← Back to Leaderboard

The AI CUDA Engineer 👷

35_LTSM • 35_lstm_load_balancing_edit_1

Level 3 • Task 35
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import _VF


def module_fn(
    x: torch.Tensor,
    lstm_weights_ih: torch.Tensor,
    lstm_weights_hh: torch.Tensor,
    lstm_biases_ih: torch.Tensor,
    lstm_biases_hh: torch.Tensor,
    fc_weight: torch.Tensor,
    fc_bias: torch.Tensor,
    h0: torch.Tensor,
    c0: torch.Tensor,
    is_training: bool,
) -> torch.Tensor:
    """
    LSTM forward pass

    Args:
        x: Input tensor of shape (batch_size, sequence_length, input_size)
        lstm_weights_ih: List of input-hidden weight tensors for each LSTM layer
        lstm_weights_hh: List of hidden-hidden weight tensors for each LSTM layer
        lstm_biases_ih: List of input-hidden bias tensors for each LSTM layer
        lstm_biases_hh: List of hidden-hidden bias tensors for each LSTM layer
        fc_weight: Weight tensor for final linear layer
        fc_bias: Bias tensor for final linear layer
        h0: Initial hidden state
        c0: Initial cell state
        is_training: Whether in training mode

    Returns:
        Output tensor of shape (batch_size, output_size)
    """
    h0 = h0.to(x.device)
    c0 = c0.to(x.device)

    # Run LSTM layers
    out = x

    for i in range(len(lstm_weights_ih)):
        params = (
            lstm_weights_ih[i],
            lstm_weights_hh[i],
            lstm_biases_ih[i],
            lstm_biases_hh[i],
        )
        out = _VF.lstm(
            out,
            (h0[i : i + 1], c0[i : i + 1]),
            params,
            True,  # has_biases
            1,  # num_layers
            0.0 if not is_training else dropout,  # dropout
            is_training,  # training
            False,  # bidirectional
            True,
        )[
            0
        ]  # batch_first, only keep output

    # Get last timestep and apply final linear layer
    out = F.linear(out[:, -1, :], fc_weight, fc_bias)

    return out


class Model(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):
        """
        Initialize the LSTM model.

        :param input_size: The number of expected features in the input `x`
        :param hidden_size: The number of features in the hidden state `h`
        :param num_layers: Number of recurrent layers
        :param output_size: The number of output features
        :param dropout: If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer
        """
        super(Model, self).__init__()

        # Initialize hidden states
        self.h0 = torch.randn((num_layers, batch_size, hidden_size))
        self.c0 = torch.randn((num_layers, batch_size, hidden_size))

        # Extract LSTM parameters
        lstm = nn.LSTM(
            input_size,
            hidden_size,
            num_layers,
            batch_first=True,
            dropout=dropout,
            bidirectional=False,
        )

        # Get weights and biases for each layer
        self.lstm_weights_ih = nn.ParameterList()
        self.lstm_weights_hh = nn.ParameterList()
        self.lstm_biases_ih = nn.ParameterList()
        self.lstm_biases_hh = nn.ParameterList()

        for i in range(num_layers):
            self.lstm_weights_ih.append(
                nn.Parameter(getattr(lstm, f"weight_ih_l{i}").data.clone())
            )
            self.lstm_weights_hh.append(
                nn.Parameter(getattr(lstm, f"weight_hh_l{i}").data.clone())
            )
            self.lstm_biases_ih.append(
                nn.Parameter(getattr(lstm, f"bias_ih_l{i}").data.clone())
            )
            self.lstm_biases_hh.append(
                nn.Parameter(getattr(lstm, f"bias_hh_l{i}").data.clone())
            )

        # Extract linear layer parameters
        fc = nn.Linear(hidden_size, output_size)
        self.fc_weight = nn.Parameter(fc.weight.data.clone())
        self.fc_bias = nn.Parameter(fc.bias.data.clone())

    def forward(self, x, fn=module_fn):
        return fn(
            x,
            self.lstm_weights_ih,
            self.lstm_weights_hh,
            self.lstm_biases_ih,
            self.lstm_biases_hh,
            self.fc_weight,
            self.fc_bias,
            self.h0,
            self.c0,
            self.training,
        )


# Test code
batch_size = 10
sequence_length = 512
input_size = 128
hidden_size = 256
num_layers = 6
output_size = 10
dropout = 0.0


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


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

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, dropout=0.0):
        """
        Initialize the LSTM model.

        :param input_size: The number of expected features in the input `x`
        :param hidden_size: The number of features in the hidden state `h`
        :param num_layers: Number of recurrent layers
        :param output_size: The number of output features
        :param dropout: If non-zero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to `dropout`
        """
        super(Model, self).__init__()
        # Initialize hidden state with random values
        self.h0 = torch.randn((num_layers, batch_size, hidden_size))
        self.c0 = torch.randn((num_layers, batch_size, hidden_size))
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout, bidirectional=False)
        self.fc = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        """
        Forward pass through the LSTM model.

        :param x: The input tensor, shape (batch_size, sequence_length, input_size)
        :return: The output tensor, shape (batch_size, sequence_length, output_size)
        """
        self.h0 = self.h0.to(x.device)
        self.c0 = self.h0.to(x.device)
        
        # Forward propagate LSTM
        out, hn = self.lstm(x, (self.h0, self.c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])  # out: tensor of shape (batch_size, output_size)
        
        return out

# Test code
batch_size = 10
sequence_length = 512
input_size = 128
hidden_size = 256
num_layers = 6
output_size = 10
dropout = 0.0

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

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

Kernel Information

Operation Name 35_LTSM
Level ID 3
Task ID 35
Kernel Name 35_lstm_load_balancing_edit_1
CUDA Speedup (Native) 0.360x
CUDA Speedup (Compile) 0.681x
CUDA Runtime 88.681 ms
PyTorch Runtime (Native) 31.945 ms
PyTorch Runtime (Compile) 60.432 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 3, Task 35 • 35_LTSM)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 35_lstm_grid_stride_base_base 72.97 0.44 0.83
🥈 35_lstm_modular_device_edit_1 75.07 0.43 0.81
🥉 35_lstm_shared_memory_base 86.54 0.37 0.70
4 35_lstm_atomic_reduction_base_base 86.99 0.37 0.69
5 35_lstm_workload_balanced_base 87.77 0.36 0.69
6 35_lstm_aligned_base 88.03 0.36 0.69
7 35_lstm_tiled_unroll_edit_1 88.19 0.36 0.69
8 35_lstm_load_balancing_base 88.28 0.36 0.68
9 fused_tiled_base 88.40 0.36 0.68
10 35_lstm_ldg_aligned_v2_base 88.50 0.36 0.68
11 35_lstm_load_balancing_edit_1 88.68 0.36 0.68
12 35_LTSM 88.90 0.36 0.68
13 35_lstm_memory_coalescing_edit_1 89.05 0.36 0.68
14 modular_35_ltsm_base 89.17 0.36 0.68
15 35_lstm_shared_memory_edit_1 89.34 0.36 0.68
16 fused_tiled_edit_1 89.35 0.36 0.68
17 35_lstm_unrolled_base 89.58 0.36 0.67
18 35_lstm_memory_coalescing_base 89.77 0.36 0.67
19 35_lstm_warp_reduce_base 89.78 0.36 0.67
20 35_lstm_warp_aligned_base 89.81 0.36 0.67 
#include <torch/extension.h>
#include <vector>
#include <cmath>

#define TILE_DIM 16

// CUDA kernel for element-wise LSTM computations (optimized for load balancing)
__global__ __launch_bounds__(256) void lstm_elementwise_forward(
    const float* __restrict__ gates,
    const float* __restrict__ prev_c,
    float* __restrict__ h,
    float* __restrict__ c,
    int batch_size,
    int hidden_size
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    int total_elements = batch_size * hidden_size;
    int stride = blockDim.x * gridDim.x;

    for (int i = idx; i < total_elements; i += stride) {
        int b = i / hidden_size;
        int n = i % hidden_size;
        int gate_idx = b * hidden_size * 4 + n;

        float i_gate = gates[gate_idx];
        float f_gate = gates[gate_idx + hidden_size];
        float g_gate = gates[gate_idx + 2 * hidden_size];
        float o_gate = gates[gate_idx + 3 * hidden_size];

        // Apply activations
        i_gate = 1.0f / (1.0f + expf(-i_gate));
        f_gate = 1.0f / (1.0f + expf(-f_gate));
        g_gate = tanhf(g_gate);
        o_gate = 1.0f / (1.0f + expf(-o_gate));

        float c_new = f_gate * prev_c[i] + i_gate * g_gate;
        float h_new = o_gate * tanhf(c_new);

        c[i] = c_new;
        h[i] = h_new;
    }
}

// Optimized linear forward kernel using tiling, loop unrolling, and load balancing
__global__ void tiled_linear_forward_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int batch_size,
    int input_dim,
    int output_dim
) {
    // Identify the row and column of the output element
    int row = blockIdx.x * TILE_DIM + threadIdx.y;
    int col = blockIdx.y * TILE_DIM + threadIdx.x;
    float sum = 0.f;

    // Shared memory for tiles from input and weight
    __shared__ float shared_A[TILE_DIM][TILE_DIM];
    __shared__ float shared_B[TILE_DIM][TILE_DIM];

    // Number of tiles to cover input_dim
    int numTiles = (input_dim + TILE_DIM - 1) / TILE_DIM;
    for (int t = 0; t < numTiles; t++) {
        int colA = t * TILE_DIM + threadIdx.x; // Column index for input
        int rowB = t * TILE_DIM + threadIdx.y; // Row index for weight tile

        // Load tile from input
        if (row < batch_size && colA < input_dim)
            shared_A[threadIdx.y][threadIdx.x] = input[row * input_dim + colA];
        else
            shared_A[threadIdx.y][threadIdx.x] = 0.f;

        // Load tile from weight
        if (col < output_dim && rowB < input_dim)
            shared_B[threadIdx.y][threadIdx.x] = weight[col * input_dim + rowB];
        else
            shared_B[threadIdx.y][threadIdx.x] = 0.f;

        __syncthreads();

        // Utilize loop unrolling for better performance
        #pragma unroll
        for (int k = 0; k < TILE_DIM; k++) {
            sum += shared_A[threadIdx.y][k] * shared_B[k][threadIdx.x];
        }

        __syncthreads();
    }

    if (row < batch_size && col < output_dim) {
        if (bias != nullptr)
            sum += bias[col];
        output[row * output_dim + col] = sum;
    }
}

// LSTM forward function using the lstm_elementwise_forward kernel
torch::Tensor lstm_forward_cuda(
    torch::Tensor input,
    torch::Tensor w_ih,
    torch::Tensor w_hh,
    torch::Tensor b_ih,
    torch::Tensor b_hh,
    torch::Tensor h0,
    torch::Tensor c0
) {
    int batch_size = input.size(0);
    int seq_len = input.size(1);
    int input_size = input.size(2);
    int hidden_size = h0.size(1);

    torch::Tensor h = h0.clone();
    torch::Tensor c = c0.clone();

    std::vector<torch::Tensor> outputs;

    int threads = 256;
    int blocks = std::min(65535, (batch_size * hidden_size + threads - 1) / threads);

    for (int t = 0; t < seq_len; t++) {
        torch::Tensor xt = input.select(1, t);

        torch::Tensor gates = torch::addmm(b_ih, xt, w_ih.t());
        gates = torch::addmm(gates, h, w_hh.t());
        gates += b_hh;

        const float* gates_ptr = gates.data_ptr<float>();
        const float* prev_c_ptr = c.data_ptr<float>();
        float* h_ptr = h.data_ptr<float>();
        float* c_ptr = c.data_ptr<float>();

        lstm_elementwise_forward<<<blocks, threads>>>(
            gates_ptr,
            prev_c_ptr,
            h_ptr,
            c_ptr,
            batch_size,
            hidden_size
        );

        outputs.push_back(h.unsqueeze(1));
    }

    torch::Tensor output = torch::cat(outputs, 1);
    return output;
}

// Linear forward function using a tiled and unrolled GEMM kernel
torch::Tensor linear_forward_cuda(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias
) {
    int batch_size = input.size(0);
    int input_dim = input.size(1);
    int output_dim = weight.size(0);

    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    auto output = torch::empty({batch_size, output_dim}, options);

    dim3 block(TILE_DIM, TILE_DIM);
    dim3 grid((batch_size + TILE_DIM - 1) / TILE_DIM, (output_dim + TILE_DIM - 1) / TILE_DIM);

    tiled_linear_forward_kernel<<<grid, block>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.defined() ? bias.data_ptr<float>() : nullptr,
        output.data_ptr<float>(),
        batch_size,
        input_dim,
        output_dim
    );

    return output;
}

// Main forward function orchestrating LSTM layers and the final linear layer
torch::Tensor forward(
    torch::Tensor x,
    std::vector<torch::Tensor> lstm_weights_ih,
    std::vector<torch::Tensor> lstm_weights_hh,
    std::vector<torch::Tensor> lstm_biases_ih,
    std::vector<torch::Tensor> lstm_biases_hh,
    torch::Tensor fc_weight,
    torch::Tensor fc_bias,
    torch::Tensor h0,
    torch::Tensor c0,
    bool is_training
) {
    h0 = h0.to(x.device());
    c0 = c0.to(x.device());
    
    torch::Tensor out = x;
    int num_layers = lstm_weights_ih.size();

    for (int i = 0; i < num_layers; i++) {
        auto w_ih = lstm_weights_ih[i].to(x.device());
        auto w_hh = lstm_weights_hh[i].to(x.device());
        auto b_ih = lstm_biases_ih[i].to(x.device());
        auto b_hh = lstm_biases_hh[i].to(x.device());

        torch::Tensor h_i = h0.narrow(0, i, 1).squeeze(0);
        torch::Tensor c_i = c0.narrow(0, i, 1).squeeze(0);
        
        out = lstm_forward_cuda(out, w_ih, w_hh, b_ih, b_hh, h_i, c_i);
    }

    // Take the last timestep and apply the final linear layer
    out = out.select(1, -1);
    out = linear_forward_cuda(out, fc_weight, fc_bias);
    
    return out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Module forward (CUDA) with load balancing and optimization");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.414 inst/cycle 0.000 5
Executed Ipc Elapsed 0.000 inst/cycle 0.000 5
Issue Slots Busy 10.404 % 0.024 5
Issued Ipc Active 0.416 inst/cycle 0.000 5
SM Busy 10.404 % 0.024 5
Memory Throughput 1747905470.902 byte/second 118276510007642.594 5
Mem Busy 2.312 % 0.001 5
Max Bandwidth 1.200 % 0.000 5
L1/TEX Hit Rate 60.060 % 0.004 5
L2 Hit Rate 99.708 % 0.418 5
Mem Pipes Busy 0.106 % 0.000 5
Warp Cycles Per Issued Instruction 18.780 cycle 0.018 5
Warp Cycles Per Executed Instruction 18.876 cycle 0.017 5
Avg. Active Threads Per Warp 31.900 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.040 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 8.000 block 0.000 5
Block Limit Shared Mem 21.000 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 12.328 % 0.001 5
Achieved Active Warps Per SM 7.890 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (12.4%) 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
cudaMemcpyAsync
CPU Time 807674.54 μs
Device Time 0.00 μs
Self CPU Time 807674.54 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::t
CPU Time 899544.75 μs
Device Time 0.00 μs
Self CPU Time 401279.41 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::addmm
CPU Time 6369443.41 μs
Device Time 2639833.25 μs
Self CPU Time 3992744.24 μs
Self Device Time 2639833.25 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 2601766.79 μs
Device Time 91594.80 μs
Self CPU Time 2601766.79 μs
Self Device Time 91594.80 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void gemmSN_TN_kernel<float, 128, 16, 2, 4, 10, 11, false, cublasGemvTensorStridedBatched<float const>, cublasGemvTensorStridedBatched<float const>, cublasGemvTensorStridedBatched<float> >(cublasGemmSmallNParams<cublasGemvTensorStridedBatched<float const>, cublasGemvTensorStridedBatched<float const>, cublasGemvTensorStridedBatched<float>, float>)
CPU Time 0.00 μs
Device Time 1908121.08 μs
Self CPU Time 0.00 μs
Self Device Time 1908121.08 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::add_
CPU Time 1250455.98 μs
Device Time 681367.86 μs
Self CPU Time 559621.95 μs
Self Device Time 681367.86 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::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast<at::native::CUDAFunctor_add<float> >(at::TensorIteratorBase&, at::native::CUDAFunctor_add<float> const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast<at::native::CUDAFunctor_add<float> >(at::TensorIteratorBase&, at::native::CUDAFunctor_add<float> const&)::{lambda(int)#1})
CPU Time 0.00 μs
Device Time 681367.86 μs
Self CPU Time 0.00 μs
Self Device Time 681367.86 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
lstm_elementwise_forward(float const*, float const*, float*, float*, int, int)
CPU Time 0.00 μs
Device Time 588972.36 μs
Self CPU Time 0.00 μs
Self Device Time 588972.36 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 
45320 warnings generated when compiling for host.
Suppressed 45332 warnings (45285 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_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:9:5 bugprone-easily-swappable-parameters
9 | const float* __restrict__ gates,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
10 | const float* __restrict__ prev_c,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:9:31: note: the first parameter in the range is 'gates'
9 | const float* __restrict__ gates,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:10:31: note: the last parameter in the range is 'prev_c'
10 | const float* __restrict__ prev_c,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:11:5: warning: 2 adjacent parameters of 'lstm_elementwise_forward' of similar type ('float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
11 | float* __restrict__ h,
| ^~~~~~~~~~~~~~~~~~~~~~
12 | float* __restrict__ c,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:11:25: note: the first parameter in the range is 'h'
11 | float* __restrict__ h,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:12:25: note: the last parameter in the range is 'c'
12 | float* __restrict__ c,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/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_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:18:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:46:5: warning: 3 adjacent parameters of 'tiled_linear_forward_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
46 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
47 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
48 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:46:31: note: the first parameter in the range is 'input'
46 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:48:31: note: the last parameter in the range is 'bias'
48 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:55:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
55 | int row = blockIdx.x * TILE_DIM + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:56:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
56 | int col = blockIdx.y * TILE_DIM + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:66:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
66 | int colA = t * TILE_DIM + threadIdx.x; // Column index for input
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:67:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | int rowB = t * TILE_DIM + threadIdx.y; // Row index for weight tile
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:101:5: warning: 2 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
101 | torch::Tensor input,
| ^~~~~~~~~~~~~~~~~~~~
102 | torch::Tensor w_ih,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:101:19: note: the first parameter in the range is 'input'
101 | torch::Tensor input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:102:19: note: the last parameter in the range is 'w_ih'
102 | torch::Tensor w_ih,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:101:19: 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]
101 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:102:19: warning: the parameter 'w_ih' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
102 | torch::Tensor w_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:103:5: warning: 4 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
103 | torch::Tensor w_hh,
| ^~~~~~~~~~~~~~~~~~~
104 | torch::Tensor b_ih,
| ~~~~~~~~~~~~~~~~~~~
105 | torch::Tensor b_hh,
| ~~~~~~~~~~~~~~~~~~~
106 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:103:19: note: the first parameter in the range is 'w_hh'
103 | torch::Tensor w_hh,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:106:19: note: the last parameter in the range is 'h0'
106 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:103:19: warning: the parameter 'w_hh' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
103 | torch::Tensor w_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:104:19: warning: the parameter 'b_ih' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
104 | torch::Tensor b_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:105:19: warning: the parameter 'b_hh' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
105 | torch::Tensor b_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:106:19: warning: the parameter 'h0' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
106 | torch::Tensor h0,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:107:19: warning: the parameter 'c0' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
107 | torch::Tensor c0
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:109:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
109 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:110:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | int seq_len = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:111:9: warning: Value stored to 'input_size' during its initialization is never read [clang-analyzer-deadcode.DeadStores]
111 | int input_size = input.size(2);
| ^~~~~~~~~~ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:111:9: note: Value stored to 'input_size' during its initialization is never read
111 | int input_size = input.size(2);
| ^~~~~~~~~~ ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:111:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
111 | int input_size = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:112:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
112 | int hidden_size = h0.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:152:19: 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]
152 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:153:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
153 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:154:19: warning: the parameter 'bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
154 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:156:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
156 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:157:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | int input_dim = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:158:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
158 | int output_dim = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:181:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
181 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:182:5: warning: 4 adjacent parameters of 'forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
182 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
183 | std::vector<torch::Tensor> lstm_weights_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
184 | std::vector<torch::Tensor> lstm_biases_ih,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
185 | std::vector<torch::Tensor> lstm_biases_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:182:32: note: the first parameter in the range is 'lstm_weights_ih'
182 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:185:32: note: the last parameter in the range is 'lstm_biases_hh'
185 | std::vector<torch::Tensor> lstm_biases_hh,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:187:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
187 | torch::Tensor fc_bias,
| ^~~~~~~~~~~~~~~~~~~~~~
188 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:187:19: note: the first parameter in the range is 'fc_bias'
187 | torch::Tensor fc_bias,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:188:19: note: the last parameter in the range is 'h0'
188 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:196:22: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
196 | int num_layers = lstm_weights_ih.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:212:36: warning: parameter 'fc_weight' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
2 | out = linear_forward_cuda(out, fc_weight, fc_bias);
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s2_35_lstm_load_balancing/edit_1/edit_1.cu:212:47: warning: parameter 'fc_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
212 | out = linear_forward_cuda(out, fc_weight, fc_bias);
| ^
| std::move( )