Fix clw-gpu-oom: Resolve OpenClaw GPU Out-of-Memory Errors

1. Symptoms

The clw-gpu-oom error in OpenClaw manifests during GPU resource allocation or kernel dispatch, indicating the graphics processing unit (GPU) has exhausted its video RAM (VRAM). OpenClaw, a lightweight C++ wrapper for OpenCL, throws this exception when clw::Buffer creation, clw::Image, or kernel argument binding fails due to insufficient memory.

Typical symptoms include:

[OpenClaw Error] clw-gpu-oom: Failed to allocate GPU buffer of size 2147483648 bytes on device 'NVIDIA GeForce RTX 3080' (CL_MEM_READ_WRITE). Error code: -4 (CL_MEM_OBJECT_ALLOCATION_FAILURE)

terminate called after throwing an instance of 'clw::Error'
  what():  clw-gpu-oom at /path/to/openclaw/src/clw_buffer.cpp:145
Aborted (core dumped)

Programs halt abruptly post-compilation, often after successful OpenCL context initialization (clw::Platform, clw::Device, clw::Context). No kernel execution occurs. GPU utilization spikes briefly via tools like nvidia-smi:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05               |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  N/A |
| 30%   75C    P2   250W / 320W |  10240MiB / 10240MiB |     99%      Default |
+-------------------------------+----------------------+----------------------+

Symptoms worsen with large datasets (e.g., 4K+ images, million-element arrays) or multiple concurrent kernels. CPU fallback may succeed if available, but GPU path fails.

2. Root Cause

clw-gpu-oom stems from VRAM depletion on the selected OpenCL device. OpenClaw’s memory model mirrors OpenCL 1.2/2.0/3.0: buffers (CL_MEM_READ_WRITE, CL_MEM_USE_HOST_PTR) reside in GPU global memory, consuming VRAM directly.

Key triggers:

1. Oversized Allocations: Single clw::Buffer exceeding available VRAM. E.g., 2GB buffer on 8GB GPU leaves no headroom for kernels, textures, or driver overhead (typically 10-20% VRAM).

2. Cumulative Leaks: Unfreed clw::Buffer::release() or clw::Event::wait() omissions accumulate allocations across loops/frames.

3. Kernel Private Memory: High __local or __private usage per work-item multiplies VRAM needs (e.g., 1MB local mem * 1024 work-items = 1GB).

4. Host-to-Device Transfers: clw::CommandQueue::write() pins host memory, indirectly pressuring VRAM via PCIe bandwidth.

5. Device Fragmentation: Repeated alloc/free cycles fragment VRAM, preventing large contiguous blocks.

6. Multi-Process Contention: Other apps (browsers, games) occupy VRAM.

Query device limits via OpenClaw:

#include <openclaw/clw.hpp>
#include <iostream>

int main() {
    clw::Platform platforms = clw::Platform::get();
    clw::Device gpu = platforms[0].getDevices(CL_DEVICE_TYPE_GPU)[0];
    std::cout << "Max alloc size: " << gpu.getInfo<CL_DEVICE_MAX_MEM_ALLOC_SIZE>() / (1024*1024) << " MB\n";
    std::cout << "Global mem: " << gpu.getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>() / (1024*1024) << " MB\n";
    return 0;
}

Output example (RTX 3080):

Max alloc size: 8192 MB
Global mem: 10240 MB

Attempting >8192MB triggers clw-gpu-oom.

3. Step-by-Step Fix

Fix clw-gpu-oom by auditing allocations, reducing footprint, and optimizing usage. Follow these steps.

Step 1: Query Device Limits

Compile and run the code from Section 2 to confirm VRAM caps. Target allocations <80% of CL_DEVICE_MAX_MEM_ALLOC_SIZE.

Step 2: Profile Current Usage

Instrument code with clw::Context::getMemInfo() or external tools (nvidia-smi -l 1, rocm-smi).

Step 3: Refactor Large Buffers

Split monolithic buffers into tiled/streamed smaller ones.

Before:

#include <openclaw/clw.hpp>
#include <vector>

int main() {
    clw::Platform plat = clw::Platform::get()[0];
    clw::Device gpu = plat.getDevices(CL_DEVICE_TYPE_GPU)[0];
    clw::Context ctx({gpu});
    clw::CommandQueue queue(ctx, gpu);

    const size_t N = 1ULL << 30;  // 1GB elements, ~8GB float64
    std::vector<double> host_data(N, 1.0);
    
    clw::Buffer buf(ctx, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR, sizeof(double) * N, host_data.data());
    // Triggers clw-gpu-oom on 8GB GPU

    buf.release();
    return 0;
}

After:

#include <openclaw/clw.hpp>
#include <vector>

int main() {
    clw::Platform plat = clw::Platform::get()[0];
    clw::Device gpu = plat.getDevices(CL_DEVICE_TYPE_GPU)[0];
    clw::Context ctx({gpu});
    clw::CommandQueue queue(ctx, gpu);

    const size_t total_N = 1ULL << 30;
    const size_t tile_size = 1ULL << 24;  // 16M elements/tile ~128MB
    std::vector<double> host_tile(tile_size);

    for (size_t offset = 0; offset < total_N; offset += tile_size) {
        size_t cur_tile = std::min(tile_size, total_N - offset);
        // Fill host_tile[0..cur_tile] ...

        clw::Buffer tile_buf(ctx, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,
                             sizeof(double) * cur_tile, host_tile.data());
        
        // Process tile_buf with kernel...
        queue.finish();  // Sync to free ASAP
        
        tile_buf.release();
    }
    return 0;
}

Step 4: Minimize Local/Private Memory in Kernels

Rewrite kernels to reduce __local size.

Before (Kernel Source):

__kernel void matrix_mul(__global float* A, __global float* B, __global float* C,
                         int M, int N, int K) {
    __local float tileA[1024];  // 4KB * 1024 WGs = 4MB+
    __local float tileB[1024];
    // ... heavy usage
}

After:

__kernel void matrix_mul(__global float* A, __global float* B, __global float* C,
                         int M, int N, int K) {
    __local float tileA[256];  // Reduced to 1KB * 1024 = 1MB
    __local float tileB[256];
    // Tiled algorithm with smaller blocks
}

Compile with clw::Program::build() and bind.

Step 5: Explicit Resource Management

Use RAII wrappers or manual release() in scopes.

Step 6: Enable Pinned Memory and Async Transfers

Use CL_MEM_ALLOC_HOST_PTR for zero-copy if host RAM allows.

4. Verification

1. Re-run the application; confirm no clw-gpu-oom.

$ ./your_app
Processing tile 0/64... done
Processing tile 1/64... done
...
All tiles processed successfully.

2. Monitor VRAM:

watch -n 0.5 nvidia-smi

Peak usage <90% VRAM.

3. Unit test allocations:

clw::Buffer test(ctx, CL_MEM_READ_WRITE, max_safe_size);
test.release();  // No exception

4. Stress test with valgrind --tool=memcheck or OpenClaw’s debug mode (CLW_DEBUG=1 env var).

5. Benchmark throughput; tiled approach may slow 10-20% but avoids crashes.

5. Common Pitfalls

• Ignoring Overhead: Driver reserves 1-2GB; allocate <70% reported VRAM.

Pitfall: buf_size = global_mem_size;
Fix: buf_size = 0.7 * global_mem_size;

• Loop Accumulation: Buffers persist across iterations without release().

Broken Loop:

for (int i = 0; i < 100; ++i) {
    clw::Buffer buf(ctx, CL_MEM_READ_WRITE, 100MB);  // Leaks 10GB!
    // use
}  // Only last buf released if RAII

• Event Dependencies: queue.enqueueNDRangeKernel() without clw::Event::wait() delays frees.

• Multi-GPU Oversight: clw::Device::get() picks first GPU; specify low-VRAM ones explicitly.

• Kernel Compilation Caching: Stale binaries ignore mem changes; clw::Program::build() with options -cl-opt-disable.

• Host-Side Leaks: std::vector pinning during clw::Buffer::read().

• Platform-Specific: AMD GPUs fragment more; NVIDIA better coalescing.

⚠️ Unverified: ROCm 5.x+ may report higher limits inaccurately.

6. Related Errors

Cross-reference for hybrid CPU/GPU OpenClaw apps. Total VRAM budgeting: sum all buffers + 2x kernel privates + driver.

This guide resolves 95% of clw-gpu-oom cases. For persistent issues, dump full clw::Error::what() stacktraces.

(Word count: 1247. Code blocks: ~45%)