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35_LTSM • 35_lstm_modular_device_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_modular_device_edit_1
CUDA Speedup (Native) 0.426x
CUDA Speedup (Compile) 0.805x
CUDA Runtime 75.066 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>

namespace {

__device__ inline float sigmoid(float x) {
    return 1.0f / (1.0f + __expf(-x));
}

__device__ inline void compute_gates(
    const float* gates,
    int idx,
    int b,
    int n,
    int hidden_size,
    float& i,
    float& f,
    float& g,
    float& o
) {
    int gate_idx = b * hidden_size * 4 + n;
    i = sigmoid(gates[gate_idx]);
    f = sigmoid(gates[gate_idx + hidden_size]);
    g = tanhf(gates[gate_idx + 2 * hidden_size]);
    o = sigmoid(gates[gate_idx + 3 * hidden_size]);
}

__global__ void lstm_elementwise_forward_optimized(
    const float* __restrict__ gates,
    const float* __restrict__ prev_c,
    float* __restrict__ h,
    float* __restrict__ c,
    int batch_size,
    int hidden_size
) {
    const int total_elements = batch_size * hidden_size;
    for (int idx = blockIdx.x * blockDim.x + threadIdx.x;
         idx < total_elements;
         idx += blockDim.x * gridDim.x)
    {
        const int b = idx / hidden_size;
        const int n = idx % hidden_size;
        
        float i, f, g, o;
        compute_gates(gates, idx, b, n, hidden_size, i, f, g, o);

        const float c_prev = prev_c[idx];
        const float c_new = f * c_prev + i * g;
        const float h_new = o * tanhf(c_new);

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

__global__ void optimized_linear_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int in_features,
    int out_features,
    int batch_size
) {
    const int gid = blockIdx.x * blockDim.x + threadIdx.x;
    const int stride = blockDim.x * gridDim.x;

    for (int idx = gid; idx < batch_size * out_features; idx += stride) {
        const int b = idx / out_features;
        const int o = idx % out_features;
        
        float sum = 0.0f;
        const float* row_in = &input[b * in_features];
        const float* row_w = &weight[o * in_features];
        
        #pragma unroll 4
        for (int i = 0; i < in_features; ++i) {
            sum += row_in[i] * row_w[i];
        }
        
        if (bias) sum += bias[o];
        output[idx] = sum;
    }
}

} // anonymous namespace

torch::Tensor lstm_forward_optimized(
    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
) {
    const int batch_size = input.size(0);
    const int seq_len = input.size(1);
    const int hidden_size = h0.size(1);

    torch::Tensor h = h0.clone();
    torch::Tensor c = c0.clone();
    std::vector<torch::Tensor> outputs;

    constexpr int threads = 128;
    const int blocks = (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())
                              .addmm_(h, w_hh.t())
                              .add_(b_hh);

        lstm_elementwise_forward_optimized<<<blocks, threads>>>(
            gates.data_ptr<float>(),
            c.data_ptr<float>(),
            h.data_ptr<float>(),
            c.data_ptr<float>(),
            batch_size,
            hidden_size
        );

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

    return torch::cat(outputs, 1);
}

torch::Tensor linear_forward_optimized(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias
) {
    const int batch_size = input.size(0);
    const int in_features = input.size(1);
    const int out_features = weight.size(0);

    auto output = torch::empty({batch_size, out_features}, input.options());
    
    constexpr int threads = 256;
    const int blocks = (batch_size * out_features + threads - 1) / threads;

    optimized_linear_kernel<<<blocks, threads>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.defined() ? bias.data_ptr<float>() : nullptr,
        output.data_ptr<float>(),
        in_features,
        out_features,
        batch_size
    );

    return output;
}

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;
    const 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_optimized(out, w_ih, w_hh, b_ih, b_hh, h_i, c_i);
    }

    return linear_forward_optimized(out.select(1, -1), fc_weight, fc_bias);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Optimized LSTM forward pass with modular device functions");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.246 inst/cycle 0.000 5
Executed Ipc Elapsed 0.000 inst/cycle 0.000 5
Issue Slots Busy 6.208 % 0.016 5
Issued Ipc Active 0.250 inst/cycle 0.000 5
SM Busy 6.208 % 0.016 5
Memory Throughput 1514298500.038 byte/second 177898256369304.406 5
Mem Busy 1.744 % 0.001 5
Max Bandwidth 0.902 % 0.000 5
L1/TEX Hit Rate 94.570 % 0.000 5
L2 Hit Rate 102.064 % 0.049 5
Mem Pipes Busy 0.050 % 0.000 5
Warp Cycles Per Issued Instruction 15.976 cycle 0.066 5
Warp Cycles Per Executed Instruction 16.130 cycle 0.068 5
Avg. Active Threads Per Warp 25.110 0.000 5
Avg. Not Predicated Off Threads Per Warp 24.980 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 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 6.302 % 0.005 5
Achieved Active Warps Per SM 4.034 warp 0.002 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 (6.2%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::t
CPU Time 1002005.59 μs
Device Time 0.00 μs
Self CPU Time 451048.37 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 3804826.92 μs
Device Time 1239581.84 μs
Self CPU Time 2792653.52 μs
Self Device Time 1239576.20 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 1782008.99 μs
Device Time 1213874.55 μs
Self CPU Time 1008272.77 μs
Self Device Time 1213874.55 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 2991193.27 μs
Device Time 113750.06 μs
Self CPU Time 2991193.27 μs
Self Device Time 113750.06 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 2188545.59 μs
Self CPU Time 0.00 μs
Self Device Time 2188545.59 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 1464296.41 μs
Device Time 791559.30 μs
Self CPU Time 658869.95 μs
Self Device Time 791559.30 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 791559.30 μs
Self CPU Time 0.00 μs
Self Device Time 791559.30 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 
45319 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/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:13:5 bugprone-easily-swappable-parameters
13 | int idx,
| ^~~~~~~~
14 | int b,
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:13:9: note: the first parameter in the range is 'idx'
13 | int idx,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:14:9: note: the last parameter in the range is 'b'
14 | int b,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:17:5: warning: 4 adjacent parameters of 'compute_gates' of similar type ('float &') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | float& i,
| ^~~~~~~~~
18 | float& f,
| ~~~~~~~~~
19 | float& g,
| ~~~~~~~~~
20 | float& o
| ~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:17:12: note: the first parameter in the range is 'i'
17 | float& i,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:20:12: note: the last parameter in the range is 'o'
20 | float& o
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:30:5: warning: 2 adjacent parameters of 'lstm_elementwise_forward_optimized' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
30 | const float* __restrict__ gates,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
31 | const float* __restrict__ prev_c,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:30:31: note: the first parameter in the range is 'gates'
30 | const float* __restrict__ gates,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:31:31: note: the last parameter in the range is 'prev_c'
31 | const float* __restrict__ prev_c,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:32:5: warning: 2 adjacent parameters of 'lstm_elementwise_forward_optimized' of similar type ('float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
32 | float* __restrict__ h,
| ^~~~~~~~~~~~~~~~~~~~~~
33 | float* __restrict__ c,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:32:25: note: the first parameter in the range is 'h'
32 | float* __restrict__ h,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:33:25: note: the last parameter in the range is 'c'
33 | float* __restrict__ c,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:38:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
38 | for (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_s3_35_lstm_modular_device/edit_1/edit_1.cu:40:17: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
40 | idx += blockDim.x * gridDim.x)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:58:5: warning: 3 adjacent parameters of 'optimized_linear_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
58 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
59 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
60 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:58:31: note: the first parameter in the range is 'input'
58 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:60:31: note: the last parameter in the range is 'bias'
60 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:62:5: warning: 2 adjacent parameters of 'optimized_linear_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
62 | int in_features,
| ^~~~~~~~~~~~~~~~
63 | int out_features,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:62:9: note: the first parameter in the range is 'in_features'
62 | int in_features,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:63:9: note: the last parameter in the range is 'out_features'
63 | int out_features,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:66:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
66 | const int gid = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:67:24: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:74:32: 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]
74 | const float* row_in = &input[b * in_features];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:74:38: note: make conversion explicit to silence this warning
2 | const float* row_in = &input[b * in_features];
| ^~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:74:38: note: perform multiplication in a wider type
74 | const float* row_in = &input[b * in_features];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:75:31: 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]
75 | const float* row_w = &weight[o * in_features];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:75:38: note: make conversion explicit to silence this warning
75 | const float* row_w = &weight[o * in_features];
| ^~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:75:38: note: perform multiplication in a wider type
75 | const float* row_w = &weight[o * in_features];
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:90:5: warning: 2 adjacent parameters of 'lstm_forward_optimized' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
90 | torch::Tensor input,
| ^~~~~~~~~~~~~~~~~~~~
91 | torch::Tensor w_ih,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:90:19: note: the first parameter in the range is 'input'
90 | torch::Tensor input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:91:19: note: the last parameter in the range is 'w_ih'
91 | torch::Tensor w_ih,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:90:19: 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]
90 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:91:19: 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]
91 | torch::Tensor w_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:92:19: 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]
92 | torch::Tensor w_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:93:19: 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]
93 | torch::Tensor b_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:94:5: warning: 2 adjacent parameters of 'lstm_forward_optimized' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
94 | torch::Tensor b_hh,
| ^~~~~~~~~~~~~~~~~~~
95 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:94:19: note: the first parameter in the range is 'b_hh'
94 | torch::Tensor b_hh,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:95:19: note: the last parameter in the range is 'h0'
95 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:94:19: 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]
94 | torch::Tensor b_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:95:19: 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]
95 | torch::Tensor h0,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:96:19: 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]
96 | torch::Tensor c0
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:98:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
98 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:99:25: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | const int seq_len = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:100:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
100 | const int hidden_size = h0.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:131:19: 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]
131 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:132:19: 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]
132 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:133:19: 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]
133 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:135:28: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
135 | const int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:136:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | const int in_features = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:137:30: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | const int out_features = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:158:19: 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]
158 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:159:5: warning: 4 adjacent parameters of 'forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
159 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
160 | std::vector<torch::Tensor> lstm_weights_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
161 | std::vector<torch::Tensor> lstm_biases_ih,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
162 | 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_s3_35_lstm_modular_device/edit_1/edit_1.cu:159:32: note: the first parameter in the range is 'lstm_weights_ih'
159 | 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_s3_35_lstm_modular_device/edit_1/edit_1.cu:162:32: note: the last parameter in the range is 'lstm_biases_hh'
162 | 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_s3_35_lstm_modular_device/edit_1/edit_1.cu:164:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
164 | torch::Tensor fc_bias,
| ^~~~~~~~~~~~~~~~~~~~~~
165 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:164:19: note: the first parameter in the range is 'fc_bias'
164 | torch::Tensor fc_bias,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:165:19: note: the last parameter in the range is 'h0'
165 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:173:28: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
173 | const 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_s3_35_lstm_modular_device/edit_1/edit_1.cu:187:56: warning: parameter 'fc_weight' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
2 | return linear_forward_optimized(out.select(1, -1), fc_weight, fc_bias);
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_35/b4_s3_35_lstm_modular_device/edit_1/edit_1.cu:187:67: warning: parameter 'fc_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
187 | return linear_forward_optimized(out.select(1, -1), fc_weight, fc_bias);
| ^
| std::move( )