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kherud / java-llama.cpp

Java Bindings for llama.cpp - A Port of Facebook's LLaMA model in C/C++
MIT License
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SIGSEGV Error During Execution of Embedding Code #49

Closed hide212131 closed 5 months ago

hide212131 commented 7 months ago

Thanks for the cool product 😊

I attempted embedding with the following code.

import de.kherud.llama.LlamaModel;
import de.kherud.llama.ModelParameters;

public class EmbedExample {

    public static void main(String... args) throws IOException {
        LlamaModel.setLogger((level, message) -> System.out.print(message));
        ModelParameters modelParams = new ModelParameters()
                .setNGpuLayers(43);
        String modelName = System.getProperty("model.name");
        String modelPath = ModelResolver.getPathToModel(modelName);
        try (LlamaModel model = new LlamaModel(modelPath, modelParams)) {
            float[] embedding = model.embed("Hello");
            System.out.print(embedding.length);
        }
    }
}

The execution command is as follows.

$ mvn exec:java -Dexec.mainClass="examples.EmbedExample" -Dmodel.home="./models" -Dmodel.name="mistral-7b-instruct-v0.2.Q2_K.gguf"

The following error occurred. It seemed to fail because there was no data in llama_get_embeddings

# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x0000000101788850, pid=10435, tid=33027
#
# JRE version: OpenJDK Runtime Environment GraalVM CE 21+35.1 (21.0+35) (build 21+35-jvmci-23.1-b15)
# Java VM: OpenJDK 64-Bit Server VM GraalVM CE 21+35.1 (21+35-jvmci-23.1-b15, mixed mode, sharing, tiered, jvmci, jvmci compiler, compressed oops, compressed class ptrs, g1 gc, bsd-aarch64)
# Problematic frame:
# C  [libjllama.dylib+0x8850]  Java_de_kherud_llama_LlamaModel_embed+0x134
[INFO] Scanning for projects...
[INFO] 
[INFO] --------------------------< de.kherud:llama >---------------------------
[INFO] Building de.kherud:llama 2.3.5
[INFO]   from pom.xml
[INFO] --------------------------------[ jar ]---------------------------------
[INFO] 
[INFO] --- exec:3.0.0:java (default-cli) @ llama ---
/de/kherud/llama/Mac/aarch64
'ggml-metal.metal' not found
Extracted 'libllama.dylib' to '/var/folders/ln/rwfhlnks5xbgfjl1_f68bn2h0000gn/T/libllama.dylib'
Extracted 'libjllama.dylib' to '/var/folders/ln/rwfhlnks5xbgfjl1_f68bn2h0000gn/T/libjllama.dylib'
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from ./models/mistral-7b-instruct-v0.2.Q2_K.gguf (version GGUF V3 (latest))
llama_model_loader: - tensor    0:                token_embd.weight q2_K     [  4096, 32000,     1,     1 ]
llama_model_loader: - tensor    1:              blk.0.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    2:              blk.0.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor    3:              blk.0.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor    4:         blk.0.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor    5:            blk.0.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor    6:              blk.0.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor    7:            blk.0.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor    8:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor    9:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   10:              blk.1.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   11:              blk.1.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   12:              blk.1.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   13:         blk.1.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   14:            blk.1.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   15:              blk.1.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   16:            blk.1.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   17:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   18:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   19:              blk.2.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   20:              blk.2.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   21:              blk.2.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   22:         blk.2.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   23:            blk.2.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   24:              blk.2.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   25:            blk.2.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   26:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   27:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   28:              blk.3.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   29:              blk.3.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   30:              blk.3.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   31:         blk.3.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   32:            blk.3.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   33:              blk.3.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   34:            blk.3.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   35:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   36:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   37:              blk.4.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   38:              blk.4.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   39:              blk.4.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   40:         blk.4.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   41:            blk.4.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   42:              blk.4.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   43:            blk.4.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   44:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   45:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   46:              blk.5.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   47:              blk.5.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   48:              blk.5.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   49:         blk.5.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   50:            blk.5.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   51:              blk.5.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   52:            blk.5.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   53:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   54:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   55:              blk.6.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   56:              blk.6.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   57:              blk.6.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   58:         blk.6.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   59:            blk.6.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   60:              blk.6.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   61:            blk.6.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   62:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   63:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   64:              blk.7.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   65:              blk.7.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   66:              blk.7.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   67:         blk.7.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   68:            blk.7.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   69:              blk.7.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   70:            blk.7.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   71:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   72:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   73:              blk.8.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   74:              blk.8.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   75:              blk.8.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   76:         blk.8.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   77:            blk.8.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   78:              blk.8.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   79:            blk.8.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   80:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   81:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   82:              blk.9.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   83:              blk.9.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   84:              blk.9.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   85:         blk.9.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   86:            blk.9.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   87:              blk.9.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   88:            blk.9.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   89:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   90:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   91:             blk.10.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   92:             blk.10.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   93:             blk.10.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor   94:        blk.10.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor   95:           blk.10.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   96:             blk.10.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor   97:           blk.10.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor   98:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor   99:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  100:             blk.11.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  101:             blk.11.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  102:             blk.11.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  103:        blk.11.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  104:           blk.11.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  105:             blk.11.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  106:           blk.11.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  107:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  108:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  109:             blk.12.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  110:             blk.12.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  111:             blk.12.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  112:        blk.12.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  113:           blk.12.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  114:             blk.12.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  115:           blk.12.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  116:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  117:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  118:             blk.13.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  119:             blk.13.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  120:             blk.13.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  121:        blk.13.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  122:           blk.13.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  123:             blk.13.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  124:           blk.13.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  125:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  126:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  127:             blk.14.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  128:             blk.14.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  129:             blk.14.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  130:        blk.14.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  131:           blk.14.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  132:             blk.14.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  133:           blk.14.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  134:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  135:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  136:             blk.15.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  137:             blk.15.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  138:             blk.15.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  139:        blk.15.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  140:           blk.15.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  141:             blk.15.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  142:           blk.15.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  143:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  144:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  145:             blk.16.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  146:             blk.16.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  147:             blk.16.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  148:        blk.16.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  149:           blk.16.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  150:             blk.16.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  151:           blk.16.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  152:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  153:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  154:             blk.17.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  155:             blk.17.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  156:             blk.17.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  157:        blk.17.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  158:           blk.17.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  159:             blk.17.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  160:           blk.17.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  161:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  162:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  163:             blk.18.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  164:             blk.18.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  165:             blk.18.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  166:        blk.18.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  167:           blk.18.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  168:             blk.18.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  169:           blk.18.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  170:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  171:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  172:             blk.19.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  173:             blk.19.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  174:             blk.19.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  175:        blk.19.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  176:           blk.19.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  177:             blk.19.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  178:           blk.19.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  179:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  180:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  181:             blk.20.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  182:             blk.20.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  183:             blk.20.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  184:        blk.20.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  185:           blk.20.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  186:             blk.20.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  187:           blk.20.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  188:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  189:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  190:             blk.21.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  191:             blk.21.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  192:             blk.21.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  193:        blk.21.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  194:           blk.21.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  195:             blk.21.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  196:           blk.21.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  197:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  198:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  199:             blk.22.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  200:             blk.22.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  201:             blk.22.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  202:        blk.22.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  203:           blk.22.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  204:             blk.22.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  205:           blk.22.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  206:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  207:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  208:             blk.23.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  209:             blk.23.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  210:             blk.23.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  211:        blk.23.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  212:           blk.23.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  213:             blk.23.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  214:           blk.23.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  215:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  216:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  217:             blk.24.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  218:             blk.24.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  219:             blk.24.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  220:        blk.24.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  221:           blk.24.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  222:             blk.24.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  223:           blk.24.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  224:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  225:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  226:             blk.25.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  227:             blk.25.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  228:             blk.25.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  229:        blk.25.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  230:           blk.25.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  231:             blk.25.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  232:           blk.25.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  233:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  234:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  235:             blk.26.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  236:             blk.26.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  237:             blk.26.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  238:        blk.26.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  239:           blk.26.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  240:             blk.26.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  241:           blk.26.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  242:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  243:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  244:             blk.27.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  245:             blk.27.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  246:             blk.27.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  247:        blk.27.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  248:           blk.27.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  249:             blk.27.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  250:           blk.27.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  251:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  252:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  253:             blk.28.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  254:             blk.28.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  255:             blk.28.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  256:        blk.28.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  257:           blk.28.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  258:             blk.28.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  259:           blk.28.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  260:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  261:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  262:             blk.29.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  263:             blk.29.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  264:             blk.29.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  265:        blk.29.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  266:           blk.29.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  267:             blk.29.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  268:           blk.29.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  269:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  270:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  271:             blk.30.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  272:             blk.30.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  273:             blk.30.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  274:        blk.30.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  275:           blk.30.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  276:             blk.30.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  277:           blk.30.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  278:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  279:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  280:             blk.31.attn_q.weight q2_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  281:             blk.31.attn_k.weight q2_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  282:             blk.31.attn_v.weight q3_K     [  4096,  1024,     1,     1 ]
llama_model_loader: - tensor  283:        blk.31.attn_output.weight q3_K     [  4096,  4096,     1,     1 ]
llama_model_loader: - tensor  284:           blk.31.ffn_gate.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  285:             blk.31.ffn_up.weight q3_K     [  4096, 14336,     1,     1 ]
llama_model_loader: - tensor  286:           blk.31.ffn_down.weight q3_K     [ 14336,  4096,     1,     1 ]
llama_model_loader: - tensor  287:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  288:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  289:               output_norm.weight f32      [  4096,     1,     1,     1 ]
llama_model_loader: - tensor  290:                    output.weight q6_K     [  4096, 32000,     1,     1 ]
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.name str              = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv   2:                       llama.context_length u32              = 32768
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv   4:                          llama.block_count u32              = 32
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  11:                          general.file_type u32              = 10
llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  19:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  20:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  21:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  22:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv  23:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   65 tensors
llama_model_loader: - type q2_K:   65 tensors
llama_model_loader: - type q3_K:  160 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format           = GGUF V3 (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      = 32768
llm_load_print_meta: n_embd           = 4096
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 8
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 128
llm_load_print_meta: n_gqa            = 4
llm_load_print_meta: f_norm_eps       = 0.0e+00
llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
llm_load_print_meta: f_clamp_kqv      = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff             = 14336
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: rope scaling     = linear
llm_load_print_meta: freq_base_train  = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 32768
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: model type       = 7B
llm_load_print_meta: model ftype      = mostly Q2_K
llm_load_print_meta: model params     = 7.24 B
llm_load_print_meta: model size       = 2.87 GiB (3.41 BPW) 
llm_load_print_meta: general.name     = mistralai_mistral-7b-instruct-v0.2
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: PAD token        = 0 ''
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.11 MiB
llm_load_tensors: mem required  = 2939.68 MiB
.................................................................................................
llama_new_context_with_model: n_ctx      = 512
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: KV self size  =   64.00 MiB, K (f16):   32.00 MiB, V (f16):   32.00 MiB
llama_build_graph: non-view tensors processed: 676/676
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M3 Pro
ggml_metal_init: picking default device: Apple M3 Pro
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = /Users/hide/Github/JM-Lab/java-llama.cpp/build/bin
ggml_metal_init: loading '/Users/hide/Github/JM-Lab/java-llama.cpp/build/bin/ggml-metal.metal'
ggml_metal_init: GPU name:   Apple M3 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple9 (1009)
ggml_metal_init: hasUnifiedMemory              = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 28991.03 MB
ggml_metal_init: maxTransferRate               = built-in GPU
llama_new_context_with_model: compute buffer total size = 76.19 MiB
llama_new_context_with_model: max tensor size =   102.54 MiB
ggml_metal_add_buffer: allocated 'data            ' buffer, size =  2940.28 MiB, ( 2945.98 / 27648.00)
ggml_metal_add_buffer: allocated 'kv              ' buffer, size =    64.03 MiB, ( 3010.02 / 27648.00)
ggml_metal_add_buffer: allocated 'alloc           ' buffer, size =    73.02 MiB, ( 3083.03 / 27648.00)
#
# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x0000000101788850, pid=10435, tid=33027
#
# JRE version: OpenJDK Runtime Environment GraalVM CE 21+35.1 (21.0+35) (build 21+35-jvmci-23.1-b15)
# Java VM: OpenJDK 64-Bit Server VM GraalVM CE 21+35.1 (21+35-jvmci-23.1-b15, mixed mode, sharing, tiered, jvmci, jvmci compiler, compressed oops, compressed class ptrs, g1 gc, bsd-aarch64)
# Problematic frame:
# C  [libjllama.dylib+0x8850]  Java_de_kherud_llama_LlamaModel_embed+0x134
#
# No core dump will be written. Core dumps have been disabled. To enable core dumping, try "ulimit -c unlimited" before starting Java again
#
# An error report file with more information is saved as:
# /Users/hide/Github/JM-Lab/java-llama.cpp/hs_err_pid10435.log
#
# If you would like to submit a bug report, please visit:
#   https://github.com/oracle/graal/issues
# The crash happened outside the Java Virtual Machine in native code.
# See problematic frame for where to report the bug.
#
[1]    10435 abort      mvn exec:java -Dexec.mainClass="examples.EmbedExample" -Dmodel.home="./models
hide212131 commented 7 months ago

Sorry. The reason was that the following settings were missing.

ModelParameters modelParams = new ModelParameters()
                .setNGpuLayers(43)
                .setEmbedding(true);
kherud commented 7 months ago

Thank you for reporting 👍 good that your issue resolved, but I think a seg fault definitely shouldn't happen even without the setting. I'll leave this ticket open to further look into it.

kherud commented 5 months ago

I just released version 3.0 and this issue should hopefully not longer be relevant. Feel free to re-open otherwise.