abetlen
llama-cpp-python not using GPU on colab
Prerequisites
Please answer the following questions for yourself before submitting an issue.
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [x] I carefully followed the README.md.
- [x] I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
- [x] I reviewed the Discussions, and have a new bug or useful enhancement to share.
Expected Behavior
I installed llama-cpp-python[server] using:
pip install llama-cpp-python[server] \ --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu122
I used cu122 after running:
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:02:13_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0
and when I run the server using this command:
!python3 -m llama_cpp.server --hf_model_repo_id Qwen/Qwen2-7B-Instruct-GGUF --model 'qwen2-7b-instruct-q6_k.gguf' \ --n_ctx 32000 --host 0.0.0.0 --port 8188 --flash_attn True --n_gpu_layers -1
using the --n_gpu_layers -1 should've loaded the model on the gpu
Current Behavior
the model is instead loaded on cpu
llama_model_loader: loaded meta data with 26 key-value pairs and 339 tensors from /root/.cache/huggingface/hub/models--Qwen--Qwen2-7B-Instruct-GGUF/snapshots/ddfd0ef0d00b055363c0fbab0efc1eb2b12186e0/./qwen2-7b-instruct-q6_k.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 = qwen2
llama_model_loader: - kv 1: general.name str = qwen2-7b-instruct
llama_model_loader: - kv 2: qwen2.block_count u32 = 28
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 3584
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 18944
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 28
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 18
llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - kv 22: quantize.imatrix.file str = ../Qwen2/gguf/qwen2-7b-imatrix/imatri...
llama_model_loader: - kv 23: quantize.imatrix.dataset str = ../sft_2406.txt
llama_model_loader: - kv 24: quantize.imatrix.entries_count i32 = 196
llama_model_loader: - kv 25: quantize.imatrix.chunks_count i32 = 1937
llama_model_loader: - type f32: 141 tensors
llama_model_loader: - type q6_K: 198 tensors
llm_load_vocab: special tokens cache size = 421
llm_load_vocab: token to piece cache size = 0.9352 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 152064
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_layer = 28
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 = 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 = 18944
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 = 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: model type = ?B
llm_load_print_meta: model ftype = Q6_K
llm_load_print_meta: model params = 7.62 B
llm_load_print_meta: model size = 5.82 GiB (6.56 BPW)
llm_load_print_meta: general.name = qwen2-7b-instruct
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
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_tensors: ggml ctx size = 0.16 MiB
llm_load_tensors: CPU buffer size = 5958.79 MiB
warning: failed to mlock 453021696-byte buffer (after previously locking 0 bytes): Cannot allocate memory
Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).
........................................................................................
llama_new_context_with_model: n_ctx = 32000
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 1
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 1750.00 MiB
llama_new_context_with_model: KV self size = 1750.00 MiB, K (f16): 875.00 MiB, V (f16): 875.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.59 MiB
llama_new_context_with_model: CPU compute buffer size = 304.00 MiB
llama_new_context_with_model: graph nodes = 875
llama_new_context_with_model: graph splits = 1
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 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: {'quantize.imatrix.entries_count': '196', 'quantize.imatrix.dataset': '../sft_2406.txt', 'quantize.imatrix.chunks_count': '1937', 'quantize.imatrix.file': '../Qwen2/gguf/qwen2-7b-imatrix/imatrix.dat', 'tokenizer.ggml.add_bos_token': 'false', 'tokenizer.ggml.bos_token_id': '151643', 'general.architecture': 'qwen2', 'qwen2.block_count': '28', 'qwen2.context_length': '32768', 'tokenizer.chat_template': "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", 'qwen2.attention.head_count_kv': '4', 'tokenizer.ggml.padding_token_id': '151643', 'qwen2.embedding_length': '3584', 'qwen2.attention.layer_norm_rms_epsilon': '0.000001', 'qwen2.attention.head_count': '28', 'tokenizer.ggml.eos_token_id': '151645', 'qwen2.rope.freq_base': '1000000.000000', 'general.file_type': '18', 'general.quantization_version': '2', 'qwen2.feed_forward_length': '18944', 'tokenizer.ggml.model': 'gpt2', 'general.name': 'qwen2-7b-instruct', 'tokenizer.ggml.pre': 'qwen2'}
Available chat formats from metadata: chat_template.default
Using gguf chat template: {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
You are a helpful assistant.<|im_end|>
' }}{% endif %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}
Using chat eos_token: <|im_end|>
Using chat bos_token: <|endoftext|>
INFO: Started server process [11741]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8188/ (Press CTRL+C to quit)
Environment and Context
Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
it is a colab environment with a T4 gpu
update
installing llama-cpp-python using:
!CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python[server]
fixed the problem, but the problem is that it takes 18 mins to install, so using a prebuilt is still preferred, then I am not closing this issue for time being.
I think the issue is that there is currently no cuda prebuild of the latest 0.2.78 version, and pip pulls latest by default. I had the same problem installing it on a local machine.
It can be worked around by specifically installing the previous version
pip install --no-cache-dir llama-cpp-python==0.2.77 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124
Hopefully, the author keeps providing updated cuda builds though.
I think the issue is that there is currently no cuda prebuild of the latest 0.2.78 version, and pip pulls latest by default. I had the same problem installing it on a local machine.
It can be worked around by specifically installing the previous version
pip install --no-cache-dir llama-cpp-python==0.2.77 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124Hopefully, the author keeps providing updated cuda builds though.
Thank you, works on my CUDA 12.3 version using this link instead:
pip install --no-cache-dir llama-cpp-python==0.2.90 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu123
I think the issue is that there is currently no cuda prebuild of the latest 0.2.78 version, and pip pulls latest by default. I had the same problem installing it on a local machine.
It can be worked around by specifically installing the previous version
pip install --no-cache-dir llama-cpp-python==0.2.77 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124Hopefully, the author keeps providing updated cuda builds though.
thank you, for me this one worked for v0.3.2:
pip install --no-cache-dir llama-cpp-python==0.3.2 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu/
I haven't installed cuda toolkit and this worked for me pip install --no-cache-dir llama-cpp-python==0.3.2 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu/
I haven't installed cuda toolkit and this worked for me pip install --no-cache-dir llama-cpp-python==0.3.2 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu/
You and the guy above you are you sure you are using the gpu?
I think the issue is that there is currently no cuda prebuild of the latest 0.2.78 version, and pip pulls latest by default. I had the same problem installing it on a local machine.
It can be worked around by specifically installing the previous version
pip install --no-cache-dir llama-cpp-python==0.2.77 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124Hopefully, the author keeps providing updated cuda builds though.
Thanks alot ! Derived it along with a few more tags and it worked !!!
CMAKE_ARGS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=75" pip install --no-cache-dir llama-cpp-python==0.2.77 --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu122
- GGML_CUDA: For my NVIDIA based GPU
- CUDA_ARCH: For Tesla T4's compute capability which I found using
nvidia-smi --query-gpu=compute_cap --format=csv
