Closed
Description
System: Arch Linux,
CPU: Intel i3 12th gen
GPU: Intel Arc A750
RAM: 16GB
llama.cpp version: b2134
Previously the build was failing with -DLLAMA_SYCL_F16=ON which has been fixed in #5411. Upon running this build, it crashes with segmentation fault.
logs:
bin/main -m ~/Public/Models/Weights/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf -p "hello " -n 1000 -ngl 99
Log start
main: build = 2134 (099afc62)
main: built with Intel(R) oneAPI DPC++/C++ Compiler 2024.0.0 (2024.0.0.20231017) for x86_64-unknown-linux-gnu
main: seed = 1707789832
GGML_SYCL_DEBUG=0
ggml_init_sycl: GGML_SYCL_F16: yes
ggml_init_sycl: SYCL_USE_XMX: yes
found 4 SYCL devices:
Device 0: Intel(R) Arc(TM) A750 Graphics, compute capability 1.3,
max compute_units 448, max work group size 1024, max sub group size 32, global mem size 8096681984
Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2,
max compute_units 4, max work group size 67108864, max sub group size 64, global mem size 16577347584
Device 2: 12th Gen Intel(R) Core(TM) i3-12100F, compute capability 3.0,
max compute_units 4, max work group size 8192, max sub group size 64, global mem size 16577347584
Device 3: Intel(R) Arc(TM) A750 Graphics, compute capability 3.0,
max compute_units 448, max work group size 1024, max sub group size 32, global mem size 8096681984
Using device 0 (Intel(R) Arc(TM) A750 Graphics) as main device
llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from /home/tensorblast/Public/Models/Weights/tinyllama-1.1b-chat-v1.0.Q4_K_M.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 = llama
llama_model_loader: - kv 1: general.name str = tinyllama_tinyllama-1.1b-chat-v1.0
llama_model_loader: - kv 2: llama.context_length u32 = 2048
llama_model_loader: - kv 3: llama.embedding_length u32 = 2048
llama_model_loader: - kv 4: llama.block_count u32 = 22
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 5632
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 64
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 4
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 11: general.file_type u32 = 15
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.merges arr[str,61249] = ["▁ t", "e r", "i n", "▁ a", "e n...
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.chat_template str = {% for message in messages %}\n{% if m...
llama_model_loader: - kv 22: general.quantization_version u32 = 2
llama_model_loader: - type f32: 45 tensors
llama_model_loader: - type q4_K: 135 tensors
llama_model_loader: - type q6_K: 21 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 = 2048
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_layer = 22
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
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 = 5632
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 = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 2048
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 1B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 1.10 B
llm_load_print_meta: model size = 636.18 MiB (4.85 BPW)
llm_load_print_meta: general.name = tinyllama_tinyllama-1.1b-chat-v1.0
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 = 2 '</s>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.15 MiB
llm_load_tensors: offloading 22 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 23/23 layers to GPU
llm_load_tensors: buffer size = 601.02 MiB
llm_load_tensors: CPU buffer size = 35.16 MiB
.....................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: KV buffer size = 11.00 MiB
llama_new_context_with_model: KV self size = 11.00 MiB, K (f16): 5.50 MiB, V (f16): 5.50 MiB
llama_new_context_with_model: CPU input buffer size = 5.01 MiB
zsh: segmentation fault (core dumped) bin/main -m -p "hello " -n
The build without -DLLAMA_SYCL_F16=ON works.
Confirmed: This crash started happening after #5411