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40_LayerNorm • layernorm_unrolled_base_base

Level 1 • Task 40
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, eps: float = 1e-5
) -> torch.Tensor:
    """
    Functional implementation of LayerNorm.

    Args:
        x (torch.Tensor): Input tensor of shape (*, normalized_shape).
        weight (torch.Tensor): Weight tensor of shape (normalized_shape).
        bias (torch.Tensor): Bias tensor of shape (normalized_shape).
        eps (float): Epsilon parameter for numerical stability.

    Returns:
        torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
    """
    # Get the normalized shape from the weight tensor
    normalized_shape = tuple(x.shape[-len(weight.shape) :])
    return F.layer_norm(
        x, normalized_shape=normalized_shape, weight=weight, bias=bias, eps=eps
    )


class Model(nn.Module):
    """
    Simple model that performs Layer Normalization.
    """

    def __init__(self, normalized_shape: tuple):
        """
        Initializes the LayerNorm layer parameters.

        Args:
            normalized_shape (tuple): Shape of the input tensor to be normalized.
        """
        super(Model, self).__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        """
        Applies Layer Normalization to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (*, normalized_shape).
            fn: Function to apply (defaults to module_fn)

        Returns:
            torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
        """
        return fn(x, self.weight, self.bias)


batch_size = 16
features = 64
dim1 = 256
dim2 = 256


def get_inputs():
    x = torch.randn(batch_size, features, dim1, dim2)
    return [x]


def get_init_inputs():
    return [(features, dim1, dim2)]

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs Layer Normalization.
    """
    def __init__(self, normalized_shape: tuple):
        """
        Initializes the LayerNorm layer.

        Args:
            normalized_shape (tuple): Shape of the input tensor to be normalized.
        """
        super(Model, self).__init__()
        self.ln = nn.LayerNorm(normalized_shape=normalized_shape)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Layer Normalization to the input tensor.

        Args:
            x (torch.Tensor): Input tensor of shape (*, normalized_shape).

        Returns:
            torch.Tensor: Output tensor with Layer Normalization applied, same shape as input.
        """
        return self.ln(x)

batch_size = 16
features = 64
dim1 = 256
dim2 = 256

def get_inputs():
    x = torch.randn(batch_size, features, dim1, dim2)
    return [x]

def get_init_inputs():
    return [(features, dim1, dim2)]
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Kernel Information

Operation Name 40_LayerNorm
Level ID 1
Task ID 40
Kernel Name layernorm_unrolled_base_base
CUDA Speedup (Native) 7.991x
CUDA Speedup (Compile) 0.652x
CUDA Runtime 1.014 ms
PyTorch Runtime (Native) 8.103 ms
PyTorch Runtime (Compile) 0.661 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 1, Task 40 • 40_LayerNorm)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_layernorm_streamed_base 0.94 8.60 0.70
🥈 layernorm_unrolled_base_base 1.01 7.99 0.65
🥉 layernorm_forward_optimized_base 1.02 7.94 0.65
4 layernorm_modular_base_base 1.02 7.91 0.65
5 layernorm_aligned_base_base 1.03 7.90 0.64
6 layernorm_forward_optimized_base 1.03 7.89 0.64
7 optimized_layernorm_2d_base 1.12 7.23 0.59
8 layernorm_vector8_aligned_base_base 1.24 6.53 0.53
9 optimized_layernorm_unrolled_base 1.63 4.96 0.40
10 optimized_layernorm_unrolled_edit_1 1.64 4.94 0.40
11 layernorm_ldg_optimized_base_edit_1 2.22 3.65 0.30
12 40_LayerNorm_stride_loops_edit_1 2.24 3.62 0.30
13 layernorm_hybrid_optimized_base 2.24 3.62 0.29
14 layernorm_ldg_optimized_base_base 2.24 3.61 0.29
15 layernorm_coalesced_base 3.26 2.49 0.20
16 layernorm_2d_indexing_base 3.26 2.48 0.20
17 optimized_layernorm_base 3.27 2.48 0.20
18 warp_shfl_layernorm_base 3.28 2.47 0.20
19 layernorm_forward_opt_base 3.28 2.47 0.20
20 layernorm_uniform_control_flow_base 3.28 2.47 0.20 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <ATen/AccumulateType.h>

template <typename scalar_t>
__global__ void layernorm_forward_kernel_unrolled(
    const scalar_t* __restrict__ input,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    const float eps,
    scalar_t* __restrict__ output,
    const int normalized_size) {

  const int instance_idx = blockIdx.x;
  const int tid = threadIdx.x;
  
  const scalar_t* __restrict__ in_ptr = input + instance_idx * normalized_size;
  scalar_t* __restrict__ out_ptr = output + instance_idx * normalized_size;

  using accscalar_t = at::acc_type<scalar_t, true>;
  
  extern __shared__ char smem[];
  accscalar_t* s_sum = reinterpret_cast<accscalar_t*>(smem);
  accscalar_t* s_sum_sq = s_sum + blockDim.x;

  accscalar_t local_sum = 0;
  accscalar_t local_sum_sq = 0;

  const int vector_size = 4;
  const int aligned_size = normalized_size / vector_size * vector_size;
  
  // Main vectorized loop with manual unrolling for better instruction scheduling
  #pragma unroll 4
  for (int i = tid * vector_size; i < aligned_size; i += blockDim.x * vector_size) {
    float4 in_vec = *reinterpret_cast<const float4*>(&in_ptr[i]);
    
    // Explicitly separate operations for better instruction-level parallelism
    accscalar_t val1 = static_cast<accscalar_t>(in_vec.x);
    accscalar_t val2 = static_cast<accscalar_t>(in_vec.y);
    accscalar_t val3 = static_cast<accscalar_t>(in_vec.z);
    accscalar_t val4 = static_cast<accscalar_t>(in_vec.w);
    
    // Compute sums and squares in parallel
    accscalar_t sum1 = val1;
    accscalar_t sum2 = val2;
    accscalar_t sum3 = val3;
    accscalar_t sum4 = val4;
    
    accscalar_t sq1 = val1 * val1;
    accscalar_t sq2 = val2 * val2;
    accscalar_t sq3 = val3 * val3;
    accscalar_t sq4 = val4 * val4;
    
    local_sum += sum1 + sum2 + sum3 + sum4;
    local_sum_sq += sq1 + sq2 + sq3 + sq4;
  }

  // Handle remaining elements
  #pragma unroll
  for (int i = aligned_size + tid; i < normalized_size; i += blockDim.x) {
    accscalar_t val = static_cast<accscalar_t>(__ldg(&in_ptr[i]));
    local_sum += val;
    local_sum_sq += val * val;
  }

  s_sum[tid] = local_sum;
  s_sum_sq[tid] = local_sum_sq;
  __syncthreads();

  // Manually unrolled reduction for better performance
  if (blockDim.x >= 1024) { if (tid < 512) { s_sum[tid] += s_sum[tid + 512]; s_sum_sq[tid] += s_sum_sq[tid + 512]; } __syncthreads(); }
  if (blockDim.x >= 512) { if (tid < 256) { s_sum[tid] += s_sum[tid + 256]; s_sum_sq[tid] += s_sum_sq[tid + 256]; } __syncthreads(); }
  if (blockDim.x >= 256) { if (tid < 128) { s_sum[tid] += s_sum[tid + 128]; s_sum_sq[tid] += s_sum_sq[tid + 128]; } __syncthreads(); }
  if (blockDim.x >= 128) { if (tid < 64) { s_sum[tid] += s_sum[tid + 64]; s_sum_sq[tid] += s_sum_sq[tid + 64]; } __syncthreads(); }
  
  // Final warp reduction unrolled
  if (tid < 32) {
    // Warp-synchronized implicit
    volatile accscalar_t* vsum = s_sum;
    volatile accscalar_t* vsum_sq = s_sum_sq;
    if (blockDim.x >= 64) { vsum[tid] += vsum[tid + 32]; vsum_sq[tid] += vsum_sq[tid + 32]; }
    if (blockDim.x >= 32) { vsum[tid] += vsum[tid + 16]; vsum_sq[tid] += vsum_sq[tid + 16]; }
    if (blockDim.x >= 16) { vsum[tid] += vsum[tid + 8]; vsum_sq[tid] += vsum_sq[tid + 8]; }
    if (blockDim.x >= 8) { vsum[tid] += vsum[tid + 4]; vsum_sq[tid] += vsum_sq[tid + 4]; }
    if (blockDim.x >= 4) { vsum[tid] += vsum[tid + 2]; vsum_sq[tid] += vsum_sq[tid + 2]; }
    if (blockDim.x >= 2) { vsum[tid] += vsum[tid + 1]; vsum_sq[tid] += vsum_sq[tid + 1]; }
  }

  __shared__ accscalar_t mean;
  __shared__ accscalar_t inv_std;
  if (tid == 0) {
    mean = s_sum[0] / static_cast<accscalar_t>(normalized_size);
    accscalar_t var = s_sum_sq[0] / static_cast<accscalar_t>(normalized_size) - mean * mean;
    inv_std = static_cast<accscalar_t>(1) / sqrt(var + static_cast<accscalar_t>(eps));
  }
  __syncthreads();

  // Process output with unrolled vectorized operations
  #pragma unroll 4
  for (int i = tid * vector_size; i < aligned_size; i += blockDim.x * vector_size) {
    float4 in_vec = *reinterpret_cast<const float4*>(&in_ptr[i]);
    float4 w_vec = *reinterpret_cast<const float4*>(&weight[i]);
    float4 b_vec = *reinterpret_cast<const float4*>(&bias[i]);
    
    float4 out_vec;
    
    // Unrolled normalization computations
    accscalar_t norm_val1 = (static_cast<accscalar_t>(in_vec.x) - mean) * inv_std;
    accscalar_t norm_val2 = (static_cast<accscalar_t>(in_vec.y) - mean) * inv_std;
    accscalar_t norm_val3 = (static_cast<accscalar_t>(in_vec.z) - mean) * inv_std;
    accscalar_t norm_val4 = (static_cast<accscalar_t>(in_vec.w) - mean) * inv_std;
    
    out_vec.x = static_cast<scalar_t>(norm_val1 * static_cast<accscalar_t>(w_vec.x) + static_cast<accscalar_t>(b_vec.x));
    out_vec.y = static_cast<scalar_t>(norm_val2 * static_cast<accscalar_t>(w_vec.y) + static_cast<accscalar_t>(b_vec.y));
    out_vec.z = static_cast<scalar_t>(norm_val3 * static_cast<accscalar_t>(w_vec.z) + static_cast<accscalar_t>(b_vec.z));
    out_vec.w = static_cast<scalar_t>(norm_val4 * static_cast<accscalar_t>(w_vec.w) + static_cast<accscalar_t>(b_vec.w));
    
    *reinterpret_cast<float4*>(&out_ptr[i]) = out_vec;
  }

  // Handle remaining elements
  #pragma unroll
  for (int i = aligned_size + tid; i < normalized_size; i += blockDim.x) {
    scalar_t in_val = __ldg(&in_ptr[i]);
    scalar_t w_val = __ldg(&weight[i]);
    scalar_t b_val = __ldg(&bias[i]);
    accscalar_t norm_val = (static_cast<accscalar_t>(in_val) - mean) * inv_std;
    out_ptr[i] = static_cast<scalar_t>(norm_val * static_cast<accscalar_t>(w_val) + static_cast<accscalar_t>(b_val));
  }
}

torch::Tensor layernorm_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double eps = 1e-5) {
  auto output = torch::empty_like(x);
  int normalized_size = weight.numel();
  int outer_size = x.numel() / normalized_size;

  // Ensure thread count is multiple of warp size (32)
  int threads = std::min(((normalized_size + 31) / 32) * 32, 1024);
  int blocks = outer_size;

  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_forward_cuda", ([&] {
    using accscalar_t = at::acc_type<scalar_t, true>;
    int shared_size = threads * 2 * sizeof(accscalar_t);
    layernorm_forward_kernel_unrolled<scalar_t><<<blocks, threads, shared_size>>>(
        x.data_ptr<scalar_t>(),
        weight.data_ptr<scalar_t>(),
        bias.data_ptr<scalar_t>(),
        static_cast<float>(eps),
        output.data_ptr<scalar_t>(),
        normalized_size);
  }));

  return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &layernorm_forward, "LayerNorm forward (CUDA) with unrolled loops",
        py::arg("x"), py::arg("weight"), py::arg("bias"), py::arg("eps") = 1e-5);
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.860 inst/cycle 0.000 5
Executed Ipc Elapsed 0.100 inst/cycle 0.000 5
Issue Slots Busy 21.518 % 0.000 5
Issued Ipc Active 0.860 inst/cycle 0.000 5
SM Busy 21.518 % 0.000 5
Memory Throughput 764273163868.376 byte/second 82237251581541616.000 5
Mem Busy 14.702 % 0.000 5
Max Bandwidth 22.798 % 0.000 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 47.188 % 0.101 5
Mem Pipes Busy 1.160 % 0.000 5
Warp Cycles Per Issued Instruction 37.090 cycle 0.000 5
Warp Cycles Per Executed Instruction 37.094 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 30.910 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 1.000 block 0.000 5
Block Limit Shared Mem 1.000 block 0.000 5
Block Limit Warps 2.000 block 0.000 5
Theoretical Active Warps per SM 32.000 warp 0.000 5
Theoretical Occupancy 50.000 % 0.000 5
Achieved Occupancy 49.890 % 0.000 5
Achieved Active Warps Per SM 31.930 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 (50.0%) is limited by the number of required registers. This kernel's theoretical occupancy (50.0%) is limited by the required amount of shared memory. 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 336270.01 μs
Device Time 32566.14 μs
Self CPU Time 57.64 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 336212.37 μs
Device Time 32566.14 μs
Self CPU Time 130.98 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 3927847.35 μs
Device Time 321146.35 μs
Self CPU Time 50243.17 μs
Self Device Time 321146.35 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 3912667.62 μs
Device Time 321146.35 μs
Self CPU Time 8323.96 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
cudaLaunchKernel
CPU Time 4110111.13 μs
Device Time 10803.78 μs
Self CPU Time 4110111.13 μs
Self Device Time 10803.78 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void layernorm_forward_kernel_unrolled<float>(float const*, float const*, float const*, float, float*, int)
CPU Time 0.00 μs
Device Time 4188414.25 μs
Self CPU Time 0.00 μs
Self Device Time 4188414.25 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 321146.35 μs
Self CPU Time 0.00 μs
Self Device Time 321146.35 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 
45293 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/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:9:5 bugprone-easily-swappable-parameters
9 | const scalar_t* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:9:34: note: the first parameter in the range is 'input'
9 | const scalar_t* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:11:34: note: the last parameter in the range is 'bias'
11 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:16:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | const int instance_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:17:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:36:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
36 | for (int i = tid * vector_size; i < aligned_size; i += blockDim.x * vector_size) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:62:62: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
62 | for (int i = aligned_size + tid; i < normalized_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:102:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | for (int i = tid * vector_size; i < aligned_size; i += blockDim.x * vector_size) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:125:62: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | for (int i = aligned_size + tid; i < normalized_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:136:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | int normalized_size = weight.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:137:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | int outer_size = x.numel() / normalized_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:143:3: 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]
143 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_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/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:145:23: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
145 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:145:23: note: make conversion explicit to silence this warning
145 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
| static_cast<unsigned long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: 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:233:44: 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:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b9_s3_layernorm_unrolled_base/base/base.cu:145:23: note: perform multiplication in a wider type
145 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
| static_cast<long>(
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:66: 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:233:44: 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:74:56: note: expanded from macro 'AT_DISPATCH_CASE'
74 | AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:70:12: note: expanded from macro 'AT_PRIVATE_CASE_TYPE_USING_HINT'
70 | return __VA_ARGS__(); \
| ^~~~~~~~~~~
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:221:7: note: expanded from macro 'AT_DISPATCH_SWITCH'
221 | __VA_ARGS__ \
| ^~~~~~~~~~~