SIGSEGV Error During Execution of Embedding Code
hide212131 opened this issue · 3 comments
hide212131 commented
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
Sorry. The reason was that the following settings were missing.
ModelParameters modelParams = new ModelParameters()
.setNGpuLayers(43)
.setEmbedding(true); kherud commented
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
I just released version 3.0 and this issue should hopefully not longer be relevant. Feel free to re-open otherwise.