Feature: Integrate with unified SYCL backend for Intel GPUs by abhilash1910 · Pull Request #2690 · ggml-org/llama.cpp (original) (raw)

Is this supposed to work with laptop/low-end iGPUs? I was getting some acceleration with openBlas but wanted this give a shot locally, but fails with:

$ GGML_SYCL_DEBUG=1 GGML_SYCL_DEVICE=0 ./bin/main -m ../../../../../models/c0c3c83d0ec33ffe925657a56b06771b -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 > logs.txt 2>&1

Log start
main: build = 2038 (ce320601)
main: built with Intel(R) oneAPI DPC++/C++ Compiler 2024.0.2 (2024.0.2.20231213) for x86_64-unknown-linux-gnu
main: seed  = 1706787641
ggml_init_sycl: GGML_SYCL_FP16:   no
ggml_init_sycl: SYCL_USE_XMX: yes
found 2 SYCL devices:
  Device 0: 12th Gen Intel(R) Core(TM) i7-1280P,	compute capability 3.0,
    max compute_units 20,	max work group size 8192,	max sub group size 64,	global mem size 67084083200
  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
    max compute_units 20,	max work group size 67108864,	max sub group size 64,	global mem size 67084083200
Using device 0 (12th Gen Intel(R) Core(TM) i7-1280P) as main device
llama_model_loader: loaded meta data with 20 key-value pairs and 325 tensors from ../../../../../models/c0c3c83d0ec33ffe925657a56b06771b (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              = phi2
llama_model_loader: - kv   1:                               general.name str              = Phi2
llama_model_loader: - kv   2:                        phi2.context_length u32              = 2048
llama_model_loader: - kv   3:                      phi2.embedding_length u32              = 2560
llama_model_loader: - kv   4:                   phi2.feed_forward_length u32              = 10240
llama_model_loader: - kv   5:                           phi2.block_count u32              = 32
llama_model_loader: - kv   6:                  phi2.attention.head_count u32              = 32
llama_model_loader: - kv   7:               phi2.attention.head_count_kv u32              = 32
llama_model_loader: - kv   8:          phi2.attention.layer_norm_epsilon f32              = 0.000010
llama_model_loader: - kv   9:                  phi2.rope.dimension_count u32              = 32
llama_model_loader: - kv  10:                          general.file_type u32              = 7
llama_model_loader: - kv  11:               tokenizer.ggml.add_bos_token bool             = false
llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,51200]   = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,51200]   = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  15:                      tokenizer.ggml.merges arr[str,50000]   = ["Ġ t", "Ġ a", "h e", "i n", "r e",...
llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 50256
llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 50256
llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 50256
llama_model_loader: - kv  19:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:  195 tensors
llama_model_loader: - type q8_0:  130 tensors
llm_load_vocab: mismatch in special tokens definition ( 910/51200 vs 944/51200 ).
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = phi2
llm_load_print_meta: vocab type       = BPE
llm_load_print_meta: n_vocab          = 51200
llm_load_print_meta: n_merges         = 50000
llm_load_print_meta: n_ctx_train      = 2048
llm_load_print_meta: n_embd           = 2560
llm_load_print_meta: n_head           = 32
llm_load_print_meta: n_head_kv        = 32
llm_load_print_meta: n_layer          = 32
llm_load_print_meta: n_rot            = 32
llm_load_print_meta: n_embd_head_k    = 80
llm_load_print_meta: n_embd_head_v    = 80
llm_load_print_meta: n_gqa            = 1
llm_load_print_meta: n_embd_k_gqa     = 2560
llm_load_print_meta: n_embd_v_gqa     = 2560
llm_load_print_meta: f_norm_eps       = 1.0e-05
llm_load_print_meta: f_norm_rms_eps   = 0.0e+00
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             = 10240
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  = 2048
llm_load_print_meta: rope_finetuned   = unknown
llm_load_print_meta: model type       = 3B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 2.78 B
llm_load_print_meta: model size       = 2.75 GiB (8.51 BPW) 
llm_load_print_meta: general.name     = Phi2
llm_load_print_meta: BOS token        = 50256 '<|endoftext|>'
llm_load_print_meta: EOS token        = 50256 '<|endoftext|>'
llm_load_print_meta: UNK token        = 50256 '<|endoftext|>'
llm_load_print_meta: LF token         = 128 'Ä'
llm_load_tensors: ggml ctx size =    0.25 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors:            buffer size =  2686.46 MiB
llm_load_tensors:        CPU buffer size =   132.81 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:            KV buffer size =   160.00 MiB
llama_new_context_with_model: KV self size  =  160.00 MiB, K (f16):   80.00 MiB, V (f16):   80.00 MiB
llama_new_context_with_model:        CPU input buffer size   =     6.01 MiB
llama_new_context_with_model:            compute buffer size =   121.00 MiB
llama_new_context_with_model:        CPU compute buffer size =     5.50 MiB
llama_new_context_with_model: graph splits (measure): 3
GGML_SYCL_DEBUG=1
call ggml_sycl_norm
The program was built for 1 devices
Build program log for '12th Gen Intel(R) Core(TM) i7-1280P':
Compilation started
Compilation done
Linking started
Linking done
Device build started
Options used by backend compiler: 
Failed to build device program
CompilerException Failed to lookup symbol _ZTSZZL13norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_
JIT session error: Symbols not found: [ _Z11fmax_commonDv32_fS_S_ ]
Failed to materialize symbols: { (main, { _ZTSZL17sum_rows_f32_syclPKfPfiiPN4sycl3_V15queueEEUlNS3_7nd_itemILi3EEEE_, _ZGVdN32uuuuuuu__ZTSZZL17soft_max_f32_syclPKfS0_PfiiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL13norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL13norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZGVdN32uuuuuu__ZTSZZL17rms_norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL19group_norm_f32_syclPKfPfiiiPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL17rms_norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL17soft_max_f32_syclPKfS0_PfiiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL17rms_norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZTSZZL19group_norm_f32_syclPKfPfiiiPN4sycl3_V15queueEENKUlRNS3_7handlerEE_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZGVdN32uuuuuu__ZTSZZL19group_norm_f32_syclPKfPfiiiPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_, _ZGVdN32uuuuuu__ZTSZZL13norm_f32_syclPKfPfiifPN4sycl3_V15queueEENKUlRNS3_7handlerEE0_clES7_EUlNS3_7nd_itemILi3EEEE_ }) }

 -11 (PI_ERROR_BUILD_PROGRAM_FAILURE)Exception caught at file:/home/mudler/_git/LocalAI/backend/cpp/llama/llama.cpp/ggml-sycl.cpp, line:12651

does that mean that the op is not supported by the onboard GPU? If so I'd be happy to add it in the known issues in the docs