Closed
Description
What happened?
When starting the server in embedding mode, requests to the /complete
endpoint result in a segmentation fault (other endpoints might be affected too).
Tested on Macbook Air M1 and RTX 4090.
The embedding API recently changed, so it might be intended, that completion and embedding mode can't be used simultaneously (?).
However, I remember both working simultaneously in the past and couldn't find this behavior documented anywhere.
Also, the error should probably be handled more gracefully.
Steps to reproduce:
cmake -B build -DLLAMA_CURL=ON
cmake --build build --config Release -j
./llama-server -mu https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q2_K.gguf --embeddings
curl --request POST \
--url http://localhost:8080/completion \
--header "Content-Type: application/json" \
--data '{
"prompt": "Hello, world!"
}'
Name and Version
$ ./llama-cli --version
version: 3276 (cb5fad4)
built with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.5.0
What operating system are you seeing the problem on?
Linux, Mac
Relevant log output
INFO [ main] build info | tid="0x1f2170c00" timestamp=1719867303 build=3276 commit="cb5fad4c"
INFO [ main] system info | tid="0x1f2170c00" timestamp=1719867303 n_threads=4 n_threads_batch=-1 total_threads=8 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | "
[...]
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] = ["<unk>", "<s>", "</s>", "<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 cache size = 259
llm_load_vocab: token to piece cache size = 0.1637 MB
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_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
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: f_logit_scale = 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: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 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_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q2_K - Medium
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 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: PAD token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size = 0.27 MiB
ggml_backend_metal_log_allocated_size: allocated buffer, size = 2898.58 MiB, ( 2898.64 / 10922.67)
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CPU buffer size = 41.02 MiB
llm_load_tensors: Metal buffer size = 2898.57 MiB
..................................................................................................
llama_new_context_with_model: n_ctx = 32768
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1
ggml_metal_init: picking default device: Apple M1
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M1
ggml_metal_init: GPU family: MTLGPUFamilyApple7 (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
llama_kv_cache_init: Metal KV buffer size = 4096.00 MiB
llama_new_context_with_model: KV self size = 4096.00 MiB, K (f16): 2048.00 MiB, V (f16): 2048.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.03 MiB
llama_new_context_with_model: Metal compute buffer size = 2144.00 MiB
llama_new_context_with_model: CPU compute buffer size = 72.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 2
INFO [ init] initializing slots | tid="0x1f2170c00" timestamp=1719867305 n_slots=1
INFO [ init] new slot | tid="0x1f2170c00" timestamp=1719867305 id_slot=0 n_ctx_slot=32768
INFO [ main] model loaded | tid="0x1f2170c00" timestamp=1719867305
INFO [ main] chat template | tid="0x1f2170c00" timestamp=1719867305 chat_example="[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] How are you? [/INST]" built_in=true
INFO [ main] HTTP server listening | tid="0x1f2170c00" timestamp=1719867305 port="8080" n_threads_http="7" hostname="127.0.0.1"
INFO [ update_slots] all slots are idle | tid="0x1f2170c00" timestamp=1719867305
INFO [ launch_slot_with_task] slot is processing task | tid="0x1f2170c00" timestamp=1719867324 id_slot=0 id_task=0
INFO [ update_slots] kv cache rm [p0, end) | tid="0x1f2170c00" timestamp=1719867324 id_slot=0 id_task=0 p0=0
llama_get_logits_ith: invalid logits id 4, reason: no logits
zsh: segmentation fault ./llama-server -mu --embeddings