Kernel Details - fused_tiled_edit

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	fused_tiled_edit_1
CUDA Speedup (Native)	0.358x
CUDA Speedup (Compile)	0.676x
CUDA Runtime	89.349 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>

// Device helper functions for activations
__device__ inline float device_sigmoid(float x) {
    return 1.0f / (1.0f + expf(-x));
}

__device__ inline float device_tanh(float x) {
    return tanhf(x);
}

// Modular device function for LSTM cell computation
__device__ inline void compute_lstm_cell(const float* __restrict__ gates,
                                          int gate_base,
                                          float prev_c,
                                          int hidden_size,
                                          float &h_out,
                                          float &c_out) {
    float i_gate = device_sigmoid(gates[gate_base]);
    float f_gate = device_sigmoid(gates[gate_base + hidden_size]);
    float g_gate = device_tanh(gates[gate_base + 2 * hidden_size]);
    float o_gate = device_sigmoid(gates[gate_base + 3 * hidden_size]);
    float c_new = f_gate * prev_c + i_gate * g_gate;
    float tanh_c_new = device_tanh(c_new); h_out = o_gate * tanh_c_new;
    c_out = c_new;
}

// CUDA kernel for element-wise LSTM computations
__global__ void lstm_elementwise_forward_modular(
    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;
    if (idx < batch_size * hidden_size) {
        int b = idx / hidden_size;
        int n = idx % hidden_size;
        int gate_base = b * hidden_size * 4 + n;

        float h_val, c_val;
        compute_lstm_cell(gates, gate_base, prev_c[idx], hidden_size, h_val, c_val);
        h[idx] = h_val;
        c[idx] = c_val;
    }
}

// Tiled linear forward kernel using shared memory reduction
// Grid dimensions: grid.x = batch index, grid.y = output index
__global__ void linear_forward_kernel_tiled(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int input_dim
) {
    int batch_idx = blockIdx.x;  // Each block row corresponds to a unique batch element
    int out_idx   = blockIdx.y;  // Each block column corresponds to a unique output neuron
    int tid = threadIdx.x;
    float sum = 0.0f;

    // Each thread computes partial dot product across the input dimension
    for (int k = tid; k < input_dim; k += blockDim.x) {
        sum += input[batch_idx * input_dim + k] * weight[out_idx * input_dim + k];
    }

    // Shared memory reduction
    extern __shared__ float shmem[];
    shmem[tid] = sum;
    __syncthreads();

    // Reduction in shared memory
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            shmem[tid] += shmem[tid + s];
        }
        __syncthreads();
    }

    // Write final result to global memory
    if (tid == 0) {
        float res = shmem[0];
        if (bias != nullptr) {
            res += bias[out_idx];
        }
        // Using gridDim.y as the output dimension stride
        output[batch_idx * gridDim.y + out_idx] = res;
    }
}

// Function to compute LSTM forward pass using the modular 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
) {
    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    int batch_size = input.size(0);
    int seq_len = input.size(1);
    int hidden_size = h0.size(1);

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

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

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

        // Compute gates: gates = x_t * W_ih^T + h_{t-1} * W_hh^T + b_ih + b_hh
        torch::Tensor gates = torch::addmm(b_ih, xt, w_ih.t());
        gates = torch::addmm(gates, h, w_hh.t());
        gates += b_hh;

        // Prepare device pointers
        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>();

        int total_elements = batch_size * hidden_size;
        int threads = 256;
        int blocks = (total_elements + threads - 1) / threads;

        // Launch CUDA kernel for LSTM cell computations
        lstm_elementwise_forward_modular<<<blocks, threads>>>(
            gates_ptr,
            prev_c_ptr,
            h_ptr,
            c_ptr,
            batch_size,
            hidden_size
        );

        // Collect output
        outputs.push_back(h.unsqueeze(1));
    }

    // Concatenate outputs
    torch::Tensor output = torch::cat(outputs, 1);

    return output;
}

// Function to compute linear forward pass using tiled reduction without atomic operations
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 = input.options();
    auto output = torch::empty({batch_size, output_dim}, options);

    // Launch one block per (batch, output) pair
    dim3 grid(batch_size, output_dim);
    int threads = 256;
    size_t shared_mem_size = threads * sizeof(float);

    linear_forward_kernel_tiled<<<grid, threads, shared_mem_size>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.defined() ? bias.data_ptr<float>() : nullptr,
        output.data_ptr<float>(),
        input_dim
    );

    return output;
}

// Main forward function integrating LSTM and Linear layers
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
) {
    // Ensure h0 and c0 are on the same device as x
    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());

        // Prepare initial hidden and cell states for the current layer
        torch::Tensor h_i = h0.narrow(0, i, 1).squeeze(0);
        torch::Tensor c_i = c0.narrow(0, i, 1).squeeze(0);

        // Run LSTM layer
        out = lstm_forward_cuda(out, w_ih, w_hh, b_ih, b_hh, h_i, c_i);
    }

    // Take the last timestep
    out = out.select(1, -1);

    // Apply final linear layer using the tiled reduction kernel
    out = linear_forward_cuda(out, fc_weight, fc_bias);

    return out;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Forward pass with fused LSTM and tiled linear kernel (CUDA)");
}

Performance Metrics

Metric	Value	Unit	Variance	Samples
Executed Ipc Active	0.346	inst/cycle	0.000	5
Executed Ipc Elapsed	0.118	inst/cycle	0.000	5
Issue Slots Busy	8.874	%	0.054	5
Issued Ipc Active	0.356	inst/cycle	0.000	5
SM Busy	8.874	%	0.054	5
Memory Throughput	6078525200.356	byte/second	41561341071578432.000	5
Mem Busy	8.344	%	0.037	5
Max Bandwidth	4.336	%	0.018	5
L1/TEX Hit Rate	0.000	%	0.000	5
L2 Hit Rate	98.794	%	0.030	5
Mem Pipes Busy	3.854	%	0.016	5
Warp Cycles Per Issued Instruction	19.304	cycle	1.197	5
Warp Cycles Per Executed Instruction	19.942	cycle	1.277	5
Avg. Active Threads Per Warp	31.460		0.000	5
Avg. Not Predicated Off Threads Per Warp	20.880		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	16.000	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	10.754	%	0.017	5
Achieved Active Warps Per SM	6.884	warp	0.007	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.
WRN ThreadDivergence	Instructions are executed in warps, which are groups of 32 threads. Optimal instruction throughput is achieved if all 32 threads of a warp execute the same instruction. The chosen launch configuration, early thread completion, and divergent flow control can significantly lower the number of active threads in a warp per cycle. This kernel achieves an average of 31.5 threads being active per cycle. This is further reduced to 20.9 threads per warp due to predication. The compiler may use predication to avoid an actual branch. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Try to avoid different execution paths within a warp when possible. In addition, ensure your kernel makes use of Independent Thread Scheduling, which allows a warp to reconverge after a data-dependent conditional block by explicitly calling __syncwarp().
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 (10.6%) 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.
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.

Operation / Metric	Value	Unit
cudaMemcpyAsync
CPU Time	809935.39	μs
Device Time	0.00	μs
Self CPU Time	809935.39	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	879451.93	μs
Device Time	0.00	μs
Self CPU Time	395555.30	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	6338524.42	μs
Device Time	2579085.48	μs
Self CPU Time	3970316.61	μs
Self Device Time	2579085.48	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	2599093.70	μs
Device Time	88824.96	μs
Self CPU Time	2599093.70	μs
Self Device Time	88824.96	μs
CPU Memory Usage	0	B
Device Memory Usage	0	B
Self CPU Memory Usage	0	B
Self Device Memory Usage	0	B
void 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	1866296.86	μs
Self CPU Time	0.00	μs
Self Device Time	1866296.86	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	1245094.34	μs
Device Time	662880.98	μs
Self CPU Time	552306.89	μs
Self Device Time	662880.98	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	662880.98	μs
Self CPU Time	0.00	μs
Self Device Time	662880.98	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_modular(float const, float const, float, float, int, int)
CPU Time	0.00	μs
Device Time	555005.28	μs
Self CPU Time	0.00	μs
Self Device Time	555005.28	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

45317 warnings generated when compiling for host.
Suppressed 45330 warnings (45283 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/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:16:43 bugprone-easily-swappable-parameters

16 | int gate_base,

| ^~~~~~~~~~~~~~

17 | float prev_c,

| ~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:16:47: note: the first parameter in the range is 'gate_base'

16 | int gate_base,

| ^~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:17:49: note: the last parameter in the range is 'prev_c'

17 | float prev_c,

| ^~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:17:43: note: 'int' and 'float' may be implicitly converted

17 | float prev_c,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:34:5: warning: 2 adjacent parameters of 'lstm_elementwise_forward_modular' of similar type ('float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]

34 | float* __restrict__ h,

| ^~~~~~~~~~~~~~~~~~~~~~

35 | float* __restrict__ c,

| ~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:34:25: note: the first parameter in the range is 'h'

34 | float* __restrict__ h,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:35:25: note: the last parameter in the range is 'c'

35 | float* __restrict__ c,

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:39:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

39 | int idx = blockIdx.x * blockDim.x + threadIdx.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:56:5: warning: 2 adjacent parameters of 'linear_forward_kernel_tiled' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]

56 | const float* __restrict__ weight,

| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

57 | const float* __restrict__ bias,

| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:56:31: note: the first parameter in the range is 'weight'

56 | const float* __restrict__ weight,

| ^~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:57:31: note: the last parameter in the range is 'bias'

57 | const float* __restrict__ bias,

| ^~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:61:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

61 | int batch_idx = blockIdx.x; // Each block row corresponds to a unique batch element

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:62:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

62 | int out_idx = blockIdx.y; // Each block column corresponds to a unique output neuron

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:63:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

63 | int tid = threadIdx.x;

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:67:43: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

67 | for (int k = tid; k < input_dim; k += blockDim.x) {

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:77:18: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

77 | for (int s = blockDim.x / 2; s > 0; s >>= 1) {

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:97:5: warning: 2 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]

97 | torch::Tensor input,

| ^~~~~~~~~~~~~~~~~~~~

98 | torch::Tensor w_ih,

| ~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:97:19: note: the first parameter in the range is 'input'

97 | torch::Tensor input,

| ^~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:98:19: note: the last parameter in the range is 'w_ih'

98 | torch::Tensor w_ih,

| ^~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:97:19: warning: the parameter 'input' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]

97 | torch::Tensor input,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:98:19: 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]

98 | torch::Tensor w_ih,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:99:5: warning: 4 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]

99 | torch::Tensor w_hh,

| ^~~~~~~~~~~~~~~~~~~

100 | torch::Tensor b_ih,

| ~~~~~~~~~~~~~~~~~~~

101 | torch::Tensor b_hh,

| ~~~~~~~~~~~~~~~~~~~

102 | torch::Tensor h0,

| ~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:99:19: note: the first parameter in the range is 'w_hh'

99 | torch::Tensor w_hh,

| ^~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:102:19: note: the last parameter in the range is 'h0'

102 | torch::Tensor h0,

| ^~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:99:19: 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]

99 | torch::Tensor w_hh,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:100:19: 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]

100 | torch::Tensor b_ih,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:101:19: 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]

101 | torch::Tensor b_hh,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:102:19: 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]

102 | torch::Tensor h0,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:103:19: 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]

103 | torch::Tensor c0

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:105:10: warning: Value stored to 'options' during its initialization is never read [clang-analyzer-deadcode.DeadStores]

105 | auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());

| ^~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:105:10: note: Value stored to 'options' during its initialization is never read

105 | auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());

| ^~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:106:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

106 | int batch_size = input.size(0);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:107:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

107 | int seq_len = input.size(1);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:108:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

108 | int hidden_size = h0.size(1);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:156:19: 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]

156 | torch::Tensor input,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:157:19: 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]

157 | torch::Tensor weight,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:158:19: 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]

158 | torch::Tensor bias

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:160:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

160 | int batch_size = input.size(0);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:161:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

161 | int input_dim = input.size(1);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:162:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

162 | int output_dim = weight.size(0);

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:185:19: 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]

185 | torch::Tensor x,

| ^

| const &

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:186:5: warning: 4 adjacent parameters of 'forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]

186 | std::vector<torch::Tensor> lstm_weights_ih,

| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

187 | std::vector<torch::Tensor> lstm_weights_hh,

| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

188 | std::vector<torch::Tensor> lstm_biases_ih,

| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

189 | std::vector<torch::Tensor> lstm_biases_hh,

| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:186:32: note: the first parameter in the range is 'lstm_weights_ih'

186 | std::vector<torch::Tensor> lstm_weights_ih,

| ^~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:189:32: note: the last parameter in the range is 'lstm_biases_hh'

189 | std::vector<torch::Tensor> lstm_biases_hh,

| ^~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:191:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]

191 | torch::Tensor fc_bias,

| ^~~~~~~~~~~~~~~~~~~~~~

192 | torch::Tensor h0,

| ~~~~~~~~~~~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:191:19: note: the first parameter in the range is 'fc_bias'

191 | torch::Tensor fc_bias,

| ^~~~~~~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:192:19: note: the last parameter in the range is 'h0'

192 | torch::Tensor h0,

| ^~

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:201:22: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]

201 | int num_layers = lstm_weights_ih.size();

| ^

/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:221:36: 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/20250208_optimize_b5_s4_e1_sweep/level_3/task_35/b4_s2_fused_tiled/edit_1/edit_1.cu:221:47: warning: parameter 'fc_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]

221 | out = linear_forward_cuda(out, fc_weight, fc_bias);

| ^

| std::move( )

The AI CUDA Engineer 👷

`35_LTSM` • `fused_tiled_edit_1`

Kernel Information

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

The AI CUDA Engineer 👷

35_LTSM • fused_tiled_edit_1

Kernel Information

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

`35_LTSM` • `fused_tiled_edit_1`