Saving weights and logs of step 60000

checkpoint_40000

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f423d7dd26898cbfa65c5e03548c4c57a401fbe7afe6a868dfe237817fac7d0
+size 769729450

events.out.tfevents.1626138389.t1v-n-f5c06ea1-w-0.330819.3.v2

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e1fa958230b00de0c045c3ab57efec8147bce881102430ec5f776e87330b9591
-size 5932729
+oid sha256:279cc56d0b6cc6fbb971cb2ba30bfa7004a58bd5eea377c189dda74cdb77b8e9
+size 8915529

flax_model.msgpack

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:04bd8612fa7089d7c97e60d2ce4546521218848aad584bba90d687e0e8f6d87a
+oid sha256:19dddbba6ad2a0aa9c5c22f1b9750b90fcd0b7c8f3007cbd6af9a17d447fa417
 size 256576390

run.sh

@@ -23,9 +23,9 @@ python ./run_mlm_flax.py \
     --per_device_train_batch_size="2" \
     --per_device_eval_batch_size="2" \
     --save_total_limit="5"\
-    --dtype="bfloat16" \
+    --gradient_accumulation_steps="2" \
     #--adafactor \
-    #--gradient_accumulation_steps="8" \
+    #--dtype="bfloat16" \
     #--resume_from_checkpoint="./"\
     
 

run_mlm_flax.py

@@ -396,8 +396,11 @@ if __name__ == "__main__":
             return train, val
         train, val = train_val_files()
         datasets = load_dataset('json', data_files={'train': train, 'validation': val})
-        datasets["train"] = datasets["train"].select(range(int(0.8*len(datasets["train"]))))
-        datasets["validation"] = datasets["validation"].select(range(int(0.8*len(datasets["validation"]))))
+        #datasets["train"] = datasets["train"].select(range(int(0.8*len(datasets["train"]))))
+        #datasets["validation"] = datasets["validation"].select(range(int(0.8*len(datasets["validation"]))))
+        datasets["train"] = datasets["train"].select(range(10000))
+        datasets["validation"] = datasets["validation"].select(range(10000))
+
 
 
 
@@ -610,9 +613,9 @@ if __name__ == "__main__":
             mask=decay_mask_fn,
         )
 
-    #if training_args.gradient_accumulation_steps > 1:
-    #    optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
-    #grad_accum_steps = training_args.gradient_accumulation_steps
+    if training_args.gradient_accumulation_steps > 1:
+        optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
+    grad_accum_steps = training_args.gradient_accumulation_steps
 
     # Setup train state
     
@@ -650,7 +653,7 @@ if __name__ == "__main__":
         new_state = state.apply_gradients(grads=grad)
 
         metrics = jax.lax.pmean(
-            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step )}, axis_name="batch" #// grad_accum_steps
+            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step // grad_accum_steps)}, axis_name="batch" #
         )
 
         return new_state, metrics, new_dropout_rng
@@ -696,10 +699,10 @@ if __name__ == "__main__":
         # Generate an epoch by shuffling sampling indices from the train dataset
         num_train_samples = len(train_dataset)
         train_samples_idx = jax.random.permutation(input_rng, jnp.arange(num_train_samples))
-        train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size) #// grad_accum_steps
+        train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size // grad_accum_steps) #
 
         # Gather the indexes for creating the batch and do a training step
-        for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1,initial=resume_step)): #// grad_accum_steps
+        for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1,initial=resume_step // grad_accum_steps)): #
             samples = [train_dataset[int(idx)] for idx in batch_idx]
             model_inputs = data_collator(samples, pad_to_multiple_of=16)
             
@@ -713,7 +716,7 @@ if __name__ == "__main__":
             if cur_step < resume_step:
                 continue
 
-            if (cur_step % training_args.logging_steps) == 0 and cur_step > 0: #* grad_accum_steps
+            if (cur_step % training_args.logging_steps * grad_accum_steps) == 0 and cur_step > 0: #
                 # Save metrics
                 train_metric = jax_utils.unreplicate(train_metric)
                 train_time += time.time() - train_start
@@ -730,7 +733,7 @@ if __name__ == "__main__":
 
                 train_metrics = []
 
-            if cur_step % (training_args.eval_steps) == 0 and cur_step > 0: #* grad_accum_steps
+            if cur_step % (training_args.eval_steps * grad_accum_steps) == 0 and cur_step > 0: #
                 # ======================== Evaluating ==============================
                 num_eval_samples = len(eval_dataset)
                 eval_samples_idx = jnp.arange(num_eval_samples)
@@ -763,7 +766,7 @@ if __name__ == "__main__":
                     _metrics = {f"eval_{k}":mb_item(v) for k, v in eval_metrics.items()}
                     wandb.log({"eval_step":cur_step, **_metrics})
 
-            if (cur_step % training_args.save_steps == 0) and cur_step > 0: #* grad_accum_steps
+            if (cur_step % training_args.save_steps == 0 ) and cur_step > 0: #
                 # save checkpoint after each epoch and push checkpoint to the hub
                 if jax.process_index() == 0:
                     params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))

wandb/run-20210713_010630-14xhiyhf/files/output.log

@@ -10815,3 +10815,5410 @@ Training...:  43%|████████▌           | 39512/92767 [6:18:50<3
 
 Training...:  43%|████████▌           | 40000/92767 [6:23:16<8:22:42,  1.75it/s]
 git-lfs/2.9.2 (GitHub; linux amd64; go 1.13.5)92767 [6:23:35<8:22:42,  1.75it/s]
+[07:31:58] - DEBUG - huggingface_hub.repository - [Repository] is a valid git repo
+[07:32:37] - INFO - huggingface_hub.repository - Uploading LFS objects: 100% (3/3), 1.0 GB | 38 MB/s, done.
+[07:32:38] - INFO - absl - Saving checkpoint at step: 40000
+tcmalloc: large alloc 1363968000 bytes == 0x2ed6e2000 @  0x7f170bb8c680 0x7f170bbacbdd 0x7f143fe0e20d 0x7f143fe1c340 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe1be87 0x7f143fe17bd3 0x7f143fe181fe 0x504d56 0x56acb6 0x568d9a 0x5f5b33 0x56bc9b 0x5f5956 0x56aadf 0x5f5956 0x56fb87 0x568d9a 0x5f5b33 0x56bc9b 0x568d9a 0x68cdc7
+[07:32:43] - INFO - absl - Saved checkpoint at checkpoint_40000
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+Training...:  44%|████████▋           | 40510/92767 [6:29:08<5:04:12,  2.86it/s]
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+Training...:  44%|████████▊           | 41010/92767 [6:33:55<6:35:41,  2.18it/s]
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+Training...:  45%|████████▌          | 41503/92767 [6:38:40<44:05:30,  3.10s/it]
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+Training...:  45%|█████████           | 42000/92767 [6:43:07<8:03:02,  1.75it/s]
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+Training...:  46%|█████████▏          | 42500/92767 [6:47:53<7:58:54,  1.75it/s]
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+Training...:  46%|████████▊          | 43001/92767 [6:52:58<85:49:55,  6.21s/it]
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+Training...:  47%|████████▉          | 43507/92767 [6:57:45<11:14:32,  1.22it/s]
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+Training...:  47%|█████████▍          | 44009/92767 [7:02:31<6:09:57,  2.20it/s]
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+Training...:  48%|█████████▌          | 44500/92767 [7:06:57<7:39:27,  1.75it/s]
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+Training...:  49%|█████████▏         | 45005/92767 [7:12:03<20:50:46,  1.57s/it]
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+Training...:  49%|█████████▊          | 45510/92767 [7:16:50<6:01:25,  2.18it/s]
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+Training...:  50%|█████████▉          | 46000/92767 [7:21:16<7:24:18,  1.75it/s]
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+Training...:  50%|██████████          | 46500/92767 [7:26:02<7:20:11,  1.75it/s]
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+Training...:  51%|██████████▏         | 47511/92767 [7:35:55<3:26:53,  3.65it/s]
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+Training...:  52%|██████████▍         | 48500/92767 [7:45:06<7:01:43,  1.75it/s]
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+Training...:  53%|██████████▌         | 49000/92767 [7:49:53<6:55:29,  1.76it/s]
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+Training...:  53%|██████████▋         | 49500/92767 [7:54:39<6:51:41,  1.75it/s]
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+Training...:  54%|██████████▏        | 50001/92767 [7:59:44<73:42:07,  6.20s/it]
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+Training...:  54%|██████████▎        | 50507/92767 [8:04:31<12:35:49,  1.07s/it]
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+Training...:  55%|██████████▉         | 51000/92767 [8:08:57<6:37:26,  1.75it/s]
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+Training...:  56%|██████████▌        | 51501/92767 [8:14:03<71:09:39,  6.21s/it]
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+Training...:  56%|██████████▋        | 52007/92767 [8:18:49<11:18:40,  1.00it/s]
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+Training...:  57%|███████████▎        | 52500/92767 [8:23:16<6:23:12,  1.75it/s]
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+Training...:  57%|███████████▍        | 53000/92767 [8:28:02<6:18:56,  1.75it/s]
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+Training...:  58%|███████████▌        | 53500/92767 [8:32:48<6:13:32,  1.75it/s]
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+Training...:  58%|███████████▋        | 54000/92767 [8:37:34<6:08:49,  1.75it/s]
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+Training...:  59%|███████████▋        | 54500/92767 [8:42:20<6:03:59,  1.75it/s]
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+Training...:  59%|███████████▎       | 55003/92767 [8:47:26<32:27:24,  3.09s/it]
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+Training...:  64%|████████████▊       | 59500/92767 [9:30:02<5:16:37,  1.75it/s]
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+git-lfs/2.9.2 (GitHub; linux amd64; go 1.13.5)92767 [9:35:07<5:11:39,  1.75it/s]

wandb/run-20210713_010630-14xhiyhf/files/wandb-summary.json

@@ -1 +1 @@
-{"training_step": 40000, "learning_rate": 4.6185992687242106e-05, "train_loss": 6.655529975891113, "_runtime": 23126, "_timestamp": 1626161516, "_step": 79}
+{"training_step": 60000, "learning_rate": 4.4006559619447216e-05, "train_loss": 6.5428876876831055, "_runtime": 34618, "_timestamp": 1626173008, "_step": 119}