Error loading grammar_dataset

[KysonYang001]

System Info

PyTorch version: 2.1.2+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0
Clang version: Could not collect
CMake version: version 3.23.5
Libc version: glibc-2.31

Python version: 3.8.19 | packaged by conda-forge | (default, Mar 20 2024, 12:47:35) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.147.05
cuDNN version: Probably one of the following:
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 40
On-line CPU(s) list: 0-39
Thread(s) per core: 2
Core(s) per socket: 10
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz
Stepping: 7
CPU MHz: 1000.000
CPU max MHz: 3200.0000
CPU min MHz: 1000.0000
BogoMIPS: 4800.00
Virtualization: VT-x
L1d cache: 640 KiB
L1i cache: 640 KiB
L2 cache: 20 MiB
L3 cache: 27.5 MiB
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
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: Mitigation; TSX disabled
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 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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] flake8-bugbear==24.2.6
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.4
[pip3] numpy-quaternion==2022.4.3
[pip3] torch==2.1.2+cu118
[pip3] torchdata==0.7.1+cpu
[pip3] torchtext==0.16.2+cpu
[pip3] triton==2.1.0
[conda] numpy 1.24.4 pypi_0 pypi
[conda] torch 2.1.2+cu118 pypi_0 pypi
[conda] torchdata 0.7.1+cpu pypi_0 pypi
[conda] torchtext 0.16.2+cpu pypi_0 pypi
[conda] triton 2.1.0 pypi_0 pypi

Information

• The official example scripts
• My own modified scripts

🐛 Describe the bug

I always get this error when using grammar_dataset, I'm sure I've prepared the dataset as given, why does it always fail to load?

Error logs

TypeError: read_csv() got an unexpected keyword argument 'mangle_dupe_cols'

Expected behavior

This is the command I ran：CUDA_VISIBLE_DEVICES=1 python finetuning.py --use_peft --peft_method lora --quantization --dataset grammar_dataset --model_name llama/Llama-2-7b-hf --output_dir llama/llama_finetuned

I want to load the CSV file correctly.

[KysonYang001]

System Info

PyTorch version: 2.1.2+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0 Clang version: Could not collect CMake version: version 3.23.5 Libc version: glibc-2.31

Python version: 3.8.19 | packaged by conda-forge | (default, Mar 20 2024, 12:47:35) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.10 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 3090 GPU 2: NVIDIA GeForce RTX 3090 GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.147.05 cuDNN version: Probably one of the following: /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 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 Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 40 On-line CPU(s) list: 0-39 Thread(s) per core: 2 Core(s) per socket: 10 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz Stepping: 7 CPU MHz: 1000.000 CPU max MHz: 3200.0000 CPU min MHz: 1000.0000 BogoMIPS: 4800.00 Virtualization: VT-x L1d cache: 640 KiB L1i cache: 640 KiB L2 cache: 20 MiB L3 cache: 27.5 MiB NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp 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: Mitigation; TSX disabled 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 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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries: [pip3] flake8==7.0.0 [pip3] flake8-bugbear==24.2.6 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.24.4 [pip3] numpy-quaternion==2022.4.3 [pip3] torch==2.1.2+cu118 [pip3] torchdata==0.7.1+cpu [pip3] torchtext==0.16.2+cpu [pip3] triton==2.1.0 [conda] numpy 1.24.4 pypi_0 pypi [conda] torch 2.1.2+cu118 pypi_0 pypi [conda] torchdata 0.7.1+cpu pypi_0 pypi [conda] torchtext 0.16.2+cpu pypi_0 pypi [conda] triton 2.1.0 pypi_0 pypi

Information

• The official example scripts
• My own modified scripts

🐛 Describe the bug

I always get this error when using grammar_dataset, I'm sure I've prepared the dataset as given, why does it always fail to load?

Error logs

TypeError: read_csv() got an unexpected keyword argument 'mangle_dupe_cols'

Expected behavior

This is the command I ran：CUDA_VISIBLE_DEVICES=1 python finetuning.py --use_peft --peft_method lora --quantization --dataset grammar_dataset --model_name llama/Llama-2-7b-hf --output_dir llama/llama_finetuned

I want to load the CSV file correctly.

https://github.com/huggingface/datasets/issues/5744This appears to be an issue with the dataset library and pandas library versions.

[wukaixingxp]

Hi! @KysonYang001 Can you confirm this issue has been solved by upgrading datasets > 2.11 using pip install -U datasets. Thanks!