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33_VanillaRNN • atomic_rnn_optimized_edit_1

Level 3 • Task 33
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    i2h_weight: torch.Tensor,
    i2h_bias: torch.Tensor,
    h2o_weight: torch.Tensor,
    h2o_bias: torch.Tensor,
    hidden: torch.Tensor,
) -> torch.Tensor:
    """
    Vanilla RNN forward pass

    Args:
        x: Input tensor of shape (batch_size, input_size)
        i2h_weight: Weight tensor for input-to-hidden layer
        i2h_bias: Bias tensor for input-to-hidden layer
        h2o_weight: Weight tensor for hidden-to-output layer
        h2o_bias: Bias tensor for hidden-to-output layer
        hidden: Hidden state tensor

    Returns:
        Output tensor of shape (batch_size, output_size)
    """
    hidden = hidden.to(x.device)
    combined = torch.cat((x, hidden), dim=1)
    hidden = torch.tanh(F.linear(combined, i2h_weight, i2h_bias))
    output = F.linear(hidden, h2o_weight, h2o_bias)
    return output


class Model(nn.Module):
    def __init__(self, input_size: int, hidden_size: int, output_size: int):
        """
        Initialize the Vanilla RNN model.

        :param input_size: The number of input features (int).
        :param hidden_size: The size of the hidden state (int).
        :param output_size: The number of output features (int).
        """
        super(Model, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.hidden = nn.Parameter(torch.randn((batch_size, hidden_size)))

        # Extract parameters from linear layers
        i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2h_weight = nn.Parameter(i2h.weight.data.clone())
        self.i2h_bias = nn.Parameter(i2h.bias.data.clone())

        h2o = nn.Linear(hidden_size, output_size)
        self.h2o_weight = nn.Parameter(h2o.weight.data.clone())
        self.h2o_bias = nn.Parameter(h2o.bias.data.clone())

    def forward(self, x: torch.Tensor, fn=module_fn) -> torch.Tensor:
        return fn(
            x,
            self.i2h_weight,
            self.i2h_bias,
            self.h2o_weight,
            self.h2o_bias,
            self.hidden,
        )


batch_size = 8
input_size = 1024
hidden_size = 256
output_size = 128
sequence_length = 256


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, input_size: int, hidden_size: int, output_size: int):
        """
        Initialize the Vanilla RNN model.
        
        :param input_size: The number of input features (int).
        :param hidden_size: The size of the hidden state (int).
        :param output_size: The number of output features (int).
        """
        super(Model, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.hidden = torch.randn((batch_size, hidden_size))
        
        # Define the RNN cell components (input to hidden, hidden to hidden, and hidden to output)
        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)  # Input to hidden
        self.h2o = nn.Linear(hidden_size, output_size)  # Hidden to output
        self.tanh = nn.Tanh()  # Activation function for hidden state
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the Vanilla RNN.
        
        :param x: Input tensor of shape (batch_size, input_size).
        :param hidden: Hidden state tensor of shape (batch_size, hidden_size).
        :return: Output tensor of shape (batch_size, output_size), and the new hidden state.
        """
        self.hidden = self.hidden.to(x.device)
        combined = torch.cat((x, self.hidden), dim=1)  # Concatenate input and hidden state
        self.hidden = self.tanh(self.i2h(combined))  # Update hidden state
        output = self.h2o(self.hidden)  # Compute output
        return output

batch_size = 8
input_size = 1024
hidden_size = 256
output_size = 128
sequence_length = 256

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

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

Kernel Information

Operation Name 33_VanillaRNN
Level ID 3
Task ID 33
Kernel Name atomic_rnn_optimized_edit_1
CUDA Speedup (Native) 1.106x
CUDA Speedup (Compile) 2.449x
CUDA Runtime 0.024 ms
PyTorch Runtime (Native) 0.027 ms
PyTorch Runtime (Compile) 0.059 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 3, Task 33 • 33_VanillaRNN)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 fused_rnn_i2h_warp_base 0.02 1.21 2.67
🥈 warp_optimized_rnn_base 0.02 1.15 2.56
🥈 optimized_rnn_reduction_base 0.02 1.15 2.56
4 atomic_rnn_optimized_edit_1 0.02 1.11 2.45
4 modular_warp_rnn_base 0.02 1.11 2.45
6 balanced_load_rnn_base_base 0.03 0.83 1.84
6 optimized_concat_kernel_base 0.03 0.83 1.84
6 optimized_unroll_concat_base 0.03 0.83 1.84
6 shared_memory_optimized_edit_1 0.03 0.83 1.84
6 stride_loops_rnn_base 0.03 0.83 1.84
6 optimal_blocksize_rnn_edit_1 0.03 0.83 1.84
6 modular_vanillarnn_edit_1 0.03 0.83 1.84
6 unroll_optimized_rnn_base_base 0.03 0.83 1.84
6 optimized_concat_base 0.03 0.83 1.84
15 unrolled_rnn_base_base 0.03 0.80 1.78
15 efficient_concat_base 0.03 0.80 1.78
15 sync_optimized_rnn_base_base 0.03 0.80 1.78
15 atomic_optimized_rnn_base 0.03 0.80 1.78
15 warp_aligned_rnn_base 0.03 0.80 1.78
15 optimized_concat_kernel_edit_1 0.03 0.80 1.78 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>

// Kernel to concatenate x and hidden into a combined tensor
__global__ void concat_kernel(
    const float* __restrict__ x,
    const float* __restrict__ hidden,
    float* __restrict__ combined,
    int batch_size,
    int x_size,
    int hidden_size,
    int total_elements
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int combined_width = x_size + hidden_size;
    for (; idx < total_elements; idx += blockDim.x * gridDim.x) {
        int row = idx / combined_width;
        int col = idx % combined_width;
        if (col < x_size) {
            combined[idx] = x[row * x_size + col];
        } else {
            combined[idx] = hidden[row * hidden_size + (col - x_size)];
        }
    }
}

// Kernel for computing the linear transformation with tanh activation
// This kernel computes: out[row, col] = tanh( bias[col] + dot( A[row, :], weight[col, :] ) )
// where A is the combined tensor of shape [B, K] and weight is i2h_weight of shape [M, K] (row-major).
// Each warp (32 threads) cooperatively computes one dot product using atomic operations to handle race conditions.
__global__ void linear_tanh_kernel(
    const float* __restrict__ A,       // Combined tensor, shape [B, K]
    const float* __restrict__ weight,  // i2h_weight, shape [M, K] (row-major)
    const float* __restrict__ bias,    // i2h_bias, shape [M]
    float* __restrict__ out,           // Output tensor, shape [B, M]
    int B, int K, int M                // Dimensions: batch, input features, output neurons
) {
    int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;
    int warp_id = global_thread_id / 32; // each warp computes one output element
    int lane_id = global_thread_id % 32;
    int row = warp_id / M;              // batch index
    int col = warp_id % M;              // neuron index

    if (row >= B) return;

    float sum = 0.0f;
    const float* a_row = A + row * K;   // Pointer to the beginning of the row in combined
    const float* w_row = weight + col * K; // weight is stored row-major; row 'col' of weight

    // Each thread in the warp processes a strided portion of the K dimension
    for (int k = lane_id; k < K; k += 32) {
        sum += a_row[k] * w_row[k];
    }

    // Warp-level reduction to sum up partial results
    #pragma unroll
    for (int offset = 16; offset > 0; offset /= 2) {
        sum += __shfl_down_sync(0xffffffff, sum, offset);
    }

    // Only the first thread in the warp writes the final result
    if (lane_id == 0) {
        float val = sum + bias[col];
        out[row * M + col] = tanhf(val);
    }
}

// Main function which launches the kernels
torch::Tensor module_fn_cuda(
    torch::Tensor x,
    torch::Tensor i2h_weight,
    torch::Tensor i2h_bias,
    torch::Tensor h2o_weight,
    torch::Tensor h2o_bias,
    torch::Tensor hidden
) {
    // Ensure all tensors are contiguous and on CUDA
    x = x.contiguous().cuda();
    i2h_weight = i2h_weight.contiguous().cuda();
    i2h_bias = i2h_bias.contiguous().cuda();
    h2o_weight = h2o_weight.contiguous().cuda();
    h2o_bias = h2o_bias.contiguous().cuda();
    hidden = hidden.contiguous().cuda();

    int batch_size = x.size(0);
    int x_size = x.size(1);
    int hidden_input_size = hidden.size(1);
    int combined_width = x_size + hidden_input_size;

    // Allocate the combined tensor
    auto options = torch::TensorOptions().dtype(x.dtype()).device(x.device());
    torch::Tensor combined = torch::empty({batch_size, combined_width}, options);

    int total_elements = batch_size * combined_width;
    int threads = 256;
    int blocks = (total_elements + threads - 1) / threads;
    concat_kernel<<<blocks, threads>>>(
        x.data_ptr<float>(),
        hidden.data_ptr<float>(),
        combined.data_ptr<float>(),
        batch_size,
        x_size,
        hidden_input_size,
        total_elements
    );

    // Compute the linear transformation with tanh activation for the i2h layer
    // i2h_weight has shape [M, combined_width] and i2h_bias has shape [M]
    int M = i2h_weight.size(0); // output neurons
    int K = combined_width;     // input dimensionality for the transformation

    // Allocate the hidden state tensor after transformation
    torch::Tensor hidden_new = torch::empty({batch_size, M}, options);

    // Each warp (32 threads) computes one output element, so total warps = batch_size * M
    int total_warps = batch_size * M;
    int total_threads = total_warps * 32; // 32 threads per warp
    int threads_per_block = 256;
    int grid = (total_threads + threads_per_block - 1) / threads_per_block;

    linear_tanh_kernel<<<grid, threads_per_block>>>(
        combined.data_ptr<float>(),
        i2h_weight.data_ptr<float>(),
        i2h_bias.data_ptr<float>(),
        hidden_new.data_ptr<float>(),
        batch_size,
        K,
        M
    );

    // Final output: compute the h2o layer: output = h2o_bias + hidden_new * h2o_weight.t()
    torch::Tensor output = torch::addmm(h2o_bias, hidden_new, h2o_weight.t());
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &module_fn_cuda, "Optimized RNN forward with atomic operations (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.716 inst/cycle 0.000 5
Executed Ipc Elapsed 0.482 inst/cycle 0.000 5
Issue Slots Busy 18.050 % 0.086 5
Issued Ipc Active 0.722 inst/cycle 0.000 5
SM Busy 18.050 % 0.086 5
Memory Throughput 204353478812.916 byte/second 7849561009270361088.000 5
Mem Busy 13.044 % 0.022 5
Max Bandwidth 14.074 % 0.264 5
L1/TEX Hit Rate 46.920 % 0.000 5
L2 Hit Rate 69.714 % 8.393 5
Mem Pipes Busy 13.040 % 0.023 5
Warp Cycles Per Issued Instruction 20.812 cycle 0.156 5
Warp Cycles Per Executed Instruction 20.986 cycle 0.160 5
Avg. Active Threads Per Warp 29.910 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.820 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 5.000 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 40.000 warp 0.000 5
Theoretical Occupancy 62.500 % 0.000 5
Achieved Occupancy 23.602 % 0.020 5
Achieved Active Warps Per SM 15.102 warp 0.008 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 (62.5%) is limited by the number of required registers. The difference between calculated theoretical (62.5%) and measured achieved occupancy (23.6%) 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 287431.54 μs
Device Time 66.37 μs
Self CPU Time 22706.27 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 264725.27 μs
Device Time 66.37 μs
Self CPU Time 127.77 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 264212.90 μs
Device Time 0.00 μs
Self CPU Time 137.57 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 263621.01 μs
Device Time 0.00 μs
Self CPU Time 263621.01 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 354269.74 μs
Device Time 26901.58 μs
Self CPU Time 354269.74 μs
Self Device Time 26901.58 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::addmm
CPU Time 263385.12 μs
Device Time 101896.70 μs
Self CPU Time 173660.84 μs
Self Device Time 101896.70 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_gemm_f32f32_f32f32_f32_tn_n_tilesize32x32x8_stage3_warpsize1x2x1_ffma_aligna4_alignc4_execute_kernel__51_cublas
CPU Time 0.00 μs
Device Time 101896.70 μs
Self CPU Time 0.00 μs
Self Device Time 101896.70 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 73059.75 μs
Device Time 705381.79 μs
Self CPU Time 16343.46 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 56717.44 μs
Device Time 705381.79 μs
Self CPU Time 19166.55 μs
Self Device Time 705381.79 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 705381.79 μs
Self CPU Time 0.00 μs
Self Device Time 705381.79 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45293 warnings generated when compiling for host.
Suppressed 45326 warnings (45279 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:8:5 bugprone-easily-swappable-parameters
8 | const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
9 | const float* __restrict__ hidden,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:8:31: note: the first parameter in the range is 'x'
8 | const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:9:31: note: the last parameter in the range is 'hidden'
9 | const float* __restrict__ hidden,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:11:5: warning: 2 adjacent parameters of 'concat_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
11 | int batch_size,
| ^~~~~~~~~~~~~~~
12 | int x_size,
| ~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:11:9: note: the first parameter in the range is 'batch_size'
11 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:12:9: note: the last parameter in the range is 'x_size'
12 | int x_size,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:13:5: warning: 2 adjacent parameters of 'concat_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
13 | int hidden_size,
| ^~~~~~~~~~~~~~~~
14 | int total_elements
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:13:9: note: the first parameter in the range is 'hidden_size'
13 | int hidden_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:14:9: note: the last parameter in the range is 'total_elements'
14 | int total_elements
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:16:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:18:41: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | for (; idx < total_elements; idx += blockDim.x * gridDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:34:5: warning: 3 adjacent parameters of 'linear_tanh_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
34 | const float* __restrict__ A, // Combined tensor, shape [B, K]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
35 | const float* __restrict__ weight, // i2h_weight, shape [M, K] (row-major)
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
36 | const float* __restrict__ bias, // i2h_bias, shape [M]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:34:31: note: the first parameter in the range is 'A'
34 | const float* __restrict__ A, // Combined tensor, shape [B, K]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:36:31: note: the last parameter in the range is 'bias'
36 | const float* __restrict__ bias, // i2h_bias, shape [M]
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:38:5: warning: 3 adjacent parameters of 'linear_tanh_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
38 | int B, int K, int M // Dimensions: batch, input features, output neurons
| ^~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:38:9: note: the first parameter in the range is 'B'
38 | int B, int K, int M // Dimensions: batch, input features, output neurons
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:38:23: note: the last parameter in the range is 'M'
38 | int B, int K, int M // Dimensions: batch, input features, output neurons
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:40:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:49:26: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
49 | const float* a_row = A + row * K; // Pointer to the beginning of the row in combined
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:49:30: note: make conversion explicit to silence this warning
5 | const float* a_row = A + row * K; // Pointer to the beginning of the row in combined
| ^~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:49:30: note: perform multiplication in a wider type
49 | const float* a_row = A + row * K; // Pointer to the beginning of the row in combined
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:50:26: warning: result of multiplication in type 'int' is used as a pointer offset after an implicit widening conversion to type 'ptrdiff_t' [bugprone-implicit-widening-of-multiplication-result]
50 | const float* w_row = weight + col * K; // weight is stored row-major; row 'col' of weight
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:50:35: note: make conversion explicit to silence this warning
50 | const float* w_row = weight + col * K; // weight is stored row-major; row 'col' of weight
| ^~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:50:35: note: perform multiplication in a wider type
50 | const float* w_row = weight + col * K; // weight is stored row-major; row 'col' of weight
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:87:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
87 | int batch_size = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:88:18: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
88 | int x_size = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:89:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
89 | int hidden_input_size = hidden.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_33/b5_s0_atomic_rnn_optimized/edit_1/edit_1.cu:111:13: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int M = i2h_weight.size(0); // output neurons
| ^