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Batch upload: 20250922_102503

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Files changed (49) hide show
  1. .gitattributes +2 -0
  2. 20250922_102503/experiment_log.txt +14 -0
  3. 20250922_102503/filtered_data/filter_info.json +44 -0
  4. 20250922_102503/filtered_data/filtered_threshold_60.parquet +3 -0
  5. 20250922_102503/filtered_data/filtered_threshold_60_auc.csv +857 -0
  6. 20250922_102503/filtered_data/filtered_threshold_70.parquet +3 -0
  7. 20250922_102503/filtered_data/filtered_threshold_70_auc.csv +949 -0
  8. 20250922_102503/filtered_data/filtered_threshold_80.parquet +3 -0
  9. 20250922_102503/filtered_data/filtered_threshold_80_auc.csv +0 -0
  10. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/added_tokens.json +24 -0
  11. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/chat_template.jinja +54 -0
  12. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/config.json +67 -0
  13. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/generation_config.json +6 -0
  14. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/merges.txt +0 -0
  15. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/model-00001-of-00003.safetensors +3 -0
  16. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/model-00002-of-00003.safetensors +3 -0
  17. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/model-00003-of-00003.safetensors +3 -0
  18. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/model.safetensors.index.json +443 -0
  19. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/special_tokens_map.json +31 -0
  20. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/tokenizer.json +3 -0
  21. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/tokenizer_config.json +207 -0
  22. 20250922_102503/sft_results/filtered_threshold_60/global_step_214/vocab.json +0 -0
  23. 20250922_102503/sft_results/filtered_threshold_60/gpu_lock_wrapper.sh +31 -0
  24. 20250922_102503/sft_results/filtered_threshold_60/run_sft_script.sh +40 -0
  25. 20250922_102503/sft_results/filtered_threshold_60/sft_training_data.parquet +3 -0
  26. 20250922_102503/sft_results/filtered_threshold_60/training_log.txt +0 -0
  27. 20250922_102503/sft_results/filtered_threshold_60/training_summary.json +15 -0
  28. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/added_tokens.json +24 -0
  29. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/chat_template.jinja +54 -0
  30. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/config.json +67 -0
  31. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/generation_config.json +6 -0
  32. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/merges.txt +0 -0
  33. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/model-00001-of-00003.safetensors +3 -0
  34. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/model-00002-of-00003.safetensors +3 -0
  35. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/model-00003-of-00003.safetensors +3 -0
  36. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/model.safetensors.index.json +443 -0
  37. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/special_tokens_map.json +31 -0
  38. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/tokenizer.json +3 -0
  39. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/tokenizer_config.json +207 -0
  40. 20250922_102503/sft_results/filtered_threshold_70/global_step_237/vocab.json +0 -0
  41. 20250922_102503/sft_results/filtered_threshold_70/gpu_lock_wrapper.sh +31 -0
  42. 20250922_102503/sft_results/filtered_threshold_70/run_sft_script.sh +40 -0
  43. 20250922_102503/sft_results/filtered_threshold_70/sft_training_data.parquet +3 -0
  44. 20250922_102503/sft_results/filtered_threshold_70/training_log.txt +0 -0
  45. 20250922_102503/sft_results/filtered_threshold_70/training_summary.json +15 -0
  46. 20250922_102503/sft_results/filtered_threshold_80/gpu_lock_wrapper.sh +31 -0
  47. 20250922_102503/sft_results/filtered_threshold_80/run_sft_script.sh +40 -0
  48. 20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet +3 -0
  49. 20250922_102503/sft_results/filtered_threshold_80/training_log.txt +109 -0
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+ Configuration:
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+ Model: Qwen/Qwen2.5-3B
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+ Filter Thresholds: [60.0, 70.0, 80.0]
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+ Filter: filtered_threshold_60 - Completed successfully at Mon Sep 22 10:45:43 UTC 2025
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+ #!/bin/bash
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+ # GPU Lock Wrapper for job: sft_filtered_threshold_60
3
+ # GPUs: [6]
4
+
5
+ # Function to cleanup GPU locks on exit
6
+ cleanup_locks() {
7
+ echo "Cleaning up GPU locks for job: sft_filtered_threshold_60"
8
+ python3 -c "
9
+ import sys
10
+ sys.path.append('/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl')
11
+ from utils.utils import release_gpu_lock
12
+ gpus = [6]
13
+ for gpu_id in gpus:
14
+ release_gpu_lock(str(gpu_id))
15
+ print(f'Released lock for GPU {gpu_id}')
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+ "
17
+ }
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+
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+ # Set up trap to cleanup locks on script exit (success, failure, or interrupt)
20
+ trap cleanup_locks EXIT INT TERM
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+
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+ # Run the actual job
23
+ echo "Starting job: sft_filtered_threshold_60 on GPUs: [6]"
24
+ CUDA_VISIBLE_DEVICES=6 bash checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_60/run_sft_script.sh
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+ JOB_EXIT_CODE=$?
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+
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+ # Explicit cleanup (trap will also run, but this ensures it happens)
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+ cleanup_locks
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+
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+ # Exit with the same code as the job
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+ exit $JOB_EXIT_CODE
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+ #!/bin/bash
2
+ # SFT Job - filtered_threshold_60
3
+ # Generated at: 20250922_102503
4
+
5
+ export TRANSFORMERS_CACHE=/home/greenland-user/verl/checkpoints/huggingface
6
+ export PYTHONHASHSEED=42
7
+
8
+ echo "Starting SFT training for filtered_threshold_60"
9
+ echo "Data file: checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_60.parquet"
10
+ echo "Output: checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_60"
11
+ echo "Model: Qwen/Qwen2.5-3B"
12
+
13
+ # Log experiment start
14
+ echo "Filter: filtered_threshold_60 - Started at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
15
+
16
+ # Run SFT training
17
+ bash scripts_cot/run_simple_sft.sh \
18
+ --model_path "Qwen/Qwen2.5-3B" \
19
+ --train_data_path "checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_60.parquet" \
20
+ --test_data_path "data/big-math_tiny_instruct/test.parquet" \
21
+ --output_dir "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_60" \
22
+ --sft_epochs "1" \
23
+ --sft_batch_size "4" \
24
+ --gpu "$CUDA_VISIBLE_DEVICES" \
25
+ 2>&1 | tee "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_60/training_log.txt"
26
+
27
+ SFT_EXIT_CODE=$?
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+
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+ if [ $SFT_EXIT_CODE -eq 0 ]; then
30
+ echo "SFT completed successfully for filtered_threshold_60"
31
+ echo "Filter: filtered_threshold_60 - Completed successfully at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
32
+ elif [ $SFT_EXIT_CODE -eq 124 ]; then
33
+ echo "SFT timed out for filtered_threshold_60 (2 hour limit)"
34
+ echo "Filter: filtered_threshold_60 - Timed out at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
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+ else
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+ echo "SFT failed for filtered_threshold_60 with exit code $SFT_EXIT_CODE"
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+ echo "Filter: filtered_threshold_60 - Failed with exit code $SFT_EXIT_CODE at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
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+ fi
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+
40
+ echo "Completed SFT training for filtered_threshold_60"
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+ {
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+ "experiment": "simple_sft_training",
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+ "timestamp": "2025-09-22_10:45:43",
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+ "model": "Qwen/Qwen2.5-3B",
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+ "test_accuracy": "N/A",
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+ #!/bin/bash
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+ # GPUs: [7]
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+
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+ for gpu_id in gpus:
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+ }
18
+
19
+ # Set up trap to cleanup locks on script exit (success, failure, or interrupt)
20
+ trap cleanup_locks EXIT INT TERM
21
+
22
+ # Run the actual job
23
+ echo "Starting job: sft_filtered_threshold_70 on GPUs: [7]"
24
+ CUDA_VISIBLE_DEVICES=7 bash checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70/run_sft_script.sh
25
+ JOB_EXIT_CODE=$?
26
+
27
+ # Explicit cleanup (trap will also run, but this ensures it happens)
28
+ cleanup_locks
29
+
30
+ # Exit with the same code as the job
31
+ exit $JOB_EXIT_CODE
20250922_102503/sft_results/filtered_threshold_70/run_sft_script.sh ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # SFT Job - filtered_threshold_70
3
+ # Generated at: 20250922_102503
4
+
5
+ export TRANSFORMERS_CACHE=/home/greenland-user/verl/checkpoints/huggingface
6
+ export PYTHONHASHSEED=42
7
+
8
+ echo "Starting SFT training for filtered_threshold_70"
9
+ echo "Data file: checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_70.parquet"
10
+ echo "Output: checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70"
11
+ echo "Model: Qwen/Qwen2.5-3B"
12
+
13
+ # Log experiment start
14
+ echo "Filter: filtered_threshold_70 - Started at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
15
+
16
+ # Run SFT training
17
+ bash scripts_cot/run_simple_sft.sh \
18
+ --model_path "Qwen/Qwen2.5-3B" \
19
+ --train_data_path "checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_70.parquet" \
20
+ --test_data_path "data/big-math_tiny_instruct/test.parquet" \
21
+ --output_dir "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70" \
22
+ --sft_epochs "1" \
23
+ --sft_batch_size "4" \
24
+ --gpu "$CUDA_VISIBLE_DEVICES" \
25
+ 2>&1 | tee "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70/training_log.txt"
26
+
27
+ SFT_EXIT_CODE=$?
28
+
29
+ if [ $SFT_EXIT_CODE -eq 0 ]; then
30
+ echo "SFT completed successfully for filtered_threshold_70"
31
+ echo "Filter: filtered_threshold_70 - Completed successfully at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
32
+ elif [ $SFT_EXIT_CODE -eq 124 ]; then
33
+ echo "SFT timed out for filtered_threshold_70 (2 hour limit)"
34
+ echo "Filter: filtered_threshold_70 - Timed out at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
35
+ else
36
+ echo "SFT failed for filtered_threshold_70 with exit code $SFT_EXIT_CODE"
37
+ echo "Filter: filtered_threshold_70 - Failed with exit code $SFT_EXIT_CODE at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
38
+ fi
39
+
40
+ echo "Completed SFT training for filtered_threshold_70"
20250922_102503/sft_results/filtered_threshold_70/sft_training_data.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ec27750e908bd9740f260fa9a04f9265a537142336a3b231166f3d30901257b5
3
+ size 819802
20250922_102503/sft_results/filtered_threshold_70/training_log.txt ADDED
The diff for this file is too large to render. See raw diff
 
20250922_102503/sft_results/filtered_threshold_70/training_summary.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment": "simple_sft_training",
3
+ "timestamp": "2025-09-22_10:45:54",
4
+ "model": "Qwen/Qwen2.5-3B",
5
+ "trained_model": "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70/global_step_237",
6
+ "train_dataset": "checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_70.parquet",
7
+ "test_dataset": "data/big-math_tiny_instruct/test.parquet",
8
+ "test_accuracy": "N/A",
9
+ "hyperparameters": {
10
+ "sft_epochs": 1,
11
+ "sft_batch_size": 4,
12
+ "nproc_per_node": 1
13
+ },
14
+ "output_directory": "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_70"
15
+ }
20250922_102503/sft_results/filtered_threshold_80/gpu_lock_wrapper.sh ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # GPU Lock Wrapper for job: sft_filtered_threshold_80
3
+ # GPUs: [0]
4
+
5
+ # Function to cleanup GPU locks on exit
6
+ cleanup_locks() {
7
+ echo "Cleaning up GPU locks for job: sft_filtered_threshold_80"
8
+ python3 -c "
9
+ import sys
10
+ sys.path.append('/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl')
11
+ from utils.utils import release_gpu_lock
12
+ gpus = [0]
13
+ for gpu_id in gpus:
14
+ release_gpu_lock(str(gpu_id))
15
+ print(f'Released lock for GPU {gpu_id}')
16
+ "
17
+ }
18
+
19
+ # Set up trap to cleanup locks on script exit (success, failure, or interrupt)
20
+ trap cleanup_locks EXIT INT TERM
21
+
22
+ # Run the actual job
23
+ echo "Starting job: sft_filtered_threshold_80 on GPUs: [0]"
24
+ CUDA_VISIBLE_DEVICES=0 bash checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/run_sft_script.sh
25
+ JOB_EXIT_CODE=$?
26
+
27
+ # Explicit cleanup (trap will also run, but this ensures it happens)
28
+ cleanup_locks
29
+
30
+ # Exit with the same code as the job
31
+ exit $JOB_EXIT_CODE
20250922_102503/sft_results/filtered_threshold_80/run_sft_script.sh ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # SFT Job - filtered_threshold_80
3
+ # Generated at: 20250922_102503
4
+
5
+ export TRANSFORMERS_CACHE=/home/greenland-user/verl/checkpoints/huggingface
6
+ export PYTHONHASHSEED=42
7
+
8
+ echo "Starting SFT training for filtered_threshold_80"
9
+ echo "Data file: checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_80.parquet"
10
+ echo "Output: checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80"
11
+ echo "Model: Qwen/Qwen2.5-3B"
12
+
13
+ # Log experiment start
14
+ echo "Filter: filtered_threshold_80 - Started at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
15
+
16
+ # Run SFT training
17
+ bash scripts_cot/run_simple_sft.sh \
18
+ --model_path "Qwen/Qwen2.5-3B" \
19
+ --train_data_path "checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_80.parquet" \
20
+ --test_data_path "data/big-math_tiny_instruct/test.parquet" \
21
+ --output_dir "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80" \
22
+ --sft_epochs "1" \
23
+ --sft_batch_size "4" \
24
+ --gpu "$CUDA_VISIBLE_DEVICES" \
25
+ 2>&1 | tee "checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/training_log.txt"
26
+
27
+ SFT_EXIT_CODE=$?
28
+
29
+ if [ $SFT_EXIT_CODE -eq 0 ]; then
30
+ echo "SFT completed successfully for filtered_threshold_80"
31
+ echo "Filter: filtered_threshold_80 - Completed successfully at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
32
+ elif [ $SFT_EXIT_CODE -eq 124 ]; then
33
+ echo "SFT timed out for filtered_threshold_80 (2 hour limit)"
34
+ echo "Filter: filtered_threshold_80 - Timed out at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
35
+ else
36
+ echo "SFT failed for filtered_threshold_80 with exit code $SFT_EXIT_CODE"
37
+ echo "Filter: filtered_threshold_80 - Failed with exit code $SFT_EXIT_CODE at $(date)" >> "checkpoints/auc_filtered_sft/20250922_102503/experiment_log.txt"
38
+ fi
39
+
40
+ echo "Completed SFT training for filtered_threshold_80"
20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8a0d0c451628418bf6e38f02f8af0a1833de686d7e0b2c76c231bbb6485895f1
3
+ size 924071
20250922_102503/sft_results/filtered_threshold_80/training_log.txt ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ==================================================
2
+ Starting Simple SFT Training
3
+ ==================================================
4
+ Model: Qwen/Qwen2.5-3B
5
+ Train dataset: checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_80.parquet
6
+ Test dataset: data/big-math_tiny_instruct/test.parquet
7
+ Output directory: checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80
8
+ SFT epochs: 1
9
+ SFT batch size: 4
10
+ GPU: 0
11
+ ==================================================
12
+ Resolved train dataset path: /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_80.parquet
13
+ Resolved test dataset path: /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/data/big-math_tiny_instruct/test.parquet
14
+ Using 1 processes per node (GPUs: 0)
15
+
16
+ ==================================================
17
+ Step 1: Converting training data for SFT
18
+ ==================================================
19
+ Loaded 1068 training examples from /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/filtered_data/filtered_threshold_80.parquet
20
+ Saved 1068 SFT training examples to /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet
21
+ SFT training data saved to: /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet
22
+
23
+ ==================================================
24
+ Step 2: Running SFT training
25
+ ==================================================
26
+ Executing: /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/bin/torchrun --standalone --nnodes=1 --nproc_per_node=1 -m verl.trainer.fsdp_sft_trainer data.train_files='/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet' data.val_files='/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet' data.prompt_key=extra_info data.response_key=extra_info +data.prompt_dict_keys=['question'] +data.response_dict_keys=['answer'] data.micro_batch_size_per_gpu=4 data.train_batch_size=4 data.max_length=2048 data.truncation=right +data.apply_chat_template=false model.partial_pretrain='Qwen/Qwen2.5-3B' model.enable_gradient_checkpointing=True model.fsdp_config.cpu_offload=False model.fsdp_config.offload_params=False trainer.default_local_dir='/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80' trainer.project_name=simple-sft trainer.experiment_name=qwen2.5-3b-simple-sft trainer.total_epochs=1 trainer.logger=['console','wandb'] trainer.default_hdfs_dir=null
27
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/transformers/utils/hub.py:111: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
28
+ warnings.warn(
29
+ INFO 09-22 10:25:26 [__init__.py:239] Automatically detected platform cuda.
30
+ Normalize batch size by dp 1
31
+ Using sequence parallel size: 1
32
+ Using remove padding: False
33
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/utils/dataset/sft_dataset.py:83: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
34
+ ls = ls[0]
35
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/utils/dataset/sft_dataset.py:83: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
36
+ ls = ls[0]
37
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/utils/dataset/sft_dataset.py:83: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
38
+ ls = ls[0]
39
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/utils/dataset/sft_dataset.py:83: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
40
+ ls = ls[0]
41
+ Using FSDP rank 0 and size 1 for data distribution
42
+ `torch_dtype` is deprecated! Use `dtype` instead!
43
+ Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
44
+ Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen2Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
45
+
46
+ functools.partial(<function _or_policy at 0x7f14cb035240>, policies=[functools.partial(<function transformer_auto_wrap_policy at 0x7f14cb035120>, transformer_layer_cls={<class 'transformers.models.qwen2.modeling_qwen2.Qwen2DecoderLayer'>})])
47
+ /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py:444: UserWarning: FSDP is switching to use `NO_SHARD` instead of ShardingStrategy.FULL_SHARD since the world size is 1.
48
+ warnings.warn(
49
+ NCCL version 2.21.5+cuda12.4
50
+ Number of steps/epoch 267, number of epochs 1, total number of steps 267
51
+ {'data': {'train_batch_size': 4, 'micro_batch_size': None, 'micro_batch_size_per_gpu': 4, 'train_files': '/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet', 'val_files': '/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet', 'prompt_key': 'extra_info', 'response_key': 'extra_info', 'max_length': 2048, 'truncation': 'right', 'balance_dp_token': False, 'num_workers': 8, 'chat_template': None, 'custom_cls': {'path': None}, 'prompt_dict_keys': ['question'], 'response_dict_keys': ['answer'], 'apply_chat_template': False}, 'model': {'partial_pretrain': 'Qwen/Qwen2.5-3B', 'fsdp_config': {'wrap_policy': {'min_num_params': 0}, 'cpu_offload': False, 'offload_params': False}, 'external_lib': None, 'enable_gradient_checkpointing': True, 'trust_remote_code': False, 'lora_rank': 0, 'lora_alpha': 16, 'target_modules': 'all-linear', 'use_liger': False}, 'optim': {'lr': 1e-05, 'betas': [0.9, 0.95], 'weight_decay': 0.01, 'warmup_steps_ratio': 0.1, 'clip_grad': 1.0}, 'ulysses_sequence_parallel_size': 1, 'use_remove_padding': False, 'trainer': {'default_local_dir': '/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80', 'default_hdfs_dir': None, 'resume_path': None, 'project_name': 'simple-sft', 'experiment_name': 'qwen2.5-3b-simple-sft', 'total_epochs': 1, 'total_training_steps': None, 'logger': ['console', 'wandb'], 'seed': 1}}
52
+ wandb: WARNING `start_method` is deprecated and will be removed in a future version of wandb. This setting is currently non-functional and safely ignored.
53
+ WARN: W&B online init failed (api_key not configured (no-tty). call wandb.login(key=[your_api_key])). Falling back to offline mode.
54
+ WARN: W&B offline init also failed (api_key not configured (no-tty). call wandb.login(key=[your_api_key])). Using console logger only.
55
+ Using LocalLogger is deprecated. The constructor API will change
56
+ Using LocalLogger is deprecated. The constructor API will change
57
+ Total training steps: 267
58
+
59
+ Error executing job with overrides: ['data.train_files=/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet', 'data.val_files=/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80/sft_training_data.parquet', 'data.prompt_key=extra_info', 'data.response_key=extra_info', '+data.prompt_dict_keys=[question]', '+data.response_dict_keys=[answer]', 'data.micro_batch_size_per_gpu=4', 'data.train_batch_size=4', 'data.max_length=2048', 'data.truncation=right', '+data.apply_chat_template=false', 'model.partial_pretrain=Qwen/Qwen2.5-3B', 'model.enable_gradient_checkpointing=True', 'model.fsdp_config.cpu_offload=False', 'model.fsdp_config.offload_params=False', 'trainer.default_local_dir=/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/checkpoints/auc_filtered_sft/20250922_102503/sft_results/filtered_threshold_80', 'trainer.project_name=simple-sft', 'trainer.experiment_name=qwen2.5-3b-simple-sft', 'trainer.total_epochs=1', 'trainer.logger=[console,wandb]', 'trainer.default_hdfs_dir=null']
60
+ Traceback (most recent call last):
61
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/trainer/fsdp_sft_trainer.py", line 529, in main
62
+ trainer.fit()
63
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/trainer/fsdp_sft_trainer.py", line 472, in fit
64
+ metric = self.training_step(data)
65
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/verl/verl/trainer/fsdp_sft_trainer.py", line 403, in training_step
66
+ self.optimizer.step()
67
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 140, in wrapper
68
+ return func.__get__(opt, opt.__class__)(*args, **kwargs)
69
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/optim/optimizer.py", line 493, in wrapper
70
+ out = func(*args, **kwargs)
71
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
72
+ ret = func(self, *args, **kwargs)
73
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/optim/adamw.py", line 232, in step
74
+ has_complex = self._init_group(
75
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/optim/adamw.py", line 175, in _init_group
76
+ state["exp_avg_sq"] = torch.zeros_like(
77
+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 296.00 MiB. GPU 0 has a total capacity of 79.11 GiB of which 41.31 MiB is free. Process 146419 has 0 bytes memory in use. Including non-PyTorch memory, this process has 0 bytes memory in use. Of the allocated memory 34.85 GiB is allocated by PyTorch, and 157.05 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
78
+
79
+ Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
80
+ E0922 10:26:41.144000 149614 site-packages/torch/distributed/elastic/multiprocessing/api.py:869] failed (exitcode: 1) local_rank: 0 (pid: 149848) of binary: /fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/bin/python3.10
81
+ Traceback (most recent call last):
82
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/bin/torchrun", line 9, in <module>
83
+ sys.exit(main())
84
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
85
+ return f(*args, **kwargs)
86
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/run.py", line 918, in main
87
+ run(args)
88
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/run.py", line 909, in run
89
+ elastic_launch(
90
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 138, in __call__
91
+ return launch_agent(self._config, self._entrypoint, list(args))
92
+ File "/fsx-sfai/dedicated-fsx-data-repo-pretraining-gl-ap-south-1/shared_experiments/wxpeng/miniconda3/envs/verl_persistent/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 269, in launch_agent
93
+ raise ChildFailedError(
94
+ torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
95
+ ============================================================
96
+ verl.trainer.fsdp_sft_trainer FAILED
97
+ ------------------------------------------------------------
98
+ Failures:
99
+ <NO_OTHER_FAILURES>
100
+ ------------------------------------------------------------
101
+ Root Cause (first observed failure):
102
+ [0]:
103
+ time : 2025-09-22_10:26:41
104
+ host : ip-10-1-111-168.ap-south-1.compute.internal
105
+ rank : 0 (local_rank: 0)
106
+ exitcode : 1 (pid: 149848)
107
+ error_file: <N/A>
108
+ traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
109
+ ============================================================