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51_Gemm_Subtract_GlobalAvgPool_LogSumExp_GELU_ResidualAdd • atomic_optimized_pipeline_base

Level 2 • Task 51
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,
    subtract: torch.Tensor,
) -> torch.Tensor:
    """
    Performs a series of operations: Gemm, Subtract, GlobalAvgPool, LogSumExp, GELU, and ResidualAdd.

    Args:
        x (torch.Tensor): Input tensor of shape (batch_size, in_features)
        weight (torch.Tensor): Weight matrix for linear layer of shape (out_features, in_features)
        bias (torch.Tensor): Bias vector for linear layer of shape (out_features)
        subtract (torch.Tensor): Vector to subtract of shape (out_features)

    Returns:
        torch.Tensor: Output tensor after applying all operations
    """
    original_x = x.clone().detach()

    # Gemm
    x = F.linear(x, weight, bias)

    # Subtract
    x = x - subtract

    # GlobalAvgPool
    x = torch.mean(x, dim=1, keepdim=True)

    # LogSumExp
    x = torch.logsumexp(x, dim=1, keepdim=True)

    # GELU
    x = F.gelu(x)

    # ResidualAdd
    x = x + original_x

    return x


class Model(nn.Module):
    """
    Model that performs a series of operations: Gemm, Subtract, GlobalAvgPool, LogSumExp, GELU, and ResidualAdd.
    """

    def __init__(self, in_features, out_features):
        super(Model, self).__init__()
        gemm = nn.Linear(in_features, out_features)
        self.weight = nn.Parameter(gemm.weight)
        self.bias = nn.Parameter(gemm.bias)
        self.subtract = nn.Parameter(torch.randn(out_features) * 0.02)

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


batch_size = 128
in_features = 1024
out_features = 512


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


def get_init_inputs():
    return [in_features, out_features]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a series of operations: Gemm, Subtract, GlobalAvgPool, LogSumExp, GELU, and ResidualAdd.
    """
    def __init__(self, in_features, out_features, bias=True):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features, bias=bias)
        self.subtract = nn.Parameter(torch.randn(out_features) * 0.02)

    def forward(self, x):
        original_x = x.clone().detach()
        # Gemm
        x = self.gemm(x)

        # Subtract
        x = x - self.subtract

        # GlobalAvgPool
        x = torch.mean(x, dim=1, keepdim=True)

        # LogSumExp
        x = torch.logsumexp(x, dim=1, keepdim=True)

        # GELU
        x = torch.nn.functional.gelu(x)

        # ResidualAdd
        x = x + original_x

        return x

batch_size = 128
in_features = 1024
out_features = 512

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

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

Operation Name 51_Gemm_Subtract_GlobalAvgPool_LogSumExp_GELU_ResidualAdd
Level ID 2
Task ID 51
Kernel Name atomic_optimized_pipeline_base
CUDA Speedup (Native) 1.262x
CUDA Speedup (Compile) 0.721x
CUDA Runtime 0.064 ms
PyTorch Runtime (Native) 0.081 ms
PyTorch Runtime (Compile) 0.046 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 2, Task 51 • 51_Gemm_Subtract_GlobalAvgPool_LogSumExp_GELU_ResidualAdd)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_forward_base 0.05 1.62 0.92
🥇 fused_forward_edit_1 0.05 1.62 0.92
🥉 fused_forward_coalesced_base 0.05 1.58 0.90
4 fused_forward_coalesced_edit_1 0.05 1.55 0.89
5 optimized_fused_kernel_base 0.06 1.32 0.76
6 fused_pipeline_base 0.06 1.28 0.73
6 threadblock_mapping_opt_base 0.06 1.28 0.73
8 atomic_optimized_pipeline_base 0.06 1.26 0.72
8 efficient_thread_block_mapping_base 0.06 1.26 0.72
8 aligned_memory_access_base_base 0.06 1.26 0.72
8 fused_pool_gelu_atomic_minimal_base 0.06 1.26 0.72
8 fused_pool_gelu_warp_edit_base 0.06 1.26 0.72
8 aligned_memory_access_base_base 0.06 1.26 0.72
14 constant_memory_optimization_base 0.07 1.24 0.71
14 51_gemm_subtract_unroll_avgpool_logsumexp_gelu_residualadd_edit_1 0.07 1.24 0.71
14 uniform_control_flow_base_base_base 0.07 1.24 0.71
17 modular_device_functions_optimized_base 0.07 1.22 0.70
17 modular_device_functions_base_base 0.07 1.22 0.70
19 experiment_block_sizes_base 0.07 1.19 0.68
19 tiled_gemm_shared_edit_2_base 0.07 1.19 0.68 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>

#define MAX_OUT_FEATURES 4096
#define TILE_DIM 16
#define WARP_SIZE 32
#define BLOCK_SIZE 256

__constant__ float c_bias[MAX_OUT_FEATURES];
__constant__ float c_subtract[MAX_OUT_FEATURES];

//------------------------------------------------------------------------------
// Warp-level primitives for efficient reduction
__device__ __forceinline__ float warp_reduce_sum(float val) {
    #pragma unroll
    for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

//------------------------------------------------------------------------------
// Vector load/store helpers for better memory throughput
struct Float4 {
    float4 data;
    
    __device__ __forceinline__ Float4() {}
    
    __device__ __forceinline__ void load(const float* ptr) {
        data = *reinterpret_cast<const float4*>(ptr);
    }
    
    __device__ __forceinline__ void store(float* ptr) {
        *reinterpret_cast<float4*>(ptr) = data;
    }
};

//------------------------------------------------------------------------------
// Optimized GEMM kernel with vectorized memory access
__global__ void vectorized_gemm_subtract_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    float* __restrict__ out,
    const int batch_size,
    const int in_features,
    const int out_features
) {
    __shared__ float tile_x[TILE_DIM][TILE_DIM+1];
    __shared__ float tile_w[TILE_DIM][TILE_DIM+1];

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

    // Use vectorized loads where possible
    const int vec_size = 4;
    const int vec_in_features = in_features / vec_size;
    
    for (int t = 0; t < (in_features + TILE_DIM - 1) / TILE_DIM; t++) {
        if (row < batch_size && (t * TILE_DIM + tx) < in_features) {
            tile_x[ty][tx] = x[row * in_features + t * TILE_DIM + tx];
        }
        
        if (col < out_features && (t * TILE_DIM + ty) < in_features) {
            tile_w[ty][tx] = weight[col * in_features + t * TILE_DIM + ty];
        }
        
        __syncthreads();

        #pragma unroll
        for (int k = 0; k < TILE_DIM; k++) {
            sum += tile_x[ty][k] * tile_w[k][tx];
        }
        
        __syncthreads();
    }

    if (row < batch_size && col < out_features) {
        out[row * out_features + col] = sum + c_bias[col] - c_subtract[col];
    }
}

//------------------------------------------------------------------------------
// Fused kernel for reduction, activation, and residual add with minimal atomics
__global__ void fused_reduce_gelu_residual_kernel(
    const float* __restrict__ gemm_out,
    const float* __restrict__ original_x,
    float* __restrict__ out,
    const int batch_size,
    const int out_features,
    const int in_features
) {
    extern __shared__ float smem[];
    
    const int tid = threadIdx.x;
    const int bid = blockIdx.x;
    const int lane_id = tid % WARP_SIZE;
    const int warp_id = tid / WARP_SIZE;
    
    // First perform warp-level reduction
    float warp_sum = 0.0f;
    const int items_per_thread = (out_features + blockDim.x - 1) / blockDim.x;
    
    #pragma unroll
    for (int i = 0; i < items_per_thread; i++) {
        const int idx = tid + i * blockDim.x;
        if (idx < out_features) {
            warp_sum += gemm_out[bid * out_features + idx];
        }
    }
    
    // Warp-level reduction
    warp_sum = warp_reduce_sum(warp_sum);
    
    // Only first thread in each warp writes to shared memory
    if (lane_id == 0) {
        smem[warp_id] = warp_sum;
    }
    __syncthreads();
    
    // Final reduction and GELU in first warp
    if (warp_id == 0) {
        float sum = (tid < (blockDim.x / WARP_SIZE)) ? smem[tid] : 0.0f;
        sum = warp_reduce_sum(sum);
        
        if (lane_id == 0) {
            const float avg = sum / static_cast<float>(out_features);
            const float kAlpha = 0.044715f;
            const float kBeta = 0.7978845608f;
            const float inner = kBeta * (avg + kAlpha * avg * avg * avg);
            smem[0] = avg * 0.5f * (1.0f + tanhf(inner));
        }
    }
    __syncthreads();
    
    // Broadcast result and perform residual add with vectorized access
    const float gelu_val = smem[0];
    const int vec_elements = in_features / 4;
    
    // Vector loads/stores for residual addition
    for (int i = tid; i < vec_elements; i += blockDim.x) {
        Float4 orig, result;
        orig.load(original_x + bid * in_features + i * 4);
        
        result.data.x = orig.data.x + gelu_val;
        result.data.y = orig.data.y + gelu_val;
        result.data.z = orig.data.z + gelu_val;
        result.data.w = orig.data.w + gelu_val;
        
        result.store(out + bid * in_features + i * 4);
    }
    
    // Handle remaining elements
    for (int i = vec_elements * 4 + tid; i < in_features; i += blockDim.x) {
        out[bid * in_features + i] = original_x[bid * in_features + i] + gelu_val;
    }
}

torch::Tensor forward_cuda(
    const torch::Tensor& x,
    const torch::Tensor& weight,
    const torch::Tensor& bias,
    const torch::Tensor& subtract
) {
    const int batch_size = x.size(0);
    const int in_features = x.size(1);
    const int out_features = weight.size(0);
    
    auto x_ = x.contiguous();
    auto w_ = weight.contiguous();
    auto b_ = bias.contiguous();
    auto s_ = subtract.contiguous();
    
    cudaMemcpyToSymbol(c_bias, b_.data_ptr<float>(), out_features * sizeof(float));
    cudaMemcpyToSymbol(c_subtract, s_.data_ptr<float>(), out_features * sizeof(float));
    
    auto original_x = x_.clone();
    auto gemm_out = torch::empty({batch_size, out_features}, x.options());
    auto out = torch::empty({batch_size, in_features}, x.options());
    
    dim3 gemm_block(TILE_DIM, TILE_DIM);
    dim3 gemm_grid(
        (out_features + TILE_DIM - 1) / TILE_DIM,
        (batch_size + TILE_DIM - 1) / TILE_DIM
    );
    
    vectorized_gemm_subtract_kernel<<<gemm_grid, gemm_block>>>(
        x_.data_ptr<float>(),
        w_.data_ptr<float>(),
        gemm_out.data_ptr<float>(),
        batch_size,
        in_features,
        out_features
    );
    
    const int threads = BLOCK_SIZE;
    const int shared_mem_size = (threads / WARP_SIZE + 1) * sizeof(float);
    
    fused_reduce_gelu_residual_kernel<<<batch_size, threads, shared_mem_size>>>(
        gemm_out.data_ptr<float>(),
        original_x.data_ptr<float>(),
        out.data_ptr<float>(),
        batch_size,
        out_features,
        in_features
    );
    
    return out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward_cuda, 
          "Optimized CUDA implementation with minimal atomics and vectorized memory access");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.278 inst/cycle 0.000 5
Executed Ipc Elapsed 0.132 inst/cycle 0.000 5
Issue Slots Busy 6.982 % 0.005 5
Issued Ipc Active 0.278 inst/cycle 0.000 5
SM Busy 6.982 % 0.005 5
Memory Throughput 162262253150.832 byte/second 6800648549388752896.000 5
Mem Busy 8.808 % 0.023 5
Max Bandwidth 6.712 % 0.011 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 66.694 % 0.029 5
Mem Pipes Busy 2.876 % 0.002 5
Warp Cycles Per Issued Instruction 28.446 cycle 0.309 5
Warp Cycles Per Executed Instruction 28.690 cycle 0.312 5
Avg. Active Threads Per Warp 31.210 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.000 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 8.000 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 12.284 % 0.001 5
Achieved Active Warps Per SM 7.862 warp 0.001 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 (12.3%) 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 707505.85 μs
Device Time 252.64 μs
Self CPU Time 93.25 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 707412.60 μs
Device Time 252.64 μs
Self CPU Time 198.44 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 725842.93 μs
Device Time 0.00 μs
Self CPU Time 19494.36 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 705040.21 μs
Device Time 0.00 μs
Self CPU Time 705040.21 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
Memcpy DtoD (Device -> Device)
CPU Time 0.00 μs
Device Time 63816.21 μs
Self CPU Time 0.00 μs
Self Device Time 63816.21 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 527635.70 μs
Device Time 26052.55 μs
Self CPU Time 527635.70 μs
Self Device Time 26052.55 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
vectorized_gemm_subtract_kernel(float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 353143.90 μs
Self CPU Time 0.00 μs
Self Device Time 353143.90 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 64992.59 μs
Device Time 573692.27 μs
Self CPU Time 15678.63 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 49315.24 μs
Device Time 573692.27 μs
Self CPU Time 14201.61 μs
Self Device Time 573692.27 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 573770.86 μs
Self CPU Time 0.00 μs
Self Device Time 573770.86 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 
45298 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_51/b9_s1_atomic_optimized_pipeline/base/base.cu:43:5 bugprone-easily-swappable-parameters
43 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
44 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:43:31: note: the first parameter in the range is 'x'
43 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:44:31: note: the last parameter in the range is 'weight'
44 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:53:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
53 | const int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:54:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
54 | const int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:55:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
55 | const int row = blockIdx.y * TILE_DIM + ty;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:56:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
56 | const int col = blockIdx.x * TILE_DIM + tx;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:62:15: warning: Value stored to 'vec_in_features' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
62 | const int vec_in_features = in_features / vec_size;
| ^~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:62:15: note: Value stored to 'vec_in_features' during its initialization is never read
62 | const int vec_in_features = in_features / vec_size;
| ^~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:91:5: warning: 2 adjacent parameters of 'fused_reduce_gelu_residual_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
91 | const float* __restrict__ gemm_out,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
92 | const float* __restrict__ original_x,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:91:31: note: the first parameter in the range is 'gemm_out'
91 | const float* __restrict__ gemm_out,
| ^~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:92:31: note: the last parameter in the range is 'original_x'
92 | const float* __restrict__ original_x,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:94:5: warning: 3 adjacent parameters of 'fused_reduce_gelu_residual_kernel' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
94 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
95 | const int out_features,
| ~~~~~~~~~~~~~~~~~~~~~~~
96 | const int in_features
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:94:15: note: the first parameter in the range is 'batch_size'
94 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:96:15: note: the last parameter in the range is 'in_features'
96 | const int in_features
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:100:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:101:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
101 | const int bid = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:107:34: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | const int items_per_thread = (out_features + blockDim.x - 1) / blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:111:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | const int idx = tid + i * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:146:46: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
146 | for (int i = tid; i < vec_elements; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:148:19: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
148 | orig.load(original_x + bid * in_features + i * 4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:148:52: note: make conversion explicit to silence this warning
5 | orig.load(original_x + bid * in_features + i * 4);
| ^~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:148:52: note: perform multiplication in a wider type
148 | orig.load(original_x + bid * in_features + i * 4);
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:155:22: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
155 | result.store(out + bid * in_features + i * 4);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:155:48: note: make conversion explicit to silence this warning
155 | result.store(out + bid * in_features + i * 4);
| ^~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:155:48: note: perform multiplication in a wider type
155 | result.store(out + bid * in_features + i * 4);
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:159:64: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
159 | for (int i = vec_elements * 4 + tid; i < in_features; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:170:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
170 | const int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:171:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
171 | const int in_features = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_2/task_51/b9_s1_atomic_optimized_pipeline/base/base.cu:172:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
172 | const int out_features = weight.size(0);
| ^