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40_LayerNorm • 40_LayerNorm_stride_loops_edit_1

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 40_LayerNorm_stride_loops_edit_1
CUDA Speedup (Native) 3.621x
CUDA Speedup (Compile) 0.295x
CUDA Runtime 2.238 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 0.00
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>

// CUDA kernel for Layer Normalization forward with stride loops.
template <typename scalar_t>
__global__ void layernorm_forward_kernel_stride(
    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) {

  // Each block processes one instance along the outer dimension.
  int instance_idx = blockIdx.x;
  int tid = threadIdx.x;

  const scalar_t* in_ptr = input + instance_idx * normalized_size;
  scalar_t* out_ptr = output + instance_idx * normalized_size;

  // Use the accumulation type for better precision.
  using accscalar_t = at::acc_type<scalar_t, true>;

  // Allocate shared memory for partial sums, partial sums of squares, and input cache
  extern __shared__ char smem[];
  accscalar_t* s_sum    = reinterpret_cast<accscalar_t*>(smem);
  accscalar_t* s_sum_sq = s_sum + blockDim.x;
  
  // Initialize accumulators
  accscalar_t local_sum = 0;
  accscalar_t local_sum_sq = 0;
  
  // Process elements in chunks to ensure coalesced memory access
  const int CHUNK_SIZE = 4;  // Process 4 elements per thread at a time
  const int num_chunks = (normalized_size + blockDim.x * CHUNK_SIZE - 1) / (blockDim.x * CHUNK_SIZE);
  
  for (int chunk = 0; chunk < num_chunks; chunk++) {
    const int chunk_start = chunk * blockDim.x * CHUNK_SIZE + tid;
    
    // Load and process CHUNK_SIZE elements per thread
    #pragma unroll
    for (int i = 0; i < CHUNK_SIZE; i++) {
      const int idx = chunk_start + i * blockDim.x;
      if (idx < normalized_size) {
        accscalar_t val = static_cast<accscalar_t>(in_ptr[idx]);
        local_sum += val;
        local_sum_sq += val * val;
      }
    }
  }
  
  s_sum[tid] = local_sum;
  s_sum_sq[tid] = local_sum_sq;
  __syncthreads();

  // Parallel reduction to compute the total sum and sum of squares.
  for (int stride = blockDim.x / 2; stride > 0; stride /= 2) {
    if (tid < stride) {
      s_sum[tid]    += s_sum[tid + stride];
      s_sum_sq[tid] += s_sum_sq[tid + stride];
    }
    __syncthreads();
  }

  // Compute mean and inverse standard deviation.
  __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();

  // Normalize the input and apply the affine transformation.
  for (int i = tid; i < normalized_size; i += blockDim.x) {
    accscalar_t val = static_cast<accscalar_t>(in_ptr[i]);
    accscalar_t norm_val = (val - mean) * inv_std;
    out_ptr[i] = static_cast<scalar_t>(norm_val * static_cast<accscalar_t>(weight[i]) +
                                         static_cast<accscalar_t>(bias[i]));
  }
}

// C++ interface. Note: eps has a default value of 1e-5.
torch::Tensor layernorm_forward_stride(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double eps = 1e-5) {
  // Create an output tensor with the same shape and options as x.
  auto output = torch::empty_like(x);

  // Determine normalized dimension size (the product of weight's dimensions)
  int normalized_size = weight.numel();
  int outer_size = x.numel() / normalized_size;

  // Choose number of threads (cap at 1024)
  int threads = (normalized_size < 1024) ? normalized_size : 1024;
  int blocks = outer_size;

  AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_forward_cuda_stride", ([&] {
    using accscalar_t = at::acc_type<scalar_t, true>;
    int shared_size = threads * 2 * sizeof(accscalar_t);
    layernorm_forward_kernel_stride<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) {
  // Bind the 'forward' function with default eps argument.
  m.def("forward", &layernorm_forward_stride, "LayerNorm forward with stride (CUDA)",
        py::arg("x"), py::arg("weight"), py::arg("bias"), py::arg("eps") = 1e-5);
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.080 inst/cycle 0.000 5
Executed Ipc Elapsed 0.130 inst/cycle 0.000 5
Issue Slots Busy 26.948 % 0.001 5
Issued Ipc Active 1.080 inst/cycle 0.000 5
SM Busy 26.948 % 0.001 5
Memory Throughput 348013044779.250 byte/second 26307305051882776.000 5
Mem Busy 6.546 % 0.000 5
Max Bandwidth 10.382 % 0.000 5
L1/TEX Hit Rate 0.000 % 0.000 5
L2 Hit Rate 45.104 % 0.004 5
Mem Pipes Busy 2.110 % 0.000 5
Warp Cycles Per Issued Instruction 29.620 cycle 0.001 5
Warp Cycles Per Executed Instruction 29.624 cycle 0.001 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 32.000 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 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 49.904 % 0.000 5
Achieved Active Warps Per SM 31.938 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (26.8%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
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::randn
CPU Time 307970.73 μs
Device Time 0.00 μs
Self CPU Time 117.56 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
CPU Time 296702.93 μs
Device Time 31470.53 μs
Self CPU Time 91.71 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 5063013.19 μs
Device Time 195389.01 μs
Self CPU Time 50561.02 μs
Self Device Time 195389.01 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 5044081.85 μs
Device Time 195389.01 μs
Self CPU Time 5198.50 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 5147735.21 μs
Device Time 6451.93 μs
Self CPU Time 5147735.21 μs
Self Device Time 6451.93 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_stride<float>(float const*, float const*, float const*, float, float*, int)
CPU Time 0.00 μs
Device Time 5629719.24 μs
Self CPU Time 0.00 μs
Self Device Time 5629719.24 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 607929.02 μs
Device Time 0.00 μs
Self CPU Time 607929.02 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 195389.01 μs
Self CPU Time 0.00 μs
Self Device Time 195389.01 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 
45292 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/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:10:5 bugprone-easily-swappable-parameters
10 | const scalar_t* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const scalar_t* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.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/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:11:34: note: the last parameter in the range is 'weight'
11 | const scalar_t* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:18:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int instance_idx = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:19:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
19 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:38:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | const int num_chunks = (normalized_size + blockDim.x * CHUNK_SIZE - 1) / (blockDim.x * CHUNK_SIZE);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:41:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
41 | const int chunk_start = chunk * blockDim.x * CHUNK_SIZE + tid;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:46:23: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | const int idx = chunk_start + i * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:60:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
60 | for (int stride = blockDim.x / 2; stride > 0; stride /= 2) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:79:47: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
79 | for (int i = tid; i < normalized_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:93:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
93 | int normalized_size = weight.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:94:20: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
94 | int outer_size = x.numel() / normalized_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:100:3: 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]
100 | AT_DISPATCH_FLOATING_TYPES(x.scalar_type(), "layernorm_forward_cuda_stride", ([&] {
| ^
/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/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:102:23: warning: performing an implicit widening conversion to type 'unsigned long' of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
102 | int shared_size = threads * 2 * sizeof(accscalar_t);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:102:23: note: make conversion explicit to silence this warning
102 | 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/20250207_optimize_b5_s4_e1_sweep/level_1/task_40/b1_s0_40_LayerNorm_stride_loops/edit_1/edit_1.cu:102:23: note: perform multiplication in a wider type
102 | 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__ \
| ^~~~~~~~~~~