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Browse files- LTA_openwebtext_dualt/logs/infer_owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8.log +70 -0
- LTA_openwebtext_dualt/logs/owt_dirichlet_len1024_Cv_to_2v_gumbel_sde_watch/infer_lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525_step_0002000.log +136 -0
- LTA_openwebtext_dualt/logs/owt_dirichlet_len1024_Cv_to_2v_gumbel_sde_watch/infer_lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525_step_0005000.log +136 -0
- LTA_openwebtext_dualt/logs/owt_genppl128_0501.log +0 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/pcg64-testset-1.csv +1001 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv +1001 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/philox-testset-2.csv +1001 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/sam/processing_sam.py +292 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_2x8gpu_50ep_lr1e3_ema0p9999_elfopt_not5_bottleneck128_unfixed_norm_stateprobadd_selfcond_ce_fast_20260609_155046/step_099000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_2x8gpu_50ep_lr1e3_ema0p9999_elfopt_not5_bottleneck128_unfixed_norm_stateprobadd_selfcond_ce_fast_20260609_155046/step_341000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_027000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_030000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_073000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_098000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_101000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_130000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_149000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_181000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_211000.pt +3 -0
- LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_242000.pt +3 -0
LTA_openwebtext_dualt/logs/infer_owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8.log
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[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
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[decode-base] n=8 max_len=1024 steps=1024 model_t=post
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[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 3/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 5/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 6/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 7/8
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[decode] temp=1.56 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/8
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[summary] {"type": "summary", "checkpoint": "eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt", "step": 101000, "decode": {"steps": 1024, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0009765625, 0.001953125, 0.0029296875, 0.00390625, 0.0048828125, 0.005859375, 0.0068359375, 0.0078125, 0.0087890625, 0.009765625, 0.0107421875, 0.01171875, 0.0126953125, 0.013671875, 0.0146484375, 0.015625, 0.0166015625, 0.017578125, 0.0185546875, 0.01953125, 0.0205078125, 0.021484375, 0.0224609375, 0.0234375, 0.0244140625, 0.025390625, 0.0263671875, 0.02734375, 0.0283203125, 0.029296875, 0.0302734375, 0.03125, 0.0322265625, 0.033203125, 0.0341796875, 0.03515625, 0.0361328125, 0.037109375, 0.0380859375, 0.0390625, 0.0400390625, 0.041015625, 0.0419921875, 0.04296875, 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"decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "dirichlet", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 128.0, "target_prob": 1.0, "endpoint_temp": 1.56, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 8, "seed": 20260519}, "raw_genppl": {"ppl": 17.691889482192725, "nll_per_token": 2.8731063132192576, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 17.892892672036336, "nll_per_token": 2.884403576570399, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 2.843557034565923, "unique_tokens": 1647, "token_count": 8192, "distinct_1": 0.2010498046875, "distinct_2": 0.2574535679374389, "top_token_mass": 0.125}}
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| 13 |
+
[done] docs/lta_samples/metrics_20260519/owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8/t1p56.jsonl
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| 14 |
+
[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
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| 15 |
+
[decode-base] n=8 max_len=1024 steps=1024 model_t=post
|
| 16 |
+
[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
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| 17 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
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| 18 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
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| 19 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 3/8
|
| 20 |
+
[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/8
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| 21 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 5/8
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| 22 |
+
[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 6/8
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| 23 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 7/8
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| 24 |
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[decode] temp=1.58 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/8
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| 25 |
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[summary] {"type": "summary", "checkpoint": "eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt", "step": 101000, "decode": {"steps": 1024, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0009765625, 0.001953125, 0.0029296875, 0.00390625, 0.0048828125, 0.005859375, 0.0068359375, 0.0078125, 0.0087890625, 0.009765625, 0.0107421875, 0.01171875, 0.0126953125, 0.013671875, 0.0146484375, 0.015625, 0.0166015625, 0.017578125, 0.0185546875, 0.01953125, 0.0205078125, 0.021484375, 0.0224609375, 0.0234375, 0.0244140625, 0.025390625, 0.0263671875, 0.02734375, 0.0283203125, 0.029296875, 0.0302734375, 0.03125, 0.0322265625, 0.033203125, 0.0341796875, 0.03515625, 0.0361328125, 0.037109375, 0.0380859375, 0.0390625, 0.0400390625, 0.041015625, 0.0419921875, 0.04296875, 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"decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "dirichlet", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 128.0, "target_prob": 1.0, "endpoint_temp": 1.58, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 8, "seed": 20260519}, "raw_genppl": {"ppl": 7.647573846985461, "nll_per_token": 2.034388453352685, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 7.646043380687294, "nll_per_token": 2.034188308902815, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 2.68936163814995, "unique_tokens": 971, "token_count": 8192, "distinct_1": 0.1185302734375, "distinct_2": 0.1573802541544477, "top_token_mass": 0.1055908203125}}
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[done] docs/lta_samples/metrics_20260519/owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8/t1p58.jsonl
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[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
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[decode-base] n=8 max_len=1024 steps=1024 model_t=post
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[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
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| 32 |
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 3/8
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| 33 |
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/8
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| 34 |
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 5/8
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| 35 |
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 6/8
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| 36 |
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 7/8
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[decode] temp=1.62 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/8
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| 38 |
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[done] docs/lta_samples/metrics_20260519/owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8/t1p62.jsonl
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[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
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[decode-base] n=8 max_len=1024 steps=1024 model_t=post
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[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
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| 44 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
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| 45 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 3/8
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| 46 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/8
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| 47 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 5/8
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| 48 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 6/8
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| 49 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 7/8
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| 50 |
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[decode] temp=1.64 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/8
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| 51 |
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[summary] {"type": "summary", "checkpoint": "eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt", "step": 101000, "decode": {"steps": 1024, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0009765625, 0.001953125, 0.0029296875, 0.00390625, 0.0048828125, 0.005859375, 0.0068359375, 0.0078125, 0.0087890625, 0.009765625, 0.0107421875, 0.01171875, 0.0126953125, 0.013671875, 0.0146484375, 0.015625, 0.0166015625, 0.017578125, 0.0185546875, 0.01953125, 0.0205078125, 0.021484375, 0.0224609375, 0.0234375, 0.0244140625, 0.025390625, 0.0263671875, 0.02734375, 0.0283203125, 0.029296875, 0.0302734375, 0.03125, 0.0322265625, 0.033203125, 0.0341796875, 0.03515625, 0.0361328125, 0.037109375, 0.0380859375, 0.0390625, 0.0400390625, 0.041015625, 0.0419921875, 0.04296875, 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"decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "dirichlet", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 128.0, "target_prob": 1.0, "endpoint_temp": 1.64, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 8, "seed": 20260519}, "raw_genppl": {"ppl": 32.72432129267036, "nll_per_token": 3.4881185718611176, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 32.6352363483664, "nll_per_token": 3.485392574235505, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 3.3980019972335276, "unique_tokens": 1852, "token_count": 8192, "distinct_1": 0.22607421875, "distinct_2": 0.30962854349951124, "top_token_mass": 0.0850830078125}}
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| 52 |
+
[done] docs/lta_samples/metrics_20260519/owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8/t1p64.jsonl
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| 53 |
+
[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
|
| 54 |
+
[decode-base] n=8 max_len=1024 steps=1024 model_t=post
|
| 55 |
+
[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
|
| 56 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
|
| 57 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
|
| 58 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 3/8
|
| 59 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 4/8
|
| 60 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 5/8
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| 61 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 6/8
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| 62 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 7/8
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| 63 |
+
[decode] temp=1.66 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 8/8
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| 64 |
+
[summary] {"type": "summary", "checkpoint": "eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt", "step": 101000, "decode": {"steps": 1024, "model_t_mode": "post", "decode_time_schedule": "linear", "decode_s_min_frac": 0.0, "decode_s_max_frac": 0.25, "decode_force_final_t": true, "decode_time_grid": [0.0, 0.0009765625, 0.001953125, 0.0029296875, 0.00390625, 0.0048828125, 0.005859375, 0.0068359375, 0.0078125, 0.0087890625, 0.009765625, 0.0107421875, 0.01171875, 0.0126953125, 0.013671875, 0.0146484375, 0.015625, 0.0166015625, 0.017578125, 0.0185546875, 0.01953125, 0.0205078125, 0.021484375, 0.0224609375, 0.0234375, 0.0244140625, 0.025390625, 0.0263671875, 0.02734375, 0.0283203125, 0.029296875, 0.0302734375, 0.03125, 0.0322265625, 0.033203125, 0.0341796875, 0.03515625, 0.0361328125, 0.037109375, 0.0380859375, 0.0390625, 0.0400390625, 0.041015625, 0.0419921875, 0.04296875, 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0.79296875, 0.7939453125, 0.794921875, 0.7958984375, 0.796875, 0.7978515625, 0.798828125, 0.7998046875, 0.80078125, 0.8017578125, 0.802734375, 0.8037109375, 0.8046875, 0.8056640625, 0.806640625, 0.8076171875, 0.80859375, 0.8095703125, 0.810546875, 0.8115234375, 0.8125, 0.8134765625, 0.814453125, 0.8154296875, 0.81640625, 0.8173828125, 0.818359375, 0.8193359375, 0.8203125, 0.8212890625, 0.822265625, 0.8232421875, 0.82421875, 0.8251953125, 0.826171875, 0.8271484375, 0.828125, 0.8291015625, 0.830078125, 0.8310546875, 0.83203125, 0.8330078125, 0.833984375, 0.8349609375, 0.8359375, 0.8369140625, 0.837890625, 0.8388671875, 0.83984375, 0.8408203125, 0.841796875, 0.8427734375, 0.84375, 0.8447265625, 0.845703125, 0.8466796875, 0.84765625, 0.8486328125, 0.849609375, 0.8505859375, 0.8515625, 0.8525390625, 0.853515625, 0.8544921875, 0.85546875, 0.8564453125, 0.857421875, 0.8583984375, 0.859375, 0.8603515625, 0.861328125, 0.8623046875, 0.86328125, 0.8642578125, 0.865234375, 0.8662109375, 0.8671875, 0.8681640625, 0.869140625, 0.8701171875, 0.87109375, 0.8720703125, 0.873046875, 0.8740234375, 0.875, 0.8759765625, 0.876953125, 0.8779296875, 0.87890625, 0.8798828125, 0.880859375, 0.8818359375, 0.8828125, 0.8837890625, 0.884765625, 0.8857421875, 0.88671875, 0.8876953125, 0.888671875, 0.8896484375, 0.890625, 0.8916015625, 0.892578125, 0.8935546875, 0.89453125, 0.8955078125, 0.896484375, 0.8974609375, 0.8984375, 0.8994140625, 0.900390625, 0.9013671875, 0.90234375, 0.9033203125, 0.904296875, 0.9052734375, 0.90625, 0.9072265625, 0.908203125, 0.9091796875, 0.91015625, 0.9111328125, 0.912109375, 0.9130859375, 0.9140625, 0.9150390625, 0.916015625, 0.9169921875, 0.91796875, 0.9189453125, 0.919921875, 0.9208984375, 0.921875, 0.9228515625, 0.923828125, 0.9248046875, 0.92578125, 0.9267578125, 0.927734375, 0.9287109375, 0.9296875, 0.9306640625, 0.931640625, 0.9326171875, 0.93359375, 0.9345703125, 0.935546875, 0.9365234375, 0.9375, 0.9384765625, 0.939453125, 0.9404296875, 0.94140625, 0.9423828125, 0.943359375, 0.9443359375, 0.9453125, 0.9462890625, 0.947265625, 0.9482421875, 0.94921875, 0.9501953125, 0.951171875, 0.9521484375, 0.953125, 0.9541015625, 0.955078125, 0.9560546875, 0.95703125, 0.9580078125, 0.958984375, 0.9599609375, 0.9609375, 0.9619140625, 0.962890625, 0.9638671875, 0.96484375, 0.9658203125, 0.966796875, 0.9677734375, 0.96875, 0.9697265625, 0.970703125, 0.9716796875, 0.97265625, 0.9736328125, 0.974609375, 0.9755859375, 0.9765625, 0.9775390625, 0.978515625, 0.9794921875, 0.98046875, 0.9814453125, 0.982421875, 0.9833984375, 0.984375, 0.9853515625, 0.986328125, 0.9873046875, 0.98828125, 0.9892578125, 0.990234375, 0.9912109375, 0.9921875, 0.9931640625, 0.994140625, 0.9951171875, 0.99609375, 0.9970703125, 0.998046875, 0.9990234375, 1.0], "decode_rule": "dirichlet_resample", "support_power": 1.0, "semantic_power": 1.0, "anchor_mode": "onehot", "cfg_scale": 0.0, "cfg_power": 1.0, "cfg_start": 0.0, "cfg_prior": "uniform", "decode_freq_penalty_alpha": 0.0, "decode_freq_penalty_beta": 0.0, "decode_freq_penalty_floor": 0.0, "decode_freq_penalty_start": 0.0, "decode_freq_penalty_end": 1.0, "decode_freq_penalty_power": 1.0, "start_t": 0.0, "start_init": "noise", "noise_init": "dirichlet", "noise_sigma": -1.0, "dirichlet_concentration": 1.0, "concentration_min": 1.0, "concentration_max": 128.0, "target_prob": 1.0, "endpoint_temp": 1.66, "final_from": "state", "final_sample_mode": "argmax", "final_sample_temp": 1.0, "final_top_k": 64, "final_top_p": 0.95, "final_freq_penalty_alpha": 0.0, "final_freq_penalty_beta": 0.0, "final_freq_penalty_floor": 0.0, "lock_bos": false, "n_samples": 8, "seed": 20260519}, "raw_genppl": {"ppl": 20.407927037225726, "nll_per_token": 3.015923405628578, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "stripped_genppl": {"ppl": 20.340524572570345, "nll_per_token": 3.0126151804830514, "tokens": 2040, "kept_samples": 8, "total_samples": 8, "empty_rate": 0.0, "skipped_samples": 0}, "diversity": {"sample_entropy": 2.6891041072755777, "unique_tokens": 1271, "token_count": 8192, "distinct_1": 0.1551513671875, "distinct_2": 0.2134652981427175, "top_token_mass": 0.148681640625}}
|
| 65 |
+
[done] docs/lta_samples/metrics_20260519/owt_t5_2node_latest_trainmatched_dirres_c128_finetemp_n8/t1p66.jsonl
|
| 66 |
+
[ckpt] eval_ckpts/lta_owt_t5_adaln_adamw_wd0p1_rollin_p50_randk0_3_uniformt_temp1_synct_gbs512_2node8gpu_1m_t-20260518224737-tftgw/latest_frozen_step101200_20260519_092441.pt step=101000
|
| 67 |
+
[decode-base] n=8 max_len=1024 steps=1024 model_t=post
|
| 68 |
+
[decode-time] schedule=linear s=[0.0,0.25] gumbel=(2.2,0.8) force_final=True t0=0.000000 t_mid=0.500000 t_end=1.000000 dt_mean=0.000977 dt_max=0.000977
|
| 69 |
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[decode] temp=1.68 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 1/8
|
| 70 |
+
[decode] temp=1.68 final=state rule=dirichlet_resample support=1 semantic=1 anchor=onehot cfg=0/1@0:uniform decode_freq_penalty=0/0/0-1^1 final_sample=argmax/1/k64/p0.95 freq_penalty=0/0/0 start_t=0 start_init=noise time_path=0.0000->1.0000 generated 2/8
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LTA_openwebtext_dualt/logs/owt_dirichlet_len1024_Cv_to_2v_gumbel_sde_watch/infer_lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525_step_0002000.log
ADDED
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[watch-gumbel] 2026-05-25_15:23:54 infer runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0002000.pt -> docs/lta_samples/metrics_20260525/owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_every1k_sde_gumbel_topp0.95_tau1.0_to_0.2_blend_c32100_64200_n128/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0002000
|
| 2 |
+
[load] runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0002000.pt
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| 3 |
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[ckpt] step=2000
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[sde] generated 2/128
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[sde] generated 128/128
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[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
|
| 69 |
+
[summary] {
|
| 70 |
+
"type": "summary",
|
| 71 |
+
"checkpoint": "runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0002000.pt",
|
| 72 |
+
"step": 2000,
|
| 73 |
+
"decode": {
|
| 74 |
+
"decode_rule": "dirichlet_resample_sde",
|
| 75 |
+
"steps": 128,
|
| 76 |
+
"model_t_mode": "support_t",
|
| 77 |
+
"mean_mode": "endpoint_only",
|
| 78 |
+
"anchor_gamma": 1.0,
|
| 79 |
+
"endpoint_floor": 0.0,
|
| 80 |
+
"concentration_min": 32100.0,
|
| 81 |
+
"concentration_max": 64200.0,
|
| 82 |
+
"endpoint_temp": 1.45,
|
| 83 |
+
"endpoint_temp_start": null,
|
| 84 |
+
"endpoint_temp_end": null,
|
| 85 |
+
"endpoint_projection": "gumbel_softmax",
|
| 86 |
+
"endpoint_top_k": 0,
|
| 87 |
+
"endpoint_top_p": 0.95,
|
| 88 |
+
"gumbel_tau_start": 1.0,
|
| 89 |
+
"gumbel_tau_end": 0.2,
|
| 90 |
+
"gumbel_noise_scale_start": 1.0,
|
| 91 |
+
"gumbel_noise_scale_end": 1.0,
|
| 92 |
+
"ban_special_tokens": false,
|
| 93 |
+
"banned_endpoint_ids": [],
|
| 94 |
+
"support_power": 1.0,
|
| 95 |
+
"semantic_power": 1.0,
|
| 96 |
+
"noise_init": "dirichlet",
|
| 97 |
+
"noise_sigma": -1.0,
|
| 98 |
+
"noise_dirichlet_concentration": 32100.0,
|
| 99 |
+
"sde_resample": "dirichlet",
|
| 100 |
+
"logistic_normal_sigma_min": 0.18,
|
| 101 |
+
"logistic_normal_sigma_max": 3.0,
|
| 102 |
+
"logistic_normal_tau_min": 0.65,
|
| 103 |
+
"logistic_normal_tau_max": 1.0,
|
| 104 |
+
"final_from": "blend_0.5",
|
| 105 |
+
"n_samples": 128,
|
| 106 |
+
"seed": 20260524
|
| 107 |
+
},
|
| 108 |
+
"raw_genppl": {
|
| 109 |
+
"ppl": 100.27064995881773,
|
| 110 |
+
"nll_per_token": 4.607873029601369,
|
| 111 |
+
"tokens": 100519,
|
| 112 |
+
"kept_samples": 128,
|
| 113 |
+
"total_samples": 128,
|
| 114 |
+
"empty_rate": 0.0,
|
| 115 |
+
"skipped_samples": 0
|
| 116 |
+
},
|
| 117 |
+
"stripped_genppl": {
|
| 118 |
+
"ppl": 112.93080964533284,
|
| 119 |
+
"nll_per_token": 4.726775327154073,
|
| 120 |
+
"tokens": 90108,
|
| 121 |
+
"kept_samples": 128,
|
| 122 |
+
"total_samples": 128,
|
| 123 |
+
"empty_rate": 0.0,
|
| 124 |
+
"skipped_samples": 0
|
| 125 |
+
},
|
| 126 |
+
"diversity": {
|
| 127 |
+
"sample_entropy": 2.5862611728643055,
|
| 128 |
+
"unique_tokens": 5299,
|
| 129 |
+
"token_count": 131072,
|
| 130 |
+
"distinct_1": 0.04042816162109375,
|
| 131 |
+
"distinct_2": 0.14362628299120234,
|
| 132 |
+
"top_token_mass": 0.30364227294921875
|
| 133 |
+
}
|
| 134 |
+
}
|
| 135 |
+
[done] docs/lta_samples/metrics_20260525/owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_every1k_sde_gumbel_topp0.95_tau1.0_to_0.2_blend_c32100_64200_n128/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0002000/sde_steps128_samples128_scored.jsonl
|
| 136 |
+
[watch-gumbel] 2026-05-25_15:30:21 done step_0002000
|
LTA_openwebtext_dualt/logs/owt_dirichlet_len1024_Cv_to_2v_gumbel_sde_watch/infer_lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525_step_0005000.log
ADDED
|
@@ -0,0 +1,136 @@
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| 1 |
+
[watch-gumbel] 2026-05-25_17:25:10 infer runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0005000.pt -> docs/lta_samples/metrics_20260525/owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_every1k_sde_gumbel_topp0.95_tau1.0_to_0.2_blend_c32100_64200_n128/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0005000
|
| 2 |
+
[load] runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0005000.pt
|
| 3 |
+
[ckpt] step=5000
|
| 4 |
+
[sde] generated 2/128
|
| 5 |
+
[sde] generated 4/128
|
| 6 |
+
[sde] generated 6/128
|
| 7 |
+
[sde] generated 8/128
|
| 8 |
+
[sde] generated 10/128
|
| 9 |
+
[sde] generated 12/128
|
| 10 |
+
[sde] generated 14/128
|
| 11 |
+
[sde] generated 16/128
|
| 12 |
+
[sde] generated 18/128
|
| 13 |
+
[sde] generated 20/128
|
| 14 |
+
[sde] generated 22/128
|
| 15 |
+
[sde] generated 24/128
|
| 16 |
+
[sde] generated 26/128
|
| 17 |
+
[sde] generated 28/128
|
| 18 |
+
[sde] generated 30/128
|
| 19 |
+
[sde] generated 32/128
|
| 20 |
+
[sde] generated 34/128
|
| 21 |
+
[sde] generated 36/128
|
| 22 |
+
[sde] generated 38/128
|
| 23 |
+
[sde] generated 40/128
|
| 24 |
+
[sde] generated 42/128
|
| 25 |
+
[sde] generated 44/128
|
| 26 |
+
[sde] generated 46/128
|
| 27 |
+
[sde] generated 48/128
|
| 28 |
+
[sde] generated 50/128
|
| 29 |
+
[sde] generated 52/128
|
| 30 |
+
[sde] generated 54/128
|
| 31 |
+
[sde] generated 56/128
|
| 32 |
+
[sde] generated 58/128
|
| 33 |
+
[sde] generated 60/128
|
| 34 |
+
[sde] generated 62/128
|
| 35 |
+
[sde] generated 64/128
|
| 36 |
+
[sde] generated 66/128
|
| 37 |
+
[sde] generated 68/128
|
| 38 |
+
[sde] generated 70/128
|
| 39 |
+
[sde] generated 72/128
|
| 40 |
+
[sde] generated 74/128
|
| 41 |
+
[sde] generated 76/128
|
| 42 |
+
[sde] generated 78/128
|
| 43 |
+
[sde] generated 80/128
|
| 44 |
+
[sde] generated 82/128
|
| 45 |
+
[sde] generated 84/128
|
| 46 |
+
[sde] generated 86/128
|
| 47 |
+
[sde] generated 88/128
|
| 48 |
+
[sde] generated 90/128
|
| 49 |
+
[sde] generated 92/128
|
| 50 |
+
[sde] generated 94/128
|
| 51 |
+
[sde] generated 96/128
|
| 52 |
+
[sde] generated 98/128
|
| 53 |
+
[sde] generated 100/128
|
| 54 |
+
[sde] generated 102/128
|
| 55 |
+
[sde] generated 104/128
|
| 56 |
+
[sde] generated 106/128
|
| 57 |
+
[sde] generated 108/128
|
| 58 |
+
[sde] generated 110/128
|
| 59 |
+
[sde] generated 112/128
|
| 60 |
+
[sde] generated 114/128
|
| 61 |
+
[sde] generated 116/128
|
| 62 |
+
[sde] generated 118/128
|
| 63 |
+
[sde] generated 120/128
|
| 64 |
+
[sde] generated 122/128
|
| 65 |
+
[sde] generated 124/128
|
| 66 |
+
[sde] generated 126/128
|
| 67 |
+
[sde] generated 128/128
|
| 68 |
+
[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
|
| 69 |
+
[summary] {
|
| 70 |
+
"type": "summary",
|
| 71 |
+
"checkpoint": "runs/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0005000.pt",
|
| 72 |
+
"step": 5000,
|
| 73 |
+
"decode": {
|
| 74 |
+
"decode_rule": "dirichlet_resample_sde",
|
| 75 |
+
"steps": 128,
|
| 76 |
+
"model_t_mode": "support_t",
|
| 77 |
+
"mean_mode": "endpoint_only",
|
| 78 |
+
"anchor_gamma": 1.0,
|
| 79 |
+
"endpoint_floor": 0.0,
|
| 80 |
+
"concentration_min": 32100.0,
|
| 81 |
+
"concentration_max": 64200.0,
|
| 82 |
+
"endpoint_temp": 1.45,
|
| 83 |
+
"endpoint_temp_start": null,
|
| 84 |
+
"endpoint_temp_end": null,
|
| 85 |
+
"endpoint_projection": "gumbel_softmax",
|
| 86 |
+
"endpoint_top_k": 0,
|
| 87 |
+
"endpoint_top_p": 0.95,
|
| 88 |
+
"gumbel_tau_start": 1.0,
|
| 89 |
+
"gumbel_tau_end": 0.2,
|
| 90 |
+
"gumbel_noise_scale_start": 1.0,
|
| 91 |
+
"gumbel_noise_scale_end": 1.0,
|
| 92 |
+
"ban_special_tokens": false,
|
| 93 |
+
"banned_endpoint_ids": [],
|
| 94 |
+
"support_power": 1.0,
|
| 95 |
+
"semantic_power": 1.0,
|
| 96 |
+
"noise_init": "dirichlet",
|
| 97 |
+
"noise_sigma": -1.0,
|
| 98 |
+
"noise_dirichlet_concentration": 32100.0,
|
| 99 |
+
"sde_resample": "dirichlet",
|
| 100 |
+
"logistic_normal_sigma_min": 0.18,
|
| 101 |
+
"logistic_normal_sigma_max": 3.0,
|
| 102 |
+
"logistic_normal_tau_min": 0.65,
|
| 103 |
+
"logistic_normal_tau_max": 1.0,
|
| 104 |
+
"final_from": "blend_0.5",
|
| 105 |
+
"n_samples": 128,
|
| 106 |
+
"seed": 20260524
|
| 107 |
+
},
|
| 108 |
+
"raw_genppl": {
|
| 109 |
+
"ppl": 1.5005041808014281,
|
| 110 |
+
"nll_per_token": 0.4058011721665977,
|
| 111 |
+
"tokens": 129460,
|
| 112 |
+
"kept_samples": 128,
|
| 113 |
+
"total_samples": 128,
|
| 114 |
+
"empty_rate": 0.0,
|
| 115 |
+
"skipped_samples": 0
|
| 116 |
+
},
|
| 117 |
+
"stripped_genppl": {
|
| 118 |
+
"ppl": 1.499758641411332,
|
| 119 |
+
"nll_per_token": 0.40530418943567065,
|
| 120 |
+
"tokens": 129453,
|
| 121 |
+
"kept_samples": 128,
|
| 122 |
+
"total_samples": 128,
|
| 123 |
+
"empty_rate": 0.0,
|
| 124 |
+
"skipped_samples": 0
|
| 125 |
+
},
|
| 126 |
+
"diversity": {
|
| 127 |
+
"sample_entropy": 0.2226418600195074,
|
| 128 |
+
"unique_tokens": 414,
|
| 129 |
+
"token_count": 131072,
|
| 130 |
+
"distinct_1": 0.0031585693359375,
|
| 131 |
+
"distinct_2": 0.009935544965786901,
|
| 132 |
+
"top_token_mass": 0.9261245727539062
|
| 133 |
+
}
|
| 134 |
+
}
|
| 135 |
+
[done] docs/lta_samples/metrics_20260525/owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_every1k_sde_gumbel_topp0.95_tau1.0_to_0.2_blend_c32100_64200_n128/lta_owt_t5elf_dirichlet_len1024_Cv_to_2v_mask1_sameT_gbs512_b32_8gpu_20k_save1k_gumbelwatch_20260525/step_0005000/sde_steps128_samples128_scored.jsonl
|
| 136 |
+
[watch-gumbel] 2026-05-25_17:31:41 done step_0005000
|
LTA_openwebtext_dualt/logs/owt_genppl128_0501.log
ADDED
|
File without changes
|
LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/pcg64-testset-1.csv
ADDED
|
@@ -0,0 +1,1001 @@
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|
| 1 |
+
seed, 0xdeadbeaf
|
| 2 |
+
0, 0x60d24054e17a0698
|
| 3 |
+
1, 0xd5e79d89856e4f12
|
| 4 |
+
2, 0xd254972fe64bd782
|
| 5 |
+
3, 0xf1e3072a53c72571
|
| 6 |
+
4, 0xd7c1d7393d4115c9
|
| 7 |
+
5, 0x77b75928b763e1e2
|
| 8 |
+
6, 0xee6dee05190f7909
|
| 9 |
+
7, 0x15f7b1c51d7fa319
|
| 10 |
+
8, 0x27e44105f26ac2d7
|
| 11 |
+
9, 0xcc0d88b29e5b415
|
| 12 |
+
10, 0xe07b1a90c685e361
|
| 13 |
+
11, 0xd2e430240de95e38
|
| 14 |
+
12, 0x3260bca9a24ca9da
|
| 15 |
+
13, 0x9b3cf2e92385adb7
|
| 16 |
+
14, 0x30b5514548271976
|
| 17 |
+
15, 0xa3a1fa16c124faf9
|
| 18 |
+
16, 0xf53e17e918e45bb6
|
| 19 |
+
17, 0x26f19faaeb833bfc
|
| 20 |
+
18, 0x95e1d605730cce1b
|
| 21 |
+
19, 0xa7b520c5c093c1aa
|
| 22 |
+
20, 0x4b68c010c9b106a3
|
| 23 |
+
21, 0x25e19fe91df703f0
|
| 24 |
+
22, 0x898364bb0bf593cb
|
| 25 |
+
23, 0x5bd6ab7dbaa125db
|
| 26 |
+
24, 0xd1fe47f25152045c
|
| 27 |
+
25, 0x3bb11919addf2409
|
| 28 |
+
26, 0x26a8cb7b3f54af8
|
| 29 |
+
27, 0xe6a27ee11200aa24
|
| 30 |
+
28, 0x7cb585ab01e22000
|
| 31 |
+
29, 0x78e60028676d2ef3
|
| 32 |
+
30, 0x5c32535e5a899528
|
| 33 |
+
31, 0x83e8b6f8c4a46fb3
|
| 34 |
+
32, 0xe56ef7668a161246
|
| 35 |
+
33, 0x36dcbc15aeb73055
|
| 36 |
+
34, 0x5ea247f0bd188acb
|
| 37 |
+
35, 0x438b547b84601a80
|
| 38 |
+
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947, 0x1088324dfa955c25
|
| 950 |
+
948, 0x516437acd4764623
|
| 951 |
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949, 0x38a31abe50d0aa03
|
| 952 |
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950, 0x72e1054e9dc02ba
|
| 953 |
+
951, 0xe6971dd664d1a2e2
|
| 954 |
+
952, 0xf6698cb095d3b702
|
| 955 |
+
953, 0xad995a5a8c19bd92
|
| 956 |
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954, 0x34e53c6936f656e6
|
| 957 |
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955, 0x10de240bc07c757a
|
| 958 |
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956, 0x3e3b9a6861c2bd1c
|
| 959 |
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957, 0x9c0b0b97d3712ec9
|
| 960 |
+
958, 0xabf1505a75043aed
|
| 961 |
+
959, 0xbdf93d3de3274179
|
| 962 |
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960, 0x28fa5904d3f62c28
|
| 963 |
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961, 0xc3b97b39ef6c5133
|
| 964 |
+
962, 0xf2b2219225b8679d
|
| 965 |
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963, 0x8be4ec0f930c0aaa
|
| 966 |
+
964, 0x47de5a56aa590643
|
| 967 |
+
965, 0xb6f871b304129856
|
| 968 |
+
966, 0x80a61c06233ab0f9
|
| 969 |
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967, 0x3ce6c3af8101b055
|
| 970 |
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968, 0x85b911708274e7d1
|
| 971 |
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969, 0x4cab65d093a488b7
|
| 972 |
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970, 0xaabc4b10661fe28e
|
| 973 |
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971, 0x35b16dea64474a68
|
| 974 |
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972, 0x1d6eb5b093361223
|
| 975 |
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973, 0xc39107b92f0fe1fb
|
| 976 |
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974, 0x1d09e048073c4841
|
| 977 |
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975, 0xc6a02f43aca8cb2f
|
| 978 |
+
976, 0xaf6613dbc7da909c
|
| 979 |
+
977, 0x5ac2a40c230aa756
|
| 980 |
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978, 0x33afb5e7c01c39a5
|
| 981 |
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979, 0xc7b0b20ea8b7d0ef
|
| 982 |
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980, 0xdf7306c8ccb1bbea
|
| 983 |
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981, 0x9710efc0c188b2a0
|
| 984 |
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982, 0xd6303eadb72c873e
|
| 985 |
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983, 0xa38ca609b118f35a
|
| 986 |
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984, 0x8390613065c6e535
|
| 987 |
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985, 0xdf9a0106757e431f
|
| 988 |
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986, 0x8bcf77039788e143
|
| 989 |
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987, 0x6026806a986b378e
|
| 990 |
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988, 0x482ff3b1394cb1dc
|
| 991 |
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989, 0x2a27d0ccac9ede9c
|
| 992 |
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990, 0x53c77f26e271b3ab
|
| 993 |
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991, 0x1ba004cf276cf3f
|
| 994 |
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992, 0xc135b0517dc81f7c
|
| 995 |
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993, 0x5d137838db75e442
|
| 996 |
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994, 0x3fe505f93d1dbdd7
|
| 997 |
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995, 0x351654ae7d598294
|
| 998 |
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996, 0x173f8d182af9d84d
|
| 999 |
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997, 0xf97dfcd164fe11c5
|
| 1000 |
+
998, 0xcda423e5ad43b290
|
| 1001 |
+
999, 0xa5cb380b8de10d10
|
LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/pcg64dxsm-testset-1.csv
ADDED
|
@@ -0,0 +1,1001 @@
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| 1 |
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seed, 0xdeadbeaf
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0, 0xdf1ddcf1e22521fe
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| 830 |
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| 831 |
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| 832 |
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| 839 |
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| 840 |
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| 841 |
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| 843 |
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| 844 |
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| 849 |
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| 850 |
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| 851 |
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| 852 |
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| 853 |
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| 854 |
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| 855 |
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| 856 |
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| 857 |
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| 858 |
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| 859 |
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| 860 |
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| 882 |
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| 888 |
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| 891 |
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| 897 |
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| 898 |
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| 900 |
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| 910 |
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| 911 |
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| 913 |
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|
| 914 |
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| 915 |
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|
| 916 |
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|
| 917 |
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|
| 918 |
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|
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|
| 920 |
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|
| 921 |
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|
| 922 |
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|
| 923 |
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|
| 924 |
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|
| 925 |
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|
| 926 |
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|
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|
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|
| 929 |
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|
| 930 |
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|
| 931 |
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|
| 932 |
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|
| 933 |
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|
| 934 |
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|
| 935 |
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|
| 936 |
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|
| 937 |
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|
| 938 |
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|
| 939 |
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|
| 940 |
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|
| 941 |
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|
| 942 |
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|
| 943 |
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|
| 944 |
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|
| 945 |
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|
| 946 |
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944, 0x9940b983d331b163
|
| 947 |
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|
| 948 |
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|
| 949 |
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|
| 950 |
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948, 0xc0a5849e23a61606
|
| 951 |
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|
| 952 |
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|
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952, 0x825ab3f10e6d330b
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| 955 |
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953, 0x1a1c31ac9d883ea0
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| 956 |
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954, 0xb49076b7155c6f47
|
| 957 |
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955, 0x920cf3085dfe3ccb
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| 958 |
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|
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|
| 960 |
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|
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| 965 |
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963, 0x885b4335457bd84a
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| 968 |
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966, 0xc6dd76a6cbf5d2b2
|
| 969 |
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967, 0xba9c22be404ee1aa
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| 970 |
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| 971 |
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969, 0x61e03a798593c177
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| 972 |
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970, 0x171671f809c68213
|
| 973 |
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971, 0x28d54872fc1d914c
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| 974 |
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972, 0x43c2fcd9bd098b53
|
| 975 |
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973, 0x172ad4c4a98b9d37
|
| 976 |
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974, 0x330860c9460f2516
|
| 977 |
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975, 0x49547f472df984f4
|
| 978 |
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976, 0x873b2436d3f0e114
|
| 979 |
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| 980 |
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980, 0x11f381ed28738d3d
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| 984 |
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| 985 |
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983, 0xf84decbd8f48c913
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| 986 |
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984, 0x4425c0ed1e9a82a5
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| 987 |
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| 988 |
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| 989 |
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| 990 |
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| 991 |
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|
| 992 |
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990, 0x3eaf7c5b0df734e1
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| 993 |
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991, 0xe71cb4064d1ede05
|
| 994 |
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992, 0x356e3cec80e418b2
|
| 995 |
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993, 0xca04306243a15be6
|
| 996 |
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994, 0x941cf3881fa18896
|
| 997 |
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995, 0x30dbb0e819d644e0
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| 998 |
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996, 0xaae22c0bef02859a
|
| 999 |
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997, 0x7bd30917bbaa8a94
|
| 1000 |
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998, 0x2672547bc8d7d329
|
| 1001 |
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999, 0x4955c92aaa231578
|
LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/numpy/random/tests/data/philox-testset-2.csv
ADDED
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|
| 1 |
+
seed, 0x0
|
| 2 |
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0, 0x399e5b222b82fa9
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| 3 |
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1, 0x41fd08c1f00f3bc5
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| 4 |
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| 5 |
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| 6 |
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| 9 |
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| 11 |
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| 12 |
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| 16 |
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14, 0x22b7697473b1dfda
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998, 0xdb4548a86f27abfd
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| 1001 |
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999, 0x57c05f47ff62380d
|
LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/sam/processing_sam.py
ADDED
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|
| 1 |
+
# Copyright 2023 The HuggingFace Inc. team.
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
#
|
| 7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
+
#
|
| 9 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
"""
|
| 15 |
+
Processor class for SAM.
|
| 16 |
+
"""
|
| 17 |
+
|
| 18 |
+
from copy import deepcopy
|
| 19 |
+
from typing import Union
|
| 20 |
+
|
| 21 |
+
import numpy as np
|
| 22 |
+
|
| 23 |
+
from ...image_utils import ImageInput
|
| 24 |
+
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin
|
| 25 |
+
from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
|
| 26 |
+
from ...utils import auto_docstring, is_torch_available
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
if is_torch_available():
|
| 30 |
+
import torch
|
| 31 |
+
|
| 32 |
+
NestedList = list[Union[float | int | None, "NestedList"]]
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
class SamImagesKwargs(ImagesKwargs, total=False):
|
| 36 |
+
"""
|
| 37 |
+
segmentation_maps (`ImageInput`, *optional*):
|
| 38 |
+
Ground truth segmentation maps to process alongside the input images. These maps are used for training
|
| 39 |
+
or evaluation purposes and are resized and normalized to match the processed image dimensions.
|
| 40 |
+
input_points (`NestedList`, *optional*):
|
| 41 |
+
Input points for prompt-based segmentation. Should be a nested list with structure
|
| 42 |
+
`[image_level, object_level, point_level, [x, y]]` where each point is specified as `[x, y]` coordinates
|
| 43 |
+
in the original image space. Points are normalized to the target image size before being passed to the model.
|
| 44 |
+
input_labels (`NestedList`, *optional*):
|
| 45 |
+
Labels for the input points, indicating whether each point is a foreground (1) or background (0) point.
|
| 46 |
+
Should be a nested list with structure `[image_level, object_level, point_level]`. Must have the same
|
| 47 |
+
structure as `input_points` (excluding the coordinate dimension).
|
| 48 |
+
input_boxes (`NestedList`, *optional*):
|
| 49 |
+
Bounding boxes for prompt-based segmentation. Should be a nested list with structure
|
| 50 |
+
`[image_level, box_level, [x1, y1, x2, y2]]` where each box is specified as `[x1, y1, x2, y2]` coordinates
|
| 51 |
+
in the original image space. Boxes are normalized to the target image size before being passed to the model.
|
| 52 |
+
point_pad_value (`int`, *optional*, defaults to `-10`):
|
| 53 |
+
The value used for padding input points when batching sequences of different lengths. This value marks
|
| 54 |
+
padded positions and is preserved during coordinate normalization to distinguish real points from padding.
|
| 55 |
+
mask_size (`dict[str, int]`, *optional*):
|
| 56 |
+
Dictionary specifying the target mask size with keys `"height"` and `"width"`. This determines the
|
| 57 |
+
resolution of the output segmentation masks generated by the model.
|
| 58 |
+
mask_pad_size (`dict[str, int]`, *optional*):
|
| 59 |
+
Dictionary specifying the padding size for masks with keys `"height"` and `"width"`. This is used when
|
| 60 |
+
batching masks of different sizes to ensure consistent dimensions.
|
| 61 |
+
"""
|
| 62 |
+
|
| 63 |
+
segmentation_maps: ImageInput | None
|
| 64 |
+
input_points: "NestedList | torch.Tensor | None"
|
| 65 |
+
input_labels: "NestedList | int | torch.Tensor | None"
|
| 66 |
+
input_boxes: "NestedList | torch.Tensor | None"
|
| 67 |
+
point_pad_value: int | None
|
| 68 |
+
mask_size: dict[str, int]
|
| 69 |
+
mask_pad_size: dict[str, int]
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
class SamProcessorKwargs(ProcessingKwargs, total=False):
|
| 73 |
+
images_kwargs: SamImagesKwargs
|
| 74 |
+
_defaults = {
|
| 75 |
+
"images_kwargs": {
|
| 76 |
+
"point_pad_value": -10,
|
| 77 |
+
}
|
| 78 |
+
}
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
@auto_docstring
|
| 82 |
+
class SamProcessor(ProcessorMixin):
|
| 83 |
+
def __init__(self, image_processor):
|
| 84 |
+
super().__init__(image_processor)
|
| 85 |
+
self.target_size = self.image_processor.size["longest_edge"]
|
| 86 |
+
|
| 87 |
+
@auto_docstring
|
| 88 |
+
def __call__(
|
| 89 |
+
self,
|
| 90 |
+
images: ImageInput | None = None,
|
| 91 |
+
text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
|
| 92 |
+
**kwargs,
|
| 93 |
+
) -> BatchEncoding:
|
| 94 |
+
output_kwargs = self._merge_kwargs(
|
| 95 |
+
SamProcessorKwargs,
|
| 96 |
+
tokenizer_init_kwargs={},
|
| 97 |
+
**kwargs,
|
| 98 |
+
)
|
| 99 |
+
input_points = output_kwargs["images_kwargs"].pop("input_points", None)
|
| 100 |
+
input_labels = output_kwargs["images_kwargs"].pop("input_labels", None)
|
| 101 |
+
input_boxes = output_kwargs["images_kwargs"].pop("input_boxes", None)
|
| 102 |
+
point_pad_value = output_kwargs["images_kwargs"].pop("point_pad_value", None)
|
| 103 |
+
|
| 104 |
+
encoding_image_processor = self.image_processor(
|
| 105 |
+
images,
|
| 106 |
+
**output_kwargs["images_kwargs"],
|
| 107 |
+
)
|
| 108 |
+
|
| 109 |
+
# pop arguments that are not used in the forward but used nevertheless
|
| 110 |
+
original_sizes = encoding_image_processor["original_sizes"]
|
| 111 |
+
|
| 112 |
+
if hasattr(original_sizes, "numpy"):
|
| 113 |
+
original_sizes = original_sizes.numpy()
|
| 114 |
+
|
| 115 |
+
input_points, input_labels, input_boxes = self._check_and_preprocess_points(
|
| 116 |
+
input_points=input_points,
|
| 117 |
+
input_labels=input_labels,
|
| 118 |
+
input_boxes=input_boxes,
|
| 119 |
+
)
|
| 120 |
+
|
| 121 |
+
encoding_image_processor = self._normalize_and_convert(
|
| 122 |
+
encoding_image_processor,
|
| 123 |
+
original_sizes,
|
| 124 |
+
input_points=input_points,
|
| 125 |
+
input_labels=input_labels,
|
| 126 |
+
input_boxes=input_boxes,
|
| 127 |
+
return_tensors=output_kwargs["images_kwargs"].get("return_tensors"),
|
| 128 |
+
point_pad_value=point_pad_value,
|
| 129 |
+
)
|
| 130 |
+
|
| 131 |
+
return encoding_image_processor
|
| 132 |
+
|
| 133 |
+
def _normalize_and_convert(
|
| 134 |
+
self,
|
| 135 |
+
encoding_image_processor,
|
| 136 |
+
original_sizes,
|
| 137 |
+
input_points=None,
|
| 138 |
+
input_labels=None,
|
| 139 |
+
input_boxes=None,
|
| 140 |
+
return_tensors="pt",
|
| 141 |
+
point_pad_value=-10,
|
| 142 |
+
):
|
| 143 |
+
if input_points is not None:
|
| 144 |
+
if len(original_sizes) != len(input_points):
|
| 145 |
+
input_points = [
|
| 146 |
+
self._normalize_coordinates(self.target_size, point, original_sizes[0]) for point in input_points
|
| 147 |
+
]
|
| 148 |
+
else:
|
| 149 |
+
input_points = [
|
| 150 |
+
self._normalize_coordinates(self.target_size, point, original_size)
|
| 151 |
+
for point, original_size in zip(input_points, original_sizes)
|
| 152 |
+
]
|
| 153 |
+
# check that all arrays have the same shape
|
| 154 |
+
if not all(point.shape == input_points[0].shape for point in input_points):
|
| 155 |
+
if input_labels is not None:
|
| 156 |
+
input_points, input_labels = self._pad_points_and_labels(
|
| 157 |
+
input_points, input_labels, point_pad_value
|
| 158 |
+
)
|
| 159 |
+
|
| 160 |
+
input_points = np.array(input_points)
|
| 161 |
+
|
| 162 |
+
if input_labels is not None:
|
| 163 |
+
input_labels = np.array(input_labels)
|
| 164 |
+
|
| 165 |
+
if input_boxes is not None:
|
| 166 |
+
if len(original_sizes) != len(input_boxes):
|
| 167 |
+
input_boxes = [
|
| 168 |
+
self._normalize_coordinates(self.target_size, box, original_sizes[0], is_bounding_box=True)
|
| 169 |
+
for box in input_boxes
|
| 170 |
+
]
|
| 171 |
+
else:
|
| 172 |
+
input_boxes = [
|
| 173 |
+
self._normalize_coordinates(self.target_size, box, original_size, is_bounding_box=True)
|
| 174 |
+
for box, original_size in zip(input_boxes, original_sizes)
|
| 175 |
+
]
|
| 176 |
+
input_boxes = np.array(input_boxes)
|
| 177 |
+
|
| 178 |
+
if input_boxes is not None:
|
| 179 |
+
if return_tensors == "pt":
|
| 180 |
+
input_boxes = torch.from_numpy(input_boxes)
|
| 181 |
+
# boxes batch size of 1 by default
|
| 182 |
+
input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
|
| 183 |
+
encoding_image_processor.update({"input_boxes": input_boxes})
|
| 184 |
+
if input_points is not None:
|
| 185 |
+
if return_tensors == "pt":
|
| 186 |
+
input_points = torch.from_numpy(input_points)
|
| 187 |
+
# point batch size of 1 by default
|
| 188 |
+
input_points = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
|
| 189 |
+
encoding_image_processor.update({"input_points": input_points})
|
| 190 |
+
if input_labels is not None:
|
| 191 |
+
if return_tensors == "pt":
|
| 192 |
+
input_labels = torch.from_numpy(input_labels)
|
| 193 |
+
# point batch size of 1 by default
|
| 194 |
+
input_labels = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
|
| 195 |
+
encoding_image_processor.update({"input_labels": input_labels})
|
| 196 |
+
|
| 197 |
+
return encoding_image_processor
|
| 198 |
+
|
| 199 |
+
def _pad_points_and_labels(self, input_points, input_labels, point_pad_value):
|
| 200 |
+
r"""
|
| 201 |
+
The method pads the 2D points and labels to the maximum number of points in the batch.
|
| 202 |
+
"""
|
| 203 |
+
expected_nb_points = max(point.shape[0] for point in input_points)
|
| 204 |
+
processed_input_points = []
|
| 205 |
+
for i, point in enumerate(input_points):
|
| 206 |
+
if point.shape[0] != expected_nb_points:
|
| 207 |
+
point = np.concatenate(
|
| 208 |
+
[point, np.zeros((expected_nb_points - point.shape[0], 2)) + point_pad_value], axis=0
|
| 209 |
+
)
|
| 210 |
+
input_labels[i] = np.append(input_labels[i], [point_pad_value])
|
| 211 |
+
processed_input_points.append(point)
|
| 212 |
+
input_points = processed_input_points
|
| 213 |
+
return input_points, input_labels
|
| 214 |
+
|
| 215 |
+
def _normalize_coordinates(
|
| 216 |
+
self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False
|
| 217 |
+
) -> np.ndarray:
|
| 218 |
+
"""
|
| 219 |
+
Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format.
|
| 220 |
+
"""
|
| 221 |
+
old_h, old_w = original_size
|
| 222 |
+
new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size)
|
| 223 |
+
coords = deepcopy(coords).astype(float)
|
| 224 |
+
|
| 225 |
+
if is_bounding_box:
|
| 226 |
+
coords = coords.reshape(-1, 2, 2)
|
| 227 |
+
|
| 228 |
+
coords[..., 0] = coords[..., 0] * (new_w / old_w)
|
| 229 |
+
coords[..., 1] = coords[..., 1] * (new_h / old_h)
|
| 230 |
+
|
| 231 |
+
if is_bounding_box:
|
| 232 |
+
coords = coords.reshape(-1, 4)
|
| 233 |
+
|
| 234 |
+
return coords
|
| 235 |
+
|
| 236 |
+
def _check_and_preprocess_points(
|
| 237 |
+
self,
|
| 238 |
+
input_points=None,
|
| 239 |
+
input_labels=None,
|
| 240 |
+
input_boxes=None,
|
| 241 |
+
):
|
| 242 |
+
r"""
|
| 243 |
+
Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they
|
| 244 |
+
are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`,
|
| 245 |
+
it is converted to a `numpy.ndarray` and then to a `list`.
|
| 246 |
+
"""
|
| 247 |
+
if input_points is not None:
|
| 248 |
+
if hasattr(input_points, "numpy"):
|
| 249 |
+
input_points = input_points.numpy().tolist()
|
| 250 |
+
|
| 251 |
+
if not isinstance(input_points, list) or not isinstance(input_points[0], list):
|
| 252 |
+
raise ValueError("Input points must be a list of list of floating points.")
|
| 253 |
+
input_points = [np.array(input_point) for input_point in input_points]
|
| 254 |
+
else:
|
| 255 |
+
input_points = None
|
| 256 |
+
|
| 257 |
+
if input_labels is not None:
|
| 258 |
+
if hasattr(input_labels, "numpy"):
|
| 259 |
+
input_labels = input_labels.numpy().tolist()
|
| 260 |
+
|
| 261 |
+
if not isinstance(input_labels, list) or not isinstance(input_labels[0], list):
|
| 262 |
+
raise ValueError("Input labels must be a list of list integers.")
|
| 263 |
+
input_labels = [np.array(label) for label in input_labels]
|
| 264 |
+
else:
|
| 265 |
+
input_labels = None
|
| 266 |
+
|
| 267 |
+
if input_boxes is not None:
|
| 268 |
+
if hasattr(input_boxes, "numpy"):
|
| 269 |
+
input_boxes = input_boxes.numpy().tolist()
|
| 270 |
+
|
| 271 |
+
if (
|
| 272 |
+
not isinstance(input_boxes, list)
|
| 273 |
+
or not isinstance(input_boxes[0], list)
|
| 274 |
+
or not isinstance(input_boxes[0][0], list)
|
| 275 |
+
):
|
| 276 |
+
raise ValueError("Input boxes must be a list of list of list of floating points.")
|
| 277 |
+
input_boxes = [np.array(box).astype(np.float32) for box in input_boxes]
|
| 278 |
+
else:
|
| 279 |
+
input_boxes = None
|
| 280 |
+
|
| 281 |
+
return input_points, input_labels, input_boxes
|
| 282 |
+
|
| 283 |
+
@property
|
| 284 |
+
def model_input_names(self):
|
| 285 |
+
image_processor_input_names = self.image_processor.model_input_names
|
| 286 |
+
return list(image_processor_input_names + ["original_sizes", "reshaped_input_sizes"])
|
| 287 |
+
|
| 288 |
+
def post_process_masks(self, *args, **kwargs):
|
| 289 |
+
return self.image_processor.post_process_masks(*args, **kwargs)
|
| 290 |
+
|
| 291 |
+
|
| 292 |
+
__all__ = ["SamProcessor"]
|
LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_2x8gpu_50ep_lr1e3_ema0p9999_elfopt_not5_bottleneck128_unfixed_norm_stateprobadd_selfcond_ce_fast_20260609_155046/step_099000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_027000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_030000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
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|
|
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|
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|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_101000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_130000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
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|
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|
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|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
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|
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|
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|
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version https://git-lfs.github.com/spec/v1
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LTA_openwebtext_dualt/mini_owt_logdirichlet/runs/owt_t5_elftokenized_full_len1024_C1_to_1024_pow1_d768_l12_h12_gbs512_8gpu_50ep_lr6e4_ema0p9999_elfopt_t5embed_unfixed_norm_stateprobadd_selfcond_ce_fast_20260606_144245/step_181000.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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