"CPU_AARCH64 model buffer" appears when not using AARCH64

[pt13762104]

Name and Version

build: 4465 (9a48399) with gcc (conda-forge gcc 13.3.0-1) 13.3.0 for x86_64-conda-linux-gnu

Operating systems

Linux

GGML backends

CPU

Hardware

2x Intel Xeon 24 core (Kaggle)

Models

DeepSeek-V2.5: https://huggingface.co/bartowski/DeepSeek-V2.5-GGUF/tree/main/DeepSeek-V2.5-Q4_0

Problem description & steps to reproduce

The problem is that a part of the memory was used for "CPU_AARCH64 model buffer". Normally the model takes only 150GB of RAM, now it takes 260GB and loads much slower. Command line: /root/llama.cpp/build/bin/llama-server -m /dev/shm/DeepSeek-V2.5-Q4_0-00001-of-00004.gguf -t 72. This doesn't appear when using Q4_K_M.

Compile commands:

git clone https://github.com/ggerganov/llama.cpp ~/llama.cpp
cd ~/llama.cpp && cmake -G Ninja -B build && cmake --build build --config Release -j 64

First Bad Commit

No response

Relevant log output

build: 4465 (9a483999) with gcc (conda-forge gcc 13.3.0-1) 13.3.0 for x86_64-conda-linux-gnu
system info: n_threads = 72, n_threads_batch = 72, total_threads = 96

system_info: n_threads = 72 (n_threads_batch = 72) / 96 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 | 

main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 95
main: loading model
srv    load_model: loading model '/dev/shm/DeepSeek-V2.5-Q4_0-00001-of-00004.gguf'
llama_model_loader: additional 3 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 53 key-value pairs and 959 tensors from /dev/shm/DeepSeek-V2.5-Q4_0-00001-of-00004.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              = deepseek2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = DeepSeek V2.5
llama_model_loader: - kv   3:                            general.version str              = V2.5
llama_model_loader: - kv   4:                           general.basename str              = DeepSeek
llama_model_loader: - kv   5:                         general.size_label str              = 160x14B
llama_model_loader: - kv   6:                            general.license str              = other
llama_model_loader: - kv   7:                       general.license.name str              = deepseek
llama_model_loader: - kv   8:                       general.license.link str              = https://github.com/deepseek-ai/DeepSe...
llama_model_loader: - kv   9:                      deepseek2.block_count u32              = 60
llama_model_loader: - kv  10:                   deepseek2.context_length u32              = 163840
llama_model_loader: - kv  11:                 deepseek2.embedding_length u32              = 5120
llama_model_loader: - kv  12:              deepseek2.feed_forward_length u32              = 12288
llama_model_loader: - kv  13:             deepseek2.attention.head_count u32              = 128
llama_model_loader: - kv  14:          deepseek2.attention.head_count_kv u32              = 128
llama_model_loader: - kv  15:                   deepseek2.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  16: deepseek2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  17:                deepseek2.expert_used_count u32              = 6
llama_model_loader: - kv  18:                          general.file_type u32              = 2
llama_model_loader: - kv  19:        deepseek2.leading_dense_block_count u32              = 1
llama_model_loader: - kv  20:                       deepseek2.vocab_size u32              = 102400
llama_model_loader: - kv  21:            deepseek2.attention.q_lora_rank u32              = 1536
llama_model_loader: - kv  22:           deepseek2.attention.kv_lora_rank u32              = 512
llama_model_loader: - kv  23:             deepseek2.attention.key_length u32              = 192
llama_model_loader: - kv  24:           deepseek2.attention.value_length u32              = 128
llama_model_loader: - kv  25:       deepseek2.expert_feed_forward_length u32              = 1536
llama_model_loader: - kv  26:                     deepseek2.expert_count u32              = 160
llama_model_loader: - kv  27:              deepseek2.expert_shared_count u32              = 2
llama_model_loader: - kv  28:             deepseek2.expert_weights_scale f32              = 16.000000
llama_model_loader: - kv  29:             deepseek2.rope.dimension_count u32              = 64
llama_model_loader: - kv  30:                deepseek2.rope.scaling.type str              = yarn
llama_model_loader: - kv  31:              deepseek2.rope.scaling.factor f32              = 40.000000
llama_model_loader: - kv  32: deepseek2.rope.scaling.original_context_length u32              = 4096
llama_model_loader: - kv  33: deepseek2.rope.scaling.yarn_log_multiplier f32              = 0.100000
llama_model_loader: - kv  34:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  35:                         tokenizer.ggml.pre str              = deepseek-llm
llama_model_loader: - kv  36:                      tokenizer.ggml.tokens arr[str,102400]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  37:                  tokenizer.ggml.token_type arr[i32,102400]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  38:                      tokenizer.ggml.merges arr[str,99757]   = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv  39:                tokenizer.ggml.bos_token_id u32              = 100000
llama_model_loader: - kv  40:                tokenizer.ggml.eos_token_id u32              = 100001
llama_model_loader: - kv  41:            tokenizer.ggml.padding_token_id u32              = 100001
llama_model_loader: - kv  42:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  43:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  44:                    tokenizer.chat_template str              = {% if not add_generation_prompt is de...
llama_model_loader: - kv  45:               general.quantization_version u32              = 2
llama_model_loader: - kv  46:                      quantize.imatrix.file str              = /models_out/DeepSeek-V2.5-GGUF/DeepSe...
llama_model_loader: - kv  47:                   quantize.imatrix.dataset str              = /training_dir/calibration_datav3.txt
llama_model_loader: - kv  48:             quantize.imatrix.entries_count i32              = 716
llama_model_loader: - kv  49:              quantize.imatrix.chunks_count i32              = 139
llama_model_loader: - kv  50:                                   split.no u16              = 0
llama_model_loader: - kv  51:                                split.count u16              = 4
llama_model_loader: - kv  52:                        split.tensors.count i32              = 959
llama_model_loader: - type  f32:  300 tensors
llama_model_loader: - type q4_0:  645 tensors
llama_model_loader: - type q4_1:   13 tensors
llama_model_loader: - type q6_K:    1 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_0
print_info: file size   = 124.23 GiB (4.53 BPW) 
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 18
load: token to piece cache size = 0.6411 MB
print_info: arch             = deepseek2
print_info: vocab_only       = 0
print_info: n_ctx_train      = 163840
print_info: n_embd           = 5120
print_info: n_layer          = 60
print_info: n_head           = 128
print_info: n_head_kv        = 128
print_info: n_rot            = 64
print_info: n_swa            = 0
print_info: n_embd_head_k    = 192
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 1
print_info: n_embd_k_gqa     = 24576
print_info: n_embd_v_gqa     = 16384
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-06
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: n_ff             = 12288
print_info: n_expert         = 160
print_info: n_expert_used    = 6
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 0
print_info: rope scaling     = yarn
print_info: freq_base_train  = 10000.0
print_info: freq_scale_train = 0.025
print_info: n_ctx_orig_yarn  = 4096
print_info: rope_finetuned   = unknown
print_info: ssm_d_conv       = 0
print_info: ssm_d_inner      = 0
print_info: ssm_d_state      = 0
print_info: ssm_dt_rank      = 0
print_info: ssm_dt_b_c_rms   = 0
print_info: model type       = 236B
print_info: model params     = 235.74 B
print_info: general.name     = DeepSeek V2.5
print_info: n_layer_dense_lead   = 1
print_info: n_lora_q             = 1536
print_info: n_lora_kv            = 512
print_info: n_ff_exp             = 1536
print_info: n_expert_shared      = 2
print_info: expert_weights_scale = 16.0
print_info: expert_weights_norm  = 0
print_info: expert_gating_func   = softmax
print_info: rope_yarn_log_mul    = 0.1000
print_info: vocab type       = BPE
print_info: n_vocab          = 102400
print_info: n_merges         = 99757
print_info: BOS token        = 100000 '<｜begin▁of▁sentence｜>'
print_info: EOS token        = 100001 '<｜end▁of▁sentence｜>'
print_info: EOT token        = 100001 '<｜end▁of▁sentence｜>'
print_info: PAD token        = 100001 '<｜end▁of▁sentence｜>'
print_info: LF token         = 126 'Ä'
print_info: FIM PRE token    = 100003 '<｜fim▁begin｜>'
print_info: FIM SUF token    = 100002 '<｜fim▁hole｜>'
print_info: FIM MID token    = 100004 '<｜fim▁end｜>'
print_info: EOG token        = 100001 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors:   CPU_Mapped model buffer size = 37602.27 MiB
load_tensors:   CPU_Mapped model buffer size = 34353.59 MiB
load_tensors:   CPU_Mapped model buffer size = 36378.63 MiB
load_tensors:   CPU_Mapped model buffer size = 12801.23 MiB
load_tensors:  CPU_AARCH64 model buffer size = 121738.36 MiB
llama_init_from_model: n_seq_max     = 1
llama_init_from_model: n_ctx         = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch       = 2048
llama_init_from_model: n_ubatch      = 512
llama_init_from_model: flash_attn    = 0
llama_init_from_model: freq_base     = 10000.0
llama_init_from_model: freq_scale    = 0.025
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 60, can_shift = 0
llama_kv_cache_init:        CPU KV buffer size = 19200.00 MiB
llama_init_from_model: KV self size  = 19200.00 MiB, K (f16): 11520.00 MiB, V (f16): 7680.00 MiB
llama_init_from_model:        CPU  output buffer size =     0.39 MiB
llama_init_from_model:        CPU compute buffer size =  1174.01 MiB
llama_init_from_model: graph nodes  = 4480
llama_init_from_model: graph splits = 1
common_init_from_params: KV cache shifting is not supported for this model, disabling KV cache shifting
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv          init: initializing slots, n_slots = 1
slot         init: id  0 | task -1 | new slot n_ctx_slot = 4096
main: model loaded
main: chat template, chat_template: (built-in), example_format: 'You are a helpful assistant

<｜User｜>Hello<｜Assistant｜>Hi there<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>'
main: server is listening on http://127.0.0.1:8080 - starting the main loop
srv  update_slots: all slots are idle