L2 · problem 35
FW-GLM-5-1 · popcorn · pop_l123_all_gpt
← all variants← popcorn← FW-GLM-5-1generated 2026-05-28 03:33:20 UTC
correct 35_FusedConv1dGroupNorm.py
model
FW-GLM-5-1
turns / max
15 / 15
tool calls
15
tokens (total)
285672
wall clock
4470.8s
truncated
no
backend
cuda fp32
variant
popcorn
runtime
0.07 μs
ref runtime
0.09 μs
speedup
1.28x
started
2026-05-11T19:42:04Z
timingkernelbench.timing
kernel runtime
0.07 μs
ref runtime
0.09 μs
speedup
1.28x
kernel mean / median / std
0.07 / — / 6.15e-04 μs
ref mean / median / std
0.09 / — / 0.02 μs
numerical precisioneval_kernel_against_ref
max abs error
9.54e-07
mean abs error
3.99e-08
max rel error
0.02
mean rel error
6.51e-08
gpu memoryextended_metrics.measure_memory
kernel peak
82.13 MB
ref peak
82.13 MB
kernel / ref
1.00x
kernel launches / fusiontorch.profiler
kernel launches
12
ref launches
21
fusion ratio (ref / kernel)
1.75x
total cuda time
794 μs
SOL (speed-of-light)nsight_hardware_counters
SOL score (0–1)
1.000
dram util %
100.0%
compute util %
45.5%
bottleneck
memory-bound
dominant pipe
tensor-core
arithmetic intensity
—
ridge point
295.22
roofline / occupancynsight_hardware_counters
dram bandwidth
4075.50 GB/s
dram util %
100.0%
fp32 tflops
—
fp32 util %
—
fp16 tflops
—
occupancy %
303.3%
bottleneck
memory-bound
dominant pipe %
45.5%
arithmetic intensity
—
ridge point
295.22
L1 hit %
—
L2 hit %
321.8%
ld sectors / req
11.25
st sectors / req
—
regs / thread
296
smem / block
283136 B
block size
1408
peak bw
3350.00 GB/s
peak fp32 tflops
67.00
peak fp16 tflops
1979.00
warp stalls (top 5)nsight
long_scoreboard
5852.1%
mio_throttle
612.0%
short_scoreboard
566.0%
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 0297.0s turn · 2.2s LLM · 294.8s tools · tokens in=2,513 · out=137 · total=2,650
reasoning
Let me analyze this task. I need to create a custom CUDA kernel that fuses a 1D convolution followed by GroupNorm. The reference implementation uses: - Conv1d with kernel_size=3, stride=1, padding=1, dilation=1 (default) - GroupNorm with num_groups=32, num_channels=256 The input shape is (N, C_in, T) = (16, 128, 1024) and output shape is (N, C_out, T') where T' is determined by conv parameters. Let me first get the GPU specs to understand the hardware I'm working with.
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) · 104.7s turn · 31.2s LLM · tokens in=2,761 · out=3,132 · total=5,893 · 17,955 chars (click to expand)
turn 1104.7s turn · 31.2s LLM · 73.4s tools · tokens in=2,761 · out=3,132 · total=5,893
reasoning
Let me analyze this task. I need to implement a fused Conv1d + GroupNorm kernel. Let me understand the dimensions: - Input: (N, C_in, T) = (16, 128, 1024) - Output: (N, C_out, T') = (16, 256, 1024) (with stride=1, padding=1, kernel=3, T' = T = 1024) - num_groups = 32, so channels_per_group = 256/32 = 8 For Conv1d: - Each output element is a dot product between the kernel and input elements - For kernel_size=3, stride=1, padding=1, dilation=1, the output sequence length = input sequence length For GroupNorm: - Normalize within each group (across channels in the group and spatial/temporal dimensions) - Groups: 32, channels_per_group: 8 - For each sample and each group, compute mean and variance over (channels_per_group, T') = (8, 1024) = 8192 elements - Then apply: y = (x - mean) / sqrt(var + eps) * gamma + beta Key optimizations: 1. Fuse the convolution with the normalization to avoid intermediate storage 2. Use shared memory for reduction operations in GroupNorm 3. Tile the computation for better cache locality Let me design the kernel: For each output position (n, c_out, t_out): 1. Compute the convolution output for that position 2. Accumulate for GroupNorm statistics Since GroupNorm requires computing mean and variance across all positions in a group, we need two passes: 1. First pass: compute convolution outputs and accumulate mean/variance 2. Second pass: normalize Actually, we can use Welford's online algorithm for mean/variance, but it's tricky to parallelize. Alternative approach: 1. Compute all convolution outputs and store them 2. Then apply GroupNorm But that defeats the purpose of fusion. Let me think about a better approach. Actually, we can use a two-stage approach: 1. Kernel 1: Compute convolution outputs, store in shared memory or global memory, and compute partial sums for mean/variance 2. Kernel 2: Apply normalization Or we could compute the convolution output in shared memory, then do a reduction for the statistics. Let me think about the tiling strategy: - Tile by output channel groups (matching GroupNorm groups) - Each thread block handles one (batch, group) pair - Within a block, threads cooperate to compute the convolution outputs for all channels in the group and all time positions - Then perform reduction for mean/variance - Then normalize But this requires a lot of shared memory. With 8 channels per group * 1024 time positions * 4 bytes = 32KB per block. That's manageable. Actually, let me simplify: - Each block handles one (batch, group) pair - Each thread handles multiple (channel_in_group, time) positions - Compute conv outputs and accumulate statistics - Then normalize For convolution: - Each output position (c_out, t_out) requires input positions (t_in) from t_out - padding to t_out + kernel_size - 1 - padding - With dilation=1, stride=1, kernel_size=3, padding=1, input range is [t_out-1, t_out+1] Let me think about how to compute the convolution efficiently: - For each output channel c_out, we need weights for (kernel_size, in_channels) - For kernel_size=3, each output channel has 3*128 = 384 weight values We can pre-load the weights into shared memory. Let me design the kernel more concretely: ```cpp // Block dimension: (batch, group) // Thread dimension: cover all (channels_in_group, time) positions // For batch=16, groups=32, we have 16*32=512 blocks // Each block handles 8 channels * 1024 time = 8192 output values // Each thread can handle multiple positions // With 1024 threads per block, each thread handles 8 positions ``` Actually, I think a simpler approach might be: - Use cuDNN for convolution (it's well optimized) - Then apply GroupNorm in a custom kernel - The fusion benefit is avoiding the intermediate write/read But the task is to write a custom CUDA kernel, so let me proceed with a fused implementation. Let me write a first version that: 1. Computes convolution output 2. Immediately accumulates for mean/variance 3. Syncs and computes final stats 4. Normalizes Here's my approach: - Each block handles one (batch, group) pair - Threads iterate over time positions and channels - Use shared memory for partial sums - Then normalize Let me write the code:
assistantLet me start by writing a fused Conv1d + GroupNorm CUDA kernel. I'll analyze the problem and create an optimized implementation.
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n int c_end = c_start + channels_per_group; // ending output channel (exclusive)\n \n // Shared memory for partial sums (mean, variance)\n // Use double for accumulation precision\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem); // sum for mean\n double* s_sum_sq = s_sum + blockDim.x; // sum of squares for variance\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x); // output buffer\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate statistics\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n \n // Accumulate for mean/variance\n local_sum += static_cast<double>(conv_val);\n local_sum_sq += static_cast<double>(conv_val) * static_cast<double>(conv_val);\n }\n \n // Store partial sums to shared memory\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n // Parallel reduction for sum and sum_sq\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 // Thread 0 computes mean and variance\n __shared__ double mean, inv_std;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n double var = s_sum_sq[0] / total_elements - mean * mean;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Second pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n // Each thread handles part of channels_per_group * T_out elements\n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n const float* gamma_ptr = gamma.has_value() ? gamma.value().data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.has_value() ? beta.value().data_ptr<float>() : nullptr;\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\n# Load the CUDA extension\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (fail)
failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'fused_conv1d_groupnorm': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv1d_groupnorm -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/main.cpp -o main.o [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_conv1d_groupnorm -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/cuda.cu -o cuda.cuda.o FAILED: [code=2] cuda.cuda.o /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_conv1d_groupnorm -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/cuda.cu(149): error: no instance of function template "fused_conv1d_groupnorm_kernel" matches the argument list argument types are: (double *, double *, const float *, const float *, double *, int, int, int, int, int, int, int, int, int, int, int, scalar_t) ([&] { fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>( input.data_ptr<scalar_t>(), weight.data_ptr<scalar_t>(), gamma_ptr, beta_ptr, output.data_ptr<scalar_t>(), N, C_in, C_out, T_in, T_out, kernel_size, stride, padding, dilation, num_groups, channels_per_group, static_cast<scalar_t>(eps) ); }) ^ /scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/cuda.cu(11): note #3326-D: function template "fused_conv1d_groupnorm_kernel" does not match because argument #3 does not match parameter void fused_conv1d_groupnorm_kernel( ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_35_cache/k_e94b148c59f4/fused_conv1d_groupnorm/cuda.cu". ninja: build stopped: subcommand failed.
turn 2 · 1 tool call(s) · 176.4s turn · 19.0s LLM · tokens in=6,098 · out=2,190 · total=8,288 · 10,133 chars (click to expand)
turn 2176.4s turn · 19.0s LLM · 157.4s tools · tokens in=6,098 · out=2,190 · total=8,288
reasoning
The issue is that gamma and beta pointers are typed as `const float*` in the host code, but the kernel template expects `const scalar_t*`. Let me fix the type mismatch.
assistantLet me fix the type mismatch issue with gamma and beta pointers:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n int c_end = c_start + channels_per_group; // ending output channel (exclusive)\n \n // Shared memory for partial sums (mean, variance)\n // Use double for accumulation precision\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem); // sum for mean\n double* s_sum_sq = s_sum + blockDim.x; // sum of squares for variance\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x); // output buffer\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate statistics\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n \n // Accumulate for mean/variance\n local_sum += static_cast<double>(conv_val);\n local_sum_sq += static_cast<double>(conv_val) * static_cast<double>(conv_val);\n }\n \n // Store partial sums to shared memory\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n // Parallel reduction for sum and sum_sq\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 // Thread 0 computes mean and variance\n __shared__ double mean, inv_std;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n double var = s_sum_sq[0] / total_elements - mean * mean;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Second pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n // Each thread handles part of channels_per_group * T_out elements\n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n // Handle optional tensors by creating empty tensors if not provided\n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n const scalar_t* gamma_ptr = gamma.defined() ? gamma.data_ptr<scalar_t>() : nullptr;\n const scalar_t* beta_ptr = beta.defined() ? beta.data_ptr<scalar_t>() : nullptr;\n \n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\n# Load the CUDA extension\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 3 · 1 tool call(s) · 311.6s turn · 17.8s LLM · tokens in=8,262 · out=2,159 · total=10,421 · 10,149 chars (click to expand)
turn 3311.6s turn · 17.8s LLM · 293.8s tools · tokens in=8,262 · out=2,159 · total=10,421
reasoning
The kernel compiled. Now let me run the correctness test to see if the output matches the reference.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n int c_end = c_start + channels_per_group; // ending output channel (exclusive)\n \n // Shared memory for partial sums (mean, variance)\n // Use double for accumulation precision\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem); // sum for mean\n double* s_sum_sq = s_sum + blockDim.x; // sum of squares for variance\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x); // output buffer\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate statistics\n double local_sum = 0.0;\n double local_sum_sq = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n \n // Accumulate for mean/variance\n local_sum += static_cast<double>(conv_val);\n local_sum_sq += static_cast<double>(conv_val) * static_cast<double>(conv_val);\n }\n \n // Store partial sums to shared memory\n s_sum[tid] = local_sum;\n s_sum_sq[tid] = local_sum_sq;\n __syncthreads();\n \n // Parallel reduction for sum and sum_sq\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 // Thread 0 computes mean and variance\n __shared__ double mean, inv_std;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n double var = s_sum_sq[0] / total_elements - mean * mean;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Second pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n // Each thread handles part of channels_per_group * T_out elements\n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n // Handle optional tensors by creating empty tensors if not provided\n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n const scalar_t* gamma_ptr = gamma.defined() ? gamma.data_ptr<scalar_t>() : nullptr;\n const scalar_t* beta_ptr = beta.defined() ? beta.data_ptr<scalar_t>() : nullptr;\n \n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\n# Load the CUDA extension\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 3) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590'] avg_difference: ['0.000233', '0.000233', '0.000233']
turn 4 · 1 tool call(s) · 252.3s turn · 32.2s LLM · tokens in=10,476 · out=3,331 · total=13,807 · 14,766 chars (click to expand)
turn 4252.3s turn · 32.2s LLM · 220.1s tools · tokens in=10,476 · out=3,331 · total=13,807
reasoning
There's a small mismatch. The differences are around 1e-3 to 1e-4, which is close to the tolerance of 1e-4 for fp32. Let me check what might be causing the difference: 1. The main difference might be due to initialization of weights - PyTorch uses kaiming_uniform_ but the way I'm initializing might not match exactly 2. Or there could be an issue with the order of operations / numerical precision Let me check if the issue is with weight initialization. In the original model, the Conv1d is created by nn.Conv1d which has its own initialization. Let me check the reference initialization and match it exactly. Actually, looking at the differences, they are in the range 1e-3 which is above the tolerance of 1e-4 (both atol and rtol). The issue might be in: 1. Weight initialization 2. The convolution formula 3. Numerical precision in the group norm Let me check if the shared memory size calculation is correct for different scalar types (float vs double). Also, I notice that in the shared memory allocation, I'm using `sizeof(float)` for the output buffer, but if scalar_t is double, this would be wrong. Let me fix that. Let me also double-check the convolution formula. The PyTorch Conv1d computes: output[n, c_out, t_out] = sum_{k, c_in} input[n, c_in, t_in] * weight[c_out, c_in, k] where t_in = t_out * stride - padding + k * dilation (valid when 0 <= t_in < T_in) This matches what I have. But let me check the weight layout - PyTorch's Conv1d weight shape is (out_channels, in_channels, kernel_size). Actually, looking more carefully at the difference, it's around 1e-3 to 1e-4, which is just at the edge of the tolerance. This might be due to numerical precision issues in the group norm computation. Let me try using higher precision for the reduction or a different approach. Actually, I think the issue might be that I need to initialize the weights to match exactly what PyTorch's Conv1d does. Let me copy the reference model's weights instead of initializing them myself. Wait, I should be using the same weight initialization as the reference. Let me check if I should use nn.init.kaiming_uniform_ with the correct parameters. Looking at PyTorch's Conv1d initialization: ```python def reset_parameters(self): # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with # uniform(-1/sqrt(in_features), 1/sqrt(in_features)). For details, see # https://github.com/pytorch/pytorch/issues/57109 init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if self.bias is not None: fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 init.uniform_(self.bias, -bound, bound) ``` So for Conv1d without bias, it uses kaiming_uniform_ with a=sqrt(5). Let me check if I'm using that correctly. Actually, I'm using `nn.init.kaiming_uniform_(self.weight, a=5**0.5)` which is sqrt(5), so that should be correct. The issue might be in the group norm computation. Let me check if the shared memory size is calculated correctly. Actually, I notice I'm using `sizeof(float)` for the output buffer in shared memory, but if scalar_t is double, I should use `sizeof(scalar_t)`. Let me fix that. Also, I need to make sure the mean and variance computation is accurate. The formula for variance is: var = E[X^2] - E[X]^2 But this can have numerical issues if the mean is large. PyTorch might use a different formula. Let me check what PyTorch's GroupNorm does. Actually, looking at PyTorch's GroupNorm implementation, it computes: mean = sum(x) / n var = sum((x - mean)^2) / n This is different from my implementation which uses: var = sum(x^2)/n - mean^2 The first formula is numerically more stable. Let me update my implementation. Actually, for numerical stability, I should compute the variance using the corrected formula. Let me change to: 1. First pass: compute sum and store values in shared memory 2. Compute mean 3. Second pass: compute sum of (x - mean)^2 4. Compute variance and normalize But this requires an extra pass through the data. Let me see if there's a more efficient way. Actually, since we're already storing intermediate values in shared memory, I can do this efficiently: 1. First pass: compute sum and store values 2. Compute mean 3. Second pass: compute sum of squared differences 4. Compute variance and normalize Let me update the kernel to use this approach.
assistantThe difference is small but above tolerance. Let me check the issue - it might be with numerical precision in the variance calculation. Let me use the numerically stable two-pass variance computation:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel with improved numerical precision\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n \n // Shared memory layout:\n // [sum (double, blockDim.x)] [sum_sq (double, blockDim.x)] [output buffer (scalar_t, total_elements)]\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem);\n double* s_sum_sq = s_sum + blockDim.x;\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n double local_sum = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n local_sum += static_cast<double>(conv_val);\n }\n \n // Store partial sum to shared memory\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Parallel reduction for sum\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes mean\n __shared__ double mean;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n }\n __syncthreads();\n \n // Second pass: compute variance using numerically stable formula\n double local_sum_sq_diff = 0.0;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n double diff = static_cast<double>(s_output[idx]) - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n // Parallel reduction for sum of squared differences\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes variance and inv_std\n __shared__ double inv_std;\n if (tid == 0) {\n double var = s_sum_sq[0] / total_elements;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n const scalar_t* gamma_ptr = gamma.defined() ? gamma.data_ptr<scalar_t>() : nullptr;\n const scalar_t* beta_ptr = beta.defined() ? beta.data_ptr<scalar_t>() : nullptr;\n \n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 5 · 1 tool call(s) · 262.0s turn · 18.9s LLM · tokens in=12,730 · out=2,213 · total=14,943 · 10,250 chars (click to expand)
turn 5262.0s turn · 18.9s LLM · 243.0s tools · tokens in=12,730 · out=2,213 · total=14,943
reasoning
Let me run the correctness test again.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel with improved numerical precision\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n \n // Shared memory layout:\n // [sum (double, blockDim.x)] [sum_sq (double, blockDim.x)] [output buffer (scalar_t, total_elements)]\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem);\n double* s_sum_sq = s_sum + blockDim.x;\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n double local_sum = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n local_sum += static_cast<double>(conv_val);\n }\n \n // Store partial sum to shared memory\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Parallel reduction for sum\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes mean\n __shared__ double mean;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n }\n __syncthreads();\n \n // Second pass: compute variance using numerically stable formula\n double local_sum_sq_diff = 0.0;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n double diff = static_cast<double>(s_output[idx]) - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n // Parallel reduction for sum of squared differences\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes variance and inv_std\n __shared__ double inv_std;\n if (tid == 0) {\n double var = s_sum_sq[0] / total_elements;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n const scalar_t* gamma_ptr = gamma.defined() ? gamma.data_ptr<scalar_t>() : nullptr;\n const scalar_t* beta_ptr = beta.defined() ? beta.data_ptr<scalar_t>() : nullptr;\n \n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 3) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590'] avg_difference: ['0.000233', '0.000233', '0.000233']
turn 6 · 1 tool call(s) · 306.9s turn · 27.3s LLM · tokens in=15,010 · out=3,034 · total=18,044 · 13,530 chars (click to expand)
turn 6306.9s turn · 27.3s LLM · 279.6s tools · tokens in=15,010 · out=3,034 · total=18,044
reasoning
The error is still the same. Let me think about this more carefully. The difference is about 0.000233 on average with max around 0.0016. This might be due to the convolution implementation. Let me check if my convolution implementation is correct. Wait, the issue might be with the weight initialization or the order of dimensions. Let me check PyTorch's conv1d weight layout. In PyTorch: - `nn.Conv1d(in_channels, out_channels, kernel_size)` has weight shape `(out_channels, in_channels, kernel_size)` - For input `(N, C_in, T_in)` with output `(N, C_out, T_out)` - The convolution formula is: `output[n, c_out, t_out] = sum over (c_in, k) of input[n, c_in, t_in] * weight[c_out, c_in, k]` My indexing looks correct. Let me double check: - `input_idx = n * C_in * T_in + c_in * T_in + t_in` → `input[n, c_in, t_in]` ✓ - `weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k` → `weight[c_out, c_in, k]` ✓ Actually wait, I need to check my `t_in` calculation again: `t_in = t_out * stride - padding + k * dilation` That should be correct for convolution. Actually, I wonder if the issue is with the `t_out` calculation. Let me check the PyTorch formula for Conv1d output size: PyTorch Conv1d output size: `T_out = floor((T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1)` Let me verify this is integer division. Actually in C++, integer division automatically floors, so that should be fine. Hmm, the error might be in my convolution implementation. Let me think... Maybe the ordering of the loop is wrong? Let me try using PyTorch's convolution first to see if the issue is there or in the GroupNorm part. Actually, let me try a different approach - compute the convolution separately using PyTorch and just implement the GroupNorm kernel. If that passes, then the issue is in the convolution kernel. But wait, let me think about this differently. The differences are small and could be due to floating point ordering. In my kernel, I compute the convolution in a different order than PyTorch's optimized code (which might use tensor cores, vectorized loads, etc.). Let me try a simpler approach: use shared memory more efficiently and check if the numerical differences come from the order of operations. Actually, I should verify if the issue is really with the convolution or with the GroupNorm. Let me create a test kernel that just does the convolution and compare against PyTorch's conv1d. Actually, let me try using more threads and see if that helps. Also, I should use Welford's algorithm for more stable variance computation, or use Kahan summation. Wait, I just realized something. I'm using `float` for the shared output buffer regardless of scalar_t. This could cause precision issues when input is double. Let me fix that. Actually, looking more carefully at the smem allocation: ```cpp size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float); ``` And then: ```cpp scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x); ``` If scalar_t is double, then I'm allocating float-size but using as double. That's a bug! Let me fix this to use scalar_t consistently.
assistantI see the issue - there's a potential type mismatch in shared memory allocation. Let me fix that and also check for any other issues:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel with improved numerical precision\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\ntemplate <typename scalar_t>\n__global__ void fused_conv1d_groupnorm_kernel(\n const scalar_t* __restrict__ input, // (N, C_in, T_in)\n const scalar_t* __restrict__ weight, // (C_out, C_in, K)\n const scalar_t* __restrict__ gamma, // (C_out,) or nullptr\n const scalar_t* __restrict__ beta, // (C_out,) or nullptr\n scalar_t* __restrict__ output, // (N, C_out, T_out)\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, scalar_t eps\n) {\n // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n \n // Shared memory layout:\n // [sum (double, blockDim.x)] [sum_sq (double, blockDim.x)] [output buffer (scalar_t, total_elements)]\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem);\n double* s_sum_sq = s_sum + blockDim.x;\n scalar_t* s_output = reinterpret_cast<scalar_t*>(s_sum_sq + blockDim.x);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n // Total elements per group: channels_per_group * T_out\n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n double local_sum = 0.0;\n \n // Each thread processes multiple output positions\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T_out; // channel within group (0 to channels_per_group-1)\n int t_out = idx % T_out; // output time position\n int c_out = c_start + c_local; // actual output channel\n \n // Compute convolution for this output position\n scalar_t conv_val = scalar_t(0);\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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 in shared memory for later normalization\n s_output[idx] = conv_val;\n local_sum += static_cast<double>(conv_val);\n }\n \n // Store partial sum to shared memory\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Parallel reduction for sum\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes mean\n __shared__ double mean;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n }\n __syncthreads();\n \n // Second pass: compute variance using numerically stable formula\n double local_sum_sq_diff = 0.0;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n double diff = static_cast<double>(s_output[idx]) - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n // Parallel reduction for sum of squared differences\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n // Thread 0 computes variance and inv_std\n __shared__ double inv_std;\n if (tid == 0) {\n double var = s_sum_sq[0] / total_elements;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n scalar_t val = s_output[idx];\n scalar_t normalized = static_cast<scalar_t>((static_cast<double>(val) - mean) * inv_std);\n \n // Apply affine transform if gamma and beta are provided\n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n // Compute output sequence length\n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n // Shared memory: sum (double), sum_sq (double), output buffer (scalar_t)\n // Size depends on scalar_t type\n int total_elements = channels_per_group * T_out;\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), \"fused_conv1d_groupnorm\", ([&] {\n const scalar_t* gamma_ptr = gamma.defined() ? gamma.data_ptr<scalar_t>() : nullptr;\n const scalar_t* beta_ptr = beta.defined() ? beta.data_ptr<scalar_t>() : nullptr;\n \n // Allocate correct shared memory size for scalar_t\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(scalar_t);\n \n fused_conv1d_groupnorm_kernel<scalar_t><<<blocks, threads, smem_size>>>(\n input.data_ptr<scalar_t>(),\n weight.data_ptr<scalar_t>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<scalar_t>(),\n N, C_in, C_out, T_in, T_out,\n kernel_size, stride, padding, dilation,\n num_groups, channels_per_group, static_cast<scalar_t>(eps)\n );\n }));\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 7 · 1 tool call(s) · 312.0s turn · 36.1s LLM · tokens in=17,278 · out=3,984 · total=21,262 · 18,074 chars (click to expand)
turn 7312.0s turn · 36.1s LLM · 275.8s tools · tokens in=17,278 · out=3,984 · total=21,262
reasoning
Let me check correctness again. The error was still happening so there must be something else. Let me look more carefully at how PyTorch's GroupNorm works - specifically how it handles the epsilon and whether it uses biased or unbiased variance. Looking at PyTorch's GroupNorm implementation, I believe they use: - var = mean(x^2) - mean(x)^2 (biased variance) - x_norm = (x - mean) / sqrt(var + eps) This should be equivalent to my implementation. But the issue might be something else... Let me check if the issue is with how I'm handling the eps - PyTorch's GroupNorm uses eps inside the sqrt. Let me verify by looking at PyTorch source. Actually, I realize the issue might be that PyTorch's GroupNorm uses `torch.var()` which computes biased variance by default (division by N, not N-1). But my implementation also uses biased variance. Wait, I just noticed - the test is running on random inputs but the model weights are initialized differently! The Model uses nn.Conv1d which has its own weight initialization, and I'm using nn.init.kaiming_uniform_ which might be different. Let me check the reference model's weight initialization. The reference Model uses: ```python self.conv = nn.Conv1d( in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=False, ) ``` PyTorch's Conv1d uses kaiming_uniform_ by default for weights when using the default initialization. So that should match. But the difference might be in the exact formula. PyTorch uses: ```python nn.init.kaiming_uniform_(self.weight, a=5**0.5) ``` I'm using `a=5**0.5` which should match the default for leaky_relu with a=sqrt(5). Actually wait, let me look at this more carefully. The Conv1d uses `a=math.sqrt(5)` for kaiming_uniform, which I'm also using. But the formula for kaiming_uniform is: - std = gain / sqrt(fan_in) - bound = sqrt(3) * std So the bounds should be the same. But the random numbers used to fill the weight tensor will be different since I'm calling init separately! That's the issue! When running correctness, both the reference model and my model get new random weights. I need to ensure my ModelNew uses the EXACT same weights as the reference Model for comparison. Let me look at the harness - it seems like it creates both models with the same init_inputs but the weights are initialized separately. I should copy the weights from the reference model to my model, OR I should initialize my model using the reference model's conv weights. Actually, looking at the reference implementation: ```python 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__() self._impl = Model(...) ``` The reference ModelNew just delegates to a Model instance. So the weights that matter are in the Model, not ModelNew. For correctness testing, both models should be initialized with the same weights. The harness likely creates the reference model and then creates a candidate ModelNew and compares outputs with the same input tensor. But the weights might be different! Let me think about this differently - the correctness test probably: 1. Creates reference Model 2. Creates candidate ModelNew 3. Gets input tensor 4. Runs both and compares outputs If the weights are initialized differently, the outputs will be different. I need to copy the weights from the reference model to my model, OR the harness might be doing weight copying already. Let me try a different approach - instead of creating my own weights, let me load the weights from the reference model when testing. But that's not possible in the current structure... Actually, looking at typical KernelBench setups, the evaluation harness probably handles weight synchronization between the reference and candidate models. Let me check if there's an issue with my implementation instead. Actually, I should check whether the GroupNorm in PyTorch has a different eps location or computation. Let me look at PyTorch's GroupNorm formula: From PyTorch docs: ``` y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta ``` This is what I'm doing. So the formula is correct. The issue might be numerical precision. Let me check if the difference is acceptable or if there's an actual bug. The max difference is about 0.0016 and avg is 0.000233. With the tolerance being rtol=atol=1e-4 for fp32, a difference of 0.0016 is definitely above tolerance. Let me think about what could cause this difference... Actually, I wonder if the issue is that I'm not using the correct initialization. Let me check PyTorch's Conv1d initialization: ```python # PyTorch's default conv initialization nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) ``` I'm doing the same thing, so that should be fine. Wait - I just realized something. The evaluation harness might be comparing the output of ModelNew directly against Model, where ModelNew is supposed to be a faster implementation that produces the SAME output as Model. This means the weights need to be copied from Model to ModelNew! Looking at the reference ModelNew implementation, it uses `self._impl = Model(...)` which creates a new Model instance. So for testing, the harness creates a ModelNew, which internally creates a Model. But if the candidate ModelNew has its own weights (not using the reference Model's weights), then the outputs will be different! The harness might be doing weight copying, or I need to accept the weights from somewhere. Let me re-read the task... Looking at the task again, I need to "Replace the forward computation of the PyTorch model below with a custom CUDA kernel." This means I need to replicate the behavior of Model (Conv1d + GroupNorm) with my own implementation. But the issue is that for correctness testing, the weights need to match. Let me check if the harness provides weight copying... Actually, I think the issue is that I'm not properly initializing my weights. Let me try using the exact same initialization as PyTorch's Conv1d and GroupNorm. Actually, PyTorch's GroupNorm weight (gamma) is initialized to ones and bias (beta) to zeros, which I'm doing correctly. For Conv1d, the weight is initialized using kaiming_uniform_ with a=sqrt(5). Let me check my initialization again... ```python self.weight = nn.Parameter(torch.empty(out_channels, in_channels, kernel_size)) nn.init.kaiming_uniform_(self.weight, a=5**0.5) ``` This should match. But maybe the random state is different? The harness might set a specific random seed before creating the model... Actually, I think the correct approach is to let the harness handle weight synchronization. The harness likely creates both models, then copies the reference model's weights to the candidate model before testing. But let me verify by checking if my kernel is computing the correct formula. Let me simplify and focus on getting the exact numerical match. Actually, I wonder if the issue is with how PyTorch's GroupNorm computes the variance. Let me check the exact formula... PyTorch's GroupNorm (from the source): ```cpp // mean = sum / size // var = sumsq / size - mean * mean // invstd = 1 / sqrt(var + eps) ``` This is the biased variance formula, which is what I'm using. So that should be correct. But wait - there might be a subtle difference in how the sum and sumsq are computed. PyTorch might be using a different accumulation order or precision. Let me try using Welford's online algorithm for computing mean and variance, which is numerically more stable: Actually, let me try a different approach - instead of using shared memory for the output buffer, let me write to global memory directly and then read it back for normalization. This might help debug the issue. Actually, I suspect the issue might be in the precision of the convolution computation itself, not the normalization. Let me try using float accumulation instead of scalar_t for the convolution: Actually, let me try using float for all intermediate computations since that's what the input data type should be (fp32): Let me also double check my indexing in the convolution. The input is (N, C_in, T_in) and I'm accessing it as: ``` input_idx = n * C_in * T_in + c_in * T_in + t_in ``` This looks correct. For the weight (C_out, C_in, K): ``` weight_idx = c_out * C_in * kernel_size + c_in * kernel_size + k ``` This also looks correct. Let me try another approach - explicitly use float for all computations:
assistantLet me check what's happening with the numerical differences. The issue might be with how I'm handling the computations. Let me use explicit float types and ensure the shared memory is properly sized:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel - simplified version for debugging\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n// Force float computation for fp32 inputs\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\n const float* __restrict__ input, // (N, C_in, T_in)\n const float* __restrict__ weight, // (C_out, C_in, K)\n const float* __restrict__ gamma, // (C_out,) or nullptr\n const float* __restrict__ beta, // (C_out,) or nullptr\n float* __restrict__ output, // (N, C_out, T_out)\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 // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n \n // Shared memory layout:\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem);\n double* s_sum_sq = s_sum + blockDim.x;\n float* s_output = reinterpret_cast<float*>(s_sum_sq + blockDim.x);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n double local_sum = 0.0;\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n local_sum += static_cast<double>(conv_val);\n }\n \n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Parallel reduction for sum\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ double mean;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n }\n __syncthreads();\n \n // Second pass: compute variance\n double local_sum_sq_diff = 0.0;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n double diff = static_cast<double>(s_output[idx]) - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ double inv_std;\n if (tid == 0) {\n double var = s_sum_sq[0] / total_elements;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = static_cast<float>((static_cast<double>(val) - mean) * inv_std);\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 8 · 1 tool call(s) · 342.8s turn · 20.4s LLM · tokens in=19,307 · out=2,306 · total=21,613 · 10,748 chars (click to expand)
turn 8342.8s turn · 20.4s LLM · 322.5s tools · tokens in=19,307 · out=2,306 · total=21,613
reasoning
Now let me run correctness again to see if there are still issues. If the issue persists, I need to understand what the differences are caused by - it could be in how PyTorch computes things internally. Let me think about this more carefully - the issue might be related to how PyTorch handles the Conv1d. Let me check: 1. PyTorch's Conv1d uses implicit GEMM or other optimized algorithms 2. PyTorch's GroupNorm has specific numerical behaviors Actually, I suspect the issue is that PyTorch uses a different algorithm for convolution. The numerical differences in convolution can propagate to the GroupNorm output. The convolution itself might be computed differently (e.g., using cuDNN which may have slightly different floating-point behavior). The difference of ~0.0002 average is within typical floating-point tolerance differences between implementations. However, since the test requires exact match, I need to be more careful. Let me try a different approach - instead of computing the convolution and group norm separately within my kernel, I should ensure my convolution matches PyTorch's exactly. Or I could just call PyTorch's convolution and then apply group norm myself. Actually, wait - I realize the issue might be that I need to ensure the outputs are computed in exactly the same way. PyTorch might be using F.conv1d which calls cuDNN, and my manual implementation might have slightly different floating-point accumulation order. Let me try calling PyTorch's convolution inside my forward, then apply the group norm in CUDA. That way at least the convolution part will match exactly.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel - simplified version for debugging\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n// Force float computation for fp32 inputs\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\n const float* __restrict__ input, // (N, C_in, T_in)\n const float* __restrict__ weight, // (C_out, C_in, K)\n const float* __restrict__ gamma, // (C_out,) or nullptr\n const float* __restrict__ beta, // (C_out,) or nullptr\n float* __restrict__ output, // (N, C_out, T_out)\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 // Each block handles one (batch, group) pair\n int n = blockIdx.x / num_groups; // batch index\n int g = blockIdx.x % num_groups; // group index\n \n int c_start = g * channels_per_group; // starting output channel for this group\n \n // Shared memory layout:\n extern __shared__ char smem[];\n double* s_sum = reinterpret_cast<double*>(smem);\n double* s_sum_sq = s_sum + blockDim.x;\n float* s_output = reinterpret_cast<float*>(s_sum_sq + blockDim.x);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n double local_sum = 0.0;\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n local_sum += static_cast<double>(conv_val);\n }\n \n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Parallel reduction for sum\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ double mean;\n if (tid == 0) {\n mean = s_sum[0] / total_elements;\n }\n __syncthreads();\n \n // Second pass: compute variance\n double local_sum_sq_diff = 0.0;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n double diff = static_cast<double>(s_output[idx]) - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ double inv_std;\n if (tid == 0) {\n double var = s_sum_sq[0] / total_elements;\n inv_std = 1.0 / sqrt(var + static_cast<double>(eps));\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = static_cast<float>((static_cast<double>(val) - mean) * inv_std);\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(double) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\n self.stride = stride\n self.padding = padding\n self.dilation = dilation\n self.eps = eps\n \n # Create weight for Conv1d (no bias)\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 gamma and beta for GroupNorm if 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 3) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590'] avg_difference: ['0.000233', '0.000233', '0.000233']
turn 9 · 1 tool call(s) · 302.3s turn · 27.8s LLM · tokens in=21,361 · out=3,042 · total=24,403 · 13,090 chars (click to expand)
turn 9302.3s turn · 27.8s LLM · 274.6s tools · tokens in=21,361 · out=3,042 · total=24,403
reasoning
The error is consistent. This suggests there's something fundamentally different between my computation and the reference. Let me think about this: 1. The convolution itself might have a different ordering of operations 2. The memory access patterns might not match PyTorch's expected layout Looking at the convolution weight shape in PyTorch Conv1d: - PyTorch: weight is (out_channels, in_channels, kernel_size) - Input is (N, C_in, T) PyTorch's formula: output[n, c_out, t_out] = sum over c_in, k of input[n, c_in, t_in] * weight[c_out, c_in, k] My kernel computes the same thing... but maybe there's a subtle issue with how I'm computing the mean and variance? Actually, wait! The issue could be that PyTorch's GroupNorm computes statistics over multiple groups together, not per-group. Let me double check... No, GroupNorm in PyTorch computes mean and variance over each group independently. Let me look more carefully at the formula. PyTorch GroupNorm: - Reshapes input from (N, C, T) to (N, G, C//G, T) - Computes mean and variance over (C//G, T) for each (N, G) pair - Normalizes: (x - mean) / sqrt(var + eps) - If affine=True, applies gamma * normalized + beta My kernel does exactly this... Wait! Let me check if there's a mismatch in how I'm indexing. The issue could be that PyTorch's GroupNorm expects a specific memory layout and the indices I'm using might be slightly different. Actually, let me think about this more carefully. The convolution output shape is (N, C_out, T_out). When GroupNorm processes this: - It views it as (N, num_groups, channels_per_group, T_out) - Computes mean/var over (channels_per_group, T_out) So the grouping is over channels: channels 0 to channels_per_group-1 are group 0, channels_per_group to 2*channels_per_group-1 are group 1, etc. In my kernel: - c_start = g * channels_per_group - c_local = 0 to channels_per_group-1 - c_out = c_start + c_local This seems correct... Actually, I wonder if the issue is that I'm computing the convolution in a different order than PyTorch's optimized implementation. The numerical result should be the same, but the accumulation order might cause small differences. But wait, the reference uses PyTorch's Conv1d and then GroupNorm. If I'm doing the exact same computation, the results should match. Unless there's something different about how PyTorch internally orders operations. Let me try using float arithmetic instead of double for the mean and variance computation to see if that's the issue. Actually, the double should be more precise, so that shouldn't cause differences. Actually, the problem could be simpler - maybe there's a bug in how I handle the memory layout for the input tensor. Let me think again about the tensor layout. PyTorch uses row-major (C) layout for tensors: - Input (N, C_in, T_in): input[n, c, t] is at index n * C_in * T_in + c * T_in + t - Weight (C_out, C_in, K): weight[c_out, c_in, k] is at index c_out * C_in * K + c_in * K + k - Output (N, C_out, T_out): output[n, c_out, t_out] is at index n * C_out * T_out + c_out * T_out + t_out That's exactly what I have in my kernel... Wait, I should double-check the convolution formula. The convolution without padding/padding/dilation: For Conv1d with stride s, padding p, dilation d: output[n, c_out, t_out] = sum over c_in, k of input[n, c_in, t_out * s - p + k * d] * weight[c_out, c_in, k] where t_in = t_out * s - p + k * d must be in range [0, T_in). This matches what I have. Let me check one more thing - the K indexing. In PyTorch Conv1d, k ranges from 0 to kernel_size-1, and the position of the kernel element is: - k=0 is the "leftmost" element of the kernel - k=kernel_size-1 is the "rightmost" element So for a 1D convolution with padding=1, kernel_size=3: - When t_out=0: k=0 looks at t_in=-1 (padded), k=1 looks at t_in=0, k=2 looks at t_in=1 - When t_out=T_out-1: k=0 looks at t_in=T_in-2, k=1 looks at t_in=T_in-1, k=2 looks at t_in=T_in (padded) This matches my implementation. Hmm, let me try a different approach - let me match PyTorch's numerical approach exactly. Maybe PyTorch computes mean and variance using float32 accumulators instead of float64? Actually, looking at PyTorch's GroupNorm implementation in C++, it uses: - welford_online_accumulator for computing mean and variance - The accumulator type depends on the scalar type For float inputs, PyTorch likely uses float accumulators. Let me try using float accumulators instead of double to see if that matches better.
assistantLet me try using float accumulators instead of double to match PyTorch's computation:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel - matching PyTorch's float accumulator behavior\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\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 / num_groups;\n int g = blockIdx.x % num_groups;\n \n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_sum = reinterpret_cast<float*>(smem);\n float* s_sum_sq = s_sum + blockDim.x;\n float* s_output = s_sum_sq + blockDim.x;\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n float local_sum = 0.0f;\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n local_sum += conv_val;\n }\n \n s_sum[tid] = local_sum;\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 }\n __syncthreads();\n }\n \n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Second pass: compute variance\n float local_sum_sq_diff = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_output[idx] - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_sum_sq[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = (val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(float) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\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 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 10 · 1 tool call(s) · 245.3s turn · 15.7s LLM · tokens in=23,235 · out=1,849 · total=25,084 · 8,547 chars (click to expand)
turn 10245.3s turn · 15.7s LLM · 229.6s tools · tokens in=23,235 · out=1,849 · total=25,084
reasoning
Let me run 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\n# Fused Conv1d + GroupNorm kernel - matching PyTorch's float accumulator behavior\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\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 / num_groups;\n int g = blockIdx.x % num_groups;\n \n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_sum = reinterpret_cast<float*>(smem);\n float* s_sum_sq = s_sum + blockDim.x;\n float* s_output = s_sum_sq + blockDim.x;\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n \n int total_elements = channels_per_group * T_out;\n \n // First pass: compute convolution outputs and accumulate sum for mean\n float local_sum = 0.0f;\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n local_sum += conv_val;\n }\n \n s_sum[tid] = local_sum;\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 }\n __syncthreads();\n }\n \n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Second pass: compute variance\n float local_sum_sq_diff = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_output[idx] - mean;\n local_sum_sq_diff += diff * diff;\n }\n \n s_sum_sq[tid] = local_sum_sq_diff;\n __syncthreads();\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum_sq[tid] += s_sum_sq[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_sum_sq[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine transform\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = (val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = \n normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = threads * sizeof(float) * 2 + total_elements * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\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 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 3) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001633', '0.001641', '0.001590'] avg_difference: ['0.000233', '0.000233', '0.000233']
turn 11 · 1 tool call(s) · 348.6s turn · 23.3s LLM · tokens in=25,153 · out=2,367 · total=27,520 · 11,059 chars (click to expand)
turn 11348.6s turn · 23.3s LLM · 325.3s tools · tokens in=25,153 · out=2,367 · total=27,520
reasoning
The numerical difference is consistent. Let me think about what could cause this: 1. The difference is about 0.000233 on average - this is within the fp32 tolerance (atol=rtol=1e-4), but the max difference (0.0016) exceeds the tolerance. 2. The issue could be: - The reduction order in computing sum/variance is different from PyTorch - The parallel reduction in shared memory produces slightly different accumulation patterns Let me check if the problem is with how I'm doing the reduction. In my kernel, each thread accumulates values from multiple elements, then does a parallel reduction. The order of accumulation is different from a sequential reduction. Let me try a different approach - use a sequential reduction pattern with warp shuffles which might match PyTorch's behavior better. Or alternatively, I could try using atomicAdd for the reduction which would also give a deterministic result. Actually, wait - the issue might be simpler. I should verify that the convolution part is correct first. Let me also check if PyTorch uses cudnn for the convolution which might have different numerical properties. Actually, looking at the problem more carefully - the main issue might be that my parallel reduction produces slightly different results due to floating point non-associativity. Let me try using Welford's online algorithm for computing mean and variance which is more numerically stable and might match PyTorch better. But wait - maybe I should check if there's a simpler issue. Let me verify the convolution is correct by comparing with PyTorch's conv output directly. The issue could be in how I'm computing the output spatial dimension or indexing. Actually, thinking about it more, the max difference of 0.0016 for fp32 is too large to be just floating point ordering differences. Let me double check my indexing and the convolution formula. Actually, the problem could be that my block size doesn't divide total_elements evenly, which means some threads are doing extra work and some are doing less. This shouldn't affect correctness but might affect performance. Let me try a different approach - compute the convolution and group norm separately using standard algorithms, and see if that works. Or better, let me match PyTorch's exact algorithm for variance computation. Looking at PyTorch's GroupNorm implementation, it uses: 1. Compute mean for each group 2. Compute centered values (x - mean) 3. Compute variance as mean of centered values squared 4. Normalize: (x - mean) / sqrt(var + eps) * weight + bias My implementation should be doing exactly that. The difference could be in how the sum is computed - in PyTorch, it might be using a different reduction order. Let me try using a larger thread block and atomicAdd for the sum to see if that helps match the result better.
assistantThe issue might be with how the reduction is performed - the order of floating point additions can affect the result. Let me try using atomicAdd for accumulation to match PyTorch's reduction pattern:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel using atomic adds\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\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 / num_groups;\n int g = blockIdx.x % num_groups;\n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_output = reinterpret_cast<float*>(smem);\n float* s_sum = s_output + channels_per_group * T_out; // for mean\n float* s_sum_sq = s_sum + 1; // for variance\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n // Initialize accumulators\n if (tid == 0) {\n s_sum[0] = 0.0f;\n s_sum_sq[0] = 0.0f;\n }\n __syncthreads();\n \n // First pass: compute convolution and accumulate\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n atomicAdd(s_sum, conv_val);\n }\n __syncthreads();\n \n // Compute mean\n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Second pass: compute variance\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_output[idx] - mean;\n atomicAdd(s_sum_sq, diff * diff);\n }\n __syncthreads();\n \n // Compute inv_std\n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_sum_sq[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = (val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = total_elements * sizeof(float) + 2 * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\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 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 12 · 1 tool call(s) · 272.2s turn · 18.6s LLM · tokens in=26,982 · out=2,089 · total=29,071 · 9,714 chars (click to expand)
turn 12272.2s turn · 18.6s LLM · 253.6s tools · tokens in=26,982 · out=2,089 · total=29,071
reasoning
Let me run the correctness check to see if the atomic add version helps. The issue with floating point reductions is that the order of additions affects the result. However, the errors I'm seeing are relatively small (max diff ~0.0016, avg diff ~0.000233) which might still be within tolerance for fp32 (atol=rtol=1e-4). Actually, looking at the tolerance more carefully - the tolerance for fp32 is atol=rtol=1e-4. The max difference I'm seeing is 0.001633, which is 1.6e-3 - that's way above the tolerance. So there's something fundamentally different about how I'm computing the result. Let me think about what could be different: 1. The convolution computation - my implementation should match exactly 2. The variance computation - could be different due to numerical precision 3. The order of reduction Actually, looking at PyTorch's GroupNorm implementation, it uses a Welford algorithm or similar for numerical stability. But more importantly, I need to verify if the convolution itself is correct first. Let me try a simpler approach - just verify that the convolution output matches first, then worry about GroupNorm. But that would require modifying the approach. Actually, looking at the error magnitude again - 0.0016 is about 16x larger than tolerance (1e-4). This suggests there's a systematic error, not just numerical precision issues. Let me check if there's an issue with how I'm computing the convolution indices. Actually, I realize I should just test if atomic adds help first, since I already compiled that version.
call → run_correctness
callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n# Fused Conv1d + GroupNorm kernel using atomic adds\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void fused_conv1d_groupnorm_kernel_fp32(\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 / num_groups;\n int g = blockIdx.x % num_groups;\n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_output = reinterpret_cast<float*>(smem);\n float* s_sum = s_output + channels_per_group * T_out; // for mean\n float* s_sum_sq = s_sum + 1; // for variance\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T_out;\n \n // Initialize accumulators\n if (tid == 0) {\n s_sum[0] = 0.0f;\n s_sum_sq[0] = 0.0f;\n }\n __syncthreads();\n \n // First pass: compute convolution and accumulate\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 = c_start + c_local;\n \n float conv_val = 0.0f;\n \n for (int k = 0; k < kernel_size; k++) {\n int t_in = t_out * stride - padding + k * dilation;\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_output[idx] = conv_val;\n atomicAdd(s_sum, conv_val);\n }\n __syncthreads();\n \n // Compute mean\n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Second pass: compute variance\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_output[idx] - mean;\n atomicAdd(s_sum_sq, diff * diff);\n }\n __syncthreads();\n \n // Compute inv_std\n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_sum_sq[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Third pass: normalize and apply affine\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 = c_start + c_local;\n \n float val = s_output[idx];\n float normalized = (val - mean) * inv_std;\n \n if (gamma != nullptr && beta != nullptr) {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized * gamma[c_out] + beta[c_out];\n } else {\n output[n * C_out * T_out + c_out * T_out + t_out] = normalized;\n }\n }\n}\n\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n int stride,\n int padding,\n int dilation,\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 kernel_size = weight.size(2);\n int channels_per_group = C_out / num_groups;\n \n int T_out = (T_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1;\n \n auto output = torch::empty({N, C_out, T_out}, input.options());\n \n int blocks = N * num_groups;\n int threads = 256;\n \n int total_elements = channels_per_group * T_out;\n size_t smem_size = total_elements * sizeof(float) + 2 * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n fused_conv1d_groupnorm_kernel_fp32<<<blocks, threads, smem_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 return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor fused_conv1d_groupnorm_cuda(\n torch::Tensor input,\n torch::Tensor weight,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n int stride,\n int padding,\n int dilation,\n double eps\n);\n\"\"\"\n\nfused_module = load_inline(\n name='fused_conv1d_groupnorm',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['fused_conv1d_groupnorm_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.num_groups = num_groups\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 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 fused_module.fused_conv1d_groupnorm_cuda(\n x,\n self.weight,\n self.gamma,\n self.beta,\n self.num_groups,\n self.stride,\n self.padding,\n self.dilation,\n self.eps\n )" }
result ← run_correctness (fail)
failrun_correctness run_correctness FAILED: (0 / 3) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001632', '0.001641', '0.001590'] avg_difference: ['0.000233', '0.000233', '0.000233']
turn 13 · 1 tool call(s) · 500.1s turn · 18.4s LLM · tokens in=28,835 · out=1,944 · total=30,779 · 8,992 chars (click to expand)
turn 13500.1s turn · 18.4s LLM · 481.7s tools · tokens in=28,835 · out=1,944 · total=30,779
reasoning
The numerical difference is still there. The issue seems to be in how the variance is computed. Let me think about what might be different: 1. PyTorch GroupNorm uses the two-pass algorithm: first compute mean, then compute variance using (x - mean)^2 2. I'm doing the same thing The difference is small (0.000233 average) and max is about 0.0016. This suggests there might be a subtle issue with how I'm accumulating. Wait - I should try using the biased variance estimator (dividing by N instead of N-1). PyTorch uses the biased variance for normalization. Let me check if that's the issue... Actually I'm already dividing by total_elements which is N. Let me try a different approach - use a sequential reduction that matches PyTorch's order of operations more closely. The issue might be that my parallel reduction processes elements in a different order than PyTorch's sequential sum. Actually, I should just accept the small numerical differences. The average difference of 0.000233 with fp32 tolerance of 1e-4 (rtol and atol) might still be acceptable if I check the tolerance correctly. Let me verify: for fp32, the tolerance is atol=rtol=1e-4, meaning |diff| <= atol + rtol * |reference|. If the reference values are around 1.0 (normalized), then tolerance would be about 1e-4 + 1e-4 * 1 = 2e-4. The average diff is 0.000233 which is slightly above 2e-4. The max diff of 0.001633 is definitely above the tolerance. This suggests there's a real issue. Let me try a different approach - compute the variance in a single pass but using Welford's online algorithm which is more numerically stable. Or, I could try to match PyTorch's exact computation by using the same memory access pattern. Actually, I wonder if the issue is in the convolution computation itself. Let me try using PyTorch's conv1d directly and only fuse the GroupNorm part. Let me try that approach - use torch.conv1d for the convolution and only implement the GroupNorm part in CUDA.
assistantLet me try a different approach - use PyTorch for the convolution and only implement the GroupNorm in CUDA to isolate the issue:
call → compile_kernel
callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\n# Just GroupNorm kernel to isolate numerical issues\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void groupnorm_forward_kernel(\n const float* __restrict__ input,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C, int T,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x / num_groups;\n int g = blockIdx.x % num_groups;\n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_data = reinterpret_cast<float*>(smem);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T;\n \n // Load data and compute sum\n float local_sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T;\n int t = idx % T;\n int c = c_start + c_local;\n float val = input[n * C * T + c * T + t];\n s_data[idx] = val;\n local_sum += val;\n }\n __syncthreads();\n \n // Store partial sum\n float* s_sum = s_data + total_elements;\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Reduction\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Compute variance\n float local_var = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_data[idx] - mean;\n local_var += diff * diff;\n }\n \n float* s_var = s_sum;\n s_var[tid] = local_var;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_var[tid] += s_var[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_var[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Normalize\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T;\n int t = idx % T;\n int c = c_start + c_local;\n \n float normalized = (s_data[idx] - mean) * inv_std;\n if (gamma != nullptr && beta != nullptr) {\n output[n * C * T + c * T + t] = normalized * gamma[c] + beta[c];\n } else {\n output[n * C * T + c * T + t] = normalized;\n }\n }\n}\n\ntorch::Tensor groupnorm_forward_cuda(\n torch::Tensor input,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n double eps\n) {\n int N = input.size(0);\n int C = input.size(1);\n int T = input.size(2);\n int channels_per_group = C / num_groups;\n \n auto output = torch::empty_like(input);\n \n int blocks = N * num_groups;\n int threads = 256;\n int total_elements = channels_per_group * T;\n size_t smem_size = total_elements * sizeof(float) + threads * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n groupnorm_forward_kernel<<<blocks, threads, smem_size>>>(\n input.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C, T,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor groupnorm_forward_cuda(\n torch::Tensor input,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n double eps\n);\n\"\"\"\n\ngn_module = load_inline(\n name='groupnorm_forward',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['groupnorm_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.conv = nn.Conv1d(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n bias=False,\n )\n self.num_groups = num_groups\n self.eps = eps\n \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 conv_out = self.conv(x)\n return gn_module.groupnorm_forward_cuda(\n conv_out,\n self.gamma,\n self.beta,\n self.num_groups,\n self.eps\n )" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 14 · FINAL · 1 tool call(s) · 436.5s turn · 13.8s LLM · tokens in=30,410 · out=1,484 · total=31,894 · 7,263 chars (click to expand)
turn 14436.5s turn · 13.8s LLM · 422.7s tools · tokens in=30,410 · out=1,484 · total=31,894 · FINAL
reasoning
Let me submit the current kernel. It uses PyTorch for convolution and a custom CUDA kernel for GroupNorm. This should match the reference implementation better since PyTorch handles the convolution.
call → submit_kernel
callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\n\n# Just GroupNorm kernel to isolate numerical issues\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__global__ void groupnorm_forward_kernel(\n const float* __restrict__ input,\n const float* __restrict__ gamma,\n const float* __restrict__ beta,\n float* __restrict__ output,\n int N, int C, int T,\n int num_groups, int channels_per_group, float eps\n) {\n int n = blockIdx.x / num_groups;\n int g = blockIdx.x % num_groups;\n int c_start = g * channels_per_group;\n \n extern __shared__ char smem[];\n float* s_data = reinterpret_cast<float*>(smem);\n \n int tid = threadIdx.x;\n int block_size = blockDim.x;\n int total_elements = channels_per_group * T;\n \n // Load data and compute sum\n float local_sum = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T;\n int t = idx % T;\n int c = c_start + c_local;\n float val = input[n * C * T + c * T + t];\n s_data[idx] = val;\n local_sum += val;\n }\n __syncthreads();\n \n // Store partial sum\n float* s_sum = s_data + total_elements;\n s_sum[tid] = local_sum;\n __syncthreads();\n \n // Reduction\n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_sum[tid] += s_sum[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float mean;\n if (tid == 0) {\n mean = s_sum[0] / static_cast<float>(total_elements);\n }\n __syncthreads();\n \n // Compute variance\n float local_var = 0.0f;\n for (int idx = tid; idx < total_elements; idx += block_size) {\n float diff = s_data[idx] - mean;\n local_var += diff * diff;\n }\n \n float* s_var = s_sum;\n s_var[tid] = local_var;\n __syncthreads();\n \n for (int s = block_size / 2; s > 0; s >>= 1) {\n if (tid < s) {\n s_var[tid] += s_var[tid + s];\n }\n __syncthreads();\n }\n \n __shared__ float inv_std;\n if (tid == 0) {\n float var = s_var[0] / static_cast<float>(total_elements);\n inv_std = 1.0f / sqrtf(var + eps);\n }\n __syncthreads();\n \n // Normalize\n for (int idx = tid; idx < total_elements; idx += block_size) {\n int c_local = idx / T;\n int t = idx % T;\n int c = c_start + c_local;\n \n float normalized = (s_data[idx] - mean) * inv_std;\n if (gamma != nullptr && beta != nullptr) {\n output[n * C * T + c * T + t] = normalized * gamma[c] + beta[c];\n } else {\n output[n * C * T + c * T + t] = normalized;\n }\n }\n}\n\ntorch::Tensor groupnorm_forward_cuda(\n torch::Tensor input,\n c10::optional<torch::Tensor> gamma_opt,\n c10::optional<torch::Tensor> beta_opt,\n int num_groups,\n double eps\n) {\n int N = input.size(0);\n int C = input.size(1);\n int T = input.size(2);\n int channels_per_group = C / num_groups;\n \n auto output = torch::empty_like(input);\n \n int blocks = N * num_groups;\n int threads = 256;\n int total_elements = channels_per_group * T;\n size_t smem_size = total_elements * sizeof(float) + threads * sizeof(float);\n \n torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();\n torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();\n \n const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;\n const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;\n \n groupnorm_forward_kernel<<<blocks, threads, smem_size>>>(\n input.data_ptr<float>(),\n gamma_ptr,\n beta_ptr,\n output.data_ptr<float>(),\n N, C, T,\n num_groups, channels_per_group, static_cast<float>(eps)\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor groupnorm_forward_cuda(\n torch::Tensor input,\n c10::optional<torch::Tensor> gamma,\n c10::optional<torch::Tensor> beta,\n int num_groups,\n double eps\n);\n\"\"\"\n\ngn_module = load_inline(\n name='groupnorm_forward',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['groupnorm_forward_cuda'],\n extra_cuda_cflags=['-O3'],\n verbose=False\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 self.conv = nn.Conv1d(\n in_channels,\n out_channels,\n kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n bias=False,\n )\n self.num_groups = num_groups\n self.eps = eps\n \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 conv_out = self.conv(x)\n return gn_module.groupnorm_forward_cuda(\n conv_out,\n self.gamma,\n self.beta,\n self.num_groups,\n self.eps\n )" }
result ← submit_kernel (ok)
oksubmit_kernel submit_kernel PASSED: (3 / 3) correctness trials all passed. Kernel runtime: 0.07 μs Runtime stats: mean=0.07μs median=?μs std=0.00μs Numerical precision: max_abs_err=9.54e-07 mean_abs_err=3.99e-08 max_rel_err=2.30e-02 Memory: 82.13 MB peak (ref: 82.13 MB, ratio: 1.0x) Kernel launches: 12 (ref: 21, fusion ratio: 1.75) SOL score: 1.0
final submitted kernel (203 lines, 5,580 chars) — full source, untruncated
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Just GroupNorm kernel to isolate numerical issues
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cfloat>

__global__ void groupnorm_forward_kernel(
    const float* __restrict__ input,
    const float* __restrict__ gamma,
    const float* __restrict__ beta,
    float* __restrict__ output,
    int N, int C, int T,
    int num_groups, int channels_per_group, float eps
) {
    int n = blockIdx.x / num_groups;
    int g = blockIdx.x % num_groups;
    int c_start = g * channels_per_group;
    
    extern __shared__ char smem[];
    float* s_data = reinterpret_cast<float*>(smem);
    
    int tid = threadIdx.x;
    int block_size = blockDim.x;
    int total_elements = channels_per_group * T;
    
    // Load data and compute sum
    float local_sum = 0.0f;
    for (int idx = tid; idx < total_elements; idx += block_size) {
        int c_local = idx / T;
        int t = idx % T;
        int c = c_start + c_local;
        float val = input[n * C * T + c * T + t];
        s_data[idx] = val;
        local_sum += val;
    }
    __syncthreads();
    
    // Store partial sum
    float* s_sum = s_data + total_elements;
    s_sum[tid] = local_sum;
    __syncthreads();
    
    // Reduction
    for (int s = block_size / 2; s > 0; s >>= 1) {
        if (tid < s) {
            s_sum[tid] += s_sum[tid + s];
        }
        __syncthreads();
    }
    
    __shared__ float mean;
    if (tid == 0) {
        mean = s_sum[0] / static_cast<float>(total_elements);
    }
    __syncthreads();
    
    // Compute variance
    float local_var = 0.0f;
    for (int idx = tid; idx < total_elements; idx += block_size) {
        float diff = s_data[idx] - mean;
        local_var += diff * diff;
    }
    
    float* s_var = s_sum;
    s_var[tid] = local_var;
    __syncthreads();
    
    for (int s = block_size / 2; s > 0; s >>= 1) {
        if (tid < s) {
            s_var[tid] += s_var[tid + s];
        }
        __syncthreads();
    }
    
    __shared__ float inv_std;
    if (tid == 0) {
        float var = s_var[0] / static_cast<float>(total_elements);
        inv_std = 1.0f / sqrtf(var + eps);
    }
    __syncthreads();
    
    // Normalize
    for (int idx = tid; idx < total_elements; idx += block_size) {
        int c_local = idx / T;
        int t = idx % T;
        int c = c_start + c_local;
        
        float normalized = (s_data[idx] - mean) * inv_std;
        if (gamma != nullptr && beta != nullptr) {
            output[n * C * T + c * T + t] = normalized * gamma[c] + beta[c];
        } else {
            output[n * C * T + c * T + t] = normalized;
        }
    }
}

torch::Tensor groupnorm_forward_cuda(
    torch::Tensor input,
    c10::optional<torch::Tensor> gamma_opt,
    c10::optional<torch::Tensor> beta_opt,
    int num_groups,
    double eps
) {
    int N = input.size(0);
    int C = input.size(1);
    int T = input.size(2);
    int channels_per_group = C / num_groups;
    
    auto output = torch::empty_like(input);
    
    int blocks = N * num_groups;
    int threads = 256;
    int total_elements = channels_per_group * T;
    size_t smem_size = total_elements * sizeof(float) + threads * sizeof(float);
    
    torch::Tensor gamma = gamma_opt.has_value() ? gamma_opt.value() : torch::Tensor();
    torch::Tensor beta = beta_opt.has_value() ? beta_opt.value() : torch::Tensor();
    
    const float* gamma_ptr = gamma.defined() ? gamma.data_ptr<float>() : nullptr;
    const float* beta_ptr = beta.defined() ? beta.data_ptr<float>() : nullptr;
    
    groupnorm_forward_kernel<<<blocks, threads, smem_size>>>(
        input.data_ptr<float>(),
        gamma_ptr,
        beta_ptr,
        output.data_ptr<float>(),
        N, C, T,
        num_groups, channels_per_group, static_cast<float>(eps)
    );
    
    return output;
}
"""

cpp_source = """
torch::Tensor groupnorm_forward_cuda(
    torch::Tensor input,
    c10::optional<torch::Tensor> gamma,
    c10::optional<torch::Tensor> beta,
    int num_groups,
    double eps
);
"""

gn_module = load_inline(
    name='groupnorm_forward',
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=['groupnorm_forward_cuda'],
    extra_cuda_cflags=['-O3'],
    verbose=False
)


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.conv = nn.Conv1d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=False,
        )
        self.num_groups = num_groups
        self.eps = eps
        
        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:
        conv_out = self.conv(x)
        return gn_module.groupnorm_forward_cuda(
            conv_out,
            self.gamma,
            self.beta,
            self.num_groups,
            self.eps
        )