Submitting job: /common/home/users/d/dh.huang.2023/code/rapget-translation/scripts/eval-mac.sh
Current Directory:
/common/home/users/d/dh.huang.2023/code/rapget-translation
Thu Oct 17 08:40:02 2024       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.12              Driver Version: 550.90.12      CUDA Version: 12.4     |
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| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA L40S                    On  |   00000000:01:00.0 Off |                    0 |
| N/A   33C    P8             33W /  350W |       1MiB /  46068MiB |      0%      Default |
|                                         |                        |                  N/A |
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+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
Linux lotus 4.18.0-553.22.1.el8_10.x86_64 #1 SMP Wed Sep 25 09:20:43 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
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VERSION="8.10 (Green Obsidian)"
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PLATFORM_ID="platform:el8"
PRETTY_NAME="Rocky Linux 8.10 (Green Obsidian)"
ANSI_COLOR="0;32"
LOGO="fedora-logo-icon"
CPE_NAME="cpe:/o:rocky:rocky:8:GA"
HOME_URL="https://rockylinux.org/"
BUG_REPORT_URL="https://bugs.rockylinux.org/"
SUPPORT_END="2029-05-31"
ROCKY_SUPPORT_PRODUCT="Rocky-Linux-8"
ROCKY_SUPPORT_PRODUCT_VERSION="8.10"
REDHAT_SUPPORT_PRODUCT="Rocky Linux"
REDHAT_SUPPORT_PRODUCT_VERSION="8.10"
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              128
On-line CPU(s) list: 0-127
Thread(s) per core:  2
Core(s) per socket:  64
Socket(s):           1
NUMA node(s):        1
Vendor ID:           AuthenticAMD
CPU family:          25
Model:               17
Model name:          AMD EPYC 9554 64-Core Processor
Stepping:            1
CPU MHz:             3100.000
CPU max MHz:         3762.9880
CPU min MHz:         1500.0000
BogoMIPS:            6190.69
Virtualization:      AMD-V
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            32768K
NUMA node0 CPU(s):   0-127
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d
MemTotal:       527521804 kB
Current Directory:
/common/home/users/d/dh.huang.2023/code/rapget-translation
Evaluating shenzhi-wang/Llama3.1-8B-Chinese-Chat
[nltk_data] Downloading package wordnet to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package omw-1.4 to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package omw-1.4 to
[nltk_data]     /common/home/users/d/dh.huang.2023/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
loading env vars from: /common/home/users/d/dh.huang.2023/common2/code/rapget-translation/.env
Adding /common/home/users/d/dh.huang.2023/common2/code/rapget-translation to sys.path
loading: /common/home/users/d/dh.huang.2023/common2/code/rapget-translation/eval_modules/calc_repetitions.py
loading /common/home/users/d/dh.huang.2023/common2/code/rapget-translation/llm_toolkit/translation_utils.py

Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]
Fetching 5 files: 100%|██████████| 5/5 [00:00<00:00, 74631.74it/s]
Lightning automatically upgraded your loaded checkpoint from v1.8.3.post1 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../../../../../../../scratch/users/d/dh.huang.2023/transformers/hub/models--Unbabel--wmt22-comet-da/snapshots/371e9839ca4e213dde891b066cf3080f75ec7e72/checkpoints/model.ckpt`
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1617: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be deprecated in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(
Encoder model frozen.
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/pytorch_lightning/core/saving.py:195: Found keys that are not in the model state dict but in the checkpoint: ['encoder.model.embeddings.position_ids']
We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).
CUDA is available, we have found  1  GPU(s)
NVIDIA L40S
CUDA version: 12.1
shenzhi-wang/Llama3.1-8B-Chinese-Chat llama-factory/saves/Llama3.1-8B-Chinese-Chat/checkpoint-105 False datasets/mac/mac.tsv results/mac-results_rpp_with_mnt_2048_generic_prompt.csv False 2048 1
(0) GPU = NVIDIA L40S. Max memory = 44.521 GB.
0.0 GB of memory reserved.
loading model: shenzhi-wang/Llama3.1-8B-Chinese-Chat with adapter: llama-factory/saves/Llama3.1-8B-Chinese-Chat/checkpoint-105

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 4/4 [00:07<00:00,  1.61s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:07<00:00,  1.85s/it]
(2) GPU = NVIDIA L40S. Max memory = 44.521 GB.
15.201 GB of memory reserved.
loading train/test data files
DatasetDict({
    train: Dataset({
        features: ['chinese', 'english', 'text', 'prompt'],
        num_rows: 4528
    })
    test: Dataset({
        features: ['chinese', 'english', 'text', 'prompt'],
        num_rows: 1133
    })
})
--------------------------------------------------
chinese: 老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。
--------------------------------------------------
english: Old Geng picked up his shotgun, squinted, and pulled the trigger. Two sparrows crashed to the ground like hailstones as shotgun pellets tore noisily through the branches.
--------------------------------------------------
text: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.

Chinese: 老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。
English:Old Geng picked up his shotgun, squinted, and pulled the trigger. Two sparrows crashed to the ground like hailstones as shotgun pellets tore noisily through the branches.<|eot_id|>
--------------------------------------------------
prompt: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.

Chinese: 老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。
English:
--------------------------------------------------
chinese: 对我一刻也不能放松。
--------------------------------------------------
english: People shouldn't let up on me for a minute.
--------------------------------------------------
text: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.

Chinese: 对我一刻也不能放松。
English:People shouldn't let up on me for a minute.<|eot_id|>
--------------------------------------------------
prompt: You will be given a Chinese sentence to translate. If it is an incomplete sentence, or if you are unsure about the meaning, simply copy the input text as your output. Do not output any additional sentence such as explanation or reasoning.

Chinese: 对我一刻也不能放松。
English:
Evaluating model: shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoint-105 on cuda
*** Evaluating with repetition_penalty: 1.06

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GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA L40S') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
SLURM auto-requeueing enabled. Setting signal handlers.
Batch output: [' Old Geng raised his gun and narrowed one eye. He pulled the trigger. The golden sparrows fell like hailstones, and the iron sand flew through the willow branches with a rattling sound........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................']
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
Using default tokenizer.
shenzhi-wang/Llama3.1-8B-Chinese-Chat/checkpoint-105/rpp-1.06 metrics: {'comet': 0.438108970370844, 'meteor': 0.18592056616993435, 'sacrebleu': {'score': 0.26417254523417455, 'counts': [16113, 6007, 2869, 1544], 'totals': [1724224, 1723091, 1721958, 1720825], 'precisions': [0.9345073493931183, 0.34861768763228407, 0.1666126583807503, 0.08972440544506269], 'bp': 1.0, 'sys_len': 1724224, 'ref_len': 30190}, 'bleu_scores': {'bleu': 0.0026417254523417445, 'precisions': [0.009345073493931183, 0.003486176876322841, 0.0016661265838075028, 0.0008972440544506269], 'brevity_penalty': 1.0, 'length_ratio': 57.11242133156674, 'translation_length': 1724224, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.18075648237510966, 'rouge2': 0.08414947177501376, 'rougeL': 0.15585202110123367, 'rougeLsum': 0.15601577418759272}, 'accuracy': 0.0, 'correct_ids': []}
(3) GPU = NVIDIA L40S. Max memory = 44.521 GB.
18.861 GB of memory reserved.
Job ID: 95394
Cluster: crimson
User/Group: dh.huang.2023/dh.huang.2023
State: COMPLETED (exit code 0)
Nodes: 1
Cores per node: 10
CPU Utilized: 2-19:43:13
CPU Efficiency: 9.97% of 28-07:16:50 core-walltime
Job Wall-clock time: 2-19:55:41
Memory Utilized: 6.82 GB
Memory Efficiency: 2.66% of 256.00 GB