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2_ShallowWideMLP • optimized_reduction_warp_v2_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 optimized_reduction_warp_v2_base
CUDA Speedup (Native) 2.238x
CUDA Speedup (Compile) 4.250x
CUDA Runtime 0.041 ms
PyTorch Runtime (Native) 0.092 ms
PyTorch Runtime (Compile) 0.174 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 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>

__constant__ char constant_bias[65536];

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

    const int row = blockIdx.x;
    const int col = blockIdx.y;
    const int tid = threadIdx.x;
    const int warp_id = tid / warpSize;
    const int lane_id = tid % warpSize;
    
    // Shared memory for partial sums
    extern __shared__ char shared_memory[];
    scalar_t* shared_data = reinterpret_cast<scalar_t*>(shared_memory);

    if (row < batch_size && col < out_features) {
        // Each thread processes multiple elements (4x unrolling)
        scalar_t sum = 0;
        const int stride = blockDim.x * 4;
        const int input_offset = row * in_features;
        const int weight_offset = col * in_features;

        // Main loop with 4x unrolling
        #pragma unroll 4
        for (int i = tid * 4; i < in_features; i += stride) {
            scalar_t temp_sum = 0;
            if (i + 3 < in_features) {
                temp_sum = input[input_offset + i] * weight[weight_offset + i]
                        + input[input_offset + i + 1] * weight[weight_offset + i + 1]
                        + input[input_offset + i + 2] * weight[weight_offset + i + 2]
                        + input[input_offset + i + 3] * weight[weight_offset + i + 3];
            } else {
                for (int j = 0; j < 4 && i + j < in_features; j++) {
                    temp_sum += input[input_offset + i + j] * weight[weight_offset + i + j];
                }
            }
            sum += temp_sum;
        }

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

        // First thread in each warp writes to shared memory
        if (lane_id == 0) {
            shared_data[warp_id] = sum;
        }
        __syncthreads();

        // Final reduction across warps by first thread
        if (tid == 0) {
            scalar_t final_sum = 0;
            const int num_warps = (blockDim.x + warpSize - 1) / warpSize;
            
            #pragma unroll
            for (int i = 0; i < num_warps; i++) {
                final_sum += shared_data[i];
            }
            
            // Add bias and write result
            final_sum += reinterpret_cast<const scalar_t*>(constant_bias)[col];
            output[row * out_features + col] = final_sum;
        }
    }
}

template <typename scalar_t>
__global__ void relu_kernel(
    scalar_t* __restrict__ data,
    const int size) {
    
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    const int stride = blockDim.x * gridDim.x;
    
    for (int i = idx; i < size; i += stride) {
        data[i] = data[i] > 0 ? data[i] : 0;
    }
}

torch::Tensor mlp_cuda_forward(
    torch::Tensor input,
    std::vector<torch::Tensor> weights,
    std::vector<torch::Tensor> biases) {
    
    auto device = input.device();
    const int num_layers = weights.size();
    auto current = input;
    
    for (size_t i = 0; i < num_layers; i++) {
        const int batch_size = current.size(0);
        const int in_features = current.size(1);
        const int out_features = weights[i].size(0);
        
        auto output = torch::empty({batch_size, out_features}, 
                                 torch::dtype(current.dtype()).device(device));
        
        // Copy bias to constant memory
        AT_DISPATCH_FLOATING_TYPES(current.type(), "bias_copy", ([&] {
            size_t bias_bytes = out_features * sizeof(scalar_t);
            if (bias_bytes <= sizeof(constant_bias)) {
                cudaMemcpyToSymbol(constant_bias, biases[i].data_ptr<scalar_t>(), bias_bytes);
            }
        }));

        // Calculate optimal thread block size and shared memory
        const int thread_block_size = 256;  // Multiple of warpSize(32)
        const int shared_mem_size = (thread_block_size / 32) * sizeof(float);
        dim3 blocks(batch_size, out_features);
        
        AT_DISPATCH_FLOATING_TYPES(current.type(), "mlp_forward_kernel_optimized", ([&] {
            mlp_forward_kernel_optimized<scalar_t><<<blocks, thread_block_size, shared_mem_size>>>(
                current.data_ptr<scalar_t>(),
                weights[i].data_ptr<scalar_t>(),
                output.data_ptr<scalar_t>(),
                batch_size,
                in_features,
                out_features
            );
        }));
        
        if (i < num_layers - 1) {
            const int size = batch_size * out_features;
            const int thread_per_block = 256;
            const int num_blocks = (size + thread_per_block - 1) / thread_per_block;
            
            AT_DISPATCH_FLOATING_TYPES(output.type(), "relu_kernel", ([&] {
                relu_kernel<scalar_t><<<num_blocks, thread_per_block>>>(
                    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)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.216 inst/cycle 0.000 5
Executed Ipc Elapsed 0.000 inst/cycle 0.000 5
Issue Slots Busy 5.994 % 0.251 5
Issued Ipc Active 0.238 inst/cycle 0.000 5
SM Busy 5.994 % 0.251 5
Memory Throughput 3334387972.878 byte/second 20641105650860552.000 5
Mem Busy 9.434 % 0.109 5
Max Bandwidth 4.852 % 0.034 5
L1/TEX Hit Rate 50.000 % 0.000 5
L2 Hit Rate 100.696 % 0.020 5
Mem Pipes Busy 0.126 % 0.000 5
Warp Cycles Per Issued Instruction 32.744 cycle 11.752 5
Warp Cycles Per Executed Instruction 36.724 cycle 14.782 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 12.014 % 0.054 5
Achieved Active Warps Per SM 7.690 warp 0.022 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 221299.97 μs
Device Time 2184.34 μs
Self CPU Time 68.42 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 221231.55 μs
Device Time 2184.34 μs
Self CPU Time 139.86 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 218486.15 μs
Device Time 0.00 μs
Self CPU Time 173.76 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 217617.08 μs
Device Time 0.00 μs
Self CPU Time 217617.08 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 109966.31 μs
Device Time 42011.40 μs
Self CPU Time 109966.31 μs
Self Device Time 42011.40 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_optimized<float>(float const*, float const*, float*, int, int, int)
CPU Time 0.00 μs
Device Time 108223.22 μs
Self CPU Time 0.00 μs
Self Device Time 108223.22 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 258448.31 μs
Device Time 16193.11 μs
Self CPU Time 258448.31 μs
Self Device Time 16193.11 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 47048.12 μs
Device Time 386908.51 μs
Self CPU Time 9308.97 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 37744.12 μs
Device Time 386908.51 μs
Self CPU Time 10410.06 μs
Self Device Time 386908.51 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 386908.51 μs
Self CPU Time 0.00 μs
Self Device Time 386908.51 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 
45305 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/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
13 | const int in_features,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:12:15: note: the first parameter in the range is 'batch_size'
12 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:13:15: note: the last parameter in the range is 'in_features'
13 | const int in_features,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:16:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | const int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:17:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | const int col = blockIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:18:21: 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/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:29:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | const int stride = blockDim.x * 4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:65:35: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
65 | const int num_warps = (blockDim.x + warpSize - 1) / warpSize;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:84:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
84 | const int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:85:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
85 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:93:19: 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]
93 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:94:5: warning: 2 adjacent parameters of 'mlp_cuda_forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
94 | std::vector<torch::Tensor> weights,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
95 | std::vector<torch::Tensor> biases) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:94:32: note: the first parameter in the range is 'weights'
94 | std::vector<torch::Tensor> weights,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:95:32: note: the last parameter in the range is 'biases'
95 | std::vector<torch::Tensor> biases) {
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:98:28: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | const int num_layers = weights.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:102:32: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
102 | const int batch_size = current.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:103:33: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
103 | const int in_features = current.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:104:34: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
104 | const int out_features = weights[i].size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:110:9: 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]
110 | AT_DISPATCH_FLOATING_TYPES(current.type(), "bias_copy", ([&] {
| ^
/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/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:110:9: warning: 'scalar_type' is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [clang-diagnostic-deprecated-declarations]
110 | AT_DISPATCH_FLOATING_TYPES(current.type(), "bias_copy", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:3: 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:218:36: note: expanded from macro 'AT_DISPATCH_SWITCH'
218 | at::ScalarType _st = ::detail::scalar_type(the_type); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:106:1: note: 'scalar_type' has been explicitly marked deprecated here
106 | C10_DEPRECATED_MESSAGE(
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:110:44: warning: 'type' is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [clang-diagnostic-deprecated-declarations]
110 | AT_DISPATCH_FLOATING_TYPES(current.type(), "bias_copy", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/core/TensorBody.h:224:3: note: 'type' has been explicitly marked deprecated here
224 | C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().")
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:122:9: 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]
122 | AT_DISPATCH_FLOATING_TYPES(current.type(), "mlp_forward_kernel_optimized", ([&] {
| ^
/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/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:122:9: warning: 'scalar_type' is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [clang-diagnostic-deprecated-declarations]
122 | AT_DISPATCH_FLOATING_TYPES(current.type(), "mlp_forward_kernel_optimized", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:3: 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:218:36: note: expanded from macro 'AT_DISPATCH_SWITCH'
218 | at::ScalarType _st = ::detail::scalar_type(the_type); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:106:1: note: 'scalar_type' has been explicitly marked deprecated here
106 | C10_DEPRECATED_MESSAGE(
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:122:44: warning: 'type' is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [clang-diagnostic-deprecated-declarations]
122 | AT_DISPATCH_FLOATING_TYPES(current.type(), "mlp_forward_kernel_optimized", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/core/TensorBody.h:224:3: note: 'type' has been explicitly marked deprecated here
224 | C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().")
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:138:13: 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]
138 | AT_DISPATCH_FLOATING_TYPES(output.type(), "relu_kernel", ([&] {
| ^
/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/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:138:13: warning: 'scalar_type' is deprecated: passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, pass an at::ScalarType instead [clang-diagnostic-deprecated-declarations]
138 | AT_DISPATCH_FLOATING_TYPES(output.type(), "relu_kernel", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:237:3: 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:218:36: note: expanded from macro 'AT_DISPATCH_SWITCH'
218 | at::ScalarType _st = ::detail::scalar_type(the_type); \
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/Dispatch.h:106:1: note: 'scalar_type' has been explicitly marked deprecated here
106 | C10_DEPRECATED_MESSAGE(
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_2/b4_s1_optimized_reduction_warp_v2/base/base.cu:138:47: warning: 'type' is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [clang-diagnostic-deprecated-declarations]
138 | AT_DISPATCH_FLOATING_TYPES(output.type(), "relu_kernel", ([&] {
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/ATen/core/TensorBody.h:224:3: note: 'type' has been explicitly marked deprecated here
224 | C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().")
| ^
/home/robert_sakana_ai/miniconda3/envs/llm2cuda/lib/python3.11/site-packages/torch/include/c10/util/Deprecated.h:24:43: note: expanded from macro 'C10_DEPRECATED_MESSAGE'
24 | #define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
| ^