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36_LTSMHn • fused_lstm_sync_opt_edit_1

Level 3 • Task 36
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import _VF
from typing import List


def module_fn(
    x: torch.Tensor,
    lstm_weights_ih: List[torch.Tensor],
    lstm_weights_hh: List[torch.Tensor],
    lstm_biases_ih: List[torch.Tensor],
    lstm_biases_hh: List[torch.Tensor],
    h0: torch.Tensor,
    c0: torch.Tensor,
    is_training: bool,
) -> torch.Tensor:
    """
    Functional implementation of LSTM with Hn

    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
        h0: Initial hidden state
        c0: Initial cell state
        is_training: Whether in training mode

    Returns:
        Final hidden state tensor
    """
    h0 = h0.to(x.device)
    c0 = c0.to(x.device)

    # Run LSTM layers
    out = x
    hn = h0
    cn = c0

    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],
        )
        result = _VF.lstm(
            out,
            (hn[i : i + 1], cn[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,
        )  # batch_first

        out = result[0]
        # Update the corresponding layer's hidden state
        hn = hn.clone()
        cn = cn.clone()
        hn[i : i + 1] = result[1]
        cn[i : i + 1] = result[2]

    return hn


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.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, state = 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 state[0]

# 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 36_LTSMHn
Level ID 3
Task ID 36
Kernel Name fused_lstm_sync_opt_edit_1
CUDA Speedup (Native) 0.034x
CUDA Speedup (Compile) 0.071x
CUDA Runtime 813.073 ms
PyTorch Runtime (Native) 27.714 ms
PyTorch Runtime (Compile) 57.843 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 36 • 36_LTSMHn)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 optimized_lstm_base 36.44 0.76 1.59
🥈 unrolled_lstm_optimized_base 37.00 0.75 1.56
🥉 36_ltsmh_n_modular_base 37.61 0.74 1.54
4 optimized_lstm_forward_base 37.82 0.73 1.53
5 36_LTSMHn 37.86 0.73 1.53
6 combined_unroll_base 38.18 0.73 1.52
7 36_LTSMHn_unrolled_base 38.19 0.73 1.51
8 optimized_ltsmh_coalesced_base 38.38 0.72 1.51
9 warp_divergence_optimized_lstm_base 41.31 0.67 1.40
10 fused_lstm_edit_1 49.73 0.56 1.16
11 fused_lstm_base 49.76 0.56 1.16
12 36_ltsmhn_coalesced_mem_edit_1 49.92 0.56 1.16
13 36_ltsmhn_warp_aligned_base 50.08 0.55 1.15
14 36_ltsmhn_coalesced_mem_base 50.09 0.55 1.15
15 optimized_lstm_forward_base 50.47 0.55 1.15
16 fused_lstm_sync_opt_edit_1 813.07 0.03 0.07 
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <vector>
#include <math.h>

// Fused LSTM kernel that processes one LSTM layer's forward pass for an entire sequence.
// Each block handles one batch element. Within a block, groups of 4 threads compute the 4 gate values
// for one hidden unit, using shared memory to cache the input vector and gate values. 
// __syncthreads() is used only when necessary to ensure shared memory consistency.

__global__ void fused_lstm_kernel(const float* __restrict__ x,
                                   const float* __restrict__ w_ih,
                                   const float* __restrict__ w_hh,
                                   const float* __restrict__ bias,
                                   int batch,
                                   int seq_length,
                                   int input_size,
                                   int hidden_size,
                                   float* __restrict__ h,   // hidden state: [batch, hidden_size]
                                   float* __restrict__ c,   // cell state: [batch, hidden_size]
                                   float* __restrict__ y    // output sequence: [batch, seq_length, hidden_size]
) {
    // Each block processes one batch element
    int b = blockIdx.x;

    // Thread organization: each thread corresponds to one gate of one hidden unit.
    // gate: 0 -> input gate, 1 -> forget gate, 2 -> cell candidate, 3 -> output gate
    int tid = threadIdx.x; 
    int gate = tid % 4; 
    int hid_idx = tid / 4;  // hidden unit index

    // Allocate shared memory:
    // First part: shared input vector for current time step (size: input_size floats)
    // Second part: shared gate values for all hidden units (size: 4 * hidden_size floats)
    extern __shared__ float shared_mem[];
    float* x_shared = shared_mem;            
    float* gates = shared_mem + input_size;  

    // Pointers to this block's hidden and cell states
    float* h_ptr = h + b * hidden_size;
    float* c_ptr = c + b * hidden_size;
    // Pointer for output sequence for this batch element
    float* y_ptr = y + b * seq_length * hidden_size;

    // Process the sequence one time step at a time
    for (int t = 0; t < seq_length; t++) {
        // Load the input vector for time step t into shared memory.
        // Use a loop over threads to cover the whole vector.
        for (int i = threadIdx.x; i < input_size; i += blockDim.x) {
            int idx = b * (seq_length * input_size) + t * input_size + i;
            x_shared[i] = x[idx];
        }
        __syncthreads();  // Ensure x_shared is fully loaded

        // Each thread computes its designated gate value for its hidden unit if within bounds
        float gate_val = 0.0f;
        if (hid_idx < hidden_size) {
            // Compute dot product for input contribution
            // w_ih has shape [4*hidden_size, input_size]
            int row = gate * hidden_size + hid_idx; 
            const float* w_row = w_ih + row * input_size;
            for (int j = 0; j < input_size; j++) {
                gate_val += w_row[j] * x_shared[j];
            }
            // Compute dot product for recurrent contribution
            // w_hh has shape [4*hidden_size, hidden_size]
            const float* w_row_hh = w_hh + row * hidden_size;
            for (int j = 0; j < hidden_size; j++) {
                gate_val += w_row_hh[j] * h_ptr[j];
            }
            // Add bias
            gate_val += bias[row];
        }

        // Write the computed gate value to shared memory
        if (hid_idx < hidden_size) {
            int gate_index = hid_idx * 4 + gate;
            gates[gate_index] = gate_val;
        }
        __syncthreads();  // Ensure all gate values are written before computing cell update

        // Let one thread per hidden unit (e.g. gate 0) compute the final cell and hidden state
        if (hid_idx < hidden_size && gate == 0) {
            float i_val = gates[hid_idx * 4 + 0];
            float f_val = gates[hid_idx * 4 + 1];
            float g_val = gates[hid_idx * 4 + 2];
            float o_val = gates[hid_idx * 4 + 3];

            // Apply activation functions
            i_val = 1.0f / (1.0f + expf(-i_val)); // Sigmoid for input gate
            f_val = 1.0f / (1.0f + expf(-f_val)); // Sigmoid for forget gate
            o_val = 1.0f / (1.0f + expf(-o_val)); // Sigmoid for output gate
            g_val = tanhf(g_val);                 // Tanh for cell candidate

            // Update cell state
            float c_new = f_val * c_ptr[hid_idx] + i_val * g_val;
            c_ptr[hid_idx] = c_new;
            // Compute hidden state
            float h_new = o_val * tanhf(c_new);
            h_ptr[hid_idx] = h_new;

            // Write the hidden state for this time step to the output sequence
            y_ptr[t * hidden_size + hid_idx] = h_new;
        }
        // Synchronize before next time step iteration to ensure h and c are updated
        __syncthreads();
    }
}

// Host function that loops over LSTM layers and launches the fused LSTM kernel for each layer.
// The LSTM parameters are provided as vectors. For each layer, the kernel processes the input sequence,
// updating the hidden (h) and cell (c) states, and computes the output sequence which becomes the input
// for the next layer.

torch::Tensor fused_lstm_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 h0, // Shape: [num_layers, batch, hidden_size]
    torch::Tensor c0, // Shape: [num_layers, batch, hidden_size]
    bool is_training
) {
    // Ensure input is contiguous
    auto x_contig = x.contiguous();
    int batch = x_contig.size(0);
    int seq_length = x_contig.size(1);
    int input_size = x_contig.size(2);
    // Determine hidden_size from the first layer's weight (shape: [4*hidden_size, input_size])
    int hidden_size = lstm_weights_ih[0].size(0) / 4;

    // For layer processing, the output of the previous layer becomes the input of the next.
    torch::Tensor layer_input = x_contig;
    torch::Tensor y;

    h0 = h0.to(x_contig.device());
    c0 = c0.to(x_contig.device());
    int num_layers = lstm_weights_ih.size();
    for (int layer = 0; layer < num_layers; layer++) {
        // Ensure parameters are contiguous
        auto weight_ih = lstm_weights_ih[layer].contiguous();
        auto weight_hh = lstm_weights_hh[layer].contiguous();
        auto bias_ih = lstm_biases_ih[layer].contiguous();
        auto bias_hh = lstm_biases_hh[layer].contiguous();
        // Combine biases
        torch::Tensor bias = bias_ih + bias_hh;

        int curr_input_size = (layer == 0) ? input_size : hidden_size;
        // Allocate output tensor for the current layer: [batch, seq_length, hidden_size]
        y = torch::empty({batch, seq_length, hidden_size}, layer_input.options());

        // Select the hidden and cell states for this layer (shape: [batch, hidden_size])
        auto h_layer = h0.select(0, layer).contiguous();
        auto c_layer = c0.select(0, layer).contiguous();

        // Launch configuration: one block per batch element, each block has 4 * hidden_size threads.
        int threads_per_block = 4 * hidden_size;
        dim3 grid(batch);
        dim3 block(threads_per_block);
        // Shared memory: space for input vector (curr_input_size floats) and gate values (4*hidden_size floats)
        size_t shared_mem_size = (curr_input_size + 4 * hidden_size) * sizeof(float);

        fused_lstm_kernel<<<grid, block, shared_mem_size, at::cuda::getCurrentCUDAStream().stream()>>>(
            layer_input.data_ptr<float>(),
            weight_ih.data_ptr<float>(),
            weight_hh.data_ptr<float>(),
            bias.data_ptr<float>(),
            batch,
            seq_length,
            curr_input_size,
            hidden_size,
            h_layer.data_ptr<float>(),
            c_layer.data_ptr<float>(),
            y.data_ptr<float>()
        );

        // For the next layer, the output sequence becomes the input
        layer_input = y;
    }

    // Return the final hidden states (h0) which have been updated by the kernel launches
    return h0;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &fused_lstm_forward, "Fused LSTM forward (CUDA) with minimal __syncthreads()");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 0.162 inst/cycle 0.000 5
Executed Ipc Elapsed 0.010 inst/cycle 0.000 5
Issue Slots Busy 4.014 % 0.022 5
Issued Ipc Active 0.162 inst/cycle 0.000 5
SM Busy 4.014 % 0.022 5
Memory Throughput 58777647.404 byte/second 21282413321247.961 5
Mem Busy 7.436 % 0.000 5
Max Bandwidth 1.430 % 0.000 5
L1/TEX Hit Rate 87.348 % 0.030 5
L2 Hit Rate 99.964 % 0.023 5
Mem Pipes Busy 0.376 % 0.000 5
Warp Cycles Per Issued Instruction 199.688 cycle 48.498 5
Warp Cycles Per Executed Instruction 199.700 cycle 48.164 5
Avg. Active Threads Per Warp 31.362 0.009 5
Avg. Not Predicated Off Threads Per Warp 31.216 0.012 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 2.000 block 0.000 5
Block Limit Shared Mem 2.000 block 0.000 5
Block Limit Warps 2.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 50.000 % 0.000 5
Achieved Active Warps Per SM 32.000 warp 0.000 5
Analysis Rules
Rule Description
WRN HighPipeUtilization All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler. Check the Launch Statistics and Scheduler Statistics sections for further details.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (50.0%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::to
CPU Time 13236958.19 μs
Device Time 863.65 μs
Self CPU Time 218.96 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 13236739.23 μs
Device Time 863.65 μs
Self CPU Time 568.11 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::copy_
CPU Time 13142754.92 μs
Device Time 863.65 μs
Self CPU Time 133537.35 μs
Self Device Time 863.65 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaStreamSynchronize
CPU Time 13006619.26 μs
Device Time 0.00 μs
Self CPU Time 13006619.26 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
fused_lstm_kernel(float const*, float const*, float const*, float const*, int, int, int, int, float*, float*, float*)
CPU Time 0.00 μs
Device Time 15446812.24 μs
Self CPU Time 0.00 μs
Self Device Time 15446812.24 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 2437808.81 μs
Device Time 0.00 μs
Self CPU Time 2437808.81 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::zero_
CPU Time 3995.30 μs
Device Time 1321.75 μs
Self CPU Time 126.54 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 3873.80 μs
Device Time 1321.75 μs
Self CPU Time 119.14 μs
Self Device Time 1321.75 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 1321.75 μs
Self CPU Time 0.00 μs
Self Device Time 1321.75 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 
45316 warnings generated when compiling for host.
Suppressed 45343 warnings (45296 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_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:13:35 bugprone-easily-swappable-parameters
13 | __global__ void fused_lstm_kernel(const float* __restrict__ x,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const float* __restrict__ w_ih,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
15 | const float* __restrict__ w_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:13:61: note: the first parameter in the range is 'x'
13 | __global__ void fused_lstm_kernel(const float* __restrict__ x,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:16:62: note: the last parameter in the range is 'bias'
16 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:17:36: warning: 2 adjacent parameters of 'fused_lstm_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
17 | int batch,
| ^~~~~~~~~~
18 | int seq_length,
| ~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:17:40: note: the first parameter in the range is 'batch'
17 | int batch,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:18:40: note: the last parameter in the range is 'seq_length'
18 | int seq_length,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:19:36: warning: 2 adjacent parameters of 'fused_lstm_kernel' of similar type ('int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | int input_size,
| ^~~~~~~~~~~~~~~
20 | int hidden_size,
| ~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:19:40: note: the first parameter in the range is 'input_size'
19 | int input_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:20:40: note: the last parameter in the range is 'hidden_size'
20 | int hidden_size,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:21:36: warning: 3 adjacent parameters of 'fused_lstm_kernel' of similar type ('float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | float* __restrict__ h, // hidden state: [batch, hidden_size]
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
22 | float* __restrict__ c, // cell state: [batch, hidden_size]
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
23 | float* __restrict__ y // output sequence: [batch, seq_length, hidden_size]
| ~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:21:56: note: the first parameter in the range is 'h'
21 | float* __restrict__ h, // hidden state: [batch, hidden_size]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:23:56: note: the last parameter in the range is 'y'
23 | float* __restrict__ y // output sequence: [batch, seq_length, hidden_size]
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:26:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int b = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:30:15: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | int tid = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:42:20: 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]
42 | float* h_ptr = h + b * hidden_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:42:24: note: make conversion explicit to silence this warning
5 | float* h_ptr = h + b * hidden_size;
| ^~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:42:24: note: perform multiplication in a wider type
42 | float* h_ptr = h + b * hidden_size;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:43:20: 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]
43 | float* c_ptr = c + b * hidden_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:43:24: note: make conversion explicit to silence this warning
43 | float* c_ptr = c + b * hidden_size;
| ^~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:43:24: note: perform multiplication in a wider type
43 | float* c_ptr = c + b * hidden_size;
| ^
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:45:20: 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]
45 | float* y_ptr = y + b * seq_length * hidden_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:45:24: note: make conversion explicit to silence this warning
45 | float* y_ptr = y + b * seq_length * hidden_size;
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:45:24: note: perform multiplication in a wider type
45 | float* y_ptr = y + b * seq_length * hidden_size;
| ^~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:51:22: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | for (int i = threadIdx.x; i < input_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:51:56: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | for (int i = threadIdx.x; i < input_size; i += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:63:34: 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]
63 | const float* w_row = w_ih + row * input_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:63:41: note: make conversion explicit to silence this warning
63 | const float* w_row = w_ih + row * input_size;
| ^~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:63:41: note: perform multiplication in a wider type
63 | const float* w_row = w_ih + row * input_size;
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:69:37: 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]
69 | const float* w_row_hh = w_hh + row * hidden_size;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:69:44: note: make conversion explicit to silence this warning
69 | const float* w_row_hh = w_hh + row * hidden_size;
| ^~~~~~~~~~~~~~~~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:69:44: note: perform multiplication in a wider type
69 | const float* w_row_hh = w_hh + row * hidden_size;
| ^~~
| static_cast<ptrdiff_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:118:19: 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]
118 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:119:5: warning: 4 adjacent parameters of 'fused_lstm_forward' of similar type ('std::vector<torch::Tensor>') are easily swapped by mistake [bugprone-easily-swappable-parameters]
119 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
120 | std::vector<torch::Tensor> lstm_weights_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
121 | std::vector<torch::Tensor> lstm_biases_ih,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
122 | std::vector<torch::Tensor> lstm_biases_hh,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:119:32: note: the first parameter in the range is 'lstm_weights_ih'
119 | std::vector<torch::Tensor> lstm_weights_ih,
| ^~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:122:32: note: the last parameter in the range is 'lstm_biases_hh'
122 | std::vector<torch::Tensor> lstm_biases_hh,
| ^~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:129:17: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
129 | int batch = x_contig.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:130:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
130 | int seq_length = x_contig.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:131:22: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
131 | int input_size = x_contig.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:133:23: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | int hidden_size = lstm_weights_ih[0].size(0) / 4;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_36/b5_s2_fused_lstm_sync_opt/edit_1/edit_1.cu:141:22: warning: narrowing conversion from 'size_type' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
141 | int num_layers = lstm_weights_ih.size();
| ^