`server` stops processing requests after the empty prompt

[z80maniac]

Current Behavior

After passing an empty prompt to the server it stops processing any further requests.

Environment and Context

Commit: d3bac7d

OS: Kubuntu 23.10

❯ lscpu | grep -P 'Model name|Flags'

Model name: AMD Ryzen 9 7900 12-Core Processor
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d

❯ uname -a
Linux comp 6.5.0-15-generic #15-Ubuntu SMP PREEMPT_DYNAMIC Tue Jan  9 17:03:36 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux

❯ make --version | head -1
GNU Make 4.3

❯ g++ --version | head -1
g++ (Ubuntu 13.2.0-4ubuntu3) 13.2.0

Steps to Reproduce

1. I used this model:
   https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/blob/main/llama-2-13b-chat.Q4_K_M.gguf

2. The server is built with just make, no other params.

3. Start the server:

./server -m /opt/models/text/llama-2-13b-chat.Q4_K_M.gguf

startup log

{"timestamp":1706720633,"level":"INFO","function":"main","line":2427,"message":"build info","build":2036,"commit":"d3bac7d5"}
{"timestamp":1706720633,"level":"INFO","function":"main","line":2430,"message":"system info","n_threads":12,"n_threads_batch":-1,"total_threads":24,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | "}

llama server listening at http://127.0.0.1:8080

{"timestamp":1706720633,"level":"INFO","function":"main","line":2534,"message":"HTTP server listening","port":"8080","hostname":"127.0.0.1"}
llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /opt/models/text/llama-2-13b-chat.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              = LLaMA v2
llama_model_loader: - kv   2:                       llama.context_length u32              = 4096
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 5120
llama_model_loader: - kv   4:                          llama.block_count u32              = 40
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 13824
llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 40
llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 40
llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
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.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2
llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  18:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q4_K:  241 tensors
llama_model_loader: - type q6_K:   41 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      = 4096
llm_load_print_meta: n_embd           = 5120
llm_load_print_meta: n_head           = 40
llm_load_print_meta: n_head_kv        = 40
llm_load_print_meta: n_layer          = 40
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            = 1
llm_load_print_meta: n_embd_k_gqa     = 5120
llm_load_print_meta: n_embd_v_gqa     = 5120
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             = 13824
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  = 4096
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: model type       = 13B
llm_load_print_meta: model ftype      = Q4_K - Medium
llm_load_print_meta: model params     = 13.02 B
llm_load_print_meta: model size       = 7.33 GiB (4.83 BPW)
llm_load_print_meta: general.name     = LLaMA v2
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 =    0.14 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/41 layers to GPU
llm_load_tensors:        CPU buffer size =  7500.85 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:        CPU KV buffer size =   400.00 MiB
llama_new_context_with_model: KV self size  =  400.00 MiB, K (f16):  200.00 MiB, V (f16):  200.00 MiB
llama_new_context_with_model:        CPU input buffer size   =    11.01 MiB
llama_new_context_with_model:        CPU compute buffer size =    81.40 MiB
llama_new_context_with_model: graph splits (measure): 1
Available slots:
 -> Slot 0 - max context: 512
{"timestamp":1706720634,"level":"INFO","function":"main","line":2555,"message":"model loaded"}
all slots are idle and system prompt is empty, clear the KV cache

4. Call the API without specifying the prompt:

curl --data '{"n_predict": 0}' http://127.0.0.1:8080/completion

It completes OK. The server output:

slot 0 is processing [task id: 0]

print_timings: prompt eval time =       0.00 ms /     0 tokens (    -nan ms per token,     -nan tokens per second)
print_timings:        eval time =       0.00 ms /     0 runs   (    -nan ms per token,     -nan tokens per second)
print_timings:       total time =       0.00 ms
{"timestamp":1706720709,"level":"INFO","function":"log_server_request","line":2368,"message":"request","remote_addr":"127.0.0.1","remote_port":37752,"status":200,"method":"POST","path":"/completion","params":{"{\"n_predict\": 0}":""}}

5. Call the API again, with the prompt (or without - it doesn't matter):

curl --data '{"n_predict": 8, "prompt": "This is"}' http://127.0.0.1:8080/completion

The server does not respond and no logs are produced.

Additional info

1. git bisect showed that the offending commit is 48c857a.

2. I used the {"n_predict": 0} trick to get the current context size from the server without clearing up the current cache. Ideally, there should be an API endpoint to return this info, though (/props maybe).

3. The docs don't say anything about an empty prompt, but I guess with n_predict: 0 it should be allowed (and the server does it correctly for the first request). At least it shouldn't block the entire server forever.

[github-actions]

This issue is stale because it has been open for 30 days with no activity.

[z80maniac]

No longer reproducible.