Bug: Segmentation fault when set --ctv to f32. #9969
Description
What happened?
Hi, there.
I have tried to use the -ctv option to improve the performance of the model. However, when I set the --ctv to f32, I got a segmentation fault without hints.
I am not sure if I am doing something wrong or if there is a bug in the code. I would appreciate it if you could help me with this issue.
- The command I used for starting the server:
./llama.cpp-b3938/build_gpu/bin/llama-cli -m ../models/Phi-3-mini-4k-instruct-q4.gguf -n 256 -np 5 -ctv f32 -fa -p "what is the meaning of life" --no-warmup- The response I got:
...
system_info: n_threads = 6 (n_threads_batch = 6) / 16 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampler seed: 4273919972
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> top-k -> tail-free -> typical -> top-p -> min-p -> xtc -> temp-ext -> softmax -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = 256, n_keep = 1
what is the meaning of life
Segmentation fault (core dumped)
Name and Version
./llama.cpp-b3938/build_gpu/bin/llama-cli --version
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
version: 7 (d9a33c5)
built with cc (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0 for x86_64-linux-gnu
What operating system are you seeing the problem on?
Linux
Relevant log output
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 7 (d9a33c5) with cc (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_load_model_from_file: using device CUDA0 (NVIDIA GeForce RTX 3060) - 10138 MiB free
llama_model_loader: loaded meta data with 24 key-value pairs and 195 tensors from ../models/Phi-3-mini-4k-instruct-q4.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 = phi3
llama_model_loader: - kv 1: general.name str = Phi3
llama_model_loader: - kv 2: phi3.context_length u32 = 4096
llama_model_loader: - kv 3: phi3.embedding_length u32 = 3072
llama_model_loader: - kv 4: phi3.feed_forward_length u32 = 8192
llama_model_loader: - kv 5: phi3.block_count u32 = 32
llama_model_loader: - kv 6: phi3.attention.head_count u32 = 32
llama_model_loader: - kv 7: phi3.attention.head_count_kv u32 = 32
llama_model_loader: - kv 8: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 9: phi3.rope.dimension_count u32 = 96
llama_model_loader: - kv 10: general.file_type u32 = 15
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.pre str = default
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32064] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32064] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32064] = [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 = 32000
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 32000
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 q4_K: 81 tensors
llama_model_loader: - type q5_K: 32 tensors
llama_model_loader: - type q6_K: 17 tensors
llm_load_vocab: control-looking token: 32007 '<|end|>' was not control-type; this is probably a bug in the model. its type will be overridden
llm_load_vocab: control-looking token: 32000 '<|endoftext|>' was not control-type; this is probably a bug in the model. its type will be overridden
llm_load_vocab: special tokens cache size = 67
llm_load_vocab: token to piece cache size = 0.1690 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = phi3
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32064
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 3072
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_rot = 96
llm_load_print_meta: n_swa = 2047
llm_load_print_meta: n_embd_head_k = 96
llm_load_print_meta: n_embd_head_v = 96
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 3072
llm_load_print_meta: n_embd_v_gqa = 3072
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 = 8192
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 = 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 = 4096
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 = 3B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 3.82 B
llm_load_print_meta: model size = 2.23 GiB (5.01 BPW)
llm_load_print_meta: general.name = Phi3
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 32000 '<|endoftext|>'
llm_load_print_meta: EOT token = 32007 '<|end|>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: PAD token = 32000 '<|endoftext|>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: EOG token = 32000 '<|endoftext|>'
llm_load_print_meta: EOG token = 32007 '<|end|>'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size = 0.10 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors: CPU buffer size = 2281.66 MiB
...........................................................................................
llama_new_context_with_model: n_ctx = 4096
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 2304.00 MiB
llama_new_context_with_model: KV self size = 2304.00 MiB, K (f16): 768.00 MiB, V (f32): 1536.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.61 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 145.68 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 24.01 MiB
llama_new_context_with_model: graph nodes = 1159
llama_new_context_with_model: graph splits = 230
main: llama threadpool init, n_threads = 6
system_info: n_threads = 6 (n_threads_batch = 6) / 16 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampler seed: 4273919972
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> top-k -> tail-free -> typical -> top-p -> min-p -> xtc -> temp-ext -> softmax -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = 256, n_keep = 1
what is the meaning of life
Segmentation fault (core dumped)