diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step01500.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step01500.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fe7ba5b76085df8fd5568ec82b03dc34d5980115
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step01500.txt
@@ -0,0 +1,4 @@
+fsrbrtkyna:,n
+yWyp b ,th ana.,p,
+Ld ,w rK ,caaeipgeliarayseygbkgS of eb, a!I aea 'OuEc  dwsvlp,,ocdsne artayn'sA nyecf
+f ; IepE!
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step10000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step10000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..94b379bef94673f30bf6e75a5cdf09e8e4bcfd84
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step10000.txt
@@ -0,0 +1,6 @@
+iocheet oohheh
+rTsh uurlps:e erp t
+D taa h
+alqasrps TaahGi ,dekdgu   Tw t aC hrfLhwp,dueot,eeo  oe; rd oB, tur?nov.'s o e
+o
+ehAv
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step45000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step45000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9ccf6c4a9387efa9add41759f451c3dc88824a2e
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step45000.txt
@@ -0,0 +1,5 @@
+eSprApShmh k pnrm CsdsI  a -
+d!sfd oz
+ oda ,ayiothnegoPasiurf n ant
+sss
+ aevohAt aua hoi etesRnwo N,hp  oned eiotrehth.ni uiBsit
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step60000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step60000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8798f4a67a092b2b4346097b5a8340072ba3dba8
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step60000.txt
@@ -0,0 +1,8 @@
+no
+noh!fivA Lsy  ati
+neanNhaIunnwowfCTAmum ee:ws O  mOeg
+oOCvdfAtSm
+lS:.nE!naTtAu
+ses s,tv
+:
+ .mwe,,  e,Io i Ie ws soaale!eiuaoO
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step65000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step65000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e408c573a0180a6fde8afe13ac5edeec0e2d4b66
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step65000.txt
@@ -0,0 +1,3 @@
+i y m  buhttra Nhmddnrel oovcheioemogtute fveng mhso!h lri eahrs owCuetnd pfteleu
+rtlrdn eosUdclld oe' seddLt,I evqarah
+pSebsems
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step70000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step70000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a228cadca04acadd8e4ed8a0b484df069d460f3
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step70000.txt
@@ -0,0 +1,3 @@
+y ooAthOieho   bdssoihhurof tr mihf secen    iTtctFg,m ge aeootdwihbeh apsee 'vlirtm l ne UtWee hddrssltdrerd,n 
+hseekhnd
+ r tnt
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step75000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step75000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..101b7ca65f6eac6a538038159cb725f6e38d99ee
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step75000.txt
@@ -0,0 +1,10 @@
+
+ngcUc.f
+N e
+p aiYgT
+f:ieEc
+oYmoantc:wm ntnilee
+IvodIhCut :ooCtrROGe aif wnkCTfateseoa
+.RnT  ::ogddlftNedA oe
+e ode:l eyea s 
+n 
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step95000.txt b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step95000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b862e67825e530add9bb09fcd6a2f08eab4b4138
--- /dev/null
+++ b/LTA_openwebtext_dualt/experiments/nanogpt_tinyshakespeare_char/runs/char_ar_lta_4gpu_5k_20260507/fully_coupled/sample_step95000.txt
@@ -0,0 +1,3 @@
+h unht,,r isS.ttiht gef TIww nybaaky'wded m eo D ndrCotdiuninellhomc Eoliem  b e sDotnssWodsl,byn
+l idb gdWsal   rrgdmptnhc oa
+n
\ No newline at end of file
diff --git a/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0005000_logistic_normal_t1p45.log b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0005000_logistic_normal_t1p45.log
new file mode 100644
index 0000000000000000000000000000000000000000..c01810ff9bc5d90481ff3ff2c7e1cd41191d2c07
--- /dev/null
+++ b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0005000_logistic_normal_t1p45.log
@@ -0,0 +1,74 @@
+[watch-lognormal-sde] 2026-05-22_22:01:55 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0005000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0005000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0005000.pt
+[ckpt] step=5000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0005000.pt",
+  "step": 5000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 40.293694094874695,
+    "nll_per_token": 3.696194982636704,
+    "tokens": 35757,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 57.00826457595132,
+    "nll_per_token": 4.043196249884694,
+    "tokens": 29812,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.7468618715707476,
+    "unique_tokens": 1741,
+    "token_count": 32768,
+    "distinct_1": 0.053131103515625,
+    "distinct_2": 0.28057332677165353,
+    "top_token_mass": 0.10113525390625
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0005000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-22_22:04:15 done step_0005000
diff --git a/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0084000_logistic_normal_t1p45.log b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0084000_logistic_normal_t1p45.log
new file mode 100644
index 0000000000000000000000000000000000000000..7df4d1633c0562b9a7bef54cfcc28a1345df5ebd
--- /dev/null
+++ b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0084000_logistic_normal_t1p45.log
@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_06:23:01 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0084000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0084000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0084000.pt
+[ckpt] step=84000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0084000.pt",
+  "step": 84000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 35.31895237279622,
+    "nll_per_token": 3.564419714276503,
+    "tokens": 35864,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 47.30153986562729,
+    "nll_per_token": 3.8565428502666492,
+    "tokens": 30084,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 3.728319685277991,
+    "unique_tokens": 2264,
+    "token_count": 32768,
+    "distinct_1": 0.069091796875,
+    "distinct_2": 0.3536540354330709,
+    "top_token_mass": 0.10986328125
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0084000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_06:24:29 done step_0084000
diff --git a/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0093000_logistic_normal_t1p45.log b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0093000_logistic_normal_t1p45.log
new file mode 100644
index 0000000000000000000000000000000000000000..51e4c70ec0909f7bf3ce76a68136ba1c6d9ba60a
--- /dev/null
+++ b/LTA_openwebtext_dualt/logs/lm1b_len128_lognormal_atoms_sde_watch/infer_lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522_step_0093000_logistic_normal_t1p45.log
@@ -0,0 +1,76 @@
+[watch-lognormal-sde] 2026-05-23_07:13:04 infer runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0093000.pt -> docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0093000
+[load] runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0093000.pt
+[ckpt] step=93000
+[sde] generated 16/256
+[sde] generated 32/256
+[sde] generated 48/256
+[sde] generated 64/256
+[sde] generated 80/256
+[sde] generated 96/256
+[sde] generated 112/256
+[sde] generated 128/256
+[sde] generated 144/256
+[sde] generated 160/256
+[sde] generated 176/256
+[sde] generated 192/256
+[sde] generated 208/256
+[sde] generated 224/256
+[sde] generated 240/256
+[sde] generated 256/256
+[score] loading scorer: /e2e-data/evad-tech-vla/wanghan58/models/flowtext_scorers/gpt2-large-standard
+[summary] {
+  "type": "summary",
+  "checkpoint": "runs/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0093000.pt",
+  "step": 93000,
+  "decode": {
+    "decode_rule": "logistic_normal_resample_sde",
+    "steps": 128,
+    "model_t_mode": "const0.5",
+    "mean_mode": "anchor_semantic",
+    "endpoint_floor": 0.0,
+    "concentration_min": 1.0,
+    "concentration_max": 1024.0,
+    "endpoint_temp": 1.45,
+    "support_power": 1.0,
+    "semantic_power": 1.0,
+    "noise_init": "logistic_normal",
+    "noise_sigma": 3.0,
+    "noise_dirichlet_concentration": 1.0,
+    "sde_resample": "logistic_normal",
+    "logistic_normal_sigma_min": 0.18,
+    "logistic_normal_sigma_max": 3.0,
+    "logistic_normal_tau_min": 0.65,
+    "logistic_normal_tau_max": 1.0,
+    "final_from": "blend_0.5",
+    "n_samples": 256,
+    "seed": 20260522
+  },
+  "raw_genppl": {
+    "ppl": 35.929049927792164,
+    "nll_per_token": 3.581546158465558,
+    "tokens": 23957,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "stripped_genppl": {
+    "ppl": 41.2177039391741,
+    "nll_per_token": 3.7188678713192624,
+    "tokens": 20786,
+    "kept_samples": 256,
+    "total_samples": 256,
+    "empty_rate": 0.0,
+    "skipped_samples": 0
+  },
+  "diversity": {
+    "sample_entropy": 2.424182708162594,
+    "unique_tokens": 1462,
+    "token_count": 32768,
+    "distinct_1": 0.04461669921875,
+    "distinct_2": 0.21287524606299213,
+    "top_token_mass": 0.430389404296875
+  }
+}
+[done] docs/lta_samples/metrics_20260522/lm1b_len128_lognormal_atoms_every1k_logistic_normal_sde_t1p45_steps128_n256/lta_lm1b_classic_c1024_len128_lognormalatoms_gbs512_4gpu_20260522/step_0093000/sde_steps128_samples256_scored.jsonl
+[watch-lognormal-sde] 2026-05-23_07:14:31 done step_0093000
diff --git a/LTA_openwebtext_dualt/mini_owt_fit/logs/build_cache_t5_20260526_154150.log b/LTA_openwebtext_dualt/mini_owt_fit/logs/build_cache_t5_20260526_154150.log
new file mode 100644
index 0000000000000000000000000000000000000000..aa42784d67dbf06db4df94e7183407c8b2d4be32
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_fit/logs/build_cache_t5_20260526_154150.log
@@ -0,0 +1,81 @@
+[cache] worker=66 seen=100172 kept=35401 dropped=64771
+[cache] worker=49 seen=100172 kept=35599 dropped=64573
+[cache] worker=4 seen=100173 kept=35633 dropped=64540
+[cache] worker=79 seen=100172 kept=35826 dropped=64346
+[cache] worker=38 seen=100172 kept=35503 dropped=64669
+[cache] worker=34 seen=100172 kept=35790 dropped=64382
+[cache] worker=51 seen=100172 kept=35768 dropped=64404
+[cache] worker=60 seen=100172 kept=35545 dropped=64627
+[cache] worker=47 seen=100172 kept=35742 dropped=64430
+[cache] worker=26 seen=100172 kept=35927 dropped=64245
+[cache] worker=8 seen=100173 kept=36068 dropped=64105
+[cache] worker=69 seen=100172 kept=35760 dropped=64412
+[cache] worker=64 seen=100172 kept=35940 dropped=64232
+[cache] worker=63 seen=100172 kept=35537 dropped=64635
+[cache] worker=13 seen=100172 kept=35772 dropped=64400
+[cache] worker=9 seen=100172 kept=35774 dropped=64398
+[cache] worker=62 seen=100172 kept=35739 dropped=64433
+[cache] worker=0 seen=100173 kept=35619 dropped=64554
+[cache] worker=25 seen=100172 kept=35907 dropped=64265
+[cache] worker=35 seen=100172 kept=35433 dropped=64739
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diff --git a/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_bert_len1024_C1_to_1024_absrope_time4_d256_l3_h4_subset8192_8gpu_20260526_144131.log b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_bert_len1024_C1_to_1024_absrope_time4_d256_l3_h4_subset8192_8gpu_20260526_144131.log
new file mode 100644
index 0000000000000000000000000000000000000000..c2c7d76f6703ff329e31266bf6d90999917af60e
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_bert_len1024_C1_to_1024_absrope_time4_d256_l3_h4_subset8192_8gpu_20260526_144131.log
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+W0526 14:41:33.303000 10232 torch/distributed/run.py:792] *****************************************
+W0526 14:41:33.303000 10232 torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0526 14:41:33.303000 10232 torch/distributed/run.py:792] *****************************************
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+t-20260526224107-qtbpx-worker-0:10301:10733 [2] NCCL INFO [Proxy Service UDS] Device 2 CPU core 6
+t-20260526224107-qtbpx-worker-0:10301:10732 [2] NCCL INFO [Proxy Service] Device 2 CPU core 5
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO Check P2P Type intraNodeP2pSupport 1 directMode 0
+t-20260526224107-qtbpx-worker-0:10302:10734 [3] NCCL INFO [Proxy Service] Device 3 CPU core 6
+t-20260526224107-qtbpx-worker-0:10302:10735 [3] NCCL INFO [Proxy Service UDS] Device 3 CPU core 6
+t-20260526224107-qtbpx-worker-0:10299:10736 [0] NCCL INFO [Proxy Service] Device 0 CPU core 12
+t-20260526224107-qtbpx-worker-0:10299:10737 [0] NCCL INFO [Proxy Service UDS] Device 0 CPU core 10
+t-20260526224107-qtbpx-worker-0:10306:10738 [7] NCCL INFO [Proxy Service] Device 7 CPU core 144
+t-20260526224107-qtbpx-worker-0:10306:10739 [7] NCCL INFO [Proxy Service UDS] Device 7 CPU core 142
+t-20260526224107-qtbpx-worker-0:10300:10740 [1] NCCL INFO [Proxy Service] Device 1 CPU core 72
+t-20260526224107-qtbpx-worker-0:10300:10741 [1] NCCL INFO [Proxy Service UDS] Device 1 CPU core 74
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO 24 coll channels, 24 collnet channels, 16 nvls channels, 32 p2p channels, 32 p2p channels per peer
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO CC Off, workFifoBytes 1048576
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO ncclCommInitRankConfig comm 0xe108cc0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 65040 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10299:10624 [0] NCCL INFO Init timings - ncclCommInitRankConfig: rank 0 nranks 8 total 2.47 (kernels 0.20, alloc 1.07, bootstrap 0.35, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.29, rest 0.03)
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO ncclCommInitRankConfig comm 0xfbef7e0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId 73020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO ncclCommInitRankConfig comm 0xf0538c0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 67020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO ncclCommInitRankConfig comm 0xf8f0c40 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 69020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO ncclCommInitRankConfig comm 0xf35f880 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 6b020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260526224107-qtbpx-worker-0:10305:10628 [6] NCCL INFO Init timings - ncclCommInitRankConfig: rank 6 nranks 8 total 2.46 (kernels 0.19, alloc 1.06, bootstrap 0.36, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.30, rest 0.02)
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO ncclCommInitRankConfig comm 0xf5ae6a0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 6f020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO ncclCommInitRankConfig comm 0xb0a6800 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId 75020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10301:10629 [2] NCCL INFO Init timings - ncclCommInitRankConfig: rank 2 nranks 8 total 2.46 (kernels 0.19, alloc 1.04, bootstrap 0.37, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.29, rest 0.03)
+t-20260526224107-qtbpx-worker-0:10300:10625 [1] NCCL INFO Init timings - ncclCommInitRankConfig: rank 1 nranks 8 total 2.46 (kernels 0.19, alloc 1.06, bootstrap 0.36, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.28, rest 0.03)
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO ncclCommInitRankConfig comm 0xfc27a00 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 71020 commId 0x7e3b74adb33c40a1 - Init COMPLETE
+t-20260526224107-qtbpx-worker-0:10302:10626 [3] NCCL INFO Init timings - ncclCommInitRankConfig: rank 3 nranks 8 total 2.46 (kernels 0.19, alloc 1.06, bootstrap 0.36, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.29, rest 0.03)
+t-20260526224107-qtbpx-worker-0:10303:10627 [4] NCCL INFO Init timings - ncclCommInitRankConfig: rank 4 nranks 8 total 2.46 (kernels 0.19, alloc 1.05, bootstrap 0.36, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.30, rest 0.02)
+t-20260526224107-qtbpx-worker-0:10306:10700 [7] NCCL INFO Init timings - ncclCommInitRankConfig: rank 7 nranks 8 total 1.21 (kernels 0.18, alloc 0.17, bootstrap 0.00, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.29, rest 0.03)
+t-20260526224107-qtbpx-worker-0:10304:10630 [5] NCCL INFO Init timings - ncclCommInitRankConfig: rank 5 nranks 8 total 2.46 (kernels 0.23, alloc 1.02, bootstrap 0.35, allgathers 0.00, topo 0.52, graphs 0.01, connections 0.30, rest 0.02)
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10304:10744 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10304:10744 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10304:10744 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10306:10748 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10300:10747 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10304:10744 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10305:10745 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10303:10746 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10304:10744 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10301:10749 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260526224107-qtbpx-worker-0:10302:10743 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM
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+{
+  "data_path": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext",
+  "tokenizer_path": "/e2e-data/evad-tech-vla/wanghan58/workspace/imagenet_handoff_20260327/nlp_dts_light/assets/distilbert-base-uncased/tokenizer.json",
+  "out_dir": "runs/mini_owt_fit_bert_len1024_C1_to_1024_absrope_time4_d256_l3_h4_subset8192_8gpu_20260526_144131",
+  "text_column": "text",
+  "subset_size": 8192,
+  "payload_len": 1022,
+  "append_eos": 1,
+  "log_skips": 20,
+  "steps": 20000,
+  "batch_size": 8,
+  "lr": 0.0003,
+  "log_every": 50,
+  "save_every": 1000,
+  "dim": 256,
+  "layers": 3,
+  "heads": 4,
+  "mlp_dim": 1024,
+  "time_tokens": 4,
+  "abs_pos": 1,
+  "rope": 1,
+  "c_min": 1.0,
+  "c_max": 1024.0,
+  "seed": 1234
+}
+[data] rows=8192 length=1024 vocab=30522 bos=101:[CLS] eos=102:[SEP]
+[head] ['[CLS]', 'port', '-', 'au', '-', 'prince', ',', 'haiti', '(', 'cnn', ')', '-', '-', 'earthquake', 'victims', ',']
+[tail] ['.', 'retired', 'army', 'lt', '.', 'gen', '.', 'russ', '##el', 'honor', '##e', ',', 'who', 'led', 'relief', '[SEP]']
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+step=50 loss=7.2670 {'pos0_bos_p': 0.0013692908687517047, 'pos0_bos_top1': 0, 'last_eos_p': 0.0007515790057368577, 'last_eos_top1': 0}
+step=100 loss=7.1070 {'pos0_bos_p': 0.0016865339130163193, 'pos0_bos_top1': 0, 'last_eos_p': 0.0014719109749421477, 'last_eos_top1': 0}
+step=150 loss=7.4180 {'pos0_bos_p': 0.4093278646469116, 'pos0_bos_top1': 4, 'last_eos_p': 0.02048138901591301, 'last_eos_top1': 0}
+step=200 loss=7.0395 {'pos0_bos_p': 0.9398485422134399, 'pos0_bos_top1': 4, 'last_eos_p': 0.8816365599632263, 'last_eos_top1': 4}
+step=250 loss=7.1683 {'pos0_bos_p': 0.918108344078064, 'pos0_bos_top1': 4, 'last_eos_p': 0.9164758920669556, 'last_eos_top1': 4}
+step=300 loss=6.8254 {'pos0_bos_p': 0.9010106325149536, 'pos0_bos_top1': 4, 'last_eos_p': 0.7880206108093262, 'last_eos_top1': 4}
+step=350 loss=5.9360 {'pos0_bos_p': 0.8149036169052124, 'pos0_bos_top1': 4, 'last_eos_p': 0.6036866307258606, 'last_eos_top1': 4}
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+step=450 loss=5.4443 {'pos0_bos_p': 0.6736606359481812, 'pos0_bos_top1': 4, 'last_eos_p': 0.6584551930427551, 'last_eos_top1': 4}
+step=500 loss=5.5645 {'pos0_bos_p': 0.6481595039367676, 'pos0_bos_top1': 4, 'last_eos_p': 0.6777088642120361, 'last_eos_top1': 4}
+step=550 loss=5.5406 {'pos0_bos_p': 0.7611885070800781, 'pos0_bos_top1': 4, 'last_eos_p': 0.7946056723594666, 'last_eos_top1': 4}
+step=600 loss=5.1291 {'pos0_bos_p': 0.7027136087417603, 'pos0_bos_top1': 4, 'last_eos_p': 0.7575668096542358, 'last_eos_top1': 4}
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+step=700 loss=5.1110 {'pos0_bos_p': 0.8670953512191772, 'pos0_bos_top1': 4, 'last_eos_p': 0.921051025390625, 'last_eos_top1': 4}
+step=750 loss=5.7300 {'pos0_bos_p': 0.9154350757598877, 'pos0_bos_top1': 4, 'last_eos_p': 0.9507423639297485, 'last_eos_top1': 4}
+step=800 loss=4.8561 {'pos0_bos_p': 0.8796617984771729, 'pos0_bos_top1': 4, 'last_eos_p': 0.9310718774795532, 'last_eos_top1': 4}
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diff --git a/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094645.log b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094645.log
new file mode 100644
index 0000000000000000000000000000000000000000..eabbff786a2d3fdd78841d883646082843192e25
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094645.log
@@ -0,0 +1,1440 @@
+W0527 09:46:46.815000 10304 torch/distributed/run.py:792] 
+W0527 09:46:46.815000 10304 torch/distributed/run.py:792] *****************************************
+W0527 09:46:46.815000 10304 torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0527 09:46:46.815000 10304 torch/distributed/run.py:792] *****************************************
+[W527 09:46:51.919878523 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:51.923161695 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:53.205912070 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.517438427 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.517934541 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.518011820 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.518203041 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.518752459 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.519817432 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.521119095 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.521184008 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.521793617 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.527163894 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.530387471 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.533817742 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.536363432 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.540098775 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.543531400 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.546424292 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.550049651 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.556674297 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.560520259 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.568579480 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.576180157 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.580258212 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.594073883 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.619539561 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[rank6]:[W527 09:46:56.587107944 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank5]:[W527 09:46:56.592083260 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank1]:[W527 09:46:56.629491480 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank2]:[W527 09:46:56.704363369 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank7]:[W527 09:46:56.708105035 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank3]:[W527 09:46:56.725349241 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[rank4]:[W527 09:46:56.738850037 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
+[data] loaded_cache=cache/owt_t5_payload1022_appendeos1.pt seen=8013769 kept=2860537 dropped=5153232
+[rank0]:[W527 09:47:02.793620559 ProcessGroupNCCL.cpp:4571] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
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+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  2:  2 10
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  2:  2 10
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  1:  1  9
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  1:  1  9
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  1:  1  9
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  2:  2 10
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  2:  2 10
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  2:  2 10
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  3:  3 11
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  4:  4 12
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/9/-1->1->-1 [2] -1/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->9 [10] -1/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  5:  5 13
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  6:  6 14
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/10/-1->2->-1 [3] -1/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->10 [11] -1/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO NVLS Head  7:  7 15
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 00/16 :  0  7  6  5  4  3  2  1  9 10 11 12 13 14 15  8
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 01/16 :  0  8 15 14 13 12 11 10  9  1  2  3  4  5  6  7
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] 0/-1/-1->7->6 [2] 0/-1/-1->7->6 [3] 0/-1/-1->7->6 [4] 0/-1/-1->7->6 [5] 0/-1/-1->7->6 [6] 0/-1/-1->7->6 [7] 0/15/-1->7->-1 [8] -1/-1/-1->7->6 [9] 0/-1/-1->7->6 [10] 0/-1/-1->7->6 [11] 0/-1/-1->7->6 [12] 0/-1/-1->7->6 [13] 0/-1/-1->7->6 [14] 0/-1/-1->7->6 [15] 0/-1/-1->7->15
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 02/16 :  0  7  6  5  4  3 11 12 13 14 15  8  9 10  2  1
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 03/16 :  0  1  2 10  9  8 15 14 13 12 11  3  4  5  6  7
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/14/-1->6->-1 [7] -1/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->14 [15] -1/-1/-1->6->5
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/11/-1->3->-1 [4] -1/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->11 [12] -1/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 04/16 :  0  7  6  5 13 14 15  8  9 10 11 12  4  3  2  1
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/13/-1->5->-1 [6] -1/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->13 [14] -1/-1/-1->5->4 [15] 6/-1/-1->5->4
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/12/-1->4->-1 [5] -1/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->12 [13] -1/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 05/16 :  0  1  2  3  4 12 11 10  9  8 15 14 13  5  6  7
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 06/16 :  0  7 15  8  9 10 11 12 13 14  6  5  4  3  2  1
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 07/16 :  0  1  2  3  4  5  6 14 13 12 11 10  9  8 15  7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 08/16 :  0  7  6  5  4  3  2  1  9 10 11 12 13 14 15  8
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 09/16 :  0  8 15 14 13 12 11 10  9  1  2  3  4  5  6  7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 10/16 :  0  7  6  5  4  3 11 12 13 14 15  8  9 10  2  1
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 11/16 :  0  1  2 10  9  8 15 14 13 12 11  3  4  5  6  7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 12/16 :  0  7  6  5 13 14 15  8  9 10 11 12  4  3  2  1
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 13/16 :  0  1  2  3  4 12 11 10  9  8 15 14 13  5  6  7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 14/16 :  0  7 15  8  9 10 11 12 13 14  6  5  4  3  2  1
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Channel 15/16 :  0  1  2  3  4  5  6 14 13 12 11 10  9  8 15  7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Trees [0] 1/8/-1->0->-1 [1] -1/-1/-1->0->7 [2] 1/-1/-1->0->7 [3] 1/-1/-1->0->7 [4] 1/-1/-1->0->7 [5] 1/-1/-1->0->7 [6] 1/-1/-1->0->7 [7] 1/-1/-1->0->7 [8] 1/-1/-1->0->8 [9] -1/-1/-1->0->7 [10] 1/-1/-1->0->7 [11] 1/-1/-1->0->7 [12] 1/-1/-1->0->7 [13] 1/-1/-1->0->7 [14] 1/-1/-1->0->7 [15] 1/-1/-1->0->7
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Check P2P Type intraNodeP2pSupport 1 directMode 0
+t-20260527161817-lhhb7-worker-0:10396:10570 [2] NCCL INFO [Proxy Service] Device 2 CPU core 8
+t-20260527161817-lhhb7-worker-0:10401:10572 [7] NCCL INFO [Proxy Service] Device 7 CPU core 100
+t-20260527161817-lhhb7-worker-0:10396:10573 [2] NCCL INFO [Proxy Service UDS] Device 2 CPU core 12
+t-20260527161817-lhhb7-worker-0:10395:10571 [1] NCCL INFO [Proxy Service] Device 1 CPU core 74
+t-20260527161817-lhhb7-worker-0:10395:10574 [1] NCCL INFO [Proxy Service UDS] Device 1 CPU core 77
+t-20260527161817-lhhb7-worker-0:10401:10575 [7] NCCL INFO [Proxy Service UDS] Device 7 CPU core 102
+t-20260527161817-lhhb7-worker-0:10400:10576 [6] NCCL INFO [Proxy Service] Device 6 CPU core 148
+t-20260527161817-lhhb7-worker-0:10394:10578 [0] NCCL INFO [Proxy Service] Device 0 CPU core 8
+t-20260527161817-lhhb7-worker-0:10394:10582 [0] NCCL INFO [Proxy Service UDS] Device 0 CPU core 12
+t-20260527161817-lhhb7-worker-0:10397:10577 [3] NCCL INFO [Proxy Service] Device 3 CPU core 10
+t-20260527161817-lhhb7-worker-0:10397:10581 [3] NCCL INFO [Proxy Service UDS] Device 3 CPU core 13
+t-20260527161817-lhhb7-worker-0:10400:10583 [6] NCCL INFO [Proxy Service UDS] Device 6 CPU core 150
+t-20260527161817-lhhb7-worker-0:10399:10579 [5] NCCL INFO [Proxy Service] Device 5 CPU core 93
+t-20260527161817-lhhb7-worker-0:10399:10584 [5] NCCL INFO [Proxy Service UDS] Device 5 CPU core 94
+t-20260527161817-lhhb7-worker-0:10398:10580 [4] NCCL INFO [Proxy Service] Device 4 CPU core 100
+t-20260527161817-lhhb7-worker-0:10398:10585 [4] NCCL INFO [Proxy Service UDS] Device 4 CPU core 96
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO CC Off, workFifoBytes 1048576
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO ncclCommInitRankConfig comm 0xa1b3b40 rank 5 nranks 16 cudaDev 5 nvmlDev 5 busId 71020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO ncclCommInitRankConfig comm 0xab3d730 rank 1 nranks 16 cudaDev 1 nvmlDev 1 busId 67020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO ncclCommInitRankConfig comm 0x9edde60 rank 3 nranks 16 cudaDev 3 nvmlDev 3 busId 6b020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO ncclCommInitRankConfig comm 0xb020f20 rank 7 nranks 16 cudaDev 7 nvmlDev 7 busId 75020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10399:10495 [5] NCCL INFO Init timings - ncclCommInitRankConfig: rank 5 nranks 16 total 2.19 (kernels 0.20, alloc 1.01, bootstrap 0.17, allgathers 0.01, topo 0.52, graphs 0.02, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO ncclCommInitRankConfig comm 0xa4177c0 rank 6 nranks 16 cudaDev 6 nvmlDev 6 busId 73020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-0:10395:10492 [1] NCCL INFO Init timings - ncclCommInitRankConfig: rank 1 nranks 16 total 2.20 (kernels 0.23, alloc 1.00, bootstrap 0.15, allgathers 0.02, topo 0.52, graphs 0.01, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO ncclCommInitRankConfig comm 0xc2b4110 rank 0 nranks 16 cudaDev 0 nvmlDev 0 busId 65040 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10397:10491 [3] NCCL INFO Init timings - ncclCommInitRankConfig: rank 3 nranks 16 total 2.20 (kernels 0.21, alloc 1.02, bootstrap 0.15, allgathers 0.02, topo 0.52, graphs 0.01, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO ncclCommInitRankConfig comm 0xb122ed0 rank 2 nranks 16 cudaDev 2 nvmlDev 2 busId 69020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10401:10493 [7] NCCL INFO Init timings - ncclCommInitRankConfig: rank 7 nranks 16 total 2.19 (kernels 0.20, alloc 1.02, bootstrap 0.16, allgathers 0.02, topo 0.52, graphs 0.01, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO ncclCommInitRankConfig comm 0xaf2d3f0 rank 4 nranks 16 cudaDev 4 nvmlDev 4 busId 6f020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-0:10400:10494 [6] NCCL INFO Init timings - ncclCommInitRankConfig: rank 6 nranks 16 total 2.19 (kernels 0.20, alloc 1.02, bootstrap 0.16, allgathers 0.01, topo 0.53, graphs 0.02, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10394:10490 [0] NCCL INFO Init timings - ncclCommInitRankConfig: rank 0 nranks 16 total 2.22 (kernels 0.19, alloc 0.94, bootstrap 0.27, allgathers 0.02, topo 0.52, graphs 0.01, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10396:10496 [2] NCCL INFO Init timings - ncclCommInitRankConfig: rank 2 nranks 16 total 2.19 (kernels 0.20, alloc 1.02, bootstrap 0.15, allgathers 0.02, topo 0.52, graphs 0.01, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10398:10497 [4] NCCL INFO Init timings - ncclCommInitRankConfig: rank 4 nranks 16 total 2.19 (kernels 0.21, alloc 1.02, bootstrap 0.15, allgathers 0.01, topo 0.52, graphs 0.02, connections 0.27, rest 0.00)
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10399:10590 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 01/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 02/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 03/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10396:10592 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 04/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 05/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 06/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 07/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 09/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 10/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 11/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 12/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 13/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 14/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10394:10589 [0] NCCL INFO Channel 15/0 : 0[0] -> 7[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10398:10587 [4] NCCL INFO Channel 04/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10595 [4] NCCL INFO [Proxy Progress] Device 4 CPU core 106
+t-20260527161817-lhhb7-worker-0:10395:10591 [1] NCCL INFO Channel 01/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
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+t-20260527161817-lhhb7-worker-0:10397:10588 [3] NCCL INFO Channel 03/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
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+t-20260527161817-lhhb7-worker-0:10396:10597 [2] NCCL INFO [Proxy Progress] Device 2 CPU core 10
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+t-20260527161817-lhhb7-worker-0:10401:10601 [7] NCCL INFO [Proxy Progress] Device 7 CPU core 100
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+t-20260527161817-lhhb7-worker-0:10401:10594 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-0:10401:10594 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-0:10401:10594 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-0:10400:10593 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM
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+{
+  "data_path": "/e2e-data/evad-tech-vla/wanghan58/data/small_benchmarks/langflow_2604_11748/openwebtext",
+  "tokenizer_path": "/e2e-data/evad-tech-vla/wanghan58/models/hf/t5-small/tokenizer.json",
+  "out_dir": "runs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094645",
+  "text_column": "text",
+  "subset_size": 0,
+  "payload_len": 1022,
+  "append_eos": 1,
+  "log_skips": 20,
+  "cache_path": "cache/owt_t5_payload1022_appendeos1.pt",
+  "rebuild_cache": 0,
+  "online_data": 0,
+  "online_buffer_size": 8192,
+  "steps": 1000000,
+  "batch_size": 32,
+  "grad_accum": 2,
+  "lr": 0.0003,
+  "log_every": 50,
+  "save_every": 1000,
+  "dim": 768,
+  "layers": 12,
+  "heads": 12,
+  "mlp_dim": 3072,
+  "time_tokens": 4,
+  "abs_pos": 1,
+  "rope": 1,
+  "c_min": 1.0,
+  "c_max": 1024.0,
+  "seed": 1234
+}
+[data] rows=2860537 length=1024 vocab=32100 seen=8013769 dropped=5153232 kept=2860537 bos=1:</s> eos=1:</s>
+[head] ['</s>', '▁Port', '-', 'au', '-', 'Pri', 'nce', ',', '▁Haiti', '▁(', 'C', 'NN', ')', '▁--', '▁Earth', 'qua']
+[tail] ['▁magnitude', '▁earthquake', '▁flat', 't', 'ened', '▁Haiti', "'", 's', '▁capital', '▁city', '▁Tuesday', '▁afternoon', ',', '▁', 'affecting', '</s>']
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+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 07/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 08/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 07/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 08/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 07/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 07/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 07/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 07/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 08/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 08/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 09/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 09/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 08/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 08/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 09/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 08/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 09/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 10/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 10/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 10/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 09/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 09/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 09/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 11/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 10/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 11/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 11/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 11/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 10/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 10/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 13/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 11/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 12/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 12/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 12/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 10/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 13/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 14/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 12/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 12/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 11/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 13/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 13/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 11/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 15/0 : 12[4] -> 4[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 14/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 14/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 13/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 14/0 : 15[7] -> 7[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 12/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 13/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 12/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 00/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 00/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 15/0 : 9[1] -> 1[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 14/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 15/0 : 10[2] -> 2[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 14/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 14/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 13/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 01/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 01/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 02/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 00/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 15/0 : 11[3] -> 3[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 15/0 : 8[0] -> 0[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 15/0 : 13[5] -> 5[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 15/0 : 14[6] -> 6[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 02/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 02/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 03/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 01/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 02/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 00/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 00/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 03/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 03/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 00/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 04/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 04/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 03/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 01/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 06/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 01/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 04/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 05/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 05/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 01/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 04/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 04/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 07/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 05/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 02/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 06/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 06/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 05/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 02/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 05/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 08/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 08/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 07/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 03/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 07/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 06/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 03/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 09/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 06/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 09/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 10/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 08/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 06/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 07/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 10/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 04/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 07/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 11/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 10/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 07/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 09/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 10/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 11/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 05/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 08/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 12/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 11/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 12/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 08/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
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+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 12/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
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+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 09/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 12/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10398:10727 [4] NCCL INFO Channel 15/0 : 4[4] -> 12[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 14/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 09/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Channel 13/0 : 7[7] -> 15[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 12/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 14/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 13/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 10/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Channel 15/0 : 1[1] -> 9[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 10/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 13/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Channel 15/0 : 2[2] -> 10[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 14/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 11/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 11/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 14/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Channel 15/0 : 0[0] -> 8[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Channel 14/0 : 5[5] -> 13[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Channel 15/0 : 3[3] -> 11[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Channel 12/0 : 6[6] -> 14[6] [send] via NET/IBext_v9/14/GDRDMA
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+t-20260527161817-lhhb7-worker-0:10396:10721 [2] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10397:10723 [3] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10394:10728 [0] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10400:10724 [6] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10395:10725 [1] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10399:10726 [5] NCCL INFO Connected NVLS tree
+t-20260527161817-lhhb7-worker-0:10401:10722 [7] NCCL INFO Connected NVLS tree
+step=50 loss=7.2060 {'pos0_bos_p': 0.8691251277923584, 'pos0_bos_top1': 4, 'last_eos_p': 0.8674331903457642, 'last_eos_top1': 4}
+step=100 loss=7.1939 {'pos0_bos_p': 0.9981945157051086, 'pos0_bos_top1': 4, 'last_eos_p': 0.9981531500816345, 'last_eos_top1': 4}
+step=150 loss=6.7558 {'pos0_bos_p': 0.8224632740020752, 'pos0_bos_top1': 4, 'last_eos_p': 0.8395155668258667, 'last_eos_top1': 4}
+step=200 loss=6.3156 {'pos0_bos_p': 0.8714870810508728, 'pos0_bos_top1': 4, 'last_eos_p': 0.8938301801681519, 'last_eos_top1': 4}
+step=250 loss=6.2027 {'pos0_bos_p': 0.9082919955253601, 'pos0_bos_top1': 4, 'last_eos_p': 0.9218266010284424, 'last_eos_top1': 4}
+step=300 loss=5.7562 {'pos0_bos_p': 0.9323737025260925, 'pos0_bos_top1': 4, 'last_eos_p': 0.9537457823753357, 'last_eos_top1': 4}
+step=350 loss=5.9900 {'pos0_bos_p': 0.9733480215072632, 'pos0_bos_top1': 4, 'last_eos_p': 0.9767816662788391, 'last_eos_top1': 4}
+step=400 loss=5.9065 {'pos0_bos_p': 0.9612581729888916, 'pos0_bos_top1': 4, 'last_eos_p': 0.9686088562011719, 'last_eos_top1': 4}
+step=450 loss=5.3696 {'pos0_bos_p': 0.9775099158287048, 'pos0_bos_top1': 4, 'last_eos_p': 0.9818893671035767, 'last_eos_top1': 4}
+step=500 loss=5.2871 {'pos0_bos_p': 0.9876610040664673, 'pos0_bos_top1': 4, 'last_eos_p': 0.989841878414154, 'last_eos_top1': 4}
+step=550 loss=5.0319 {'pos0_bos_p': 0.9799777865409851, 'pos0_bos_top1': 4, 'last_eos_p': 0.9847173094749451, 'last_eos_top1': 4}
+step=600 loss=5.1627 {'pos0_bos_p': 0.9889834523200989, 'pos0_bos_top1': 4, 'last_eos_p': 0.9917683601379395, 'last_eos_top1': 4}
+step=650 loss=5.5194 {'pos0_bos_p': 0.9911477565765381, 'pos0_bos_top1': 4, 'last_eos_p': 0.9935898780822754, 'last_eos_top1': 4}
+step=700 loss=4.5972 {'pos0_bos_p': 0.9895609617233276, 'pos0_bos_top1': 4, 'last_eos_p': 0.9928007125854492, 'last_eos_top1': 4}
+step=750 loss=5.0060 {'pos0_bos_p': 0.9873014688491821, 'pos0_bos_top1': 4, 'last_eos_p': 0.9933567643165588, 'last_eos_top1': 4}
+step=800 loss=4.5090 {'pos0_bos_p': 0.9910714626312256, 'pos0_bos_top1': 4, 'last_eos_p': 0.9947707056999207, 'last_eos_top1': 4}
+step=850 loss=4.5166 {'pos0_bos_p': 0.990709662437439, 'pos0_bos_top1': 4, 'last_eos_p': 0.9951351284980774, 'last_eos_top1': 4}
+step=900 loss=4.7911 {'pos0_bos_p': 0.9938993453979492, 'pos0_bos_top1': 4, 'last_eos_p': 0.995788037776947, 'last_eos_top1': 4}
+step=950 loss=4.0477 {'pos0_bos_p': 0.9952994585037231, 'pos0_bos_top1': 4, 'last_eos_p': 0.9970601201057434, 'last_eos_top1': 4}
+step=1000 loss=4.0758 {'pos0_bos_p': 0.9955964684486389, 'pos0_bos_top1': 4, 'last_eos_p': 0.9973605275154114, 'last_eos_top1': 4}
+step=1050 loss=3.6664 {'pos0_bos_p': 0.9963505268096924, 'pos0_bos_top1': 4, 'last_eos_p': 0.9975348711013794, 'last_eos_top1': 4}
+step=1100 loss=4.5737 {'pos0_bos_p': 0.9966078996658325, 'pos0_bos_top1': 4, 'last_eos_p': 0.997703492641449, 'last_eos_top1': 4}
+step=1150 loss=4.6073 {'pos0_bos_p': 0.9966827034950256, 'pos0_bos_top1': 4, 'last_eos_p': 0.9975451827049255, 'last_eos_top1': 4}
+step=1200 loss=4.3905 {'pos0_bos_p': 0.9974169731140137, 'pos0_bos_top1': 4, 'last_eos_p': 0.9979470372200012, 'last_eos_top1': 4}
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+step=1350 loss=4.2711 {'pos0_bos_p': 0.9975121021270752, 'pos0_bos_top1': 4, 'last_eos_p': 0.9969311952590942, 'last_eos_top1': 4}
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+step=1700 loss=4.0769 {'pos0_bos_p': 0.9981991648674011, 'pos0_bos_top1': 4, 'last_eos_p': 0.9979619979858398, 'last_eos_top1': 4}
+step=1750 loss=4.6301 {'pos0_bos_p': 0.9979648590087891, 'pos0_bos_top1': 4, 'last_eos_p': 0.9977269768714905, 'last_eos_top1': 4}
+step=1800 loss=3.9023 {'pos0_bos_p': 0.9978443384170532, 'pos0_bos_top1': 4, 'last_eos_p': 0.9975651502609253, 'last_eos_top1': 4}
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+step=1900 loss=4.0203 {'pos0_bos_p': 0.9982709884643555, 'pos0_bos_top1': 4, 'last_eos_p': 0.9981733560562134, 'last_eos_top1': 4}
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+step=2000 loss=3.8185 {'pos0_bos_p': 0.9983890056610107, 'pos0_bos_top1': 4, 'last_eos_p': 0.9983370304107666, 'last_eos_top1': 4}
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+step=2100 loss=4.2204 {'pos0_bos_p': 0.9983493089675903, 'pos0_bos_top1': 4, 'last_eos_p': 0.9983355402946472, 'last_eos_top1': 4}
+step=2150 loss=3.7729 {'pos0_bos_p': 0.9986503720283508, 'pos0_bos_top1': 4, 'last_eos_p': 0.998673677444458, 'last_eos_top1': 4}
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+step=2250 loss=4.0841 {'pos0_bos_p': 0.9988443851470947, 'pos0_bos_top1': 4, 'last_eos_p': 0.9989006519317627, 'last_eos_top1': 4}
+step=2300 loss=3.9133 {'pos0_bos_p': 0.9990023970603943, 'pos0_bos_top1': 4, 'last_eos_p': 0.9991791844367981, 'last_eos_top1': 4}
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+step=2400 loss=3.4481 {'pos0_bos_p': 0.9988929629325867, 'pos0_bos_top1': 4, 'last_eos_p': 0.9991099238395691, 'last_eos_top1': 4}
+step=2450 loss=3.7647 {'pos0_bos_p': 0.9992520213127136, 'pos0_bos_top1': 4, 'last_eos_p': 0.999434769153595, 'last_eos_top1': 4}
+step=2500 loss=3.9265 {'pos0_bos_p': 0.9987446069717407, 'pos0_bos_top1': 4, 'last_eos_p': 0.9991039633750916, 'last_eos_top1': 4}
+step=2550 loss=3.8639 {'pos0_bos_p': 0.9987595081329346, 'pos0_bos_top1': 4, 'last_eos_p': 0.9991376399993896, 'last_eos_top1': 4}
+step=2600 loss=3.9966 {'pos0_bos_p': 0.998921275138855, 'pos0_bos_top1': 4, 'last_eos_p': 0.9992075562477112, 'last_eos_top1': 4}
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+step=2750 loss=3.2091 {'pos0_bos_p': 0.9985511898994446, 'pos0_bos_top1': 4, 'last_eos_p': 0.9989625215530396, 'last_eos_top1': 4}
+step=2800 loss=3.9735 {'pos0_bos_p': 0.998927891254425, 'pos0_bos_top1': 4, 'last_eos_p': 0.9992038607597351, 'last_eos_top1': 4}
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+step=2950 loss=3.6658 {'pos0_bos_p': 0.9992419481277466, 'pos0_bos_top1': 4, 'last_eos_p': 0.9993651509284973, 'last_eos_top1': 4}
+step=3000 loss=3.3869 {'pos0_bos_p': 0.999355137348175, 'pos0_bos_top1': 4, 'last_eos_p': 0.9995129108428955, 'last_eos_top1': 4}
+step=3050 loss=3.6482 {'pos0_bos_p': 0.9993554949760437, 'pos0_bos_top1': 4, 'last_eos_p': 0.999516487121582, 'last_eos_top1': 4}
+step=3100 loss=3.3051 {'pos0_bos_p': 0.9992934465408325, 'pos0_bos_top1': 4, 'last_eos_p': 0.9994844198226929, 'last_eos_top1': 4}
+step=3150 loss=3.6381 {'pos0_bos_p': 0.9994621872901917, 'pos0_bos_top1': 4, 'last_eos_p': 0.9995926022529602, 'last_eos_top1': 4}
+step=3200 loss=3.2033 {'pos0_bos_p': 0.999346911907196, 'pos0_bos_top1': 4, 'last_eos_p': 0.9994736313819885, 'last_eos_top1': 4}
+step=3250 loss=3.1631 {'pos0_bos_p': 0.9993060827255249, 'pos0_bos_top1': 4, 'last_eos_p': 0.9994847774505615, 'last_eos_top1': 4}
+step=3300 loss=3.6425 {'pos0_bos_p': 0.9993155002593994, 'pos0_bos_top1': 4, 'last_eos_p': 0.9994688630104065, 'last_eos_top1': 4}
+step=3350 loss=3.7051 {'pos0_bos_p': 0.9994664788246155, 'pos0_bos_top1': 4, 'last_eos_p': 0.9996002316474915, 'last_eos_top1': 4}
+step=3400 loss=3.3412 {'pos0_bos_p': 0.9995267391204834, 'pos0_bos_top1': 4, 'last_eos_p': 0.9996466636657715, 'last_eos_top1': 4}
+step=3450 loss=3.7186 {'pos0_bos_p': 0.999510645866394, 'pos0_bos_top1': 4, 'last_eos_p': 0.9996852874755859, 'last_eos_top1': 4}
+step=3500 loss=3.4544 {'pos0_bos_p': 0.9994316697120667, 'pos0_bos_top1': 4, 'last_eos_p': 0.9996050000190735, 'last_eos_top1': 4}
+step=3550 loss=3.4978 {'pos0_bos_p': 0.9996265172958374, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997459053993225, 'last_eos_top1': 4}
+step=3600 loss=3.0744 {'pos0_bos_p': 0.9995922446250916, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997416138648987, 'last_eos_top1': 4}
+step=3650 loss=3.5563 {'pos0_bos_p': 0.9996224641799927, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997537732124329, 'last_eos_top1': 4}
+step=3700 loss=3.7291 {'pos0_bos_p': 0.9996058344841003, 'pos0_bos_top1': 4, 'last_eos_p': 0.99973064661026, 'last_eos_top1': 4}
+step=3750 loss=3.4164 {'pos0_bos_p': 0.9996237754821777, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997472167015076, 'last_eos_top1': 4}
+step=3800 loss=3.6656 {'pos0_bos_p': 0.9995741248130798, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997406601905823, 'last_eos_top1': 4}
+step=3850 loss=3.3697 {'pos0_bos_p': 0.9995781779289246, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997665286064148, 'last_eos_top1': 4}
+step=3900 loss=3.7419 {'pos0_bos_p': 0.9996947050094604, 'pos0_bos_top1': 4, 'last_eos_p': 0.9998236298561096, 'last_eos_top1': 4}
+step=3950 loss=3.0580 {'pos0_bos_p': 0.9995964169502258, 'pos0_bos_top1': 4, 'last_eos_p': 0.9997518658638, 'last_eos_top1': 4}
+step=4000 loss=3.2966 {'pos0_bos_p': 0.9997994303703308, 'pos0_bos_top1': 4, 'last_eos_p': 0.9998799562454224, 'last_eos_top1': 4}
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diff --git a/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094647.log b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094647.log
new file mode 100644
index 0000000000000000000000000000000000000000..375a4d63e3fc02f8b62dd05ae16bcac51bde8478
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_fit/logs/mini_owt_fit_t5_bernoulliwrong_len1024_bos_eos_C1_to_1024_absrope_time4_d768_l12_h12_native_nofloor_full_gbs512_8gpu_20260527_094647.log
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+W0527 09:46:48.799000 10308 torch/distributed/run.py:792] 
+W0527 09:46:48.799000 10308 torch/distributed/run.py:792] *****************************************
+W0527 09:46:48.799000 10308 torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0527 09:46:48.799000 10308 torch/distributed/run.py:792] *****************************************
+[W527 09:46:52.956795156 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.535223854 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.536856506 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.537774449 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.555919129 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.556279296 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.561685108 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.607021220 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
+[W527 09:46:54.635818888 socket.cpp:202] [c10d] The hostname of the client socket cannot be retrieved. err=-3
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+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO Trees [0] 9/-1/-1->8->0 [1] -1/-1/-1->8->15 [2] 9/-1/-1->8->15 [3] 9/-1/-1->8->15 [4] 9/-1/-1->8->15 [5] 9/-1/-1->8->15 [6] 9/-1/-1->8->15 [7] 9/-1/-1->8->15 [8] 9/0/-1->8->-1 [9] -1/-1/-1->8->15 [10] 9/-1/-1->8->15 [11] 9/-1/-1->8->15 [12] 9/-1/-1->8->15 [13] 9/-1/-1->8->15 [14] 9/-1/-1->8->15 [15] 9/-1/-1->8->15
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO P2P Chunksize set to 131072
+t-20260527161817-lhhb7-worker-1:10401:10574 [4] NCCL INFO [Proxy Service] Device 4 CPU core 99
+t-20260527161817-lhhb7-worker-1:10403:10572 [6] NCCL INFO [Proxy Service] Device 6 CPU core 126
+t-20260527161817-lhhb7-worker-1:10404:10576 [7] NCCL INFO [Proxy Service] Device 7 CPU core 90
+t-20260527161817-lhhb7-worker-1:10403:10578 [6] NCCL INFO [Proxy Service UDS] Device 6 CPU core 128
+t-20260527161817-lhhb7-worker-1:10400:10575 [3] NCCL INFO [Proxy Service] Device 3 CPU core 18
+t-20260527161817-lhhb7-worker-1:10402:10573 [5] NCCL INFO [Proxy Service] Device 5 CPU core 150
+t-20260527161817-lhhb7-worker-1:10401:10579 [4] NCCL INFO [Proxy Service UDS] Device 4 CPU core 100
+t-20260527161817-lhhb7-worker-1:10400:10577 [3] NCCL INFO [Proxy Service UDS] Device 3 CPU core 26
+t-20260527161817-lhhb7-worker-1:10404:10581 [7] NCCL INFO [Proxy Service UDS] Device 7 CPU core 92
+t-20260527161817-lhhb7-worker-1:10398:10582 [1] NCCL INFO [Proxy Service] Device 1 CPU core 24
+t-20260527161817-lhhb7-worker-1:10402:10580 [5] NCCL INFO [Proxy Service UDS] Device 5 CPU core 153
+t-20260527161817-lhhb7-worker-1:10399:10583 [2] NCCL INFO [Proxy Service] Device 2 CPU core 2
+t-20260527161817-lhhb7-worker-1:10398:10584 [1] NCCL INFO [Proxy Service UDS] Device 1 CPU core 28
+t-20260527161817-lhhb7-worker-1:10399:10585 [2] NCCL INFO [Proxy Service UDS] Device 2 CPU core 4
+t-20260527161817-lhhb7-worker-1:10397:10586 [0] NCCL INFO [Proxy Service] Device 0 CPU core 5
+t-20260527161817-lhhb7-worker-1:10397:10587 [0] NCCL INFO [Proxy Service UDS] Device 0 CPU core 6
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO threadThresholds 8/8/64 | 128/8/64 | 512 | 512
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO 16 coll channels, 16 collnet channels, 16 nvls channels, 16 p2p channels, 2 p2p channels per peer
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO ncclCommInitRankConfig comm 0xa55d950 rank 9 nranks 16 cudaDev 1 nvmlDev 1 busId 67020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v4 symbol.
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10398:10499 [1] NCCL INFO Init timings - ncclCommInitRankConfig: rank 9 nranks 16 total 2.24 (kernels 0.25, alloc 1.05, bootstrap 0.07, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO ncclCommInitRankConfig comm 0xa14e440 rank 8 nranks 16 cudaDev 0 nvmlDev 0 busId 65040 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v3 symbol.
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO ncclCommInitRankConfig comm 0xa45ce50 rank 12 nranks 16 cudaDev 4 nvmlDev 4 busId 6f020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO ncclCommInitRankConfig comm 0x93a1db0 rank 14 nranks 16 cudaDev 6 nvmlDev 6 busId 73020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO ncclCommInitRankConfig comm 0xaa89000 rank 13 nranks 16 cudaDev 5 nvmlDev 5 busId 71020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10397:10493 [0] NCCL INFO Init timings - ncclCommInitRankConfig: rank 8 nranks 16 total 2.24 (kernels 0.24, alloc 1.06, bootstrap 0.09, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO TUNER/Plugin: Failed to find ncclTunerPlugin_v2 symbol, using internal tuner instead.
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO ncclCommInitRankConfig comm 0xa8b0170 rank 11 nranks 16 cudaDev 3 nvmlDev 3 busId 6b020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10403:10498 [6] NCCL INFO Init timings - ncclCommInitRankConfig: rank 14 nranks 16 total 2.24 (kernels 0.23, alloc 1.06, bootstrap 0.08, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO ncclCommInitRankConfig comm 0x99983b0 rank 15 nranks 16 cudaDev 7 nvmlDev 7 busId 75020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10401:10497 [4] NCCL INFO Init timings - ncclCommInitRankConfig: rank 12 nranks 16 total 2.24 (kernels 0.24, alloc 1.06, bootstrap 0.07, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10402:10496 [5] NCCL INFO Init timings - ncclCommInitRankConfig: rank 13 nranks 16 total 2.24 (kernels 0.24, alloc 1.06, bootstrap 0.07, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO ncclCommInitRankConfig comm 0xa80bbe0 rank 10 nranks 16 cudaDev 2 nvmlDev 2 busId 69020 commId 0x2652bda43819d8ca - Init COMPLETE
+t-20260527161817-lhhb7-worker-1:10400:10494 [3] NCCL INFO Init timings - ncclCommInitRankConfig: rank 11 nranks 16 total 2.24 (kernels 0.24, alloc 1.06, bootstrap 0.08, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10404:10492 [7] NCCL INFO Init timings - ncclCommInitRankConfig: rank 15 nranks 16 total 2.24 (kernels 0.24, alloc 1.07, bootstrap 0.07, allgathers 0.01, topo 0.53, graphs 0.01, connections 0.31, rest 0.00)
+t-20260527161817-lhhb7-worker-1:10399:10495 [2] NCCL INFO Init timings - ncclCommInitRankConfig: rank 10 nranks 16 total 2.24 (kernels 0.23, alloc 1.06, bootstrap 0.09, allgathers 0.00, topo 0.53, graphs 0.02, connections 0.31, rest 0.01)
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 00/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 02/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 03/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 04/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 05/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 06/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 07/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 08/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 00/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 10/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 01/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 11/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 02/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 12/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 03/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 13/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 05/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 14/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 06/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 15/0 : 8[0] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 07/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 08/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 09/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 00/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 00/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 10/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 01/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 00/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 01/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 11/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 02/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 01/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 02/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 13/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 03/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 02/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 03/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 14/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 04/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 04/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 04/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 15/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 05/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 05/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 05/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 07/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 06/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 07/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 08/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 09/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 00/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 10/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 01/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 12/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 00/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 02/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 13/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 01/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 03/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 14/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 03/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 04/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 15/0 : 10[2] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 04/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 06/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 05/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 07/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 06/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 08/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 06/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 08/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 08/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 09/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 07/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 09/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 09/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 10/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 08/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 10/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 10/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 11/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 09/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 11/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 11/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 12/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 11/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 12/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 12/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 13/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 12/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 14/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 13/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 15/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 13/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 15/0 : 12[4] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 14/0 : 14[6] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 14/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 15/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 01/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 02/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 03/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 04/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 05/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 06/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 07/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 09/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 10/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 11/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 12/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 13/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 14/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 15/0 : 8[0] -> 15[7] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 06/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 04/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 01/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10598 [6] NCCL INFO [Proxy Progress] Device 6 CPU core 128
+t-20260527161817-lhhb7-worker-1:10402:10597 [5] NCCL INFO [Proxy Progress] Device 5 CPU core 152
+t-20260527161817-lhhb7-worker-1:10401:10599 [4] NCCL INFO [Proxy Progress] Device 4 CPU core 102
+t-20260527161817-lhhb7-worker-1:10400:10596 [3] NCCL INFO [Proxy Progress] Device 3 CPU core 20
+t-20260527161817-lhhb7-worker-1:10398:10600 [1] NCCL INFO [Proxy Progress] Device 1 CPU core 16
+t-20260527161817-lhhb7-worker-1:10399:10601 [2] NCCL INFO [Proxy Progress] Device 2 CPU core 6
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 07/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 00/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 08/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10602 [7] NCCL INFO [Proxy Progress] Device 7 CPU core 92
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 00/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10603 [0] NCCL INFO [Proxy Progress] Device 0 CPU core 6
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Channel 08/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 12/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 14/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 09/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 05/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 15/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 02/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 01/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 04/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 06/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 13/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 07/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 10/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 05/0 : 13[5] -> 5[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 09/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 12/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 14/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 15/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 02/0 : 10[2] -> 2[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 13/0 : 13[5] -> 5[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 10/0 : 10[2] -> 2[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 01/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 03/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 11/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 03/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 11/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 02/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 03/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 04/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 05/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 06/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 07/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 09/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 10/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 11/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 12/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 13/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 14/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 15/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 00/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 00/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 00/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 00/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 00/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 00/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 01/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 01/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 01/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 01/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 02/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 01/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 00/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 02/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 02/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 02/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 02/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 03/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 03/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 01/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 03/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 03/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 03/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 03/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 04/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 04/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 02/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 04/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 04/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 04/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 05/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 05/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 05/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 04/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 05/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 06/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 05/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 06/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 06/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 06/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 05/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 07/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 07/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 06/0 : 15[7] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 07/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 08/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 07/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 07/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 09/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 08/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 08/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 10/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 10/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 09/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 11/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 11/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 11/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 12/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 12/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 12/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 14/0 : 13[5] -> 12[4] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 13/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Channel 15/0 : 13[5] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 14/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 14/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Channel 15/0 : 9[1] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Channel 15/0 : 10[2] -> 9[1] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Channel 08/0 : 14[6] -> 13[5] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 06/0 : 11[3] -> 10[2] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 07/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 09/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 10/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 08/0 : 11[3] -> 10[2] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 09/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 13/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 10/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Channel 14/0 : 12[4] -> 11[3] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 12/0 : 11[3] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Channel 13/0 : 11[3] -> 10[2] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 11/0 : 15[7] -> 14[6] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Channel 12/0 : 15[7] -> 14[6] via P2P/CUMEM
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+t-20260527161817-lhhb7-worker-1:10404:10576 [7] NCCL INFO NCCL_IB_TIMEOUT set by environment to 23.
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+t-20260527161817-lhhb7-worker-1:10399:10583 [2] NCCL INFO NCCL_IB_TIMEOUT set by environment to 23.
+t-20260527161817-lhhb7-worker-1:10399:10583 [2] NCCL INFO NCCL_IB_RETRY_CNT set by environment to 7.
+t-20260527161817-lhhb7-worker-1:10397:10586 [0] NCCL INFO NCCL_IB_GID_INDEX set by environment to 7.
+t-20260527161817-lhhb7-worker-1:10397:10586 [0] NCCL INFO NCCL_IB_TIMEOUT set by environment to 23.
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+t-20260527161817-lhhb7-worker-1:10402:10573 [5] NCCL INFO NCCL_IB_TIMEOUT set by environment to 23.
+t-20260527161817-lhhb7-worker-1:10402:10573 [5] NCCL INFO NCCL_IB_RETRY_CNT set by environment to 7.
+t-20260527161817-lhhb7-worker-1:10403:10572 [6] NCCL INFO NCCL_IB_GID_INDEX set by environment to 7.
+t-20260527161817-lhhb7-worker-1:10403:10572 [6] NCCL INFO NCCL_IB_TIMEOUT set by environment to 23.
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+t-20260527161817-lhhb7-worker-1:10399:10590 [2] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10400:10588 [3] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10401:10594 [4] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10404:10592 [7] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10397:10591 [0] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10403:10593 [6] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10402:10589 [5] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10398:10595 [1] NCCL INFO Connected all trees
+t-20260527161817-lhhb7-worker-1:10402:10638 [5] NCCL INFO Channel 06/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10638 [5] NCCL INFO Channel 14/0 : 13[5] -> 14[6] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10402:10638 [5] NCCL INFO Channel 04/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10641 [6] NCCL INFO Channel 07/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10638 [5] NCCL INFO Channel 12/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10641 [6] NCCL INFO Channel 15/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10640 [0] NCCL INFO Channel 01/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10640 [0] NCCL INFO Channel 09/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10642 [3] NCCL INFO Channel 04/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10644 [7] NCCL INFO Channel 06/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10644 [7] NCCL INFO Channel 14/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10645 [1] NCCL INFO Channel 02/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10644 [7] NCCL INFO Channel 00/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10400:10642 [3] NCCL INFO Channel 12/0 : 11[3] -> 12[4] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10404:10644 [7] NCCL INFO Channel 08/0 : 15[7] -> 8[0] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10398:10645 [1] NCCL INFO Channel 10/0 : 9[1] -> 10[2] via P2P/CUMEM
+t-20260527161817-lhhb7-worker-1:10401:10643 [4] NCCL INFO Channel 05/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10642 [3] NCCL INFO Channel 02/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10643 [4] NCCL INFO Channel 13/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10642 [3] NCCL INFO Channel 10/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10645 [1] NCCL INFO Channel 00/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10639 [2] NCCL INFO Channel 03/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10645 [1] NCCL INFO Channel 08/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10639 [2] NCCL INFO Channel 11/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10640 [0] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10398:10645 [1] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10399:10639 [2] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10404:10644 [7] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10403:10641 [6] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10402:10638 [5] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10400:10642 [3] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10401:10643 [4] NCCL INFO Connected all rings, use ring PXN 0 GDR 1
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO NVLS comm 0xa45ce50 headRank 4 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO NVLS comm 0xa14e440 headRank 0 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO NVLS comm 0xaa89000 headRank 5 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO NVLS comm 0x93a1db0 headRank 6 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO NVLS comm 0xa8b0170 headRank 3 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO NVLS comm 0xa55d950 headRank 1 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO NVLS comm 0xa80bbe0 headRank 2 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO NVLS comm 0x99983b0 headRank 7 nHeads 8 buffSize 1048576 nvlsPerRankSize 33554432 nvlsTotalSize 268435456
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 00/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 00/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 00/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 00/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 02/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 01/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 02/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 00/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 01/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 00/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 01/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 03/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 02/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 01/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 01/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 03/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 02/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 01/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 04/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 02/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 04/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 03/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 03/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 04/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 05/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 03/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 02/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 04/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 04/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 04/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 05/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 05/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 06/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 06/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 05/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 03/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 05/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 05/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 06/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 06/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 07/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 07/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 08/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 08/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 06/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 07/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 06/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 10/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 08/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 07/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 09/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 09/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 07/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 08/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 11/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 09/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 07/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 10/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 10/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 10/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 09/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 08/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 10/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 12/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 08/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 11/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 11/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 11/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 12/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 11/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 13/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 09/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 12/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 09/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 12/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 14/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 14/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 13/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 12/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 10/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 13/0 : 7[7] -> 15[7] [receive] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 12/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 15/0 : 1[1] -> 9[1] [receive] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 13/0 : 6[6] -> 14[6] [receive] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 14/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 13/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 15/0 : 4[4] -> 12[4] [receive] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 11/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 00/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 13/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 00/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 00/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 00/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 15/0 : 3[3] -> 11[3] [receive] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 01/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 14/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 14/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 14/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 02/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 01/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 01/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 02/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 00/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 15/0 : 0[0] -> 8[0] [receive] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 15/0 : 5[5] -> 13[5] [receive] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 03/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 02/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 03/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 15/0 : 2[2] -> 10[2] [receive] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 02/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 01/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 01/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 03/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 04/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 04/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 00/0 : 13[5] -> 5[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 00/0 : 10[2] -> 2[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 03/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 02/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 02/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 05/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 05/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 05/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 01/0 : 10[2] -> 2[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 01/0 : 13[5] -> 5[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 04/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 04/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 03/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 06/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10398:10728 [1] NCCL INFO Channel 06/0 : 9[1] -> 1[1] [send] via NET/IBext_v9/9/GDRDMA
+t-20260527161817-lhhb7-worker-1:10401:10715 [4] NCCL INFO Channel 06/0 : 12[4] -> 4[4] [send] via NET/IBext_v9/12/GDRDMA
+t-20260527161817-lhhb7-worker-1:10403:10726 [6] NCCL INFO Channel 05/0 : 14[6] -> 6[6] [send] via NET/IBext_v9/14/GDRDMA
+t-20260527161817-lhhb7-worker-1:10399:10729 [2] NCCL INFO Channel 03/0 : 10[2] -> 2[2] [send] via NET/IBext_v9/10/GDRDMA
+t-20260527161817-lhhb7-worker-1:10402:10725 [5] NCCL INFO Channel 02/0 : 13[5] -> 5[5] [send] via NET/IBext_v9/13/GDRDMA
+t-20260527161817-lhhb7-worker-1:10400:10727 [3] NCCL INFO Channel 05/0 : 11[3] -> 3[3] [send] via NET/IBext_v9/11/GDRDMA
+t-20260527161817-lhhb7-worker-1:10404:10730 [7] NCCL INFO Channel 08/0 : 15[7] -> 7[7] [send] via NET/IBext_v9/15/GDRDMA
+t-20260527161817-lhhb7-worker-1:10397:10724 [0] NCCL INFO Channel 04/0 : 8[0] -> 0[0] [send] via NET/IBext_v9/8/GDRDMA
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diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.deps/muon.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.deps/muon.py
new file mode 100644
index 0000000000000000000000000000000000000000..7deeb069aad45d42d49ca17af10d76563fe9b908
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.deps/muon.py
@@ -0,0 +1,238 @@
+import torch
+import torch.distributed as dist
+
+
+def zeropower_via_newtonschulz5(G, steps: int):
+    """
+    Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
+    quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
+    of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
+    zero even beyond the point where the iteration no longer converges all the way to one everywhere
+    on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
+    where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model
+    performance at all relative to UV^T, where USV^T = G is the SVD.
+    """
+    assert G.ndim >= 2 # batched Muon implementation by @scottjmaddox, and put into practice in the record by @YouJiacheng
+    a, b, c = (3.4445, -4.7750,  2.0315)
+    X = G.bfloat16()
+    if G.size(-2) > G.size(-1):
+        X = X.mT
+
+    # Ensure spectral norm is at most 1
+    X = X / (X.norm(dim=(-2, -1), keepdim=True) + 1e-7)
+    # Perform the NS iterations
+    for _ in range(steps):
+        A = X @ X.mT
+        B = b * A + c * A @ A # quintic computation strategy adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
+        X = a * X + B @ X
+    
+    if G.size(-2) > G.size(-1):
+        X = X.mT
+    return X
+
+
+def muon_update(grad, momentum, beta=0.95, ns_steps=5, nesterov=True):
+    momentum.lerp_(grad, 1 - beta)
+    update = grad.lerp_(momentum, beta) if nesterov else momentum
+    if update.ndim == 4: # for the case of conv filters
+        update = update.view(len(update), -1)
+    update = zeropower_via_newtonschulz5(update, steps=ns_steps)
+    update *= max(1, grad.size(-2) / grad.size(-1))**0.5
+    return update
+
+
+class Muon(torch.optim.Optimizer):
+    """
+    Muon - MomentUm Orthogonalized by Newton-schulz
+
+    https://kellerjordan.github.io/posts/muon/
+
+    Muon internally runs standard SGD-momentum, and then performs an orthogonalization post-
+    processing step, in which each 2D parameter's update is replaced with the nearest orthogonal
+    matrix. For efficient orthogonalization we use a Newton-Schulz iteration, which has the
+    advantage that it can be stably run in bfloat16 on the GPU.
+
+    Muon should only be used for hidden weight layers. The input embedding, final output layer,
+    and any internal gains or biases should be optimized using a standard method such as AdamW.
+    Hidden convolutional weights can be trained using Muon by viewing them as 2D and then
+    collapsing their last 3 dimensions.
+
+    Arguments:
+        lr: The learning rate, in units of spectral norm per update.
+        weight_decay: The AdamW-style weight decay.
+        momentum: The momentum. A value of 0.95 here is usually fine.
+    """
+    def __init__(self, params, lr=0.02, weight_decay=0, momentum=0.95):
+        defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum)
+        assert isinstance(params, list) and len(params) >= 1 and isinstance(params[0], torch.nn.Parameter)
+        params = sorted(params, key=lambda x: x.size(), reverse=True)
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self):
+        for group in self.param_groups:
+            params = group["params"]
+            params_pad = params + [torch.empty_like(params[-1])] * (len(params) % dist.get_world_size())
+            for base_i in range(len(params))[::dist.get_world_size()]:
+                if base_i + dist.get_rank() < len(params):
+                    p = params[base_i + dist.get_rank()]
+                    state = self.state[p]
+                    if len(state) == 0:
+                        state["momentum_buffer"] = torch.zeros_like(p)
+                    update = muon_update(p.grad, state["momentum_buffer"], beta=group["momentum"])
+                    p.mul_(1 - group["lr"] * group["weight_decay"])
+                    p.add_(update, alpha=-group["lr"])
+                dist.all_gather(params_pad[base_i:base_i + dist.get_world_size()], params_pad[base_i + dist.get_rank()])
+
+
+class SingleDeviceMuon(torch.optim.Optimizer):
+    """
+    Muon variant for usage in non-distributed settings.
+    """
+    def __init__(self, params, lr=0.02, weight_decay=0, momentum=0.95):
+        defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum)
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self):
+        for group in self.param_groups:
+            for p in group["params"]:
+                state = self.state[p]
+                if len(state) == 0:
+                    state["momentum_buffer"] = torch.zeros_like(p)
+                update = muon_update(p.grad, state["momentum_buffer"], beta=group["momentum"])
+                p.mul_(1 - group["lr"] * group["weight_decay"])
+                p.add_(update, alpha=-group["lr"])
+
+
+def adam_update(grad, buf1, buf2, step, betas, eps):
+    buf1.lerp_(grad, 1 - betas[0])
+    buf2.lerp_(grad.square(), 1 - betas[1])
+    buf1c = buf1 / (1 - betas[0]**step)
+    buf2c = buf2 / (1 - betas[1]**step)
+    return buf1c / (buf2c.sqrt() + eps)
+
+
+class MuonWithAuxAdam(torch.optim.Optimizer):
+    """
+    Distributed Muon variant that can be used for all parameters in the network, since it runs an
+    internal AdamW for the parameters that are not compatible with Muon. The user must manually
+    specify which parameters shall be optimized with Muon and which with Adam by passing in a
+    list of param_groups with the `use_muon` flag set.
+
+    The point of this class is to allow the user to have a single Opimizer in their code, rather
+    than having both a Muon and an Adam which each need to be stepped.
+
+    You can see an example usage below:
+
+    https://github.com/KellerJordan/modded-nanogpt/blob/master/records/052525_MuonWithAuxAdamExample/b01550f9-03d8-4a9c-86fe-4ab434f1c5e0.txt#L470
+    ```
+    hidden_matrix_params = [p for n, p in model.blocks.named_parameters() if p.ndim >= 2 and "embed" not in n]
+    embed_params = [p for n, p in model.named_parameters() if "embed" in n]
+    scalar_params = [p for p in model.parameters() if p.ndim < 2]
+    head_params = [model.lm_head.weight]
+
+    from muon import MuonWithAuxAdam
+    adam_groups = [dict(params=head_params, lr=0.22), dict(params=embed_params, lr=0.6), dict(params=scalar_params, lr=0.04)]
+    adam_groups = [dict(**g, betas=(0.8, 0.95), eps=1e-10, use_muon=False) for g in adam_groups]
+    muon_group = dict(params=hidden_matrix_params, lr=0.05, momentum=0.95, use_muon=True)
+    param_groups = [*adam_groups, muon_group]
+    optimizer = MuonWithAuxAdam(param_groups)
+    ```
+    """
+    def __init__(self, param_groups):
+        for group in param_groups:
+            assert "use_muon" in group
+            if group["use_muon"]:
+                group["params"] = sorted(group["params"], key=lambda x: x.size(), reverse=True)
+                # defaults
+                group["lr"] = group.get("lr", 0.02)
+                group["momentum"] = group.get("momentum", 0.95)
+                group["weight_decay"] = group.get("weight_decay", 0)
+                assert set(group.keys()) == set(["params", "lr", "momentum", "weight_decay", "use_muon"])
+            else:
+                # defaults
+                group["lr"] = group.get("lr", 3e-4)
+                group["betas"] = group.get("betas", (0.9, 0.95))
+                group["eps"] = group.get("eps", 1e-10)
+                group["weight_decay"] = group.get("weight_decay", 0)
+                assert set(group.keys()) == set(["params", "lr", "betas", "eps", "weight_decay", "use_muon"])
+        super().__init__(param_groups, dict())
+
+    @torch.no_grad()
+    def step(self):
+        for group in self.param_groups:
+            if group["use_muon"]:
+                params = group["params"]
+                params_pad = params + [torch.empty_like(params[-1])] * (len(params) % dist.get_world_size())
+                for base_i in range(len(params))[::dist.get_world_size()]:
+                    if base_i + dist.get_rank() < len(params):
+                        p = params[base_i + dist.get_rank()]
+                        state = self.state[p]
+                        if len(state) == 0:
+                            state["momentum_buffer"] = torch.zeros_like(p)
+                        update = muon_update(p.grad, state["momentum_buffer"], beta=group["momentum"])
+                        p.mul_(1 - group["lr"] * group["weight_decay"])
+                        p.add_(update, alpha=-group["lr"])
+                    dist.all_gather(params_pad[base_i:base_i + dist.get_world_size()], params_pad[base_i + dist.get_rank()])
+            else:
+                beta1, beta2 = group["betas"]
+                for p in group["params"]:
+                    state = self.state[p]
+                    if len(state) == 0:
+                        state["exp_avg"] = torch.zeros_like(p)
+                        state["exp_avg_sq"] = torch.zeros_like(p)
+                        state["step"] = 0
+                    state["step"] += 1
+                    update = adam_update(p.grad, state["exp_avg"], state["exp_avg_sq"],
+                                         state["step"], group["betas"], group["eps"])
+                    p.mul_(1 - group["lr"] * group["weight_decay"])
+                    p.add_(update, alpha=-group["lr"])
+
+
+class SingleDeviceMuonWithAuxAdam(torch.optim.Optimizer):
+    """
+    Non-distributed variant of MuonWithAuxAdam.
+    """
+    def __init__(self, param_groups):
+        for group in param_groups:
+            assert "use_muon" in group
+            if group["use_muon"]:
+                # defaults
+                group["lr"] = group.get("lr", 0.02)
+                group["momentum"] = group.get("momentum", 0.95)
+                group["weight_decay"] = group.get("weight_decay", 0)
+                assert set(group.keys()) == set(["params", "lr", "momentum", "weight_decay", "use_muon"])
+            else:
+                # defaults
+                group["lr"] = group.get("lr", 3e-4)
+                group["betas"] = group.get("betas", (0.9, 0.95))
+                group["eps"] = group.get("eps", 1e-10)
+                group["weight_decay"] = group.get("weight_decay", 0)
+                assert set(group.keys()) == set(["params", "lr", "betas", "eps", "weight_decay", "use_muon"])
+        super().__init__(param_groups, dict())
+
+    @torch.no_grad()
+    def step(self):
+        for group in self.param_groups:
+            if group["use_muon"]:
+                for p in group["params"]:
+                    state = self.state[p]
+                    if len(state) == 0:
+                        state["momentum_buffer"] = torch.zeros_like(p)
+                    update = muon_update(p.grad, state["momentum_buffer"], beta=group["momentum"])
+                    p.mul_(1 - group["lr"] * group["weight_decay"])
+                    p.add_(update, alpha=-group["lr"])
+            else:
+                beta1, beta2 = group["betas"]
+                for p in group["params"]:
+                    state = self.state[p]
+                    if len(state) == 0:
+                        state["exp_avg"] = torch.zeros_like(p)
+                        state["exp_avg_sq"] = torch.zeros_like(p)
+                        state["step"] = 0
+                    state["step"] += 1
+                    update = adam_update(p.grad, state["exp_avg"], state["exp_avg_sq"],
+                                         state["step"], group["betas"], group["eps"])
+                    p.mul_(1 - group["lr"] * group["weight_decay"])
+                    p.add_(update, alpha=-group["lr"])
diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/deepseek_vl/configuration_deepseek_vl.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/deepseek_vl/configuration_deepseek_vl.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb6e1d5061678b6957fdf3110c85190f14ef2f61
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/deepseek_vl/configuration_deepseek_vl.py
@@ -0,0 +1,77 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_vl/modular_deepseek_vl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_vl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring, logging
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="deepseek-community/deepseek-vl-1.3b-chat")
+@strict
+class DeepseekVLConfig(PreTrainedConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import DeepseekVLConfig, DeepseekVLModel
+
+    >>> # Initializing a DeepseekVL deepseek-community/deepseek-vl-1.3b-chat style configuration
+    >>> configuration = DeepseekVLConfig()
+
+    >>> # Initializing a model (with random weights) from the deepseek-community/deepseek-vl-1.3b-chat style configuration
+    >>> model = DeepseekVLModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_vl"
+    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+    image_token_id: int = 100015
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self, **kwargs):
+        if self.text_config is None:
+            self.text_config = {}
+            logger.info("`text_config` is `None`. Initializing the `LlamaConfig` with default values.")
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "llama")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+
+        if self.vision_config is None:
+            self.vision_config = {}
+            logger.info("`vision_config` is `None`. Initializing the `SiglipVisionConfig` with default values.")
+        if isinstance(self.vision_config, dict):
+            self.vision_config["model_type"] = self.vision_config.get("model_type", "siglip_vision_model")
+            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["DeepseekVLConfig"]
diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/__init__.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f04d056d6e08afd696c8f2eb93cbe75dd84f713d
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_mt5 import *
+    from .modeling_mt5 import *
+    from .tokenization_mt5 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/modeling_mt5.py b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/modeling_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1ea0c8e51f5bd0ff971f3b0ffe993573aae2ab3
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/.venv_qwen35_uv/lib/python3.12/site-packages/transformers/models/mt5/modeling_mt5.py
@@ -0,0 +1,1682 @@
+# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch mT5 model."""
+
+import copy
+import math
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_bidirectional_mask, create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    Seq2SeqLMOutput,
+    Seq2SeqModelOutput,
+    Seq2SeqQuestionAnsweringModelOutput,
+    Seq2SeqSequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import DUMMY_INPUTS, DUMMY_MASK, auto_docstring, logging, torch_compilable_check
+from .configuration_mt5 import MT5Config
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->MT5
+class MT5LayerNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Construct a layernorm module in the MT5 style. No bias and no subtraction of mean.
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        # MT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+        # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated
+        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
+        # half-precision inputs is done in fp32
+
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        # convert into half-precision if necessary
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->MT5
+class MT5DenseActDense(nn.Module):
+    def __init__(self, config: MT5Config):
+        super().__init__()
+        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = ACT2FN[config.dense_act_fn]
+
+    def forward(self, hidden_states):
+        hidden_states = self.wi(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        if (
+            isinstance(self.wo.weight, torch.Tensor)
+            and hidden_states.dtype != self.wo.weight.dtype
+            and self.wo.weight.dtype != torch.int8
+        ):
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
+        hidden_states = self.wo(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->MT5
+class MT5DenseGatedActDense(nn.Module):
+    def __init__(self, config: MT5Config):
+        super().__init__()
+        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
+        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+        self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = ACT2FN[config.dense_act_fn]
+
+    def forward(self, hidden_states):
+        hidden_gelu = self.act(self.wi_0(hidden_states))
+        hidden_linear = self.wi_1(hidden_states)
+        hidden_states = hidden_gelu * hidden_linear
+        hidden_states = self.dropout(hidden_states)
+
+        # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
+        # See https://github.com/huggingface/transformers/issues/20287
+        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
+        if (
+            isinstance(self.wo.weight, torch.Tensor)
+            and hidden_states.dtype != self.wo.weight.dtype
+            and self.wo.weight.dtype != torch.int8
+        ):
+            hidden_states = hidden_states.to(self.wo.weight.dtype)
+
+        hidden_states = self.wo(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->MT5
+class MT5LayerFF(nn.Module):
+    def __init__(self, config: MT5Config):
+        super().__init__()
+        if config.is_gated_act:
+            self.DenseReluDense = MT5DenseGatedActDense(config)
+        else:
+            self.DenseReluDense = MT5DenseActDense(config)
+
+        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, hidden_states):
+        forwarded_states = self.layer_norm(hidden_states)
+        forwarded_states = self.DenseReluDense(forwarded_states)
+        hidden_states = hidden_states + self.dropout(forwarded_states)
+        return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->MT5
+class MT5Attention(nn.Module):
+    def __init__(
+        self,
+        config: MT5Config,
+        has_relative_attention_bias=False,
+        layer_idx: int | None = None,
+    ):
+        super().__init__()
+        self.is_decoder = config.is_decoder
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.relative_attention_num_buckets = config.relative_attention_num_buckets
+        self.relative_attention_max_distance = config.relative_attention_max_distance
+        self.d_model = config.d_model
+        self.key_value_proj_dim = config.d_kv
+        self.n_heads = config.num_heads
+        self.dropout = config.dropout_rate
+        self.inner_dim = self.n_heads * self.key_value_proj_dim
+        self.layer_idx = layer_idx
+        if layer_idx is None and self.is_decoder:
+            logger.warning_once(
+                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+
+        self.gradient_checkpointing = False
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+        """
+        Adapted from Mesh Tensorflow:
+        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+        Translate relative position to a bucket number for relative attention. The relative position is defined as
+        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+        This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+        Args:
+            relative_position: an int32 Tensor
+            bidirectional: a boolean - whether the attention is bidirectional
+            num_buckets: an integer
+            max_distance: an integer
+
+        Returns:
+            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+        """
+        relative_buckets = 0
+        if bidirectional:
+            num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+        # now relative_position is in the range [0, inf)
+
+        # half of the buckets are for exact increments in positions
+        max_exact = num_buckets // 2
+        is_small = relative_position < max_exact
+
+        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length, device=None, past_seen_tokens=0):
+        """Compute binned relative position bias"""
+        if device is None:
+            device = self.relative_attention_bias.weight.device
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + past_seen_tokens
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+        relative_position = memory_position - context_position  # shape (query_length, key_length)
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,  # shape (query_length, key_length)
+            bidirectional=(not self.is_decoder),
+            num_buckets=self.relative_attention_num_buckets,
+            max_distance=self.relative_attention_max_distance,
+        )
+        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
+        return values
+
+    def forward(
+        self,
+        hidden_states,
+        mask=None,
+        key_value_states=None,
+        position_bias=None,
+        past_key_values=None,
+        output_attentions=False,
+        **kwargs,
+    ):
+        """
+        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+        """
+        # Input is (batch_size, seq_length, dim)
+        # Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.key_value_proj_dim)
+        past_seen_tokens = past_key_values.get_seq_length(self.layer_idx) if past_key_values is not None else 0
+        # We clone here for StaticCache, as we get the value before updating it, but use it after and it's the same ref
+        past_seen_tokens = past_seen_tokens.clone() if isinstance(past_seen_tokens, torch.Tensor) else past_seen_tokens
+
+        # if key_value_states are provided this layer is used as a cross-attention layer for the decoder
+        is_cross_attention = key_value_states is not None
+
+        query_states = self.q(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
+        is_updated = False
+        if isinstance(past_key_values, EncoderDecoderCache):
+            is_updated = past_key_values.is_updated.get(self.layer_idx)
+            if is_cross_attention:
+                # after the first generated id, we can subsequently re-use all key/value_states from cache
+                curr_past_key_values = past_key_values.cross_attention_cache
+            else:
+                curr_past_key_values = past_key_values.self_attention_cache
+        else:
+            curr_past_key_values = past_key_values
+
+        current_states = key_value_states if is_cross_attention else hidden_states
+        if is_cross_attention and past_key_values is not None and is_updated:
+            # reuse k,v, cross_attentions
+            key_states = curr_past_key_values.layers[self.layer_idx].keys
+            value_states = curr_past_key_values.layers[self.layer_idx].values
+        else:
+            kv_shape = (*current_states.shape[:-1], -1, self.key_value_proj_dim)
+            key_states = self.k(current_states).view(kv_shape).transpose(1, 2)
+            value_states = self.v(current_states).view(kv_shape).transpose(1, 2)
+
+            if past_key_values is not None:
+                key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx)
+                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+                if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
+                    past_key_values.is_updated[self.layer_idx] = True
+
+        # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
+        scores = torch.matmul(query_states, key_states.transpose(3, 2))
+
+        if position_bias is None:
+            key_length = key_states.shape[-2]
+            if not self.has_relative_attention_bias:
+                position_bias = torch.zeros(
+                    (1, query_states.shape[1], input_shape[1], key_length), device=scores.device, dtype=scores.dtype
+                )
+                if self.gradient_checkpointing and self.training:
+                    position_bias.requires_grad = True
+            else:
+                position_bias = self.compute_bias(
+                    input_shape[1], key_length, device=scores.device, past_seen_tokens=past_seen_tokens
+                )
+
+            if mask is not None:
+                causal_mask = mask[:, :, :, : key_states.shape[-2]]
+                position_bias = position_bias + causal_mask
+
+        position_bias_masked = position_bias
+        scores += position_bias_masked
+
+        # (batch_size, n_heads, seq_length, key_length)
+        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(*input_shape, -1)
+        attn_output = self.o(attn_output)
+
+        outputs = (attn_output, position_bias)
+
+        if output_attentions:
+            outputs = outputs + (attn_weights,)
+        return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->MT5
+class MT5LayerSelfAttention(nn.Module):
+    def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None):
+        super().__init__()
+        self.SelfAttention = MT5Attention(
+            config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
+        )
+        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_bias=None,
+        past_key_values=None,
+        use_cache=False,
+        output_attentions=False,
+        **kwargs,
+    ):
+        normed_hidden_states = self.layer_norm(hidden_states)
+        attention_output = self.SelfAttention(
+            normed_hidden_states,
+            mask=attention_mask,
+            position_bias=position_bias,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        hidden_states = hidden_states + self.dropout(attention_output[0])
+        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->MT5
+class MT5LayerCrossAttention(nn.Module):
+    def __init__(self, config, layer_idx: int | None = None):
+        super().__init__()
+        self.EncDecAttention = MT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
+        self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        hidden_states,
+        key_value_states,
+        attention_mask=None,
+        position_bias=None,
+        past_key_values=None,
+        output_attentions=False,
+        **kwargs,
+    ):
+        normed_hidden_states = self.layer_norm(hidden_states)
+        attention_output = self.EncDecAttention(
+            normed_hidden_states,
+            mask=attention_mask,
+            key_value_states=key_value_states,
+            position_bias=position_bias,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+        )
+        layer_output = hidden_states + self.dropout(attention_output[0])
+        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Block with T5->MT5
+class MT5Block(GradientCheckpointingLayer):
+    def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None):
+        super().__init__()
+        self.is_decoder = config.is_decoder
+        self.layer = nn.ModuleList()
+        self.layer.append(
+            MT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx)
+        )
+        if self.is_decoder:
+            self.layer.append(MT5LayerCrossAttention(config, layer_idx=layer_idx))
+
+        self.layer.append(MT5LayerFF(config))
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_bias=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        encoder_decoder_position_bias=None,
+        past_key_values=None,
+        use_cache=False,
+        output_attentions=False,
+        return_dict=True,
+        **kwargs,
+    ):
+        self_attention_outputs = self.layer[0](
+            hidden_states,
+            attention_mask=attention_mask,
+            position_bias=position_bias,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        hidden_states = self_attention_outputs[0]
+        attention_outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights
+
+        # clamp inf values to enable fp16 training
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(hidden_states).any(),
+                torch.finfo(hidden_states.dtype).max - 1000,
+                torch.finfo(hidden_states.dtype).max,
+            )
+            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
+        if do_cross_attention:
+            cross_attention_outputs = self.layer[1](
+                hidden_states,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                position_bias=encoder_decoder_position_bias,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+            )
+            hidden_states = cross_attention_outputs[0]
+
+            # clamp inf values to enable fp16 training
+            if hidden_states.dtype == torch.float16:
+                clamp_value = torch.where(
+                    torch.isinf(hidden_states).any(),
+                    torch.finfo(hidden_states.dtype).max - 1000,
+                    torch.finfo(hidden_states.dtype).max,
+                )
+                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+            # Keep cross-attention outputs and relative position weights
+            attention_outputs = attention_outputs + cross_attention_outputs[1:]
+
+        # Apply Feed Forward layer
+        hidden_states = self.layer[-1](hidden_states)
+
+        # clamp inf values to enable fp16 training
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(hidden_states).any(),
+                torch.finfo(hidden_states.dtype).max - 1000,
+                torch.finfo(hidden_states.dtype).max,
+            )
+            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+        outputs = (hidden_states,)
+
+        return (
+            outputs + attention_outputs
+        )  # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
+
+
+# Copied from transformers.models.t5.modeling_t5.T5ClassificationHead with T5->MT5
+class MT5ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config: MT5Config):
+        super().__init__()
+        self.dense = nn.Linear(config.d_model, config.d_model)
+        self.dropout = nn.Dropout(p=config.classifier_dropout)
+        self.out_proj = nn.Linear(config.d_model, config.num_labels)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = torch.tanh(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+@auto_docstring
+# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel with T5->MT5, t5->mt5
+class MT5PreTrainedModel(PreTrainedModel):
+    config: MT5Config
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _can_compile_fullgraph = True
+
+    _no_split_modules = ["MT5Block"]
+    _keep_in_fp32_modules = ["wo"]
+
+    @property
+    def dummy_inputs(self):
+        input_ids = torch.tensor(DUMMY_INPUTS)
+        input_mask = torch.tensor(DUMMY_MASK)
+        dummy_inputs = {
+            "decoder_input_ids": input_ids,
+            "input_ids": input_ids,
+            "decoder_attention_mask": input_mask,
+        }
+        return dummy_inputs
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        factor = self.config.initializer_factor  # Used for testing weights initialization
+        if isinstance(module, MT5LayerNorm):
+            init.constant_(module.weight, factor * 1.0)
+        elif isinstance(
+            module,
+            (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel, MT5ForQuestionAnswering),
+        ):
+            init.normal_(module.shared.weight, mean=0.0, std=factor * 1.0)
+            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
+                init.normal_(module.lm_head.weight, mean=0.0, std=factor * 1.0)
+            if hasattr(module, "qa_outputs"):
+                init.normal_(module.qa_outputs.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+                init.zeros_(module.qa_outputs.bias)
+        elif isinstance(module, MT5ForTokenClassification):
+            if hasattr(module, "classifier"):
+                init.normal_(module.classifier.weight, mean=0.0, std=factor * 1.0)
+                init.zeros_(module.classifier.bias)
+        elif isinstance(module, MT5ClassificationHead):
+            init.normal_(module.dense.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            if hasattr(module.dense, "bias") and module.dense.bias is not None:
+                init.zeros_(module.dense.bias)
+            init.normal_(module.out_proj.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
+                init.zeros_(module.out_proj.bias)
+        elif isinstance(module, MT5DenseActDense):
+            init.normal_(module.wi.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            if hasattr(module.wi, "bias") and module.wi.bias is not None:
+                init.zeros_(module.wi.bias)
+            init.normal_(module.wo.weight, mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+            if hasattr(module.wo, "bias") and module.wo.bias is not None:
+                init.zeros_(module.wo.bias)
+        elif isinstance(module, MT5DenseGatedActDense):
+            init.normal_(module.wi_0.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
+                init.zeros_(module.wi_0.bias)
+            init.normal_(module.wi_1.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
+                init.zeros_(module.wi_1.bias)
+            init.normal_(module.wo.weight, mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+            if hasattr(module.wo, "bias") and module.wo.bias is not None:
+                init.zeros_(module.wo.bias)
+        elif isinstance(module, MT5Attention):
+            d_model = self.config.d_model
+            key_value_proj_dim = self.config.d_kv
+            n_heads = self.config.num_heads
+            init.normal_(module.q.weight, mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
+            init.normal_(module.k.weight, mean=0.0, std=factor * (d_model**-0.5))
+            init.normal_(module.v.weight, mean=0.0, std=factor * (d_model**-0.5))
+            init.normal_(module.o.weight, mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
+            if module.has_relative_attention_bias:
+                init.normal_(module.relative_attention_bias.weight, mean=0.0, std=factor * ((d_model) ** -0.5))
+
+    def _shift_right(self, input_ids):
+        decoder_start_token_id = self.config.decoder_start_token_id
+        pad_token_id = self.config.pad_token_id
+
+        if decoder_start_token_id is None:
+            raise ValueError(
+                "self.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id. "
+                "See MT5 docs for more information."
+            )
+
+        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+        shifted_input_ids[..., 0] = decoder_start_token_id
+
+        if pad_token_id is None:
+            raise ValueError("self.model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+        return shifted_input_ids
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Stack with T5->MT5
+class MT5Stack(MT5PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
+        self.is_decoder = config.is_decoder
+
+        self.block = nn.ModuleList(
+            [MT5Block(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) for i in range(config.num_layers)]
+        )
+        self.final_layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+        self.gradient_checkpointing = False
+
+    def set_input_embeddings(self, new_embeddings):
+        self.embed_tokens = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        inputs_embeds=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            err_msg_prefix = "decoder_" if self.is_decoder else ""
+            raise ValueError(
+                f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            err_msg_prefix = "decoder_" if self.is_decoder else ""
+            raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if inputs_embeds is None:
+            if self.embed_tokens is None:
+                raise ValueError("You have to initialize the model with valid token embeddings")
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        batch_size, seq_length = input_shape
+
+        if use_cache is True:
+            if not self.is_decoder:
+                raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
+
+        if self.is_decoder:
+            if use_cache and past_key_values is None:
+                if self.config.is_encoder_decoder:
+                    past_key_values = EncoderDecoderCache(
+                        DynamicCache(config=self.config), DynamicCache(config=self.config)
+                    )
+                else:
+                    past_key_values = DynamicCache(config=self.config)
+        elif not self.is_decoder:
+            # do not pass cache object down the line for encoder stack
+            # it messes indexing later in decoder-stack because cache object is modified in-place
+            past_key_values = None
+
+        if self.config.is_decoder:
+            attention_mask = create_causal_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values.self_attention_cache
+                if isinstance(past_key_values, EncoderDecoderCache)
+                else past_key_values,
+            )
+        else:
+            attention_mask = create_bidirectional_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+            )
+
+        encoder_extended_attention_mask = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            encoder_extended_attention_mask = create_bidirectional_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=encoder_attention_mask,
+                encoder_hidden_states=encoder_hidden_states,
+            )
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
+        position_bias = None
+        encoder_decoder_position_bias = None
+
+        hidden_states = self.dropout(inputs_embeds)
+
+        for layer_module in self.block:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_outputs = layer_module(
+                hidden_states,
+                attention_mask,
+                position_bias,
+                encoder_hidden_states,
+                encoder_extended_attention_mask,
+                encoder_decoder_position_bias,  # as a positional argument for gradient checkpointing
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                return_dict=return_dict,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            # We share the position biases between the layers - the first layer store them
+            # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
+            # (cross-attention position bias), (cross-attention weights)
+            position_bias = layer_outputs[1]
+            if self.is_decoder and encoder_hidden_states is not None:
+                encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[2],)
+                if self.is_decoder:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
+
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    past_key_values,
+                    all_hidden_states,
+                    all_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+@auto_docstring
+class MT5Model(MT5PreTrainedModel):
+    r"""
+    Examples:
+
+    ```python
+    >>> from transformers import MT5Model, AutoTokenizer
+
+    >>> model = MT5Model.from_pretrained("google/mt5-small")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+    >>> summary = "Weiter Verhandlung in Syrien."
+    >>> inputs = tokenizer(article, return_tensors="pt")
+    >>> labels = tokenizer(text_target=summary, return_tensors="pt")
+
+    >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
+    >>> hidden_states = outputs.last_hidden_state
+    ```"""
+
+    model_type = "mt5"
+    config: MT5Config
+    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+    _tied_weights_keys = {
+        "encoder.embed_tokens.weight": "shared.weight",
+        "decoder.embed_tokens.weight": "shared.weight",
+    }
+
+    # Copied from transformers.models.t5.modeling_t5.T5Model.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        self.encoder = MT5Stack(encoder_config)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = MT5Stack(decoder_config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.t5.modeling_t5.T5Model.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.shared
+
+    # Copied from transformers.models.t5.modeling_t5.T5Model.set_input_embeddings
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    @auto_docstring
+    # Copied from transformers.models.t5.modeling_t5.T5Model.forward with google-t5/->google/, T5->MT5, t5->mt5
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.FloatTensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.BoolTensor | None = None,
+        encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        decoder_inputs_embeds: torch.Tensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor] | Seq2SeqModelOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+            MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+            Training](./mt5#training).
+        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MT5Model
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+        >>> model = MT5Model.from_pretrained("google/mt5-small")
+
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
+
+        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MT5Model.
+        >>> # This is not needed for torch's MT5ForConditionalGeneration as it does this internally using labels arg.
+        >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
+        >>> # forward pass
+        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+        >>> last_hidden_states = outputs.last_hidden_state
+        ```"""
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if not return_dict:
+            return decoder_outputs + encoder_outputs
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    MT5 Model with a `language modeling` head on top.
+    """
+)
+class MT5ForConditionalGeneration(MT5PreTrainedModel, GenerationMixin):
+    r"""
+    Examples:
+
+    ```python
+    >>> from transformers import MT5ForConditionalGeneration, AutoTokenizer
+
+    >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+    >>> summary = "Weiter Verhandlung in Syrien."
+    >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt")
+
+    >>> outputs = model(**inputs)
+    >>> loss = outputs.loss
+    ```"""
+
+    model_type = "mt5"
+    config: MT5Config
+    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+    _tied_weights_keys = {
+        "encoder.embed_tokens.weight": "shared.weight",
+        "decoder.embed_tokens.weight": "shared.weight",
+        "lm_head.weight": "shared.weight",
+    }
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        self.encoder = MT5Stack(encoder_config)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = MT5Stack(decoder_config)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.FloatTensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.BoolTensor | None = None,
+        encoder_outputs: tuple[tuple[torch.Tensor]] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+            MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+            Training](./mt5#training).
+        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
+            labels in `[0, ..., config.vocab_size]`
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MT5ForConditionalGeneration
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+        >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+
+        >>> # training
+        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
+        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
+        >>> outputs = model(input_ids=input_ids, labels=labels)
+        >>> loss = outputs.loss
+        >>> logits = outputs.logits
+
+        >>> # inference
+        >>> input_ids = tokenizer(
+        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> outputs = model.generate(input_ids)
+        >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+        >>> # studies have shown that owning a dog is good for you.
+        ```"""
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            # Convert encoder inputs in embeddings if needed
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+            # get decoder inputs from shifting lm labels to the right
+            decoder_input_ids = self._shift_right(labels)
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            # move labels to correct device to enable PP
+            labels = labels.to(lm_logits.device)
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.prepare_decoder_input_ids_from_labels
+    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+        return self._shift_right(labels)
+
+
+@auto_docstring
+class MT5EncoderModel(MT5PreTrainedModel):
+    r"""
+    Examples:
+
+    ```python
+    >>> from transformers import MT5EncoderModel, AutoTokenizer
+
+    >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
+    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+    >>> input_ids = tokenizer(article, return_tensors="pt").input_ids
+    >>> outputs = model(input_ids)
+    >>> hidden_state = outputs.last_hidden_state
+    ```"""
+
+    model_type = "mt5"
+    config: MT5Config
+    _tied_weights_keys = {
+        "encoder.embed_tokens.weight": "shared.weight",
+    }
+
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = config
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = MT5Stack(encoder_config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.shared
+
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.set_input_embeddings
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+
+    @auto_docstring
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.forward with google-t5/->google/, T5->MT5, t5->mt5
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.FloatTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor] | BaseModelOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MT5EncoderModel
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+        >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        ... ).input_ids  # Batch size 1
+        >>> outputs = model(input_ids=input_ids)
+        >>> last_hidden_states = outputs.last_hidden_state
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        encoder_outputs = self.encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        return encoder_outputs
+
+
+@auto_docstring(
+    custom_intro="""
+    MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+    tasks.
+    """
+)
+class MT5ForSequenceClassification(MT5PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.transformer = MT5Model(config)
+        self.classification_head = MT5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @auto_docstring
+    # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.forward with T5->MT5, t5->mt5
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        encoder_outputs: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple | Seq2SeqSequenceClassifierOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+            MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+            Training](./mt5#training).
+        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        if labels is not None:
+            use_cache = False
+
+        if input_ids is None and inputs_embeds is not None:
+            raise NotImplementedError(
+                f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+            )
+
+        # Copied from models.bart.modeling_bart.BartModel.forward different to other models, MT5 automatically creates
+        # decoder_input_ids from input_ids if no decoder_input_ids are provided
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            if input_ids is None:
+                raise ValueError(
+                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+                    "passed, `input_ids` cannot be `None`. Please pass either "
+                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+                )
+            decoder_input_ids = self._shift_right(input_ids)
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+
+        eos_mask = input_ids.eq(self.config.eos_token_id).to(sequence_output.device)
+
+        torch_compilable_check(
+            torch.unique_consecutive(eos_mask.sum(1)).numel() == 1,
+            "All examples must have the same number of <eos> tokens.",
+        )
+        batch_size, _, hidden_size = sequence_output.shape
+        sentence_representation = sequence_output[eos_mask, :].view(batch_size, -1, hidden_size)[:, -1, :]
+        logits = self.classification_head(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.config.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+
+@auto_docstring
+class MT5ForTokenClassification(MT5PreTrainedModel):
+    # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.transformer = MT5EncoderModel(config)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @auto_docstring
+    # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.forward with T5->MT5
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor] | TokenClassifierOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./t5#training).
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits, outputs[2:-1])
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring
+class MT5ForQuestionAnswering(MT5PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+    _tied_weights_keys = {
+        "encoder.embed_tokens.weight": "shared.weight",
+        "decoder.embed_tokens.weight": "shared.weight",
+    }
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        self.encoder = MT5Stack(encoder_config)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = MT5Stack(decoder_config)
+
+        self.num_labels = config.num_labels
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.shared
+
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.set_input_embeddings
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+        self.encoder.set_input_embeddings(new_embeddings)
+        self.decoder.set_input_embeddings(new_embeddings)
+
+    @auto_docstring
+    # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.forward
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.FloatTensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.BoolTensor | None = None,
+        encoder_outputs: tuple[tuple[torch.Tensor]] | None = None,
+        start_positions: torch.LongTensor | None = None,
+        end_positions: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor] | Seq2SeqQuestionAnsweringModelOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+            T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
+            Training](./t5#training).
+        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if start_positions is not None and end_positions is not None:
+            use_cache = False
+
+        # Copied from models.bart.modeling_bart.BartModel.forward
+        #   different to other models, T5 automatically creates decoder_input_ids from
+        #   input_ids if no decoder_input_ids are provided
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            if input_ids is None:
+                raise ValueError(
+                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+                    "passed, `input_ids` cannot be `None`. Please pass either "
+                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+                )
+            decoder_input_ids = self._shift_right(input_ids)
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=None,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + decoder_outputs[1:] + encoder_outputs
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return Seq2SeqQuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+
+__all__ = [
+    "MT5EncoderModel",
+    "MT5ForConditionalGeneration",
+    "MT5ForQuestionAnswering",
+    "MT5ForSequenceClassification",
+    "MT5ForTokenClassification",
+    "MT5Model",
+    "MT5PreTrainedModel",
+]
diff --git a/LTA_openwebtext_dualt/mini_owt_logdirichlet/run_owt_t5_elftokenized_full_len1024_d768_16gpu_50epochs_C1_toV_exp_elfopt_t5embed_unfixed_adaln_rope_noabspos_stateprobadd_selfcond_ce_fast_lr3e4.sh b/LTA_openwebtext_dualt/mini_owt_logdirichlet/run_owt_t5_elftokenized_full_len1024_d768_16gpu_50epochs_C1_toV_exp_elfopt_t5embed_unfixed_adaln_rope_noabspos_stateprobadd_selfcond_ce_fast_lr3e4.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c315507e13256eee74907a34804b85495fb0407
--- /dev/null
+++ b/LTA_openwebtext_dualt/mini_owt_logdirichlet/run_owt_t5_elftokenized_full_len1024_d768_16gpu_50epochs_C1_toV_exp_elfopt_t5embed_unfixed_adaln_rope_noabspos_stateprobadd_selfcond_ce_fast_lr3e4.sh
@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+cd /e2e-data/evad-tech-vla/wanghan58/workspace/LTA_openwebtext_dualt/mini_owt_logdirichlet
+
+export T5_MODEL_PATH="${T5_MODEL_PATH:-/e2e-data/evad-tech-vla/wanghan58/models/hf/t5-small}"
+export TOKENIZER_PATH="${TOKENIZER_PATH:-${T5_MODEL_PATH}/tokenizer.json}"
+export CMIN="${CMIN:-1}"
+export C_SCHEDULE="${C_SCHEDULE:-exp}"
+if [[ -z "${CMAX:-}" ]]; then
+  CMAX="$(TOKENIZER_PATH="${TOKENIZER_PATH}" python3 - <<'PY'
+import os
+from tokenizers import Tokenizer
+print(Tokenizer.from_file(os.environ["TOKENIZER_PATH"]).get_vocab_size())
+PY
+)"
+fi
+export CMAX
+
+DATE_TAG="${DATE_TAG:-$(date +%Y%m%d_%H%M%S)}"
+export DATE_TAG
+export RUN_NAME="${RUN_NAME:-owt_t5_elftokenized_full_len1024_C1_toV_exp_d768_l12_h12_gbs512_16gpu_50ep_lr3e4_elfopt_t5embed_unfixed_adaln_rope_noabspos_stateprobadd_selfcond_ce_fast_${DATE_TAG}}"
+export OUT_DIR="${OUT_DIR:-runs/${RUN_NAME}}"
+export LOG_FILE="${LOG_FILE:-logs/${RUN_NAME}.log}"
+
+exec bash run_owt_t5_elftokenized_full_len1024_d768_16gpu_50epochs_C1_to1024_pow1_elfopt_t5embed_unfixed_adaln_rope_noabspos_stateprobadd_selfcond_ce_fast_lr3e4.sh