Misc. bug: llama-server --ctx-size is divided by --parallel and cannot be increased? #11681
Description
Name and Version
./llama-server --version
version: 4621 (6eecde3)
built with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
Operating systems
Linux
Which llama.cpp modules do you know to be affected?
llama-server
Command line
./llama-server \
--model models/deepseek-v3-q2_k_xs-00001-of-00005.gguf --alias full \
--host 0.0.0.0 \
--port 55055 \
--ctx-size 0 \
--cache-type-k q5_0 \
--slot-save-path "saved_slots.kvc" \
--threads 94 \
--threads-http 12 \
--parallel 6 \
--mirostat 2 \
--mirostat-ent 5.7 \
--mirostat-lr 0.14Problem description & steps to reproduce
Context size is being divided by number of parallel inferences allowed to run on llama-server. This does not make sense. Attempting to rectify it by increasing --ctx-size to compensate for this results in an assertion fail. In order to not break previous behavior, I suggest adding a new command line argument --ctz-size-per-seq to override this behavior.
If llama-server is run with --ctx-size 0 --parallel 6 or --ctx-size 163840 --parallel 6, it divides the total ctx by 6.
print_info: file format = GGUF V3 (latest)
print_info: file type = Q2_K - Medium
print_info: file size = 206.05 GiB (2.64 BPW)
[...]
print_info: arch = deepseek2
print_info: n_ctx_train = 163840
[...]
llama_init_from_model: n_seq_max = 6
llama_init_from_model: n_ctx = 163840
llama_init_from_model: n_ctx_per_seq = 27306
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 (27306) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 163840, offload = 1, type_k = 'q5_0', type_v = 'f16', n_layer = 61, can_shift = 0
Okay, so I increase the ctx to 327680 so it gives more to each parallel. That almost works, but then fails on GGML_ASSERT.
llama_init_from_model: n_seq_max = 6
llama_init_from_model: n_ctx = 327680
llama_init_from_model: n_ctx_per_seq = 54613
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 (54613) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 327680, offload = 1, type_k = 'q5_0', type_v = 'f16', n_layer = 61, can_shift = 0
llama_kv_cache_init: CPU KV buffer size = 275232.00 MiB
llama_init_from_model: KV self size = 275232.00 MiB, K (q5_0): 150304.00 MiB, V (f16): 124928.00 MiB
llama_init_from_model: CPU output buffer size = 2.96 MiB
/home/main/llama.cpp/ggml/src/ggml.c:1578: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed
So it's impossible to use more than 27306 ctx size, even though I have 1.1TB of RAM.
First Bad Commit
No response
Relevant log output
./llama-server \
> --model models/deepseek-v3-q2_k_xs-00001-of-00005.gguf --alias full \
> --host 0.0.0.0 \
> --port 55055 \
> --ctx-size 327680 \
> --cache-type-k q5_0 \
> --slot-save-path "saved_slots.kvc" \
> --threads 94 \
> --threads-http 12 \
> --parallel 6 \
> --mirostat 2 \
> --mirostat-ent 5.7 \
> --mirostat-lr 0.14
build: 4621 (6eecde3c) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for x86_64-linux-gnu
system info: n_threads = 94, n_threads_batch = 94, total_threads = 96
system_info: n_threads = 94 (n_threads_batch = 94) / 96 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
main: HTTP server is listening, hostname: 0.0.0.0, port: 55055, http threads: 12
main: loading model
srv load_model: loading model 'models/deepseek-v3-q2_k_xs-00001-of-00005.gguf'
llama_model_loader: additional 4 GGUFs metadata loaded.
llama_model_loader: loaded meta data with 46 key-value pairs and 1025 tensors from models/deepseek-v3-q2_k_xs-00001-of-00005.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 V3 BF16
llama_model_loader: - kv 3: general.size_label str = 256x20B
llama_model_loader: - kv 4: deepseek2.block_count u32 = 61
llama_model_loader: - kv 5: deepseek2.context_length u32 = 163840
llama_model_loader: - kv 6: deepseek2.embedding_length u32 = 7168
llama_model_loader: - kv 7: deepseek2.feed_forward_length u32 = 18432
llama_model_loader: - kv 8: deepseek2.attention.head_count u32 = 128
llama_model_loader: - kv 9: deepseek2.attention.head_count_kv u32 = 128
llama_model_loader: - kv 10: deepseek2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 11: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 12: deepseek2.expert_used_count u32 = 8
llama_model_loader: - kv 13: general.file_type u32 = 10
llama_model_loader: - kv 14: deepseek2.leading_dense_block_count u32 = 3
llama_model_loader: - kv 15: deepseek2.vocab_size u32 = 129280
llama_model_loader: - kv 16: deepseek2.attention.q_lora_rank u32 = 1536
llama_model_loader: - kv 17: deepseek2.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 18: deepseek2.attention.key_length u32 = 192
llama_model_loader: - kv 19: deepseek2.attention.value_length u32 = 128
llama_model_loader: - kv 20: deepseek2.expert_feed_forward_length u32 = 2048
llama_model_loader: - kv 21: deepseek2.expert_count u32 = 256
llama_model_loader: - kv 22: deepseek2.expert_shared_count u32 = 1
llama_model_loader: - kv 23: deepseek2.expert_weights_scale f32 = 2.500000
llama_model_loader: - kv 24: deepseek2.expert_weights_norm bool = true
llama_model_loader: - kv 25: deepseek2.expert_gating_func u32 = 2
llama_model_loader: - kv 26: deepseek2.rope.dimension_count u32 = 64
llama_model_loader: - kv 27: deepseek2.rope.scaling.type str = yarn
llama_model_loader: - kv 28: deepseek2.rope.scaling.factor f32 = 40.000000
llama_model_loader: - kv 29: deepseek2.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 30: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.100000
llama_model_loader: - kv 31: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 32: tokenizer.ggml.pre str = deepseek-v3
llama_model_loader: - kv 33: tokenizer.ggml.tokens arr[str,129280] = ["<｜begin▁of▁sentence｜>", "<�...
llama_model_loader: - kv 34: tokenizer.ggml.token_type arr[i32,129280] = [3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 35: tokenizer.ggml.merges arr[str,127741] = ["Ġ t", "Ġ a", "i n", "Ġ Ġ", "h e...
llama_model_loader: - kv 36: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 37: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 38: tokenizer.ggml.padding_token_id u32 = 1
llama_model_loader: - kv 39: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 40: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 41: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 42: general.quantization_version u32 = 2
llama_model_loader: - kv 43: split.no u16 = 0
llama_model_loader: - kv 44: split.count u16 = 5
llama_model_loader: - kv 45: split.tensors.count i32 = 1025
llama_model_loader: - type f32: 361 tensors
llama_model_loader: - type q2_K: 662 tensors
llama_model_loader: - type q4_K: 1 tensors
llama_model_loader: - type q6_K: 1 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q2_K - Medium
print_info: file size = 206.05 GiB (2.64 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 818
load: token to piece cache size = 0.8223 MB
print_info: arch = deepseek2
print_info: vocab_only = 0
print_info: n_ctx_train = 163840
print_info: n_embd = 7168
print_info: n_layer = 61
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 = 18432
print_info: n_expert = 256
print_info: n_expert_used = 8
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 = 671B
print_info: model params = 671.03 B
print_info: general.name = DeepSeek V3 BF16
print_info: n_layer_dense_lead = 3
print_info: n_lora_q = 1536
print_info: n_lora_kv = 512
print_info: n_ff_exp = 2048
print_info: n_expert_shared = 1
print_info: expert_weights_scale = 2.5
print_info: expert_weights_norm = 1
print_info: expert_gating_func = sigmoid
print_info: rope_yarn_log_mul = 0.1000
print_info: vocab type = BPE
print_info: n_vocab = 129280
print_info: n_merges = 127741
print_info: BOS token = 0 '<｜begin▁of▁sentence｜>'
print_info: EOS token = 1 '<｜end▁of▁sentence｜>'
print_info: EOT token = 1 '<｜end▁of▁sentence｜>'
print_info: PAD token = 1 '<｜end▁of▁sentence｜>'
print_info: LF token = 201 'Ċ'
print_info: FIM PRE token = 128801 '<｜fim▁begin｜>'
print_info: FIM SUF token = 128800 '<｜fim▁hole｜>'
print_info: FIM MID token = 128802 '<｜fim▁end｜>'
print_info: EOG token = 1 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors: CPU_Mapped model buffer size = 42690.54 MiB
load_tensors: CPU_Mapped model buffer size = 42108.14 MiB
load_tensors: CPU_Mapped model buffer size = 42049.55 MiB
load_tensors: CPU_Mapped model buffer size = 42101.11 MiB
load_tensors: CPU_Mapped model buffer size = 42049.55 MiB
llama_init_from_model: n_seq_max = 6
llama_init_from_model: n_ctx = 327680
llama_init_from_model: n_ctx_per_seq = 54613
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 (54613) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 327680, offload = 1, type_k = 'q5_0', type_v = 'f16', n_layer = 61, can_shift = 0
llama_kv_cache_init: CPU KV buffer size = 275232.00 MiB
llama_init_from_model: KV self size = 275232.00 MiB, K (q5_0): 150304.00 MiB, V (f16): 124928.00 MiB
llama_init_from_model: CPU output buffer size = 2.96 MiB
/home/main/llama.cpp/ggml/src/ggml.c:1578: GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)) failed