Misc. bug: Embedding crashes on macOS when not using Metal #10702
Description
Name and Version
version: 4277 (c5ede38)
built with Apple clang version 16.0.0 (clang-1600.0.26.4) for arm64-apple-darwin24.1.0
Operating systems
Mac, M1 Max
Which llama.cpp modules do you know to be affected?
libllama (core library), Other (Please specify in the next section)
Problem description & steps to reproduce
Using llama-embedding with bge-small-en-v1.5-q8_0.gguf on macOS without Metal crashes with 85246 illegal hardware instruction, but works when using Metal.
This is the command I used:
./bin/llama-embedding -m ./models/bge-small-en-v1.5-q8_0.gguf --prompt "hi" -c 0Running with lldb gave me this stack trace:
Process 88174 stopped
* thread #7, stop reason = EXC_BAD_INSTRUCTION (code=1, subcode=0x4e84a653)
frame #0: 0x0000000100187128 libggml-cpu.dylib`ggml_vec_dot_q8_0_q8_0 + 192
libggml-cpu.dylib`ggml_vec_dot_q8_0_q8_0:
-> 0x100187128 <+192>: smmla
0x10018712c <+196>: smmla
0x100187130 <+200>: zip2.2d v1, v6, v16
0x100187134 <+204>: smmla
Target 0: (llama-embedding) stopped.
I built using these commands:
mkdir build
cd build
cmake -DGGML_METAL=0 ..
cmake --build . --config Release --clean-first --parallel 9First Bad Commit
This issue appeared since release b4179.
Relevant log output
build: 4277 (c5ede3849) with Apple clang version 16.0.0 (clang-1600.0.26.4) for arm64-apple-darwin24.1.0
llama_model_loader: loaded meta data with 24 key-value pairs and 197 tensors from ./models/bge-small-en-v1.5-q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = bert
llama_model_loader: - kv 1: general.name str = bge-small-en-v1.5
llama_model_loader: - kv 2: bert.block_count u32 = 12
llama_model_loader: - kv 3: bert.context_length u32 = 512
llama_model_loader: - kv 4: bert.embedding_length u32 = 384
llama_model_loader: - kv 5: bert.feed_forward_length u32 = 1536
llama_model_loader: - kv 6: bert.attention.head_count u32 = 12
llama_model_loader: - kv 7: bert.attention.layer_norm_epsilon f32 = 0.000000
llama_model_loader: - kv 8: general.file_type u32 = 7
llama_model_loader: - kv 9: bert.attention.causal bool = false
llama_model_loader: - kv 10: bert.pooling_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 101
llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 102
llama_model_loader: - kv 14: tokenizer.ggml.model str = bert
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,30522] = ["[PAD]", "[unused0]", "[unused1]", "...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,30522] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,30522] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 100
llama_model_loader: - kv 19: tokenizer.ggml.seperator_token_id u32 = 102
llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.cls_token_id u32 = 101
llama_model_loader: - kv 22: tokenizer.ggml.mask_token_id u32 = 103
llama_model_loader: - kv 23: general.quantization_version u32 = 2
llama_model_loader: - type f32: 124 tensors
llama_model_loader: - type q8_0: 73 tensors
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.2032 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = bert
llm_load_print_meta: vocab type = WPM
llm_load_print_meta: n_vocab = 30522
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 512
llm_load_print_meta: n_embd = 384
llm_load_print_meta: n_layer = 12
llm_load_print_meta: n_head = 12
llm_load_print_meta: n_head_kv = 12
llm_load_print_meta: n_rot = 32
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 32
llm_load_print_meta: n_embd_head_v = 32
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 384
llm_load_print_meta: n_embd_v_gqa = 384
llm_load_print_meta: f_norm_eps = 1.0e-12
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 1536
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 0
llm_load_print_meta: pooling type = 2
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 512
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 33M
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 33.21 M
llm_load_print_meta: model size = 34.38 MiB (8.68 BPW)
llm_load_print_meta: general.name = bge-small-en-v1.5
llm_load_print_meta: BOS token = 101 '[CLS]'
llm_load_print_meta: EOS token = 102 '[SEP]'
llm_load_print_meta: UNK token = 100 '[UNK]'
llm_load_print_meta: SEP token = 102 '[SEP]'
llm_load_print_meta: PAD token = 0 '[PAD]'
llm_load_print_meta: CLS token = 101 '[CLS]'
llm_load_print_meta: MASK token = 103 '[MASK]'
llm_load_print_meta: LF token = 0 '[PAD]'
llm_load_print_meta: EOG token = 102 '[SEP]'
llm_load_print_meta: max token length = 21
llm_load_tensors: CPU_Mapped model buffer size = 34.38 MiB
.................................................
llama_new_context_with_model: n_seq_max = 1
llama_new_context_with_model: n_ctx = 4096
llama_new_context_with_model: n_ctx_per_seq = 4096
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 2048
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_pre_seq (4096) > n_ctx_train (512) -- possible training context overflow
llama_kv_cache_init: CPU KV buffer size = 72.00 MiB
llama_new_context_with_model: KV self size = 72.00 MiB, K (f16): 36.00 MiB, V (f16): 36.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.00 MiB
llama_new_context_with_model: CPU compute buffer size = 220.02 MiB
llama_new_context_with_model: graph nodes = 429
llama_new_context_with_model: graph splits = 169 (with bs=2048), 1 (with bs=1)
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
[1] 89180 illegal hardware instruction ./llama-embedding -m ./models/bge-small-en-v1.5-q8_0.gguf