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Model loading failed with --gpulayer 80 on Metal  #744

@beebopkim

Description

@beebopkim

Commit hash: edb05e7
Branch: concedo_experimental

With --gpulayers 80:

% python koboldcpp.py --noblas --gpulayers 80 --model $LLM_MODEL_Q/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf
***
Welcome to KoboldCpp - Version 1.61
Warning: OpenBLAS library file not found. Non-BLAS library will be used.
Initializing dynamic library: koboldcpp_default.so
==========
Namespace(model='/Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf', model_param='/Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf', port=5001, port_param=5001, host='', launch=False, config=None, threads=4, usecublas=None, usevulkan=None, useclblast=None, noblas=True, gpulayers=80, tensor_split=None, contextsize=2048, ropeconfig=[0.0, 10000.0], blasbatchsize=512, blasthreads=4, lora=None, smartcontext=False, noshift=False, bantokens=None, forceversion=0, nommap=False, usemlock=False, noavx2=False, debugmode=0, skiplauncher=False, hordeconfig=None, onready='', benchmark=None, multiuser=0, remotetunnel=False, highpriority=False, foreground=False, preloadstory='', quiet=False, ssl=None, nocertify=False, sdconfig=None, mmproj='', password=None)
==========
Loading model: /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf 
[Threads: 4, BlasThreads: 4, SmartContext: False, ContextShift: True]

The reported GGUF Arch is: llama

---
Identified as GGUF model: (ver 6)
Attempting to Load...
---
Using automatic RoPE scaling. If the model has customized RoPE settings, they will be used directly instead!
System Info: AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | 
llama_model_loader: loaded meta data with 24 key-value pairs and 723 tensors from /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf (version GGUF V3 (latest))
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      = 32764
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          = 80
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             = 28672
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attm      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 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  = 32764
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 70B
llm_load_print_meta: model ftype      = Q4_1, some F16
llm_load_print_meta: model params     = 68.98 B
llm_load_print_meta: model size       = 23.71 GiB (2.95 BPW) 
llm_load_print_meta: general.name     = models
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        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.64 MiB
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloaded 80/81 layers to GPU
llm_load_tensors:        CPU buffer size = 24282.14 MiB
llm_load_tensors:      Metal buffer size = 24200.09 MiB
....................................................................................................
Automatic RoPE Scaling: Using model internal value.
llama_new_context_with_model: n_ctx      = 2128
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: failed to initialize Metal backend
gpttype_load_model: error: failed to load model '/Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf'
Load Text Model OK: False
Could not load text model: /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf
% 

Without --gpulayers 80:

% python koboldcpp.py --noblas --model $LLM_MODEL_Q/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf 
***
Welcome to KoboldCpp - Version 1.61
Warning: OpenBLAS library file not found. Non-BLAS library will be used.
Initializing dynamic library: koboldcpp_default.so
==========
Namespace(model='/Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf', model_param='/Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf', port=5001, port_param=5001, host='', launch=False, config=None, threads=4, usecublas=None, usevulkan=None, useclblast=None, noblas=True, gpulayers=0, tensor_split=None, contextsize=2048, ropeconfig=[0.0, 10000.0], blasbatchsize=512, blasthreads=4, lora=None, smartcontext=False, noshift=False, bantokens=None, forceversion=0, nommap=False, usemlock=False, noavx2=False, debugmode=0, skiplauncher=False, hordeconfig=None, onready='', benchmark=None, multiuser=0, remotetunnel=False, highpriority=False, foreground=False, preloadstory='', quiet=False, ssl=None, nocertify=False, sdconfig=None, mmproj='', password=None)
==========
Loading model: /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf 
[Threads: 4, BlasThreads: 4, SmartContext: False, ContextShift: True]

The reported GGUF Arch is: llama

---
Identified as GGUF model: (ver 6)
Attempting to Load...
---
Using automatic RoPE scaling. If the model has customized RoPE settings, they will be used directly instead!
System Info: AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | 
llama_model_loader: loaded meta data with 24 key-value pairs and 723 tensors from /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf (version GGUF V3 (latest))
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      = 32764
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          = 80
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             = 28672
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attm      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 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  = 32764
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 70B
llm_load_print_meta: model ftype      = Q4_1, some F16
llm_load_print_meta: model params     = 68.98 B
llm_load_print_meta: model size       = 23.71 GiB (2.95 BPW) 
llm_load_print_meta: general.name     = models
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        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.32 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/81 layers to GPU
llm_load_tensors:        CPU buffer size = 24282.14 MiB
....................................................................................................
Automatic RoPE Scaling: Using model internal value.
llama_new_context_with_model: n_ctx      = 2128
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 =   665.00 MiB
llama_new_context_with_model: KV self size  =  665.00 MiB, K (f16):  332.50 MiB, V (f16):  332.50 MiB
llama_new_context_with_model:        CPU input buffer size   =    21.18 MiB
llama_new_context_with_model:        CPU compute buffer size =   330.00 MiB
llama_new_context_with_model: graph splits (measure): 1
Load Text Model OK: True
Embedded Kobold Lite loaded.
Starting Kobold API on port 5001 at http://localhost:5001/api/
Starting OpenAI Compatible API on port 5001 at http://localhost:5001/v1/
======
Please connect to custom endpoint at http://localhost:5001

For comparison - server with -ngl 999 from llama.cpp commit hash 306d34b:

% ./server -m /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.gguf -ngl 999 -c 16384
{"build":2409,"commit":"306d34be","function":"main","level":"INFO","line":2732,"msg":"build info","tid":"0x1e27d5c40","timestamp":1710286276}
{"function":"main","level":"INFO","line":2739,"msg":"system info","n_threads":8,"n_threads_batch":-1,"system_info":"AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | ","tid":"0x1e27d5c40","timestamp":1710286276,"total_threads":10}
llama_model_loader: loaded meta data with 24 key-value pairs and 723 tensors from /Volumes/cuttingedge/large_language_models/models_ggml_converted/maywell_kiqu-70b-GGUF/kiqu-70b.Q2_K.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              = models
llama_model_loader: - kv   2:                       llama.context_length u32              = 32764
llama_model_loader: - kv   3:                     llama.embedding_length u32              = 8192
llama_model_loader: - kv   4:                          llama.block_count u32              = 80
llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 28672
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              = 10
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:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  20:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  21:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  22:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv  23:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  161 tensors
llama_model_loader: - type q2_K:  321 tensors
llama_model_loader: - type q3_K:  160 tensors
llama_model_loader: - type q5_K:   80 tensors
llama_model_loader: - type q6_K:    1 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      = 32764
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          = 80
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             = 28672
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attm      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 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  = 32764
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: ssm_d_conv       = 0
llm_load_print_meta: ssm_d_inner      = 0
llm_load_print_meta: ssm_d_state      = 0
llm_load_print_meta: ssm_dt_rank      = 0
llm_load_print_meta: model type       = 70B
llm_load_print_meta: model ftype      = Q2_K - Medium
llm_load_print_meta: model params     = 68.98 B
llm_load_print_meta: model size       = 23.71 GiB (2.95 BPW) 
llm_load_print_meta: general.name     = models
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        = 0 '<unk>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.55 MiB
ggml_backend_metal_buffer_from_ptr: allocated buffer, size = 24200.12 MiB, (24200.19 / 49152.00)
llm_load_tensors: offloading 80 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 81/81 layers to GPU
llm_load_tensors:      Metal buffer size = 24200.12 MiB
llm_load_tensors:        CPU buffer size =    82.03 MiB
....................................................................................................
llama_new_context_with_model: n_ctx      = 16384
llama_new_context_with_model: freq_base  = 1000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Max
ggml_metal_init: picking default device: Apple M1 Max
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/******/test/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name:   Apple M1 Max
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction support   = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory              = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 51539.61 MB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =  5120.00 MiB, (29322.00 / 49152.00)
llama_kv_cache_init:      Metal KV buffer size =  5120.00 MiB
llama_new_context_with_model: KV self size  = 5120.00 MiB, K (f16): 2560.00 MiB, V (f16): 2560.00 MiB
llama_new_context_with_model:        CPU input buffer size   =    49.13 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size =  2144.02 MiB, (31466.02 / 49152.00)
llama_new_context_with_model:      Metal compute buffer size =  2144.00 MiB
llama_new_context_with_model:        CPU compute buffer size =    16.00 MiB
llama_new_context_with_model: graph splits (measure): 2
{"function":"init","level":"INFO","line":700,"msg":"initializing slots","n_slots":1,"tid":"0x1e27d5c40","timestamp":1710286281}
{"function":"init","id_slot":0,"level":"INFO","line":712,"msg":"new slot","n_ctx_slot":16384,"tid":"0x1e27d5c40","timestamp":1710286281}
{"function":"main","level":"INFO","line":2828,"msg":"model loaded","tid":"0x1e27d5c40","timestamp":1710286281}
{"built_in":true,"chat_example":"[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] How are you? [/INST]","function":"main","level":"INFO","line":2853,"msg":"chat template","tid":"0x1e27d5c40","timestamp":1710286281}
{"function":"main","hostname":"127.0.0.1","level":"INFO","line":3494,"msg":"HTTP server listening","n_threads_http":"9","port":"8080","tid":"0x1e27d5c40","timestamp":1710286281}
{"function":"update_slots","level":"INFO","line":1647,"msg":"all slots are idle","tid":"0x1e27d5c40","timestamp":1710286281}

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