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2_ShallowWideMLP • coalesced_vectorized_base

Level 3 • Task 2
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor, weights: nn.ParameterList, biases: nn.ParameterList
) -> torch.Tensor:
    """
    Implements a shallow wide multi-layer perceptron with ReLU activation.

    Args:
        x (torch.Tensor): The input tensor, shape (batch_size, input_size)
        weights (nn.ParameterList): A list of weight tensors for each linear layer
        biases (nn.ParameterList): A list of bias tensors for each linear layer

    Returns:
        torch.Tensor: The output tensor, shape (batch_size, output_size)
    """
    for weight, bias in zip(weights[:-1], biases[:-1]):
        x = F.linear(x, weight, bias)
        x = F.relu(x)
    x = F.linear(x, weights[-1], biases[-1])
    return x


class Model(nn.Module):
    def __init__(self, input_size, hidden_layer_sizes, output_size):
        """
        :param input_size: The number of input features
        :param hidden_layer_sizes: A list of ints containing the sizes of each hidden layer
        :param output_size: The number of output features
        """
        super(Model, self).__init__()

        self.weights = nn.ParameterList()
        self.biases = nn.ParameterList()

        current_input_size = input_size
        for hidden_size in hidden_layer_sizes:
            linear = nn.Linear(current_input_size, hidden_size)
            self.weights.append(nn.Parameter(linear.weight.data.clone()))
            self.biases.append(nn.Parameter(linear.bias.data.clone()))
            current_input_size = hidden_size

        linear = nn.Linear(current_input_size, output_size)
        self.weights.append(nn.Parameter(linear.weight.data.clone()))
        self.biases.append(nn.Parameter(linear.bias.data.clone()))

    def forward(self, x, fn=module_fn):
        return fn(x, self.weights, self.biases)


# Test code
batch_size = 1
input_size = 1000
hidden_layer_sizes = [2000, 2000]  # Example of deep and narrow layers
output_size = 10


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


def get_init_inputs():
    return [input_size, hidden_layer_sizes, output_size]

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self, input_size, hidden_layer_sizes, output_size):
        """
        :param input_size: The number of input features
        :param hidden_layer_sizes: A list of ints containing the sizes of each hidden layer
        :param output_size: The number of output features
        """
        super(Model, self).__init__()
        
        layers = []
        current_input_size = input_size
        
        for hidden_size in hidden_layer_sizes:
            layers.append(nn.Linear(current_input_size, hidden_size))
            layers.append(nn.ReLU())
            current_input_size = hidden_size
        
        layers.append(nn.Linear(current_input_size, output_size))
        
        self.network = nn.Sequential(*layers)
    
    def forward(self, x):
        """
        :param x: The input tensor, shape (batch_size, input_size)
        :return: The output tensor, shape (batch_size, output_size)
        """
        return self.network(x)

# Test code
batch_size = 1
input_size = 1000
hidden_layer_sizes = [2000, 2000]  # Example of deep and narrow layers
output_size = 10

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

def get_init_inputs():
    return [input_size, hidden_layer_sizes, output_size]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 2_ShallowWideMLP
Level ID 3
Task ID 2
Kernel Name coalesced_vectorized_base
CUDA Speedup (Native) 2.185x
CUDA Speedup (Compile) 4.149x
CUDA Runtime 0.042 ms
PyTorch Runtime (Native) 0.092 ms
PyTorch Runtime (Compile) 0.174 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 3, Task 2 • 2_ShallowWideMLP)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 min_sync_warp_base 0.04 2.29 4.36
🥈 optimized_reduction_warp_v2_base 0.04 2.24 4.25
🥈 efficient_mlp_forward_kernel_base 0.04 2.24 4.25
4 warp_index_optimized_base 0.04 2.18 4.15
4 warp_shfl_optimized_base 0.04 2.18 4.15
4 stride_loop_optimization_base 0.04 2.18 4.15
4 warp_dot_base 0.04 2.18 4.15
4 coalesced_vectorized_base 0.04 2.18 4.15
4 tiled_warp_forward_base 0.04 2.18 4.15
4 uniform_control_flow_base_base 0.04 2.18 4.15
4 warp_dot_optimized_block_base 0.04 2.18 4.15
4 tuned_block_size_reduction_v2_base 0.04 2.18 4.15
4 tuned_block_size_reduction_v2_edit_1 0.04 2.18 4.15
14 warp_uniform_control_flow_base 0.04 2.13 4.05
15 block_reduce_mlp_2d_base 0.04 2.09 3.96
16 warp_coalesced_optimized_block_base 0.05 1.99 3.79
17 reduced_shared_warp_edit_1 0.05 1.91 3.63
18 reduced_shared_warp_base 0.05 1.87 3.56
19 hybrid_mlp_forward_base 0.06 1.61 3.06
19 hybrid_mlp_forward_base 0.06 1.61 3.06 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <type_traits>

// Define warp and block parameters
#define WARP_SIZE 32
#define WARPS_PER_BLOCK 8
#define RELU_BLOCK_SIZE 256

// Kernel to perform MLP forward pass with vectorized loads for improved memory coalescing.
// For float types, we use float4 (i.e., 4 floats per load); for double, we use double2 (2 doubles per load).

template <typename scalar_t>
__global__ void mlp_forward_kernel_coalesced(
    const scalar_t* __restrict__ input,
    const scalar_t* __restrict__ weight,
    const scalar_t* __restrict__ bias,
    scalar_t* __restrict__ output,
    int batch_size,
    int in_features,
    int out_features) {

    int lane = threadIdx.x;           // Lane index within a warp [0, 31]
    int warpId = threadIdx.y;           // Warp index within the block

    int batch_row = blockIdx.x;         // Each block in x-dim handles one batch row
    int neuron = blockIdx.y * WARPS_PER_BLOCK + warpId;  // Each warp computes one output neuron

    if (batch_row >= batch_size || neuron >= out_features) return;

    scalar_t sum = 0;

    // Determine vector width based on scalar size.
    // For float (4 bytes): use 4 (float4) -> 4 floats per load.
    // For double (8 bytes): use 2 (double2) -> 2 doubles per load.
    constexpr int vec_width = (sizeof(scalar_t) == 4 ? 4 : (sizeof(scalar_t) == 8 ? 2 : 1));

    int vec_count = in_features / vec_width;  // Number of complete vectorized loads
    int remainder = in_features % vec_width;  // Remaining elements

    if (vec_width > 1) {
        // Define vectorized type based on scalar_t
        using vec_t = typename std::conditional<sizeof(scalar_t) == 4, float4, double2>::type;

        // Reinterpret input pointers as vectorized pointers.
        const vec_t* input_vec = reinterpret_cast<const vec_t*>(input + batch_row * in_features);
        const vec_t* weight_vec = reinterpret_cast<const vec_t*>(weight + neuron * in_features);

        // Each thread in the warp processes multiple vectorized chunks with stride equal to warp size
        for (int idx = lane; idx < vec_count; idx += WARP_SIZE) {
            vec_t in_val = input_vec[idx];
            vec_t w_val = weight_vec[idx];
            if constexpr (sizeof(scalar_t) == 4) {
                sum += in_val.x * w_val.x + in_val.y * w_val.y + in_val.z * w_val.z + in_val.w * w_val.w;
            } else if constexpr (sizeof(scalar_t) == 8) {
                sum += in_val.x * w_val.x + in_val.y * w_val.y;
            }
        }
    } else {
        // Fallback to scalar loads if vectorization is not applicable
        for (int i = lane; i < in_features; i += WARP_SIZE) {
            sum += input[batch_row * in_features + i] * weight[neuron * in_features + i];
        }
    }

    // Handle remainder elements if any
    int start = vec_count * vec_width;
    for (int i = start + lane; i < in_features; i += WARP_SIZE) {
        sum += input[batch_row * in_features + i] * weight[neuron * in_features + i];
    }

    // Warp-level reduction using shuffle operations
    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
        sum += __shfl_down_sync(0xffffffff, sum, offset);
    }

    // The first thread in the warp writes the result along with bias
    if (lane == 0) {
        output[batch_row * out_features + neuron] = sum + bias[neuron];
    }
}

// ReLU kernel (unchanged, with a stride loop for full coverage)
template <typename scalar_t>
__global__ void relu_kernel_coalesced(scalar_t* __restrict__ data, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int stride = blockDim.x * gridDim.x;
    for (; idx < size; idx += stride) {
        data[idx] = data[idx] > 0 ? data[idx] : static_cast<scalar_t>(0);
    }
}

// Main forward function applying the MLP layer by layer

torch::Tensor mlp_cuda_forward(
    torch::Tensor input,
    std::vector<torch::Tensor> weights,
    std::vector<torch::Tensor> biases) {

    auto device = input.device();
    auto num_layers = weights.size();
    torch::Tensor current = input;

    for (size_t layer = 0; layer < num_layers; layer++) {
        const int batch_size = current.size(0);
        const int in_features = current.size(1);
        const int out_features = weights[layer].size(0);

        auto output = torch::empty({batch_size, out_features}, 
                                     torch::dtype(current.dtype()).device(device));

        // Configure grid and block dimensions
        // Block: 32 threads in x (warp size) and WARPS_PER_BLOCK in y
        dim3 block(WARP_SIZE, WARPS_PER_BLOCK);
        dim3 grid(batch_size, (out_features + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK);

        AT_DISPATCH_FLOATING_TYPES(current.scalar_type(), "mlp_forward_kernel_coalesced", ([&] {
            mlp_forward_kernel_coalesced<scalar_t><<<grid, block>>>(
                current.data_ptr<scalar_t>(),
                weights[layer].data_ptr<scalar_t>(),
                biases[layer].data_ptr<scalar_t>(),
                output.data_ptr<scalar_t>(),
                batch_size,
                in_features,
                out_features
            );
        }));

        // Apply ReLU activation for all layers except the final one
        if (layer < num_layers - 1) {
            int size = batch_size * out_features;
            int num_blocks = (size + RELU_BLOCK_SIZE - 1) / RELU_BLOCK_SIZE;
            AT_DISPATCH_FLOATING_TYPES(output.scalar_type(), "relu_kernel_coalesced", ([&] {
                relu_kernel_coalesced<scalar_t><<<num_blocks, RELU_BLOCK_SIZE>>>(
                    output.data_ptr<scalar_t>(),
                    size
                );
            }));
        }

        current = output;
    }

    return current;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &mlp_cuda_forward, "MLP forward (CUDA coalesced vectorized)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.228 inst/cycle 0.000 5
Executed Ipc Elapsed 0.000 inst/cycle 0.000 5
Issue Slots Busy 6.406 % 0.089 5
Issued Ipc Active 0.256 inst/cycle 0.000 5
SM Busy 6.406 % 0.089 5
Memory Throughput 3387580281.836 byte/second 10715618343123728.000 5
Mem Busy 9.430 % 0.051 5
Max Bandwidth 4.878 % 0.014 5
L1/TEX Hit Rate 50.000 % 0.000 5
L2 Hit Rate 100.604 % 0.354 5
Mem Pipes Busy 0.126 % 0.000 5
Warp Cycles Per Issued Instruction 30.258 cycle 1.099 5
Warp Cycles Per Executed Instruction 33.934 cycle 1.381 5
Avg. Active Threads Per Warp 31.800 0.000 5
Avg. Not Predicated Off Threads Per Warp 27.370 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 10.000 block 0.000 5
Block Limit Shared Mem 32.000 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 12.094 % 0.016 5
Achieved Active Warps Per SM 7.742 warp 0.006 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 (12.2%) 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 213765.51 μs
Device Time 2565.11 μs
Self CPU Time 73.40 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 213692.11 μs
Device Time 2565.11 μs
Self CPU Time 120.52 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 210613.18 μs
Device Time 0.00 μs
Self CPU Time 187.19 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 209854.60 μs
Device Time 0.00 μs
Self CPU Time 209854.60 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 271111.32 μs
Device Time 31052.96 μs
Self CPU Time 271111.32 μs
Self Device Time 31052.96 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void mlp_forward_kernel_coalesced<float>(float const*, float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 111621.85 μs
Self CPU Time 0.00 μs
Self Device Time 111621.85 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 43748.21 μs
Device Time 270838.23 μs
Self CPU Time 7067.61 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 36682.28 μs
Device Time 270838.23 μs
Self CPU Time 9051.27 μs
Self Device Time 270838.23 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 270838.23 μs
Self CPU Time 0.00 μs
Self Device Time 270838.23 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 45323 warnings (45276 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_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:17:5 bugprone-easily-swappable-parameters
17 | const scalar_t* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
18 | const scalar_t* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:17:34: note: the first parameter in the range is 'weight'
17 | const scalar_t* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:18:34: note: the last parameter in the range is 'bias'
18 | const scalar_t* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:20:5: warning: 2 adjacent parameters of 'mlp_forward_kernel_coalesced' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
20 | int batch_size,
| ^~~~~~~~~~~~~~~
21 | int in_features,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:20:9: note: the first parameter in the range is 'batch_size'
20 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:21:9: note: the last parameter in the range is 'in_features'
21 | int in_features,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:24:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
24 | int lane = threadIdx.x; // Lane index within a warp [0, 31]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:25:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int warpId = threadIdx.y; // Warp index within the block
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:27:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int batch_row = blockIdx.x; // Each block in x-dim handles one batch row
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:28:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | int neuron = blockIdx.y * WARPS_PER_BLOCK + warpId; // Each warp computes one output neuron
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:87:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:88:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:97:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
97 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:106:32: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
106 | const int batch_size = current.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:107:33: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
107 | const int in_features = current.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:108:34: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
108 | const int out_features = weights[layer].size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:118:9: 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(current.scalar_type(), "mlp_forward_kernel_coalesced", ([&] {
| ^
/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/20250203_optimize_b10_s4_e0_sweep/level_3/task_2/b10_s1_coalesced_vectorized/base/base.cu:134:13: 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]
134 | AT_DISPATCH_FLOATING_TYPES(output.scalar_type(), "relu_kernel_coalesced", ([&] {
| ^
/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__, \
| ^