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h2oai / h2ogpt

Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/
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GPU offloading mistralai_mistral-7b-instruct-v0.2 #1606

Open InesBenAmor99 opened 3 months ago

InesBenAmor99 commented 3 months ago

Any reasons why mistralai_mistral-7b-instruct-v0.2 does not offload on gpu ?

load INSTRUCTOR_Transformer max_seq_length 512 Starting get_model: llama Failed to listen to n_gpus: No module named 'llama_cpp_cuda', trying llama_cpp module llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from llamacpp_path\mistral-7b-instruct-v0.2.Q5_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_mistral-7b-instruct-v0.2 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.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 11: general.file_type u32 = 17 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "", "", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess... llama_model_loader: - kv 23: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q5_K: 193 tensors llama_model_loader: - type q6_K: 33 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 = 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 = 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 = 8B llm_load_print_meta: model ftype = Q5_K - Medium llm_load_print_meta: model params = 7.24 B llm_load_print_meta: model size = 4.78 GiB (5.67 BPW) llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.2 llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 '' llm_load_print_meta: PAD token = 0 '' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.15 MiB llm_load_tensors: CPU buffer size = 4892.99 MiB ................................................................................................... llama_new_context_with_model: n_ctx = 4096 llama_new_context_with_model: n_batch = 1024 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: CPU KV buffer size = 512.00 MiB llama_new_context_with_model: KV self size = 512.00 MiB, K (f16): 256.00 MiB, V (f16): 256.00 MiB llama_new_context_with_model: CPU output buffer size = 0.12 MiB llama_new_context_with_model: CPU compute buffer size = 296.01 MiB llama_new_context_with_model: graph nodes = 1030 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 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | Model metadata: {'general.name': 'mistralai_mistral-7b-instruct-v0.2', 'general.architecture': 'llama', 'llama.context_length': '32768', 'llama.rope.dimension_count': '128', 'llama .embedding_length': '4096', 'llama.block_count': '32', 'llama.feed_forward_length': '14336', 'llama.attention.head_count': '32', 'tokenizer.ggml.eos_token_id': '2', 'general.file_t ype': '17', 'llama.attention.head_count_kv': '8', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.freq_base': '1000000.000000', 'tokenizer.ggml.model': 'llama', ' general.quantization_version': '2', 'tokenizer.ggml.bos_token_id': '1', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.add_bos_toke n': 'true', 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 = = 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"} Guessed chat format: mistral-instruct Model {'base_model': 'llama', 'base_model0': 'llama', 'tokenizer_base_model': '', 'lora_weights': '', 'inference_server': '', 'prompt_type': 'mistral', 'prompt_dict': {'promptA': ' ', 'promptB': '', 'PreInstruct': '[INST] ', 'PreInput': None, 'PreResponse': '[/INST]', 'terminate_response': ['[INST]', ''], 'chat_sep': ' ', 'chat_turn_sep': ' ', 'hum anstr': '[INST]', 'botstr': '[/INST]', 'generates_leading_space': False, 'system_prompt': '', 'can_handle_system_prompt': False}, 'visible_models': None, 'h2ogptkey': None, 'load 8bit': False, 'load_4bit': False, 'low_bit_mode': 1, 'load_half': True, 'use_flash_attention_2': False, 'load_gptq': '', 'load_awq': '', 'load_exllama': False, 'use_safetensors': F alse, 'revision': None, 'use_gpu_id': True, 'gpu_id': 0, 'compile_model': None, 'use_cache': None, 'llamacpp_dict': {'n_gpu_layers': 100, 'use_mlock': True, 'n_batch': 1024, 'n_gqa ': 0, 'model_path_llama': 'https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf?download=true', 'model_name_gptj': '', 'm odel_name_gpt4all_llama': '', 'model_name_exllama_if_no_config': ''}, 'rope_scaling': {}, 'max_seq_len': 4096, 'max_output_seq_len': None, 'exllama_dict': {}, 'gptq_dict': {}, 'attention_sinks': False, 'sink_dict': {}, 'truncation_generation': False, 'hf_model_dict': {}, 'force_seq2seq_type': False, 'force_t5_type': False, 'trust_remote_code': True} Begin auto-detect HF cache text generation models End auto-detect HF cache text generation models Begin auto-detect llama.cpp models

pseudotensor commented 3 months ago

How did you install h2oGPT? It requires steps like this for cuda:

https://github.com/h2oai/h2ogpt/blob/5b6b7b362d64266936151feb26e983dc82f18804/docs/README_LINUX.md#L107-L111

InesBenAmor99 commented 3 months ago

I'm on windows, i followed the exact steps in the README_Windows. Additionally, when I ran 'import torch' and 'print(torch.cuda.is_available())', I got 'True'.

pseudotensor commented 3 months ago

So you did this step in the readme_windows?

* For non-CPU case, choose llama_cpp_python ARGS for your system according to [llama_cpp_python backend documentation](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#supported-backends), e.g. for CUDA:
  ```cmdline
   set CMAKE_ARGS=-DLLAMA_CUBLAS=on -DCMAKE_CUDA_ARCHITECTURES=all
   set LLAMA_CUBLAS=1
   set FORCE_CMAKE=1

Note for some reason things will fail with llama_cpp_python if don't add all cuda arches, and building with all those arches does take some time.