L1 P7 · popcorn

compile_fail 7_Conv1d_FFT.py

model

FW-GLM-5-1

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tokens (total)

6633

wall clock

85.0s

truncated

backend

cuda fp32

variant

popcorn

runtime

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speedup

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started

2026-05-11T09:10:19Z

compile error: Error building extension 'fft_conv_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fft_conv_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp -o main.o

timingkernelbench.timing

kernel runtime

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ref runtime

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speedup

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kernel mean / median / std

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ref mean / median / std

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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 0 · FINAL · 1 tool call(s) · 85.0s turn · 64.1s LLM · tokens in=1,186 · out=5,447 · total=6,633 · 58,601 chars (click to expand)

turn 085.0s turn · 64.1s LLM · 20.9s tools · tokens in=1,186 · out=5,447 · total=6,633 · FINAL

reasoning

Let me analyze this task. I need to implement a custom CUDA kernel for FFT-based convolution that's faster than the PyTorch reference. The operation is: 1. Pad input x (B, C, L) with zeros to length 2L 2. Pad filter (C, L) with zeros to length 2L 3. Compute RFFT on both 4. Multiply in frequency domain 5. Compute IRFFT 6. Take first L elements Key observations: - The FFT size is 2 * seq_len = 16384 for seq_len = 8192 - We're using RFFT (real FFT), so output size is fft_size/2 + 1 = 8193 complex values - Batch size is 16, channels is 256 - Total FFTs: 16 * 256 = 4096 for input, 256 for filter - Filter FFT can be precomputed! Optimization opportunities: 1. Precompute the filter FFT in __init__ or cache it 2. Use cuFFT for batched FFTs 3. Optimize memory layout and transfers Let me write a CUDA kernel that uses cuFFT for the FFT operations and custom kernels for the multiplication and padding. Actually, looking at this more carefully: - The filter is a learnable parameter (nn.Parameter), so its FFT needs to be recomputed when the filter changes - However, during inference, the filter is fixed - We could cache the filter FFT Let me design the kernel: 1. Use cuFFTPlanMany for batched RFFT/IRFFT 2. Custom kernel for complex multiplication 3. Custom kernel for extracting first seq_len elements For the forward pass: 1. Pad x to length 2*seq_len (zero padding) - we can embed this in the FFT by using strides 2. Compute RFFT of padded x 3. Get RFFT of padded filter (could be cached) 4. Multiply in frequency domain 5. Compute IRFFT 6. Extract first seq_len elements 7. I can leverage cuFFT's advanced data layout features to handle padding directly rather than creating separate padded tensors. This eliminates the intermediate memory allocation and copy operations. The core approach is to set up cuFFT plans that handle the padding implicitly through input/output strides, then execute the FFTs directly on the original data with the appropriate layout parameters. The output of RFFT has n//2+1 elements, so we need to handle this correctly. I should use cuFFT's advanced data layout to embed padding directly - this avoids explicit zero-padding operations. The plan configuration allows specifying input and output strides, distances, and sizes to handle non-contiguous memory layouts efficiently. For a 2D layout with (channels, length), the stride between consecutive elements is 1 and the stride between rows (channels) is seq_len. This lets me perform FFTs across the length dimension while treating the channels as separate 1D signals. cuFFT provides an efficient way to handle batched 1D FFTs with `cufftPlanMany`. I can configure the plan to work directly on the existing tensor layout without requiring explicit padding operations. The key is setting up the input and output parameters correctly—specifying the element count per FFT, the number of batches, and the strides between elements and between different FFTs in the batch. For zero-padding, I'll set `idist` to the larger padded size while keeping `istride` at 1 and `inembed` pointing to the padded length. This lets cuFFT read the actual data with the correct spacing while treating the rest as zeros. The plan creation takes the dimensions, inembed array for input layout, istride for element spacing, idist for batch spacing, and corresponding output parameters. However, there's a distinction here—I need to ensure the zero padding actually happens. Simply adjusting strides won't automatically zero out the extra space. cuFFT's advanced layout lets you specify how to walk through memory for FFT, but zero initialization requires either doing it manually or finding another approach. The cleaner strategy is to allocate a workspace buffer once during initialization, then in the forward pass: clear the buffer, copy input data into it, run the FFT operation, handle filter multiplication, apply inverse FFT, and extract the result. This means I need to set up cuFFT plans for both forward and inverse transforms during initialization, along with pre-allocating the workspace. The filter transform should be cached for efficiency. I'll store it as a persistent buffer rather than recomputing it each time. For the forward pass, the workflow becomes: workspace ← zeros, workspace ← x (copy), compute FFT on workspace, multiply by cached filter FFT, apply IRFFT, and return the first portion of the output. Actually, a fused kernel combining zero-filling and copying would be more efficient than two separate operations. I should also consider using multiple streams or CUDA graphs for further optimization, though batch size is fixed so dynamic batching improvements aren't possible. My implementation strategy: initialize cuFFT plans for forward and inverse transforms, pre-allocate a filter FFT buffer and compute it once during setup, then in the forward pass zero the workspace, copy input data, execute the FFT chain, multiply in frequency domain, and extract the result. For memory sizing, the workspace needs to hold 2L elements per channel across all batches, so that's B * C * 2L floats for real inputs and B * C * (L+1) complex values for the frequency domain representation. The key optimization is realizing the workspace can serve both the time-domain padded input and the frequency-domain result simultaneously. Since padding requires B*C*L elements and the RFFT output produces B*C*(L+1) complex values, I can reuse the same buffer—just being careful about whether to interpret it as float or complex. The size difference between these two representations is minimal (approximately 2 elements per channel), so a single workspace sized for the larger frequency-domain output works for both stages. To implement this, I'll allocate a single buffer of B*C*2L floats (approximately 67M elements for the typical problem dimensions), then interpret different slices as either float arrays for padding or complex float arrays for RFFT output as needed during the computation. The filter FFT needs precomputation once. Since the filter has shape (C, L) and must be padded to (C, 2L), its RFFT produces (C, L+1) complex values. For batch processing, I should store this with an explicit batch dimension as (C, L+1) complex values, which totals roughly 4.2M elements. My implementation plan involves creating three cuFFT plans: one for the forward RFFT with `CufftR2C` type, one for the batched RFFT across all channels and batches, and one for the inverse transform. I'll precompute the filter's FFT during initialization and store it as persistent state within the model object. The challenging part is ensuring the filter FFT gets recomputed when the filter parameters change. I could track this with a flag that signals when the cached FFT is stale, then regenerate it before each forward pass if needed. The filter can change during training, so I'll need to recompute its FFT on every forward pass or track when it's modified. I'll go with recomputing it each time for correctness. Using torch.fft.rfft on the filter and keeping the rest in a custom kernel should be efficient since the filter FFT is relatively small. I can optimize memory by reusing buffers: padding the input to the same size as the frequency representation, then performing in-place FFT and multiplication directly on that buffer. The padded input needs B*C*L floats for zero-padded regions, which fits within the frequency buffer size. The optimal approach is preallocating workspace during initialization, then each forward pass zeros the workspace, copies input data into the first L positions, performs the RFFT, multiplies by the filter FFT, applies IRFFT, and extracts the result. Since the workspace is reused across calls, zeroing is necessary, but I can fuse the zeroing with the copy operation. Alternatively, cuFFT supports zero-padding through `cufftPlanMany` with proper configuration, which could eliminate the explicit zeroing step. For a 1D R2C FFT with zero-padding from L to 2L elements across B*C transforms, the input layout has L valid elements per transform with the stride set to match the original tensor arrangement, while the FFT size itself processes 2L elements. This lets cuFFT handle the padding internally without allocating a separate zero-filled buffer. However, cuFFT doesn't actually zero-pad automatically—it reads memory at the specified strides regardless of whether that memory contains zeros or garbage values. I'd need to manually zero-initialize the padding regions first, or use a different approach: allocate a buffer of B*C*(L+1) complex elements, perform in-place R2C FFT to fill it directly with the padded result, then run IRFFT and extract the first L elements. Actually, cuFFT's R2C in-place transform overwrites the input buffer with the complex output, so zeroing and copying input data into the buffer, then applying the FFT in-place gives me the frequency domain data in the same memory. The implementation needs to handle filter FFT caching and recomputation. I can register a hook to invalidate the cache whenever filter parameters change, then lazily recompute the filter FFT when needed. The forward pass would zero-pad the input, compute its FFT in-place, multiply with the cached filter FFT, apply inverse FFT, and extract the result. However, there's a memory layout consideration: the filter is stored as (C, L) while the input is (B, C, L), so the filter FFT should be computed per-channel with (C, L+1) complex output. For broadcasting correctly during multiplication, the filter FFT needs to be reshaped to (1, C, L+1) to align with the input's (B, C, L+1) frequency representation. The IRFFT operation takes complex input and produces real output. To optimize memory usage, I can use in-place transforms where possible—R2C can operate in-place by allocating sufficient buffer space (2L floats per sequence), and IRFFT can similarly work in-place. This means after zeroing the buffer and copying the input, the RFFT transform can be applied directly, then multiplied by the filter FFT, and finally the IRFFT can proceed in the same memory space. The challenge is that IRFFT requires 2L floats of output space but only has (L+1) complex values available. However, since cuFFT allows in-place C2R transforms where the complex input size equals the real output size (both 2L elements), the transform can work in-place as long as the buffer is sized appropriately from the start. The in-place R2C transform overwrites the real input buffer with complex output. For a length 2L transform, I get L+1 complex numbers, which fits within the original 2L float slots since that's enough space for L+1 complex elements. After the transform completes, the extra padding space (roughly L-1 complex values worth) remains unused but available for subsequent operations.

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# CUDA kernel for FFT-based convolution\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cufft.h>\n#include <stdio.h>\n\n#define CHECK_CUFFT(call) \\\\\n do { \\\\\n cufftResult_t err = call; \\\\\n if (err != CUFFT_SUCCESS) { \\\\\n printf(\"cuFFT error %d at line %d\\\\n\", err, __LINE__); \\\\\n } \\\\\n } while(0)\n\n#define CHECK_CUDA(call) \\\\\n do { \\\\\n cudaError_t err = call; \\\\\n if (err != cudaSuccess) { \\\\\n printf(\"CUDA error %s at line %d\\\\n\", cudaGetErrorString(err), __LINE__); \\\\\n } \\\\\n } while(0)\n\n// Kernel to zero pad input and copy to workspace\n// Input: (B, C, L) contiguous\n// Output: (B, C, 2L) contiguous, with zeros in positions [L, 2L)\n__global__ void pad_input_kernel(const float* __restrict__ input,\n float* __restrict__ workspace,\n int B, int C, int L) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * (2 * L);\n \n // Zero out entire workspace first\n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n workspace[i] = 0.0f;\n }\n \n // Then copy input values\n int input_size = B * C * L;\n for (int i = idx; i < input_size; i += blockDim.x * gridDim.x) {\n workspace[i] = input[i];\n }\n}\n\n// Kernel to multiply frequency domain representations\n// x_f: (B, C, L+1) complex\n// k_f: (C, L+1) complex\n// Result: x_f = x_f * k_f (broadcast over batch dimension)\n__global__ void multiply_freq_kernel(cufftComplex* __restrict__ x_f,\n const cufftComplex* __restrict__ k_f,\n int B, int C, int freq_len) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * freq_len;\n \n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n int freq_idx = i % freq_len;\n int c = (i / freq_len) % C;\n int k_idx = c * freq_len + freq_idx;\n \n float a = x_f[i].x;\n float b = x_f[i].y;\n float c_val = k_f[k_idx].x;\n float d = k_f[k_idx].y;\n \n // Complex multiplication: (a + bi)(c + di) = (ac - bd) + (ad + bc)i\n x_f[i].x = a * c_val - b * d;\n x_f[i].y = a * d + b * c_val;\n }\n}\n\n// Kernel to extract output (first L elements from IRFFT result)\n__global__ void extract_output_kernel(const float* __restrict__ irfft_out,\n float* __restrict__ output,\n int B, int C, int L) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * L;\n \n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n output[i] = irfft_out[i];\n }\n}\n\n// Main forward function\nvoid fft_conv_forward_cuda(\n const float* input,\n const float* filter,\n float* output,\n float* workspace,\n cufftComplex* filter_fft,\n int B, int C, int L,\n cudaStream_t stream,\n cufftHandle plan_r2c,\n cufftHandle plan_c2r,\n bool recompute_filter\n) {\n int fft_size = 2 * L;\n int freq_len = L + 1; // RFFT output length\n int total_input = B * C * L;\n int total_workspace = B * C * fft_size;\n \n // Fused kernel: zero workspace and copy input\n int block_size = 256;\n int grid_size = (total_workspace + block_size - 1) / block_size;\n grid_size = min(grid_size, 65535);\n \n pad_input_kernel<<<grid_size, block_size, 0, stream>>>(\n input, workspace, B, C, L);\n \n // In-place RFFT: workspace (float[2L]) -> workspace (complex[L+1])\n // We need to interpret workspace as cufftComplex for the transform\n CHECK_CUFFT(cufftSetStream(plan_r2c, stream));\n CHECK_CUFFT(cufftExecR2C(plan_r2c, (cufftReal*)workspace, (cufftComplex*)workspace));\n \n // Recompute filter FFT if needed\n if (recompute_filter) {\n // Use same plan for filter (single batch, C channels)\n // Actually, we need a separate plan for the filter\n // For simplicity, we'll use the same logic\n // We need to pad filter and compute its FFT\n // Let's do this with a separate kernel\n \n // Zero and copy filter to the beginning of workspace (we can reuse)\n // Actually we need to be careful - workspace is being used for x_f\n // Let's use output buffer temporarily for filter padding\n int filter_size = C * L;\n int filter_padded = C * fft_size;\n \n // Zero output buffer (we'll use it as temp for filter FFT)\n cudaMemsetAsync(output, 0, filter_padded * sizeof(float), stream);\n \n // Copy filter values\n cudaMemcpyAsync(output, filter, filter_size * sizeof(float), \n cudaMemcpyDeviceToDevice, stream);\n \n // Compute filter RFFT\n // We need a plan for (C, 2L) -> (C, L+1)\n // The existing plan_r2c is for (B*C, 2L) -> (B*C, L+1)\n // We'll use it but with n[0] = C and idist/odist for single batch\n // Actually this is tricky... let's make a separate filter plan\n \n // For now, use atomic approach: create temp buffer for filter FFT\n // This is computed once per forward pass when filter changes\n // Use output buffer (which we haven't written results to yet)\n \n // We need a plan for filter specifically\n // Create it on first call or use existing\n static cufftHandle filter_plan = 0;\n if (filter_plan == 0) {\n int n_filter[1] = {fft_size};\n int inembed_filter[1] = {fft_size};\n int onembed_filter[1] = {freq_len};\n CHECK_CUFFT(cufftPlanMany(&filter_plan, 1, n_filter,\n inembed_filter, 1, fft_size,\n onembed_filter, 1, freq_len,\n CUFFT_R2C, C));\n }\n CHECK_CUFFT(cufftSetStream(filter_plan, stream));\n CHECK_CUFFT(cufftExecR2C(filter_plan, (cufftReal*)output, filter_fft));\n }\n \n // Multiply in frequency domain\n int freq_total = B * C * freq_len;\n int mult_grid = (freq_total + block_size - 1) / block_size;\n mult_grid = min(mult_grid, 65535);\n \n multiply_freq_kernel<<<mult_grid, block_size, 0, stream>>>(\n (cufftComplex*)workspace, filter_fft, B, C, freq_len);\n \n // In-place IRFFT\n CHECK_CUFFT(cufftSetStream(plan_c2r, stream));\n CHECK_CUFFT(cufftExecC2R(plan_c2r, (cufftComplex*)workspace, (cufftReal*)workspace));\n \n // Normalize by FFT size (cuFFT IRFFT doesn't normalize)\n // Scale by 1/fft_size\n // Actually, we can integrate this into extract kernel\n \n // Extract first L elements to output\n int extract_grid = (total_input + block_size - 1) / block_size;\n extract_grid = min(extract_grid, 65535);\n \n // Fused extract and normalize kernel\n extract_output_kernel<<<extract_grid, block_size, 0, stream>>>(\n workspace, output, B, C, L);\n}\n\n// Helper function to create plans\ncufftHandle create_r2c_plan(int batch_size, int fft_size, int freq_len) {\n cufftHandle plan;\n int n[1] = {fft_size};\n int inembed[1] = {fft_size};\n int onembed[1] = {freq_len};\n CHECK_CUFFT(cufftPlanMany(&plan, 1, n, inembed, 1, fft_size,\n onembed, 1, freq_len, CUFFT_R2C, batch_size));\n return plan;\n}\n\ncufftHandle create_c2r_plan(int batch_size, int fft_size, int freq_len) {\n cufftHandle plan;\n int n[1] = {fft_size};\n int inembed[1] = {freq_len};\n int onembed[1] = {fft_size};\n CHECK_CUFFT(cufftPlanMany(&plan, 1, n, inembed, 1, freq_len,\n onembed, 1, fft_size, CUFFT_C2R, batch_size));\n return plan;\n}\n\n// Normalize by 1/N for IRFFT\n__global__ void normalize_kernel(float* data, int size, float scale) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n for (int i = idx; i < size; i += blockDim.x * gridDim.x) {\n data[i] *= scale;\n }\n}\n\"\"\"\n\ncpp_source = \"\"\"\n#include <cufft.h>\n\nextern \"C\" {\n void fft_conv_forward_cuda(\n const float* input,\n const float* filter,\n float* output,\n float* workspace,\n cufftComplex* filter_fft,\n int B, int C, int L,\n cudaStream_t stream,\n cufftHandle plan_r2c,\n cufftHandle plan_c2r,\n bool recompute_filter\n );\n \n cufftHandle create_r2c_plan(int batch_size, int fft_size, int freq_len);\n cufftHandle create_c2r_plan(int batch_size, int fft_size, int freq_len);\n}\n\"\"\"\n\n# Load the CUDA extension\nfft_conv_ext = load_inline(\n name='fft_conv_ext',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fft_conv_forward_cuda', 'create_r2c_plan', 'create_c2r_plan'],\n extra_cuda_cflags=['-O3', '-arch=sm_80'],\n verbose=False\n)\n\nimport torch\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.freq_len = seq_len + 1\n self.filter = nn.Parameter(torch.randn(channels, seq_len))\n \n # Use typical batch size from get_inputs()\n self.default_batch_size = 16\n \n # Pre-create cuFFT plans for default batch size\n self.plans = {}\n self._create_plans(self.default_batch_size)\n \n # Pre-allocate workspace and filter FFT buffer\n self.workspace = None\n self.filter_fft = None\n self.filter_version = -1\n \n def _create_plans(self, batch_size):\n key = batch_size\n if key not in self.plans:\n total_batch = batch_size * self.channels\n plan_r2c = fft_conv_ext.create_r2c_plan(total_batch, self.fft_size, self.freq_len)\n plan_c2r = fft_conv_ext.create_c2r_plan(total_batch, self.fft_size, self.freq_len)\n self.plans[key] = (plan_r2c, plan_c2r)\n return self.plans[key]\n \n def _ensure_buffers(self, batch_size, device):\n total_batch = batch_size * self.channels\n \n # Workspace for padded input and frequency data\n workspace_size = total_batch * self.fft_size\n if self.workspace is None or self.workspace.size(0) < workspace_size:\n self.workspace = torch.zeros(workspace_size, dtype=torch.float32, device=device)\n \n # Filter FFT buffer\n filter_fft_size = self.channels * self.freq_len\n if self.filter_fft is None or self.filter_fft.size(0) < filter_fft_size:\n self.filter_fft = torch.zeros(filter_fft_size * 2, dtype=torch.float32, device=device) # complex = 2 floats\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, C, L = x.shape\n assert C == self.channels and L == self.seq_len\n \n # Ensure tensors are contiguous and on same device\n x = x.contiguous()\n filter_data = self.filter.contiguous()\n \n # Get or create plans for this batch size\n plan_r2c, plan_c2r = self._create_plans(B)\n \n # Ensure buffers are allocated\n self._ensure_buffers(B, x.device)\n \n # Check if filter changed\n recompute_filter = self.filter._version != self.filter_version\n if recompute_filter:\n self.filter_version = self.filter._version\n \n # Allocate output\n output = torch.empty(B, C, L, dtype=torch.float32, device=x.device)\n \n # Call CUDA kernel\n stream = torch.cuda.current_stream().cuda_stream\n \n import ctypes\n fft_conv_ext.fft_conv_forward_cuda(\n ctypes.c_void_p(x.data_ptr()),\n ctypes.c_void_p(filter_data.data_ptr()),\n ctypes.c_void_p(output.data_ptr()),\n ctypes.c_void_p(self.workspace.data_ptr()),\n ctypes.c_void_p(self.filter_fft.data_ptr()),\n B, C, L,\n stream,\n plan_r2c,\n plan_c2r,\n recompute_filter\n )\n \n return output" }

result ← submit_kernel (fail)

failsubmit_kernel submit_kernel FAILED: kernel did not compile. Error building extension 'fft_conv_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fft_conv_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp -o main.o FAILED: [code=1] main.o /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fft_conv_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp -o main.o In file included from /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/bits/move.h:37, from /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/bits/stl_function.h:60, from /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/functional:49, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/headeronly/core/DeviceType.h:13, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/c10/core/DeviceType.h:8, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/c10/core/Device.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:12, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/Tensor.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/Tensor.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/function_hook.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:3, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/variable.h:7, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/autograd.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:4, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:6, from /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp:1: /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/type_traits: In instantiation of 'struct std::is_base_of<pybind11::detail::pyobject_tag, CUstream_st>': /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/cast.h:2099:68: recursively required by substitution of 'template<class T> class pybind11::detail::type_caster<T, typename std::enable_if<std::is_base_of<pybind11::detail::pyobject_tag, typename std::remove_reference<_Tp>::type>::value, void>::type> [with T = CUstream_st]' 2099 | = ::pybind11::detail::concat(type_descr(make_caster<Args>::name)...); | ^~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/cast.h:2099:68: required from 'constexpr const auto pybind11::detail::argument_loader<const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool>::arg_names' /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/cast.h:2098:27: required from 'class pybind11::detail::argument_loader<const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool>' 2098 | static constexpr auto arg_names | ^~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:397:43: required from 'void pybind11::cpp_function::initialize(Func&&, Return (*)(Args ...), const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Return = void; Args = {const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool}; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}]' 397 | sizeof...(Args), cast_in::args_pos >= 0, cast_in::has_kwargs), | ^~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:275:19: required from 'pybind11::cpp_function::cpp_function(Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}; <template-parameter-1-3> = void]' 275 | initialize( | ~~~~~~~~~~^ 276 | std::forward<Func>(f), (detail::function_signature_t<Func> *) nullptr, extra...); | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1437:22: required from 'pybind11::module_& pybind11::module_::def(const char*, Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {char [22]}]' 1437 | cpp_function func(std::forward<Func>(f), | ^~~~ /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp:24:6: required from here 24 | m.def("fft_conv_forward_cuda", torch::wrap_pybind_function(fft_conv_forward_cuda), "fft_conv_forward_cuda"); | ~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/type_traits:1493:30: error: invalid use of incomplete type 'struct CUstream_st' 1493 | : public __bool_constant<__is_base_of(_Base, _Derived)> | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In file included from /usr/local/cuda/include/cufft.h:59, from /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp:3: /usr/local/cuda/include/driver_types.h:3035:35: note: forward declaration of 'struct CUstream_st' 3035 | typedef __device_builtin__ struct CUstream_st *cudaStream_t; | ^~~~~~~~~~~ In file included from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/cast.h:15, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/attr.h:15, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/detail/class.h:13, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:13, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/Exceptions.h:13, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/python.h:12, from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:10: /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/detail/descr.h: In instantiation of 'static constexpr std::array<const std::type_info*, (sizeof... (Ts) + 1)> pybind11::detail::descr<N, Ts>::types() [with long unsigned int N = 294; Ts = {float2, CUstream_st}]': /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:487:73: required from 'void pybind11::cpp_function::initialize(Func&&, Return (*)(Args ...), const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Return = void; Args = {const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool}; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}]' 487 | PYBIND11_DESCR_CONSTEXPR auto types = decltype(signature)::types(); | ~~~~~~~~~~~~~~~~~~~~~~~~~~^~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:275:19: required from 'pybind11::cpp_function::cpp_function(Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}; <template-parameter-1-3> = void]' 275 | initialize( | ~~~~~~~~~~^ 276 | std::forward<Func>(f), (detail::function_signature_t<Func> *) nullptr, extra...); | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1437:22: required from 'pybind11::module_& pybind11::module_::def(const char*, Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {char [22]}]' 1437 | cpp_function func(std::forward<Func>(f), | ^~~~ /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp:24:6: required from here 24 | m.def("fft_conv_forward_cuda", torch::wrap_pybind_function(fft_conv_forward_cuda), "fft_conv_forward_cuda"); | ~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/detail/descr.h:41:19: error: invalid use of incomplete type 'struct CUstream_st' 41 | return {{&typeid(Ts)..., nullptr}}; | ^~~~~~~~~~ /usr/local/cuda/include/driver_types.h:3035:35: note: forward declaration of 'struct CUstream_st' 3035 | typedef __device_builtin__ struct CUstream_st *cudaStream_t; | ^~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/detail/descr.h:41:42: error: could not convert '{{<expression error>, nullptr}}' from '<brace-enclosed initializer list>' to 'std::array<const std::type_info*, 3>' 41 | return {{&typeid(Ts)..., nullptr}}; | ^ | | | <brace-enclosed initializer list> /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h: In instantiation of 'void pybind11::cpp_function::initialize(Func&&, Return (*)(Args ...), const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Return = void; Args = {const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool}; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}]': /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:275:19: required from 'pybind11::cpp_function::cpp_function(Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {pybind11::name, pybind11::scope, pybind11::sibling, char [22]}; <template-parameter-1-3> = void]' 275 | initialize( | ~~~~~~~~~~^ 276 | std::forward<Func>(f), (detail::function_signature_t<Func> *) nullptr, extra...); | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:1437:22: required from 'pybind11::module_& pybind11::module_::def(const char*, Func&&, const Extra& ...) [with Func = torch::detail::wrap_pybind_function_impl_<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, false>(void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), std::index_sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11>, std::bool_constant<false>)::<lambda(torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 0>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 1>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 2>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 3>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 4>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 5>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 6>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 7>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 8>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 9>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 10>, torch::detail::Arg<void (&)(const float*, const float*, float*, float*, float2*, int, int, int, CUstream_st*, int, int, bool), 11>)>; Extra = {char [22]}]' 1437 | cpp_function func(std::forward<Func>(f), | ^~~~ /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/main.cpp:24:6: required from here 24 | m.def("fft_conv_forward_cuda", torch::wrap_pybind_function(fft_conv_forward_cuda), "fft_conv_forward_cuda"); | ~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:487:73: in 'constexpr' expansion of 'pybind11::detail::descr<294, float2, CUstream_st>::types()' /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/pybind11/pybind11.h:487:73: error: 'constexpr' call flows off the end of the function 487 | PYBIND11_DESCR_CONSTEXPR auto types = decltype(signature)::types(); | ~~~~~~~~~~~~~~~~~~~~~~~~~~^~ [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fft_conv_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -arch=sm_80 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_7_cache/k_67442fa5ee5c/fft_conv_ext/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used ninja: build stopped: subcommand failed.

final submitted kernel (336 lines, 12,102 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

# CUDA kernel for FFT-based convolution
cuda_source = """
#include <cuda.h>
#include <cuda_runtime.h>
#include <cufft.h>
#include <stdio.h>

#define CHECK_CUFFT(call) \\
    do { \\
        cufftResult_t err = call; \\
        if (err != CUFFT_SUCCESS) { \\
            printf("cuFFT error %d at line %d\\n", err, __LINE__); \\
        } \\
    } while(0)

#define CHECK_CUDA(call) \\
    do { \\
        cudaError_t err = call; \\
        if (err != cudaSuccess) { \\
            printf("CUDA error %s at line %d\\n", cudaGetErrorString(err), __LINE__); \\
        } \\
    } while(0)

// Kernel to zero pad input and copy to workspace
// Input: (B, C, L) contiguous
// Output: (B, C, 2L) contiguous, with zeros in positions [L, 2L)
__global__ void pad_input_kernel(const float* __restrict__ input,
                                  float* __restrict__ workspace,
                                  int B, int C, int L) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = B * C * (2 * L);
    
    // Zero out entire workspace first
    for (int i = idx; i < total; i += blockDim.x * gridDim.x) {
        workspace[i] = 0.0f;
    }
    
    // Then copy input values
    int input_size = B * C * L;
    for (int i = idx; i < input_size; i += blockDim.x * gridDim.x) {
        workspace[i] = input[i];
    }
}

// Kernel to multiply frequency domain representations
// x_f: (B, C, L+1) complex
// k_f: (C, L+1) complex
// Result: x_f = x_f * k_f (broadcast over batch dimension)
__global__ void multiply_freq_kernel(cufftComplex* __restrict__ x_f,
                                      const cufftComplex* __restrict__ k_f,
                                      int B, int C, int freq_len) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = B * C * freq_len;
    
    for (int i = idx; i < total; i += blockDim.x * gridDim.x) {
        int freq_idx = i % freq_len;
        int c = (i / freq_len) % C;
        int k_idx = c * freq_len + freq_idx;
        
        float a = x_f[i].x;
        float b = x_f[i].y;
        float c_val = k_f[k_idx].x;
        float d = k_f[k_idx].y;
        
        // Complex multiplication: (a + bi)(c + di) = (ac - bd) + (ad + bc)i
        x_f[i].x = a * c_val - b * d;
        x_f[i].y = a * d + b * c_val;
    }
}

// Kernel to extract output (first L elements from IRFFT result)
__global__ void extract_output_kernel(const float* __restrict__ irfft_out,
                                       float* __restrict__ output,
                                       int B, int C, int L) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total = B * C * L;
    
    for (int i = idx; i < total; i += blockDim.x * gridDim.x) {
        output[i] = irfft_out[i];
    }
}

// Main forward function
void fft_conv_forward_cuda(
    const float* input,
    const float* filter,
    float* output,
    float* workspace,
    cufftComplex* filter_fft,
    int B, int C, int L,
    cudaStream_t stream,
    cufftHandle plan_r2c,
    cufftHandle plan_c2r,
    bool recompute_filter
) {
    int fft_size = 2 * L;
    int freq_len = L + 1;  // RFFT output length
    int total_input = B * C * L;
    int total_workspace = B * C * fft_size;
    
    // Fused kernel: zero workspace and copy input
    int block_size = 256;
    int grid_size = (total_workspace + block_size - 1) / block_size;
    grid_size = min(grid_size, 65535);
    
    pad_input_kernel<<<grid_size, block_size, 0, stream>>>(
        input, workspace, B, C, L);
    
    // In-place RFFT: workspace (float[2L]) -> workspace (complex[L+1])
    // We need to interpret workspace as cufftComplex for the transform
    CHECK_CUFFT(cufftSetStream(plan_r2c, stream));
    CHECK_CUFFT(cufftExecR2C(plan_r2c, (cufftReal*)workspace, (cufftComplex*)workspace));
    
    // Recompute filter FFT if needed
    if (recompute_filter) {
        // Use same plan for filter (single batch, C channels)
        // Actually, we need a separate plan for the filter
        // For simplicity, we'll use the same logic
        // We need to pad filter and compute its FFT
        // Let's do this with a separate kernel
        
        // Zero and copy filter to the beginning of workspace (we can reuse)
        // Actually we need to be careful - workspace is being used for x_f
        // Let's use output buffer temporarily for filter padding
        int filter_size = C * L;
        int filter_padded = C * fft_size;
        
        // Zero output buffer (we'll use it as temp for filter FFT)
        cudaMemsetAsync(output, 0, filter_padded * sizeof(float), stream);
        
        // Copy filter values
        cudaMemcpyAsync(output, filter, filter_size * sizeof(float), 
                       cudaMemcpyDeviceToDevice, stream);
        
        // Compute filter RFFT
        // We need a plan for (C, 2L) -> (C, L+1)
        // The existing plan_r2c is for (B*C, 2L) -> (B*C, L+1)
        // We'll use it but with n[0] = C and idist/odist for single batch
        // Actually this is tricky... let's make a separate filter plan
        
        // For now, use atomic approach: create temp buffer for filter FFT
        // This is computed once per forward pass when filter changes
        // Use output buffer (which we haven't written results to yet)
        
        // We need a plan for filter specifically
        // Create it on first call or use existing
        static cufftHandle filter_plan = 0;
        if (filter_plan == 0) {
            int n_filter[1] = {fft_size};
            int inembed_filter[1] = {fft_size};
            int onembed_filter[1] = {freq_len};
            CHECK_CUFFT(cufftPlanMany(&filter_plan, 1, n_filter,
                                      inembed_filter, 1, fft_size,
                                      onembed_filter, 1, freq_len,
                                      CUFFT_R2C, C));
        }
        CHECK_CUFFT(cufftSetStream(filter_plan, stream));
        CHECK_CUFFT(cufftExecR2C(filter_plan, (cufftReal*)output, filter_fft));
    }
    
    // Multiply in frequency domain
    int freq_total = B * C * freq_len;
    int mult_grid = (freq_total + block_size - 1) / block_size;
    mult_grid = min(mult_grid, 65535);
    
    multiply_freq_kernel<<<mult_grid, block_size, 0, stream>>>(
        (cufftComplex*)workspace, filter_fft, B, C, freq_len);
    
    // In-place IRFFT
    CHECK_CUFFT(cufftSetStream(plan_c2r, stream));
    CHECK_CUFFT(cufftExecC2R(plan_c2r, (cufftComplex*)workspace, (cufftReal*)workspace));
    
    // Normalize by FFT size (cuFFT IRFFT doesn't normalize)
    // Scale by 1/fft_size
    // Actually, we can integrate this into extract kernel
    
    // Extract first L elements to output
    int extract_grid = (total_input + block_size - 1) / block_size;
    extract_grid = min(extract_grid, 65535);
    
    // Fused extract and normalize kernel
    extract_output_kernel<<<extract_grid, block_size, 0, stream>>>(
        workspace, output, B, C, L);
}

// Helper function to create plans
cufftHandle create_r2c_plan(int batch_size, int fft_size, int freq_len) {
    cufftHandle plan;
    int n[1] = {fft_size};
    int inembed[1] = {fft_size};
    int onembed[1] = {freq_len};
    CHECK_CUFFT(cufftPlanMany(&plan, 1, n, inembed, 1, fft_size,
                              onembed, 1, freq_len, CUFFT_R2C, batch_size));
    return plan;
}

cufftHandle create_c2r_plan(int batch_size, int fft_size, int freq_len) {
    cufftHandle plan;
    int n[1] = {fft_size};
    int inembed[1] = {freq_len};
    int onembed[1] = {fft_size};
    CHECK_CUFFT(cufftPlanMany(&plan, 1, n, inembed, 1, freq_len,
                              onembed, 1, fft_size, CUFFT_C2R, batch_size));
    return plan;
}

// Normalize by 1/N for IRFFT
__global__ void normalize_kernel(float* data, int size, float scale) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    for (int i = idx; i < size; i += blockDim.x * gridDim.x) {
        data[i] *= scale;
    }
}
"""

cpp_source = """
#include <cufft.h>

extern "C" {
    void fft_conv_forward_cuda(
        const float* input,
        const float* filter,
        float* output,
        float* workspace,
        cufftComplex* filter_fft,
        int B, int C, int L,
        cudaStream_t stream,
        cufftHandle plan_r2c,
        cufftHandle plan_c2r,
        bool recompute_filter
    );
    
    cufftHandle create_r2c_plan(int batch_size, int fft_size, int freq_len);
    cufftHandle create_c2r_plan(int batch_size, int fft_size, int freq_len);
}
"""

# Load the CUDA extension
fft_conv_ext = load_inline(
    name='fft_conv_ext',
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=['fft_conv_forward_cuda', 'create_r2c_plan', 'create_c2r_plan'],
    extra_cuda_cflags=['-O3', '-arch=sm_80'],
    verbose=False
)

import torch

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.freq_len = seq_len + 1
        self.filter = nn.Parameter(torch.randn(channels, seq_len))
        
        # Use typical batch size from get_inputs()
        self.default_batch_size = 16
        
        # Pre-create cuFFT plans for default batch size
        self.plans = {}
        self._create_plans(self.default_batch_size)
        
        # Pre-allocate workspace and filter FFT buffer
        self.workspace = None
        self.filter_fft = None
        self.filter_version = -1
        
    def _create_plans(self, batch_size):
        key = batch_size
        if key not in self.plans:
            total_batch = batch_size * self.channels
            plan_r2c = fft_conv_ext.create_r2c_plan(total_batch, self.fft_size, self.freq_len)
            plan_c2r = fft_conv_ext.create_c2r_plan(total_batch, self.fft_size, self.freq_len)
            self.plans[key] = (plan_r2c, plan_c2r)
        return self.plans[key]
    
    def _ensure_buffers(self, batch_size, device):
        total_batch = batch_size * self.channels
        
        # Workspace for padded input and frequency data
        workspace_size = total_batch * self.fft_size
        if self.workspace is None or self.workspace.size(0) < workspace_size:
            self.workspace = torch.zeros(workspace_size, dtype=torch.float32, device=device)
        
        # Filter FFT buffer
        filter_fft_size = self.channels * self.freq_len
        if self.filter_fft is None or self.filter_fft.size(0) < filter_fft_size:
            self.filter_fft = torch.zeros(filter_fft_size * 2, dtype=torch.float32, device=device)  # complex = 2 floats
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, C, L = x.shape
        assert C == self.channels and L == self.seq_len
        
        # Ensure tensors are contiguous and on same device
        x = x.contiguous()
        filter_data = self.filter.contiguous()
        
        # Get or create plans for this batch size
        plan_r2c, plan_c2r = self._create_plans(B)
        
        # Ensure buffers are allocated
        self._ensure_buffers(B, x.device)
        
        # Check if filter changed
        recompute_filter = self.filter._version != self.filter_version
        if recompute_filter:
            self.filter_version = self.filter._version
        
        # Allocate output
        output = torch.empty(B, C, L, dtype=torch.float32, device=x.device)
        
        # Call CUDA kernel
        stream = torch.cuda.current_stream().cuda_stream
        
        import ctypes
        fft_conv_ext.fft_conv_forward_cuda(
            ctypes.c_void_p(x.data_ptr()),
            ctypes.c_void_p(filter_data.data_ptr()),
            ctypes.c_void_p(output.data_ptr()),
            ctypes.c_void_p(self.workspace.data_ptr()),
            ctypes.c_void_p(self.filter_fft.data_ptr()),
            B, C, L,
            stream,
            plan_r2c,
            plan_c2r,
            recompute_filter
        )
        
        return output