Bug: igpu

[ayttop]

What happened?

C:\Users\ArabTech\Desktop\5\LlamaCppExe>C:/Users/ArabTech/Desktop/5\LlamaCppExe/llama-cli -m C:/Users/ArabTech/Desktop/5/phi-3.5-mini-instruct-q4_k_m.gguf -p "Who is Napoleon Bonaparte?" --gpu-layers 30 --no-mmap -t 2 warning: not compiled with GPU offload support, --gpu-layers option will be ignored warning: see main README.md for information on enabling GPU BLAS support Log start main: build = 3618 (3ba780e2)

Name and Version

last

What operating system are you seeing the problem on?

No response

Relevant log output

C:\Users\ArabTech\Desktop\5\LlamaCppExe>C:/Users/ArabTech/Desktop/5\LlamaCppExe/llama-cli -m C:/Users/ArabTech/Desktop/5/phi-3.5-mini-instruct-q4_k_m.gguf -p "Who is Napoleon Bonaparte?" --gpu-layers 30 --no-mmap -t 2
warning: not compiled with GPU offload support, --gpu-layers option will be ignored
warning: see main README.md for information on enabling GPU BLAS support
Log start
main: build = 3618 (3ba780e2)
main: built with MSVC 19.41.34120.0 for x64
main: seed  = 1724457404
llama_model_loader: loaded meta data with 36 key-value pairs and 197 tensors from C:/Users/ArabTech/Desktop/5/phi-3.5-mini-instruct-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              = phi3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Phi 3.5 Mini Instruct
llama_model_loader: - kv   3:                           general.finetune str              = instruct
llama_model_loader: - kv   4:                           general.basename str              = Phi-3.5
llama_model_loader: - kv   5:                         general.size_label str              = mini
llama_model_loader: - kv   6:                            general.license str              = mit
llama_model_loader: - kv   7:                       general.license.link str              = https://huggingface.co/microsoft/Phi-...
llama_model_loader: - kv   8:                               general.tags arr[str,3]       = ["nlp", "code", "text-generation"]
llama_model_loader: - kv   9:                          general.languages arr[str,1]       = ["multilingual"]
llama_model_loader: - kv  10:                        phi3.context_length u32              = 131072
llama_model_loader: - kv  11:  phi3.rope.scaling.original_context_length u32              = 4096
llama_model_loader: - kv  12:                      phi3.embedding_length u32              = 3072
llama_model_loader: - kv  13:                   phi3.feed_forward_length u32              = 8192
llama_model_loader: - kv  14:                           phi3.block_count u32              = 32
llama_model_loader: - kv  15:                  phi3.attention.head_count u32              = 32
llama_model_loader: - kv  16:               phi3.attention.head_count_kv u32              = 32
llama_model_loader: - kv  17:      phi3.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  18:                  phi3.rope.dimension_count u32              = 96
llama_model_loader: - kv  19:                        phi3.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  20:                          general.file_type u32              = 15
llama_model_loader: - kv  21:              phi3.attention.sliding_window u32              = 262144
llama_model_loader: - kv  22:              phi3.rope.scaling.attn_factor f32              = 1.190238
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,32064]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv  26:                      tokenizer.ggml.scores arr[f32,32064]   = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  27:                  tokenizer.ggml.token_type arr[i32,32064]   = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv  28:                tokenizer.ggml.bos_token_id u32              = 1
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 32000
llama_model_loader: - kv  30:            tokenizer.ggml.unknown_token_id u32              = 0
llama_model_loader: - kv  31:            tokenizer.ggml.padding_token_id u32              = 32000
llama_model_loader: - kv  32:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  33:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  34:                    tokenizer.chat_template str              = {% for message in messages %}{% if me...
llama_model_loader: - kv  35:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   67 tensors
llama_model_loader: - type q4_K:   81 tensors
llama_model_loader: - type q5_K:   32 tensors
llama_model_loader: - type q6_K:   17 tensors
llm_load_vocab: special tokens cache size = 14
llm_load_vocab: token to piece cache size = 0.1685 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = phi3
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 32064
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: vocab_only       = 0
llm_load_print_meta: n_ctx_train      = 131072
llm_load_print_meta: n_embd           = 3072
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_rot            = 96
llm_load_print_meta: n_swa            = 262144
llm_load_print_meta: n_embd_head_k    = 96
llm_load_print_meta: n_embd_head_v    = 96
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 3072
llm_load_print_meta: n_embd_v_gqa     = 3072
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: f_logit_scale    = 0.0e+00
llm_load_print_meta: n_ff             = 8192
llm_load_print_meta: n_expert         = 0
llm_load_print_meta: n_expert_used    = 0
llm_load_print_meta: causal attn      = 1
llm_load_print_meta: pooling type     = 0
llm_load_print_meta: rope type        = 2
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_ctx_orig_yarn  = 4096
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: ssm_dt_b_c_rms   = 0
llm_load_print_meta: model type       = 3B
llm_load_print_meta: model ftype      = Q4_K - Medium
llm_load_print_meta: model params     = 3.82 B
llm_load_print_meta: model size       = 2.23 GiB (5.01 BPW)
llm_load_print_meta: general.name     = Phi 3.5 Mini Instruct
llm_load_print_meta: BOS token        = 1 '<s>'
llm_load_print_meta: EOS token        = 32000 '<|endoftext|>'
llm_load_print_meta: UNK token        = 0 '<unk>'
llm_load_print_meta: PAD token        = 32000 '<|endoftext|>'
llm_load_print_meta: LF token         = 13 '<0x0A>'
llm_load_print_meta: EOT token        = 32007 '<|end|>'
llm_load_print_meta: max token length = 48
llm_load_tensors: ggml ctx size =    0.10 MiB
llm_load_tensors:        CPU buffer size =  2281.67 MiB
............................................................................................
llama_new_context_with_model: n_ctx      = 131072
llama_new_context_with_model: n_batch    = 2048
llama_new_context_with_model: n_ubatch   = 512
llama_new_context_with_model: flash_attn = 0
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 = 49152.00 MiB
llama_new_context_with_model: KV self size  = 49152.00 MiB, K (f16): 24576.00 MiB, V (f16): 24576.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB
llama_new_context_with_model:        CPU compute buffer size =  8484.01 MiB
llama_new_context_with_model: graph nodes  = 1286
llama_new_context_with_model: graph splits = 1

system_info: n_threads = 2 / 28 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampling:
        repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
        top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature
generate: n_ctx = 131072, n_batch = 2048, n_predict = -1, n_keep = 0

 Who is Napoleon Bonaparte? Napoleon Bonaparte is a famous French military leader and political leader who rose to power during the French Revolution and became the Emperor of France. Born on Corsica in 1769, Napoleon led many

llama_print_timings:        load time =   10388.70 ms
llama_print_timings:      sample time =       0.92 ms /    43 runs   (    0.02 ms per token, 46637.74 tokens per second)
llama_print_timings: prompt eval time =     357.89 ms /     7 tokens (   51.13 ms per token,    19.56 tokens per second)
llama_print_timings:        eval time =    3391.20 ms /    42 runs   (   80.74 ms per token,    12.39 tokens per second)
llama_print_timings:       total time =    3819.72 ms /    49 tokens

C:\Users\ArabTech\Desktop\5\LlamaCppExe>

[ayttop]

How do I upload on igpu

C:\Users\ArabTech\Desktop\5\LlamaCppExe>sycl-ls.exe [opencl:fpga][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2024.18.7.0.11_160000] [opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Core(TM) i7-14700K OpenCL 3.0 (Build 0) [2024.18.7.0.11_160000] [opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 770 OpenCL 3.0 NEO [31.0.101.4577] [level_zero:gpu][level_zero:0] Intel(R) Level-Zero, Intel(R) UHD Graphics 770 1.3 [1.3.26561]

[Djip007]

May be have a look her: https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md

:crossed_fingers:

[piDack]

Using the Vulkan version seems to be more hassle-free.

[ayttop]

thank you igpu intel work on llama cpp only

not work on llama cpp python

[NeoZhangJianyu]

@ayttop "warning: not compiled with GPU offload support, --gpu-layers option will be ignored" looks like the compile is without GPU support. The workload is executed on CPU in fact.

Could you refer to https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md

[ayttop]

it is run on llama cpp but not run on llama cpp python

[github-actions[bot]]

This issue was closed because it has been inactive for 14 days since being marked as stale.