Ollama似乎在启用多GPU并行处理的多CPU线程机器上受到限制,

mepcadol  于 2个月前  发布在  其他
关注(0)|答案(2)|浏览(45)

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

ollama

2条答案

按热度按时间
q7solyqu

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：

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：

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"

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

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：

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"

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。

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7d7tgy0s

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。

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