Closed
Description
Name and Version
C:\Users\xeden\Downloads\llama-b5255-bin-win-vulkan-x64>llama-cli --version
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon(TM) 8060S Graphics (AMD proprietary driver) | uma: 1 | fp16: 1 | warp size: 64 | shared memory: 32768 | int dot: 1 | matrix cores: KHR_coopmat
version: 5255 (d24d592)
built with MSVC 19.43.34808.0 for x64
Operating systems
Windows
GGML backends
Vulkan
Hardware
CPU AMD Ryzen AI MAX 395 Memory 128G （CPU 64G GPU 64G）
Models
Qwen2.5-VL-3B-Instruct-f16.gguf
Problem description & steps to reproduce
Device
CPU AMD Ryzen AI MAX 395
Memory
128GB GPU 64mb, CPU 64mb
Operating system
win11
Used llama.cpp version
llama-b5255-bin-win-vulkan-x64
Since AMD does not support ROCM of Ryzen AI MAX 395, I used vulkan as the backends, and it is no problem to run most of the llm models, including deepseek.
First Bad Commit
No response
Relevant log output
C:\Users\xeden\Downloads\llama-b5255-bin-win-vulkan-x64>llama-mtmd-cli -m Qwen2.5-VL-3B-Instruct-f16.gguf --mmproj mmproj-Qwen2.5-VL-3B-Instruct-f16.gguf -p '描述图片内容.' --image demo.jpg
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon(TM) 8060S Graphics (AMD proprietary driver) | uma: 1 | fp16: 1 | warp size: 64 | shared memory: 32768 | int dot: 1 | matrix cores: KHR_coopmat
build: 5255 (d24d5928) with MSVC 19.43.34808.0 for x64
llama_model_load_from_file_impl: using device Vulkan0 (AMD Radeon(TM) 8060S Graphics) - 65536 MiB free
llama_model_loader: loaded meta data with 27 key-value pairs and 434 tensors from Qwen2.5-VL-3B-Instruct-f16.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 = qwen2vl
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen2.5 VL 3B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Qwen2.5-VL
llama_model_loader: - kv 5: general.size_label str = 3B
llama_model_loader: - kv 6: qwen2vl.block_count u32 = 36
llama_model_loader: - kv 7: qwen2vl.context_length u32 = 128000
llama_model_loader: - kv 8: qwen2vl.embedding_length u32 = 2048
llama_model_loader: - kv 9: qwen2vl.feed_forward_length u32 = 11008
llama_model_loader: - kv 10: qwen2vl.attention.head_count u32 = 16
llama_model_loader: - kv 11: qwen2vl.attention.head_count_kv u32 = 2
llama_model_loader: - kv 12: qwen2vl.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: qwen2vl.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 14: general.file_type u32 = 1
llama_model_loader: - kv 15: qwen2vl.rope.dimension_sections arr[i32,4] = [16, 24, 24, 0]
llama_model_loader: - kv 16: general.quantization_version u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 18: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 21: tokenizer.ggml.merges arr[str,151387] = ["臓 臓", "臓臓 臓臓", "i n", "臓 t",...
llama_model_loader: - kv 22: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 23: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 24: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 25: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 26: tokenizer.chat_template str = {% set image_count = namespace(value=...
llama_model_loader: - type f32: 181 tensors
llama_model_loader: - type f16: 253 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = F16
print_info: file size = 5.75 GiB (16.00 BPW)
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2vl
print_info: vocab_only = 0
print_info: n_ctx_train = 128000
print_info: n_embd = 2048
print_info: n_layer = 36
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 128
print_info: n_swa = 0
print_info: n_swa_pattern = 1
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 256
print_info: n_embd_v_gqa = 256
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 = 0.0e+00
print_info: n_ff = 11008
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 = 8
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 128000
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 = 3B
print_info: model params = 3.09 B
print_info: general.name = Qwen2.5 VL 3B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 151936
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 '膴'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/37 layers to GPU
load_tensors: CPU_Mapped model buffer size = 5886.42 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 = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (128000) -- the full capacity of the model will not be utilized
llama_context: CPU output buffer size = 0.58 MiB
init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 36, can_shift = 1
init: CPU KV buffer size = 144.00 MiB
llama_context: KV self size = 144.00 MiB, K (f16): 72.00 MiB, V (f16): 72.00 MiB
llama_context: Vulkan0 compute buffer size = 941.25 MiB
llama_context: Vulkan_Host compute buffer size = 12.01 MiB
llama_context: graph nodes = 1338
llama_context: graph splits = 508 (with bs=512), 1 (with bs=1)
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)
mtmd_cli_context: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
clip_ctx: CLIP using Vulkan0 backend
clip_model_loader: model name: Qwen2.5 VL 3B Instruct
clip_model_loader: description:
clip_model_loader: GGUF version: 3
clip_model_loader: alignment: 32
clip_model_loader: n_tensors: 519
clip_model_loader: n_kv: 22
load_hparams: projector: qwen2.5vl_merger
load_hparams: has_llava_proj: 0
load_hparams: minicpmv_version: 0
load_hparams: proj_scale_factor: 0
load_hparams: n_wa_pattern: 8
load_hparams: use_silu: 1
load_hparams: use_gelu: 0
load_hparams: model size: 1276.39 MiB
load_hparams: metadata size: 0.18 MiB
alloc_compute_meta: Vulkan0 compute buffer size = 208.69 MiB
alloc_compute_meta: CPU compute buffer size = 13.38 MiB
main: Qwen2.5-VL-3B-Instruct-f16.gguf
encoding image or slice...
ggml_vulkan: Device memory allocation of size 4342230552 failed.
ggml_vulkan: Requested buffer size exceeds device memory allocation limit: ErrorOutOfDeviceMemory
ggml_gallocr_reserve_n: failed to allocate Vulkan0 buffer of size 4342230552
D:\a\llama.cpp\llama.cpp\ggml\src\ggml-backend.cpp:1663: GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <= (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer)) failed