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[Bug]: Can't use yarn rope config for long context in Qwen2 model #10293

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FlyCarrot opened this issue Nov 13, 2024 · 1 comment
Open
1 task done

[Bug]: Can't use yarn rope config for long context in Qwen2 model #10293

FlyCarrot opened this issue Nov 13, 2024 · 1 comment
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bug Something isn't working

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@FlyCarrot
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Your current environment

The output of `python collect_env.py` 

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.12.7 (main, Oct  1 2024, 08:52:12) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 550.90.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8462Y+
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        8
CPU max MHz:                     4100.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5600.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req hfi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        128 MiB (64 instances)
L3 cache:                        120 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.6+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	NIC9	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	PXB	PXB	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	0-31,64-95	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	PXB	PXB	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	0-31,64-95	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	0-31,64-95	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	0-31,64-95	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	NODE	NODE	32-63,96-127	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	NODE	NODE	32-63,96-127	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	PXB	PXB	32-63,96-127	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	PXB	PXB	32-63,96-127	1		N/A
NIC0	PXB	PXB	NODE	NODE	SYS	SYS	SYS	SYS	 X 	PXB	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS				
NIC1	PXB	PXB	NODE	NODE	SYS	SYS	SYS	SYS	PXB	 X 	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS				
NIC2	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	 X 	PIX	NODE	NODE	SYS	SYS	SYS	SYS				
NIC3	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	PIX	 X 	NODE	NODE	SYS	SYS	SYS	SYS				
NIC4	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	 X 	PXB	SYS	SYS	SYS	SYS				
NIC5	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	PXB	 X 	SYS	SYS	SYS	SYS				
NIC6	SYS	SYS	SYS	SYS	PXB	PXB	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	PXB	NODE	NODE				
NIC7	SYS	SYS	SYS	SYS	PXB	PXB	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	PXB	 X 	NODE	NODE				
NIC8	SYS	SYS	SYS	SYS	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	 X 	PXB				
NIC9	SYS	SYS	SYS	SYS	NODE	NODE	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	PXB	 X 				

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9

NVIDIA_VISIBLE_DEVICES=GPU-4874116f-3878-1e53-8e46-120150b0b458,GPU-f56ccdda-e68b-fe5e-c5bf-12e8f08225fe,GPU-209e16ec-a6b6-a4a1-fbdd-04d686dda7b5,GPU-7f17f885-b8df-830e-ddba-6a9245e12adb,GPU-cc477212-a933-02f2-181a-eb37183f118e,GPU-6a3c81de-e3ca-fa3d-007d-23c3028efcce,GPU-f11f6342-4207-db4b-d663-74abe28e4046,GPU-ec7601fc-87f5-1bfe-4fbe-1161dd1ae0e9
NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NVIDIA_DRIVER_CAPABILITIES=compute,utility
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.4.1
NCCL_IB_AR_THRESHOLD=0
NCCL_IB_TIMEOUT=22
LD_LIBRARY_PATH=/usr/local/lib/python3.12/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_IB_RETRY_CNT=13
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

when I add rope config in Qwen/Qwen2-72B-Instruct/config.json

"rope_scaling": {
            "factor": 4.0,
            "original_max_position_embeddings": 32768,
            "type": "yarn"
        }

I get transformers warning:

"Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}"

And, when LLM's input length is shorter than original_position_embedding_len, the response is OK. However, if input's len is larger than 32768(original_position_embedding_len), the model's output will be something confusing, similar to a kind of repetition of the input.
this error happened in the version of 0.6.3.post1, but when I switch to v0.6.0, everything is OK.

I find that transformers's repo from recent versions don't accept "original_max_position_embeddings", but vllm need it. Maybe this is a confict between transformers and vllm ?

Does anyone know how to correctly enable the long context feature? Thanks ^_^

PS: I can't run collect_env.py script in v0.6.0's docker image, but 0.6.3.post1's docker image is OK.
PPS: I just search issues about "original_max_position_embeddings", but got nothing releated.

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@FlyCarrot FlyCarrot added the bug Something isn't working label Nov 13, 2024
@DarkLight1337
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@WoosukKwon do you know about the context of this config field?

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@FlyCarrot @DarkLight1337