Jetson - "ollama run"命令加载直到超时

7gyucuyw  于 5个月前  发布在  其他
关注(0)|答案(5)|浏览(53)

问题是什么？

在运行 "ollama run gemma:2b"(尽管这种情况也适用于所有测试模型：llama3、phi、tinyllama)时，加载动画出现，大约5分钟后(估计值，无时间限制),命令的响应/结果为：
Error: timed out waiting for llama runner to start - progress 1.00 -
服务器显示了此命令的日志：

2024/06/06 11:21:53 routes.go:1007: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST: OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS: OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 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:*] OLLAMA_RUNNERS_DIR: OLLAMA_TMPDIR:]"
time=2024-06-06T11:21:53.848-04:00 level=INFO source=images.go:729 msg="total blobs: 11"
time=2024-06-06T11:21:53.849-04:00 level=INFO source=images.go:736 msg="total unused blobs removed: 0"
time=2024-06-06T11:21:53.849-04:00 level=INFO source=routes.go:1053 msg="Listening on 127.0.0.1:11434 (version 0.1.41)"
time=2024-06-06T11:21:53.850-04:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama3794080172/runners
time=2024-06-06T11:21:58.984-04:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cuda_v11]"
time=2024-06-06T11:21:59.082-04:00 level=INFO source=types.go:71 msg="inference compute" id=GPU-42638932-6929-58db-a006-34d50a6799c1 library=cuda compute=8.7 driver=11.4 name=Orin total="29.9 GiB" available="21.7 GiB"
[GIN] 2024/06/06 - 11:22:14 | 200 |      64.512µs |       127.0.0.1 | HEAD     "/"
[GIN] 2024/06/06 - 11:22:14 | 200 |    1.232036ms |       127.0.0.1 | POST     "/api/show"
[GIN] 2024/06/06 - 11:22:14 | 200 |     717.058µs |       127.0.0.1 | POST     "/api/show"
time=2024-06-06T11:22:16.239-04:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.7 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="918.6 MiB"
time=2024-06-06T11:22:16.239-04:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.7 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="918.6 MiB"
time=2024-06-06T11:22:16.240-04:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama3794080172/runners/cuda_v11/ollama_llama_server --model /home/harper/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 19 --parallel 1 --port 42781"
time=2024-06-06T11:22:16.240-04:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-06T11:22:16.240-04:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-06T11:22:16.241-04:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="5921b8f" tid="281473278327040" timestamp=1717687336
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="281473278327040" timestamp=1717687336 total_threads=8
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="42781" tid="281473278327040" timestamp=1717687336
llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from /home/harper/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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              = gemma
llama_model_loader: - kv   1:                               general.name str              = gemma-2b-it
llama_model_loader: - kv   2:                       gemma.context_length u32              = 8192
llama_model_loader: - kv   3:                          gemma.block_count u32              = 18
llama_model_loader: - kv   4:                     gemma.embedding_length u32              = 2048
llama_model_loader: - kv   5:                  gemma.feed_forward_length u32              = 16384
llama_model_loader: - kv   6:                 gemma.attention.head_count u32              = 8
llama_model_loader: - kv   7:              gemma.attention.head_count_kv u32              = 1
llama_model_loader: - kv   8:                 gemma.attention.key_length u32              = 256
llama_model_loader: - kv   9:               gemma.attention.value_length u32              = 256
llama_model_loader: - kv  10:     gemma.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  11:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  12:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  13:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  14:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  15:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  16:                      tokenizer.ggml.tokens arr[str,256128]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
time=2024-06-06T11:22:16.493-04:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv  17:                      tokenizer.ggml.scores arr[f32,256128]  = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,256128]  = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  19:               general.quantization_version u32              = 2
llama_model_loader: - kv  20:                          general.file_type u32              = 2
llama_model_loader: - type  f32:   37 tensors
llama_model_loader: - type q4_0:  126 tensors
llama_model_loader: - type q8_0:    1 tensors
llm_load_vocab: special tokens cache size = 388
llm_load_vocab: token to piece cache size = 3.2028 MB
llm_load_print_meta: format           = GGUF V3 (latest)
llm_load_print_meta: arch             = gemma
llm_load_print_meta: vocab type       = SPM
llm_load_print_meta: n_vocab          = 256128
llm_load_print_meta: n_merges         = 0
llm_load_print_meta: n_ctx_train      = 8192
llm_load_print_meta: n_embd           = 2048
llm_load_print_meta: n_head           = 8
llm_load_print_meta: n_head_kv        = 1
llm_load_print_meta: n_layer          = 18
llm_load_print_meta: n_rot            = 256
llm_load_print_meta: n_embd_head_k    = 256
llm_load_print_meta: n_embd_head_v    = 256
llm_load_print_meta: n_gqa            = 8
llm_load_print_meta: n_embd_k_gqa     = 256
llm_load_print_meta: n_embd_v_gqa     = 256
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             = 16384
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  = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx  = 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       = 2B
llm_load_print_meta: model ftype      = Q4_0
llm_load_print_meta: model params     = 2.51 B
llm_load_print_meta: model size       = 1.56 GiB (5.34 BPW) 
llm_load_print_meta: general.name     = gemma-2b-it
llm_load_print_meta: BOS token        = 2 '<bos>'
llm_load_print_meta: EOS token        = 1 '<eos>'
llm_load_print_meta: UNK token        = 3 '<unk>'
llm_load_print_meta: PAD token        = 0 '<pad>'
llm_load_print_meta: LF token         = 227 '<0x0A>'
llm_load_print_meta: EOT token        = 107 '<end_of_turn>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   yes
ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: Orin, compute capability 8.7, VMM: yes
llm_load_tensors: ggml ctx size =    0.17 MiB
llm_load_tensors: offloading 18 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 19/19 layers to GPU
llm_load_tensors:        CPU buffer size =   531.52 MiB
llm_load_tensors:      CUDA0 buffer size =  1594.93 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: flash_attn = 0
llama_new_context_with_model: freq_base  = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init:      CUDA0 KV buffer size =    36.00 MiB
llama_new_context_with_model: KV self size  =   36.00 MiB, K (f16):   18.00 MiB, V (f16):   18.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB
llama_new_context_with_model:      CUDA0 compute buffer size =   504.25 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =     8.01 MiB
llama_new_context_with_model: graph nodes  = 601
llama_new_context_with_model: graph splits = 2
time=2024-06-06T11:29:16.920-04:00 level=ERROR source=sched.go:344 msg="error loading llama server" error="timed out waiting for llama runner to start - progress 1.00 - "
[GIN] 2024/06/06 - 11:29:16 | 500 |          7m2s |       127.0.0.1 | POST     "/api/chat"
time=2024-06-06T11:29:22.037-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.117139389
time=2024-06-06T11:29:22.288-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.367497274
time=2024-06-06T11:29:22.537-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.616874999

如果有帮助的话，这是在 Jetson AGX Orin with 32GB of memory 上运行的。

操作系统

Linux

GPU

Nvidia

CPU

其他：8核 NVIDIA Arm® Cortex A78AE v8.2 64位 CPU 2MB L2 + 4MB L3

Ollama版本

0.1.41

ollama

5条答案

按热度按时间
juzqafwq

juzqafwq1#

在更新ollama镜像后，我遇到了相同的问题。移除并重新拉取模型将是一个解决方案，对我有效。

赞(0）分享  回复(0）举报  5个月前
tzdcorbm

tzdcorbm2#

你能尝试禁用mmap加载看看是否会改变加载时间吗？

curl http://localhost:11434/api/generate -d '{
  "model": "gemma:2b",
  "prompt": "Why is the sky blue?",
  "stream": false, "options": {"use_mmap": false}
}'

此外，PR #4741 可能也相关。

赞(0）分享  回复(0）举报  5个月前
ux6nzvsh

ux6nzvsh3#

在更新ollama镜像后，我遇到了相同的问题。移除并重新拉取模型将是一个解决方案，对我有效。
尝试了完全相同的方法，但似乎对我不起作用。

赞(0）分享  回复(0）举报  5个月前
tgabmvqs

tgabmvqs4#

请尝试禁用mmap加载，看看是否会改变加载时间？

curl http://localhost:11434/api/generate -d '{
  "model": "gemma:2b",
  "prompt": "Why is the sky blue?",
  "stream": false, "options": {"use_mmap": false}
}'

此外，PR #4741 可能也相关。
运行此命令，我得到相同的结果。第一次运行时，出现CUDA错误。第二次，出现超时错误。交替进行。在发生错误之前的时间似乎与之前相同(几分钟)。

赞(0）分享  回复(0）举报  5个月前
hgtggwj0

hgtggwj05#

@Vassar-HARPER-Project 听起来你需要 #4741 才能继续前进，而不需要从源代码构建。

赞(0）分享  回复(0）举报  5个月前
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