bf16 GGUF fails with GGML_ASSERT on CUDA
ddh0 opened this issue · comments
Hi!
I have just cloned the latest llama.cpp repo, and I'm getting this error when trying to run a BF16 GGUF model with llama.cpp:
GGML_ASSERT: ggml-cuda.cu:1277: to_fp32_cuda != nullptr
I am using ddh0/Meta-Llama-3-8B-Instruct-bf16-GGUF, which I made myself, and I have confirmed to work on Metal.
This is the command I'm running:
./llama.cpp/main -m ./models/Meta-Llama-3-8B-Instruct-bf16.gguf -ngl 0 -nkvo -c 8192 -n 1 -p 'nfuiresnfiuesnfuisnfuiesnfiusnfiusenfiusenfiusnfiusenfuisenfiusenfiusenfuisenfiusenfuisenfiuenfisenfuesncuesnfaensifonqfnoeqiwnfiowinefioewnfowienfoiewtgowefeioncwncioenciencisncenicsnicneicnejfoiwejfoiwefoewifnenfoiewnfiohgewoiewhdcmioesiuoghscnklesioughsmkcwkhgiowenweiopdciuewfuowncoiwenfiuwefwnueoicwe'
Important note:
The program only crashes with batch size 32 or greater -- if I only do text generation and small prompts, it works fine.
The full output of the command is here:
Click to expand full llama.cpp output
./llama.cpp/main -m ./models/Meta-Llama-3-8B-Instruct-bf16.gguf -ngl 0 -nkvo -c 8192 -n 1 -p 'nfuiresnfiuesnfuisnfuiesnfiusnfiusenfiusenfiusnfiusenfuisenfiusenfiusenfuisenfiusenfuisenfiuenfisenfuesncuesnfaensifonqfnoeqiwnfiowinefioewnfowienfoiewtgowefeioncwncioenciencisncenicsnicneicnejfoiwejfoiwefoewifnenfoiewnfiohgewoiewhdcmioesiuoghscnklesioughsmkcwkhgiowenweiopdciuewfuowncoiwenfiuwefwnueoicwe'
Log start
main: build = 2852 (fae9d234)
main: built with cc (Debian 12.2.0-14) 12.2.0 for x86_64-linux-gnu
main: seed = 1715439608
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from ./models/Meta-Llama-3-8B-Instruct-bf16.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 = Meta-Llama-3-8B-Instruct
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 8192
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 32
llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type bf16: 226 tensors
llm_load_vocab: special tokens definition check successful ( 256/128256 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
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 = 4
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: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
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 = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 8192
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 = 8B
llm_load_print_meta: model ftype = BF16
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 14.96 GiB (16.00 BPW)
llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size = 0.15 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/33 layers to GPU
llm_load_tensors: CPU buffer size = 15317.02 MiB
.........................................................................................
llama_new_context_with_model: n_ctx = 8192
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 = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 1024.00 MiB
llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.49 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 1260.50 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 540.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 324
system_info: n_threads = 8 / 16 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | 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 = 8192, n_batch = 2048, n_predict = 1, n_keep = 0
<|begin_of_text|>nfuiresnfiuesnfuisnfuiesnfiusnfiusenfiusenfiusnfiusenfuisenfiusenfiusenfuisenfiusenfuisenfiuenfisenfuesncuesnfaensifonqfnoeqiwnfiowinefioewnfowienfoiewtgowefeioncwncioenciencisncenicsnicneicnejfoiwejfoiwefoewifnenfoiewnfiohgewoiewhdcmioesiuoghscnklesioughsmkcwkhgiowenweiopdciuewfuowncoiwenfiuwefwnueoicweGGML_ASSERT: ggml-cuda.cu:1277: to_fp32_cuda != nullptr
Aborted
I'd welcome any insight. Please let me know if I can provide any other information.
