问题是什么?
我有2个GPU,并设置了OLLAMA_NUM_PARALLEL环境变量。当多个请求到来时,我可以看到模型在两个GPU内存上加载,但两个GPU的使用率都徘徊在40%左右。当我看到CPU使用率时,只有一个线程被使用并达到100%。
我怀疑它被一个单独的CPU线程限制了。我们如何让这两个并发请求由两个单独的线程服务?我在docker中运行ollama(如果这有关系)。
操作系统:Linux
GPU:Nvidia
CPU:Intel
Ollama版本:0.2.5
q7solyqu1#
使用Ollama v0.2.7在Nvidia MIG 20GB GPU上,我发现手动将OLLAMA_NUM_PARALLEL设置为高值会导致层被卸载到CPU而不是GPU。不设置OLLAMA_NUM_PARALLEL(设置为0)实际上允许并发调用。
OLLAMA_NUM_PARALLEL
当我有64个可用的CPU核心时,将其设置为OLLAMA_NUM_PARALLEL=64导致模型无法完全加载到GPU:OLLAMA_NUM_PARALLEL=64 ollama serve然后执行run llama3,ollama ps的输出为:
OLLAMA_NUM_PARALLEL=64
OLLAMA_NUM_PARALLEL=64 ollama serve
run llama3
ollama ps
ollama ps NAME ID SIZE PROCESSOR UNTIL llama3:latest 365c0bd3c000 32 GB 36%/64% CPU/GPU Forever
日志显示并非所有层都被加载到GPU,尽管应该有足够的空间:llm_load_tensors: offloaded 15/33 layers to GPU完整日志:
llm_load_tensors: offloaded 15/33 layers to GPU
OLLAMA_NUM_PARALLEL=64 ollama serve 2024/07/19 18:21:38 routes.go:1096: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:4 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/home/jovyan/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:64 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-07-19T18:21:38.990Z level=INFO source=images.go:778 msg="total blobs: 19" time=2024-07-19T18:21:38.996Z level=INFO source=images.go:785 msg="total unused blobs removed: 0" time=2024-07-19T18:21:38.998Z level=INFO source=routes.go:1143 msg="Listening on 127.0.0.1:11434 (version 0.2.7)" time=2024-07-19T18:21:38.999Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama856793591/runners time=2024-07-19T18:21:42.069Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60102]" time=2024-07-19T18:21:42.069Z level=INFO source=gpu.go:205 msg="looking for compatible GPUs" time=2024-07-19T18:21:42.385Z level=INFO source=types.go:105 msg="inference compute" id=GPU-e97eebac-1c40-8e02-9f2e-83b4b7117af9 library=cuda compute=8.0 driver=12.2 name="NVIDIA A100-SXM4-80GB MIG 2g.20gb" total="19.5 GiB" available="19.4 GiB" [GIN] 2024/07/19 - 18:21:48 | 200 | 96.058µs | 127.0.0.1 | HEAD "/" [GIN] 2024/07/19 - 18:21:49 | 200 | 390.318361ms | 127.0.0.1 | POST "/api/show" time=2024-07-19T18:21:49.433Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=33 layers.offload=15 layers.split="" memory.available="[19.4 GiB]" memory.required.full="29.9 GiB" memory.required.partial="19.0 GiB" memory.required.kv="16.0 GiB" memory.required.allocations="[19.0 GiB]" memory.weights.total="19.7 GiB" memory.weights.repeating="19.3 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="8.3 GiB" memory.graph.partial="8.8 GiB" time=2024-07-19T18:21:49.434Z level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama856793591/runners/cuda_v11/ollama_llama_server --model /home/jovyan/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 131072 --batch-size 512 --embedding --log-disable --n-gpu-layers 15 --parallel 64 --port 37799" time=2024-07-19T18:21:49.435Z level=INFO source=sched.go:437 msg="loaded runners" count=1 time=2024-07-19T18:21:49.435Z level=INFO source=server.go:571 msg="waiting for llama runner to start responding" time=2024-07-19T18:21:49.435Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="a8db2a9" tid="139664873738240" timestamp=1721413309 INFO [main] system info | n_threads=128 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | " tid="139664873738240" timestamp=1721413309 total_threads=128 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="127" port="37799" tid="139664873738240" timestamp=1721413309 llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /home/jovyan/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (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 = Meta-Llama-3-8B-Instruct llama_model_loader: - kv 2: llama.block_count u32 = 32 llama_model_loader: - kv 3: llama.context_length u32 = 8192 llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.attention.head_count u32 = 32 llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000 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: llama.vocab_size u32 = 128256 llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_0: 225 tensors llama_model_loader: - type q6_K: 1 tensors time=2024-07-19T18:21:49.687Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special tokens cache size = 256 llm_load_vocab: token to piece cache size = 0.8000 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 128256 llm_load_print_meta: n_merges = 280147 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 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 = 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 = 500000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 8192 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 = Q4_0 llm_load_print_meta: model params = 8.03 B llm_load_print_meta: model size = 4.33 GiB (4.64 BPW) llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128009 '<|eot_id|>' llm_load_print_meta: LF token = 128 'Ä' llm_load_print_meta: EOT token = 128009 '<|eot_id|>' llm_load_print_meta: max token length = 256 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA A100-SXM4-80GB MIG 2g.20gb, compute capability 8.0, VMM: yes llm_load_tensors: ggml ctx size = 0.27 MiB llm_load_tensors: offloading 15 repeating layers to GPU llm_load_tensors: offloaded 15/33 layers to GPU llm_load_tensors: CPU buffer size = 4437.80 MiB llm_load_tensors: CUDA0 buffer size = 1755.47 MiB llama_new_context_with_model: n_ctx = 131072 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 = 500000.0 llama_new_context_with_model: freq_scale = 1 time=2024-07-19T18:21:56.162Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server not responding" time=2024-07-19T18:21:57.768Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server loading model" llama_kv_cache_init: CUDA_Host KV buffer size = 8704.00 MiB llama_kv_cache_init: CUDA0 KV buffer size = 7680.00 MiB llama_new_context_with_model: KV self size = 16384.00 MiB, K (f16): 8192.00 MiB, V (f16): 8192.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 32.31 MiB llama_new_context_with_model: CUDA0 compute buffer size = 8985.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 264.01 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 191 time=2024-07-19T18:21:59.332Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server not responding" time=2024-07-19T18:21:59.633Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server loading model" INFO [main] model loaded | tid="139664873738240" timestamp=1721413328 time=2024-07-19T18:22:08.795Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server not responding" time=2024-07-19T18:22:09.502Z level=INFO source=server.go:617 msg="llama runner started in 20.07 seconds" [GIN] 2024/07/19 - 18:22:09 | 200 | 20.407256829s | 127.0.0.1 | POST "/api/chat"
然而,如果我只是取消设置环境变量(或手动设置为默认的OLLAMA_NUM_PARALLEL=0),那么一切都正常;所有模型层都加载到GPU。我甚至可以并发调用相同的模型(或多个模型)。OLLAMA_NUM_PARALLEL=0 ollama serveollama run llama3
OLLAMA_NUM_PARALLEL=0
OLLAMA_NUM_PARALLEL=0 ollama serve
ollama run llama3
ollama ps NAME ID SIZE PROCESSOR UNTIL llama3:latest 365c0bd3c000 6.7 GB 100% GPU Forever
感兴趣的日志:llm_load_tensors: offloaded 33/33 layers to GPU完整日志:
llm_load_tensors: offloaded 33/33 layers to GPU
OLLAMA_NUM_PARALLEL=0 ollama serve 2024/07/19 18:24:40 routes.go:1096: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://127.0.0.1:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:4 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/home/jovyan/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-07-19T18:24:40.448Z level=INFO source=images.go:778 msg="total blobs: 19" time=2024-07-19T18:24:40.454Z level=INFO source=images.go:785 msg="total unused blobs removed: 0" time=2024-07-19T18:24:40.456Z level=INFO source=routes.go:1143 msg="Listening on 127.0.0.1:11434 (version 0.2.7)" time=2024-07-19T18:24:40.457Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama384950711/runners time=2024-07-19T18:24:43.521Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60102]" time=2024-07-19T18:24:43.521Z level=INFO source=gpu.go:205 msg="looking for compatible GPUs" time=2024-07-19T18:24:43.848Z level=INFO source=types.go:105 msg="inference compute" id=GPU-e97eebac-1c40-8e02-9f2e-83b4b7117af9 library=cuda compute=8.0 driver=12.2 name="NVIDIA A100-SXM4-80GB MIG 2g.20gb" total="19.5 GiB" available="19.4 GiB" [GIN] 2024/07/19 - 18:28:50 | 200 | 195.543µs | 127.0.0.1 | HEAD "/" [GIN] 2024/07/19 - 18:28:50 | 200 | 177.047µs | 127.0.0.1 | GET "/api/ps" [GIN] 2024/07/19 - 18:28:57 | 200 | 45.46µs | 127.0.0.1 | HEAD "/" [GIN] 2024/07/19 - 18:28:57 | 200 | 37.127058ms | 127.0.0.1 | POST "/api/show" time=2024-07-19T18:28:57.599Z level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=/home/jovyan/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa gpu=GPU-e97eebac-1c40-8e02-9f2e-83b4b7117af9 parallel=4 available=20787953664 required="6.2 GiB" time=2024-07-19T18:28:57.603Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[19.4 GiB]" memory.required.full="6.2 GiB" memory.required.partial="6.2 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[6.2 GiB]" memory.weights.total="4.7 GiB" memory.weights.repeating="4.3 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB" time=2024-07-19T18:28:57.604Z level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama384950711/runners/cuda_v11/ollama_llama_server --model /home/jovyan/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 4 --port 43208" time=2024-07-19T18:28:57.605Z level=INFO source=sched.go:437 msg="loaded runners" count=1 time=2024-07-19T18:28:57.605Z level=INFO source=server.go:571 msg="waiting for llama runner to start responding" time=2024-07-19T18:28:57.606Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="a8db2a9" tid="140059309072384" timestamp=1721413737 INFO [main] system info | n_threads=128 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | " tid="140059309072384" timestamp=1721413737 total_threads=128 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="127" port="43208" tid="140059309072384" timestamp=1721413737 llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /home/jovyan/.ollama/models/blobs/sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa (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 = Meta-Llama-3-8B-Instruct llama_model_loader: - kv 2: llama.block_count u32 = 32 llama_model_loader: - kv 3: llama.context_length u32 = 8192 llama_model_loader: - kv 4: llama.embedding_length u32 = 4096 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.attention.head_count u32 = 32 llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000 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: llama.vocab_size u32 = 128256 llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ... llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_0: 225 tensors llama_model_loader: - type q6_K: 1 tensors time=2024-07-19T18:28:57.857Z level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special tokens cache size = 256 llm_load_vocab: token to piece cache size = 0.8000 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 128256 llm_load_print_meta: n_merges = 280147 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 8192 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 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 = 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 = 500000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 8192 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 = Q4_0 llm_load_print_meta: model params = 8.03 B llm_load_print_meta: model size = 4.33 GiB (4.64 BPW) llm_load_print_meta: general.name = Meta-Llama-3-8B-Instruct llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>' llm_load_print_meta: EOS token = 128009 '<|eot_id|>' llm_load_print_meta: LF token = 128 'Ä' llm_load_print_meta: EOT token = 128009 '<|eot_id|>' llm_load_print_meta: max token length = 256 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA A100-SXM4-80GB MIG 2g.20gb, compute capability 8.0, VMM: yes llm_load_tensors: ggml ctx size = 0.27 MiB 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 = 281.81 MiB llm_load_tensors: CUDA0 buffer size = 4155.99 MiB llama_new_context_with_model: n_ctx = 8192 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 = 500000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 1024.00 MiB llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 2.02 MiB llama_new_context_with_model: CUDA0 compute buffer size = 560.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 24.01 MiB llama_new_context_with_model: graph nodes = 1030 llama_new_context_with_model: graph splits = 2 INFO [main] model loaded | tid="140059309072384" timestamp=1721413744 time=2024-07-19T18:29:04.137Z level=INFO source=server.go:617 msg="llama runner started in 6.53 seconds" [GIN] 2024/07/19 - 18:29:04 | 200 | 6.873128858s | 127.0.0.1 | POST "/api/chat"
我创建了一个小型bash脚本,以找到开始将层卸载到CPU(不将所有层加载到GPU)的最小值OLLAMA_NUM_PARALLEL。这取决于模型,可能还取决于GPU内存大小。
7d7tgy0s2#
并发调用多个模型 - 当加载不同的模型并同时查询时,它们会并行生成响应。两个GPU都被充分利用了。问题在于,同时调用相同的模型似乎没有充分利用多个GPU,它似乎是CPU绑定的(只有1个CPU达到100%),而GPUs徘徊在35%左右。
2条答案
按热度按时间q7solyqu1#
使用Ollama v0.2.7在Nvidia MIG 20GB GPU上,我发现手动将
OLLAMA_NUM_PARALLEL
设置为高值会导致层被卸载到CPU而不是GPU。不设置OLLAMA_NUM_PARALLEL
(设置为0)实际上允许并发调用。OLLAMA_NUM_PARALLEL=64(意外行为)
当我有64个可用的CPU核心时,将其设置为
OLLAMA_NUM_PARALLEL=64
导致模型无法完全加载到GPU:OLLAMA_NUM_PARALLEL=64 ollama serve
然后执行
run llama3
,ollama ps
的输出为:日志显示并非所有层都被加载到GPU,尽管应该有足够的空间:
llm_load_tensors: offloaded 15/33 layers to GPU
完整日志:
OLLAMA_NUM_PARALLEL=0(正常工作)
然而,如果我只是取消设置环境变量(或手动设置为默认的
OLLAMA_NUM_PARALLEL=0
),那么一切都正常;所有模型层都加载到GPU。我甚至可以并发调用相同的模型(或多个模型)。OLLAMA_NUM_PARALLEL=0 ollama serve
ollama run llama3
感兴趣的日志:
llm_load_tensors: offloaded 33/33 layers to GPU
完整日志:
检测何时不会将层加载到GPU
我创建了一个小型bash脚本,以找到开始将层卸载到CPU(不将所有层加载到GPU)的最小值
OLLAMA_NUM_PARALLEL
。这取决于模型,可能还取决于GPU内存大小。7d7tgy0s2#
并发调用多个模型 - 当加载不同的模型并同时查询时,它们会并行生成响应。两个GPU都被充分利用了。问题在于,同时调用相同的模型似乎没有充分利用多个GPU,它似乎是CPU绑定的(只有1个CPU达到100%),而GPUs徘徊在35%左右。