mutex_);
auto it = plans_.find(key);
if (it != plans_.end()) return it->second;
cufftHandle plan;
CUFFT_CHECK(cufftPlan1d(&plan, n, CUFFT_C2R, batch));
plans_[key] = plan;
return plan;
}
private:
std::mutex mutex_;
std::map<size_t, cufftHandle> plans_;
};
static FFTPlanCache g_planCache;
__global__ void padInputKernel(const float* __restrict__ input, float* __restrict__ padded,
int B, int C, int seq_len, int fft_size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = B * C * fft_size;
if (idx < total) {
int t = idx % fft_size;
int bc = idx / fft_size;
padded[idx] = (t < seq_len) ? input[(bc / C) * C * seq_len + (bc % C) * seq_len + t] : 0.0f;
}
}
__global__ void padFilterKernel(const float* __restrict__ filter, float* __restrict__ padded,
int C, int seq_len, int fft_size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < C * fft_size) {
int t = idx % fft_size;
int c = idx / fft_size;
padded[idx] = (t < seq_len) ? filter[c * seq_len + t] : 0.0f;
}
}
__global__ void complexMulKernel(const cufftComplex* __restrict__ x_freq,
const cufftComplex* __restrict__ k_freq,
cufftComplex* __restrict__ out_freq,
int B, int C, int half_fft) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = B * C * half_fft;
if (idx < total) {
int t = idx % half_fft;
int c = (idx / half_fft) % C;
cufftComplex x = x_freq[idx];
cufftComplex k = k_freq[c * half_fft + t];
out_freq[idx].x = x.x * k.x - x.y * k.y;
out_freq[idx].y = x.x * k.y + x.y * k.x;
}
}
__global__ void sliceAndNormalizeKernel(const float* __restrict__ padded_out, float* __restrict__ output,
int B, int C, int seq_len, int fft_size, float inv_fft_size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = B * C * seq_len;
if (idx < total) {
int t = idx % seq_len;
int bc = idx / seq_len;
output[idx] = padded_out[bc * fft_size + t] * inv_fft_size;
}
}
torch::Tensor fft_conv_forward_cuda(
torch::Tensor x,
torch::Tensor filter,
torch::Tensor workspace,
int64_t seq_len,
int64_t fft_size
) {
int B = x.size(0);
int C = x.size(1);
int n = static_cast<int>(fft_size);
int half_fft = n / 2 + 1;
float inv_fft_size = 1.0f / static_cast<float>(n);
float* x_padded = workspace.data_ptr<float>();
float* k_padded = x_padded + B * C * n;
c10::complex<float>* x_freq = reinterpret_cast<c10::complex<float>*>(k_padded + C * n);
c10::complex<float>* k_freq = x_freq + B * C * half_fft;
c10::complex<float>* out_freq = k_freq + C * half_fft;
float* out_padded = reinterpret_cast<float*>(out_freq + B * C * half_fft);
const int threads = 256;
int blocks;
blocks = (B * C * n + threads - 1) / threads;
padInputKernel<<<blocks, threads>>>(x.data_ptr<float>(), x_padded, B, C, seq_len, n);
blocks = (C * n + threads - 1) / threads;
padFilterKernel<<<blocks, threads>>>(filter.data_ptr<float>(), k_padded, C, seq_len, n);
CUDA_CHECK(cudaGetLastError());
CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, B * C),
reinterpret_cast<cufftReal*>(x_padded),
reinterpret_cast<cufftComplex*>(x_freq)));
CUFFT_CHECK(cufftExecR2C(g_planCache.getOrCreateR2C(n, C),
reinterpret_cast<cufftReal*>(k_padded),
reinterpret_cast<cufftComplex*>(k_freq)));
blocks = (B * C * half_fft + threads - 1) / threads;
complexMulKernel<<<blocks, threads>>>(
reinterpret_cast<cufftComplex*>(x_freq),
reinterpret_cast<cufftComplex*>(k_freq),
reinterpret_cast<cufftComplex*>(out_freq),
B, C, half_fft);
CUDA_CHECK(cudaGetLastError());
CUFFT_CHECK(cufftExecC2R(g_planCache.getOrCreateC2R(n, B * C),
reinterpret_cast<cufftComplex*>(out_freq),
reinterpret_cast<cufftReal*>(out_padded)));
auto output = torch::empty({B, C, seq_len}, x.options());
blocks = (B * C * seq_len + threads - 1) / threads;
sliceAndNormalizeKernel<<<blocks, threads>>>(
out_padded, output.data_ptr<float>(), B, C, seq_len, n, inv_fft_size);
CUDA_CHECK(cudaGetLastError());
return output;
}
"""
cpp_source = "torch::Tensor fft_conv_forward_cuda(torch::Tensor x, torch::Tensor filter, torch::Tensor workspace, int64_t seq_len, int64_t fft_size);"
fft_conv_ext = load_inline(
name='fft_conv_cuda',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['fft_conv_forward_cuda'],
extra_cuda_cflags=['-O3'],
extra_ldflags=['-lcufft'],
verbose=True,
with_cuda=True
)
class ModelNew(nn.Module):
def __init__(self, channels: int, seq_len: int):
super(ModelNew, self).__init__()
self.channels = channels
self.seq_len = seq_len
self.fft_size = 2 * seq_len
self.filter = nn.Parameter(torch.randn(channels, seq_len))
self._workspace = None
self._workspace_batch = None
def _get_workspace(self, B: int, device, dtype):
n = self.fft_size
C = self.channels
half_fft = n // 2 + 1
size_floats = B * C * n + C * n + B * C * n
size_complex = (B * C * half_fft + C * half_fft + B * C * half_fft) * 2
total_size = size_floats + size_complex
if self._workspace is None or self._workspace_batch != B:
self._workspace = torch.empty(total_size, dtype=dtype, device=device)
self._workspace_batch = B
return self._workspace
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, C, L = x.shape
workspace = self._get_workspace(B, x.device, x.dtype)
return fft_conv_ext.fft_conv_forward_cuda(x, self.filter, workspace, self.seq_len, self.fft_size)