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35_LTSM • 35_lstm_ldg_aligned_v2_base

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_ldg_aligned_v2_base
CUDA Speedup (Native) 0.361x
CUDA Speedup (Compile) 0.683x
CUDA Runtime 88.503 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 1.00
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>

// Optimized LSTM elementwise kernel using __ldg() for read-only accesses and assuming 128-bit aligned memory
__global__ void lstm_elementwise_forward_aligned(
    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_idx = b * hidden_size * 4 + n;

        // Use __ldg() to load read-only global memory with assumed 128-bit alignment
        float i_gate = __ldg(&gates[gate_idx]);
        float f_gate = __ldg(&gates[gate_idx + hidden_size]);
        float g_gate = __ldg(&gates[gate_idx + 2 * hidden_size]);
        float o_gate = __ldg(&gates[gate_idx + 3 * hidden_size]);

        // Activation computations
        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 * __ldg(&prev_c[idx]) + i_gate * g_gate;
        float h_new = o_gate * tanhf(c_new);

        // Write operations (assumed to be naturally aligned)
        c[idx] = c_new;
        h[idx] = h_new;
    }
}

// Optimized linear layer kernel using __ldg() for read-only loads and assuming 128-bit alignment
__global__ void linear_forward_kernel_aligned(
    const float* __restrict__ input,    // [batch_size, input_dim]
    const float* __restrict__ weight,   // [output_dim, input_dim]
    const float* __restrict__ bias,     // [output_dim]
    float* __restrict__ output,         // [batch_size, output_dim]
    int batch_size,
    int input_dim,
    int output_dim
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < batch_size * output_dim) {
        int batch_idx = idx / output_dim;
        int out_idx = idx % output_dim;
        float sum = 0.0f;
        
        // Use __ldg() for each load; assuming input arrays are 128-bit aligned
        for (int k = 0; k < input_dim; ++k) {
            float in_val = __ldg(&input[batch_idx * input_dim + k]);
            float wt_val = __ldg(&weight[out_idx * input_dim + k]);
            sum += in_val * wt_val;
        }

        if (bias != nullptr) {
            sum += __ldg(&bias[out_idx]);
        }
        output[batch_idx * output_dim + out_idx] = sum;
    }
}

// LSTM forward pass: processes each timestep using the optimized LSTM 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 hidden_size = h0.size(1);

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

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

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

        // Compute gates: gates = xt * W_ih^T + h * 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;

        // Launch the optimized LSTM kernel that uses __ldg() for read-only accesses
        lstm_elementwise_forward_aligned<<<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);
}

// Optimized linear forward pass using the aligned kernel with __ldg() loads
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());
    torch::Tensor output = torch::empty({batch_size, output_dim}, options);

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

    linear_forward_kernel_aligned<<<blocks, threads>>>(
        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 combining multiple 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
) {
    // Ensure initial states 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());

        // Use the corresponding slice of h0 and c0 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);

        out = lstm_forward_cuda(out, w_ih, w_hh, b_ih, b_hh, h_i, c_i);
    }

    // Use the last timestep's output for 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, "CUDA forward pass with __ldg() and 128-bit alignment optimizations");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.168 inst/cycle 0.000 5
Executed Ipc Elapsed 0.000 inst/cycle 0.000 5
Issue Slots Busy 4.264 % 0.021 5
Issued Ipc Active 0.168 inst/cycle 0.000 5
SM Busy 4.472 % 0.022 5
Memory Throughput 1535046747.734 byte/second 1661044752077763.250 5
Mem Busy 1.886 % 0.002 5
Max Bandwidth 1.004 % 0.001 5
L1/TEX Hit Rate 94.570 % 0.000 5
L2 Hit Rate 101.096 % 0.477 5
Mem Pipes Busy 0.060 % 0.000 5
Warp Cycles Per Issued Instruction 23.868 cycle 0.695 5
Warp Cycles Per Executed Instruction 24.152 cycle 0.718 5
Avg. Active Threads Per Warp 25.140 0.000 5
Avg. Not Predicated Off Threads Per Warp 24.840 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 6.396 % 0.002 5
Achieved Active Warps Per SM 4.094 warp 0.001 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 (6.3%) 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 797862.70 μs
Device Time 0.00 μs
Self CPU Time 797862.70 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 863876.58 μs
Device Time 0.00 μs
Self CPU Time 374231.97 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::addmm
CPU Time 6242132.47 μs
Device Time 2553156.31 μs
Self CPU Time 3892976.09 μs
Self Device Time 2553156.31 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 2609743.31 μs
Device Time 88012.02 μs
Self CPU Time 2609743.31 μs
Self Device Time 88012.02 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 1852422.75 μs
Self CPU Time 0.00 μs
Self Device Time 1852422.75 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 1248675.87 μs
Device Time 659766.26 μs
Self CPU Time 538259.72 μs
Self Device Time 659766.26 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 659766.26 μs
Self CPU Time 0.00 μs
Self Device Time 659766.26 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_aligned(float const*, float const*, float*, float*, int, int)
CPU Time 0.00 μs
Device Time 518649.78 μs
Self CPU Time 0.00 μs
Self Device Time 518649.78 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 
45313 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/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:7:5 bugprone-easily-swappable-parameters
7 | const float* __restrict__ gates,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 | const float* __restrict__ prev_c,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:7:31: note: the first parameter in the range is 'gates'
7 | const float* __restrict__ gates,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:8:31: note: the last parameter in the range is 'prev_c'
8 | const float* __restrict__ prev_c,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:9:5: warning: 2 adjacent parameters of 'lstm_elementwise_forward_aligned' of similar type ('float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
9 | float* __restrict__ h,
| ^~~~~~~~~~~~~~~~~~~~~~
10 | float* __restrict__ c,
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:9:25: note: the first parameter in the range is 'h'
9 | float* __restrict__ h,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:10:25: note: the last parameter in the range is 'c'
10 | float* __restrict__ c,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:14:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
14 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:43:5: warning: 3 adjacent parameters of 'linear_forward_kernel_aligned' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
43 | const float* __restrict__ input, // [batch_size, input_dim]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
44 | const float* __restrict__ weight, // [output_dim, input_dim]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
45 | const float* __restrict__ bias, // [output_dim]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:43:31: note: the first parameter in the range is 'input'
43 | const float* __restrict__ input, // [batch_size, input_dim]
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:45:31: note: the last parameter in the range is 'bias'
45 | const float* __restrict__ bias, // [output_dim]
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:47:5: warning: 2 adjacent parameters of 'linear_forward_kernel_aligned' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
47 | int batch_size,
| ^~~~~~~~~~~~~~~
48 | int input_dim,
| ~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:47:9: note: the first parameter in the range is 'batch_size'
47 | int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:48:9: note: the last parameter in the range is 'input_dim'
48 | int input_dim,
| ^~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:51:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:73:5: warning: 2 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
73 | torch::Tensor input,
| ^~~~~~~~~~~~~~~~~~~~
74 | torch::Tensor w_ih,
| ~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:73:19: note: the first parameter in the range is 'input'
73 | torch::Tensor input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:74:19: note: the last parameter in the range is 'w_ih'
74 | torch::Tensor w_ih,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:73:19: 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]
73 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:74:19: 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]
74 | torch::Tensor w_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:75:5: warning: 4 adjacent parameters of 'lstm_forward_cuda' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
75 | torch::Tensor w_hh,
| ^~~~~~~~~~~~~~~~~~~
76 | torch::Tensor b_ih,
| ~~~~~~~~~~~~~~~~~~~
77 | torch::Tensor b_hh,
| ~~~~~~~~~~~~~~~~~~~
78 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:75:19: note: the first parameter in the range is 'w_hh'
75 | torch::Tensor w_hh,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:78:19: note: the last parameter in the range is 'h0'
78 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:75:19: 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]
75 | torch::Tensor w_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:76:19: 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]
76 | torch::Tensor b_ih,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:77:19: 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]
77 | torch::Tensor b_hh,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:78:19: 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]
78 | torch::Tensor h0,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:79:19: 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]
79 | torch::Tensor c0
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:81:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:82:19: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
82 | int seq_len = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:83:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
83 | int hidden_size = h0.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:120:19: 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]
120 | torch::Tensor input,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:121:19: 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]
121 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:122:19: 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]
122 | torch::Tensor bias
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:124:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | int batch_size = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:125:21: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | int input_dim = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:126:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | int output_dim = weight.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:150:19: 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]
150 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:151:5: warning: 4 adjacent parameters of 'forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
151 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
152 | std::vector<torch::Tensor> lstm_weights_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
153 | std::vector<torch::Tensor> lstm_biases_ih,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
154 | std::vector<torch::Tensor> lstm_biases_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:151:32: note: the first parameter in the range is 'lstm_weights_ih'
151 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:154:32: note: the last parameter in the range is 'lstm_biases_hh'
154 | std::vector<torch::Tensor> lstm_biases_hh,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:156:5: warning: 2 adjacent parameters of 'forward' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
156 | torch::Tensor fc_bias,
| ^~~~~~~~~~~~~~~~~~~~~~
157 | torch::Tensor h0,
| ~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:156:19: note: the first parameter in the range is 'fc_bias'
156 | torch::Tensor fc_bias,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:157:19: note: the last parameter in the range is 'h0'
157 | torch::Tensor h0,
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:165:22: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
165 | int num_layers = lstm_weights_ih.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:182:36: 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/20250203_optimize_b10_s4_e0_sweep/level_3/task_35/b5_s1_35_lstm_ldg_aligned_v2/base/base.cu:182:47: warning: parameter 'fc_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
182 | out = linear_forward_cuda(out, fc_weight, fc_bias);
| ^
| std::move( )