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30_Gemm_GroupNorm_Hardtanh • shared_mem_reuse_v1_base

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 shared_mem_reuse_v1_base
CUDA Speedup (Native) 0.830x
CUDA Speedup (Compile) 0.861x
CUDA Runtime 0.058 ms
PyTorch Runtime (Native) 0.048 ms
PyTorch Runtime (Compile) 0.050 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
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 <cuda.h>
#include <cuda_runtime.h>
#include <stdio.h>

// TILE_SIZE defines the block dimensions for matrix multiplication tiling
constexpr int TILE_SIZE = 16;

// Linear Forward Kernel with shared memory bias caching
// Computes output = x * weight^T + bias
// x: [batch_size, in_features]
// weight: [out_features, in_features]
// bias: [out_features]
// output: [batch_size, out_features]

template <typename scalar_t, int TILE_SIZE>
__global__ void linear_forward_kernel_optimized(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ output,
    int batch_size,
    int in_features,
    int out_features) {

  int row = blockIdx.y * TILE_SIZE + threadIdx.y; // batch index
  int col = blockIdx.x * TILE_SIZE + threadIdx.x; // output feature index

  __shared__ scalar_t A_tile[TILE_SIZE][TILE_SIZE];
  __shared__ scalar_t B_tile[TILE_SIZE][TILE_SIZE];
  __shared__ scalar_t bias_tile[TILE_SIZE];  // Shared bias for this block's column tile

  // Load bias values for the columns in this block once
  if (threadIdx.y == 0) {
    int bias_idx = blockIdx.x * TILE_SIZE + threadIdx.x;
    bias_tile[threadIdx.x] = (bias_idx < out_features) ? bias[bias_idx] : static_cast<scalar_t>(0);
  }
  __syncthreads();

  scalar_t sum = 0;
  int numTiles = (in_features + TILE_SIZE - 1) / TILE_SIZE;

  for (int t = 0; t < numTiles; t++) {
    int a_col = t * TILE_SIZE + threadIdx.x;
    A_tile[threadIdx.y][threadIdx.x] = (row < batch_size && a_col < in_features) 
                                         ? x[row * in_features + a_col] 
                                         : static_cast<scalar_t>(0);

    int k = t * TILE_SIZE + threadIdx.y;
    B_tile[threadIdx.y][threadIdx.x] = (col < out_features && k < in_features) 
                                         ? weight[col * in_features + k] 
                                         : static_cast<scalar_t>(0);
    __syncthreads();

    #pragma unroll
    for (int i = 0; i < TILE_SIZE; i++) {
      if (t * TILE_SIZE + i < in_features) {
        sum += A_tile[threadIdx.y][i] * B_tile[i][threadIdx.x];
      }
    }
    __syncthreads();
  }

  if (row < batch_size && col < out_features) {
    output[row * out_features + col] = sum + bias_tile[threadIdx.x];
  }
}

// Group Normalization Kernel with shared memory caching
// Each block processes one (batch, group) pair.
// x: [batch_size, num_channels]
// gamma, beta: [num_channels] used for scaling and shifting
// num_groups divides num_channels evenly.

template <typename scalar_t>
__global__ void group_norm_forward_kernel_shared(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ gamma,
    const scalar_t* __restrict__ beta,
    scalar_t* __restrict__ output,
    int batch_size,
    int num_channels,
    int num_groups) {

  int channels_per_group = num_channels / num_groups;
  int idx = blockIdx.x; // Each block corresponds to one (batch, group) pair
  int batch = idx / num_groups;
  int group = idx % num_groups;

  // Allocate shared memory for group data and a reduction buffer
  // The first part holds the group's data; the second part is used for reduction.
  extern __shared__ char shared_mem[];
  scalar_t* group_data = reinterpret_cast<scalar_t*>(shared_mem);
  scalar_t* reduction = reinterpret_cast<scalar_t*>(shared_mem + channels_per_group * sizeof(scalar_t));

  // Load the entire group's data from global memory into shared memory
  for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
    int channel = group * channels_per_group + i;
    group_data[i] = x[batch * num_channels + channel];
  }
  __syncthreads();

  // Compute the mean using a reduction in shared memory
  scalar_t local_sum = 0;
  for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
    local_sum += group_data[i];
  }
  reduction[threadIdx.x] = local_sum;
  __syncthreads();
  for (int s = blockDim.x >> 1; s > 0; s >>= 1) {
    if (threadIdx.x < s) {
      reduction[threadIdx.x] += reduction[threadIdx.x + s];
    }
    __syncthreads();
  }
  scalar_t mean = reduction[0] / channels_per_group;
  __syncthreads();

  // Compute the variance using the shared group data
  scalar_t local_var = 0;
  for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
    scalar_t diff = group_data[i] - mean;
    local_var += diff * diff;
  }
  reduction[threadIdx.x] = local_var;
  __syncthreads();
  for (int s = blockDim.x >> 1; s > 0; s >>= 1) {
    if (threadIdx.x < s) {
      reduction[threadIdx.x] += reduction[threadIdx.x + s];
    }
    __syncthreads();
  }
  scalar_t var = reduction[0] / channels_per_group;
  scalar_t inv_std = rsqrtf(var + 1e-5f);
  __syncthreads();

  // Normalize the group data and apply scale (gamma) and shift (beta)
  for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
    int channel = group * channels_per_group + i;
    scalar_t norm = (group_data[i] - mean) * inv_std;
    output[batch * num_channels + channel] = norm * gamma[channel] + beta[channel];
  }
}

// Hardtanh Activation Kernel with grid-stride loop
// Applies activation: output = min(max(x, min_val), max_val)

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;
  int stride = blockDim.x * gridDim.x;
  for (; idx < total_elements; idx += stride) {
    scalar_t val = x[idx];
    if (val < min_val) val = min_val;
    if (val > max_val) val = max_val;
    output[idx] = val;
  }
}

// Host function to launch the linear kernel
void linear_forward_cuda_optimized(
    at::Tensor x, 
    at::Tensor weight, 
    at::Tensor bias, 
    at::Tensor output) {
  const int batch_size = x.size(0);
  const int in_features = x.size(1);
  const int out_features = weight.size(0);

  dim3 block(TILE_SIZE, TILE_SIZE);
  dim3 grid((out_features + TILE_SIZE - 1) / TILE_SIZE,
            (batch_size + TILE_SIZE - 1) / TILE_SIZE);

  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "linear_forward_cuda_optimized", ([&] {
    linear_forward_kernel_optimized<scalar_t, TILE_SIZE><<<grid, block>>>(
        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_optimized: %s\n", cudaGetErrorString(err));
  }
}

// Host function to launch the group normalization kernel using shared memory
void group_norm_forward_cuda_shared(
    at::Tensor x, 
    at::Tensor gamma, 
    at::Tensor beta, 
    int64_t num_groups, 
    at::Tensor output) {
  const int batch_size = x.size(0);
  const int num_channels = x.size(1);
  int channels_per_group = num_channels / num_groups;

  int total_blocks = batch_size * num_groups;
  int threads = (channels_per_group < 256) ? channels_per_group : 256;
  
  // Shared memory: group_data (channels_per_group elements) + reduction (threads elements)
  size_t shared_mem = channels_per_group * sizeof(float) + threads * sizeof(float);

  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "group_norm_forward_cuda_shared", ([&] {
    group_norm_forward_kernel_shared<scalar_t><<<total_blocks, threads, shared_mem>>>(
        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);
  }));

  cudaError_t err = cudaGetLastError();
  if (err != cudaSuccess) {
    printf("Error in group_norm_forward_cuda_shared: %s\n", cudaGetErrorString(err));
  }
}

// Host function to launch the hardtanh kernel
void hardtanh_forward_cuda(
    at::Tensor x, 
    float min_val, 
    float max_val,
    at::Tensor output) {
  size_t total_elements = x.numel();
  int threads = 256;
  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 host function that runs linear, group norm, and hardtanh sequentially
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) {
  
  // Ensure inputs are contiguous
  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);

  // Run the linear layer with shared memory bias caching
  linear_forward_cuda_optimized(x, weight, bias, linear_output);

  // Run group normalization with shared memory caching of group data
  group_norm_forward_cuda_shared(linear_output, group_norm_weight, group_norm_bias, num_groups, group_norm_output);

  // Apply hardtanh activation
  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, "Forward pass (CUDA shared memory optimized)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.312 inst/cycle 0.000 5
Executed Ipc Elapsed 0.106 inst/cycle 0.000 5
Issue Slots Busy 8.854 % 0.015 5
Issued Ipc Active 0.354 inst/cycle 0.000 5
SM Busy 8.854 % 0.015 5
Memory Throughput 77733491443.322 byte/second 3458267285033996288.000 5
Mem Busy 10.676 % 0.077 5
Max Bandwidth 6.970 % 0.040 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 83.264 % 0.039 5
Mem Pipes Busy 1.734 % 0.002 5
Warp Cycles Per Issued Instruction 41.550 cycle 10.070 5
Warp Cycles Per Executed Instruction 47.166 cycle 12.986 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.270 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 22.298 % 0.022 5
Achieved Active Warps Per SM 14.270 warp 0.009 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 (22.3%) 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 242428.59 μs
Device Time 174.17 μs
Self CPU Time 56.85 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
aten::_to_copy
CPU Time 242371.73 μs
Device Time 174.17 μs
Self CPU Time 136.30 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 241723.73 μs
Device Time 0.00 μs
Self CPU Time 131.20 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 241235.60 μs
Device Time 0.00 μs
Self CPU Time 241235.60 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 51578.99 μs
Device Time 587029.25 μs
Self CPU Time 18941.92 μs
Self Device Time 587029.25 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 65579.02 μs
Device Time 587029.25 μs
Self CPU Time 14023.10 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 770628.13 μs
Device Time 31882.66 μs
Self CPU Time 770628.13 μs
Self Device Time 31882.66 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_optimized<float, 16>(float const*, float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 359499.35 μs
Self CPU Time 0.00 μs
Self Device Time 359499.35 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 587107.39 μs
Self CPU Time 0.00 μs
Self Device Time 587107.39 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 
45317 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/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:19:5 bugprone-easily-swappable-parameters
19 | const scalar_t* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
20 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
21 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:19:34: note: the first parameter in the range is 'x'
19 | const scalar_t* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:21:34: note: the last parameter in the range is 'bias'
21 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:78:5: warning: 2 adjacent parameters of 'group_norm_forward_kernel_shared' of similar type ('const scalar_t *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
78 | const scalar_t* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
79 | const scalar_t* __restrict__ gamma,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:78:34: note: the first parameter in the range is 'x'
78 | const scalar_t* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:79:34: note: the last parameter in the range is 'gamma'
79 | const scalar_t* __restrict__ gamma,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:82:5: warning: 2 adjacent parameters of 'group_norm_forward_kernel_shared' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
82 | int batch_size,
| ^~~~~~~~~~~~~~~
83 | int num_channels,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:82:9: note: the first parameter in the range is 'batch_size'
82 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:83:9: note: the last parameter in the range is 'num_channels'
83 | int num_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:87:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | int idx = blockIdx.x; // Each block corresponds to one (batch, group) pair
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:98:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:98:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:106:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:106:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:111:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | for (int s = blockDim.x >> 1; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:122:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:122:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
122 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:128:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
128 | for (int s = blockDim.x >> 1; s > 0; s >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:139:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:139:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | for (int i = threadIdx.x; i < channels_per_group; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:152:5: warning: 2 adjacent parameters of 'hardtanh_forward_kernel' of similar type ('scalar_t') are easily swapped by mistake [bugprone-easily-swappable-parameters]
152 | scalar_t min_val,
| ^~~~~~~~~~~~~~~~~
153 | scalar_t max_val,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:152:14: note: the first parameter in the range is 'min_val'
152 | scalar_t min_val,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:153:14: note: the last parameter in the range is 'max_val'
153 | scalar_t max_val,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:156:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
156 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:157:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:172:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
172 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:173:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
173 | const int in_features = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:174:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | const int out_features = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:180:3: 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]
180 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "linear_forward_cuda_optimized", ([&] {
| ^
/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/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:204:26: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
204 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:205:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
205 | const int num_channels = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:206:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
206 | int channels_per_group = num_channels / num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:208:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
208 | int total_blocks = batch_size * num_groups;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:214:3: 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]
214 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "group_norm_forward_cuda_shared", ([&] {
| ^
/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/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:239:16: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
239 | int blocks = (total_elements + threads - 1) / threads;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:241:3: 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]
241 | 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/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:263:5: warning: 2 adjacent parameters of 'module_fn_cuda_forward' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
263 | int64_t num_groups,
| ^~~~~~~~~~~~~~~~~~~
264 | float hardtanh_min,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:263:13: note: the first parameter in the range is 'num_groups'
263 | int64_t num_groups,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:264:11: note: the last parameter in the range is 'hardtanh_min'
264 | float hardtanh_min,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:263:5: note:
263 | int64_t num_groups,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:264:5: note: 'int64_t' and 'float' may be implicitly converted: 'int64_t' (as 'long') -> 'float', 'float' -> 'int64_t' (as 'long')
264 | float hardtanh_min,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:275:11: warning: Value stored to 'in_features' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
275 | int64_t in_features = x.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_2/task_30/b4_s3_shared_mem_reuse_v1/base/base.cu:275:11: note: Value stored to 'in_features' during its initialization is never read
275 | int64_t in_features = x.size(1);
| ^~~~~~~~~~~ ~~~~~~~~~