L2 · problem 35
FW-GLM-5-1 · popcorn · pop_l123_default_gpt
← all variants← popcorn← FW-GLM-5-1generated 2026-05-28 03:33:23 UTC
incorrect 35_FusedConv1dGroupNorm.py
model
FW-GLM-5-1
turns / max
12 / 12
tool calls
12
tokens (total)
162885
wall clock
1589.2s
truncated
no
backend
cuda fp32
variant
popcorn
runtime
—
ref runtime
0.08 μs
speedup
—
started
2026-05-11T12:20:11Z
correctness issue: Output mismatch
timingkernelbench.timing
kernel runtime
—
ref runtime
0.08 μs
speedup
—
kernel mean / median / std
—
ref mean / median / std
0.08 / — / 0.01 μs
SOL (speed-of-light)nsight_hardware_counters
SOL score (0–1)
0.229
dram util %
0.2%
compute util %
22.9%
bottleneck
compute-bound
dominant pipe
FP32
arithmetic intensity
—
ridge point
20.00
roofline / occupancynsight_hardware_counters
dram bandwidth
7.41 GB/s
dram util %
0.2%
fp32 tflops
—
fp32 util %
—
fp16 tflops
—
occupancy %
48.2%
bottleneck
compute-bound
dominant pipe %
22.9%
arithmetic intensity
—
ridge point
20.00
L1 hit %
—
L2 hit %
97.1%
ld sectors / req
2.82
st sectors / req
—
regs / thread
32
smem / block
35840 B
block size
256
peak bw
3350.00 GB/s
peak fp32 tflops
67.00
peak fp16 tflops
1979.00
warp stalls (top 5)nsight
long_scoreboard
1876.1%
short_scoreboard
30.0%
mio_throttle
0.5%
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

# Default ModelNew is PyTorch-only. run_and_check: use check_kernel=False with backend=cuda
# until the candidate file contains a real CUDA kernel (see kernel_static_checker).

class Model(nn.Module):
    """
    Fused 1D convolution + GroupNorm for temporal sequences (N, C, T).

    Typical in time-series heads, audio / motor trajectories, and sequence models
    that use strided or padded Conv1d followed by per-channel normalization without
    collapsing the time dimension (GroupNorm over `out_channels` on (N, C_out, T')).

    Reference fusion target: `Conv1d` → `GroupNorm` (no activation), matching a
    common stem or residual branch in temporal backbones.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_groups: int,
        kernel_size: int = 3,
        stride: int = 1,
        padding: int = 1,
        dilation: int = 1,
        eps: float = 1e-5,
        affine: bool = True,
    ):
        super().__init__()
        assert out_channels % num_groups == 0
        self.conv = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=False,
        )
        self.norm = nn.GroupNorm(
            num_groups=num_groups,
            num_channels=out_channels,
            eps=eps,
            affine=affine,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (N, C_in, T) temporal feature map
        Returns:
            (N, C_out, T') with T' determined by conv stride / padding / kernel
        """
        return self.norm(self.conv(x))


class ModelNew(nn.Module):
    """
    KernelBench candidate entry point. Replace with a fused CUDA/Triton kernel;
    default delegates to `Model` for harness smoke tests.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_groups: int,
        kernel_size: int = 3,
        stride: int = 1,
        padding: int = 1,
        dilation: int = 1,
        eps: float = 1e-5,
        affine: bool = True,
    ):
        super().__init__()
        self._impl = Model(
            in_channels,
            out_channels,
            num_groups,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            eps=eps,
            affine=affine,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self._impl(x)


batch_size = 16
in_channels = 128
out_channels = 256
num_groups = 32
seq_len = 1024
kernel_size = 3
stride = 1
padding = 1


def get_inputs():
    p = popcorn_pri
    x = torch.randn(p.jitter_int(batch_size), in_channels, p.jitter_int(seq_len))
    return [x]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        num_groups,
        kernel_size,
        stride,
        padding,
    ]
turn 027.9s turn · 4.2s LLM · 23.7s tools · tokens in=1,942 · out=201 · total=2,143
reasoning
Let me analyze this task: 1. We need to replace a fused Conv1d + GroupNorm operation with a custom CUDA kernel 2. The model takes (N, C_in, T) input and outputs (N, C_out, T') 3. Conv1d has kernel_size=3, stride=1, padding=1, dilation=1, bias=False 4. GroupNorm normalizes over out_channels with num_groups groups Let me first get the GPU specs to understand the hardware constraints. The key operations: 1. Conv1d: Each output element is a dot product of kernel_size elements from input channels 2. GroupNorm: Normalize within groups, then apply affine transformation (gamma, beta) For optimization: - Fuse the convolution output computation directly into groupnorm - Avoid writing intermediate convolution results to global memory - Use shared memory efficiently for the convolution computation Let me start by getting GPU specs and then write a kernel.
call → get_gpu_specs
callget_gpu_specs {}
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
turn 1 · 1 tool call(s) · 188.6s turn · 52.9s LLM · tokens in=2,190 · out=4,111 · total=6,301 · 17,421 chars (click to expand)
turn 1188.6s turn · 52.9s LLM · 135.6s tools · tokens in=2,190 · out=4,111 · total=6,301
reasoning
Now let me write the initial kernel. The key operations are: 1. Conv1d: Input (N, C_in, T) → Output (N, C_out, T_out) 2. GroupNorm: Normalize across (T_out) dimension per group For Conv1d: - Output length: T_out = (T + 2*padding - dilation*(kernel_size-1) - 1) / stride + 1 - With default params: T_out = (T + 2 - 2 - 1) / 1 + 1 = T For GroupNorm: - num_groups = 32, out_channels = 256 - channels per group = 256 / 32 = 8 - For each group, compute mean and variance across all T positions - Then normalize: (x - mean) / sqrt(var + eps) - If affine=True, apply weight and bias Let me write a fused kernel. I'll use one thread block per output position (n, c_out, t_out) to compute the convolution, then a separate kernel for GroupNorm. Actually, a better approach is to fuse them. Actually, for better performance, let me think about the approach: 1. Compute Conv1d output first 2. Then apply GroupNorm But for a truly fused kernel, we could: - Compute mean and variance for each group in a first pass - Then compute normalized output in a second pass However, the naive approach would be to have two separate kernels. Let me start with that and see if correctness passes, then optimize. Actually, let me think about a more optimized approach: For Conv1d: - Each output element requires summing over kernel_size * in_channels elements - With default params: 3 * 128 = 384 multiplications per output element - Total outputs: 16 * 256 * 1024 = 4M outputs For GroupNorm: - Need mean and variance for each group: 32 groups * 16 batch = 512 groups - Each group has 8 channels * 1024 time = 8192 elements Let me write a fused kernel approach: 1. First, launch a kernel to compute Conv1d output and store intermediate results 2. Then, launch a reduction kernel to compute mean and variance per group 3. Finally, launch a kernel to normalize and apply affine transform Actually, for simplicity, let me first try a two-kernel approach: 1. Conv1d kernel → store output in temporary buffer 2. GroupNorm kernel → read from buffer, normalize, write to output This is similar to PyTorch's approach but with custom kernels. Let me start there. ```python import torch import torch.nn as nn from torch.utils.cpp_extension import load_inline # Define CUDA source cuda_source = """ #include <torch/extension.h> #include <cuda.h> #include <cuda_runtime.h> // Conv1d kernel: each thread computes one output element __global__ void conv1d_kernel( const float* __restrict__ input, const float* __restrict__ weight, float* __restrict__ output, int N, int C_in, int C_out, int T_in, int T_out, int kernel_size, int stride, int padding, int dilation ) { int n = blockIdx.x; int c_out = blockIdx.y; int t_out = threadIdx.x + blockIdx.z * blockDim.x; if (n >= N || c_out >= C_out || t_out >= T_out) return; float sum = 0.0f; for (int k = 0; k < kernel_size; k++) { int t_in = t_out * stride + k * dilation - padding; if (t_in >= 0 && t_in < T_in) { for (int c_in = 0; c_in < C_in; c_in++) { int input_idx = n * C_in * T_in + c_in * T_in + t_in; int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k; sum += input[input_idx] * weight[weight_idx]; } } } int output_idx = n * C_out * T_out + c_out * T_out + t_out; output[output_idx] = sum; } // GroupNorm forward kernel __global__ void groupnorm_kernel( const float* __restrict__ input, const float* __restrict__ weight, const float* __restrict__ bias, float* __restrict__ output, int N, int C, int T, int num_groups, int channels_per_group, float eps ) { int n = blockIdx.x; int g = blockIdx.y; int c_local = threadIdx.x; // channel within group int t = threadIdx.y; // time position int c = g * channels_per_group + c_local; if (n >= N || g >= num_groups) return; // Shared memory for reduction extern __shared__ float sdata[]; float* s_sum = sdata; float* s_sum_sq = &sdata[blockDim.x * blockDim.y]; // Each thread loads one element float val = 0.0f; if (c < C && t < T) { int idx = n * C * T + c * T + t; val = input[idx]; } int tid = threadIdx.y * blockDim.x + threadIdx.x; s_sum[tid] = val; s_sum_sq[tid] = val * val; __syncthreads(); // Reduce for (int s = (blockDim.x * blockDim.y) / 2; s > 0; s >>= 1) { if (tid < s) { s_sum[tid] += s_sum[tid + s]; s_sum_sq[tid] += s_sum_sq[tid + s]; } __syncthreads(); } float mean = s_sum[0] / (channels_per_group * T); float var = s_sum_sq[0] / (channels_per_group * T) - mean * mean; float inv_std = rsqrtf(var + eps); // Write normalized output if (c < C && t < T) { int idx = n * C * T + c * T + t; float normalized = (val - mean) * inv_std; if (weight != nullptr && bias != nullptr) { output[idx] = normalized * weight[c] + bias[c]; } else { output[idx] = normalized; } } } """ cpp_source = """ torch::Tensor conv1d_cuda(torch::Tensor input, torch::Tensor weight, int kernel_size, int stride, int padding, int dilation); torch::Tensor groupnorm_cuda(torch::Tensor input, torch::Tensor weight, torch::Tensor bias, int num_groups, float eps); """ # Actually, I need to be more careful. Let me write a proper implementation. ``` Let me think more carefully about the tiling strategy. The issue is that T can be large (1024), so I can't have all threads in one block. For GroupNorm, I need to: 1. Compute mean and variance across all elements in each group 2. Normalize each element With T=1024 and channels_per_group=8, each group has 8192 elements. I can use a 2D thread block with 8 threads for channels and multiple warps for time. Actually, let me simplify by using a two-pass approach: 1. First pass: compute mean and variance per group (reduction) 2. Second pass: normalize Or I can use cooperative groups for the reduction. Let me write a simpler, correct version first and then optimize. Actually, let me reconsider the approach. Since PyTorch's approach is already optimized, I should focus on: 1. Fusing the two operations to save memory bandwidth 2. Using shared memory efficiently 3. Coalesced memory access For Conv1d, the memory access pattern is: - Input: strided access across channels - Weight: contiguous access - Output: contiguous write For GroupNorm, the memory access pattern is: - Input: strided access within a group - Output: strided write A fused kernel could: 1. Compute Conv1d output for a tile 2. Store in shared memory 3. After all tiles for a group are computed, normalize But this is complex because Conv1d produces T_out elements, and GroupNorm needs all of them. Let me start with a simpler approach: separate kernels but with better memory access patterns. Actually, the simplest approach that should still be faster than PyTorch is: 1. Use a single kernel that does Conv1d and then immediately applies GroupNorm But for GroupNorm, I need statistics from all elements in the group, which means I need to compute the entire group before normalizing. Let me try this approach: 1. First kernel: Compute Conv1d output and store in a temporary buffer, also compute sum and sum_sq per group 2. Second kernel: Read from buffer, normalize using precomputed statistics This saves one read of the Conv1d output. Actually, let me think about a different approach. What if I: 1. Compute Conv1d output elements and atomically add to sum/sum_sq per group 2. Then normalize in a separate pass But atomic operations are slow. A better approach is to use a parallel reduction. Let me just write a straightforward implementation first: ```python import torch import torch.nn as nn from torch.utils.cpp_extension import load_inline # ... rest of the implementation ``` Let me write the complete implementation now.
assistantLet me write a fused CUDA kernel for Conv1d + GroupNorm. I'll start with a straightforward implementation and optimize from there.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Strategy: \n// 1. Each thread block handles one (batch, group) pair\n// 2. Threads compute conv1d output for their assigned positions\n// 3. Use shared memory to store conv output and compute group norm statistics\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n // Block indices\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n // Shared memory for conv output and reduction\n // We need to store conv output for all channels in group and all time positions\n // This may not fit in shared memory, so we'll use a two-phase approach\n \n extern __shared__ float shared_mem[];\n float* s_sum = shared_mem; // size: blockDim.x\n float* s_sum_sq = &shared_mem[blockDim.x]; // size: blockDim.x\n \n // Phase 1: Compute conv1d outputs and accumulate statistics\n int group_start_c = g * channels_per_group;\n int group_end_c = group_start_c + channels_per_group;\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n float local_sum = 0.0f;\n float local_sum_sq = 0.0f;\n \n // Each thread iterates over assigned (c, t) pairs\n int total_elements = channels_per_group * T_out;\n \n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Accumulate for statistics\n local_sum += conv_val;\n local_sum_sq += conv_val * conv_val;\n \n // Store conv output temporarily (we'll reload for normalization)\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val; // Temporary store\n }\n \n // Parallel reduction for sum and sum_sq\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std\n float mean = 0.0f;\n float inv_std = 0.0f;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n float var = s_sum_sq[0] / total_elements - mean * mean;\n inv_std = rsqrtf(var + eps);\n s_sum[0] = mean;\n s_sum[1] = inv_std;\n }\n __syncthreads();\n \n mean = s_sum[0];\n inv_std = s_sum[1];\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx]; // Read back\n float normalized = (conv_val - mean) * inv_std;\n \n // Apply affine transform if needed\n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 105.2s turn · 18.6s LLM · tokens in=4,242 · out=2,025 · total=6,267 · 9,368 chars (click to expand)
turn 2105.2s turn · 18.6s LLM · 86.6s tools · tokens in=4,242 · out=2,025 · total=6,267
reasoning
Good, the kernel compiled. Now let me test correctness.
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 <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Strategy: \n// 1. Each thread block handles one (batch, group) pair\n// 2. Threads compute conv1d output for their assigned positions\n// 3. Use shared memory to store conv output and compute group norm statistics\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n // Block indices\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n // Shared memory for conv output and reduction\n // We need to store conv output for all channels in group and all time positions\n // This may not fit in shared memory, so we'll use a two-phase approach\n \n extern __shared__ float shared_mem[];\n float* s_sum = shared_mem; // size: blockDim.x\n float* s_sum_sq = &shared_mem[blockDim.x]; // size: blockDim.x\n \n // Phase 1: Compute conv1d outputs and accumulate statistics\n int group_start_c = g * channels_per_group;\n int group_end_c = group_start_c + channels_per_group;\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n float local_sum = 0.0f;\n float local_sum_sq = 0.0f;\n \n // Each thread iterates over assigned (c, t) pairs\n int total_elements = channels_per_group * T_out;\n \n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Accumulate for statistics\n local_sum += conv_val;\n local_sum_sq += conv_val * conv_val;\n \n // Store conv output temporarily (we'll reload for normalization)\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val; // Temporary store\n }\n \n // Parallel reduction for sum and sum_sq\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std\n float mean = 0.0f;\n float inv_std = 0.0f;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n float var = s_sum_sq[0] / total_elements - mean * mean;\n inv_std = rsqrtf(var + eps);\n s_sum[0] = mean;\n s_sum[1] = inv_std;\n }\n __syncthreads();\n \n mean = s_sum[0];\n inv_std = s_sum[1];\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx]; // Read back\n float normalized = (conv_val - mean) * inv_std;\n \n // Apply affine transform if needed\n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590', '0.001567', '0.001547'] avg_difference: ['0.000233', '0.000233', '0.000233', '0.000233', '0.000233']
turn 3 · 1 tool call(s) · 110.4s turn · 26.8s LLM · tokens in=6,358 · out=2,708 · total=9,066 · 12,129 chars (click to expand)
turn 3110.4s turn · 26.8s LLM · 83.6s tools · tokens in=6,358 · out=2,708 · total=9,066
reasoning
The output mismatch is small but consistent. Let me check what might be wrong. The differences are in the range of 0.001, which is still within tolerance for fp32 (rtol=atol=1e-4). Let me check if there's an issue with the computation. Actually, looking at the differences more carefully: - max_difference ~0.0015-0.0016 - avg_difference ~0.000233 This could be due to numerical precision differences in how the statistics are computed. Let me check if the convolution loop indexing is correct. Looking at my weight indexing: ``` int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k; ``` This should be `weight[c_out, c_in, k]` in PyTorch which is correct. Wait, I think the issue might be in how I'm iterating over the kernel. Let me think about the conv1d formula: - For each output position t_out, we look at input positions starting at t_out * stride - padding - Each kernel position k corresponds to input position t_out * stride + k * dilation - padding Actually, I think the convolution indexing is correct. Let me check the variance computation. In GroupNorm, the formula is: mean = mean(x) var = mean((x - mean)^2) output = (x - mean) / sqrt(var + eps) I'm computing: mean = sum(x) / N var = sum(x^2) / N - mean^2 This should be equivalent. Let me verify: sum((x - mean)^2) = sum(x^2 - 2*x*mean + mean^2) = sum(x^2) - 2*mean*sum(x) + N*mean^2 = sum(x^2) - 2*mean*N*mean + N*mean^2 = sum(x^2) - N*mean^2 So var = (sum(x^2) - N*mean^2) / N = sum(x^2)/N - mean^2 That's correct. Let me check if there's a numerical precision issue. Actually, the issue might be that I'm using float precision throughout but the reference might be using double precision in some intermediate computations. Or there could be a small numerical stability issue in how I'm computing the variance. Let me try using a more numerically stable variance computation or double check the indexing. Actually wait - I think the issue might be that in the GroupNorm computation, I'm normalizing over (channels_per_group * T_out) elements, but each channel position should be weighted equally. Let me check if the reference does the same. Looking at PyTorch's GroupNorm: - For input (N, C, *), where * is any number of additional dimensions - Groups divide the channels - For each (n, g), compute mean and variance over (C/G, *) elements So for (N, C_out, T_out), for each (n, g), we compute statistics over (channels_per_group, T_out) elements. This is what I'm doing. Let me re-check the kernel. Maybe there's a subtle bug in the indexing or the variance computation. Actually, let me try using Welford's online algorithm for more numerical stability in variance computation, and also try using double precision for the statistics. But first, let me try a simpler fix - using more parallel reduction and maybe fix any potential precision issues. Actually, looking more carefully at my kernel, I notice that I'm reading output[output_idx] back in phase 2. This should work fine, but let me make sure the output buffer is properly allocated and initialized. Hmm, the output is created with torch::empty(), which should work fine. Let me check if there's an issue with the order of operations or something else. Let me try using a different approach - instead of storing the conv output and reading it back, let me use shared memory to store intermediate results and do the normalization directly. Actually, let me first check if the convolution output is correct by comparing just the conv output without normalization. Let me rewrite the kernel to be more careful about numerical precision and correctness:
assistantThe output mismatch is small but I need to fix it. Let me improve the kernel with better numerical precision for the variance computation and double-check the indexing.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Uses shared memory to store conv outputs and compute statistics\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float shared_mem[];\n float* s_data = shared_mem; // Store conv outputs\n float* s_sum = &shared_mem[block_size]; // Reduction buffer\n float* s_sum_sq = &shared_mem[2 * block_size];\n \n // Use double for better numerical precision in statistics\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Phase 1: Compute conv1d outputs and store in shared memory\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n s_data[idx] = conv_val;\n local_sum += conv_val;\n local_sum_sq += (double)conv_val * (double)conv_val;\n }\n \n // Parallel reduction\n s_sum[tid] = (float)local_sum;\n s_sum_sq[tid] = (float)local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std\n float mean = s_sum[0] / total_elements;\n float var = s_sum_sq[0] / total_elements - mean * mean;\n float inv_std = rsqrtf(var + eps);\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float conv_val = s_data[idx];\n float normalized = (conv_val - mean) * inv_std;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = block_size * sizeof(float) + 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 4 · 1 tool call(s) · 202.5s turn · 18.6s LLM · tokens in=8,199 · out=1,997 · total=10,196 · 9,224 chars (click to expand)
turn 4202.5s turn · 18.6s LLM · 183.9s tools · tokens in=8,199 · out=1,997 · total=10,196
reasoning
Hmm, wait - I still have the shared memory issue. The total_elements is channels_per_group * T_out = 8 * 1024 = 8192 elements, but my block_size is 256. So I can't store all elements in shared memory directly indexed by idx (0-8191). I need to reconsider. Actually wait, looking at my code, I'm using: ``` s_data[idx] = conv_val; ``` But shared_mem has only `block_size` elements allocated. That's a problem - I'm writing out of bounds! Let me fix this properly. For the problem sizes (channels_per_group=8, T_out=1024), total_elements=8192. This won't fit in shared memory (228KB/4 = 57K floats max). Actually it should fit! 8192 floats = 32KB. But I need to allocate it properly. Let me rework this.
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 <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Uses shared memory to store conv outputs and compute statistics\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float shared_mem[];\n float* s_sum = shared_mem;\n float* s_sum_sq = &shared_mem[block_size];\n \n // Use double for better numerical precision in statistics\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Phase 1: Compute conv1d outputs, accumulate statistics, store to output temporarily\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Store conv output to global memory temporarily\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val;\n \n local_sum += conv_val;\n local_sum_sq += (double)conv_val * (double)conv_val;\n }\n \n // Parallel reduction\n s_sum[tid] = (float)local_sum;\n s_sum_sq[tid] = (float)local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std\n float mean = s_sum[0] / total_elements;\n float var = s_sum_sq[0] / total_elements - mean * mean;\n float inv_std = rsqrtf(var + eps);\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx]; // Read back\n \n float normalized = (conv_val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590', '0.001567', '0.001547'] avg_difference: ['0.000233', '0.000233', '0.000233', '0.000233', '0.000233']
turn 5 · 1 tool call(s) · 105.1s turn · 17.8s LLM · tokens in=10,103 · out=2,065 · total=12,168 · 9,597 chars (click to expand)
turn 5105.1s turn · 17.8s LLM · 87.3s tools · tokens in=10,103 · out=2,065 · total=12,168
reasoning
The mismatch is very small (max ~0.0016, avg ~0.00023). The tolerance for fp32 is atol=rtol=1e-4, which means the relative tolerance is 1e-4. The max difference of 0.0016 might be slightly above the tolerance. Let me check what might be causing the issue. The mismatch is consistent across runs. The problem could be: 1. Numerical precision in variance computation 2. Different order of operations Wait - I'm using float for the reduction, but maybe I need to use double throughout. Also, PyTorch's GroupNorm uses Welford's algorithm for computing variance which is more numerically stable. Let me try using Welford's algorithm or double precision for the entire statistics computation. Actually, looking at the differences, they seem to be related to numerical precision in the variance/mean computation. Let me use double precision throughout the reduction and then compute the final values.
assistantThe small numerical mismatch is likely due to floating-point precision in the reduction. Let me use double precision throughout for the statistics computation:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Uses shared memory to store conv outputs and compute statistics with high precision\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ char shared_mem[];\n double* s_sum = reinterpret_cast<double*>(shared_mem);\n double* s_sum_sq = &s_sum[block_size];\n \n // Use double for better numerical precision\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Phase 1: Compute conv1d outputs, accumulate statistics, store to output temporarily\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Store conv output to global memory temporarily\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val;\n \n double val_d = static_cast<double>(conv_val);\n local_sum += val_d;\n local_sum_sq += val_d * val_d;\n }\n \n // Parallel reduction with double precision\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std in double precision\n double mean = s_sum[0] / static_cast<double>(total_elements);\n double var = s_sum_sq[0] / static_cast<double>(total_elements) - mean * mean;\n float inv_std = static_cast<float>(rsqrt(static_cast<double>(var + eps)));\n float mean_f = static_cast<float>(mean);\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx]; // Read back\n \n float normalized = (conv_val - mean_f) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(double);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 6 · 1 tool call(s) · 16.1s turn · 15.9s LLM · tokens in=11,981 · out=1,846 · total=13,827 · 8,785 chars (click to expand)
turn 616.1s turn · 15.9s LLM · 0.2s tools · tokens in=11,981 · out=1,846 · total=13,827
reasoning
Let me check correctness again.
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 <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Uses shared memory to store conv outputs and compute statistics with high precision\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ char shared_mem[];\n double* s_sum = reinterpret_cast<double*>(shared_mem);\n double* s_sum_sq = &s_sum[block_size];\n \n // Use double for better numerical precision\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Phase 1: Compute conv1d outputs, accumulate statistics, store to output temporarily\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Store conv output to global memory temporarily\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val;\n \n double val_d = static_cast<double>(conv_val);\n local_sum += val_d;\n local_sum_sq += val_d * val_d;\n }\n \n // Parallel reduction with double precision\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std in double precision\n double mean = s_sum[0] / static_cast<double>(total_elements);\n double var = s_sum_sq[0] / static_cast<double>(total_elements) - mean * mean;\n float inv_std = static_cast<float>(rsqrt(static_cast<double>(var + eps)));\n float mean_f = static_cast<float>(mean);\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx]; // Read back\n \n float normalized = (conv_val - mean_f) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(double);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590', '0.001567', '0.001547'] avg_difference: ['0.000233', '0.000233', '0.000233', '0.000233', '0.000233']
turn 7 · 1 tool call(s) · 130.4s turn · 42.9s LLM · tokens in=13,924 · out=4,494 · total=18,418 · 19,645 chars (click to expand)
turn 7130.4s turn · 42.9s LLM · 87.5s tools · tokens in=13,924 · out=4,494 · total=18,418
reasoning
The error is still the same. The issue might be in the convolution calculation itself. Let me think about what could be different: 1. The convolution indexing - maybe I'm getting the indexing wrong somehow 2. The dilation calculation in PyTorch might be different 3. Maybe the weight layout is different Actually, looking at PyTorch's Conv1d, the weight shape is (out_channels, in_channels, kernel_size). My indexing seems correct: - input: (N, C_in, T_in) - accessed as n * C_in * T_in + c_in * T_in + t_in - weight: (C_out, C_in, kernel_size) - accessed as c_out * C_in * kernel_size + c_in * kernel_size + k - output: (N, C_out, T_out) - accessed as n * C_out * T_out + c_out * T_out + t_out Wait, I should check the output dimension formula. For Conv1d: output_size = (input_size + 2*padding - dilation*(kernel_size-1) - 1) / stride + 1 That seems correct. Actually, maybe the issue is that I'm computing the output size wrong in the formula. Let me look at PyTorch's formula: Actually, the formula I have is: T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1 Let me double-check this matches PyTorch. For dilation=1: T_out = (T_in + 2 * padding - kernel_size + 1 - 1) / stride + 1 = (T_in + 2 * padding - kernel_size) / stride + 1 For T_in=1024, padding=1, kernel_size=3, stride=1: T_out = (1024 + 2 - 3) + 1 = 1024 That seems right. Wait, maybe the issue is in the weight initialization. I'm using kaiming_uniform_, but PyTorch's default initialization for Conv1d might be slightly different. Actually, let me check - the reference Model uses nn.Conv1d which has its own weight initialization. My ModelNew uses a different initialization. I should copy the weights from the reference model, not reinitialize them! That's the issue! The evaluation framework is probably comparing my output with the reference model, but my weights are initialized differently than the reference model's weights. Let me check the evaluation more carefully. Actually, looking at the code, the `get_init_inputs()` function returns the initialization arguments. The framework will call `ModelNew(*get_init_inputs())` to create the model. But wait, the issue is that both models initialize weights differently. I need to make sure I'm using the SAME initialization scheme as PyTorch's Conv1d and GroupNorm. Let me check PyTorch's initialization for Conv1d and GroupNorm: - Conv1d weight: kaiming_uniform_ with a=sqrt(5) (not a=5**0.5 which is sqrt(5)) - GroupNorm gamma: ones - GroupNorm beta: zeros Actually, let me check more carefully. PyTorch's Conv1d init: ```python nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) ``` I used `a=5**0.5` which is the same as `math.sqrt(5)`. So that should be correct. Hmm, maybe the issue is in how the framework evaluates. Let me think... Actually, maybe the framework creates both the reference and my model, then copies the weights from reference to my model before running. In that case, the initialization wouldn't matter as long as the weights match. Wait, I should re-read the task more carefully. It says: "Replace the forward computation of the PyTorch model below with a custom CUDA kernel" So the framework should be copying weights. Let me re-think... Actually, looking at the difference more carefully: - max_difference: ~0.0016 - avg_difference: ~0.00023 This is within fp32 tolerance (atol=rtol=1e-4), but the check is failing. Let me check the tolerance again... Wait, fp32 uses atol=rtol=1e-4. The max difference is 0.0016 which is above 1e-4. So the tolerance is the issue. Let me think about what could cause a consistent difference... Oh! I think I see the issue now. The way I'm computing the conv output. In my kernel, I have: ``` for (int k = 0; k < kernel_size; k++) { int t_in = t_out * stride + k * dilation - padding; ``` But this is iterating from k=0 to kernel_size-1. In PyTorch, the weight is indexed from kernel position 0 to kernel_size-1, where position 0 corresponds to the leftmost position in the sliding window. Wait, but there's also the issue of how PyTorch handles the dilation. Let me think... For dilation=2, kernel_size=3: - The kernel spans positions: 0, 2, 4 relative to center - The weight indices are still 0, 1, 2 My code computes: - k=0: t_in = t_out * stride + 0 * dilation - padding = t_out * stride - padding - k=1: t_in = t_out * stride + 1 * dilation - padding = t_out * stride + dilation - padding - k=2: t_in = t_out * stride + 2 * dilation - padding = t_out * stride + 2*dilation - padding This seems correct for dilation. Hmm, let me try a different approach. Maybe I should print out some debug info, but that's not possible. Let me try a simpler test - just the conv part without groupnorm. Actually, let me re-examine the convolution formula more carefully. PyTorch's formula for computing the output size is: output_size = floor((input_size + 2*padding - dilation*(kernel_size-1) - 1) / stride + 1) And my formula is: T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1 This uses integer division which should give floor. That's correct. Wait, I just realized something. In the task definition, there's this: ```python def get_init_inputs(): return [ in_channels, out_channels, num_groups, kernel_size, stride, padding, ] ``` This doesn't include dilation and eps! The default values are used for those. Let me check if my model handles that correctly... Actually, looking at my ModelNew.__init__, I have default values for dilation=1, eps=1e-5, affine=True. The reference Model also has these defaults. So that should be fine. Let me think about another possibility. Maybe the issue is the affine parameters. In my model, I create gamma and beta parameters that are initialized to ones and zeros respectively. In PyTorch's GroupNorm, these are also initialized to ones and zeros. So that should match. But wait, what if the framework is copying weights between models? If the framework creates both models and then copies the weights, my initialization doesn't matter. But then my indexing would need to match exactly. Actually, let me re-read how the evaluation works. The framework: 1. Creates Model reference model with specific parameters 2. Creates ModelNew with same parameters 3. Runs both on same input 4. Compares outputs The weights are not copied! Each model has its own weight initialization. But wait, that doesn't make sense because then how would it be a fair comparison? Actually, looking at the task again, I think the framework might be loading pre-trained weights or something. Or maybe the comparison is supposed to work regardless of the specific weight values, as long as the computation is correct. Let me try another approach - use the exact same initialization that PyTorch uses for Conv1d and GroupNorm. Actually wait, I just realized the issue might be much simpler. The problem is that the reference Model class has its own conv and norm modules, and when the framework instantiates ModelNew, it expects ModelNew to have the SAME weights as the reference Model. But the current ModelNew creates its own weight, gamma, beta parameters from scratch! The framework is likely comparing the output of ModelNew with the output of Model (the reference), but they have different weights because they're initialized separately. Let me look at the reference implementation again: ```python class ModelNew(nn.Module): def __init__(self, ...): super().__init__() self._impl = Model(...) # Delegates to Model def forward(self, x): return self._impl(x) ``` The default ModelNew just wraps Model. So when the framework creates ModelNew, it creates Model internally. The framework must be comparing the output of ModelNew with the expected output from Model (or some other reference). OK I think I understand now. The framework: 1. Creates ModelNew with specific parameters 2. Creates an internal reference (maybe another Model instance?) 3. Runs both on the same input 4. Compares outputs If the internal reference is created with the same random seed, then my ModelNew should produce the same output IF the initialization matches exactly. Let me check PyTorch's exact initialization for Conv1d: ```python # From PyTorch source def _reset_parameters(self): # Kaiming uniform for weight nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) # Uniform for bias if present if self.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(self.bias, -bound, bound) ``` For GroupNorm: ```python def reset_parameters(self): if self.weight is not None: nn.init.ones_(self.weight) if self.bias is not None: nn.init.zeros_(self.bias) ``` I think my initialization is correct. The issue must be something else. Wait! I just realized something important. Let me check the actual call: ```python def get_init_inputs(): return [ in_channels, out_channels, num_groups, kernel_size, stride, padding, ] ``` This returns 6 positional arguments, but both Model and ModelNew have 9 parameters: - in_channels - out_channels - num_groups - kernel_size (default 3) - stride (default 1) - padding (default 1) - dilation (default 1) - eps (default 1e-5) - affine (default True) So the framework passes 6 arguments: in_channels, out_channels, num_groups, kernel_size, stride, padding. This means dilation=1, eps=1e-5, affine=True by default. This matches my implementation. Let me think about this differently. Maybe the issue is numerical precision in the normalization step. The small differences (~0.0016) suggest numerical precision issues rather than logical errors. Actually, I wonder if the issue is the order of operations. In PyTorch: 1. conv output is computed 2. GroupNorm computes mean and var 3. Normalization is applied In my kernel, I'm computing the mean and variance from the conv output that I've stored in global memory. But what if there's some subtle difference? Actually, I think I see the issue now. When I use float for conv_val but double for sum/sum_sq, the accumulation happens in double precision. But when PyTorch computes, it might use float throughout. Let me try a version that uses float throughout to match PyTorch's behavior exactly. Actually, let me think more carefully. PyTorch's GroupNorm uses float accumulation for the mean and variance. My kernel uses double. This could cause small differences. But wait, PyTorch's CUDA kernels might also use double for accumulation in normalization layers. Let me try using float throughout and see if that matches better. Actually, thinking about it more, using double should be MORE accurate, not less. The issue might be elsewhere. Let me try a completely different approach. Instead of using the two-phase approach with global memory, let me use shared memory to store all conv outputs and then normalize. But this might not fit for large T_out. Actually, let me just use float throughout and see what happens. PyTorch probably uses float for accumulation.
assistantLet me try using float throughout for the statistics computation to match PyTorch's behavior more closely:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Strategy: two-phase approach - compute conv, then normalize\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float shared_data[];\n float* s_sum = shared_data;\n float* s_sum_sq = &shared_data[block_size];\n \n // Use float to match PyTorch behavior\n float local_sum = 0.0f;\n float local_sum_sq = 0.0f;\n \n // Phase 1: Compute conv1d outputs\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n // Store conv output temporarily\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n output[output_idx] = conv_val;\n \n local_sum += conv_val;\n local_sum_sq += conv_val * conv_val;\n }\n \n // Parallel reduction\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Compute mean and inv_std\n float mean = s_sum[0] / total_elements;\n float var = s_sum_sq[0] / total_elements - mean * mean;\n float inv_std = rsqrtf(var + eps);\n \n // Phase 2: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float conv_val = output[output_idx];\n float normalized = (conv_val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 8 · 1 tool call(s) · 188.8s turn · 28.6s LLM · tokens in=15,732 · out=2,206 · total=17,938 · 10,193 chars (click to expand)
turn 8188.8s turn · 28.6s LLM · 160.2s tools · tokens in=15,732 · out=2,206 · total=17,938
reasoning
Let me try another approach - maybe I need to match the exact order of operations that PyTorch uses. Let me compute the exact reduction differently by using a more sequential approach. Actually, let me think about this more carefully. The differences are small (~0.001) and consistent. This suggests there might be a subtle numerical difference in how I'm computing things. One issue: In the two-pass approach, I'm storing to global memory and reading back. This might introduce issues. Let me try a single-pass approach where I keep the conv outputs in shared memory (if it fits) or use a more careful approach. Actually, wait - let me check if the shared memory size calculation is correct. If the problem is 8 * T_out = 8 * 1024 = 8192 floats per group, that's 32KB per block for the data alone, plus reduction buffers. This might be okay for H100's 228KB per SM. But the key insight is that PyTorch's GroupNorm uses Welford's algorithm or a two-pass approach with specific numerical properties. Let me try a different approach: use warp-level primitives and match PyTorch's order of operations more closely. Actually, let me try a sequential accumulation approach instead of tree reduction:
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 <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Uses sequential reduction for more accurate statistics\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x; // batch\n int g = blockIdx.y; // group\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float shared_data[];\n float* s_data = shared_data; // Store conv outputs\n float* s_sum = &shared_data[total_elements]; // Reduction\n \n // Phase 1: Compute conv1d outputs and store in shared memory\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n // Compute conv1d output\n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n int input_idx = n * C_in * T_in + c_in * T_in + t_in;\n int weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k;\n conv_val += input[input_idx] * weight[weight_idx];\n }\n }\n }\n \n s_data[idx] = conv_val;\n }\n __syncthreads();\n \n // Phase 2: Sequential sum reduction for mean\n // Each thread accumulates a subset\n float local_sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n local_sum += s_data[idx];\n }\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Final reduction by thread 0\n float mean = 0.0f;\n if (tid == 0) {\n float total_sum = 0.0f;\n for (int i = 0; i < block_size; i++) {\n total_sum += s_sum[i];\n }\n mean = total_sum / total_elements;\n s_sum[0] = mean;\n }\n __syncthreads();\n mean = s_sum[0];\n \n // Phase 3: Sequential sum of squares for variance\n float local_sum_sq = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_data[idx] - mean;\n local_sum_sq += diff * diff;\n }\n s_sum[tid] = local_sum_sq;\n __syncthreads();\n \n // Final reduction by thread 0\n float inv_std = 0.0f;\n if (tid == 0) {\n float total_sum_sq = 0.0f;\n for (int i = 0; i < block_size; i++) {\n total_sum_sq += s_sum[i];\n }\n float var = total_sum_sq / total_elements;\n inv_std = rsqrtf(var + eps);\n s_sum[0] = inv_std;\n }\n __syncthreads();\n inv_std = s_sum[0];\n \n // Phase 4: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n float normalized = (s_data[idx] - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n int total_elements = channels_per_group * T_out;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = total_elements * sizeof(float) + block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight parameter for conv (no bias as per reference)\n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n # Create affine parameters for GroupNorm\n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.kernel_size,\n self.stride,\n self.padding,\n self.dilation,\n self.num_groups,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001632', '0.001641', '0.001590', '0.001566', '0.001547'] avg_difference: ['0.000233', '0.000233', '0.000233', '0.000233', '0.000233']
turn 9 · 1 tool call(s) · 108.8s turn · 24.3s LLM · tokens in=17,811 · out=2,507 · total=20,318 · 11,127 chars (click to expand)
turn 9108.8s turn · 24.3s LLM · 84.5s tools · tokens in=17,811 · out=2,507 · total=20,318
reasoning
The differences are very small (max ~0.0016, avg ~0.00023) but still failing. The issue might be in the weight initialization - PyTorch's Conv1d uses a specific initialization that I need to match exactly. Let me check if my initialization is correct. Actually, looking at the reference model more carefully, it creates a `Conv1d` and `GroupNorm` as separate modules with their own parameters. My approach creates new parameters with random initialization, which would differ from the reference's initialization. The key insight is that during the correctness test, the reference model is instantiated with its own parameters, and my model is instantiated with its own parameters. They won't have the same weights! Wait, that's not how the testing works. Let me think about this more carefully... Actually, I think the test creates both models and runs them on the same input. But each model has its own randomly initialized weights, so outputs will differ by more than just floating-point error. No wait, the test harness likely uses pre-initialized weights that are loaded into both models. The issue might be that I'm not using the exact same weight layout or format as PyTorch. Let me try a different approach - store the conv output in shared memory with the same layout that PyTorch uses, and ensure the statistics computation matches exactly. Actually, I realize I should just check the tolerance - the error is ~0.001 which is still within fp32 tolerance of 1e-4? No wait, 0.0016 is larger than 1e-4. The issue might be the variance formula. PyTorch uses Bessel's correction (dividing by N-1 instead of N)? No, GroupNorm uses biased variance (dividing by N). Let me try a cleaner implementation where I store everything in shared memory and compute the exact same statistics as PyTorch.
assistantThe error is small but persistent. Let me check if the issue is with how the shared memory layout handles edge cases. I'll try a simpler approach that mirrors PyTorch's exact computation:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Each block handles one (batch, group) pair\n// Compute conv, store in shared mem, compute stats, normalize\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x;\n int g = blockIdx.y;\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float s_mem[];\n float* s_data = s_mem;\n \n // Phase 1: Compute conv and store\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n conv_val += input[n * C_in * T_in + c_in * T_in + t_in] *\n weight[c_out * C_in * kernel_size + c_in * kernel_size + k];\n }\n }\n }\n s_data[idx] = conv_val;\n }\n __syncthreads();\n \n // Phase 2: Compute mean (sequential reduction for consistency)\n float sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n sum += s_data[idx];\n }\n \n // Block-level reduction\n for (int offset = block_size / 2; offset > 0; offset >>= 1) {\n if (tid < offset) {\n s_data[tid] = sum;\n s_data[tid + offset] = 0.0f; // temp storage\n }\n __syncthreads();\n if (tid < offset) {\n sum = s_data[tid] + s_data[tid + offset];\n }\n __syncthreads();\n }\n \n // Actually use simpler approach - warp shuffle\n sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n sum += s_data[idx];\n }\n __syncthreads();\n \n // Store partial sums\n s_data[tid] = sum;\n __syncthreads();\n \n // Thread 0 computes final sum\n float mean = 0.0f;\n if (tid == 0) {\n float total = 0.0f;\n int active_threads = (total_elements < block_size) ? total_elements : block_size;\n for (int i = 0; i < active_threads; i++) {\n total += s_data[i];\n }\n mean = total / total_elements;\n }\n __syncthreads();\n \n // Broadcast mean\n if (tid == 0) s_data[0] = mean;\n __syncthreads();\n mean = s_data[0];\n \n // Phase 3: Compute variance\n float sum_sq = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_data[idx] - mean;\n sum_sq += diff * diff;\n }\n s_data[tid] = sum_sq;\n __syncthreads();\n \n float inv_std = 0.0f;\n if (tid == 0) {\n float total_sq = 0.0f;\n int active_threads = (total_elements < block_size) ? total_elements : block_size;\n for (int i = 0; i < active_threads; i++) {\n total_sq += s_data[i];\n }\n float var = total_sq / total_elements;\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Broadcast inv_std\n if (tid == 0) s_data[0] = inv_std;\n __syncthreads();\n inv_std = s_data[0];\n \n // Phase 4: Normalize\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float normalized = (s_data[idx] - mean) * inv_std;\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = max((int)(channels_per_group * T_out * sizeof(float)), (int)(block_size * sizeof(float)));\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x, self.weight, self.gamma, self.beta,\n self.kernel_size, self.stride, self.padding, self.dilation,\n self.num_groups, self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 10 · 1 tool call(s) · 173.5s turn · 19.9s LLM · tokens in=19,975 · out=2,237 · total=22,212 · 9,914 chars (click to expand)
turn 10173.5s turn · 19.9s LLM · 153.6s tools · tokens in=19,975 · out=2,237 · total=22,212
reasoning
I have been getting small numerical mismatches consistently. The errors are very small (max ~0.0016, avg ~0.00023) which could be due to floating point precision differences in the statistics computation between my kernel and PyTorch's GroupNorm. Let me think about this more carefully. The tolerance for fp32 is atol=rtol=1e-4. My errors are around 0.00023 average and 0.0016 max, which is above the tolerance. The issue might be that I need to match PyTorch's exact implementation of GroupNorm. PyTorch's GroupNorm uses a specific way to compute mean and variance. Let me check if the issue is in how I compute statistics or in how the reduction is done. Actually, let me just submit the kernel and see what happens. The errors might be within tolerance after all, or I might need to revisit the approach. Wait, let me think about this more carefully. The tolerance is rtol=1e-4, atol=1e-4. This means the error needs to satisfy: |a - b| <= atol + rtol * |b| If the output values are around 1.0, then the error needs to be <= 1e-4 + 1e-4 * 1 = 2e-4 = 0.0002 My avg error is 0.000233, which is just above that. But max error is 0.0016 which is way above. The issue is likely in my reduction approach. Let me think about what PyTorch does differently... Actually, let me just submit and see if it passes or fails. If it fails, I'll need to reconsider the approach.
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 <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Each block handles one (batch, group) pair\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x;\n int g = blockIdx.y;\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n extern __shared__ float s_mem[];\n float* s_data = s_mem;\n \n // Phase 1: Compute conv and store\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n conv_val += input[n * C_in * T_in + c_in * T_in + t_in] *\n weight[c_out * C_in * kernel_size + c_in * kernel_size + k];\n }\n }\n }\n s_data[idx] = conv_val;\n }\n __syncthreads();\n \n // Phase 2: Compute mean\n float sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n sum += s_data[idx];\n }\n s_data[tid] = sum;\n __syncthreads();\n \n float mean = 0.0f;\n if (tid == 0) {\n float total = 0.0f;\n int active_threads = (total_elements < block_size) ? total_elements : block_size;\n for (int i = 0; i < active_threads; i++) {\n total += s_data[i];\n }\n mean = total / total_elements;\n }\n __syncthreads();\n \n if (tid == 0) s_data[0] = mean;\n __syncthreads();\n mean = s_data[0];\n \n // Phase 3: Compute variance\n float sum_sq = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_data[idx] - mean;\n sum_sq += diff * diff;\n }\n s_data[tid] = sum_sq;\n __syncthreads();\n \n float inv_std = 0.0f;\n if (tid == 0) {\n float total_sq = 0.0f;\n int active_threads = (total_elements < block_size) ? total_elements : block_size;\n for (int i = 0; i < active_threads; i++) {\n total_sq += s_data[i];\n }\n float var = total_sq / total_elements;\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n if (tid == 0) s_data[0] = inv_std;\n __syncthreads();\n inv_std = s_data[0];\n \n // Phase 4: Normalize\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float normalized = (s_data[idx] - mean) * inv_std;\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = max((int)(channels_per_group * T_out * sizeof(float)), (int)(block_size * sizeof(float)));\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x, self.weight, self.gamma, self.beta,\n self.kernel_size, self.stride, self.padding, self.dilation,\n self.num_groups, self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['307.612000', '375.023590', '296.459747', '316.004883', '240.656906'] avg_difference: ['1.030662', '1.029010', '1.031238', '1.033010', '1.031134']
turn 11 · FINAL · 1 tool call(s) · 231.9s turn · 18.2s LLM · tokens in=22,054 · out=1,977 · total=24,031 · 9,022 chars (click to expand)
turn 11231.9s turn · 18.2s LLM · 213.7s tools · tokens in=22,054 · out=1,977 · total=24,031 · FINAL
reasoning
The last test had huge errors which indicates a bug in my kernel. Let me go back to a simpler approach that I know works better - the original version that had small errors. The issue in the last version is that I'm overwriting s_data when computing the sums and then trying to use it later for normalization. I need to keep the conv outputs separate. Let me submit a cleaner version that stores conv outputs separately and handles the shared memory properly.
call → submit_kernel
callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n\n// Conv1d + GroupNorm fused kernel\n// Each block handles one (batch, group) pair\n\n__global__ void conv1d_groupnorm_kernel(\n const float* __restrict__ input,\n const float* __restrict__ weight,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C_in, int C_out, int T_in, int T_out,\n int kernel_size, int stride, int padding, int dilation,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x;\n int g = blockIdx.y;\n \n if (n >= N || g >= num_groups) return;\n \n int group_start_c = g * channels_per_group;\n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n // Shared memory layout: [conv_outputs (total_elements)] [sums (block_size)] [sum_sq (block_size)]\n extern __shared__ char shared_mem[];\n float* s_conv = reinterpret_cast<float*>(shared_mem);\n float* s_sums = &s_conv[total_elements];\n float* s_sum_sq = &s_sums[block_size];\n \n // Phase 1: Compute conv and store to shared memory\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float conv_val = 0.0f;\n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride + k * dilation - padding;\n if (t_in >= 0 && t_in < T_in) {\n for (int c_in = 0; c_in < C_in; c_in++) {\n conv_val += input[n * C_in * T_in + c_in * T_in + t_in] *\n weight[c_out * C_in * kernel_size + c_in * kernel_size + k];\n }\n }\n }\n s_conv[idx] = conv_val;\n }\n __syncthreads();\n \n // Phase 2: Compute mean\n float local_sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n local_sum += s_conv[idx];\n }\n s_sums[tid] = local_sum;\n __syncthreads();\n \n float mean = 0.0f;\n if (tid == 0) {\n float total = 0.0f;\n for (int i = 0; i < block_size; i++) {\n total += s_sums[i];\n }\n mean = total / total_elements;\n s_sums[0] = mean;\n }\n __syncthreads();\n mean = s_sums[0];\n \n // Phase 3: Compute variance\n float local_sum_sq = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_conv[idx] - mean;\n local_sum_sq += diff * diff;\n }\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n float inv_std = 0.0f;\n if (tid == 0) {\n float total_sq = 0.0f;\n for (int i = 0; i < block_size; i++) {\n total_sq += s_sum_sq[i];\n }\n float var = total_sq / total_elements;\n inv_std = 1.0f / sqrtf(var + eps);\n s_sums[0] = inv_std;\n }\n __syncthreads();\n inv_std = s_sums[0];\n \n // Phase 4: Normalize and write output\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out;\n int t_out = idx % T_out;\n int c_out = group_start_c + c_local;\n \n float normalized = (s_conv[idx] - mean) * inv_std;\n int output_idx = n * C_out * T_out + c_out * T_out + t_out;\n \n if (gamma != nullptr && beta != nullptr) {\n output[output_idx] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[output_idx] = normalized;\n }\n }\n}\n\ntorch::Tensor conv1d_groupnorm_forward(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int64_t kernel_size,\n int64_t stride,\n int64_t padding,\n int64_t dilation,\n int64_t num_groups,\n double eps\n) {\n int N = input.size(0);\n int C_in = input.size(1);\n int T_in = input.size(2);\n int C_out = weight.size(0);\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n int channels_per_group = C_out / num_groups;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;\n const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;\n \n int block_size = 256;\n dim3 grid(N, num_groups);\n size_t shared_mem_size = channels_per_group * T_out * sizeof(float) + 2 * block_size * sizeof(float);\n \n conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(\n input.data_ptr<float>(),\n weight.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n return output;\n}\n\"\"\"\n\ncpp_source = \"torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);\"\n\nconv1d_gn_module = load_inline(\n name='conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['conv1d_groupnorm_forward'],\n extra_cuda_cflags=['-O3'],\n verbose=True\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_groups: int,\n kernel_size: int = 3,\n stride: int = 1,\n padding: int = 1,\n dilation: int = 1,\n eps: float = 1e-5,\n affine: bool = True,\n ):\n super().__init__()\n assert out_channels % num_groups == 0\n \n self.in_channels = in_channels\n self.out_channels = out_channels\n self.num_groups = num_groups\n self.kernel_size = kernel_size\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))\n nn.init.kaiming_uniform_(self.weight, a=5**0.5)\n \n self.affine = affine\n if affine:\n self.gamma = nn.Parameter(torch.ones(out_channels))\n self.beta = nn.Parameter(torch.zeros(out_channels))\n else:\n self.register_parameter('gamma', None)\n self.register_parameter('beta', None)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return conv1d_gn_module.conv1d_groupnorm_forward(\n x, self.weight, self.gamma, self.beta,\n self.kernel_size, self.stride, self.padding, self.dilation,\n self.num_groups, self.eps\n )" }
result ← submit_kernel (fail)
failsubmit_kernel submit_kernel FAILED: correctness check did not pass ((0 / 5) trials). correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590', '0.001566', '0.001547'] avg_difference: ['0.000233', '0.000233', '0.000233', '0.000233', '0.000233']
final submitted kernel (216 lines, 7,029 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 <cuda.h>
#include <cuda_runtime.h>
#include <c10/cuda/CUDAException.h>

// Conv1d + GroupNorm fused kernel
// Each block handles one (batch, group) pair

__global__ void conv1d_groupnorm_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ gamma,
    const float* __restrict__ beta,
    float* __restrict__ output,
    int N, int C_in, int C_out, int T_in, int T_out,
    int kernel_size, int stride, int padding, int dilation,
    int num_groups, int channels_per_group, float eps
) {
    int n = blockIdx.x;
    int g = blockIdx.y;
    
    if (n >= N || g >= num_groups) return;
    
    int group_start_c = g * channels_per_group;
    int tid = threadIdx.x;
    int block_size = blockDim.x;
    int total_elements = channels_per_group * T_out;
    
    // Shared memory layout: [conv_outputs (total_elements)] [sums (block_size)] [sum_sq (block_size)]
    extern __shared__ char shared_mem[];
    float* s_conv = reinterpret_cast<float*>(shared_mem);
    float* s_sums = &s_conv[total_elements];
    float* s_sum_sq = &s_sums[block_size];
    
    // Phase 1: Compute conv and store to shared memory
    for (int idx = tid; idx < total_elements; idx += block_size) {
        int c_local = idx / T_out;
        int t_out = idx % T_out;
        int c_out = group_start_c + c_local;
        
        float conv_val = 0.0f;
        for (int k = 0; k < kernel_size; k++) {
            int t_in = t_out * stride + k * dilation - padding;
            if (t_in >= 0 && t_in < T_in) {
                for (int c_in = 0; c_in < C_in; c_in++) {
                    conv_val += input[n * C_in * T_in + c_in * T_in + t_in] *
                                weight[c_out * C_in * kernel_size + c_in * kernel_size + k];
                }
            }
        }
        s_conv[idx] = conv_val;
    }
    __syncthreads();
    
    // Phase 2: Compute mean
    float local_sum = 0.0f;
    for (int idx = tid; idx < total_elements; idx += block_size) {
        local_sum += s_conv[idx];
    }
    s_sums[tid] = local_sum;
    __syncthreads();
    
    float mean = 0.0f;
    if (tid == 0) {
        float total = 0.0f;
        for (int i = 0; i < block_size; i++) {
            total += s_sums[i];
        }
        mean = total / total_elements;
        s_sums[0] = mean;
    }
    __syncthreads();
    mean = s_sums[0];
    
    // Phase 3: Compute variance
    float local_sum_sq = 0.0f;
    for (int idx = tid; idx < total_elements; idx += block_size) {
        float diff = s_conv[idx] - mean;
        local_sum_sq += diff * diff;
    }
    s_sum_sq[tid] = local_sum_sq;
    __syncthreads();
    
    float inv_std = 0.0f;
    if (tid == 0) {
        float total_sq = 0.0f;
        for (int i = 0; i < block_size; i++) {
            total_sq += s_sum_sq[i];
        }
        float var = total_sq / total_elements;
        inv_std = 1.0f / sqrtf(var + eps);
        s_sums[0] = inv_std;
    }
    __syncthreads();
    inv_std = s_sums[0];
    
    // Phase 4: Normalize and write output
    for (int idx = tid; idx < total_elements; idx += block_size) {
        int c_local = idx / T_out;
        int t_out = idx % T_out;
        int c_out = group_start_c + c_local;
        
        float normalized = (s_conv[idx] - mean) * inv_std;
        int output_idx = n * C_out * T_out + c_out * T_out + t_out;
        
        if (gamma != nullptr && beta != nullptr) {
            output[output_idx] = normalized * gamma[c_out] + beta[c_out];
        } else {
            output[output_idx] = normalized;
        }
    }
}

torch::Tensor conv1d_groupnorm_forward(
    torch::Tensor input,
    torch::Tensor weight,
    c10::optional<torch::Tensor> gamma_opt,
    c10::optional<torch::Tensor> beta_opt,
    int64_t kernel_size,
    int64_t stride,
    int64_t padding,
    int64_t dilation,
    int64_t num_groups,
    double eps
) {
    int N = input.size(0);
    int C_in = input.size(1);
    int T_in = input.size(2);
    int C_out = weight.size(0);
    int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;
    int channels_per_group = C_out / num_groups;
    
    auto output = torch::empty({N, C_out, T_out}, input.options());
    
    const float* gamma_ptr = gamma_opt.has_value() ? gamma_opt->data_ptr<float>() : nullptr;
    const float* beta_ptr = beta_opt.has_value() ? beta_opt->data_ptr<float>() : nullptr;
    
    int block_size = 256;
    dim3 grid(N, num_groups);
    size_t shared_mem_size = channels_per_group * T_out * sizeof(float) + 2 * block_size * sizeof(float);
    
    conv1d_groupnorm_kernel<<<grid, block_size, shared_mem_size>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        gamma_ptr,
        beta_ptr,
        output.data_ptr<float>(),
        N, C_in, C_out, T_in, T_out,
        kernel_size, stride, padding, dilation,
        num_groups, channels_per_group, static_cast<float>(eps)
    );
    
    C10_CUDA_CHECK(cudaGetLastError());
    return output;
}
"""

cpp_source = "torch::Tensor conv1d_groupnorm_forward(torch::Tensor input, torch::Tensor weight, c10::optional<torch::Tensor> gamma, c10::optional<torch::Tensor> beta, int64_t kernel_size, int64_t stride, int64_t padding, int64_t dilation, int64_t num_groups, double eps);"

conv1d_gn_module = load_inline(
    name='conv1d_groupnorm',
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=['conv1d_groupnorm_forward'],
    extra_cuda_cflags=['-O3'],
    verbose=True
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_groups: int,
        kernel_size: int = 3,
        stride: int = 1,
        padding: int = 1,
        dilation: int = 1,
        eps: float = 1e-5,
        affine: bool = True,
    ):
        super().__init__()
        assert out_channels % num_groups == 0
        
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_groups = num_groups
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.eps = eps
        
        self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size))
        nn.init.kaiming_uniform_(self.weight, a=5**0.5)
        
        self.affine = affine
        if affine:
            self.gamma = nn.Parameter(torch.ones(out_channels))
            self.beta = nn.Parameter(torch.zeros(out_channels))
        else:
            self.register_parameter('gamma', None)
            self.register_parameter('beta', None)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return conv1d_gn_module.conv1d_groupnorm_forward(
            x, self.weight, self.gamma, self.beta,
            self.kernel_size, self.stride, self.padding, self.dilation,
            self.num_groups, self.eps
        )