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README.md

@@ -60,7 +60,7 @@ An ensemble of six Uni-Sign models reaches 10.36 BLEU-4 and 44.37 BLEURT on MS.
 | `unisign/unisign-pjm-{cslnews,how2sign,openasl,scratch}-{ms,si}/` | ONNX export of each Uni-Sign row: `projector.onnx`, `encoder.onnx`, `decoder.onnx`, `prefix.npy`, `meta.json` |
 | `spamo/{spamo,mod-spamo}-pjm-{phoenix,scratch}-{ms,si}/` | ONNX export of the SpaMo and Mod-SpaMo rows, sharing `spamo/shared/` (CLIP ViT, VideoMAE, hand ViT); the PHOENIX-2014T SI rows have no ONNX export, only checkpoints |
 | `assets/` | Tokenizers (mT5, Flan-T5), YOLO person detector and MediaPipe models used by `signsdk` |
-| `checkpoints/unisign/<run>/best_checkpoint.pth` | PyTorch checkpoints: the eight table rows (`pjm_phase3_full_{ms,si}` from CSL-News, `_how2sign`, `_openasl`, `_scratch`), the second seeds (`_s43`), the chained and distilled models (`chain_openasl_how2sign`, `_ms_chain`, `_ms_how2sign_kfold_mix`) and the two models trained on Polish targets (`pjm_pl_{ms,si}_how2sign`) |
+| `checkpoints/unisign/<run>/best_checkpoint.pth` | PyTorch checkpoints: the eight table rows (`pjm_phase3_full_{ms,si}` from CSL-News, `_how2sign`, `_openasl`, `_scratch`), the second seeds (`_s43`), the chained and distilled models (`chain_openasl_how2sign`, `_ms_chain`, `_ms_how2sign_kfold_mix`) and the two models trained on Polish targets (`pjm_pl_{ms,si}_how2sign`), and the MS training-size control (`pjm_ms_sub_openasl`, see `results/README.md`) |
 | `checkpoints/spamo/<model>/*.ckpt` | PyTorch Lightning checkpoints of the eight SpaMo and Mod-SpaMo rows; the BLEU-4 in a file name is the validation score at that epoch |
 | `results/` | Test outputs, references, metric logs and training logs of every row (see `results/README.md`) |
 

checkpoints/unisign/pjm_ms_sub_openasl/best_checkpoint.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41dcf722ef7579da8e924557ff4f440e0b5459f1be78017e2d82dd1cae9be3ec
+size 1175634096

results/README.md

@@ -43,3 +43,11 @@ is not stored here.
 `CrocoSign/pjm-data/texts/texts_fixed.csv`), weights in `checkpoints/unisign/pjm_pl_{ms,si}_how2sign/`.
 `en2pl_{ms,si}_how2sign`: the English How2Sign outputs translated to Polish by a language model (Claude Sonnet).
 Both are scored against `pl_fixed`; see the "Polish targets" paragraph of the paper.
+
+## Training-size control (`unisign/eval_pjm_ms_sub_openasl/`)
+
+Uni-Sign OpenASL trained on the MS training set cut to the SI size (10,062 pairs, every MS signer kept with about
+42% of their clips, `split_train_ms_sub.csv` here) with the SI schedule, and tested on the full MS test set: 7.19
+BLEU-4 and 39.75 BLEURT, against 8.53 and 42.43 with the full MS training set. Weights in
+`checkpoints/unisign/pjm_ms_sub_openasl/`; scripts `make_ms_subset.py` and `queue_ms_subset.sh` in Uni-Sign-PJM.
+

results/unisign/eval_pjm_ms_sub_openasl/bleurt_BLEURT-20.json

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results/unisign/eval_pjm_ms_sub_openasl/log.txt

@@ -0,0 +1,340 @@
+[2026-11-01 20:35:32,686] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+| distributed init (rank 0): env://
+[2026-11-01 20:35:33,377] [INFO] [comm.py:652:init_distributed] cdb=None
+[2026-11-01 20:35:33,377] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+Namespace(batch_size=8, gradient_accumulation_steps=8, gradient_clipping=1.0, epochs=20, world_size=1, dist_url='env://', local_rank=0, hidden_dim=256, finetune='out/ms_subset/pjm_ms_sub_openasl/best_checkpoint.pth', opt='adamw', opt_eps=1e-09, opt_betas=None, clip_grad=None, momentum=0.9, weight_decay=0.0001, sched='cosine', lr=0.001, min_lr=1e-08, warmup_epochs=0, output_dir='out/ms_subset/eval_pjm_ms_sub_openasl', seed=42, eval=True, num_workers=4, pin_mem=True, offload=False, dtype='bf16', zero_stage=2, compute_fp32_loss=False, quick_break=0, rgb_support=False, max_length=256, dataset='PJM', wandb=False, wandb_project='uni-sign', wandb_dir='./wandb_logs', bertscore=True, num_examples=5, patience=0, pjm_train_texts='', pjm_train_texts_p=1.0, pjm_train_csv='', pjm_texts='../CrocoSign/data/texts_eng.h5', pjm_lang='', pjm_split='ms', lora=False, lora_rank=16, lora_alpha=32, lora_dropout=0.05, lora_target='q,v', lora_ckpt='', freeze_visual=False, task='SLT', label_smoothing=0.2, online_video='', rank=0, gpu=0, distributed=True, dist_backend='nccl')
+Creating dataset:
+  PJM split family: ms (suffix='_ms')
+<datasets.S2T_Dataset_PJM object at 0x700746b8e650>
+<datasets.S2T_Dataset_PJM object at 0x70067d317460>
+<datasets.S2T_Dataset_PJM object at 0x70067d317760>
+Creating model:
+/home/croco/Uni-Sign/.venv/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+***********************************
+Load Checkpoint...
+***********************************
+Missing keys: 
+ 
+Unexpected keys: 
+ 
+number of params: 587.747368M
+Using deepspeed to train...
+Initializing deepspeed...
+[2026-11-01 20:35:36,494] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed info: version=0.16.3, git-hash=unknown, git-branch=unknown
+[2026-11-01 20:35:36,494] [INFO] [comm.py:677:init_distributed] Distributed backend already initialized
+[2026-11-01 20:35:36,494] [INFO] [config.py:733:__init__] Config mesh_device None world_size = 1
+[2026-11-01 20:35:36,526] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
+[2026-11-01 20:35:36,526] [INFO] [logging.py:128:log_dist] [Rank 0] Using client Optimizer as basic optimizer
+[2026-11-01 20:35:36,526] [INFO] [logging.py:128:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
+[2026-11-01 20:35:36,535] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Basic Optimizer = AdamW
+[2026-11-01 20:35:36,535] [INFO] [utils.py:59:is_zero_supported_optimizer] Checking ZeRO support for optimizer=AdamW type=<class 'torch.optim.adamw.AdamW'>
+[2026-11-01 20:35:36,535] [INFO] [logging.py:128:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 2 optimizer
+[2026-11-01 20:35:36,535] [INFO] [stage_1_and_2.py:149:__init__] Reduce bucket size 500000000
+[2026-11-01 20:35:36,535] [INFO] [stage_1_and_2.py:150:__init__] Allgather bucket size 500000000
+[2026-11-01 20:35:36,535] [INFO] [stage_1_and_2.py:151:__init__] CPU Offload: False
+[2026-11-01 20:35:36,535] [INFO] [stage_1_and_2.py:152:__init__] Round robin gradient partitioning: False
+[2026-11-01 20:35:36,968] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
+[2026-11-01 20:35:36,968] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 6.57 GB         CA 7.31 GB         Max_CA 7 GB 
+[2026-11-01 20:35:36,969] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.51 GB, percent = 10.8%
+[2026-11-01 20:35:37,030] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
+[2026-11-01 20:35:37,031] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 7.66 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-11-01 20:35:37,031] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.5 GB, percent = 10.8%
+[2026-11-01 20:35:37,031] [INFO] [stage_1_and_2.py:545:__init__] optimizer state initialized
+[2026-11-01 20:35:37,091] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
+[2026-11-01 20:35:37,092] [INFO] [utils.py:782:see_memory_usage] MA 5.48 GB         Max_MA 5.48 GB         CA 9.5 GB         Max_CA 10 GB 
+[2026-11-01 20:35:37,092] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory:  used = 6.5 GB, percent = 10.8%
+[2026-11-01 20:35:37,094] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer
+[2026-11-01 20:35:37,094] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed using client LR scheduler
+[2026-11-01 20:35:37,094] [INFO] [logging.py:128:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x700648937820>
+[2026-11-01 20:35:37,094] [INFO] [logging.py:128:log_dist] [Rank 0] step=0, skipped=0, lr=[0.001, 0.001], mom=[(0.9, 0.999), (0.9, 0.999)]
+[2026-11-01 20:35:37,095] [INFO] [config.py:999:print] DeepSpeedEngine configuration:
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   activation_checkpointing_config  {
+    "partition_activations": false, 
+    "contiguous_memory_optimization": false, 
+    "cpu_checkpointing": false, 
+    "number_checkpoints": null, 
+    "synchronize_checkpoint_boundary": false, 
+    "profile": false
+}
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True, 'use_gds': False}
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   amp_enabled .................. False
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   amp_params ................... False
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   autotuning_config ............ {
+    "enabled": false, 
+    "start_step": null, 
+    "end_step": null, 
+    "metric_path": null, 
+    "arg_mappings": null, 
+    "metric": "throughput", 
+    "model_info": null, 
+    "results_dir": "autotuning_results", 
+    "exps_dir": "autotuning_exps", 
+    "overwrite": true, 
+    "fast": true, 
+    "start_profile_step": 3, 
+    "end_profile_step": 5, 
+    "tuner_type": "gridsearch", 
+    "tuner_early_stopping": 5, 
+    "tuner_num_trials": 50, 
+    "model_info_path": null, 
+    "mp_size": 1, 
+    "max_train_batch_size": null, 
+    "min_train_batch_size": 1, 
+    "max_train_micro_batch_size_per_gpu": 1.024000e+03, 
+    "min_train_micro_batch_size_per_gpu": 1, 
+    "num_tuning_micro_batch_sizes": 3
+}
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   bfloat16_enabled ............. True
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   bfloat16_immediate_grad_update  False
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   checkpoint_parallel_write_pipeline  False
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   checkpoint_tag_validation_enabled  True
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   checkpoint_tag_validation_fail  False
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7006485dcee0>
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   communication_data_type ...... None
+[2026-11-01 20:35:37,095] [INFO] [config.py:1003:print]   compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   curriculum_enabled_legacy .... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   curriculum_params_legacy ..... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   data_efficiency_enabled ...... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   dataloader_drop_last ......... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   disable_allgather ............ False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   dump_state ................... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   dynamic_loss_scale_args ...... None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_enabled ........... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_gas_boundary_resolution  1
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_layer_name ........ bert.encoder.layer
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_layer_num ......... 0
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_max_iter .......... 100
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_stability ......... 1e-06
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_tol ............... 0.01
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   eigenvalue_verbose ........... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   elasticity_enabled ........... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   flops_profiler_config ........ {
+    "enabled": false, 
+    "recompute_fwd_factor": 0.0, 
+    "profile_step": 1, 
+    "module_depth": -1, 
+    "top_modules": 1, 
+    "detailed": true, 
+    "output_file": null
+}
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   fp16_auto_cast ............... None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   fp16_enabled ................. False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   fp16_master_weights_and_gradients  False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   global_rank .................. 0
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   grad_accum_dtype ............. None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   gradient_accumulation_steps .. 8
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   gradient_clipping ............ 1.0
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   gradient_predivide_factor .... 1.0
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   graph_harvesting ............. False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   initial_dynamic_scale ........ 1
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   load_universal_checkpoint .... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   loss_scale ................... 1.0
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   memory_breakdown ............. False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   mics_hierarchial_params_gather  False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   mics_shard_size .............. -1
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='/ds_tensorboard_logs/', job_name='_tensorboard') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName')
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   nebula_config ................ {
+    "enabled": false, 
+    "persistent_storage_path": null, 
+    "persistent_time_interval": 100, 
+    "num_of_version_in_retention": 2, 
+    "enable_nebula_load": true, 
+    "load_path": null
+}
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   optimizer_legacy_fusion ...... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   optimizer_name ............... None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   optimizer_params ............. None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   pld_enabled .................. False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   pld_params ................... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   prescale_gradients ........... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   scheduler_name ............... None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   scheduler_params ............. None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   seq_parallel_communication_data_type  torch.float32
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   sparse_attention ............. None
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   sparse_gradients_enabled ..... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   steps_per_print .............. 10
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   timers_config ................ enabled=True synchronized=True
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   train_batch_size ............. 64
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   train_micro_batch_size_per_gpu  8
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   use_data_before_expert_parallel_  False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   use_node_local_storage ....... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   wall_clock_breakdown ......... False
+[2026-11-01 20:35:37,096] [INFO] [config.py:1003:print]   weight_quantization_config ... None
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   world_size ................... 1
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   zero_allow_untested_optimizer  False
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100000000, max_in_cpu=1000000000, pin_memory=False) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=False, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=30000000 param_persistence_threshold=10000 model_persistence_threshold=9223372036854775807 max_live_parameters=30000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=False module_granularity_threshold=0 use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False zeropp_loco_param=None mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=False pipeline_loading_checkpoint=False override_module_apply=True
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   zero_enabled ................. True
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   zero_force_ds_cpu_optimizer .. True
+[2026-11-01 20:35:37,097] [INFO] [config.py:1003:print]   zero_optimization_stage ...... 2
+[2026-11-01 20:35:37,097] [INFO] [config.py:989:print_user_config]   json = {
+    "steps_per_print": 10, 
+    "zero_optimization": {
+        "stage": 2, 
+        "offload_param": {
+            "device": "none"
+        }, 
+        "offload_optimizer": {
+            "device": "none"
+        }, 
+        "stage3_param_persistence_threshold": 1.000000e+04, 
+        "stage3_max_live_parameters": 3.000000e+07, 
+        "stage3_prefetch_bucket_size": 3.000000e+07, 
+        "memory_efficient_linear": false
+    }, 
+    "bfloat16": {
+        "enabled": true
+    }, 
+    "gradient_clipping": 1.0, 
+    "prescale_gradients": false, 
+    "wall_clock_breakdown": false, 
+    "hybrid_engine": {
+        "enabled": false, 
+        "max_out_tokens": 512, 
+        "inference_tp_size": 1, 
+        "release_inference_cache": false, 
+        "pin_parameters": true, 
+        "tp_gather_partition_size": 8
+    }, 
+    "tensorboard": {
+        "enabled": false, 
+        "output_path": "/ds_tensorboard_logs/", 
+        "job_name": "_tensorboard"
+    }, 
+    "train_micro_batch_size_per_gpu": 8, 
+    "gradient_accumulation_steps": 8
+}
+<deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer object at 0x7006485df430>
+📄 dev result
+Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
+Test:  [  0/375]  eta: 0:07:08  loss: 5.3125 (5.3125)  time: 1.1434  data: 0.1178  max mem: 5844
+Test:  [ 10/375]  eta: 0:04:54  loss: 5.3438 (5.4432)  time: 0.8067  data: 0.0108  max mem: 6223
+Test:  [ 20/375]  eta: 0:04:26  loss: 5.3125 (5.2961)  time: 0.7305  data: 0.0001  max mem: 6223
+Test:  [ 30/375]  eta: 0:04:07  loss: 5.2812 (5.3619)  time: 0.6695  data: 0.0001  max mem: 6467
+Test:  [ 40/375]  eta: 0:04:01  loss: 5.4375 (5.3925)  time: 0.6887  data: 0.0001  max mem: 6467
+Test:  [ 50/375]  eta: 0:03:48  loss: 5.4375 (5.3915)  time: 0.6770  data: 0.0001  max mem: 6467
+Test:  [ 60/375]  eta: 0:03:35  loss: 5.4688 (5.4308)  time: 0.6128  data: 0.0001  max mem: 6467
+Test:  [ 70/375]  eta: 0:03:30  loss: 5.5625 (5.4353)  time: 0.6636  data: 0.0001  max mem: 6467
+Test:  [ 80/375]  eta: 0:03:21  loss: 5.3125 (5.4082)  time: 0.6803  data: 0.0001  max mem: 6467
+Test:  [ 90/375]  eta: 0:03:11  loss: 5.1562 (5.3877)  time: 0.6031  data: 0.0001  max mem: 6467
+Test:  [100/375]  eta: 0:03:03  loss: 5.2188 (5.3738)  time: 0.6091  data: 0.0001  max mem: 6467
+Test:  [110/375]  eta: 0:02:55  loss: 5.1875 (5.3561)  time: 0.6188  data: 0.0001  max mem: 6467
+Test:  [120/375]  eta: 0:02:48  loss: 5.3125 (5.3833)  time: 0.6176  data: 0.0001  max mem: 6467
+Test:  [130/375]  eta: 0:02:42  loss: 5.6250 (5.3860)  time: 0.6780  data: 0.0001  max mem: 6467
+Test:  [140/375]  eta: 0:02:35  loss: 5.4375 (5.3956)  time: 0.6665  data: 0.0001  max mem: 6467
+Test:  [150/375]  eta: 0:02:26  loss: 5.4375 (5.3899)  time: 0.5521  data: 0.0001  max mem: 6467
+Test:  [160/375]  eta: 0:02:18  loss: 5.2812 (5.3892)  time: 0.5185  data: 0.0001  max mem: 6467
+Test:  [170/375]  eta: 0:02:08  loss: 5.3438 (5.3975)  time: 0.4719  data: 0.0001  max mem: 6467
+Test:  [180/375]  eta: 0:02:02  loss: 5.5312 (5.4092)  time: 0.4827  data: 0.0000  max mem: 6495
+Test:  [190/375]  eta: 0:01:55  loss: 5.5625 (5.4216)  time: 0.5783  data: 0.0001  max mem: 6495
+Test:  [200/375]  eta: 0:01:48  loss: 5.5312 (5.4235)  time: 0.5499  data: 0.0001  max mem: 6495
+Test:  [210/375]  eta: 0:01:40  loss: 5.4375 (5.4236)  time: 0.4457  data: 0.0001  max mem: 6495
+Test:  [220/375]  eta: 0:01:33  loss: 5.4375 (5.4318)  time: 0.4636  data: 0.0001  max mem: 6495
+Test:  [230/375]  eta: 0:01:27  loss: 5.4375 (5.4347)  time: 0.5536  data: 0.0001  max mem: 6495
+Test:  [240/375]  eta: 0:01:20  loss: 5.4062 (5.4352)  time: 0.4946  data: 0.0001  max mem: 6495
+Test:  [250/375]  eta: 0:01:14  loss: 5.6250 (5.4438)  time: 0.4771  data: 0.0001  max mem: 6495
+Test:  [260/375]  eta: 0:01:07  loss: 5.5625 (5.4452)  time: 0.4647  data: 0.0001  max mem: 6495
+Test:  [270/375]  eta: 0:01:00  loss: 5.4375 (5.4473)  time: 0.3969  data: 0.0000  max mem: 6495
+Test:  [280/375]  eta: 0:00:54  loss: 5.4062 (5.4480)  time: 0.4419  data: 0.0001  max mem: 6495
+Test:  [290/375]  eta: 0:00:48  loss: 5.3750 (5.4435)  time: 0.4621  data: 0.0001  max mem: 6495
+Test:  [300/375]  eta: 0:00:42  loss: 5.4375 (5.4451)  time: 0.4310  data: 0.0000  max mem: 6495
+Test:  [310/375]  eta: 0:00:36  loss: 5.8125 (5.4579)  time: 0.4277  data: 0.0001  max mem: 6495
+Test:  [320/375]  eta: 0:00:30  loss: 5.7500 (5.4608)  time: 0.3427  data: 0.0001  max mem: 6495
+Test:  [330/375]  eta: 0:00:24  loss: 5.5000 (5.4623)  time: 0.3853  data: 0.0001  max mem: 6496
+Test:  [340/375]  eta: 0:00:18  loss: 5.5000 (5.4577)  time: 0.3906  data: 0.0000  max mem: 6496
+Test:  [350/375]  eta: 0:00:13  loss: 5.5000 (5.4617)  time: 0.2921  data: 0.0000  max mem: 6496
+Test:  [360/375]  eta: 0:00:07  loss: 5.5000 (5.4601)  time: 0.3705  data: 0.0001  max mem: 6496
+Test:  [370/375]  eta: 0:00:02  loss: 5.5000 (5.4619)  time: 0.3859  data: 0.0000  max mem: 6496
+Test:  [374/375]  eta: 0:00:00  loss: 5.4375 (5.4604)  time: 0.4066  data: 0.0000  max mem: 6496
+Test: Total time: 0:03:17 (0.5269 s / it)
+{'bleu1': 31.348860175810604, 'bleu2': 17.02286737246585, 'bleu3': 10.940283748484829, 'bleu4': 7.440620722581526, 'chrf': 24.568965492452367, 'avg_ref_len': 8.992666666666667, 'avg_hyp_len': 9.106333333333334, 'length_ratio': 1.0126399288308994, 'pct_empty': 0.0, 'pct_very_short': 0.43333333333333335, 'pct_repetitive': 0.13333333333333333}
+Rouge: 20.26
+--- 5 example translations (dev) ---
+[183_033_R]
+  ref: Because he didn't make it.
+  hyp: To the movies with a friend.
+[183_126_R]
+  ref: How do you flash "bench"?
+  hyp: He doesn't want to.
+[183_137_R]
+  ref: I blink twice.
+  hyp: I'm cleaning up.
+[183_006_R]
+  ref: Be careful, because it is slippery.
+  hyp: I have a lot on my mind.
+[183_134_R]
+  ref: I ask - I'm deaf.
+  hyp: I don't know.
+--- end examples ---
+/home/croco/Uni-Sign/.venv/lib/python3.10/site-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+BERTScore F1: 88.46
+📄 test result
+Test:  [  0/374]  eta: 0:02:24  loss: 5.6875 (5.6875)  time: 0.3863  data: 0.1488  max mem: 6779
+Test:  [ 10/374]  eta: 0:02:03  loss: 5.3750 (5.4830)  time: 0.3394  data: 0.0136  max mem: 6779
+Test:  [ 20/374]  eta: 0:01:39  loss: 5.3750 (5.4940)  time: 0.2754  data: 0.0001  max mem: 6779
+Test:  [ 30/374]  eta: 0:01:46  loss: 5.5312 (5.5454)  time: 0.2924  data: 0.0001  max mem: 6779
+Test:  [ 40/374]  eta: 0:01:42  loss: 5.7500 (5.5595)  time: 0.3346  data: 0.0001  max mem: 6779
+Test:  [ 50/374]  eta: 0:01:42  loss: 5.3125 (5.5214)  time: 0.3238  data: 0.0001  max mem: 6779
+Test:  [ 60/374]  eta: 0:01:33  loss: 5.5625 (5.5625)  time: 0.2772  data: 0.0001  max mem: 6779
+Test:  [ 70/374]  eta: 0:01:36  loss: 5.6562 (5.5625)  time: 0.3241  data: 0.0001  max mem: 6779
+Test:  [ 80/374]  eta: 0:01:32  loss: 5.5000 (5.5478)  time: 0.3707  data: 0.0001  max mem: 6779
+Test:  [ 90/374]  eta: 0:01:29  loss: 5.2500 (5.5247)  time: 0.3030  data: 0.0001  max mem: 6779
+Test:  [100/374]  eta: 0:01:26  loss: 5.5000 (5.5371)  time: 0.3100  data: 0.0001  max mem: 6779
+Test:  [110/374]  eta: 0:01:23  loss: 5.6250 (5.5391)  time: 0.3345  data: 0.0001  max mem: 6779
+Test:  [120/374]  eta: 0:01:20  loss: 5.6250 (5.5615)  time: 0.3306  data: 0.0001  max mem: 6779
+Test:  [130/374]  eta: 0:01:16  loss: 5.6875 (5.5608)  time: 0.2931  data: 0.0001  max mem: 6779
+Test:  [140/374]  eta: 0:01:12  loss: 5.3750 (5.5463)  time: 0.2636  data: 0.0001  max mem: 6779
+Test:  [150/374]  eta: 0:01:09  loss: 5.2188 (5.5290)  time: 0.2744  data: 0.0001  max mem: 6779
+Test:  [160/374]  eta: 0:01:05  loss: 5.2188 (5.5099)  time: 0.2717  data: 0.0001  max mem: 6779
+Test:  [170/374]  eta: 0:01:01  loss: 5.3125 (5.5239)  time: 0.2339  data: 0.0000  max mem: 6779
+Test:  [180/374]  eta: 0:00:59  loss: 5.6562 (5.5259)  time: 0.3050  data: 0.0001  max mem: 6779
+Test:  [190/374]  eta: 0:00:55  loss: 5.5000 (5.5249)  time: 0.3274  data: 0.0001  max mem: 6779
+Test:  [200/374]  eta: 0:00:53  loss: 5.6250 (5.5353)  time: 0.3102  data: 0.0001  max mem: 6779
+Test:  [210/374]  eta: 0:00:50  loss: 5.8125 (5.5532)  time: 0.3410  data: 0.0001  max mem: 6779
+Test:  [220/374]  eta: 0:00:47  loss: 5.8125 (5.5595)  time: 0.3158  data: 0.0001  max mem: 6779
+Test:  [230/374]  eta: 0:00:43  loss: 5.5312 (5.5628)  time: 0.2863  data: 0.0001  max mem: 6779
+Test:  [240/374]  eta: 0:00:41  loss: 5.5312 (5.5624)  time: 0.3059  data: 0.0001  max mem: 6779
+Test:  [250/374]  eta: 0:00:37  loss: 5.4375 (5.5549)  time: 0.3095  data: 0.0001  max mem: 6779
+Test:  [260/374]  eta: 0:00:34  loss: 5.3750 (5.5483)  time: 0.2629  data: 0.0000  max mem: 6779
+Test:  [270/374]  eta: 0:00:31  loss: 5.4375 (5.5431)  time: 0.2353  data: 0.0000  max mem: 6779
+Test:  [280/374]  eta: 0:00:28  loss: 5.3438 (5.5377)  time: 0.2471  data: 0.0000  max mem: 6779
+Test:  [290/374]  eta: 0:00:25  loss: 5.2812 (5.5229)  time: 0.2855  data: 0.0001  max mem: 6779
+Test:  [300/374]  eta: 0:00:21  loss: 5.0938 (5.5159)  time: 0.2560  data: 0.0001  max mem: 6779
+Test:  [310/374]  eta: 0:00:19  loss: 5.4062 (5.5177)  time: 0.3184  data: 0.0000  max mem: 6779
+Test:  [320/374]  eta: 0:00:16  loss: 5.4688 (5.5135)  time: 0.3399  data: 0.0001  max mem: 6779
+Test:  [330/374]  eta: 0:00:13  loss: 5.3750 (5.5094)  time: 0.2674  data: 0.0001  max mem: 6779
+Test:  [340/374]  eta: 0:00:10  loss: 5.3750 (5.5036)  time: 0.2581  data: 0.0000  max mem: 6779
+Test:  [350/374]  eta: 0:00:07  loss: 5.3750 (5.5023)  time: 0.2514  data: 0.0000  max mem: 6779
+Test:  [360/374]  eta: 0:00:04  loss: 5.4375 (5.5020)  time: 0.3240  data: 0.0001  max mem: 6779
+Test:  [370/374]  eta: 0:00:01  loss: 5.4688 (5.5003)  time: 0.3550  data: 0.0000  max mem: 6779
+Test:  [373/374]  eta: 0:00:00  loss: 5.3750 (5.4969)  time: 0.3674  data: 0.0000  max mem: 6779
+Test: Total time: 0:01:52 (0.3000 s / it)
+{'bleu1': 30.852240265122578, 'bleu2': 16.42445266375037, 'bleu3': 10.25544027985281, 'bleu4': 6.912859100391263, 'chrf': 24.05964790512074, 'avg_ref_len': 8.989286909943086, 'avg_hyp_len': 9.022765316370942, 'length_ratio': 1.0037242560798483, 'pct_empty': 0.0, 'pct_very_short': 0.33478406427854035, 'pct_repetitive': 0.06695681285570806}
+Rouge: 19.89
+--- 5 example translations (test) ---
+[183_116_R]
+  ref: He came up with it and on that basis he does it, that's cool.
+  hyp: Hamburgers and drinking are not allowed, there is a ban.
+[472_011_M]
+  ref: He steals the basket and puts it on the bike.
+  hyp: He takes the basket and puts it on the bike.
+[247_042_R]
+  ref: You know, fence....
+  hyp: He threw it away.
+[183_002_R]
+  ref: It's slippery and you can fall over.
+  hyp: How about you?
+[001_003_M]
+  ref: I said, the dog looks down at the frog sitting in the jar.
+  hyp: The dog leans out of the window and the owl sits in it.
+--- end examples ---
+Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+BERTScore F1: 88.43
+BLEURT-20 mean: 0.3975  (n=2987)

results/unisign/eval_pjm_ms_sub_openasl/train.log.gz

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