Running LLamaSharp on gpu #189

jonty-esterhuizen · 2023-10-09T11:35:22Z

so i am currently using LLamaSharp like this

    public StatefulChatService ( IConfiguration configuration )
        {
            _configuration = configuration;

            modelPath = Path.Combine(_configuration [ "ModelPath" ], _configuration [ "ModelName" ]);
            systemPrompt = File.ReadAllText(_configuration [ "ChatWithBase" ]);
            _context = new LLamaContext(new LLama.Common.ModelParams(modelPath)
            {
                ContextSize = 2048,
                GpuLayerCount = 50,
                MainGpu=0
            });

            _chatSession = new ChatSession(new InteractiveExecutor(_context));
            _chatSession.AddInputTransform(new MyInputTransform1());
            _chatSession.AddInputTransform(new MyInputTransform2());
            _chatSession.AddInputTransform(new MyInputTransform3());
        } 
        public void Dispose ()
        {
            _chatSession.SaveSession(_configuration [ "SavedSessionPath" ]);
            _context?.Dispose();
        }

        public async Task<string> ChatAsync ( SendMessageInput input )
        {
            var userInput = input.Text;
            if (!_continue)
            {
                userInput = systemPrompt + userInput;
                Console.Write(systemPrompt);
                _continue = true;
            }

            Console.ForegroundColor = ConsoleColor.Green;
            Console.Write(input.Text);

            Console.ForegroundColor = ConsoleColor.White;
            var outputs = _chatSession.ChatAsync(userInput, new LLama.Common.InferenceParams()
            {
                RepeatPenalty = 1.0f,
                AntiPrompts = new string [ ] { "User:" },
                FrequencyPenalty = 1.0f,
                Temperature=0.5f
            });
            var result = "";
            await foreach (var output in outputs)
            {
                Console.Write(output);
                result += output;
            }

            return result;
        }

but the issue i am encountering is that i cant seem to let this run on my gpu it only uses my cpu and ram

The text was updated successfully, but these errors were encountered:

AsakusaRinne · 2023-10-11T09:49:54Z

Hi, which backend package did you install for your project? There's three kinds of backends: cpu, cuda and Metal (MAC), be care that cpu and cuda backend shouldn't be installed at the same time.

jonty-esterhuizen · 2023-10-11T10:35:07Z

so when using LLamaSharp.Backend.cuda12 and LLamaSharp both versions 0.5.1
i encountered this error
RuntimeError: The native library cannot be found. It could be one of the following reasons:

No LLamaSharp backend was installed. Please search LLamaSharp.Backend and install one of them.
You are using a device with only CPU but installed cuda backend. Please install cpu backend instead.
The backend is not compatible with your system cuda environment. Please check and fix it. If the environment is expected not to be changed, then consider build llama.cpp from source or submit an issue to LLamaSharp.
One of the dependencies of the native library is missing.

here is my system specs

jonty-esterhuizen · 2023-10-12T11:56:59Z

i also moved over to importing

and using this as a direct reference to my project
my debugger is set to gpu

this is how i set my contex

    _context = LLamaWeights.LoadFromFile(new LLama.Common.ModelParams(configPaths.ModelPath)
    {
        ContextSize = 1024,
        GpuLayerCount = 50,
        LowVram = false,
        Seed = random.Next(1, 99999),
        ConvertEosToNewLine = true,
        EmbeddingMode=true,
        Encoding = Encoding.UTF8,
        

    }).CreateContext(new LLama.Common.ModelParams(configPaths.ModelPath)
    {
        ContextSize = 1024,
        GpuLayerCount = 50,
        LowVram = false,
        Seed = random.Next(1, 99999),
        ConvertEosToNewLine = true,
        EmbeddingMode = true,
        Encoding = Encoding.UTF8,


    });
  _chatSession = new ChatSession(new InteractiveExecutor(_context));

and this is my chat method


        public async Task<string> ChatAsync ( SendMessageInput input )
        {
            var userInput = input.Text;
            if (!_continue)
            {
                userInput = systemPrompt + userInput;
                Console.Write(systemPrompt);
                _continue = true;
            }

            Console.ForegroundColor = ConsoleColor.Green;
            Console.Write(input.Text);

            Console.ForegroundColor = ConsoleColor.White;
            var outputs = _chatSession.ChatAsync(userInput, new LLama.Common.InferenceParams()
            {
                RepeatPenalty = 1.0f,
                AntiPrompts = new string [ ] { "User:" },
                FrequencyPenalty = 1.0f,
                Temperature = 0.5f
            });
            var result = "";
            await foreach (var output in outputs)
            {
                Console.Write(output);
                result += output;
            }

            return result;
        }

martindevans · 2023-10-12T12:24:39Z

With this new setup do you get the RuntimeError: The native library cannot be found message, or does it run but on CPU only?

jonty-esterhuizen · 2023-10-12T12:25:56Z

No errors just running on cpu

…

On Thu, 12 Oct 2023, 2:24 pm Martin Evans, ***@***.***> wrote: With this new setup do you get the RuntimeError: The native library cannot be found message, or does it run but on CPU only? — Reply to this email directly, view it on GitHub <#189 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ATR54HTUX3TYUFIOTYQAHTDX67ORFANCNFSM6AAAAAA5YU6RUI> . You are receiving this because you authored the thread.Message ID: ***@***.***>

martindevans · 2023-10-12T12:31:07Z

I think if you've got all of the dependencies it prefers to use the CPU ones at the moment, so you may have to delete all the non-CUDA DLLs.

jonty-esterhuizen · 2023-10-12T12:33:53Z

what dlls do I delete and do I keep to get this to work

and where can I get the correct dlls

martindevans · 2023-10-12T12:40:46Z

If you've cloned this repo you should have the right DLLs, you just need to trim it down to just the ones you want:

Having all the DLLs is like having all of the backends installed.

I'm guessing that once you're loading the CUDA DLL you'll get the Runtime error again. If so that's probably because some dependencies it requires are missing.

jonty-esterhuizen · 2023-10-12T12:45:16Z

System.TypeInitializationException: The type initializer for 'LLama.Native.NativeApi' threw an exception.
 ---> LLama.Exceptions.RuntimeError: The native library cannot be found. It could be one of the following reasons: 
1. No LLamaSharp backend was installed. Please search LLamaSharp.Backend and install one of them. 
2. You are using a device with only CPU but installed cuda backend. Please install cpu backend instead. 
3. The backend is not compatible with your system cuda environment. Please check and fix it. If the environment is expected not to be changed, then consider build llama.cpp from source or submit an issue to LLamaSharp.
4. One of the dependency of the native library is missed.

yea it seems to pop out that error is there a way i can see what is missing

martindevans · 2023-10-12T12:51:57Z

Unfortunately no, as far as I know. I've always wondered why that exception doesn't just list exactly what it's missing :'(

A couple of things to check:

Run nvidia-smi and check what CUDA version is listed
Make sure the CUDA stuff is actually installed (https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)

jonty-esterhuizen · 2023-10-12T13:10:40Z

jonty-esterhuizen · 2023-10-12T17:05:43Z

so after downloading and installing (https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)

 Microsoft.AspNetCore.Diagnostics.DeveloperExceptionPageMiddleware[1]
      An unhandled exception has occurred while executing the request.
      System.TypeInitializationException: The type initializer for 'LLama.Native.NativeApi' threw an exception.
       ---> LLama.Exceptions.RuntimeError: The native library cannot be found. It could be one of the following reasons:

martindevans · 2023-10-12T17:34:51Z

I'm at a bit of a loss what could be the problem in that case :/

Does the normal llama.cpp demo application (main.exe) work on GPU?

saddam213 · 2023-10-19T20:25:36Z

did you rename the file libllama-cuda12.dll to libllama.dll?

This has tripped up a few people I have helped with LLamaSharp

PaulaScholz · 2023-10-20T03:45:12Z

I have a very similar issue. I have one machine, a 10 core I9 with 128gb and 3090 GPU. LlamaSharp runs very fast and is certainly using the GPU. On another machine, a 56 core Xeon with 128GB, 4 NVIDIA A6000 GPUs, it runs dog slow, uses up almost all the CPU. It does seem to use one GPU, but not much.

I have CUDA 12 and the latest NVIDIA drivers on each system. Output from both say they are using the GPU, but 3090 is super fast and the other is super slow. I am using the 0.5.1 Nuget package and the 0.5.1 Backend.Cuda12 on both.

Also, how would I get LlamaSharp to use more than 1 GPU?

martindevans · 2023-10-20T12:49:23Z

Also, how would I get LlamaSharp to use more than 1 GPU?

Currently not possible, but I've just opened this PR to add support for it: #202

I don't personally use CUDA and I don't have multiple GPUs to test with, so if you could test it and confirm that it works for you that would be much appreciated!

PaulaScholz · 2023-10-20T15:14:34Z

I would be delighted to try it but I can't get it working at all with any GPU on the multi-GPU system. Also, FYI, the 70b model doesn't seem to work on my single GPU system (128gb ram, 3090 card). Nonetheless, I will try the updated code, maybe it will fix the underlying issue.

PaulaScholz · 2023-10-21T04:03:51Z

So, I cloned the MultiGpu branch and tried it. Got SystemAccessViolationException when trying to load the 13b model in native.cs, SafeLlamaModelHandle at
var model_ptr = NativeApi.llama_load_model_from_file(modelPath, lparams);

Here is the model output:

ggml_init_cublas: found 4 CUDA devices:
Device 0: NVIDIA RTX A6000, compute capability 8.6
Device 1: NVIDIA RTX A6000, compute capability 8.6
Device 2: NVIDIA RTX A6000, compute capability 8.6
Device 3: NVIDIA RTX A6000, compute capability 8.6
llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from C:\PaulaLlamaModels\llama-2-13b-chat.Q5_K_M.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor 0: token_embd.weight q5_K [ 5120, 32000, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 20: blk.10.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 22: blk.10.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 24: blk.10.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 25: blk.10.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 26: blk.10.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 27: blk.10.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 29: blk.11.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 31: blk.11.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 33: blk.11.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 34: blk.11.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 35: blk.11.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 36: blk.11.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 38: blk.12.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 40: blk.12.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 42: blk.12.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 43: blk.12.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 44: blk.12.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 45: blk.12.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 47: blk.13.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 49: blk.13.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 51: blk.13.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 52: blk.13.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 53: blk.13.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 54: blk.13.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 56: blk.14.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 57: blk.14.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 58: blk.14.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 59: blk.14.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 60: blk.14.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 61: blk.14.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 62: blk.14.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 63: blk.14.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 64: blk.15.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 65: blk.15.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 66: blk.2.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 67: blk.2.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 68: blk.2.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 69: blk.2.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 70: blk.2.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 71: blk.2.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 72: blk.2.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 73: blk.2.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 74: blk.2.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 75: blk.3.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 76: blk.3.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 77: blk.3.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 78: blk.3.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 79: blk.3.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 80: blk.3.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 81: blk.3.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 82: blk.3.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 83: blk.3.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 84: blk.4.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 85: blk.4.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 86: blk.4.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 87: blk.4.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 88: blk.4.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 89: blk.4.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 90: blk.4.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 91: blk.4.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 92: blk.4.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 93: blk.5.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 94: blk.5.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 95: blk.5.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 96: blk.5.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 97: blk.5.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 98: blk.5.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 99: blk.5.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 100: blk.5.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 101: blk.5.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 102: blk.6.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 103: blk.6.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 104: blk.6.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 105: blk.6.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 106: blk.6.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 107: blk.6.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 108: blk.6.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 109: blk.6.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 110: blk.6.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 111: blk.7.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 112: blk.7.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 113: blk.7.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 114: blk.7.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 115: blk.7.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 116: blk.7.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 117: blk.7.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 118: blk.7.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 119: blk.7.attn_v.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 120: blk.8.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 121: blk.8.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 122: blk.8.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 123: blk.8.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 124: blk.8.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 125: blk.8.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 126: blk.8.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 127: blk.8.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 128: blk.8.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 129: blk.9.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 130: blk.9.ffn_down.weight q5_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 131: blk.9.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
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llama_model_loader: - tensor 341: blk.37.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 342: blk.37.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 343: blk.37.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 344: blk.38.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 345: blk.38.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 346: blk.38.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 347: blk.38.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 348: blk.38.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 349: blk.38.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 350: blk.38.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 351: blk.38.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 352: blk.38.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 353: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 354: blk.39.ffn_down.weight q6_K [ 13824, 5120, 1, 1 ]
llama_model_loader: - tensor 355: blk.39.ffn_gate.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 356: blk.39.ffn_up.weight q5_K [ 5120, 13824, 1, 1 ]
llama_model_loader: - tensor 357: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - tensor 358: blk.39.attn_k.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 359: blk.39.attn_output.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 360: blk.39.attn_q.weight q5_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 361: blk.39.attn_v.weight q6_K [ 5120, 5120, 1, 1 ]
llama_model_loader: - tensor 362: output_norm.weight f32 [ 5120, 1, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: general.file_type u32
llama_model_loader: - kv 11: tokenizer.ggml.model str
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr
llama_model_loader: - kv 13: tokenizer.ggml.scores arr
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 18: general.quantization_version u32
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q5_K: 241 tensors
llama_model_loader: - type q6_K: 41 tensors
llm_load_print_meta: format = GGUF V2 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_ctx = 1024
llm_load_print_meta: n_embd = 5120
llm_load_print_meta: n_head = 40
llm_load_print_meta: n_head_kv = 40
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: f_norm_eps = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff = 13824
llm_load_print_meta: freq_base = 0.0
llm_load_print_meta: freq_scale = 0
llm_load_print_meta: model type = 13B
llm_load_print_meta: model ftype = mostly Q5_K - Medium
llm_load_print_meta: model size = 13.02 B
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 ''
llm_load_print_meta: EOS token = 2 ''
llm_load_print_meta: UNK token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MB
llm_load_tensors: using CUDA for GPU acceleration
warning: cannot set main_device=916964780 because there are only 4 devices. Using device 0 instead.
llm_load_tensors: mem required = 8581.45 MB (+ 800.00 MB per state)
llm_load_tensors: offloading 1 repeating layers to GPU
llm_load_tensors: offloaded 1/43 layers to GPU
llm_load_tensors: VRAM used: 221 MB
Fatal error. System.AccessViolationException: Attempted to read or write protected memory. This is often an indication that other memory is corrupt.
Repeat 2 times:

at LLama.Native.NativeApi.llama_load_model_from_file(System.String, LLama.Native.LLamaContextParams)

at LLama.Native.SafeLlamaModelHandle.LoadFromFile(System.String, LLama.Native.LLamaContextParams)
at LLama.LLamaWeights.LoadFromFile(LLama.Abstractions.IModelParams)
at Program.

$(System.String[])

PaulaScholz · 2023-10-21T04:05:04Z

Maybe you need to make changes in the CUDA 12 backend? Maybe you could debug on Azure VM with multiple GPUs? I might could get one for a small while.

martindevans · 2023-10-21T12:35:35Z

How did you setup your test?

The error here:

warning: cannot set main_device=916964780 because there are only 4 devices. Using device 0 instead.

Makes it looks like you've set IModelParams.MainGpu = 916964780!

I assume you didn't actually do that, so I guess that there must be some mismatch between the C# and C++ sides. For example a field in the wrong place in the llama_model_params struct. That would also explain the AccessViolationException if it tried to use some other random data as a pointer.

PaulaScholz · 2023-10-21T20:28:18Z

Must be a mismatch because I didn't set that in the ModelParams, just these:

ContextSize = 2048,
GpuLayerCount = 48

martindevans · 2023-10-21T21:55:51Z

I don't have any good ideas to debug that I'm afraid.

The relevent bit of llama.cpp is this struct and the equivalent in that PR is this. As far as I can see those two agree with each other.

I can't reproduce this on my PC (with just one GPU), which is odd because if there really was something misaligned in that struct I'd expect everything to fail very quickly (even basic loading of weights).

jonty-esterhuizen · 2023-10-23T11:45:56Z

is there a way i can test this for you or if we can have a discord call ?

JeremyBickel · 2023-10-24T15:12:44Z

I'm at a bit of a loss what could be the problem in that case :/

Does the normal llama.cpp demo application (main.exe) work on GPU?

I can get llama.cpp main to work with cpu, but it just drops back to command line after a couple seconds with cuda:

C:\Users\Jeremy\Downloads\llama-b1414-bin-win-cublas-cu12.2.0-x64>main -m C:\Users\Jeremy\LLM\MODELS\TheBloke\CausalLM-14B-GGUF\causallm-14b-q5_1.gguf --file prompt.txt
Log start
main: build = 1414 (96981f3)
main: built with MSVC 19.35.32217.1 for x64
main: seed  = 1698159182
ggml_init_cublas: found 1 CUDA devices:
  Device 0: Quadro K1200, compute capability 5.0

C:\Users\Jeremy\Downloads\llama-b1414-bin-win-cublas-cu12.2.0-x64>

I also tried renaming libllama-cuda12.dll (and the .so file for good measure). When that didn't work, I went a step further and copied libllama-cuda12.dll and .so and pasted them with the names of the other dll and so files:

 Directory of C:\Users\Jeremy\LLM\LLamaSharp\LLama\runtimes

10/24/2023  10:45 AM    <DIR>          .
10/24/2023  10:45 AM    <DIR>          ..
10/24/2023  10:31 AM    <DIR>          baks
10/23/2023  09:59 PM    <DIR>          build
10/23/2023  09:59 PM         5,168,128 ggml-metal.metal
10/23/2023  09:59 PM         5,168,128 libllama-cuda11.dll
10/23/2023  09:59 PM         5,253,824 libllama-cuda11.so
10/23/2023  09:59 PM         5,168,128 libllama-cuda12.dll
10/23/2023  09:59 PM         5,253,824 libllama-cuda12.so
10/23/2023  09:59 PM         5,168,128 libllama.dll
10/23/2023  09:59 PM         5,253,824 libllama.dylib
10/23/2023  09:59 PM         5,253,824 libllama.so

I followed some advice to replace the dll and run it from the command line from the debug folder

\LLamaSharp\LLama.Examples\bin\Debug\net6.0>LLama.Examples.exe
and I got this error:

CUDA error 209 at D:\a\LLamaSharp\LLamaSharp\ggml-cuda.cu:6862: no kernel image is available for execution on the device
current device: 0

BTW, I don't have a "D" drive, and the code isn't in an "a" directory. Is this path hardwired somewhere in the code? Maybe it doesn't matter here, but it's curious.

martindevans · 2023-10-24T15:46:44Z

I don't have a "D" drive, and the code isn't in an "a" directory. Is this path hardwired somewhere in the code?

That's just a path that was baked in from the build environment, it's not a problem.

no kernel image is available for execution on the device

This is the error that's causing you problems - it means that the compiled code does not support your specific CUDA version.

It's odd that llama.cpp isn't printing the same error, but I think that's the fundamental problem in both cases.

JeremyBickel · 2023-10-24T15:57:01Z

I thought I saw it over at llama.cpp, but the error message there was "invalid configuration file" coming from the same line of code (ggml-cuda.cu:6862). I don't know if it's related or not.

ggml-org/llama.cpp#3740

atonalfreerider · 2023-10-26T22:18:47Z

EDIT: I swapped GPUs from one of my PCs to the other. The RTX 2070 has intermittent performance on both PCs. The GTX 1070 has none at all.

If it helps, I'm experiencing similar GPU issues to OP. I have tried the following combinations and have seen only one case where one of my GPUs intermittently functions:

PC1: Windows 10, GTX 1070

tried CUDA 11.8
tried CUDA 12.3
tried building libllama-cuda11.dll from latest source (today) and renamed to libllama.dll
Result: NO GPU USAGE

PC2: Windows 10, RTX 2070, CUDA 11.8
Result: INTERMITTENT GPU USAGE. The output from the model is still incredibly slow. The GPU spikes every time a token is generated, but it takes 6-10 seconds between spikes.

Results displayed below:

I think this is a great library. I really hope to be able to use it. Thank you.

AsakusaRinne · 2023-10-27T04:49:30Z

@atonalfreerider Hey, noticed that you have compiled the DLL from llama.cpp yourself, could you please further test if GPU is used when running directly with llama.cpp examples? That will help us to see if it's an issue of llama.cpp or LLamaSharp. :)

atonalfreerider · 2023-10-27T14:33:59Z

Someone posted a similar issue here to llama.cpp today:
ggml-org/llama.cpp#3806

From llama.cpp I downloaded the latest release b1429

I downgraded my CUDA SDK to 12.2 to match the release version and ran on RTX 2070

I ran main.exe with several different models. I ran it with the exact same model that I was using in my comment above, with the same prompt. Experiencing the same behavior as posted above.

I also ran LLaMaSharp.Examples, with choice (14) coding assistant and gave the same prompt, and experienced the same behavior.

Here is the output from llama-bench.exe. I'll post to the llamacpp issues:

C:\Users\johnb\Desktop\llamacpp-bin\llama-b1429-bin-win-cublas-cu12.2.0-x64>llama-bench.exe
ggml_init_cublas: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070 SUPER, compute capability 7.5
| model                          |       size |     params | backend    | ngl | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | pp 512     |  1165.85 ± 60.52 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | tg 128     |     76.31 ± 0.11 |

atonalfreerider · 2023-10-27T19:04:22Z

I may have found a partial explanation for the behavior. I explained on the llama.cpp issue ticket referenced above:
ggml-org/llama.cpp#3806 (comment)

It would appear from testing that the model needs to fit into the GPU memory in order to run efficiently. Please take this result with a grain of salt.

AsakusaRinne · 2023-10-28T02:57:55Z

Thank you for the feedback!

atonalfreerider · 2023-10-31T14:47:51Z

IMPORTANT UPDATE from the llamacpp thread. It is now possible to disable VRAM overflow with driver version 564.01:

https://old.reddit.com/r/LocalLLaMA/comments/17kl8gu/psa_with_nvidia_driver_56401_its_now_possible_to/

Guide:
https://nvidia.custhelp.com/app/answers/detail/a_id/5490

You have to individually add the path to your program executable to the NVIDIA 3D settings and opt out of RAM fallback.

WARNING: This will cause your program to crash, rather than just slow down, if you overflow the memory with a large model. But it should make full use of VRAM.

AsakusaRinne · 2023-11-05T04:53:39Z

It seems to have been resolved by ggml-org/llama.cpp#3906. I think it will be included in the future release of LLamaSharp.

martindevans · 2023-11-06T23:16:56Z

That 2 character change has caused a lot of grief 😆 😢

New binaries have been merged into master (thru #249) which should speed everything up.

sania96 · 2024-02-05T13:57:02Z

I have a very similar issue. I have one machine, a 10 core I9 with 128gb and 3090 GPU. LlamaSharp runs very fast and is certainly using the GPU. On another machine, a 56 core Xeon with 128GB, 4 NVIDIA A6000 GPUs, it runs dog slow, uses up almost all the CPU. It does seem to use one GPU, but not much.

I have CUDA 12 and the latest NVIDIA drivers on each system. Output from both say they are using the GPU, but 3090 is super fast and the other is super slow. I am using the 0.5.1 Nuget package and the 0.5.1 Backend.Cuda12 on both.

Also, how would I get LlamaSharp to use more than 1 GPU?

HI, how did you run it on gpu? i cannot find .dll files for cuda from where i can get that?

martindevans · 2024-02-05T14:31:55Z

At the moment you need to have the CUDA toolkit installed (https://developer.nvidia.com/cuda-toolkit).

jonty-esterhuizen closed this as completed Oct 12, 2023

jonty-esterhuizen reopened this Oct 12, 2023

martindevans mentioned this issue Oct 22, 2023

Multi GPU #202

Merged

1 task

jonty-esterhuizen closed this as completed Oct 23, 2023

jonty-esterhuizen reopened this Oct 23, 2023

JeremyBickel mentioned this issue Oct 24, 2023

[Baichuan2 Error] : CUDA error 9 at xxx/llama.cpp/ggml-cuda.cu:6862: invalid configuration argument ggml-org/llama.cpp#3740

Closed

atonalfreerider mentioned this issue Oct 27, 2023

GPU not being utilized on Windows ggml-org/llama.cpp#3806

Closed

4 tasks

martindevans added the bug Something isn't working label Nov 6, 2023

martindevans closed this as completed Dec 2, 2023

This was referenced Jan 26, 2024

using CUDA when both CPU and Cuda12 back-ends are present. #456

Open

[Question] CUDA use in LLamaSharp microsoft/kernel-memory#266

Closed

swharden mentioned this issue Feb 22, 2024

Gemma Is Not Supported #533

Closed

Running LLamaSharp on gpu #189

Running LLamaSharp on gpu #189

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at LLama.Native.NativeApi.llama_load_model_from_file(System.String, LLama.Native.LLamaContextParams)

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