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为什么LlamaCPP在推理过程中冻结?

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  • Calder Johnson  · 技术社区  · 1 年前

    我正在使用以下代码尝试从LlamaCPP接收响应,该响应通过LlamaIndex库使用。我的模型存储在本地的gguf文件中。我正试图在CPU上进行推理,因为我的VRAM是有限的。我的程序打印出初始化代码(也粘贴在下面),但随后无限期挂起,不产生任何响应。

    import json
    
    from llama_index.llms.llama_cpp import LlamaCPP
    
    MODEL_URL = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_0.gguf"
    MODEL_PATH = None
    
    with open("./paths.json", "r") as f:
        paths = json.load(f)
        if "llama-2-13b-chat" in paths:
            MODEL_URL = None
            MODEL_PATH = paths["llama-2-13b-chat"]
    
    llm = LlamaCPP(
        model_url=MODEL_URL,
        model_path=MODEL_PATH,
        temperature=0.1,
        max_new_tokens=256,
        context_window=3900,
        model_kwargs={"n_gpu_layers": 0}, # Use CPU for inference
        verbose=True,
    )
    
    response = llm.complete("Hello, how are you?")
    print(str(response))
    

    输出:初始化,然后无限期挂起。我期望的输出是,它打印出详细的初始化,然后是LLM响应,然后终止。

    llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from ../models/llama-2-13b-chat.Q4_0.gguf (version GGUF V2)
    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              = LLaMA v2
    llama_model_loader: - kv   2:                       llama.context_length u32              = 4096
    llama_model_loader: - kv   3:                     llama.embedding_length u32              = 5120
    llama_model_loader: - kv   4:                          llama.block_count u32              = 40
    llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 13824
    llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128
    llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 40
    llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 40
    llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
    llama_model_loader: - kv  10:                          general.file_type u32              = 2
    llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
    llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr[str,32000]   = ["<unk>", "<s>", "</s>", "<0x00>", "<...
    llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...
    llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
    llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32              = 1
    llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32              = 2
    llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32              = 0
    llama_model_loader: - kv  18:               general.quantization_version u32              = 2
    llama_model_loader: - type  f32:   81 tensors
    llama_model_loader: - type q4_0:  281 tensors
    llama_model_loader: - type q6_K:    1 tensors
    llm_load_vocab: special tokens definition check successful ( 259/32000 ).
    llm_load_print_meta: format           = GGUF V2
    llm_load_print_meta: arch             = llama
    llm_load_print_meta: vocab type       = SPM
    llm_load_print_meta: n_vocab          = 32000
    llm_load_print_meta: n_merges         = 0
    llm_load_print_meta: n_ctx_train      = 4096
    llm_load_print_meta: n_embd           = 5120
    llm_load_print_meta: n_head           = 40
    llm_load_print_meta: n_head_kv        = 40
    llm_load_print_meta: n_layer          = 40
    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            = 1
    llm_load_print_meta: n_embd_k_gqa     = 5120
    llm_load_print_meta: n_embd_v_gqa     = 5120
    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             = 13824
    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  = 10000.0
    llm_load_print_meta: freq_scale_train = 1
    llm_load_print_meta: n_yarn_orig_ctx  = 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: model type       = 13B
    llm_load_print_meta: model ftype      = Q4_0
    llm_load_print_meta: model params     = 13.02 B
    llm_load_print_meta: model size       = 6.86 GiB (4.53 BPW) 
    llm_load_print_meta: general.name     = LLaMA v2
    llm_load_print_meta: BOS token        = 1 '<s>'
    llm_load_print_meta: EOS token        = 2 '</s>'
    llm_load_print_meta: UNK token        = 0 '<unk>'
    llm_load_print_meta: LF token         = 13 '<0x0A>'
    llm_load_tensors: ggml ctx size =    0.18 MiB
    llm_load_tensors:        CPU buffer size =  7023.90 MiB
    ...................................................................................................
    llama_new_context_with_model: n_ctx      = 4096
    llama_new_context_with_model: n_batch    = 512
    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 =  3200.00 MiB
    llama_new_context_with_model: KV self size  = 3200.00 MiB, K (f16): 1600.00 MiB, V (f16): 1600.00 MiB
    llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB
    llama_new_context_with_model:        CPU compute buffer size =   368.01 MiB
    llama_new_context_with_model: graph nodes  = 1286
    llama_new_context_with_model: graph splits = 1
    AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 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 | 
    Model metadata: {'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'llama.embedding_length': '5120', 'llama.feed_forward_length': '13824', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'llama.attention.head_count': '40', 'tokenizer.ggml.bos_token_id': '1', 'llama.block_count': '40', 'llama.attention.head_count_kv': '40', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '2'}
    Using fallback chat format: llama-2
    

    我的RAM利用率最终约为9.5GB/16,CPU利用率约为50%。如果能深入了解为什么会发生这种情况,我们将不胜感激。

    1 回复  |  直到 1 年前
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  •  1
  •   Johnny Cheesecutter    1 年前

    尝试使用流输出。模型正在生成响应,但如果没有gpu,它会非常慢。总体而言,13B型号相当大,如果它们需要超过10gb的内存也没关系。

    response_iter = llm.stream_complete("Can you write me a poem about fast cars?")
    for response in response_iter:
        print(response.delta, end="", flush=True)
    

    还可以考虑使用较小的模型来加快输出速度:

    MODEL_URL = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf"
    
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