llama.cpp - ValueError: Failed to create llama_context - ggml-common.h file not found
I’ve been playing around with the outlines library and needed to install llama.cpp as a result. I ran into trouble when trying to offload model layers to the GPU and in this post, I’ll explain how to install llama.cpp so that you don’t have the same issues.
This was how I installed the library initially:
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-pythonAnd then let’s try to load a GGUF model with some layers offloaded to the GPU:
from llama_cpp import Llama
model_name = "mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf"
Llama(
model_path=model_name,
n_ctx=2048,
n_gpu_layers=-1
)We’ll get the following error:
Output
llama_model_loader: loaded meta data with 26 key-value pairs and 995 tensors from mixtral-8x7b-instruct-v0.1.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 = llama
llama_model_loader: - kv 1: general.name str = mistralai_mixtral-8x7b-instruct-v0.1
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.expert_count u32 = 8
llama_model_loader: - kv 10: llama.expert_used_count u32 = 2
llama_model_loader: - kv 11: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 12: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: general.file_type u32 = 15
llama_model_loader: - kv 14: tokenizer.ggml.model str = llama
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type f16: 32 tensors
llama_model_loader: - type q8_0: 64 tensors
llama_model_loader: - type q4_K: 833 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 V3 (latest)
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 = 32768
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 = 8
llm_load_print_meta: n_expert_used = 2
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 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: model type = 7B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 46.70 B
llm_load_print_meta: model size = 24.62 GiB (4.53 BPW)
llm_load_print_meta: general.name = mistralai_mixtral-8x7b-instruct-v0.1
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: PAD token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.76 MiB
ggml_backend_metal_buffer_from_ptr: allocated buffer, size = 25145.58 MiB, (75437.11 / 49152.00)ggml_backend_metal_log_allocated_size: warning: current allocated size is greater than the recommended max working set size
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: CPU buffer size = 70.31 MiB
llm_load_tensors: Metal buffer size = 25145.56 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Max
ggml_metal_init: picking default device: Apple M1 Max
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/markhneedham/projects/learndatawithmark/outlines-playground/.venv/lib/python3.11/site-packages/llama_cpp/ggml-metal.metal'
ggml_metal_init: error: Error Domain=MTLLibraryErrorDomain Code=3 "program_source:3:10: fatal error: 'ggml-common.h' file not found
#include "ggml-common.h"
^~~~~~~~~~~~~~~
" UserInfo={NSLocalizedDescription=program_source:3:10: fatal error: 'ggml-common.h' file not found
#include "ggml-common.h"
^~~~~~~~~~~~~~~
}
llama_new_context_with_model: failed to initialize Metal backend
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[29], line 3
1 from llama_cpp import Llama
----> 3 Llama(
4 model_path=model_name,
5 n_ctx=2048,
6 n_gpu_layers=-1
7 )
File ~/projects/learndatawithmark/outlines-playground/.venv/lib/python3.11/site-packages/llama_cpp/llama.py:328, in Llama.__init__(self, model_path, n_gpu_layers, split_mode, main_gpu, tensor_split, vocab_only, use_mmap, use_mlock, kv_overrides, seed, n_ctx, n_batch, n_threads, n_threads_batch, rope_scaling_type, pooling_type, rope_freq_base, rope_freq_scale, yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow, yarn_orig_ctx, logits_all, embedding, offload_kqv, last_n_tokens_size, lora_base, lora_scale, lora_path, numa, chat_format, chat_handler, draft_model, tokenizer, verbose, **kwargs)
325 self.context_params.n_ctx = self._model.n_ctx_train()
326 self.context_params.n_batch = self.n_batch
--> 328 self._ctx = _LlamaContext(
329 model=self._model,
330 params=self.context_params,
331 verbose=self.verbose,
332 )
334 self._batch = _LlamaBatch(
335 n_tokens=self.n_batch,
336 embd=0,
337 n_seq_max=self.context_params.n_ctx,
338 verbose=self.verbose,
339 )
341 if self.lora_path:
File ~/projects/learndatawithmark/outlines-playground/.venv/lib/python3.11/site-packages/llama_cpp/_internals.py:265, in _LlamaContext.__init__(self, model, params, verbose)
260 self.ctx = llama_cpp.llama_new_context_with_model(
261 self.model.model, self.params
262 )
264 if self.ctx is None:
--> 265 raise ValueError("Failed to create llama_context")
ValueError: Failed to create llama_contextI came across this GitHub issue which had a few suggestions. The one that worked for me was this:
CMAKE_ARGS="-DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_METAL=on" pip install -U llama-cpp-python --no-cache-dirThe extra argument that we need to set is LLAMA_METAL_EMBED_LIBRARY=ON.
My impression is that this argument will eventually be redundant, but as of now you still need it!
About the author
I'm currently working on short form content at ClickHouse. I publish short 5 minute videos showing how to solve data problems on YouTube @LearnDataWithMark. I previously worked on graph analytics at Neo4j, where I also co-authored the O'Reilly Graph Algorithms Book with Amy Hodler.