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22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish • reduced_sync_fused_kernel_base

Level 2 • Task 22
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    scale_factor: float,
    clamp_min: float,
    clamp_max: float,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Applies matrix multiplication, scaling, residual connection, clamping, LogSumExp and Mish activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, input_size)
        scale_factor (float): Factor to scale the output by
        clamp_min (float): Minimum value for clamping
        clamp_max (float): Maximum value for clamping
        weight (torch.Tensor): Weight matrix of shape (hidden_size, input_size)
        bias (torch.Tensor): Bias vector of shape (hidden_size)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, hidden_size)
    """
    x = F.linear(x, weight, bias)
    x = x * scale_factor
    x = x + x
    x = torch.clamp(x, clamp_min, clamp_max)
    x = torch.logsumexp(x, dim=1, keepdim=True)
    x = x * F.mish(x)
    return x


class Model(nn.Module):
    """
    Model that performs a matrix multiplication, scales the result, adds a residual connection, clamps the output,
    applies LogSumExp, and finally applies the Mish activation function.
    """

    def __init__(self, input_size, hidden_size, scale_factor, clamp_min, clamp_max):
        super(Model, self).__init__()
        matmul = nn.Linear(input_size, hidden_size)
        self.weight = matmul.weight
        self.bias = nn.Parameter(
            matmul.bias + torch.ones_like(matmul.bias) * 0.02
        )  # make sure its nonzero

    def forward(self, x, scale_factor, clamp_min, clamp_max, fn=module_fn):
        return fn(x, scale_factor, clamp_min, clamp_max, self.weight, self.bias)


batch_size = 128
input_size = 512
hidden_size = 1024
scale_factor = 2.0
clamp_min = -10.0
clamp_max = 10.0


def get_inputs():
    return [torch.randn(batch_size, input_size), scale_factor, clamp_min, clamp_max]


def get_init_inputs():
    return [input_size, hidden_size, scale_factor, clamp_min, clamp_max]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a matrix multiplication, scales the result, adds a residual connection, clamps the output,
    applies LogSumExp, and finally applies the Mish activation function.
    """
    def __init__(self, input_size, hidden_size, scale_factor, clamp_min, clamp_max):
        super(Model, self).__init__()
        self.matmul = nn.Linear(input_size, hidden_size)
        self.matmul.bias = nn.Parameter(self.matmul.bias + torch.ones_like(self.matmul.bias) * 0.02)
        self.scale_factor = scale_factor
        self.clamp_min = clamp_min
        self.clamp_max = clamp_max

    def forward(self, x):
        """
        Args:
            x: Input tensor of shape (batch_size, input_size).

        Returns:
            Output tensor of shape (batch_size, hidden_size).
        """
        x = self.matmul(x)
        x = x * self.scale_factor
        x = x + x
        x = torch.clamp(x, self.clamp_min, self.clamp_max)
        x = torch.logsumexp(x, dim=1, keepdim=True)
        x = x * torch.nn.functional.mish(x)  # Mish activation
        return x

batch_size = 128
input_size = 512
hidden_size = 1024
scale_factor = 2.0
clamp_min = -10.0
clamp_max = 10.0

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

def get_init_inputs():
    return [input_size, hidden_size, scale_factor, clamp_min, clamp_max]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish
Level ID 2
Task ID 22
Kernel Name reduced_sync_fused_kernel_base
CUDA Speedup (Native) 1.671x
CUDA Speedup (Compile) 1.136x
CUDA Runtime 0.038 ms
PyTorch Runtime (Native) 0.064 ms
PyTorch Runtime (Compile) 0.043 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 2, Task 22 • 22_Matmul_Scale_ResidualAdd_Clamp_LogSumExp_Mish)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_syncthreads_optimized_base 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_even_workload_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_ldg_aligned_edit_1 0.03 2.19 1.49
🥇 22_matmul_scale_residualadd_clamp_logsumexp_mish_ldg_aligned_base 0.03 2.19 1.49
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_warp_optimized_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_warp_optimized_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_warp_optimized_edit_1 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_stride_loop_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_even_workload_base 0.03 2.12 1.44
6 22_matmul_scale_residualadd_clamp_logsumexp_mish_2dindex_optimized_base 0.03 2.12 1.44
12 coalesced_fused_kernel_base_base 0.04 1.76 1.20
12 block_size_optimization_base 0.04 1.76 1.20
12 atomic_optimized_fused_kernel_base 0.04 1.76 1.20
15 reduced_sync_fused_kernel_base 0.04 1.67 1.14
16 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_optimized_base 0.04 1.63 1.11
16 fused_kernel_base 0.04 1.63 1.11
16 22_matmul_scale_residualadd_clamp_logsumexp_mish_shared_memory_optimized_edit_1 0.04 1.63 1.11
19 aligned_ldg_fused_kernel_base 0.04 1.44 0.98
20 unroll_optimization_base_base 0.04 1.41 0.96 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <cuda_runtime.h>
#include <vector>
#include <math.h>

#define TILE_SIZE 16

__global__ void gemm_post_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ intermediate,
    int batch_size,
    int input_size,
    int hidden_size,
    float scale_factor,
    float clamp_min,
    float clamp_max
) {
    __shared__ float input_shared[TILE_SIZE][TILE_SIZE];
    __shared__ float weight_shared[TILE_SIZE][TILE_SIZE];

    int row = blockIdx.y * TILE_SIZE + threadIdx.y;
    int col = blockIdx.x * TILE_SIZE + threadIdx.x;
    float sum = 0.0f;

    // Single loop to handle all tiles
    for (int t = 0; t < (input_size + TILE_SIZE - 1) / TILE_SIZE; t++) {
        // Load input and weight data into shared memory
        int input_col = t * TILE_SIZE + threadIdx.x;
        int weight_row = t * TILE_SIZE + threadIdx.y;
        
        input_shared[threadIdx.y][threadIdx.x] = (row < batch_size && input_col < input_size) ? 
            input[row * input_size + input_col] : 0.0f;
        
        weight_shared[threadIdx.y][threadIdx.x] = (col < hidden_size && weight_row < input_size) ? 
            weight[col * input_size + weight_row] : 0.0f;

        // Single sync point after shared memory loads
        __syncthreads();

        // Compute partial dot product
        #pragma unroll
        for (int i = 0; i < TILE_SIZE; i++) {
            sum += input_shared[threadIdx.y][i] * weight_shared[i][threadIdx.x];
        }

        // Only sync if we're not on the last iteration
        if (t < (input_size + TILE_SIZE - 1) / TILE_SIZE - 1) {
            __syncthreads();
        }
    }

    // Write result with post-processing
    if (row < batch_size && col < hidden_size) {
        sum += bias[col];
        sum *= scale_factor;
        sum += sum;
        sum = fmaxf(fminf(sum, clamp_max), clamp_min);
        intermediate[row * hidden_size + col] = sum;
    }
}

__global__ void fused_logsumexp_mish_kernel(
    const float* __restrict__ intermediate,
    float* __restrict__ output,
    int hidden_size,
    int batch_size
) {
    extern __shared__ float sdata[];
    int tid = threadIdx.x;
    int row = blockIdx.x;
    
    if (row >= batch_size) return;

    // Initialize shared memory for max and sum
    float thread_max = -INFINITY;
    float thread_sum = 0.0f;
    
    // Each thread processes multiple elements
    for (int i = tid; i < hidden_size; i += blockDim.x) {
        float val = intermediate[row * hidden_size + i];
        thread_max = fmaxf(thread_max, val);
    }
    
    // Store thread max in shared memory
    sdata[tid] = thread_max;
    __syncthreads();
    
    // Reduce to find max (only log2(blockDim.x) iterations)
    for (int offset = blockDim.x/2; offset > 0; offset >>= 1) {
        if (tid < offset) {
            sdata[tid] = fmaxf(sdata[tid], sdata[tid + offset]);
        }
        __syncthreads();
    }
    
    // Get the row max
    float row_max = sdata[0];
    
    // Compute sum of exponentials using the same threads
    for (int i = tid; i < hidden_size; i += blockDim.x) {
        float val = intermediate[row * hidden_size + i];
        thread_sum += expf(val - row_max);
    }
    
    // Store thread sum in shared memory
    sdata[tid] = thread_sum;
    __syncthreads();
    
    // Reduce to find total sum (reuse shared memory)
    for (int offset = blockDim.x/2; offset > 0; offset >>= 1) {
        if (tid < offset) {
            sdata[tid] += sdata[tid + offset];
        }
        __syncthreads();
    }
    
    // Final computation in single thread
    if (tid == 0) {
        float lse = row_max + logf(sdata[0]);
        float softplus = logf(1.0f + expf(lse));
        float mish_val = lse * tanhf(softplus);
        output[row] = lse * mish_val;
    }
}

torch::Tensor fused_forward(
    torch::Tensor x,
    float scale_factor,
    float clamp_min,
    float clamp_max,
    torch::Tensor weight,
    torch::Tensor bias
) {
    int batch_size = x.size(0);
    int input_size = x.size(1);
    int hidden_size = weight.size(0);

    auto options = torch::TensorOptions().dtype(x.dtype()).device(x.device());
    auto intermediate = torch::empty({batch_size, hidden_size}, options);

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

    gemm_post_kernel<<<grid, block>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        intermediate.data_ptr<float>(),
        batch_size,
        input_size,
        hidden_size,
        scale_factor,
        clamp_min,
        clamp_max
    );

    auto output = torch::empty({batch_size}, options);
    
    int threads_per_block = 256;
    int shared_mem_size = threads_per_block * sizeof(float);
    
    fused_logsumexp_mish_kernel<<<batch_size, threads_per_block, shared_mem_size>>>(
        intermediate.data_ptr<float>(),
        output.data_ptr<float>(),
        hidden_size,
        batch_size
    );

    return output.reshape({batch_size, 1});
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &fused_forward, "Optimized forward pass (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.380 inst/cycle 0.000 5
Executed Ipc Elapsed 0.220 inst/cycle 0.000 5
Issue Slots Busy 9.586 % 0.001 5
Issued Ipc Active 0.380 inst/cycle 0.000 5
SM Busy 9.586 % 0.001 5
Memory Throughput 84785193293.524 byte/second 1134217601484009728.000 5
Mem Busy 5.860 % 0.006 5
Max Bandwidth 4.740 % 0.003 5
L1/TEX Hit Rate 49.810 % 0.000 5
L2 Hit Rate 65.052 % 0.061 5
Mem Pipes Busy 4.740 % 0.003 5
Warp Cycles Per Issued Instruction 20.338 cycle 0.431 5
Warp Cycles Per Executed Instruction 20.472 cycle 0.437 5
Avg. Active Threads Per Warp 31.600 0.000 5
Avg. Not Predicated Off Threads Per Warp 22.370 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 16.000 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 11.960 % 0.000 5
Achieved Active Warps Per SM 7.650 warp 0.000 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 ThreadDivergence Instructions are executed in warps, which are groups of 32 threads. Optimal instruction throughput is achieved if all 32 threads of a warp execute the same instruction. The chosen launch configuration, early thread completion, and divergent flow control can significantly lower the number of active threads in a warp per cycle. This kernel achieves an average of 31.6 threads being active per cycle. This is further reduced to 22.4 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
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 (12.0%) 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 302786.53 μs
Device Time 172.54 μs
Self CPU Time 47.59 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 302738.94 μs
Device Time 172.54 μs
Self CPU Time 107.58 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 302215.61 μs
Device Time 0.00 μs
Self CPU Time 96.58 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 300165.29 μs
Device Time 0.00 μs
Self CPU Time 300165.29 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 74663.73 μs
Device Time 638737.59 μs
Self CPU Time 17701.09 μs
Self Device Time 638737.59 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 603707.20 μs
Device Time 39164.93 μs
Self CPU Time 603707.20 μs
Self Device Time 39164.93 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
gemm_post_kernel(float const*, float const*, float const*, float*, int, int, int, float, float, float)
CPU Time 0.00 μs
Device Time 242910.78 μs
Self CPU Time 0.00 μs
Self Device Time 242910.78 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 87707.68 μs
Device Time 638737.59 μs
Self CPU Time 13056.01 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
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 638737.59 μs
Self CPU Time 0.00 μs
Self Device Time 638737.59 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 
45299 warnings generated when compiling for host.
Suppressed 45326 warnings (45279 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/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:10:31: note: the first parameter in the range is 'input'
10 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:12:31: note: the last parameter in the range is 'bias'
12 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:16:5: warning: 3 adjacent parameters of 'gemm_post_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
16 | int hidden_size,
| ^~~~~~~~~~~~~~~~
17 | float scale_factor,
| ~~~~~~~~~~~~~~~~~~~
18 | float clamp_min,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:16:9: note: the first parameter in the range is 'hidden_size'
16 | int hidden_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:18:11: note: the last parameter in the range is 'clamp_min'
18 | float clamp_min,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:17:5: note: 'int' and 'float' may be implicitly converted
17 | float scale_factor,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:24:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | int row = blockIdx.y * TILE_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:25:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int col = blockIdx.x * TILE_SIZE + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:31:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | int input_col = t * TILE_SIZE + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:32:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
32 | int weight_row = t * TILE_SIZE + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:68:5: warning: 2 adjacent parameters of 'fused_logsumexp_mish_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
68 | int hidden_size,
| ^~~~~~~~~~~~~~~~
69 | int batch_size
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:68:9: note: the first parameter in the range is 'hidden_size'
68 | int hidden_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:69:9: note: the last parameter in the range is 'batch_size'
69 | int batch_size
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:72:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
72 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:73:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:82:45: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | for (int i = tid; i < hidden_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:92:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
92 | for (int offset = blockDim.x/2; offset > 0; offset >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:103:45: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | for (int i = tid; i < hidden_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:113:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
113 | for (int offset = blockDim.x/2; offset > 0; offset >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:130:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
130 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:134:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
134 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:135:19: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
135 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:137:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:138:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
138 | int input_size = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:139:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | int hidden_size = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_22/b9_s3_reduced_sync_fused_kernel/base/base.cu:164:27: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
164 | int shared_mem_size = threads_per_block * sizeof(float);
| ^