使用lmdeploy部署后模型返回空

[ChingKwanCheung]

#1991 (comment) 问题跟这里类似，问一些比较短的问题能正常输出，但是问一些比较长的问题（超过1万字，不超过session-len里面设置的长度）结果返回空。
模型：qwen1.5-7b-chat
启动脚本：lmdeploy serve api_server qwen1half-7b-chat的模型路径 --server-name 0.0.0.0 --server-port 6002 --tp 1 --cache-max-entry-count 0.2 --rope-scaling-factor 0.2 --session-len 32000
日志报错：lmdeploy - ERROR - Truncate max_new_tokens to 19663
请问这是什么原因呢？

[lvhan028]

lmdeploy - ERROR - Truncate max_new_tokens to 19663
这表信息表示，最多生成 19663 个token。它会根据 session_len 和 input prompt tokens 来计算。
能不能运行下 lmdeploy check_env，把环境信息发一下？

[lvhan028]

另外，还请在执行 lmdeploy serve 命令时，加上 --log-level INFO 选项，分享下启动日志。

[ChingKwanCheung]

lmdeploy - ERROR - Truncate max_new_tokens to 19663 这表信息表示，最多生成 19663 个token。它会根据 session_len 和 input prompt tokens 来计算。 能不能运行下 lmdeploy check_env，把环境信息发一下？

我同样的问题，问离线转换成turbomind的internlm2-7b是有正常结果输出的，我的环境信息是：
sys.platform: linux
Python: 3.11.4 (main, Jul 5 2023, 14:15:25) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB
CUDA_HOME: /usr
NVCC: Cuda compilation tools, release 11.7, V11.7.64
GCC: gcc (GCC) 7.3.0
PyTorch: 2.0.1+cu117
PyTorch compiling details: PyTorch built with:

• GCC 9.3
• C++ Version: 201703
• Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.7
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
• CuDNN 8.9.2 (built against CUDA 12.1)
  • Built with CuDNN 8.5
• Magma 2.6.1
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2+cu117
LMDeploy: 0.5.0+
transformers: 4.38.2
gradio: 4.16.0
fastapi: 0.109.0
pydantic: 2.5.2
triton: 2.0.0

[ChingKwanCheung]

另外，还请在执行 lmdeploy serve 命令时，加上 --log-level INFO 选项，分享下启动日志。

我加上这个选项后报错：usage: lmdeploy [-h] [-v] {lite,serve,convert,list,check_env,chat} ...
lmdeploy: error: unrecognized arguments:
是哪里不对吗

[lvhan028]

另外，还请在执行 lmdeploy serve 命令时，加上 --log-level INFO 选项，分享下启动日志。

我加上这个选项后报错：usage: lmdeploy [-h] [-v] {lite,serve,convert,list,check_env,chat} ... lmdeploy: error: unrecognized arguments: 是哪里不对吗

lmdeploy serve api_server qwen1half-7b-chat的模型路径 --server-name 0.0.0.0 --server-port 6002 --tp 1 --cache-max-entry-count 0.2 --rope-scaling-factor 0.2 --session-len 32000 --log-level INFO

[ChingKwanCheung]

另外，还请在执行 lmdeploy serve 命令时，加上 --log-level INFO 选项，分享下启动日志。

我加上这个选项后报错：usage: lmdeploy [-h] [-v] {lite,serve,convert,list,check_env,chat} ... lmdeploy: error: unrecognized arguments: 是哪里不对吗

lmdeploy serve api_server qwen1half-7b-chat的模型路径 --server-name 0.0.0.0 --server-port 6002 --tp 1 --cache-max-entry-count 0.2 --rope-scaling-factor 0.2 --session-len 32000 --log-level INFO

日志如下：
usage: lmdeploy serve api_server [-h] [--server-name SERVER_NAME] [--server-port SERVER_PORT] [--allow-origins ALLOW_ORIGINS [ALLOW_ORIGINS ...]] [--allow-credentials]
[--allow-methods ALLOW_METHODS [ALLOW_METHODS ...]] [--allow-headers ALLOW_HEADERS [ALLOW_HEADERS ...]] [--qos-config-path QOS_CONFIG_PATH]
[--backend {pytorch,turbomind}] [--log-level {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}] [--api-keys [API_KEYS ...]] [--ssl]
[--meta-instruction META_INSTRUCTION] [--chat-template CHAT_TEMPLATE] [--cap {completion,infilling,chat,python}] [--revision REVISION]
[--download-dir DOWNLOAD_DIR] [--adapters [ADAPTERS ...]] [--tp TP] [--model-name MODEL_NAME] [--session-len SESSION_LEN]
[--max-batch-size MAX_BATCH_SIZE] [--cache-max-entry-count CACHE_MAX_ENTRY_COUNT] [--cache-block-seq-len CACHE_BLOCK_SEQ_LEN] [--enable-prefix-caching]
[--model-format {hf,llama,awq}] [--quant-policy {0,4,8}] [--rope-scaling-factor ROPE_SCALING_FACTOR] [--num-tokens-per-iter NUM_TOKENS_PER_ITER]
[--max-prefill-iters MAX_PREFILL_ITERS] [--vision-max-batch-size VISION_MAX_BATCH_SIZE]
model_path
lmdeploy serve api_server: error: argument --log-level: invalid choice: 'INFO\r' (choose from 'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET')
(lmdeploy) [z00854892@aiservice-01 InternLM-main]$ sh lmdeploy_start.sh
2024-07-12 16:46:42,189 - lmdeploy - INFO - input backend=turbomind, backend_config=TurbomindEngineConfig(model_name=None, model_format=None, tp=1, session_len=32000, max_batch_size=128, cache_max_entry_count=0.2, cache_block_seq_len=64, enable_prefix_caching=False, quant_policy=0, rope_scaling_factor=0.2, use_logn_attn=False, download_dir=None, revision=None, max_prefill_token_num=8192, num_tokens_per_iter=0, max_prefill_iters=1)
2024-07-12 16:46:42,189 - lmdeploy - INFO - input chat_template_config=None
2024-07-12 16:46:42,379 - lmdeploy - INFO - updated chat_template_onfig=ChatTemplateConfig(model_name='qwen', system=None, meta_instruction=None, eosys=None, user=None, eoh=None, assistant=None, eoa=None, separator=None, capability=None, stop_words=None)
2024-07-12 16:46:42,379 - lmdeploy - INFO - model_source: ModelSource.HF_MODEL
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Device does not support bfloat16. Set float16 forcefully
2024-07-12 16:46:42,857 - lmdeploy - INFO - model_config:

[llama]
model_name = qwen
model_arch = Qwen2ForCausalLM
tensor_para_size = 1
head_num = 32
kv_head_num = 32
vocab_size = 151936
num_layer = 32
inter_size = 11008
norm_eps = 1e-06
attn_bias = 1
start_id = 151643
end_id = 151645
session_len = 32000
weight_type = fp16
rotary_embedding = 128
rope_theta = 1000000.0
size_per_head = 128
group_size = 0
max_batch_size = 128
max_context_token_num = 1
step_length = 1
cache_max_entry_count = 0.2
cache_block_seq_len = 64
cache_chunk_size = -1
enable_prefix_caching = False
num_tokens_per_iter = 8192
max_prefill_iters = 4
extra_tokens_per_iter = 0
use_context_fmha = 1
quant_policy = 0
max_position_embeddings = 32768
rope_scaling_factor = 0.0
use_dynamic_ntk = 0
use_logn_attn = 0
lora_policy =
lora_r = 0
lora_scale = 0.0
lora_max_wo_r = 0
lora_rank_pattern =
lora_scale_pattern =

[TM][WARNING] [LlamaTritonModel] max_context_token_num = 32000.
2024-07-12 16:46:44,801 - lmdeploy - WARNING - get 291 model params