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80_Gemm_Max_Subtract_GELU • warp_optimized_reduction_base_base

Level 2 • Task 80
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    max_dim: int,
    weight: torch.Tensor,
    bias: torch.Tensor,
) -> torch.Tensor:
    """
    Performs a GEMM, followed by a max operation, subtraction, and GELU activation.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        max_dim (int): Dimension to perform max operation over
        weight (torch.Tensor): Weight matrix of shape (out_features, in_features)
        bias (torch.Tensor): Bias vector of shape (out_features)

    Returns:
        torch.Tensor: Output tensor of shape (batch_size, out_features)
    """
    x = F.linear(x, weight, bias)
    x = torch.max(x, dim=max_dim, keepdim=True).values
    x = x - x.mean(dim=1, keepdim=True)
    x = F.gelu(x)
    return x


class Model(nn.Module):
    """
    Model that performs a GEMM, followed by a max operation, subtraction, and GELU activation.
    """

    def __init__(self, in_features, out_features, max_dim):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        self.weight = nn.Parameter(gemm.weight)
        self.bias = nn.Parameter(gemm.bias)

    def forward(self, x, max_dim, fn=module_fn):
        return fn(x, max_dim, self.weight, self.bias)


batch_size = 128
in_features = 512
out_features = 1024
max_dim = 1


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


def get_init_inputs():
    return [in_features, out_features, max_dim]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a GEMM, followed by a max operation, subtraction, and GELU activation.
    """
    def __init__(self, in_features, out_features, max_dim):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.max_dim = max_dim

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

        Returns:
            Output tensor of shape (batch_size, out_features)
        """
        x = self.gemm(x)
        x = torch.max(x, dim=self.max_dim, keepdim=True).values
        x = x - x.mean(dim=1, keepdim=True)
        x = torch.nn.functional.gelu(x)
        return x

batch_size = 128
in_features = 512
out_features = 1024
max_dim = 1

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

def get_init_inputs():
    return [in_features, out_features, max_dim]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 80_Gemm_Max_Subtract_GELU
Level ID 2
Task ID 80
Kernel Name warp_optimized_reduction_base_base
CUDA Speedup (Native) 1.091x
CUDA Speedup (Compile) 1.159x
CUDA Runtime 0.039 ms
PyTorch Runtime (Native) 0.043 ms
PyTorch Runtime (Compile) 0.045 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 80 • 80_Gemm_Max_Subtract_GELU)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 warp_optimized_gemm_max_gelu_base 0.03 1.70 1.81
🥇 warp_optimized_shared_memory_edit_1 0.03 1.70 1.81
🥇 warp_aligned_gemm_base_edit_1 0.03 1.70 1.81
🥇 warp_optimized_shared_memory_base 0.03 1.70 1.81
🥇 warp_balanced_gemm_optimization_base 0.03 1.70 1.81
6 warp_aligned_gemm_base_base 0.03 1.58 1.67
7 warp_aligned_gemm_const_bias_base 0.03 1.47 1.56
8 warp_aligned_gemm_const_bias_edit_1 0.03 1.25 1.33
8 ldg_memory_optimized_kernel_base 0.03 1.25 1.33
10 indexing_optimized_fused_kernel_base 0.04 1.22 1.29
10 workload_balanced_kernel_base_base 0.04 1.22 1.29
10 shared_memory_reduction_warp_optimization_base_base 0.04 1.22 1.29
10 efficient_thread_mapping_kernel_base 0.04 1.22 1.29
14 block_tuned_fused_kernel_base_base 0.04 1.18 1.26
14 minimal_sync_optimized_kernel_base_base 0.04 1.18 1.26
16 warp_balanced_gemm_optimization_edit_1 0.04 1.15 1.22
17 warp_optimized_reduction_base_base 0.04 1.09 1.16
18 evenly_distributed_base 0.04 1.06 1.13
18 fused_gemm_max_reduce_gelu_base 0.04 1.06 1.13
20 fused_stride_loops_base 0.04 1.04 1.10 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#include <float.h>

#define TILE_DIM 16
#define WARP_SIZE 32
#define FULL_MASK 0xffffffff

__device__ inline float gelu(float x) {
    return 0.5f * x * (1.0f + erf(x * 0.70710678118654752440f));
}

// Warp-level reduction using shuffle instructions
__device__ inline float warp_reduce_max(float val) {
    for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
        float other = __shfl_down_sync(FULL_MASK, val, offset);
        val = fmaxf(val, other);
    }
    return val;
}

__device__ inline float warp_reduce_sum(float val) {
    for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
        val += __shfl_down_sync(FULL_MASK, val, offset);
    }
    return val;
}

__global__ void tiled_gemm_kernel(const float* __restrict__ x,
                                   const float* __restrict__ weight,
                                   const float* __restrict__ bias,
                                   float* __restrict__ y,
                                   int batch, int in_features, int out_features) {
    __shared__ float tile_x[TILE_DIM][TILE_DIM];
    __shared__ float tile_w[TILE_DIM][TILE_DIM];

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

    for (int t = 0; t < (in_features + TILE_DIM - 1) / TILE_DIM; t++) {
        int idx = t * TILE_DIM + threadIdx.x;
        int idy = t * TILE_DIM + threadIdx.y;

        tile_x[threadIdx.y][threadIdx.x] = (row < batch && idx < in_features) ?
            x[row * in_features + idx] : 0.0f;
        tile_w[threadIdx.y][threadIdx.x] = (col < out_features && idy < in_features) ?
            weight[col * in_features + idy] : 0.0f;
        __syncthreads();

        #pragma unroll
        for (int k = 0; k < TILE_DIM; k++) {
            sum += tile_x[threadIdx.y][k] * tile_w[k][threadIdx.x];
        }
        __syncthreads();
    }

    if (row < batch && col < out_features) {
        y[row * out_features + col] = sum + bias[col];
    }
}

__global__ void fused_warp_reduce_gelu_kernel(float* data,
                                               int batch,
                                               int out_features,
                                               int max_dim) {
    extern __shared__ float smem[];
    const int tid = threadIdx.x;
    const int lane_id = tid % WARP_SIZE;
    const int warp_id = tid / WARP_SIZE;
    const int warps_per_block = blockDim.x / WARP_SIZE;

    float thread_max = -FLT_MAX;
    float thread_sum = 0.0f;

    if (max_dim == 0) {
        // Reduce along batch dimension
        const int col = blockIdx.x;
        
        // Each thread processes multiple elements with grid stride
        for (int i = tid; i < batch; i += blockDim.x) {
            float val = data[i * out_features + col];
            thread_max = fmaxf(thread_max, val);
        }

        // Warp-level reduction
        float warp_max = warp_reduce_max(thread_max);

        // Store warp results in shared memory
        if (lane_id == 0) {
            smem[warp_id] = warp_max;
        }
        __syncthreads();

        // Final reduction across warps
        if (warp_id == 0) {
            float block_max = (tid < warps_per_block) ? smem[tid] : -FLT_MAX;
            block_max = warp_reduce_max(block_max);

            // First thread has the final result
            if (tid == 0) {
                smem[0] = block_max;
            }
        }
        __syncthreads();

        float max_val = smem[0];
        
        // Compute mean using the same pattern
        for (int i = tid; i < batch; i += blockDim.x) {
            int idx = i * out_features + col;
            float val = data[idx] - max_val;
            thread_sum += val;
        }

        float warp_sum = warp_reduce_sum(thread_sum);
        if (lane_id == 0) {
            smem[warp_id] = warp_sum;
        }
        __syncthreads();

        if (warp_id == 0) {
            float block_sum = (tid < warps_per_block) ? smem[tid] : 0.0f;
            block_sum = warp_reduce_sum(block_sum);
            if (tid == 0) {
                smem[0] = block_sum / batch;
            }
        }
        __syncthreads();

        float mean = smem[0];

        // Apply GELU with grid stride loop
        for (int i = tid; i < batch; i += blockDim.x) {
            int idx = i * out_features + col;
            float val = data[idx] - mean;
            data[idx] = gelu(val);
        }
    } else {
        // Reduce along feature dimension
        const int row = blockIdx.x;
        
        for (int j = tid; j < out_features; j += blockDim.x) {
            float val = data[row * out_features + j];
            thread_max = fmaxf(thread_max, val);
        }

        float warp_max = warp_reduce_max(thread_max);
        if (lane_id == 0) {
            smem[warp_id] = warp_max;
        }
        __syncthreads();

        if (warp_id == 0) {
            float block_max = (tid < warps_per_block) ? smem[tid] : -FLT_MAX;
            block_max = warp_reduce_max(block_max);
            if (tid == 0) {
                smem[0] = block_max;
            }
        }
        __syncthreads();

        float max_val = smem[0];

        for (int j = tid; j < out_features; j += blockDim.x) {
            int idx = row * out_features + j;
            float val = data[idx] - max_val;
            thread_sum += val;
        }

        float warp_sum = warp_reduce_sum(thread_sum);
        if (lane_id == 0) {
            smem[warp_id] = warp_sum;
        }
        __syncthreads();

        if (warp_id == 0) {
            float block_sum = (tid < warps_per_block) ? smem[tid] : 0.0f;
            block_sum = warp_reduce_sum(block_sum);
            if (tid == 0) {
                smem[0] = block_sum / out_features;
            }
        }
        __syncthreads();

        float mean = smem[0];

        for (int j = tid; j < out_features; j += blockDim.x) {
            int idx = row * out_features + j;
            float val = data[idx] - mean;
            data[idx] = gelu(val);
        }
    }
}

torch::Tensor forward(torch::Tensor x, int max_dim, torch::Tensor weight, torch::Tensor bias) {
    int batch = x.size(0);
    int in_features = x.size(1);
    int out_features = weight.size(0);

    auto y = torch::empty({batch, out_features}, x.options());

    dim3 blockDimGEMM(TILE_DIM, TILE_DIM);
    dim3 gridDimGEMM((out_features + TILE_DIM - 1) / TILE_DIM,
                     (batch + TILE_DIM - 1) / TILE_DIM);
    
    tiled_gemm_kernel<<<gridDimGEMM, blockDimGEMM>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        y.data_ptr<float>(),
        batch, in_features, out_features
    );

    auto max_out = torch::empty({max_dim == 0 ? 1 : batch, max_dim == 0 ? out_features : 1}, y.options());

    int threads = 256; // Must be multiple of WARP_SIZE
    int blocks = max_dim == 0 ? out_features : batch;
    int sharedMemSize = (threads / WARP_SIZE) * sizeof(float);

    fused_warp_reduce_gelu_kernel<<<blocks, threads, sharedMemSize>>>(
        max_out.data_ptr<float>(),
        batch,
        out_features,
        max_dim
    );

    return max_out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Warp-optimized GEMM and reduction with GELU");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.452 inst/cycle 0.000 5
Executed Ipc Elapsed 0.248 inst/cycle 0.000 5
Issue Slots Busy 11.636 % 0.000 5
Issued Ipc Active 0.466 inst/cycle 0.000 5
SM Busy 11.636 % 0.000 5
Memory Throughput 89466428590.386 byte/second 2285018570214138368.000 5
Mem Busy 7.004 % 0.012 5
Max Bandwidth 5.320 % 0.012 5
L1/TEX Hit Rate 75.000 % 0.000 5
L2 Hit Rate 75.786 % 0.337 5
Mem Pipes Busy 2.692 % 0.002 5
Warp Cycles Per Issued Instruction 17.068 cycle 0.005 5
Warp Cycles Per Executed Instruction 17.494 cycle 0.005 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.670 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 10.000 block 0.000 5
Block Limit Shared Mem 28.000 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 12.460 % 0.000 5
Achieved Active Warps Per SM 7.976 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 Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (12.5%) 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 263590.30 μs
Device Time 160.54 μs
Self CPU Time 48.39 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 263541.91 μs
Device Time 160.54 μs
Self CPU Time 90.09 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 263021.73 μs
Device Time 0.00 μs
Self CPU Time 104.06 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 262454.62 μs
Device Time 0.00 μs
Self CPU Time 262454.62 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 575278.80 μs
Device Time 34966.73 μs
Self CPU Time 575278.80 μs
Self Device Time 34966.73 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
tiled_gemm_kernel(float const*, float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 235181.71 μs
Self CPU Time 0.00 μs
Self Device Time 235181.71 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
fused_warp_reduce_gelu_kernel(float*, int, int, int)
CPU Time 0.00 μs
Device Time 42481.16 μs
Self CPU Time 0.00 μs
Self Device Time 42481.16 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 87429.08 μs
Device Time 600776.99 μs
Self CPU Time 11789.96 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 75640.31 μs
Device Time 600776.99 μs
Self CPU Time 15600.21 μs
Self Device Time 600776.99 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 600854.53 μs
Self CPU Time 0.00 μs
Self Device Time 600854.53 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 
45302 warnings generated when compiling for host.
Suppressed 45324 warnings (45277 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_80/b7_s1_warp_optimized_reduction_base/base/base.cu:31:35 bugprone-easily-swappable-parameters
31 | __global__ void tiled_gemm_kernel(const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
32 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
33 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:31:61: note: the first parameter in the range is 'x'
31 | __global__ void tiled_gemm_kernel(const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:33:62: note: the last parameter in the range is 'bias'
33 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:39:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | int row = blockIdx.y * TILE_DIM + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:40:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | int col = blockIdx.x * TILE_DIM + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:44:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
44 | int idx = t * TILE_DIM + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:45:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | int idy = t * TILE_DIM + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:66:48: warning: 3 adjacent parameters of 'fused_warp_reduce_gelu_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
66 | int batch,
| ^~~~~~~~~~
67 | int out_features,
| ~~~~~~~~~~~~~~~~~
68 | int max_dim) {
| ~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:66:52: note: the first parameter in the range is 'batch'
66 | int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:68:52: note: the last parameter in the range is 'max_dim'
68 | int max_dim) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:70:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
70 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:73:33: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
73 | const int warps_per_block = blockDim.x / WARP_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:80:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | const int col = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:83:43: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | for (int i = tid; i < batch; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:112:43: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | for (int i = tid; i < batch; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:128:39: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
128 | smem[0] = block_sum / batch;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:136:43: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | for (int i = tid; i < batch; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:143:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
143 | const int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:145:50: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
145 | for (int j = tid; j < out_features; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:167:50: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
167 | for (int j = tid; j < out_features; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:183:39: warning: narrowing conversion from 'int' to 'float' [bugprone-narrowing-conversions]
183 | smem[0] = block_sum / out_features;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:190:50: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
190 | for (int j = tid; j < out_features; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:198:37: 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]
198 | torch::Tensor forward(torch::Tensor x, int max_dim, torch::Tensor weight, torch::Tensor bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:198:67: 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]
198 | torch::Tensor forward(torch::Tensor x, int max_dim, torch::Tensor weight, torch::Tensor bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:198:89: 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]
198 | torch::Tensor forward(torch::Tensor x, int max_dim, torch::Tensor weight, torch::Tensor bias) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:199:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
199 | int batch = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:200:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
200 | int in_features = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:201:24: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
201 | int out_features = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_80/b7_s1_warp_optimized_reduction_base/base/base.cu:221:25: warning: narrowing conversion from 'unsigned long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
221 | int sharedMemSize = (threads / WARP_SIZE) * sizeof(float);
| ^