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40_LayerNorm • layernorm_forward_optimized_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_forward_optimized_base
CUDA Speedup (Native) 7.936x
CUDA Speedup (Compile) 0.647x
CUDA Runtime 1.021 ms
PyTorch Runtime (Native) 8.103 ms
PyTorch Runtime (Compile) 0.661 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
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>

// Optimized CUDA kernel for LayerNorm forward combining memory coalescing and vectorized operations.
template <typename scalar_t>
__global__ void layernorm_forward_kernel_optimized(
    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;
  
  // Align pointers to 128-bit boundary
  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>;
  
  // Shared memory
  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;

  // Calculate number of float4 elements we can process
  const int vector_size = 4;
  const int aligned_size = normalized_size / vector_size * vector_size;
  
  // Process elements in chunks of 4 (float4) for coalesced and vectorized access
  for (int i = tid * vector_size; i < aligned_size; i += blockDim.x * vector_size) {
    float4 in_vec = *reinterpret_cast<const float4*>(&in_ptr[i]);
    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);
    local_sum += val1 + val2 + val3 + val4;
    local_sum_sq += val1 * val1 + val2 * val2 + val3 * val3 + val4 * val4;
  }

  // Handle remaining elements
  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();

  // Parallel reduction
  for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
    if (tid < stride) {
      s_sum[tid] += s_sum[tid + stride];
      s_sum_sq[tid] += s_sum_sq[tid + stride];
    }
    __syncthreads();
  }

  // Compute statistics
  __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 in vectors of 4 when possible
  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;
    out_vec.x = static_cast<scalar_t>((static_cast<accscalar_t>(in_vec.x) - mean) * inv_std * 
                                     static_cast<accscalar_t>(w_vec.x) + static_cast<accscalar_t>(b_vec.x));
    out_vec.y = static_cast<scalar_t>((static_cast<accscalar_t>(in_vec.y) - mean) * inv_std * 
                                     static_cast<accscalar_t>(w_vec.y) + static_cast<accscalar_t>(b_vec.y));
    out_vec.z = static_cast<scalar_t>((static_cast<accscalar_t>(in_vec.z) - mean) * inv_std * 
                                     static_cast<accscalar_t>(w_vec.z) + static_cast<accscalar_t>(b_vec.z));
    out_vec.w = static_cast<scalar_t>((static_cast<accscalar_t>(in_vec.w) - mean) * inv_std * 
                                     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
  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));
  }
}

// C++ interface function for the LayerNorm forward pass.
torch::Tensor layernorm_forward_optimized(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_optimized<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_optimized, "LayerNorm forward (CUDA) optimized",
        py::arg("x"), py::arg("weight"), py::arg("bias"), py::arg("eps") = 1e-5);
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.880 inst/cycle 0.000 5
Executed Ipc Elapsed 0.110 inst/cycle 0.000 5
Issue Slots Busy 21.986 % 0.000 5
Issued Ipc Active 0.880 inst/cycle 0.000 5
SM Busy 21.986 % 0.000 5
Memory Throughput 759717562238.766 byte/second 148347964913555936.000 5
Mem Busy 14.650 % 0.000 5
Max Bandwidth 22.662 % 0.000 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 47.522 % 0.021 5
Mem Pipes Busy 1.150 % 0.000 5
Warp Cycles Per Issued Instruction 36.314 cycle 0.000 5
Warp Cycles Per Executed Instruction 36.314 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.990 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 2.000 block 0.000 5
Block Limit Shared Mem 3.000 block 0.000 5
Block Limit Warps 2.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 49.894 % 0.000 5
Achieved Active Warps Per SM 31.932 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 (49.9%) 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 503012.35 μs
Device Time 39638.39 μs
Self CPU Time 66.65 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 502945.70 μs
Device Time 39638.39 μs
Self CPU Time 141.79 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 3335650.75 μs
Device Time 266091.72 μs
Self CPU Time 132977.83 μs
Self Device Time 266091.72 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 3251627.08 μs
Device Time 266091.72 μs
Self CPU Time 7167.11 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 3396694.36 μs
Device Time 8923.65 μs
Self CPU Time 3396694.36 μs
Self Device Time 8923.65 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_optimized<float>(float const*, float const*, float const*, float, float*, int)
CPU Time 0.00 μs
Device Time 3499036.66 μs
Self CPU Time 0.00 μs
Self Device Time 3499036.66 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void 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 266091.72 μs
Self CPU Time 0.00 μs
Self Device Time 266091.72 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 
45294 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/b8_s3_layernorm_forward_optimized/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const scalar_t* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
12 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:10:34: note: the first parameter in the range is 'input'
10 | const scalar_t* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:12:34: note: the last parameter in the range is 'bias'
12 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:17:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | const int instance_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:18:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | const int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:39:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:50:62: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
50 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:61:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
61 | for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:80:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
80 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:99:62: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:111:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int normalized_size = weight.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:112:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | int outer_size = x.numel() / normalized_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250201_optimize_b10_s4_e0_sweep/level_1/task_40/b8_s3_layernorm_forward_optimized/base/base.cu:118:3: 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]
118 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:120:23: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
120 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:120:23: note: make conversion explicit to silence this warning
120 | 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/b8_s3_layernorm_forward_optimized/base/base.cu:120:23: note: perform multiplication in a wider type
120 | 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__ \
| ^~~~~~~~~~~