failed to VirtualLock 101449728-byte buffer (after previously locking 17901879296 bytes): Invalid access to memory location #5293
Description
llama.cpp version llama-b2050-bin-win-avx2-x64
version: 2050 (1912211)
Windows 10
Running on AMD 3900x CPU
Command: server --threads 23 --ctx-size 16384 --mlock --model models\phind-codellama-34b-python-v1.Q4_K_M.gguf
(removing the --ctx-size 16384 --mlock parameters gets rid of the warning but, doesn't fix the error where it exits.)
Happens with all of these models:
I'm probably doing something stupid or missing something.
When I try to run these models, I get the following output and then the program exits:
{"timestamp":1706910929,"level":"INFO","function":"main","line":2428,"message":"build info","build":2050,"commit":"19122117"}
{"timestamp":1706910929,"level":"INFO","function":"main","line":2435,"message":"system info","n_threads":23,"n_threads_batch":-1,"total_threads":24,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | "}
llama server listening at http://127.0.0.1:8080
{"timestamp":1706910929,"level":"INFO","function":"main","line":2534,"message":"HTTP server listening","hostname":"127.0.0.1","port":"8080"}
llama_model_loader: loaded meta data with 20 key-value pairs and 435 tensors from models\phind-codellama-34b-python-v1.Q4_K_M.gguf (version GGUF V2)
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 = phind_phind-codellama-34b-python-v1
llama_model_loader: - kv 2: llama.context_length u32 = 16384
llama_model_loader: - kv 3: llama.embedding_length u32 = 8192
llama_model_loader: - kv 4: llama.block_count u32 = 48
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 22016
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 64
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 = 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.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: general.quantization_version u32 = 2
llama_model_loader: - type f32: 97 tensors
llama_model_loader: - type q4_K: 289 tensors
llama_model_loader: - type q6_K: 49 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V2
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 = 16384
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 48
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 8
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: n_ff = 22016
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 = 16384
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 34B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 33.74 B
llm_load_print_meta: model size = 18.83 GiB (4.79 BPW)
llm_load_print_meta: general.name = phind_phind-codellama-34b-python-v1
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: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.17 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/49 layers to GPU
llm_load_tensors: CPU buffer size = 19282.48 MiB
........................................................................................warning: failed to VirtualLock 101449728-byte buffer (after previously locking 17901879296 bytes): Invalid access to memory location.
...........
llama_new_context_with_model: n_ctx = 32000
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 6000.00 MiB
llama_new_context_with_model: KV self size = 6000.00 MiB, K (f16): 3000.00 MiB, V (f16): 3000.00 MiB
llama_new_context_with_model: CPU input buffer size = 78.63 MiB
llama_new_context_with_model: CPU compute buffer size = 4452.80 MiB
llama_new_context_with_model: graph splits (measure): 1