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30_Gemm_GroupNorm_Hardtanh • min_warp_divergence_edit_1

Level 2 • Task 30
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    group_norm_weight: torch.Tensor,
    group_norm_bias: torch.Tensor,
    num_groups: int,
    hardtanh_min: float,
    hardtanh_max: float,
) -> torch.Tensor:
    """
    Applies linear layer, group normalization and hardtanh activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        weight (torch.Tensor): Weight matrix of shape (out_features, in_features)
        bias (torch.Tensor): Bias vector of shape (out_features)
        group_norm_weight (torch.Tensor): Group norm weight of shape (out_features)
        group_norm_bias (torch.Tensor): Group norm bias of shape (out_features)
        num_groups (int): Number of groups for group normalization
        hardtanh_min (float): Minimum value for hardtanh
        hardtanh_max (float): Maximum value for hardtanh

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_features)
    """
    x = F.linear(x, weight, bias)
    x = F.group_norm(x, num_groups, group_norm_weight, group_norm_bias)
    x = F.hardtanh(x, hardtanh_min, hardtanh_max)
    return x


class Model(nn.Module):
    """
    Simple model that performs a GEMM, applies Group Normalization, and then HardTanh.
    """

    def __init__(
        self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max
    ):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        group_norm = nn.GroupNorm(num_groups, out_features)
        self.weight = nn.Parameter(gemm.weight)
        self.bias = nn.Parameter(gemm.bias + torch.ones_like(gemm.bias) * 0.02)
        self.group_norm_weight = nn.Parameter(group_norm.weight)
        self.group_norm_bias = nn.Parameter(
            group_norm.bias + torch.ones_like(group_norm.bias) * 0.02
        )

    def forward(self, x, num_groups, hardtanh_min, hardtanh_max, fn=module_fn):
        return fn(
            x,
            self.weight,
            self.bias,
            self.group_norm_weight,
            self.group_norm_bias,
            num_groups,
            hardtanh_min,
            hardtanh_max,
        )


batch_size = 128
in_features = 1024
out_features = 512
num_groups = 8
hardtanh_min = -2.0
hardtanh_max = 2.0


def get_inputs():
    return [
        torch.randn(batch_size, in_features),
        num_groups,
        hardtanh_min,
        hardtanh_max,
    ]


def get_init_inputs():
    return [in_features, out_features, num_groups, hardtanh_min, hardtanh_max]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a GEMM, applies Group Normalization, and then HardTanh.
    """
    def __init__(self, in_features, out_features, num_groups, hardtanh_min, hardtanh_max):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.group_norm = nn.GroupNorm(num_groups, out_features)
        self.hardtanh = nn.Hardtanh(min_val=hardtanh_min, max_val=hardtanh_max)
        # Add the same noise as in functional implementation
        self.gemm.bias = nn.Parameter(self.gemm.bias + torch.ones_like(self.gemm.bias) * 0.02)
        self.group_norm.bias = nn.Parameter(self.group_norm.bias + torch.ones_like(self.group_norm.bias) * 0.02)

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_features).
        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_features).
        """
        x = self.gemm(x)
        x = self.group_norm(x)
        x = self.hardtanh(x)
        return x

batch_size = 128
in_features = 1024
out_features = 512
num_groups = 8
hardtanh_min = -2.0
hardtanh_max = 2.0

def get_inputs():
    return [torch.randn(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, num_groups, hardtanh_min, hardtanh_max]
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Kernel Information

Operation Name 30_Gemm_GroupNorm_Hardtanh
Level ID 2
Task ID 30
Kernel Name min_warp_divergence_edit_1
CUDA Speedup (Native) 0.790x
CUDA Speedup (Compile) 0.819x
CUDA Runtime 0.061 ms
PyTorch Runtime (Native) 0.048 ms
PyTorch Runtime (Compile) 0.050 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model gpt-4o-mini-2024-07-18
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 2, Task 30 • 30_Gemm_GroupNorm_Hardtanh)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 warp_divergence_minimization_base 0.06 0.88 0.91
🥈 warp_divergence_minimization_edit_1 0.06 0.86 0.89
🥉 optimized_block_sizes_base_edit_1 0.06 0.85 0.88
🥉 optimized_gemm_groupnorm_hardtanh_edit_1 0.06 0.85 0.88
🥉 ldg_memory_alignment_optimization_base 0.06 0.85 0.88
🥉 optimized_kernel_unroll_loops_base 0.06 0.85 0.88
🥉 modular_device_functions_optimized_v2_base 0.06 0.85 0.88
🥉 modular_device_functions_refactor_base 0.06 0.85 0.88
🥉 optimized_kernel_unroll_loops_edit_1 0.06 0.85 0.88
10 shared_mem_reuse_v1_base 0.06 0.83 0.86
11 unroll_loops_optim_base 0.06 0.79 0.82
11 min_warp_divergence_edit_1 0.06 0.79 0.82
11 sync_reduction_optim_edit_1 0.06 0.79 0.82
14 sync_reduction_optim_base 0.06 0.78 0.81
14 min_warp_divergence_base 0.06 0.78 0.81
14 modular_kernel_edit_1 0.06 0.78 0.81
14 constant_memory_optimization_base_edit_1 0.06 0.78 0.81
18 optimized_kernel_constant_memory_base 0.07 0.74 0.77
18 const_memory_optimized_kernel_edit_1 0.07 0.74 0.77
20 const_memory_optimized_kernel_base 0.07 0.73 0.76 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <vector>
#include <cmath>

// Inline device clamp function for both float and double types to ensure branchless clamping.

template <typename scalar_t>
__device__ inline scalar_t device_clamp(scalar_t v, scalar_t min_val, scalar_t max_val);

template <>
__device__ inline float device_clamp<float>(float v, float min_val, float max_val) {
    return fminf(fmaxf(v, min_val), max_val);
}

template <>
__device__ inline double device_clamp<double>(double v, double min_val, double max_val) {
    return fmin(fmax(v, min_val), max_val);
}

// Compute mean and variance for a group in GroupNorm with loop unrolling
template <typename scalar_t>
__device__ inline void compute_group_mean_var(
    const scalar_t* __restrict__ x,
    int batch,
    int group,
    int channels_per_group,
    int num_channels,
    scalar_t &mean,
    scalar_t &var) {
  mean = 0;
  #pragma unroll
  for (int c = 0; c < channels_per_group; ++c) {
    int channel = group * channels_per_group + c;
    mean += x[batch * num_channels + channel];
  }
  mean /= static_cast<scalar_t>(channels_per_group);
  var = 0;
  #pragma unroll
  for (int c = 0; c < channels_per_group; ++c) {
    int channel = group * channels_per_group + c;
    scalar_t diff = x[batch * num_channels + channel] - mean;
    var += diff * diff;
  }
  var /= static_cast<scalar_t>(channels_per_group);
}

// Normalize and apply scale (gamma) and shift (beta)
template <typename scalar_t>
__device__ inline scalar_t group_norm_normalize(
    scalar_t val,
    scalar_t mean,
    scalar_t var,
    scalar_t eps,
    scalar_t gamma,
    scalar_t beta) {
  scalar_t inv_std = __frsqrt_rn(var + eps);
  return ((val - mean) * inv_std) * gamma + beta;
}

// Tiled linear kernel with uniform control flow
template <typename scalar_t>
__global__ void linear_forward_kernel(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ output,
    size_t batch_size,
    size_t in_features,
    size_t out_features) {

  int row = blockIdx.y * blockDim.y + threadIdx.y;  // batch index
  int col = blockIdx.x * blockDim.x + threadIdx.x;      // output feature index
  const int TILE_DIM = 16;
  
  if (row < batch_size && col < out_features) {
    scalar_t sum = bias[col];
    __shared__ scalar_t tile_x[TILE_DIM][TILE_DIM];
    __shared__ scalar_t tile_w[TILE_DIM][TILE_DIM];
    int numTiles = (in_features + TILE_DIM - 1) / TILE_DIM;
    for (int t = 0; t < numTiles; t++) {
      int x_idx = t * TILE_DIM + threadIdx.x;
      int w_idx = t * TILE_DIM + threadIdx.y;
      tile_x[threadIdx.y][threadIdx.x] = (x_idx < in_features) ? x[row * in_features + x_idx] : static_cast<scalar_t>(0);
      tile_w[threadIdx.y][threadIdx.x] = (w_idx < in_features) ? weight[col * in_features + w_idx] : static_cast<scalar_t>(0);
      __syncthreads();
      #pragma unroll
      for (int k = 0; k < TILE_DIM; k++) {
        sum += tile_x[threadIdx.y][k] * tile_w[k][threadIdx.x];
      }
      __syncthreads();
    }
    output[row * out_features + col] = sum;
  }
}

// Group normalization kernel with loop unrolling; using one thread per block minimizes divergence.
template <typename scalar_t>
__global__ void group_norm_forward_kernel(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ gamma,
    const scalar_t* __restrict__ beta,
    scalar_t* __restrict__ output,
    int64_t batch_size,
    int64_t num_channels,
    int64_t num_groups,
    int64_t channels_per_group,
    float eps = 1e-5f) {

  int batch = blockIdx.x;
  int group = blockIdx.y;
  
  if (batch < batch_size && group < num_groups) {
    scalar_t mean, var;
    compute_group_mean_var(x, batch, group, channels_per_group, num_channels, mean, var);
    #pragma unroll
    for (int c = 0; c < channels_per_group; ++c) {
      int channel = group * channels_per_group + c;
      scalar_t val = x[batch * num_channels + channel];
      output[batch * num_channels + channel] = group_norm_normalize(val, mean, var, static_cast<scalar_t>(eps), gamma[channel], beta[channel]);
    }
  }
}

// Hardtanh activation kernel refactored to remove divergent branches using branchless clamping
template <typename scalar_t>
__global__ void hardtanh_forward_kernel(
    const scalar_t* __restrict__ x,
    scalar_t min_val,
    scalar_t max_val,
    scalar_t* __restrict__ output,
    size_t total_elements) {

  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  if (idx < total_elements) {
    scalar_t val = x[idx];
    // Replace conditional branches with branchless clamp to minimize warp divergence
    output[idx] = device_clamp<scalar_t>(val, min_val, max_val);
  }
}

// C++ interface functions

void linear_forward_cuda(
    at::Tensor x, 
    at::Tensor weight, 
    at::Tensor bias, 
    at::Tensor output) {

  const auto batch_size = x.size(0);
  const auto in_features = x.size(1);
  const auto out_features = weight.size(0);

  const int threads = 16;
  const dim3 threadsPerBlock(threads, threads);
  const dim3 numBlocks((out_features + threads - 1) / threads, (batch_size + threads - 1) / threads);

  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "linear_forward_cuda", ([&] {
    linear_forward_kernel<scalar_t><<<numBlocks, threadsPerBlock>>>(
        x.data_ptr<scalar_t>(),
        weight.data_ptr<scalar_t>(),
        bias.data_ptr<scalar_t>(),
        output.data_ptr<scalar_t>(),
        batch_size,
        in_features,
        out_features);
  }));
  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess) {
    printf("Error in linear_forward_cuda: %s\n", cudaGetErrorString(err));
  }
}

void group_norm_forward_cuda(
    at::Tensor x, 
    at::Tensor gamma, 
    at::Tensor beta, 
    int64_t num_groups,
    at::Tensor output) {

  const int64_t batch_size = x.size(0);
  const int64_t num_channels = x.size(1);
  const int64_t channels_per_group = num_channels / num_groups;

  const dim3 blocks(batch_size, num_groups);
  const int threads = 1; // One thread per block minimizes divergence in group norm
  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "group_norm_forward_cuda", ([&] {
    group_norm_forward_kernel<scalar_t><<<blocks, threads>>>(
        x.data_ptr<scalar_t>(),
        gamma.data_ptr<scalar_t>(),
        beta.data_ptr<scalar_t>(),
        output.data_ptr<scalar_t>(),
        batch_size,
        num_channels,
        num_groups,
        channels_per_group);
  }));
  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess) {
    printf("Error in group_norm_forward_cuda: %s\n", cudaGetErrorString(err));
  }
}

void hardtanh_forward_cuda(
    at::Tensor x, 
    float min_val, 
    float max_val,
    at::Tensor output) {

  const size_t total_elements = x.numel();
  const int threads = 256;
  const int blocks = (total_elements + threads - 1) / threads;
  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "hardtanh_forward_cuda", ([&] {
    hardtanh_forward_kernel<scalar_t><<<blocks, threads>>>(
        x.data_ptr<scalar_t>(),
        static_cast<scalar_t>(min_val),
        static_cast<scalar_t>(max_val),
        output.data_ptr<scalar_t>(),
        total_elements);
  }));
  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess) {
    printf("Error in hardtanh_forward_cuda: %s\n", cudaGetErrorString(err));
  }
}

// Combined module function integrating linear, group norm and hardtanh kernels
at::Tensor module_fn_cuda_forward(
    at::Tensor x,
    at::Tensor weight,
    at::Tensor bias,
    at::Tensor group_norm_weight,
    at::Tensor group_norm_bias,
    int64_t num_groups,
    float hardtanh_min,
    float hardtanh_max) {

  x = x.contiguous();
  weight = weight.contiguous();
  bias = bias.contiguous();
  group_norm_weight = group_norm_weight.contiguous();
  group_norm_bias = group_norm_bias.contiguous();

  int64_t batch_size = x.size(0);
  int64_t in_features = x.size(1);
  int64_t out_features = weight.size(0);
  auto options = x.options();
  
  at::Tensor linear_output = at::empty({batch_size, out_features}, options);
  at::Tensor group_norm_output = at::empty({batch_size, out_features}, options);
  at::Tensor output = at::empty({batch_size, out_features}, options);

  linear_forward_cuda(x, weight, bias, linear_output);
  group_norm_forward_cuda(linear_output, group_norm_weight, group_norm_bias, num_groups, group_norm_output);
  hardtanh_forward_cuda(group_norm_output, hardtanh_min, hardtanh_max, output);

  return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &module_fn_cuda_forward, "Module function forward (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.218 inst/cycle 0.000 5
Executed Ipc Elapsed 0.074 inst/cycle 0.000 5
Issue Slots Busy 5.906 % 0.236 5
Issued Ipc Active 0.238 inst/cycle 0.000 5
SM Busy 5.906 % 0.236 5
Memory Throughput 80892175397.416 byte/second 2660996417951900672.000 5
Mem Busy 11.090 % 0.040 5
Max Bandwidth 7.242 % 0.018 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 83.418 % 0.088 5
Mem Pipes Busy 3.010 % 0.004 5
Warp Cycles Per Issued Instruction 58.222 cycle 13.908 5
Warp Cycles Per Executed Instruction 62.880 cycle 16.224 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.720 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 16.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 22.250 % 0.033 5
Achieved Active Warps Per SM 14.238 warp 0.014 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (22.2%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::to
CPU Time 197927.54 μs
Device Time 163.42 μs
Self CPU Time 66.53 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 197861.01 μs
Device Time 163.42 μs
Self CPU Time 140.30 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::empty_strided
CPU Time 197224.29 μs
Device Time 0.00 μs
Self CPU Time 164.85 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceGetStreamPriorityRange
CPU Time 196651.82 μs
Device Time 0.00 μs
Self CPU Time 196651.82 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 50963.56 μs
Device Time 604226.45 μs
Self CPU Time 16050.05 μs
Self Device Time 604226.45 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 64823.84 μs
Device Time 604226.45 μs
Self CPU Time 13895.47 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 803537.38 μs
Device Time 32788.66 μs
Self CPU Time 803537.38 μs
Self Device Time 32788.66 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void linear_forward_kernel<float>(float const*, float const*, float const*, float*, unsigned long, unsigned long, unsigned long)
CPU Time 0.00 μs
Device Time 366107.38 μs
Self CPU Time 0.00 μs
Self Device Time 366107.38 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void group_norm_forward_kernel<float>(float const*, float const*, float const*, float*, long, long, long, long, float)
CPU Time 0.00 μs
Device Time 53612.79 μs
Self CPU Time 0.00 μs
Self Device Time 53612.79 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 604226.45 μs
Self CPU Time 0.00 μs
Self Device Time 604226.45 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45312 warnings generated when compiling for host.
Suppressed 45328 warnings (45281 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:25:5 bugprone-easily-swappable-parameters
25 | int batch,
| ^~~~~~~~~~
26 | int group,
| ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:25:9: note: the first parameter in the range is 'batch'
25 | int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:26:9: note: the last parameter in the range is 'group'
26 | int group,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:27:5: warning: 2 adjacent parameters of 'compute_group_mean_var' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
27 | int channels_per_group,
| ^~~~~~~~~~~~~~~~~~~~~~~
28 | int num_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:27:9: note: the first parameter in the range is 'channels_per_group'
27 | int channels_per_group,
| ^~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:28:9: note: the last parameter in the range is 'num_channels'
28 | int num_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:29:5: warning: 2 adjacent parameters of 'compute_group_mean_var' of similar type ('scalar_t &') are easily swapped by mistake [bugprone-easily-swappable-parameters]
29 | scalar_t &mean,
| ^~~~~~~~~~~~~~~
30 | scalar_t &var) {
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:29:15: note: the first parameter in the range is 'mean'
29 | scalar_t &mean,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:30:15: note: the last parameter in the range is 'var'
30 | scalar_t &var) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:52:5: warning: 2 adjacent parameters of 'group_norm_normalize' of similar type ('scalar_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
52 | scalar_t mean,
| ^~~~~~~~~~~~~~
53 | scalar_t var,
| ~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:52:14: note: the first parameter in the range is 'mean'
52 | scalar_t mean,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:53:14: note: the last parameter in the range is 'var'
53 | scalar_t var,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:54:5: warning: 2 adjacent parameters of 'group_norm_normalize' of similar type ('scalar_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
54 | scalar_t eps,
| ^~~~~~~~~~~~~
55 | scalar_t gamma,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:54:14: note: the first parameter in the range is 'eps'
54 | scalar_t eps,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:55:14: note: the last parameter in the range is 'gamma'
55 | scalar_t gamma,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:64:5: warning: 3 adjacent parameters of 'linear_forward_kernel' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
64 | const scalar_t* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
65 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
66 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:64:34: note: the first parameter in the range is 'x'
64 | const scalar_t* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:66:34: note: the last parameter in the range is 'bias'
66 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:68:5: warning: 2 adjacent parameters of 'linear_forward_kernel' of similar type ('size_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
68 | size_t batch_size,
| ^~~~~~~~~~~~~~~~~~
69 | size_t in_features,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:68:12: note: the first parameter in the range is 'batch_size'
68 | size_t batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:69:12: note: the last parameter in the range is 'in_features'
69 | size_t in_features,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:72:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | int row = blockIdx.y * blockDim.y + threadIdx.y; // batch index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:73:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | int col = blockIdx.x * blockDim.x + threadIdx.x; // output feature index
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:80:20: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | int numTiles = (in_features + TILE_DIM - 1) / TILE_DIM;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:82:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int x_idx = t * TILE_DIM + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:83:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int w_idx = t * TILE_DIM + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:100:5: warning: 2 adjacent parameters of 'group_norm_forward_kernel' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
100 | const scalar_t* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
101 | const scalar_t* __restrict__ gamma,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:100:34: note: the first parameter in the range is 'x'
100 | const scalar_t* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:101:34: note: the last parameter in the range is 'gamma'
101 | const scalar_t* __restrict__ gamma,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:104:5: warning: 2 adjacent parameters of 'group_norm_forward_kernel' of similar type ('int64_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
104 | int64_t batch_size,
| ^~~~~~~~~~~~~~~~~~~
105 | int64_t num_channels,
| ~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:104:13: note: the first parameter in the range is 'batch_size'
104 | int64_t batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:105:13: note: the last parameter in the range is 'num_channels'
105 | int64_t num_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:106:5: warning: 3 adjacent parameters of 'group_norm_forward_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
106 | int64_t num_groups,
| ^~~~~~~~~~~~~~~~~~~
107 | int64_t channels_per_group,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~
108 | float eps = 1e-5f) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:106:13: note: the first parameter in the range is 'num_groups'
106 | int64_t num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:108:11: note: the last parameter in the range is 'eps'
108 | float eps = 1e-5f) {
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:106:5: note:
106 | int64_t num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:108:5: note: 'int64_t' and 'float' may be implicitly converted: 'int64_t' (as 'long') -> 'float', 'float' -> 'int64_t' (as 'long')
108 | float eps = 1e-5f) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:110:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int batch = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:111:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int group = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:118:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
118 | int channel = group * channels_per_group + c;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:134:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
134 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:158:3: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
158 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "linear_forward_cuda", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:187:3: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
187 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "group_norm_forward_cuda", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:212:22: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
212 | const int blocks = (total_elements + threads - 1) / threads;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:213:3: warning: inside a lambda, '__func__' expands to the name of the function call operator; consider capturing the name of the enclosing function explicitly [bugprone-lambda-function-name]
213 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "hardtanh_forward_cuda", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:34: note: expanded from macro 'AT_DISPATCH_FLOATING_TYPES'
237 | AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:233:3: note: expanded from macro 'AT_DISPATCH_CASE_FLOATING_TYPES'
233 | AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:74:3: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^
note: (skipping 1 expansions in backtrace; use -fmacro-backtrace-limit=0 to see all)
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:58:7: note: expanded from macro 'AT_PRIVATE_CHECK_SELECTIVE_BUILD'
58 | AT_ERROR( \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:711:32: note: expanded from macro 'AT_ERROR'
711 | C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Exception.h:536:9: note: expanded from macro 'TORCH_CHECK'
536 | __func__, \
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:234:5: warning: 2 adjacent parameters of 'module_fn_cuda_forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
234 | int64_t num_groups,
| ^~~~~~~~~~~~~~~~~~~
235 | float hardtanh_min,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:234:13: note: the first parameter in the range is 'num_groups'
234 | int64_t num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:235:11: note: the last parameter in the range is 'hardtanh_min'
235 | float hardtanh_min,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:234:5: note:
234 | int64_t num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:235:5: note: 'int64_t' and 'float' may be implicitly converted: 'int64_t' (as 'long') -> 'float', 'float' -> 'int64_t' (as 'long')
235 | float hardtanh_min,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:245:11: warning: Value stored to 'in_features' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
245 | int64_t in_features = x.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_2/task_30/b5_s0_min_warp_divergence/edit_1/edit_1.cu:245:11: note: Value stored to 'in_features' during its initialization is never read
245 | int64_t in_features = x.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~