llava: Fix cpu-only clip image encoding sefault #12907

mattjcly · 2025-04-11T20:32:30Z

Overview

Properly manage/use backend unique_ptrs and the memory that they point to in clip.cpp when only using cpu backend. Note that I've observed CPU-only to perform better than Metal on my M3 Pro 36GB for image encoding in certain situations.

Issue

In clip.cpp, backend_cpu is moved from here:

backend = std::move(backend_cpu);

and then pushed to backend_ptrs here:

backend_ptrs.push_back(backend_cpu.get());

which causes a segfault in ggml-backend.cpp when that backend (NULL) is attempted to have its device member accessed:

ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
    return ggml_backend_dev_buffer_type(backend->device);
}

This PR fixes that, and then also properly checks which backend unique_ptr is pointing to a cpu backend to set n_threads.

Repro

The issue can be observed by setting use_gpu to false in gemma3-cli.cpp.

Click to see segfault in output

-> % /Users/matt/Workspace/forks/llama.cpp/build/bin/llama-gemma3-cli -m /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/gemma-3-4b-it-Q4_K_M.gguf --mmproj /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/mmproj-model-f16.gguf --image /Users/matt/Documents/dice.jpg -p "what is this"
build: 5120 (e8a62631) with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.3.0
llama_model_load_from_file_impl: using device Metal (Apple M3 Pro) - 27647 MiB free
llama_model_loader: loaded meta data with 40 key-value pairs and 444 tensors from /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/gemma-3-4b-it-Q4_K_M.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              = gemma3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Gemma 3 4b It
llama_model_loader: - kv   3:                           general.finetune str              = it
llama_model_loader: - kv   4:                           general.basename str              = gemma-3
llama_model_loader: - kv   5:                         general.size_label str              = 4B
llama_model_loader: - kv   6:                            general.license str              = gemma
llama_model_loader: - kv   7:                   general.base_model.count u32              = 1
llama_model_loader: - kv   8:                  general.base_model.0.name str              = Gemma 3 4b Pt
llama_model_loader: - kv   9:          general.base_model.0.organization str              = Google
llama_model_loader: - kv  10:              general.base_model.0.repo_url str              = https://huggingface.co/google/gemma-3...
llama_model_loader: - kv  11:                               general.tags arr[str,1]       = ["image-text-to-text"]
llama_model_loader: - kv  12:                      gemma3.context_length u32              = 131072
llama_model_loader: - kv  13:                    gemma3.embedding_length u32              = 2560
llama_model_loader: - kv  14:                         gemma3.block_count u32              = 34
llama_model_loader: - kv  15:                 gemma3.feed_forward_length u32              = 10240
llama_model_loader: - kv  16:                gemma3.attention.head_count u32              = 8
llama_model_loader: - kv  17:    gemma3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  18:                gemma3.attention.key_length u32              = 256
llama_model_loader: - kv  19:              gemma3.attention.value_length u32              = 256
llama_model_loader: - kv  20:                      gemma3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  21:            gemma3.attention.sliding_window u32              = 1024
llama_model_loader: - kv  22:             gemma3.attention.head_count_kv u32              = 4
llama_model_loader: - kv  23:                   gemma3.rope.scaling.type str              = linear
llama_model_loader: - kv  24:                 gemma3.rope.scaling.factor f32              = 8.000000
llama_model_loader: - kv  25:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  26:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  27:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  28:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,262144]  = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  32:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv  37:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  38:               general.quantization_version u32              = 2
llama_model_loader: - kv  39:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  205 tensors
llama_model_loader: - type q4_K:  204 tensors
llama_model_loader: - type q6_K:   35 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 2.31 GiB (5.12 BPW) 
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch             = gemma3
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 2560
print_info: n_layer          = 34
print_info: n_head           = 8
print_info: n_head_kv        = 4
print_info: n_rot            = 256
print_info: n_swa            = 1024
print_info: n_swa_pattern    = 6
print_info: n_embd_head_k    = 256
print_info: n_embd_head_v    = 256
print_info: n_gqa            = 2
print_info: n_embd_k_gqa     = 1024
print_info: n_embd_v_gqa     = 1024
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: f_attn_scale     = 6.2e-02
print_info: n_ff             = 10240
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 2
print_info: rope scaling     = linear
print_info: freq_base_train  = 1000000.0
print_info: freq_scale_train = 0.125
print_info: n_ctx_orig_yarn  = 131072
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       = 4B
print_info: model params     = 3.88 B
print_info: general.name     = Gemma 3 4b It
print_info: vocab type       = SPM
print_info: n_vocab          = 262144
print_info: n_merges         = 0
print_info: BOS token        = 2 '<bos>'
print_info: EOS token        = 1 '<eos>'
print_info: EOT token        = 106 '<end_of_turn>'
print_info: UNK token        = 3 '<unk>'
print_info: PAD token        = 0 '<pad>'
print_info: LF token         = 248 '<0x0A>'
print_info: EOG token        = 1 '<eos>'
print_info: EOG token        = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 34 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 35/35 layers to GPU
load_tensors: Metal_Mapped model buffer size =  2368.18 MiB
load_tensors:   CPU_Mapped model buffer size =   525.00 MiB
.................................................................
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 0
llama_context: freq_base     = 1000000.0
llama_context: freq_scale    = 0.125
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M3 Pro
ggml_metal_init: picking default device: Apple M3 Pro
ggml_metal_load_library: using embedded metal library
ggml_metal_init: GPU name:   Apple M3 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple9  (1009)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction   = true
ggml_metal_init: simdgroup matrix mul. = true
ggml_metal_init: has residency sets    = false
ggml_metal_init: has bfloat            = true
ggml_metal_init: use bfloat            = false
ggml_metal_init: hasUnifiedMemory      = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 28991.03 MB
ggml_metal_init: skipping kernel_get_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_1row              (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_l4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_bf16                  (not supported)
ggml_metal_init: skipping kernel_mul_mv_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_mul_mm_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h64           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h80           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h96           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h112          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h128          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h192          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk192_hv128   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h256          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h128      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h192      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk192_hv128 (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h256      (not supported)
ggml_metal_init: skipping kernel_cpy_f32_bf16                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_f32                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_bf16                     (not supported)
llama_context:        CPU  output buffer size =     1.00 MiB
init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 34, can_shift = 1
init:      Metal KV buffer size =   544.00 MiB
llama_context: KV self size  =  544.00 MiB, K (f16):  272.00 MiB, V (f16):  272.00 MiB
llama_context:      Metal compute buffer size =   517.00 MiB
llama_context:        CPU compute buffer size =    21.01 MiB
llama_context: graph nodes  = 1435
llama_context: graph splits = 2
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)
clip_ctx: CLIP using CPU backend
[1]    56301 segmentation fault  /Users/matt/Workspace/forks/llama.cpp/build/bin/llama-gemma3-cli -m  --mmproj

Fix

Output with these changes

Click to see correct output

-> % /Users/matt/Workspace/forks/llama.cpp/build/bin/llama-gemma3-cli -m /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/gemma-3-4b-it-Q4_K_M.gguf --mmproj /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/mmproj-model-f16.gguf --image /Users/matt/Documents/dice.jpg -p "what is this"
build: 5121 (a7fb802d) with Apple clang version 15.0.0 (clang-1500.3.9.4) for arm64-apple-darwin23.3.0
llama_model_load_from_file_impl: using device Metal (Apple M3 Pro) - 27647 MiB free
llama_model_loader: loaded meta data with 40 key-value pairs and 444 tensors from /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/gemma-3-4b-it-Q4_K_M.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              = gemma3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Gemma 3 4b It
llama_model_loader: - kv   3:                           general.finetune str              = it
llama_model_loader: - kv   4:                           general.basename str              = gemma-3
llama_model_loader: - kv   5:                         general.size_label str              = 4B
llama_model_loader: - kv   6:                            general.license str              = gemma
llama_model_loader: - kv   7:                   general.base_model.count u32              = 1
llama_model_loader: - kv   8:                  general.base_model.0.name str              = Gemma 3 4b Pt
llama_model_loader: - kv   9:          general.base_model.0.organization str              = Google
llama_model_loader: - kv  10:              general.base_model.0.repo_url str              = https://huggingface.co/google/gemma-3...
llama_model_loader: - kv  11:                               general.tags arr[str,1]       = ["image-text-to-text"]
llama_model_loader: - kv  12:                      gemma3.context_length u32              = 131072
llama_model_loader: - kv  13:                    gemma3.embedding_length u32              = 2560
llama_model_loader: - kv  14:                         gemma3.block_count u32              = 34
llama_model_loader: - kv  15:                 gemma3.feed_forward_length u32              = 10240
llama_model_loader: - kv  16:                gemma3.attention.head_count u32              = 8
llama_model_loader: - kv  17:    gemma3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  18:                gemma3.attention.key_length u32              = 256
llama_model_loader: - kv  19:              gemma3.attention.value_length u32              = 256
llama_model_loader: - kv  20:                      gemma3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  21:            gemma3.attention.sliding_window u32              = 1024
llama_model_loader: - kv  22:             gemma3.attention.head_count_kv u32              = 4
llama_model_loader: - kv  23:                   gemma3.rope.scaling.type str              = linear
llama_model_loader: - kv  24:                 gemma3.rope.scaling.factor f32              = 8.000000
llama_model_loader: - kv  25:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  26:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  27:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  28:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,262144]  = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  32:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv  37:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  38:               general.quantization_version u32              = 2
llama_model_loader: - kv  39:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  205 tensors
llama_model_loader: - type q4_K:  204 tensors
llama_model_loader: - type q6_K:   35 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 2.31 GiB (5.12 BPW) 
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch             = gemma3
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 2560
print_info: n_layer          = 34
print_info: n_head           = 8
print_info: n_head_kv        = 4
print_info: n_rot            = 256
print_info: n_swa            = 1024
print_info: n_swa_pattern    = 6
print_info: n_embd_head_k    = 256
print_info: n_embd_head_v    = 256
print_info: n_gqa            = 2
print_info: n_embd_k_gqa     = 1024
print_info: n_embd_v_gqa     = 1024
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: f_attn_scale     = 6.2e-02
print_info: n_ff             = 10240
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 2
print_info: rope scaling     = linear
print_info: freq_base_train  = 1000000.0
print_info: freq_scale_train = 0.125
print_info: n_ctx_orig_yarn  = 131072
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       = 4B
print_info: model params     = 3.88 B
print_info: general.name     = Gemma 3 4b It
print_info: vocab type       = SPM
print_info: n_vocab          = 262144
print_info: n_merges         = 0
print_info: BOS token        = 2 '<bos>'
print_info: EOS token        = 1 '<eos>'
print_info: EOT token        = 106 '<end_of_turn>'
print_info: UNK token        = 3 '<unk>'
print_info: PAD token        = 0 '<pad>'
print_info: LF token         = 248 '<0x0A>'
print_info: EOG token        = 1 '<eos>'
print_info: EOG token        = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 34 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 35/35 layers to GPU
load_tensors: Metal_Mapped model buffer size =  2368.18 MiB
load_tensors:   CPU_Mapped model buffer size =   525.00 MiB
.................................................................
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 0
llama_context: freq_base     = 1000000.0
llama_context: freq_scale    = 0.125
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M3 Pro
ggml_metal_init: picking default device: Apple M3 Pro
ggml_metal_load_library: using embedded metal library
ggml_metal_init: GPU name:   Apple M3 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple9  (1009)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction   = true
ggml_metal_init: simdgroup matrix mul. = true
ggml_metal_init: has residency sets    = false
ggml_metal_init: has bfloat            = true
ggml_metal_init: use bfloat            = false
ggml_metal_init: hasUnifiedMemory      = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 28991.03 MB
ggml_metal_init: skipping kernel_get_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_1row              (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_l4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_bf16                  (not supported)
ggml_metal_init: skipping kernel_mul_mv_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_mul_mm_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h64           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h80           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h96           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h112          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h128          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h192          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk192_hv128   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h256          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h128      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h192      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk192_hv128 (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h256      (not supported)
ggml_metal_init: skipping kernel_cpy_f32_bf16                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_f32                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_bf16                     (not supported)
llama_context:        CPU  output buffer size =     1.00 MiB
init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 34, can_shift = 1
init:      Metal KV buffer size =   544.00 MiB
llama_context: KV self size  =  544.00 MiB, K (f16):  272.00 MiB, V (f16):  272.00 MiB
llama_context:      Metal compute buffer size =   517.00 MiB
llama_context:        CPU compute buffer size =    21.01 MiB
llama_context: graph nodes  = 1435
llama_context: graph splits = 2
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)
clip_ctx: CLIP using CPU backend
clip_model_loader: model name:   
clip_model_loader: description:  
clip_model_loader: GGUF version: 3
clip_model_loader: alignment:    32
clip_model_loader: n_tensors:    439
clip_model_loader: n_kv:         16

load_hparams: text_encoder:       0
load_hparams: vision_encoder:     1
load_hparams: llava_projector:    0
load_hparams: minicpmv_projector: 0
load_hparams: minicpmv_version:   2
load_hparams: glm_projector:      0
load_hparams: model size:         811.79 MiB
load_hparams: metadata size:      0.15 MiB
alloc_compute_meta:        CPU compute buffer size =  1128.81 MiB
main: /Users/matt/.cache/lm-studio/models/ggml-org/gemma-3-4b-it-GGUF/gemma-3-4b-it-Q4_K_M.gguf
encoding image...
image encoded in 25698 ms
image decoded in 465 ms
Based on the image, those are **wooden dice**. 

You can see they have the standard six sides with dots representing the numbers 1 through 6. 

They're arranged on a dark, likely black, surface.
ggml_metal_free: deallocating

ngxson

After more careful examine, I think the backend_cpu is only used by ggml_backend_cpu_set_n_threads, so probably it's not that useful to use a smart pointer for it.

I'll simplify this by remove the smart ptr altogether for ggml_backend_t

ngxson · 2025-04-11T20:51:32Z

Could you give this a try @mattjcly ? Thanks!!

mattjcly · 2025-04-11T21:02:06Z

That caused a gpu enabled failure:

clip_ctx: CLIP using Metal backend
/Users/matt/Workspace/forks/llama.cpp/ggml/src/ggml-backend.cpp:1458: GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU) failed
[1]    60661 abort      /Users/matt/Workspace/forks/llama.cpp/build/bin/llama-gemma3-cli -m  --mmproj

Put up the fix (tiny typo). @ngxson lmk if that looks correct to you.

ngxson

Yes this looks perfect, thanks 🔥

(merging once the CI is green)

* llava: Fix cpu-only clip image encoding * clip : no smart ptr for ggml_backend_t * Fix for backend_ptr push_back --------- Co-authored-by: Xuan Son Nguyen <[email protected]>

llava: Fix cpu-only clip image encoding

a7fb802

github-actions bot added the examples label Apr 11, 2025

ngxson reviewed Apr 11, 2025

View reviewed changes

clip : no smart ptr for ggml_backend_t

afb1eaf

Fix for backend_ptr push_back

676dc0c

ngxson approved these changes Apr 11, 2025

View reviewed changes

ngxson merged commit e59ea53 into ggml-org:master Apr 12, 2025
51 checks passed

Tianyue-Zhao mentioned this pull request Apr 14, 2025

Misc. bug: ALL gguf models fail to run (no log, docker exit code 139), #12205

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llava: Fix cpu-only clip image encoding sefault #12907

llava: Fix cpu-only clip image encoding sefault #12907

mattjcly commented Apr 11, 2025 •

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llava: Fix cpu-only clip image encoding sefault #12907

llava: Fix cpu-only clip image encoding sefault #12907

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mattjcly commented Apr 11, 2025 • edited Loading

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