Bug: Failed to run qwen2-57b-a14b-instruct-fp16. #240
Description
Note: This issue was copied from ggml-org#9628
Original Author: @tang-t21
Original Issue Number: ggml-org#9628
Created: 2024-09-24T13:47:44Z
What happened?
I am trying to run Qwen2-57B-A14B-instruct, and I used llama-gguf-split to merge the gguf files from Qwen/Qwen2-57B-A14B-Instruct-GGUF. But it's aborted with terminate called after throwing an instance of 'std::length_error' what(): vector::_M_default_append Aborted (core dumped)。
Name and Version
./build/bin/llama-cli --version
version: 3808 (699a0dc)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
What operating system are you seeing the problem on?
Linux
Relevant log output
`(llama) root@201edf3683be:/home/llama.cpp# ./build/bin/llama-cli -m ./models/qwen2-57b-a14b-instruct-fp16.gguf -p "Beijing is the capital of" -n 64 -c 4096
build: 3808 (699a0dc1) with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu (debug)
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_loader: loaded meta data with 28 key-value pairs and 479 tensors from ./models/qwen2-57b-a14b-instruct-fp16.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 = qwen2moe
llama_model_loader: - kv 1: general.name str = Qwen2-MoE-A14.2B-Chat
llama_model_loader: - kv 2: qwen2moe.block_count u32 = 28
llama_model_loader: - kv 3: qwen2moe.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2moe.embedding_length u32 = 3584
llama_model_loader: - kv 5: qwen2moe.attention.head_count u32 = 28
llama_model_loader: - kv 6: qwen2moe.attention.head_count_kv u32 = 4
llama_model_loader: - kv 7: qwen2moe.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 8: qwen2moe.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 9: qwen2moe.expert_used_count u32 = 8
llama_model_loader: - kv 10: qwen2moe.expert_count u32 = 64
llama_model_loader: - kv 11: qwen2moe.expert_feed_forward_length u32 = 2560
llama_model_loader: - kv 12: qwen2moe.feed_forward_length u32 = 20480
llama_model_loader: - kv 13: general.file_type u32 = 1
llama_model_loader: - kv 14: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 15: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 21: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.chat_template str = {% for message in messages %}{{'<|im_...
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 24: general.quantization_version u32 = 2
llama_model_loader: - kv 25: split.no u16 = 0
llama_model_loader: - kv 26: split.count u16 = 0
llama_model_loader: - kv 27: split.tensors.count i32 = 479
llama_model_loader: - type f32: 197 tensors
llama_model_loader: - type f16: 282 tensors
llm_load_vocab: special tokens cache size = 293
llm_load_vocab: token to piece cache size = 0.9338 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2moe
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 20480
llm_load_print_meta: n_expert = 64
llm_load_print_meta: n_expert_used = 8
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
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 = 57B.A14B
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 57.41 B
llm_load_print_meta: model size = 106.94 GiB (16.00 BPW)
llm_load_print_meta: general.name = Qwen2-MoE-A14.2B-Chat
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151643 '<|endoftext|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
llm_load_print_meta: n_ff_exp = 2560
llm_load_print_meta: n_ff_shexp = 0
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 4 CUDA devices:
Device 0: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes
Device 1: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes
Device 2: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes
Device 3: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes
llm_load_tensors: ggml ctx size = 0.20 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/29 layers to GPU
llm_load_tensors: CPU buffer size = 109511.40 MiB
.............................................................................................
llama_new_context_with_model: n_ctx = 4096
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 = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 224.00 MiB
llama_new_context_with_model: KV self size = 224.00 MiB, K (f16): 112.00 MiB, V (f16): 112.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.58 MiB
ggml_gallocr_reserve_n: reallocating CUDA0 buffer from size 0.00 MiB to 1349.38 MiB
ggml_gallocr_reserve_n: reallocating CUDA1 buffer from size 0.00 MiB to 0.00 MiB
ggml_gallocr_reserve_n: reallocating CUDA2 buffer from size 0.00 MiB to 0.00 MiB
ggml_gallocr_reserve_n: reallocating CUDA3 buffer from size 0.00 MiB to 0.00 MiB
ggml_gallocr_reserve_n: reallocating CUDA_Host buffer from size 0.00 MiB to 15.01 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 1349.38 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 15.01 MiB
llama_new_context_with_model: graph nodes = 1910
llama_new_context_with_model: graph splits = 536
llama_init_from_gpt_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
ggml_backend_sched_alloc_splits: failed to allocate graph, reserving (backend_ids_changed = 1)
main: llama threadpool init, n_threads = 128
system_info: n_threads = 128 (n_threads_batch = 128) / 255 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
terminate called after throwing an instance of 'std::length_error'
what(): vector::_M_default_append
Aborted (core dumped)`